Key Principles in Computation 9781774696408, 9781774694367

Table of contents :
Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 Fundamentals of Computation
1.1. Introduction
1.2. Computing
1.3. Hardware
1.4. Processors
1.5. Software
1.6. Processing
References
Chapter 2 Principles and Applications of DNA Computing
2.1. Introduction
2.2. Construction of DNA Logic Gates as the Basic Computing Components
2.3. Scaling Up DNA Logic Gates for Building Computing Systems
2.4. DNA Molecular Computing for Intelligent Diagnostics
2.5. DNA Arithmetical Computation For Intelligent Diagnostics
2.6. Summary
References
Chapter 3 Stochastic Computing Principles
3.1. Introduction
3.2. Stochastic Thinking
3.3. Fundamentals of Stochastic Computing
3.4. Stochastic Computing Techniques
3.5. Optimization Methods For Stochastic Systems
3.6. Technology and Design
3.7. Stochastic Computing Applications and Potential Research Areas
3.8. Summary
References
Chapter 4 Principles and Applications of Social Computing
4.1. Introduction
4.2. The Nature of Social Computing
4.3. Challenges
4.4. Approach
4.5. Summary
References
Chapter 5 Computational Principles in Memory Storage
5.1. Introduction
5.2. Creating Persistence From Memory-Less Components
5.3. Robustness to Noise
5.4. Memory Capacity
5.5. Model Mechanisms: Tests and Questions
5.6. Biological Versus Computer Memory
References
Chapter 6 Application of Computational Models in Clinical Applications
6.1. Introduction
6.2. Modeling Approaches for Clinical Applications in Personalized Medicine
6.3. Models in Clinical Research for Discovery, Diagnosis, and Therapy
6.4. Challenges and Recommendations
References
Chapter 7 Application of Computational Models in Climate Analysis and Remote Sensing
7.1. Introduction
7.2. Theoretical Background
7.3. Analyzing Remote Sensing and Climate Data Over Data Mining Techniques
7.4. Future Research Directions
References
Chapter 8 A Socio-Technical Perspective of Computational Sustainability
8.1. Introduction
8.2. Background of Computational Sustainability
8.3. Sustainability in General
8.4. Computational Sustainability
References
Index
Back Cover

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本书版权归Arcler所有

本书版权归Arcler所有

Key Principles in Computation

本书版权归Arcler所有

本书版权归Arcler所有

KEY PRINCIPLES IN COMPUTATION

S.P. Upadhyay

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www.arclerpress.com

Key Principles in Computation S.P. Upadhyay

Arcler Press 224 Shoreacres Road Burlington, ON L7L 2H2 Canada www.arclerpress.com Email: [email protected]

e-book Edition 2023 ISBN: 978-1-77469-640-8 (e-book)

This book contains information obtained from highly regarded resources. Reprinted material sources are indicated and copyright remains with the original owners. Copyright for images and other graphics remains with the original owners as indicated. A Wide variety of references are listed. Reasonable efforts have been made to publish reliable data. Authors or Editors or Publishers are not responsible for the accuracy of the information in the published chapters or consequences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The authors or editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify. Notice: Registered trademark of products or corporate names are used only for explanation and identification without intent of infringement.

© 2023 Arcler Press ISBN: 978-1-77469-436-7 (Hardcover)

Arcler Press publishes wide variety of books and eBooks. For more information about Arcler Press and its products, visit our website at www.arclerpress.com

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ABOUT THE AUTHOR

Dr. Satya Prakash Upadhyay holds Ph.D. in Computer Science from DDU Gorakhpur University, Gorakhpur (U.P.), 2013. He has Technical/Teaching Experience (15 Years 08 Months) in DDU Gorakhpur University, Gorakhpur(UP). He has been the member of Academic/Administrative Committees. He has also participated in a large number on seminars/workshops/conferences, presented papers/keynote address and chaired technical sessions. He has authored several books. His areas of interest are Integrating University Administration with IT, Research on Pattern Recognition/Optimization of Algorithms/Big data analysis, and Innovation in Examination System.

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TABLE OF CONTENTS

List of Figures.........................................................................................................xi List of Tables.........................................................................................................xv List of Abbreviations........................................................................................... xvii Preface............................................................................................................ ....xxi Chapter 1

Fundamentals of Computation................................................................... 1 1.1. Introduction......................................................................................... 2 1.2. Computing........................................................................................... 3 1.3. Hardware............................................................................................ 4 1.4. Processors.......................................................................................... 10 1.5. Software............................................................................................ 13 1.6. Processing......................................................................................... 25 References................................................................................................ 29

Chapter 2

Principles and Applications of DNA Computing...................................... 39 2.1. Introduction....................................................................................... 40 2.2. Construction of DNA Logic Gates as the Basic Computing Components................................................................. 42 2.3. Scaling Up DNA Logic Gates for Building Computing Systems.......... 48 2.4. DNA Molecular Computing for Intelligent Diagnostics...................... 52 2.5. DNA Arithmetical Computation For Intelligent Diagnostics................ 59 2.6. Summary........................................................................................... 61 References................................................................................................ 62

Chapter 3

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Stochastic Computing Principles.............................................................. 69 3.1. Introduction....................................................................................... 70 3.2. Stochastic Thinking............................................................................ 72 3.3. Fundamentals of Stochastic Computing............................................. 75

3.4. Stochastic Computing Techniques...................................................... 80 3.5. Optimization Methods For Stochastic Systems................................... 84 3.6. Technology and Design...................................................................... 86 3.7. Stochastic Computing Applications and Potential Research Areas...... 88 3.8. Summary........................................................................................... 92 References ............................................................................................... 93 Chapter 4

Principles and Applications of Social Computing................................... 101 4.1. Introduction..................................................................................... 102 4.2. The Nature of Social Computing...................................................... 104 4.3. Challenges....................................................................................... 107 4.4. Approach......................................................................................... 109 4.5. Summary......................................................................................... 111 References.............................................................................................. 113

Chapter 5

Computational Principles in Memory Storage........................................ 117 5.1. Introduction..................................................................................... 118 5.2. Creating Persistence From Memory-Less Components...................... 122 5.3. Robustness to Noise......................................................................... 130 5.4. Memory Capacity............................................................................ 133 5.5. Model Mechanisms: Tests and Questions......................................... 140 5.6. Biological Versus Computer Memory............................................... 142 References.............................................................................................. 145

Chapter 6

Application of Computational Models in Clinical Applications.............. 155 6.1. Introduction..................................................................................... 156 6.2. Modeling Approaches for Clinical Applications in Personalized Medicine.................................................................. 159 6.3. Models in Clinical Research for Discovery, Diagnosis, and Therapy.................................................................................. 169 6.4. Challenges and Recommendations.................................................. 177 References.............................................................................................. 184

Chapter 7

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Application of Computational Models in Climate Analysis and Remote Sensing............................................................................... 193 7.1. Introduction..................................................................................... 194 7.2. Theoretical Background................................................................... 199 viii

7.3. Analyzing Remote Sensing and Climate Data Over Data Mining Techniques........................................................................ 202 7.4. Future Research Directions.............................................................. 203 References.............................................................................................. 205 Chapter 8

A Socio-Technical Perspective of Computational Sustainability............. 209 8.1. Introduction..................................................................................... 210 8.2. Background of Computational Sustainability.................................... 211 8.3. Sustainability in General.................................................................. 212 8.4. Computational Sustainability........................................................... 218 References.............................................................................................. 230

Index...................................................................................................... 237

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LIST OF FIGURES Figure 1.1. The computation theories Figure 1.2. The computer’s block diagram Figure 1.3. The demonstration of the ancient abacus Figure 1.4. Pascal’s computer Figure 1.5. The world’s first vacuum tube, as well as an electronic numerical computer and integrator Figure 1.6. Base-2 numeral system Figure 1.7. Number 18 in the binary system Figure 1.8. Number 255 in the binary system Figure 1.9. Number 18 depicted with only five vacuum tubes Figure 1.10. Number 255 depicted with just eight vacuum tubes Figure 1.11. The processor’s operating principle Figure 1.12. Analytical Engine developed by Charles Babbage Figure 1.13. First Dynabook idea by Alan Kay Figure 1.14. The blank graphical output window and program editor window Figure 1.15. Text-based hello-world program Figure 1.16. Graphical hello-world program Figure 1.17. Graphical hello-world having color Figure 1.18. Hello-world program having a bitmap Earth’s image Figure 2.1. A schematic illustration of DNA computing as well as its applications Figure 2.2. (a) AND gate: DNAzyme is only dynamic when both of the inputs are available. (b) Fluorescence output of logic gates with the following input configuration (from the beginning to end): IA and IB (green line output), just IB, just IA, and no input. The Cu2+-dependent DNAzyme and logic gates are shown in c. (d) DNAzyme secondary structures: DNAzyme X (“YES” gate), DNAzyme Y (“NOT” gate I), and DNAzyme Z (“NOT” gate II). Right: graphs of reaction time vs cleaved product portion (“ON” (•), “OFF” ()) Figure 2.3. (a) Design of a full-adder using several DNAzymes. (b) Development of an OR gate based on DNAzyme for detecting enterovirus 71 strains. (c) Top: Yes gate for Lyssavirus detection. Bottom: the first 5 3 wells with the label R1 represent the viral

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genotype name, and the second 5 3 wells with the label R2 show the viral genotype name. The viral genotype frequency is shown in 5 3 well (Vijayakumar & Macdonald, 2017) Figure 2.4. Illustration of a displacement reaction mediated by a toehold. (b) Schematic of a displacement reaction mediated by a toehold exchange Figure 2.5. (a) Toehold-mediated displacement reactions drive a two-input AND gate. (b) The “seesaw” gate DNA motif. (c) The design of a three-input dominant gate relay on DNA strand displacement responses (Li et al., 2013) Figure 2.6. (a) A: Irradiation at 365 and 532 nm as incoming signal for a light-triggered DNA-based AND gate. (b) DNA computing in mammalian cells using miRNA as a source of information. (c) Design of DNA logic gates based on Staudinger reduction for tiny molecule production. (d) Principles of operation of a 3D DNA logic gate based on aptamers (Peng et al., 2018) Figure 2.7. (a) DNA-origami-based molecular computation for multiplication computations (Figure 2.7). (i) The DNA chip’s design; (ii) Mechanism of translation; (iii) During translation fluorescent pictures. (b) A top–down image of the origami-based localized circuit for implementing digital circuits. (c) A schematic representation of the “burntbridges” DNA walker on a computational scaffold (Chatterjee et al., 2017) Figure 2.8. (a) Schematic diagram of the simulated acquired immune system using a DNA circuit. (b) Schematic representation of protocells with an integrated DNA circuit simulating an immune response (Lyu et al., 2018) Figure 2.9. (a) Schematic of a triple-aptamer-based AND logic circuit for cell membrane target cell identification. (b) Using an AND logic gate, compare fluorescence signals on cell membranes. (c) The AND logic gate [45] is depend on multiaptamer-mediated proximity ligation Figure 2.10. DNA computation system for cancer diagnosis. (a) Scheme for multiplication (Wn c(An) = Tn). (b) Scheme for summation (T1 + T2 = E (2); T3 + T4 = F (3)). (c) Scheme for subtraction (E F). (d) Illustration of catalytic amplification and reporting (Ma et al., 2021) Figure 3.1. (a) and (b) Both demonstrate the operation of probabilistic Computing. For both simulation and inference issues, the program is the same as the background assumptions. However, observations are the system’s input in the inference issue Figure 3.2. Deterministic computing in many setups, as well as metrics conversion from deterministic to stochastic computing Figure 3.3. VOS method explained using a simple block. Various probability distributions of error are caused by the same clock frequency and different voltage levels Figure 3.4. Basic Structure of Detection block and Statistical Estimation Figure 3.5. Statistical branching of stochastic computing

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Figure 3.6. Using an AND with an unsynchronized bitstream to perform stochastic multiplication Figure 3.7. (a) framework of the ANT system. (b) error distributions Figure 3.8. The framework of the SSNoC system Figure 3.9. (a) is a block diagram of a Random Number Generator (RNG). (b) block diagram of a 3-bit Linear feedback shift register (LFSR) Figure 3.10. (a) Concept of invertible logic, (b) simple invertible AND Figure 3.11. Realization of Hamiltonian Full Adder Figure 3.12. (a) IBM’s original Truenorth chip layout, (b) Based on IBM’s Truenorth chip, neuromorphic computing Figure 3.13. Application of stochastic computation in BLSI. (a) Various blocks of Vision chip, (b) Fundamental structure of Equivalent-to-Stochastic converter Figure 3.14. A comparison of the fault tolerance characteristics of several image processing algorithm hardware implementations. A traditional implementation is used to create the graphics in the first row. A stochastic implementation is used to create the pictures in the second row. Soft mistakes are inserted at a rate of one per second(a) 0%; (b) 1%; (c) 2%; (d) 5%; (e) 10%; (f) 15%; (g) 30% Figure 4.1. Different paradigms of social computing Figure 4.2. Social computing architecture Figure 4.3. Structural model for social computing Figure 4.4. How abstraction, formalization, and implementation contribute to the next stage of modeling and simulation Figure 5.1. Illustration of in-memory computation system Figure 5.2. Stable states from positive feedback Figure 5.3. Depending on how the memory is used for computing data, four main inmemory computing approaches can be defined Figure 5.4. Long-term synapse size maintenance. Green hexagons in both images depict synapse-produced molecules that catch centrally transported resources for synapse upkeep Figure 5.5. Robustness of persistent activity architectures Figure 5.6. The tradeoff between robustness and capacity Figure 5.7. Intricacy cost of storing a constant variable in a set of well-separated distinct attractors Figure 5.8. Applications of cloud computing technology Figure 5.9. Types of long term memory Figure 5.10. A generalized representation of Hopfield network

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Figure 5.11. According to the Atkinson-Shiffrin model of memory, information passes through three distinct stages in order for it to be stored in long-term memory Figure 5.12. The cost of storing a variable in a series of discrete attractors is complex Figure 6.1. Computational methods are being used to generate and investigate complicated biological processes Figure 6.2. Computational theories for individual stratification in personalized medicine Figure 6.3. Integrated model of precision medicine. Physicians and patients are both active participants in the integrated procedures Figure 6.4. An example of molecular interaction maps (MIM) diagram Figure 6.5. Prespecified vs. constraint-based process models Figure 6.6. Hypotheses of the quantitative model Figure 6.7. Flow diagram of a physiologically based pharmacokinetic (PBPK) model Figure 6.8. Difference between machine learning and deep learning Figure 6.9. Flowchart for Planning and Conducting Clinical Research Figure 6.10. Model of the medical diagnostic process Figure 6.11. Workflow and responsibilities for the iterative harmonization process in the SAIL (sample availability) method, involving multiple curation teams and facilitated by a web-based application Figure 6.12. The lifecycle of a predictive model Figure 6.13. Data standardization model Figure 6.14. Basic recommendations for the use of computational models from early ideation to implementation in clinical practice. For each of the four key challenges (outer circle), a specific set of basic recommendations is given in the corresponding color Figure 7.1. Climate change modeling Figure 7.2. Computational intelligence modeling in remote sensing Figure 7.3. Application of big data in climate change modeling Figure 8.1. Computational sustainability; the multidisciplinary academic field Figure 8.2. These are three fundamental sustainability dimensions (Elkington 1998; Rodriguez et al. 2002; Todorov & Marinova 2011) Figure 8.3. The Earth’s carbon cycle Figure 8.4. The life cycle of sustainable manufacture Figure 8.5. A smart building, as well as the computationally sophisticated characteristics that can help it become more sustainable Figure 8.6. How artificial intelligence methods (maximum entropy model) may be utilized in species distribution modelling for conservation of biodiversity Figure 8.7. Sustainable data centers

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LIST OF TABLES Table 2.1. Contrast of dissimilar instruments of intelligent diagnosis Table 6.1. Tools and resources used to develop MIMs, pharmacokinetic models and qualitative and quantitative models (Li et al., 2010; Seaver et al., 2021) Table 6.2. Examples for mechanistic modelling in discovery (Väremo et al., 2017; Singh et al., 2018) Table 6.3. Examples for the application of machine learning and deep learning algorithms in diagnosis (Okser et al., 2014; Paré et al., 2017) Table 6.4. Examples for mechanistic modelling in therapy (Rasool et al., 2021) Table 7.1. Table of symbols

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LIST OF ABBREVIATIONS

ABA

ATP-binding aptamer

ABM

Agent-based modeling

ACSN

Atlas of cancer signaling network

AD

Alzheimer’s disease

AI

artificial intelligence

AM

adaptive multi-voltage scaling

ANN

artificial neural network

ANT

algorithmic noise tolerance

API

application programming interface

BM

Boolean modeling

CaMKII

calmodulin-dependent protein kinase II

CAN

CellNetAnalyzer

CCS

capture and sequestration

CDN

clock distribution networks

CDNs

content delivery networks

CMOS

complementary metal-oxide-semiconductor

CNN

convolutional neural network

COMBINE

computational modeling in biology network

CPEB

cytoplasmatic polyadenylation element-binding protein

CPU

central processing unit

CRP

C-reactive protein

CT

computed tomography

DL

deep learning

DNAzyme deoxyribozyme DNNs

deep neural networks

DPM

dynamic power management

DRAM

dynamic random-access memory

DTP

DNA triangle prism

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DVFS

dynamic voltage and frequency scaling

EMA

European Medicines Agency

ESBs

enterprise services busses

EV71

enterovirus 71

EVs

electric vehicles

FDA

Food and Drug Administration

GDPR

general data protection regulation

GEM

GEnome-scale Metabolic models

GFR

glomerular filtration rate

GHGs

greenhouse gasses

GINsim

gene interaction network simulation suite

GIS

geographic information systems

GUI

graphical user interfaces

GWAS

genome-wide association studies

GWP

global warming potential

HCR

hybridization chain reaction

IBIS

issue-based information systems

IC

integrated circuit

IDEs

integrated development environments

IT

information technology

JWS

java web simulation

KEGG

Kyoto encyclopedia of genes and genomes

KNN

K-nearest neighbors

LAI

leaf area index

LCA

life cycle analysis

LTM

long-term memory

MDPs

Markov decision processes

MDR

medical equipment regulation

MIMs

Molecular interaction maps

MINERVA

Molecular Interaction NEtwoRks VisuAlization

MIP

mixed-integer programming

MIPD

model-informed precision dosage

ML

machine learning

MTJ

magnetic tunnel junction

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NAFLD

non-alcoholic fatty liver disease

NeuroML

neural open markup language

NoC

network-on-chip

ODEs

ordinary differential equations

OOP

object-oriented programming

PARC

Palo Alto Research Center

PBPK

physiologically based PK

PCA

principal component analysis

PCR

polymerase chain reaction

PGM

personal glycemic meter

PK

pharmacokinetic

PKM

protein kinase M

PLL

phase-locked loop

POC

point-of-care

PRS

polygenic risk scores

PV

photovoltaic

RCTs

randomized clinical trials

RE

requirements engineering

RF

random forests

RING

regulatory interaction graph

RNG

random number generator

RS

remote sensing

SBGN

systems biology graphical notation

SBML

systems biology markup language

SC

stochastic computing

SED-ML

simulation experiment description markup language

SIGNOR

signaling network open resource

SND

Stochastic network design

SNG

stochastic number generator

SNNs

spiking neural networks

SOM

Self-Organizing Maps

SP

signal peptides

SSC

stochastic sensor network on chip

ssDNA

single-stranded DNA

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STM

short-term memory

SVM

support vector machines

TDC

time digital converter

tiNIT

integrated networking inference for tissues

TSP

truncated square pyramid

UAVs

unmanned aerial vehicles

UN

United Nations

VM

virtual machine

VOS

voltage over scaling

WSNs

wireless sensor networks

XML

extensible markup language

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PREFACE Computing is frequently portrayed as a high-tech field that grows at the fast pace dictated by Moore’s Law. If we avert our gaze for a few minutes, we risk missing a gamechanging technology advancement or a paradigm-shifting theoretical discovery. This book offers a different approach, presenting computers as a science guided by universally applicable rules. Computer science is the study of data processing. We require a new vocabulary to explain science, and this book proposes the framework of big principles as such a language. This book is on computing as a whole—its algorithms, architectures, and designs. The main computing concepts are divided into six areas in this book: communication, computation, coordination, recollection, evaluation, and design. The book opens with an overview of computing, its history, numerous connections with other areas, practice domains, and the organization of the framework of the major principles. The book’s chapters continue to study fundamental ideas in a variety of fields, including information, machines, programming, computation, memory, parallelism, queueing, and design. Finally, the essay applies these lofty ideals to networking, specifically the Internet. In the early years of computer science, the interconnections between hardware, software, compilers, and operating systems were basic enough for students to get a broad understanding of how computers operated. Such clarity is frequently lost when computer technology becomes more complicated, and knowledge becomes more specialized. Unlike other publications that focus exclusively on one area of the discipline, The elements of computing systems provides students with a comprehensive and rigorous view of applied computer science as it relates to the design of a basic yet powerful computer system. Indeed, the greatest way to learn how computers operate is to construct one from the ground up, and this textbook guides students through twelve chapters and exercises that gradually develop a basic hardware platform and a contemporary software hierarchy from scratch. Students receive practical experience with hardware architecture, operating systems, programming languages, compilers, data structures, algorithms, and software engineering during this process. By taking this constructive approach, the book exposes a substantial amount of computer science knowledge and illustrates how the theoretical and applied approaches taught in earlier courses fit into the larger picture. The book is organized around an abstraction-implementation paradigm, with each chapter presenting a crucial hardware or software abstraction, a proposed implementation that concretizes it, and an actual project. The growing computer system can be

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constructed by following the chapters; however, this is only one option, as the projects are self-contained and can be completed or skipped in any sequence. The book contains all of the computer science knowledge necessary to complete the tasks; the only prerequisite is programming expertise. This book is required reading for professionals in science and engineering fields that include a “computational” component, for practitioners in computing seeking overviews of less familiar areas of computer science, and for non-computer science majors seeking an accessible introduction to the field.

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—Author

1

CHAPTER

FUNDAMENTALS OF COMPUTATION

CONTENTS

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1.1. Introduction......................................................................................... 2 1.2. Computing........................................................................................... 3 1.3. Hardware............................................................................................ 4 1.4. Processors.......................................................................................... 10 1.5. Software............................................................................................ 13 1.6. Processing......................................................................................... 25 References................................................................................................ 29

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Key Principles in Computation

1.1. INTRODUCTION The computer, and the communication and information technologies constructed on it, have altered business, government, society, and science and have impacted practically every area of our life, just as industrialization did in the 19th century. Amongst the most significant advancement of the 20th century is the electronic computer. The essential principles and procedures utilized in the advancements of computer apps are detailed in this literature, which covers the discipline of computing (Abramowitz & Stegun, 1965). Getting into a novel sector like computers is going to a workstation in a foreign country for the first time. Whereas all countries have certain fundamental characteristics, like the need for language and cultural and trade preferences, the vast variances in such characteristics from one country to the next may be confusing and even devastating for newbies. Furthermore, describing the characteristics of a country in any universal way is challenging since they differ widely and change with time. Similarly, joining the world of computers may be unsettling, and defining its characteristics may be challenging (Figure 1.1).

Figure 1.1. The computation theories. Source: https://www.proprofs.com/quiz-school/story.php?title=njuyodkw2z8j.

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Fundamentals of Computation

3

Although there are basic notions that underpin the subject of computers which may be defined, learnt and applied practically (Acar, 2009). All calculation is dependent upon the associated utilization of computer types of equipment, referred to as hardware, and the programs of computers that make them referred to as software. All the software apps have been designed utilizing data and the specifications of the process, referred to as algorithms and data structures, and all hardware devices are manufactured by utilizing algorithms and data structures. Although software and hardware technology have advanced continuously during the history of computing, and novel paradigms for process and data descriptions have emerged regularly, such foundations have maintained relatively consistent throughout that period (Agrawal et al., 2008). It begins with defining the idea of computing, and then moves on to examine the notions of software and hardware, before concluding with a foreword to the creation of software (also known as the programming of the computer). After that, most work is devoted to the production of computer software, with an in-depth explanation of software concepts and a glimpse of contemporary culture in the software development area. For the first half of the work, the author makes extensive usage of processing, a Java-based programming environment; after that, the author makes extensive usage of the complete Java development environment.

1.2. COMPUTING As previously said, defining computing is difficult, however, according to the curriculum of Computing 2005: The Overview Report issued through a joint committee of AIS, ACM, and IEEE, computing is defined as follows: “We may define computing in a broad sense as any goal-oriented activity that needs, advantages from, or creates computers. It is a wide idea that contains computer hardware creation, the development of the application, and the development of software. The production of the software of a computer is the latest of such businesses and the subject of this text” (Agrawal et al., 2006).

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Key Principles in Computation

Figure 1.2. The computer’s block diagram. Source: https://www.tutorialandexample.com/block-diagram-of-a-computer/.

Since the development of software is reliant on computer hardware (Figure 1.2), we will cover the basics of computer hardware and how it relates to software to better get ready students for the construction of software. The authors believe that it would connect a fresh era of software engineers, such as not just scientifically and mathematically inclined students who seem to be prevalent in the courses of programming, as well as a younger youth of students in the social sciences and civilizations that have been discovering that calculation is as pertinent to their disciplines as it is always in the fields of science (Agrawal et al., 2004).

1.3. HARDWARE The word “computer” was first used in the 1600s. Till the 1950s, the word nearly exclusively applied to a human who did calculations. The task of conducting massive quantities of calculations is hard, time-consuming, and error-prone for humans. As a result, the wish to automate mathematics is a long-standing human ambition (Aho et al., 1983).

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Figure 1.3. The demonstration of the ancient abacus. Source: https://www.opencolleges.edu.au/informed/learning-strategies/whywe-need-ancient-forms-of-learning-in-the-21st-century/.

The abacus (Figure 1.3), which had been in usage in Indian, ancient Mesopotamia, Persian, Asian, Mesoamerican communities and Mesoamerican communities, and Greco-Roman is still in use worldwide nowadays, was among the first instruments devised for easing human arithmetic (Ahuja et al., 1989). An abacus is a digitized arithmetic device, similar to the contemporary computer since its actions imitate the variations in digits that happen when people perform fundamental arithmetic operations. It is made up of an orderly collection of stones or beads moving along rods or in grooves. A few of these cultures employed base-60, base-20, or base16 numeral systems; therefore not all abacus systems utilized decimal–base10–numerals.

Figure 1.4. Pascal’s computer. Source: https://www.britannica.com/technology/Pascaline.

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Key Principles in Computation

Blaise Pascal (1623–1662), a young French mathematician, built one of the earliest adding machines which are gear-based to aid in the massive number of computations required in tax calculation (Figure 1.4). The decimal edition of the Pascaline operated similarly to a type of calculator popular among grocery store consumers in the United States and internationally throughout the late 1950s and early 1960s (Ahuja et al., 1995). Charles Babbage (1792–1871), an English mathematician, introduced the 1st stage of his anticipated “Difference Engine” in 1822; it is similarly employed 10-position gears to express decimal digits. This had been able of further sophisticated computations as compared to addition machines such as Pascaline’s basic arithmetic. Although, the engineering of the Difference Engine proved so difficult that Babbage discontinued the project for this and several other reasons (Ajtai et al., 2001). There are two primary challenges here, each exhibiting two important computing ideas. Firstly, such gadgets were mechanical, requiring physically moving and connecting pieces. A device with moving components is usually always slower, more likely to fail, and more complex to build as compared to the equipment which is immoveable (Akra & Bazzi, 1998). Electronic equipment, on the other hand, like vacuum tubes utilized in primitive radios, have no moving components by design. As a result, the ENIAC, one of the first electronic digital computers, depicted every decimal digit with a column of 10 vacuum tubes that might electrically switch off and on to depict the 0 to 9 counting order of a decimaldigit without necessitating a physical movement.

Figure 1.5. The world’s first vacuum tube, as well as an electronic numerical computer and integrator. Source: https://www.hpcwire.com/2021/02/15/eniac-at-75-celebrating-theworlds-first-supercomputer/.

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Fundamentals of Computation

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The 30-ton ENIAC, developed by John Mauchly and J. Presper Eckert at the Pennsylvania University between 1943 and 1946, needed used massive quantities of electricity and 18,000 vacuum tubes for their running time. This is because every decimal digit in the ENIAC needed ten vacuum tubes to express (Figure 1.5) (Alon, 1990).

Figure 1.6. Base-2 numeral system. Source: https://eduquestionbank.blogspot.com/2020/05/chapter-6-number-system.html.

Contrarily, the 1st electrical digital computer, created by Clifford Berry and John Atanasoff at Iowa State University between 1937 and 1942, employed a binary – or Base-2–numeral system, as do all electronic digital computers nowadays (Figure 1.6) (Amir et al., 2009). Decimal digits are dependent upon powers of ten, with each number representing a different power of ten: ones (100), tens (101), hundreds (102), thousands (103), as well as so on. Therefore, the decimal number ‘two hundred and fifty-five’ is expressed as 255, arithmetically equivalent to the sum of 2 hundred, 5 tens, and 5 ones. Therefore, ENIAC will only need to switch on 3 vacuum tubes to keep that number, but there will be a maximum of thirty vacuum tubes necessary to depict all of the possibilities of such 3 numbers (Amtoft et al., 2002). On either side, Binary digits also referred to as bits are dependent upon powers of 2, with each number moving to the left representing a different

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power of 2: ones (20), twos (21), fours (102), eights (103), sixteen’s (104), as well as so on. Therefore, the number 18 will be expressed in binary as 10010, which is the total of 1 sixteen, 0 eights, 0 fours, 1 two, and 0 ones in Base-2:

Figure 1.7. Number 18 in the binary system. Source: https://cs.calvin.edu/activities/books/processing/text/01computing.pdf.

To put it another way, the number two hundred & fifty-five will be represented in the binary numeric by the number 11111111, which can be thought of arithmetically as the total of the 1 one, two, 1 four, 1 eight, 1 sixteen, 1 thirty-two, 1 sixty-four, and 1 one hundred twenty-eight:

Figure 1.8. Number 255 in the binary system. Source: https://cs.calvin.edu/activities/books/processing/text/01computing.pdf.

Why’d computer developers want to construct a system that performs arithmetic utilizing a binary, Base-2 numeral strategy, which is obscure and unfamiliar? Every digit in a digital numeral system should be capable to count down to one lesser as compared to the base. As a result, under the Base-10 system, every decimal digit’s counting order ranges between 0 and 9, subsequently reversing to zero. To depict a decimal-digit, we should be capable to account for all ten probabilities in the counting order, zero through nine, which necessitates the usage of either a device with ten feasible states, such as the Pascaline’s ten-position gear, or ten separate types of equipment, such as the ENIAC’s ten different vacuum tubes for every digit (Andersson, 1995). The binary number system, on the other hand, is Base-2. Given that its digits must only be capable to count a maximum of one lesser as compared

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to the base, it implies that every binary digit’s counting order runs between 0 and 1 and afterwards back to 0. In the other sense, although a decimal digit may include ten distinct integers ranging between 0 and 9, a binary digit may only contain a 0 or a 1. Instead of needing to account for the ten potential states of a decimal digit, a binary digit may be represented using only one device with 2 feasible states. For instance, every binary digit may be represented by a simple off or on the switch, with the position of ON representing a 1 and the position of OFF representing a 0 (Andersson, 1996). Similarly, every binary digit in the Atanasoff-Berry Computer might be replicated by a single vacuum tube. Therefore, rather than the twenty vacuum tubes needed by the ENIAC, the number 18 may be replicated with only five (Figure 1.9).

Figure 1.9. Number 18 depicted with only five vacuum tubes. Source: https://www.cuemath.com/numbers/18-in-binary/.

Similarly, rather than the Thirty vacuum tubes used by ENIAC, just Eight vacuum tubes might be used to show the number two hundred fiftyfive (Figure 1.10).

Figure 1.10. Number 255 depicted with just eight vacuum tubes. Source: https://www.sciencedirect.com/topics/computer-science/binary-to-decimal-conversion.

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As a result, computer developers obtained an easy means to create electrical digital computers by the usage of 2-state electronic devices in return for the obscure unfamiliarity of binary representation. This 1st generation of digital computers depending upon vacuum tubes gradually gave way to a 2nd generation to utilize the transistor-like an even quicker and much lesser and non-moving, off or on switch for depicting the 1/0 of a binary-digit, much like vacuum tube radios had been superseded through transistor radios beginning in the 1950s (Sjöberg et al., 2021).

1.4. PROCESSORS It’s quite simple to grasp the basics of how a series of interlocking, 10-position gears may simulate decimal arithmetic processes. However, how a vacuum tubes arrangement or transistors utilization as electrical off or on switches replicates binary arithmetic processes are significantly less evident. The analogy of a succession of dominoes may be quite effective. A domino program on late-night television, for example, in which a domino champion constructs a complicated maze of dominoes, pushes one of them over and thereby initiates a lengthy chain reaction of dominoes falling. In the end, the succession of falling dominoes concludes with a dramatic flourish when the final set of dominoes falls over in a magnificent flourish. Think of a set of dominoes laid out on a table with a line of eight dominoes on one side and the second line of eight dominoes on the other side, divided by a maze of other dominoes in between. You may start a chain reaction of dominoes falling by going to the 8 dominoes on one side and knocking some or all of them over. The chain reaction will eventually come to a stop at another side when all or most of those 8 dominoes will be knocked over as a result of the chain reaction (Sandel et al., 2011).

Source: https://cs.calvin.edu/activities/books/processing/text/01computing.pdf.

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There are a few resemblances between this and how a processor operates. A domino, such as a transistor, is indeed a 2-state piece of equipment: it may be in either the on or off position and it may also be standing at the top or laying at the bottom. A domino or transistor, like every 2-state device, may represent the two alternatives for a binary digit: a 0 or a 1. Consider a domino that’s also standing up like a 1 and a domino that is laying down like a 0 for instance. It’s “inputting” an 8-digit binary number inside this domino “machine” by beating over part or every dominoes in the 1st row of 8 (Arora et al., 2001).

Source: https://cs.calvin.edu/activities/books/processing/text/01computing.pdf.

As such, this binary number serves as a command to the machine, indicating the specific set of chain reactions between such 2-state equipment that must take place. The result is that after this chain reaction is finished, and a few of the entire of the 8 dominoes at either side have been thrown over, it seems as though this domino machine is “output” an 8-digit binary number. Such domino similarity gives several resemblances to the method a processor chip composed of transistors functions in terms of functionality. In a processor, binary numbers that indicate the fundamental arithmetic operations addition, subtraction, multiplication, and division come into the processor in the format of electrical impulses that are either “low” or “high”. As a result, a chain reaction occurs amongst the literally millions of tiny transistors as well as the off or on switches, that comprise the processor (Taylor-Robinson et al., 2011). A binary value indicating the outcome of the chain reaction is sent out on the wires heading away from the processor once it has been completed. The processor’s maze of transistors is programmed in such a way that the outcome of the chain reaction is the 1 that corresponds to the “correct answer” to the arithmetic command that was provided as

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input. This was true because the Intel 8088 processor, which was utilized in the genuine IBM personal computer, had been an 8-bit processor. This meant that, during every cycle of instruction, an 8-digit binary number will be input, the processing (chain reaction) will occur, and the subsequently eight-digit binary number will be output (Kolliopoulos & Stein, 2004).

Source: https://cs.calvin.edu/activities/books/processing/text/01computing.pdf.

Consequently nowadays, at the core of its hardware, a contemporary electronic digital computer is indeed a machine that executes simple arithmetic operations. Furthermore, this has a computer machine that replicates or models the fact that numbers change as people perform simple arithmetic. When it comes to modeling arithmetic, the astounding speed with which today’s computers do this task is something to behold. Today’s microprocessors are generally 32-bits or high, which means that their commands have been made up of binary integers with 32 or more digits, as opposed to earlier generations. Command cycles for these kinds of computers machines are measured in “gigahertz,” which means that they can do billions of command cycles per second, according to the specifications.

Figure 1.11. The processor’s operating principle. Source: https://cs.wellesley.edu/~cs110/lectures/compModel/computer.html.

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1.5. SOFTWARE This hardware’s arithmetic capability is meaningless unless it may be put to work executing relevant computations in proper orders using meaningful integers. It’s indeed computer software that delivers such helpful and relevant guidance. Moreover, it is because of the advancement of software that computer has progressed from simple number crunchers to technologies that nowadays benefit a lot of aspects of human existence (Aslam & Montague, 2001). By considering the case of calculating taxes, a calculator may surely help with this procedure, since it may speed up and improve the precision of the arithmetic which is included. A calculator, on the other hand, can’t calculate one’s taxes for one. Instead, the tax format defines which mathematical operations, under what sequence, and with what numbers to be done. A tax format, in this respect, is similar to a computer program, that is likewise a specified series of activities including proper information that, when executed, achieves the desired outcome. For instance, with today’s readily accessible tax software, the program of a computer is modeled after the program for the personal activity that is specified by the tax format.

Figure 1.12. Analytical engine developed by Charles Babbage. Source: https://en.wikipedia.org/wiki/Analytical_Engine.

Charles Babbage, who is well-known for his contributions to the history of computer hardware, is equally well-known for his contributions

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to software history (Figure 1.12). After abandoning the difference Engine, Babbage started work on his Analytical Engine, a far more advanced engine. This gadget was supposed to be significantly more adaptable and automated as compared to his previous innovation. In the case of hardware, Babbage envisioned a machine that could execute simple arithmetic operations on numerical digits – a calculator (Van Horn et al., 2001). Babbage wanted to feed the order of metal cards having punched holes into his Analytical Engine, using a technique from the mechanized Jacquard looms which started to emerge in the early 1800s. Rather than defining an order of threads to include into a specific weave, the cuffed cards will be utilized to specify an order of fundamental operations of arithmetic for the Analytical Engine to execute, when combined, it produced a desired mathematical result. In another sense, except for previous computing machines, Babbage’s Analytical Engine will be programmable: as a single automated loom can execute various weaves by switching computation of punched cards, Babbage’s Analytical Engine might switch among various mathematical calculations by modifying the set of punched cards. It had been arranged into the four basic processing subsystems, input, storage, and outcome; the Analytical Engine predicted the essential “architecture” of the current electronic computer to a surprising extent (Shamir & Avidan, 2009). Ada Lovelace, Lord Byron’s daughter, had one of the certain persons, aside from Babbage, who saw the Analytical Engine’s immense potential. The Analytical Engine constructs algebraic designs exactly like the Jacquard loom weaves leaves and flowers, in both scenarios, just through conducting a properly defined series of simple operations, she explained. Lovelace devised and documented illustrations of how sophisticated mathematical calculations may be generated purely from the order of the Analytical Engine’s core set of arithmetic operations. Ada Lovelace is sometimes referred to as “the 1st programmer,” and her work supports this claim – maybe much like is generally acknowledged. One of the main properties of a computer program, according to Lovelace’s ideas, is its perfectly sequenced structure. The term “algorithm” is frequently used by mathematicians to indicate a specified series of operations that, if followed, would generate a certain intended outcome. As a result, one of the most important aspects of computer programming is “algorithm design,” which is the procedure of properly breaking down a procedure into an order of actions that a computer can accomplish. This view of programming as creating a series of procedures to achieve a goal

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is also consistent with what is known as the procedural model of computer programming (Babaioff et al., 2008). “A successful computer program according to Lovelace, must be universal in the concept that the order of the specified operations must be capable to perform on an endless number of specific numeric values,” instead of being specified to function on only a single set of arithmetic operations. Instead of creating a program to execute a specific task, a “generic” program will take any 3 integers, multiplies the 1st two, and then subtracts the 3rd number from the output. (2 x 10) – 5 This demonstrates whatever Lovelace termed as “independent” operations, which are operations that are not reliant upon the objects being operated on. It is commonly stated in words of the segregation which is kept between the “data” as well as the operations which are performed on that data in current computing terminology (Bach et al., 1996). As part of her description of cycles of operations, Lovelace explained how a specific desired outcome might be reached by repeating a specific subset of operations repeatedly until the intended result was attained. This is referred to as a “loop” in contemporary computer programming. A basic instance of this is the method where each multiplication operation (5 x 4) and it is also possible to do this by continually conducting a single addition operation, as follows: 5 + 5 + 5 + 5. Because of the computer’s impressive capability to automatically do the cycles of operations, with the outcomes building on one another, Lovelace boldly predicted that this machine will be capable to do computations that had not been worked out before by any person. This prediction proved to be correct. Moreover, it brings to light a crucial feature of programming that is commonly neglected: specifically, it is the procedure of programming usually comprises in the simple transcription into code of a fully conceptualized notion of the software that has already been theoretically worked out in its completeness. Instead, the procedure of programming comprises research, experimentation, originality, discovery, and innovation, all of which are essential elements of the creative process (Bae & Takaoka, 2006). What’s rather more astounding is the extent to which Ada Lovelace predicted that computers will have uses that went well beyond the realm of math and science. Lovelace was keen to stress out that the analytical engine did not work on real numbers but instead of the symbols of numbers, and

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as such the engine may organize and mix these signs of numbers almost as easily as it may arrange and mix letters or other basic symbols. In reality, according to Lovelace, whatever is meant by the term operation, in that case, might relate to any activity that affects the reciprocal relationship between 2 or more entities, regardless of the nature of the relationship in question. As a result, according to Lovelace, a significant portion of the computer’s strength comes from its capacity to build and modify symbolic illustrations of the various things and huge truths of the natural world (Bailey et al., 1991). In addition to this, Lovelace recognizes the exceptional capability of the computer to generate symbolic illustrations of some aspects of the associations among objects and facts, as well as the capability to manipulate those symbolic illustrations to make models of such unceasing adjustments of mutual relationship, whether it is visible or invisible, knowingly or unknowingly to our direct physical conceptions, are interminably taking place in the organizations of the formation we work more closely with. Provided this the broadest concept of a computer, which is a trickster of symbolic illustrations of factual relationships and entities (also known as factual relationships and entities manipulation), Lovelace proposed that the novel, fast, and influential language which we now know as computer programming might potentially be of usage not only in math and science, as well as in all subjects throughout the universe (Bakker et al., 2012). The computers are capable of far greater than the simple mathematical calculations required to file taxes. It is due to the computers are prompted software programmers to investigate how many real and imagined particulars and things may be described in the format of symbols that may be manipulated to replicate actions, phenomena, procedures, and connections, at least to an extent.

1.5.1. Object-Oriented Programming and Personal Computing Lovelace foresaw a change in computer programming concepts in certain aspects. She envisaged computation and programming as expanding beyond statistical information and conventional notions of algebraic order of mathematical operations. Rather, she regarded programming as a tool for creating and manipulating which we refer to as the models of computers. Virtual objects with linked qualities and behaviors are the essential building blocks of software in this change from a solely algorithmic paradigm to one that also provides for the choice of what has come to be known as objectoriented programming (Baswana et al., 2007).

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Object-oriented programming (OOP) arose largely from efforts to create the latest generation of computers and graphical user interfaces (GUI) in the latter half of the 1970s, and the fast surge in popularity of such technologies in the latter 50% of the 1980s had been associated through the same surge in popularity of the object-oriented programming conceptions that empowered people (Bateni et al., 2013).

Figure 1.13. First Dynabook idea by Alan Kay. Source: https://www.quora.com/American-computer-pioneer-Alan-Kay-s-concept-the-Dynabook-was-published-in-1972-How-come-Steve-Jobs-and-AppleiPad-get-the-credit-for-tablet-invention.

Alan Kay was among the 1st people to utilize the phrase “personal computer,” and he had been also the leader of a team at Xerox Corporation’s Palo Alto Research Center (PARC) that worked on the development of a compact, personal computer throughout the 1970s. The hoped-for outcome The Dynabook, as it had been dubbed at the time, had a striking resemblance to the “notebook” computers that will first appear on the scene after two decades. Its GUI, allows customers to interact with the computer through choosing and attempting to manipulate onscreen menus to utilize a relatively unfamiliar novel pointing device called a “mouse,” instead of having to memorize and type cryptic commands as had been the case with the prior “command-line interface,” had been one of its most notable features and a novel paradigm (Bayer, 1972). The Dynabook’s graphical user interface also enables the users to work in several on-screen windows that may deliver various viewpoints at the identical time the user’s data was displayed on the screen as interactive virtual “objects” that included text passages, drawings, images, music and

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sounds, descriptive icons, and more (Figure 1.13). A graphical user interface like this had been created to produce a dynamic on-screen depiction of an active environment that the user could identify and change quickly (Fiorini et al., 2021). Moreover, Dynabook’s Smalltalk software system had a graphical object-oriented programming environment. Kay’s team had a vision wherein Dynabook users will indeed start by engaging with instinctive software developed through others, but once they had been prepared, they will be capable to analyze and even transform the virtual character’s specified characteristics and behaviors; such software programs were made up of objects. In reality, the idea had been to create a version of the Smalltalk programming language that had been so simple to use that even children who used the Dynabook could want to write their software. In this sense, early testing with Palo Alto children had shown to be quite promising (Beauchemin et al., 1998). An instance of this graphical user interface was developed as part of an effort to assess the value of the Dynabook project, and it represented a virtual “desktop” developed to resemble the actions and working space usually office work in a manner that even a worker with really no preceding computer skills will locate interacting with the system to just be enjoyable, productive, and intuitive. This effort failed to persuade Xerox officials; nonetheless, Apple Computer co-founder Steve Jobs viewed a demonstration of such graphical user interfaces some years back, in 1979.

Source: https://en.wikipedia.org/wiki/Command-line_interface.

The Apple II portable computer system, which debuted in 1977, used a command-line interface to connect with its customers, similar to certain other earlier microcomputer systems. Upon viewing the PARC system, Jobs stated, “This was evident to me for about 10 min that almost all computers will function such as this eventually.” Jobs got right to work building the

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next generation of Apple computers, which started in January 1984 with the introduction of the Macintosh. The Macintosh’s GUI, and that of Microsoft’s Windows OS, which debuted per year after, was strikingly similar to the desktop graphical user interface developed at Xerox PARC (Karger et al., 1998). The Apple II, such as other earlier microcomputers, had been developed and sold on the premise that consumers will build their software (perhaps utilizing one of the introductory technical programming languages of that age, like the extensively utilized BASIC). Meanwhile, in 1979, a new significant occasion occurred, propelling the Apple II to new heights of fame that extended much further than computer programmers and hobbyists. In 1979, VisiCalc, a revolutionary novel and sophisticated spreadsheet software package that was created for bigger computer systems some years before, was successfully transferred to the Apple II microcomputer system. VisiCalc is an instance of an application software program, which is pre-written software that is given for others to use. VisiCalc is sometimes referred to as the Apple II’s killer app, a word that has come to reference a novel technology’s utilization that helps to legitimize its acceptance by a broad variety of new users. The accessibility of an edition of the widely respected VisiCalc spreadsheet software for the Apple II and, later, for numerous other microcomputers, such as those developed by Commodore and Atari, helped to persuade numerous people that the Apple II wasn’t just a new device, but a genuine computer capable of significant scientific and business applications. Likewise, when IBM Corporation hustled to initiate its personal computer in 1981 in response to the sudden and dramatic rise in fame of microcomputers in the late 1970s, fueled largely through VisiCalc, the killer app that persuaded the majority of users to purchase the IBM PC had been, once again, a spreadsheet program: 1–2–3 is the Lotus formula (Bender et al., 2005). With the advent of spreadsheet software, inexperienced computer users were able to perform the kinds of computational operations on textual and numerical information that had previously been linked with computers without attempting to know what they will have considered being a genuine programming language. Rather than being a stretch, particularly considering the broader history of computers, it doesn’t appear to be very farfetched to say that spreadsheet software introduced a novel form of the computer programming environment. The layout of calculations inside spreadsheets was, without a doubt, considerably more straightforward for novice users than the design of computations within traditional programming languages.

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The fact that spreadsheet software itself became so strongly associated with “application software,” instead of considering this kind of spreadsheet software to be a kind of programming environment, in which the spreadsheets that this software system facilitated one to develop for usage by others had been considered to be the resultant application software, appears regrettable in retrospect, the failure of this project may reflect the larger shift in public understanding of the concept of “personal computing” that occurred throughout the 1980s, in which Alan Kay’s dream of a desktop pc which was so intuitive and graphical that even a child could write software programs for it was deflected by a conception of the desktop pc as a machine that nobody must have to learn how to program for it was displaced (Ben, 1983; Siesjö et al., 1989). In the adaptations of PARC’s Dynabook vision made by Apple and Microsoft, the concept of empowering new computer consumers not just to utilize software written through others but also to continue conveniently across a pathway of learning that will ultimately lead to the capability to begin modifying those software programs and, ultimately, the capability to create simple software programs of their own appears to are completely lost. The concept of Alan Kay’s for the desktop pc like a medium, one that allowed the ability to develop new sorts of models of concepts that might be experienced as a type of performance, was based on this. The ideals of musical and theatrical performance, in reality, were recognized by Kay as having a significant effect on the formation of the Dynabook. Kay has stated that he views the desktop PC as like an instrument, with the music being composed of thoughts. Instead, the “air guitar”-style user experience that Microsoft and Apple pioneered in the 1980s has resulted in more than 20 years of “air guitar” style personal computing, according to Kay (Halpert et al., 1994). As a result, rather than utilizing the personal computer like a medium by which to design and stage performances of our concepts, we perform inside of models of consumer experience that are developed by others, models which are often restricting, troublingly universal, and complicated to associate with one’s concepts (Bentley & Shaw, 1980). However, with the adoption of the Macintosh and Windows GUIs as the main interface paradigms, a novel idea of the computer’s “user” emerged: the “end-user,” who was, by definition, a non-programmer. For more than two decades, people have thought of personal computers as the polar opposite of computer programming.

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Despite the numerous resemblances in the GUIs, part of the reason why the Macintosh and Windows OS were eventually such significant compromises in the PARC vision of personal computing is that it just might not be completely realized on the hardware technology accessible at the time. Furthermore, despite early triumphs, PARC researchers realized that their idea of an intuitive version of Smalltalk like a programming language was proven to be harder to implement than they had anticipated. Ada Lovelace, it’s worth noting, foresee the type of enthusiasm and disillusionment that may accompany computer technology. She stated that it is important to protect against the danger of overblown beliefs about the Analytical Engine’s capabilities. When examining a novel subject, we often have a propensity to exaggerate what we already find fascinating or noteworthy. Furthermore, when we learn that our beliefs have passed those that are truly tenable, Lovelace observed that we have a propensity to swing too far in the opposite direction, undervaluing the genuine status of the situation as a natural reaction (Bentley & Shaw, 2012). In the case of the binary link between computer programming and personal computing, something similar may have occurred in popular conceptions of computer programming versus personal computing. It is true that at times, this diametric association may even approach the brink of intolerance, with programmers decrying end-users as “ignorant” and endusers labeling programmers like “geeks” (Ring et al., 2012). Individuals whose enthusiastic use of personal computer software has prompted them to seek ways to break outside the confines of the end user box to customize their user experience or to invent entirely new forms of user experience have experienced a resurgence and gradual increase in interest in the idea of programming in the new millennium. That’s also particularly true in the scenario of certain areas of computing that have come to be known by a variety of names, including digital design, digital media, digital imaging, and digital art, in which the personal computer is expressly considered as an artistic medium. Considering that computer programming is typically linked with numeric values in the fields of science, mathematics, and business, it may appear strange that those who approach computers from a more esthetic perspective will be interested in learning to program (Bienstock, 2008). People who engage in the more expressive and artistic design procedures, on either side, are much less likely to be receptive to the more general and restrictive workflows which are intrinsic in most commercially accessible application software, according to the

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research. In this context, it may be unsurprising that a large number of digital designers and photographers of digital graphics, for instance, choose to customize the applications they utilize by creating their extensions and filters for this kind of software, despite the high level of difficulty which is frequently associated with developing such customizations. Certain people are so inspired by the artistic pursuits that they decide to teach themselves a conventional programming language that is perhaps the most challenging approach to learning this knowledge. According to a recent article in the New York Times, the Processing programming language used in that book had been firstly created at the Massachusetts Institute of Technology in 2001 to make it simpler for artists to build dynamic graphical art on computers through the use of a combination of interactions, animation, and images (Bienstock & McClosky, 2012).

1.5.2. Programming Languages and Compiling As previously stated, a contemporary electronic computer is still, at its most basic hardware level, a binary, digital arithmetic machine that performs binary operations. The processor, which is at the heart of the system, acts on data that is thought to be in the form of binary integers. This type of data item is depicted and then sent to the processor as an electrical signal with a voltage which is assessed to be either low or high, that also serves to switch certain of the processor’s off/on transistor buttons in a manner that simulates the inputting of this binary digit into to the processor (Blasgen et al., 1977). As with the simple arithmetic operations performed by the CPU, every one of these operations (such as add, subtract, multiply, and divide) is defined via a distinctive binary number as well. This means that the binary digit representing the command to execute a particular arithmetic operation may be communicated to the processor. To produce the precise chain reaction of transistor switches that replicates that specific arithmetic operation, a set of binary numbers expressed as low/higher electrical signals trigger the processor in a certain manner. The outcome of this reaction is a depiction of the binary digit that corresponds to the right outcome for the operation performed with the provided input (Little et al., 1989). To put it another way, a software program that is executed by a computer’s processor is made up of an extremely lengthy stream of binary integers, some representing a specific arithmetic operation to be done and others representing the data that will be utilized in that operation (Blelloch, 1996).

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100011 00011 01000 00000 00001 00100 In reality, to load a software program into an earlier electronic computer in the 1940s and 1950s, a programmer had to input lengthy series of binary digits of this type. This is, thankfully, no longer true. Throughout time, several higher degree programming languages have emerged that enable programmers to create software that uses arithmetic characters and decimal numbers that are comparable to those used by humans while doing mathematics. As an example, adding ten to the existing value of the variable x may be represented as: x + 10 Besides allowing data made up of letters and punctuation marks, higher degree programming languages also permit instructions that are extremely close to words utilized in human languages to be included in the code. A typical higher-degree programming language, for instance, would include something like the following syntax for an order to print on the screen the greeting Hello! print(“Hello!”) Nonetheless, to be performed on a computer, software created in a higher degree language of programming should be translated into orders of binary integers, consisting exclusively of 0 second and 1 second. It’s because, at the computer’s hardware level, just one element that may be entered into a chip of processor is higher and lower electrical signals that show these binary numbers and that would on/off the necessary chain reaction in the processor’s maze of off/on transistor buttons (Blelloch & Gibbons, 2004). Compiling a program refers to the procedure of converting software compiled in a higher degree language into binary commands that may be transmitted to the processor. A compiler is a specific translation software program that transforms source code compiled in a specific higher-degree programming language into the necessary order of binary commands. The instructions defined in the higher degree programming language are referred to as the “source code” of the software program wherein they are used. Binary code, object code, machine code, or executable code are all terms that are used to refer to the binary output produced by a compiler that may be processed through a processor while a software program is being performed by the processor (Blelloch & Greiner, 1996). Furthermore, there are always several challenges associated with the compilation of computer software applications. In the 1st place, the sequence

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of binary instructions which may be delivered to a CPU varies from one type of processor to the next. Therefore, source code that is generated for 1 type of processor chip may not be readable on various types of processor chips as a consequence. The fact that PC utilization software, like a word processor, would usually be made accessible in distinct Macintosh and Personal Computer editions is because binary commands in the executable code have been conveyed to a processor in the direction of a specific OS, and also that the Macintosh and Windows OS themselves are software programs which are developed and compiled for completely distinctive categories of processor chips. However, the very comparable Linux and Unix families of OS, which may be designed to run on a huge range of processor chips, such as the ones present in both Macintosh and Windows computers, are not restricted to a single processor chip (Blelloch & Greiner, 1996).

1.5.3. Platform Independence Moreover, the surge in the fame of the World Wide Web that began in the last part of the 1990s has brought the issue of incompatible PC systems to the forefront of public discourse. The Internet was intended to be a platformindependent foundation from the very beginning of its existence. This is due to the JPEG digital picture form and it had been created primarily for usage on the World Wide Web, and as a result, it was created to be platformindependent. Therefore, consumers of Windows/PC, Macintosh, and Linux/ Unix PC are capable of accessing, altering, examining, and sharing any given JPEG picture file via the Internet without difficulty (De Matteis et al., 2020). The platform-independent, multimedia data formats of the World Wide Web acted as a catalyst for investigating the viability of constructing platformindependent software applications. Certainly, it is one of the primary reasons behind the platform-independent Java programming language’s meteoric increase in fame in the past years. A Java software program’s source code has not immediately turned into binary code for a particular chip of the processor. Instead, the source code of the Java is generated for a virtual machine, which is a type of general processor that has not resided in all formats of the hardware. As a result, the code generated by the compiler of Java is still byte code, which is an intermediary step between binary code and executable code. Sun Microsystems, which created the Java platform, supplies free software for every of the main computer medium that changes

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Java byte code into the executable binary code needed by that medium, obviating the requirement for numerous built copies of the similar software application (Blelloch et al., 1999). Similarly to Java, C++ and Python are object-oriented programming languages, wherein the basic units of a software program are envisioned as a wide range of virtual elements, everyone is described to have some uniqueness and behaviors which may be used to develop the general function of the application which is preferred. Firstly, the object-oriented aspect of Java had also a contributing factor to its attractiveness and quick increase in fame in the industrial sector. Thus, starting in the late 1990s, Java had been positively welcomed as the higher degree programming of chosen language for beginner computer programming classes, and it had been also used in a broad range of industries, including the entertainment sector (Blelloch et al., 1995).

1.6. PROCESSING Because of the intricacy of the technique needed to input a program, type it, and run it, usually referred to as the environment of programming, learning how to program can be difficult. One can need to learn a set of commands for the OS (e.g., Unix) which is being utilized as well as editor commands for inputting and editing the program while working in a command-line environment. Furthermore, a range of integrated development environments (IDEs), like Microsoft’s Visual Studio and the widely used open-source IDE Eclipse, are accessible that enable this substantially easier to do. Generally, one starts by creating a fresh project and defining the programming language to be utilized. After that, one might add a few libraries to the project, reorganize certain windows, and develop a text file and package, into which one enters the code of source for the program and keeps it, and afterwards build the project from the source code. It is possible to run the object program that was generated (Blelloch et al., 1997). Processing, on the other hand, is among the most straightforward environments to utilize. The basic “sketch window” with 6 buttons at the upside, a program editing window down to this one, and a textual outputting window at the bottom are displayed when it is first launched, as seen on the right in Figure 1.14.

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Figure 1.14. The blank graphical output window and program editor window. Source: https://tekeye.uk/visual_studio/hello-world-in-c-sharp.

There is now no program code in the program editor, thus it is a vacant program. However, when we press the left-most Run button 4 at the peak of the sketch window, a new window emerges (seen on the left shown in Figure 1.14). Since it comprises graphical output generated by a program, it is termed as the visual output window. Since the program is blank in this instance, no outcome is shown (Blelloch et al., 1994).

Figure 1.15. Text-based hello-world program. Source: https://tekeye.uk/visual_studio/hello-world-in-c-sharp.

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We include this line because a program without any helpful code is not particularly fascinating. println(“Hello World!”); it is represented in Figure 1.15. When we pressed the Run button once more the outcome: Hello World! It emerges in the text output window. (As we’re exporting text rather than images, nothing output displays in the visual output window.) To generate a basic visual depiction and also to the text outcome, you may add the line ellipse(50, 50, 50, 50); In the visual output window, this would generate an ellipse having a center (50, 50), minor and main axes both 50 (pixels), such that, a circle having a center (50, 50) and having the radius of 50 as illustrated in Figure 1.16.

Figure 1.16. Graphical hello-world program. Source: https://www.c-programming-simple-steps.com/c-hello-world.html.

If we wish to add certain color, we could use the following two code lines: background(0); fill(0, 0, 255); Fill the backdrop with 1 color (blackish color, as defined via the color value 0, which signifies zero light) and the circle with each other, as illustrated in Figure 1.17. (Blue color is specified via the red-green-blue color triple representing no green, no red, and 255, blue, complete intensity).

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Figure 1.17. Graphical hello-world having color. Source: https://www.programmerall.com/article/94551981263/.

When we like a rather extra practical representation of the Earth, all we must do is download the Earth’s image file into your PC folder holding our software and make the necessary modifications to the program, as seen in Figure 1.18.

Figure 1.18. Hello-world program having a bitmap Earth’s image. Source: https://cs.calvin.edu/activities/books/processing/text/01computing.pdf.

The instances that follow are designed to give you your 1st taste of the processing environment as well as to provide a sense of how simple it is to develop some interesting programming in it.

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87. Kolliopoulos, S. G., & Stein, C., (2004). Approximating disjoint-path problems using packing integer programs. Mathematical Programming, 99(1), 63–87. 88. Lenstra, H. W., & Pomerance, C., (1992). A rigorous time bound for factoring integers. Journal of the American Mathematical Society, 5(3), 483–516. 89. Little, J. J., Blelloch, G. E., & Cass, T. A., (1989). Algorithmic techniques for computer vision on a fine-grained parallel machine. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(3), 244–257. 90. Papadimitriou, C. H., & Yannakakis, M., (1991). Optimization, approximation, and complexity classes. Journal of Computer and System Sciences, 43(3), 425–440. 91. Parsons, S., & Jones, G., (2000). Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks. Journal of Experimental Biology, 203(17), 2641–2656. 92. Ring, J. D., Lindberg, J., Howls, C. J., & Dennis, M. R., (2012). Aberration-like cusped focusing in the post-paraxial talbot effect. Journal of Optics, 14(7), 075702. 93. Sandel, B., Arge, L., Dalsgaard, B., Davies, R. G., Gaston, K. J., Sutherland, W. J., & Svenning, J. C., (2011). The influence of late quaternary climate-change velocity on species endemism. Science, 334(6056), 660–664. 94. Shamir, A., & Avidan, S., (2009). Seam carving for media retargeting. Communications of the ACM, 52(1), 77–85. 95. Siesjö, B. K., & Bengtsson, F., (1989). Calcium fluxes, calcium antagonists, and calcium-related pathology in brain ischemia, hypoglycemia, and spreading depression: A unifying hypothesis. Journal of Cerebral Blood Flow & Metabolism, 9(2), 127–140. 96. Sjöberg, S., Malmiga, G., Nord, A., Andersson, A., Bäckman, J., Tarka, M., & Hasselquist, D., (2021). Extreme altitudes during diurnal flights in a nocturnal songbird migrant. Science, 372(6542), 646–648. 97. Stogryn, A. P., Butler, C. T., & Bartolac, T. J., (1994). Ocean surface wind retrievals from special sensor microwave imager data with neural networks. Journal of Geophysical Research: Oceans, 99(C1), 981– 984.

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98. Taylor-Robinson, S. D., Toledano, M. B., Arora, S., Keegan, T. J., Hargreaves, S., Beck, A., & Thomas, H. C., (2001). Increase in mortality rates from intrahepatic cholangiocarcinoma in England and Wales 1968–1998. Gut, 48(6), 816–820. 99. Van, H. J. D., Grethe, J. S., Kostelec, P., Woodward, J. B., Aslam, J. A., Rus, D., & Gazzaniga, M. S., (2001). The functional magnetic resonance imaging data center (fMRIDC): The challenges and rewards of large–scale databasing of neuroimaging studies. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356(1412), 1323–1339.

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PRINCIPLES AND APPLICATIONS OF DNA COMPUTING

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2.1. Introduction....................................................................................... 40 2.2. Construction of DNA Logic Gates as the Basic Computing Components................................................................. 42 2.3. Scaling Up DNA Logic Gates for Building Computing Systems.......... 48 2.4. DNA Molecular Computing for Intelligent Diagnostics...................... 52 2.5. DNA Arithmetical Computation For Intelligent Diagnostics................ 59 2.6. Summary........................................................................................... 61 References................................................................................................ 62

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2.1. INTRODUCTION New methods of computation are being initiated by demands for faster data processing speeds and compact data storage. DNA has demonstrated its ability in data storage and processing because of its predetermined base combinations and nano size for computer controlled and large-throughput coding, and computing, since DNA is the essential biomolecule that possesses genetic information (Amos et al., 2002). DNA also has important biological functions, such as regulation of gene expression and tracking biochemical responses in cells. The creation of advanced domains of DNA computation in biology as well as biomedicine is the outcome of combining these capabilities (Tsaftaris et al., 2004; Xu & Zhang, 2003). The current and significant advancement in DNA-based analysis for biomedical field are discussed here, with a focus on biosensing and detectives. A formulation as well as simulation of DNA computational systems are also explored at various levels of the pipeline. The goal is to show DNA computing is like a significant means for smart diagnosis (Condon, 2010; Paun et al., 2005). In today’s “digital” civilization, data processing and storage are essential issues (Hao et al., 2021). However, current data storage as well as computing modes, and manufacturing technology limitations, are among the most significant barriers to a complete sustainable “digital” lifetime in the future. As a result, it is curious to investigate new methods of computing that apply data more effectively, and DNA computing is developing fast in this direction. DNA computing is a novel type of computing that integrates information technology and biology (Kumar et al., 2015). To represent a specific computing procedure, a sequence of consistent biochemical processes with DNA molecules can be used. Adleman (1994) was the first to suggest the notion of DNA computing, based on the theory of thermodynamic equilibrium state of base pairing, and was the first to solve an NP-complete non-deterministic polynomial problem: the Hamiltonian route problem is an issue in mathematics. He employed the automated recognition capability to encode the paths in the Hamiltonian route problem to differ single-stranded DNA (ssDNA) series to establish the right Hamiltonian routes among DNA bases and polymerase chain reaction (PCR). Okamoto et al. (2004) coupled digital logic with DNA molecules for the very first time 2004 to create DNA logic gates. Multiple logic gates might construct circuits through DNA flowing reactions in their architecture, allowing for DNA-based computing as designed. This study demonstrated how to build DNA-based logic circuits and expanded an area of DNA computation (Green et al., 2017). In 2009, IBM unveiled the proposal to use DNA and nanotechnology to produce the

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next stage of the evolution of microprocessing chips, ushering in the new age of DNA computing. Generally, DNA computation relies on biological molecules’ capacity to interpret information to substitute the switching elements of a digital circuit. Information can be encrypted into a particular DNA series and then coupled to the various formats in the power of external parameters like light, temperature, enzymes, molecular concentrations, and so on, through the double helix shape of DNA particles and the existence of a complementary base combination. Using DNA displacement processes, for example, DNA molecules may be controlled to generate logic circuits by forming and breaking down hydrogen atoms in the duplex (Li et al., 2013). The core of DNA computing, in our perspective, is programming distinct DNA molecules in a controlled and coherent way that encodes data on to the basis of set of constraints. Until now, DNA-based logic processes have been showed to perform logical computing like transduction speed, amplification, and signal storage (Dong et al., 2009). However, due to its low modularity and computational power, DNA computing is still a long way from meeting the standards of today’s semiconductor computer technology. On the other hand, because DNA computing combines operations of nucleic acids like biomolecules that recognize as well as control other biomolecules with their capability to analyze as well as implement computational functions, this is rational to investigate the ability of utilizing DNA computing in biomedical as well as biological studies, particularly in a diagnostic model, at which multiplex sensing and in situ analysis are required (Watada, 2008; Ignatova et all., 2008). Using DNA computation technique for diagnostics has various benefits, in our opinion. Firstly, it may acquire a greater degree of intelligence in terms of executing tasks without the assistance of humans or computers. Like, the system may combine biomarker detection with analysis and presentation of results at the same time. Furthermore, the methodology of information processing may be programmed to give more precise diagnostic results (Zhang et al., 2019). Finally, DNA computation does not require expensive apparatus and may be completely integrated into existing biosensing as well as clinical diagnostics settings. DNA computing as a whole presents the universal and flexible foundation for cheap and efficient biomarker analysis techniques. As a result, the focus of this assessment is on a development of intelligent DNA computing systems as well as the most recent diagnostic research advances (Xu & Tan, 2007) (Figure 2.1).

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2.2. CONSTRUCTION OF DNA LOGIC GATES AS THE BASIC COMPUTING COMPONENTS The logic gate is an instrument that utilizes logical processes to transform a collection of inputs to observable outputs. Modern computers’ primary computing elements are basic logic gates like YES, NOT, AND, NAND, OR, XOR and NOR (Liu et al., 2015). Thus, in order to develop a DNA-based computer, logic gates of DNA must first be built. Numerous arrangements depend on various biological response processes have been established, such as deoxyribozyme (DNAzyme)-based reactions, scaffold-based models, and strand displacement reactions, laying the groundwork for the development of DNA logic circuits (Ma et al., 2021).

2.2.1. DNA Logic Gates Based on DNAzymes Ribozyme was initially found by researcher from rRNA precursor chemicals of tetrahymena and the ribonuclease P complexes of microbes. They discovered as RNA can self-catalyze. Breaker and Joyce (1994) identified a DNAzyme capable of particularly cleaving DNA substrates along metal ion co-factors utilizing an in vitro screening process. Since ribozyme and DNAzyme are components of nucleic acids in existence, they are compatible with nucleic-acid-based computer systems and have emerged as important paradigms for developing DNA logic gates (Bi et al., 2010; Zhang et al., 2013). Penchovsky and Breaker (2005) proposed a computational technique for managing an operation of allosteric ribozymes (RNA switches) in response to various DNA inputs. Four main RNA switches with OR, NOT, AND, and YES logic gates of Boolean were built in a modular manner which could provide high reply selectivity to inputs with the absences of modifying the catalytic central of ribozyme, exhibiting exact as well as universal creation of various logic gates using allosteric ribozymes. Stojanovic et al. (2002) initially disclosed a creation of a logic gate relying on DNAzyme. They employed the DNAzyme like the operational module (Figure 2.2a) to logically construct distinct output configurations by cutting the DNA substrates in reply to IA and IB (Figure 2.2b). They successfully developed logic gates i.e. NOT and AND, and the more complicated XOR gate, using this design guideline. They built a solution-stage array of three logic gates depend on DNAzymes and utilized it is like half-adder for logic computation based on a similar concept (Stojanović & Stefanović, 2003). This is the first instance

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of a DNAzyme-based computer system having two initiators and two products that might be utilized to train higher-scale circuits. Though using DNAzyme for DNA logic gate creation has several benefits, it necessitates the utilization of RNA-having heterozygous DNA like a medium (Li et al., 2009). This resulted in certain limitations, such as RNA’s low chemical stability and expensive synthesis costs, which eventually limited the uses of such logic gates (Guo et al., 2016).

Figure 2.1. A schematic illustration of DNA computing as well as its applications. Source: https://onlinelibrary.wiley.com/doi/full/10.1002/sstr.202100051.

Knowledge from this failure, Chen et al. (2006) created a logic gate depend on DNAzyme (Figure 2.2c) utilizing only DNA and an ion of copper as a partner for DNAzyme. DNAzyme can be triggered in the existence of an input of DNA strand to split a base material as well as form a DNA output, according to their design (Figure 2.2d). They utilized this strategy to create more logic gates as well as a sophisticated three-input logic circuit. Elbaz et al. (2010) regulated a DNAzyme’s catalytic activity by manipulating its shape and constructed a set of logic gates (XOR, AND, YES and INHIBIT). They linked such logic gates together to create a more complex DNA logic circuit capable of performing smart actions (YES– AND–InhibAND) through autonomously responding to corresponding inputs. It can adjust gene expression as well as enzyme activities along the

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required logic operations by adding antisense DNA and aptamers into the system. Orbach et al. (2015) built a higher sized logic circuit by splice and combining various DNAzymes, achieving the functionality of the 1/2 adder and a full-adder for binary integers (Figure 2.2a).

Figure 2.2. (a) AND gate: DNAzyme is only dynamic when both of the inputs are available. (b) Fluorescence output of logic gates with the following input configuration (from the beginning to end): IA and IB (green line output), just IB, just IA, and no input. The Cu2+-dependent DNAzyme and logic gates are shown in c. (d) DNAzyme secondary structures: DNAzyme X (“YES” gate), DNAzyme Y (“NOT” gate I), and DNAzyme Z (“NOT” gate II). Right: graphs of reaction time vs cleaved product portion (“ON” (•), “OFF” ()). Source: https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.200502511.

Furthermore, logic gates depend on DNAzyme have been developed to identify viral genes. Kamar et al. (2017) created an OR logic gate in 2017 that was created by three DNAzyme-specific biosensors, every of them with a binding pocket to the viral genome sets (Figure 2.3b). This was able to fluorescently indicate the existence of several forms of enterovirus 71 (EV71), spanning 90% of known EV71 mutant variants. Vijayakumar and Macdonald (2017) coupled Lyssavirus balancing series to ribozyme

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structures and made a set of YES gates for Lyssavirus detection (Figure 2.3c). This technique, in particular, might detect seven distinct genotypes of Lyssavirus, offering significant promise for extremely multiplex virus analysis.

Figure 2.3. (a) Design of a full-adder using several DNAzymes. (b) Development of an OR gate based on DNAzyme for detecting enterovirus 71 strains. (c) Top: Yes gate for Lyssavirus detection. Bottom: the first wells with the label R1 represent the viral genotype name, and the second wells with the label R2 show the viral genotype name. The viral genotype frequency is shown in well (Vijayakumar & Macdonald, 2017). Source: https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/ cphc.201700072.

2.2.2. DNA Logic Gates Based on Strand Displacement Reactions Numerous research groups have investigated the ability and adaptability of enzyme-based logic gates. However, certain inherent disadvantages preclude the utilization of such logic gates for computation (Zhang & Seelig, 2011). For instance, scaling up the circuit that use the ribozyme-based method is challenging. Additionally, the stringent need for experimental circumstances when using ribozymes and DNAzymes creates barriers

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to additional biological uses (Miao & Tang, 2021; Su et al., 2019). DNA strand replacement is other important technology for operating DNAbased computations. It has the benefits of being highly programmable, nonenzymatic, and scalable. Toehold-mediated displacement and toeholdexchange displacement are two distinct reaction schemes (Han et al., 2014). In toehold-mediated displacement reactions, a ssDNA (input) combines along one dsDNA to substitute another dsDNA’s ssDNA (output) by binding to the dsDNA’s overhang ssDNA region (toehold) (Figure 2.4a). Variation in the number of toehold bases could be used to control the displacement rate pioneered the use of toehold-mediated displacement reactions to construct DNA logic gates like OR, AND (Figure 5a), and NOT (Seelig et al., 2006). They attained larger orders of digital computation by combining different gates, like amplification, feedback, signal recovery, and cascading. Li et al. developed as well as applied a three-input logic gate relying on a circular ssDNA along three distinct input coding domains using a similar rule (Figure 2.5c) (Li et al., 2013). In general, the adaptability of toehold-mediated displacement allows the production of much more complicated logic circuits without the assistance of enzymes. Though, because the numbers of base of the output strands gradually decrease, it is problematic to programmed over three coatings of cascading reactions using this mechanism. Zhang and Winfree (2009) devised a toehold-exchange displacement reaction system to further scale up logic circuits (Figure 2.4b). In this case, the arriving ssDNA might be constructed like a complimentary “shift sequence” to the lower strand.

Figure 2.4. Illustration of a displacement reaction mediated by a toehold. (b) Schematic of a displacement reaction mediated by a toehold exchange. Source: https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/ chem.201304891.

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Although the displacement reaction will happen, the displacement strand has an area of single-strand toehold, that may initiate a converse displacement reaction. An increasing level of fuel strand was then pouring to create an entropy-driven cyclization which maintained the reaction going. Thus, the signal might be flowed forever, based on the precise plan of the input and output DNA sequences, as well as the quantities of fuel DNA. Qian et al. (2021) developed the “seesaw gate,” a novel logic gate relying just on toehold-exchange displacement response (Figure 2.5b). They demonstrated as using such global DNA gate motif, they could assemble large-scale circuits with up to tens of linked logic gates. Additionally, they proved the system’s computational capability by demonstrating its capacity to provide accurate response to a four-bit square root computation. The majority of DNA logic gates use arbitrarily dispersed DNA molecules with no dynamic functions as its inputs and outputs, restricting their biological uses. As a result, it is required to increase the variety and functionality of the outputs and inputs of DNA logic gates. Prokup et al. (2012) built an AND logic gate that used light as an input. Their process relies on the photochemical regulation of strand displacement processes using imprinted thymine nucleotides in DNA series (Figure 6a). This photochemical input might decrease a distance among DNA logic instruments and silicon-based circuits that operate logic gates directly with light. Hemphill and Deiters (2013) broadened the scope of this optical logic gate’s applicability to biological systems. They employed miRNAs as inputs to logic gates and created computational sensors capable of detecting endogenous miR-21 and miR-122 in human cells (Figure 2.6b). Morihiro et al. (2017) created a logic instrument that accepts miRNA as an input and outputs a tiny molecule (Figure 2.6c). To accommodate the increasingly intricate calculation functions, the DNA logic gate’s structural complexity has been raised proportionately. Tang et al. constructed a three-dimensional DNA logic gate based on aptamers to operate AND logic calculations on to surface of cancer cell membranes in order to simultaneously recognize overexpressed cancerous cells markers (Figure 2.6d) (Peng et al., 2018). The unique three-dimensional structure merges the identification and computation modules, hence increasing the overall system’s precision and effectiveness. Fan et al. programmed the adherence of mammalian cells on

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a chip using a smart DNA system with various logic gates (AND, XOR, OR, and AND–OR) (Qu et al., 2017). This discovery establishes a highly ubiquitous mechanism for biological systems to self-organize. Without the need of enzymes, Toehold-mediated DNA strand substitution processes are an ideal instrument for getting and transferring DNA input data, and they have been coded as a formal computer language (Phillips & Cardelli, 2009). Lakin et al. (2011) created Microsoft Visual DSD, a computational tool for building and executing complex logic systems containing such responses. Numerous DNA logic gates, signal amplifiers, circuits, and sometimes even artificial neuronal networks may be modeled in silico and interpreted into precise DNA pattern for application using this approach. Simultaneously, by combining diverse functional molecules like antisense DNA, messenger RNA, optical signals, microRNA, aptamers, and small molecules, a variety of uses in disease diagnosis, targeted drug administration, and molecular diagnostics may be accomplished (Li et al., 2013).

2.3. SCALING UP DNA LOGIC GATES FOR BUILDING COMPUTING SYSTEMS 2.3.1. DNA Computing in Solution The physical foundation of DNA computations in solution is DNA and test tubes, which are analogous to transistors and silicon in microchips. Stefanovic and colleagues in 2003, created Maya, a molecular automaton that could perform tic-tac-toe in the buffer with human players (Pei et al., 2010). A DNAzyme and 23 molecular logic gates made up the full DNA circuit.

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Figure 2.5. (a) Toehold-mediated displacement reactions drive a two-input AND gate. (b) The “seesaw” gate DNA motif. (c) The design of a three-input dominant gate relay on DNA strand displacement responses (Li et al., 2013). Source: https://pubs.acs.org/doi/abs/10.1021/nl4016107.

It might communicate with a real user by replying to additional inputs and reporting results with fluorescent signals till a game ended (Agrawal & Glotzer, 2020). The researchers put every game tree’s 19 options to the test. Maya had executed a flawless plan of victory or tying every time, according to the findings. MAYA II, a second generation molecular automaton with over 100 logic gates, was announced in 2006. On a bigger scale of tic-tac-toe, MAYA II might easily win all 76 possibilities. MAYA III, which diverged from the last two generations in terms of design concept, was released in 2010 (Pei et al., 2010). This was essentially a “primitive” automaton that could serve as an acceptable whiteboard before being educated to execute certain duties after learning. This paved the way for machine learning in DNA computing.

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Figure 2.6. (a) A: Irradiation at 365 and 532 nm as incoming signal for a lighttriggered DNA-based AND gate. (b) DNA computing in mammalian cells using miRNA as a source of information. (c) Design of DNA logic gates based on Staudinger reduction for tiny molecule production. (d) Principles of operation of a 3D DNA logic gate based on aptamers (Peng et al., 2018). Source: https://pubs.acs.org/doi/abs/10.1021/jacs.8b04319.

Later, Qian and Winfree (2011) used two modules: a seesaw gate and a threshold gate, to build a large-scale DNA circuit. The longest circuit in their system comprised of 130 DNA strands that could calculate the integer component of a four-bit binary number’s square root (Qian & Winfree, 2011). Despite the fact that it was nothing new in contemporary computing, it was a huge achievement in molecular computing at the time, with no other equivalent approaches for performing such complex computations. Later, they developed a DNA circuit with four completely linked artificial neurons comprised of many layers of DNA logic gates that could apply a Hopfield associative memory (Qian et al., 2011). When an incomplete pattern was presented to the system, these artificial neurons could distinguish ssDNA sequences and ever recall the most comparable ones. Qian’s second groundbreaking breakthrough involves pattern recognition using numerous

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layers of DNA neurons. The neural networks based on DNA, could be trained with the use of a “winner-take-all” rule and were effectively categorized writing sequences with up to 30 of the 100 bits inverted relative to the right digit sequences (i.e., writing digits from 1 to 9) “remembered” during training. This research showed that DNA computing can be utilized to effectively do highly parallel computations in the same way that a graphical computing unit can (Patwardhan, 2006). Song et al. have published an alternate technique for computing square roots with DNA circuits lately (Shah et al., 2022). To do so, they used DNA polymerase-mediated DNA strand displacement in the circuitry to manufacture individual DNA logic gates. In comparison to standard strand displacement, their technique increased cascade speed and reduced the amount of strands needed for computation, allowing them to complete the square root of a value in 25 minutes using just 37 DNA strands. This is also one of the future options for creating DNA circuits with lesser and simpler arrangements, as well as a simpler cascade system.

2.3.2. DNA Computing on a Scaffold It is appropriate to build localized DNA cascading reactions on the surface to enhance the slower reply time of DNA computing, which is restricted by the dispersion dynamics of DNA molecules in solution (Bošković et al., 2021). DNA nanotechnology gives an appropriate foundation for molecular circuit localization (Bui et al., 2018). Liu et al., (2015) for example, employed a DNA origami sheet like a molecular breadboard for multiplication computations. The system could find the library for a matched solution to a single-digit multiplying computation (1–5) and show the output on the DNA origami sheet (Figure 2.7a). Chatterjee et al. created the “DNA Domino” computing technique, which allowed DNA hairpins on a surface of origami to be triggered via a specific signal show pathway (Figure 2.7b) (Chatterjee et al., 2017). They developed unique logic gates and multi-input logic circuits, like the digital architecture of circuitry, in the Domino scheme and finished the related calculation in various minutes. Boemo et al. presented a comparable “DNA walker” system that employed spatial restrictions to accomplish more robust and quicker computations (Figure 2.7c) (Chatterjee et al., 2017). The “DNA walker” in their system was driven by enzymatic processes and designed with distinct Boolean logic for computation. Furthermore, DNA-breadboard-based computing might be effortlessly written on paper and executed with realistic pattern when paired with

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a software-added design (e.g., Microsoft DSD and Origami studio). To summarize, there are several advantages to developing DNA computer systems on scaffolding. First, the nanoscale addressability of the DNA nanostructure allows for the exact assembly of computer units. Furthermore, partitioning the calculating substrate decreases the problems of molecular circuit design in solution because beneficial contacts may be fostered and unfavorable interactions can be spatially segregated by carefully organizing the parts. Finally, the reaction mechanism on the surface of the DNA origami may be fine-tuned by accurately adjusting the distance among the two reaction DNA strands (Huang et al., 2020).

2.4. DNA MOLECULAR COMPUTING FOR INTELLIGENT DIAGNOSTICS Generally, DNA molecular computation can be classified as logical or arithmetic. Logical computation is concerned with the usage of various logic gates like AND, OR, and NOR, and creates related computing outputs as a result of logical cascade of the various gates. In other words, DNA arithmetic computation enables the precise application of mathematical concepts at the molecular level and the complex examination of diagnostic biomarkers. As a result, we discussed current breakthroughs in the use of DNA logical computing for in vitro biomarker examination, membranebased cell profiling, and intracellular biomarker analysis, and the use of arithmetic computing for intelligent diagnostics (Table 2.1).

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Figure 2.7. (a) DNA-origami-based molecular computation for multiplication computations (Figure 2.7). (i) The DNA chip’s design; (ii) mechanism of translation; (iii) during translation fluorescent pictures. (b) A top–down image of the origami-based localized circuit for implementing digital circuits. (c) A schematic representation of the “burntbridges” DNA walker on a computational scaffold (Chatterjee et al., 2017). Source: https://pubs.acs.org/doi/abs/10.1021/acssynbio.5b00275. Table 2.1. Contrast of Dissimilar Instruments of Intelligent Diagnosis

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Computing mechanism Logical computing

Target

Applications

mRNA

Small-cell lung cancer and prostate cancer diagnosis

PPAP2B, GSTP1, PIM1, HPN miRNA miR-21, miR-122 DNA SARS-CoV Peptide

Hepatocellular carcinoma, hepatitis C virus diagnosis Immune response simulator Hela cell isolation

RGD (Arg-GlyAsp) Protein PTK7 Antibody anti-CD3ϵ, anti-Flg

Cancer cell (CEM, Ramos, K562, Hela) identification Transporting molecular payloads to T cells

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Lymphocyte analysis

Protein

Cancer cell (CEM, Ramos, K562, Hela) targeting and therapy

PTK7 Protein PTK7 Protein PTK7 miRNA miR-21, miR-155 ATP, H+ mRNA TK1 Protein IgM Enzyme Thrombin Glucose/NADH Arithmetical computing

mRNA hTERT, GAPDH miRNA

Cancer cell (CEM, Ramos, K562) identification Acute T-lymphocytic leukemia diagnosis Breast cancer (MCF-7) identification Cell imaging Cellular mRNA imaging and targeted transport of molecular payloads in living cells Monitoring dynamic and transient molecular encounters Autonomous blood anticoagulation Cation, anion, organic metabolite, and enzyme tracking in cells Cancer diagnosis, respiratory infection diagnosis Lung cancer diagnosis

miR-148a-3p, miR182–5p, miR-30d-5p, miR-30a-3p

2.4.1. DNA Logical Computation for Biomarker Analysis In Vitro Benenson et al. (2004) published the initial description of an independent biomolecular computer in 2004, which rationally assessed the amount of mRNA class and created reaction outputs able of managing gene expression levels in vitro. Such computer has three programmable components: a computational module that analyzes illness indications logically, an input component which responds to mRNAs or alterations, and an output component which regulates the discharge of antisense DNA molecules. Biochemical sensing, genetic engineering, and clinical diagnosis and therapy are all possible applications for the system. Hemphill and Deiters (2013)

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created a computer system for tracing microRNAs in cells at the same time, and the logic circuit might be utilized to identify illnesses in the future. In Escherichia coli cells, Yin et al. devised and built an RNA logic system which can reliably perform multistep logic operations including AND, OR, and NOT (Green et al., 2017). They believe this technique might be useful in treating intracellular genetic disorders. Our study team was able to create a DNA and enzymebased structure that might mimic an acquired immune system’s essential activities (Han et al., 2015). In vitro, this system could detect pathogen DNA and create unique immune function and memory consequences on pathogen patterns (Figure 2.8a). In addition, we showed that such an artificial immune system might work in model cells (Figure 2.8b) (Lyu et al., 2018). The idea of employing an intelligent nucleic acid computing system at the heart of prototype cell computing paved the way for the development of artificial cells or nanobots for future biological research.

Figure 2.8. (a) Schematic diagram of the simulated acquired immune system using a DNA circuit. (b) Schematic representation of protocells with an integrated DNA circuit simulating an immune response (Lyu et al., 2018). Source: https://pubs.acs.org/doi/abs/10.1021/jacs.8b01960.

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2.4.2. DNA Logical Computation for Analysis of Cell Membrane Biomarkers In molecular diagnostics, DNA computing has demonstrated its promise for intelligently categorizing illness signs and sequence in clinical data. Additionally, it has been used to profile cells based on their membrane function (Qu et al., 2017; Song et al., 2019). Douglas et al. (2012) created a DNA origami-based system capable of analyzing the complex marker arrays on cell membranes and intelligently labeling or delivering medications to cells. To increase the accuracy of cell type, Stojanovic and colleagues developed a computer approach depend on antibody–DNA hybrids which conceptually recognize antigens on the cell membrane (Wang et al., 2021). Additionally, our research team developed an aptamer-based computational scheme that is based on the cell membrane. The method separates targeted cancer cells from healthy cells by the selective identification and binding of aptamers, while creating a high level of singlet oxygen surrounding the cancer cells to produce cell poisonousness, thus minimizing harm to healthy cells. Additionally, the Nano-Claw was created for identical purposes. It is a DNA-based intelligent tool that joins the unique structural transformation characteristics of DNA aptamers with toehold-mediated strand displacement events (You et al., 2014). To enhance the efficacy of the DNA-computing system on cell membranes, we combined a hybridization chain reaction signal amplification technique with AND Boolean logic examination of several biomarkers to accomplish exact profiling of distinct cell subtypes among huge populations of comparable cells (Figure 2.9a) (Chang et al., 2019). Later, with the assistance of enzymes on living cell membranes, we created a more potent in situ amplification approach to significantly increase the effectiveness of cell profiling (Figure 2.9c) (Gao et al., 2020). We proved that our technique is highly effective in differentiating tumor cell subgroups in clinical data. By and large, these DNA-based computer devices have demonstrated potential for executing logic activities on cell membranes, and for cell profiling and disease diagnosis.

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Figure 2.9. (a) Schematic of a triple-aptamer-based AND logic circuit for cell membrane target cell identification. (b) Using an AND logic gate, compare fluorescence signals on cell membranes. (c) The AND logic gate [45] is depend on multiaptamer-mediated proximity ligation. Source: https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.202011198.

2.4.3. DNA Logical Computation for Analysis of Intracellular Biomarkers After cell uptake, DNA logic gates were expanded for identification of intracellular indicators. Using Lipofectamine 3000, a DNA logic gate was recently transfected into cells, permitting direct measurement of endogenous microRNA expression pattern. A range of endogenous microRNA-induced biological computing operations, comprising binary logic gates (AND, OR, XOR and INHIBIT) and more complex cascade logic circuits (XOROR, XOR-AND, and XOR-Inhibit,) in various live cells, are accomplished through the development of modular sensing modules. A hybridization chain reaction (HCR) strategy was utilized for increasing the FRET output indications in order to check the microRNA signals in the cell nanodevice

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for recognizing frame nucleic acid (FNA) (Gong et al., 2019). The logic control parts of this instrument are an i-motif and an ATP-binding aptamer (ABA) integrated into a DNA triangle prism (DTP). The FNA nanodevice has the ability to describe a lysosomal response to lysosomal pH and ATP through the folding of the i-motif and ABA, prompting structural variation in the FNA and the discharge of reporting agents for subcellular imaging, to govern the initiation of DNA-logic nanodevices in particular cell compartments (Du et al., 2019). Programming intelligent DNA nanocarriers for the selective transport of molecular payloads in living cells has also garnered a lot of interest. Li et al. describe the integration of a DNA circuit with a FNA nanocarrier that includes a truncated square pyramid (TSP) cage and a developed double stranded payload that contains the target mRNA’s antisense strand. In reaction to H+ and intracellular ATP, the i-motif and ATP aptamers incorporated in the TSP act as logical control units, prompting the release of sensor components for fluorescent mRNA imaging (Wang et al., 2020). DNA computing has also been shown to be beneficial in modulating protein activities. Kolpashchikov and Stojanovic (2005) developed aptamerbased Boolean logic circuits which conduct protein logic operations. They discovered that molecular computation conducted by a DNAzyme-based logic circuit may be utilized to regulate the useful condition of aptamers in order to regulate enzyme functionalities. To precisely regulate thrombin function, our team built a sophisticated DNA computer system. The system has the ability to detect the presence of thrombin in the system. The system is inactive when the enzyme concentration falls below the threshold value. When the enzyme concentration exceeds the predetermined threshold, the software will automatically discharge enzyme inhibitors (aptamers) to prevent hypercoagulation by inhibiting the enzyme’s action. As a result, this innovative technology regulates enzymes intelligently, providing a useful tool and framework for the generation of tailored medications. Our study team further expanded the breadth of the intelligent DNA computing system’s application to include regulating protein activity in vivo and at the cell membrane (You et al., 2017). For autonomous anticoagulation in human plasma, we also constructed an intelligent DNA nanorobot with chemical reaction cascades as the core of computation (Yang et al., 2020). In the presence of excessive thrombin, this nanobot may intelligently recognize the level of thrombin in the local surroundings and initiate autonomic anticoagulation. This enables nanobots to perform independent anticoagulation in a number of medical circumstances, resulting in a more

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effective and harmless technique for future intelligent diagnostics and therapy. DNA logical computing has also been used in point-of-care (POC) analyzes in addition to these systems. Zhang and Lu (2018) describe the development of a biocomputing-based personal glycemic meter (PGM) that uses several DNAzymes and protein enzymes as logical computing parts and glucose/NADH as signal-reporting components. They demonstrated how different logic gates react to diverse combinations of biological substances. Despite the system’s portable design, the strong surroundings signal is a significant issue that should be addressed. However, this method combines the advantages of both a POC device and DNA computation, opening up a new avenue for the creation of intelligent POC devices for diagnostic purposes.

2.5. DNA ARITHMETICAL COMPUTATION FOR INTELLIGENT DIAGNOSTICS Early DNA computing research centered on developing molecular computers capable of solving mathematical problems, mostly via logical analysis. Today’s DNA computation researchers can logically develop devices that can do arithmetic computations at the molecular level. This brings up the possibility of employing DNA computing to run complicated calculation models for sickness diagnosis and profiling that are more accurate. So far, DNA has been programmed to do addition, subtraction, and multiplication, among other fundamental arithmetic operations (Figure 2.10). Multiplication is accomplished by hybridizing numerous probes into various areas of the similar input and producing related outputs using an accurate multiplier. Designing distinct set for various inputs and presenting the same result that can be summed up is one way to achieve summation. Subtraction may be generated by using a cooperative hybridization method to annihilate two separate strands. Just the extra strand may report the signals in this way. Overall, these arithmetical processes enable intelligent biological diagnostics to execute increasingly sophisticated computing tasks. In recent research, a mixture of DNA computing and bioinformatics methodologies has been applied for biomarker screening and illness detection (Husser et al., 2021). The categorization of genes, whose concentrations and variabilities are directly linked to certain disorders, may benefit from DNA computing (Feng et al., 2020). Lopez et al. (2018) published a molecular computing technique in 2018 to categorize the information in complicated

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gene expression profiles in simulated examples. On existing gene expression data, they trained a computer classifier to differentiate viral and microbial respiratory illnesses in silico. The classifier was then implemented using a DNA circuit capable of performing computational operations using artificially duplicated RNA samples. They found that the approaches were accurate in distinguishing between viral and bacterial respiratory illnesses in simulated samples, and that they might be used for low-cost gene expression analysis. This approach, however, cannot be straightly applied to patients’ samples because to the tiny quantity of mRNA in clinical samples. Later, our research team incorporated the DNA-computing-based classifier into a whole diagnostic procedure using actual clinical samples for the first time, resulting in trustworthy diagnostic findings (Ma et al., 2021). We accomplished the in-situ computation of additions, subtractions, and multiplications of miRNA particles in serum (Figure 2.10) and detected non-small-cell lung cancer in 6 hours with an accuracy rate of 86.4 percent to use a close-to-linear amplified technique that could transmit the miRNA profiles in clinical samples to calculable DNA inputs without changing the intensity and variability information (Ma et al., 2021). This will spur additional clinical uses of DNA computing for noninvasive and routine disease detection and categorization.

Figure 2.10. DNA computation system for cancer diagnosis. (a) Scheme for multiplication (Wn c(An) = Tn). (b) Scheme for summation (T1 + T2 = E (2); T3 + T4 = F (3)). (c) Scheme for subtraction (E F). (d) Illustration of catalytic amplification and reporting (Ma et al., 2021).

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Source: https://www.nature.com/articles/s41565–020–0699–0.

2.6. SUMMARY Today, researchers may create practically any logic function logically utilizing nucleic acid-based smart computing devices. Additionally, computer software may be utilized to forecast and simulate the system’s series layout, dynamic efficiency, and thermodynamic behavior, hence increasing the system’s success rate. As a result, the quality and quantity of DNA computing systems have increased dramatically during the last decade (Ma et al., 2021). Meanwhile, DNA computation is still in its infancy and faces several obstacles: Computing complexity: As computing complexity rises, the amount of DNA molecules needed in the method grows exponentially. A greater quantity of DNA molecules in the system might result in a higher mistake rate throughout the synthesis and purifying process. • Accuracy: DNA biological processes are easily restricted by reaction circumstances, and enhancing reaction responses, a predictor of computing accuracy, is a pressing issue in DNA computational methods. • Universality: The majority of DNA computing models are limited to a single type of issue. The absence of a global computing system precludes widespread adoption of DNA computing. • Environmental compatibility: DNA computing’s distinctive feature is its ability to dock with a variety of physiological settings. Direct interaction along physiological environment allows cells to react to biological signals in vivo, allowing intelligent diagnosis and therapy to be implemented in live creatures. However, it has to be seen if the DNA computer system can function correctly in the complicated physiological conditions (temperature, enzyme, and pH value). Despite these obstacles, with the introduction of numerous new technologies, these issues are on the verge of being resolved. Researchers will undoubtedly develop more sophisticated DNA computer systems and expand associated applications for better diagnostics and therapies.

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12. Condon, A., (2010). Designed DNA molecules: Principles and applications of molecular nanotechnology. Nanoscience and Technology: A Collection of Reviews from Nature Journals, 113–123. 13. Dong, M., Wang, N., & Tao, J., (2009). DNA computing in control systems: A survey. In: 2009 Chinese Control and Decision Conference (pp. 4942–4946). IEEE. 14. Douglas, S. M., Bachelet, I., & Church, G. M., (2012). A logic-gated nanorobot for targeted transport of molecular payloads. Science, 335(6070), 831–834. 15. Du, Y., Peng, P., & Li, T., (2019). DNA logic operations in living cells utilizing lysosome-recognizing framework nucleic acid nanodevices for subcellular imaging. ACS Nano, 13(5), 5778–5784. 16. Elbaz, J., Lioubashevski, O., Wang, F., Remacle, F., Levine, R. D., & Willner, I., (2010). DNA computing circuits using libraries of DNAzyme subunits. Nature Nanotechnology, 5(6), 417–422. 17. Feng, J., Li, B., Ying, J., Pan, W., Liu, C., Luo, T., & Zheng, L., (2020). Liquid biopsy: Application in early diagnosis and monitoring of cancer. Small Structures, 1(3), 2000063. 18. Gao, Q., Zhao, Y., Xu, K., Zhang, C., Ma, Q., Qi, L., & Han, D., (2020). Highly specific, single‐step cancer cell isolation with multi‐aptamer‐ mediated proximity ligation on live cell membranes. Angewandte Chemie International Edition, 59(52), 23564–23568. 19. Gong, X., Wei, J., Liu, J., Li, R., Liu, X., & Wang, F., (2019). Programmable intracellular DNA biocomputing circuits for reliable cell recognitions. Chemical Science, 10(10), 2989–2997. 20. Green, A. A., Kim, J., Ma, D., Silver, P. A., Collins, J. J., & Yin, P., (2017). Complex cellular logic computation using ribocomputing devices. Nature, 548(7665), 117–121. 21. Guo, Y., Yao, W., Xie, Y., Zhou, X., Hu, J., & Pei, R., (2016). Logic gates based on G-quadruplexes: Principles and sensor applications. Microchimica Acta, 183(1), 21–34. 22. Han, D., Kang, H., Zhang, T., Wu, C., Zhou, C., You, M., & Tan, W., (2014). Nucleic acid based logical systems. Chemistry–A European Journal, 20(20), 5866–5873. 23. Han, D., Wu, C., You, M., Zhang, T., Wan, S., Chen, T., & Tan, W., (2015). A cascade reaction network mimicking the basic functional steps of adaptive immune response. Nature Chemistry, 7(10), 835–841.

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24. Hao, Y., Li, Q., Fan, C., & Wang, F., (2021). Data storage based on DNA. Small Structures, 2(2), 2000046. 25. Hemphill, J., & Deiters, A., (2013). DNA computation in mammalian cells: MicroRNA logic operations. Journal of the American Chemical Society, 135(28), 10512–10518. 26. Huang, J., Suma, A., Cui, M., Grundmeier, G., Carnevale, V., Zhang, Y., & Keller, A., (2020). Arranging small molecules with subnanometer precision on DNA origami substrates for the single‐molecule investigation of protein–ligand interactions. Small Structures, 1(1), 2000038. 27. Husser, C., Dentz, N., & Ryckelynck, M., (2021). Structure‐switching RNAs: From gene expression regulation to small molecule detection. Small Structures, 2(4), 2000132. 28. Ignatova, Z., Martinez-Perez, I., & Zimmermann, K. H., (2008). DNA Computing Models. Springer Science & Business Media. 29. Kamar, O., Sun, S. C., Lin, C. H., Chung, W. Y., Lee, M. S., Liao, Y. C., & Chuang, M. C., (2017). A mutation-resistant deoxyribozyme OR gate for highly selective detection of viral nucleic acids. Chemical Communications, 53(76), 10592–10595. 30. Kolpashchikov, D. M., & Stojanovic, M. N., (2005). Boolean control of aptamer binding states. Journal of the American Chemical Society, 127(32), 11348–11351. 31. Kumar, I., Verma, A., Srivastava, A., & Shukla, R. C., (2015). Idiopathic hypertrophic pachymeningitis? MRI diagnosis and follow up. J. Neurol. Disord., 3(1), 1000198. 32. Lakin, M. R., Youssef, S., Polo, F., Emmott, S., & Phillips, A., (2011). Visual DSD: A design and analysis tool for DNA strand displacement systems. Bioinformatics, 27(22), 3211–3213. 33. Li, T., Wang, E., & Dong, S., (2009). Potassium−lead-switched G-quadruplexes: A new class of DNA logic gates. Journal of the American Chemical Society, 131(42), 15082, 15083. 34. Li, W., Yang, Y. A. N. G., Yan, H., & Liu, Y., (2013). Three-input majority logic gate and multiple input logic circuit based on DNA strand displacement. Nano Letters, 13(6), 2980–2988. 35. Liu, H., Wang, J., Song, S., Fan, C., & Gothelf, K. V., (2015). A DNA-

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based system for selecting and displaying the combined result of two input variables. Nature Communications, 6(1), 1–7. Liu, X., Suo, J., Li, Z., Zou, Q., Liu, J., & Ju, Y., (2015). Reusable logic gates based on DNA strand branch migration. Journal of Computational and Theoretical Nanoscience, 12(8), 1624–1629. Lopez, R., Wang, R., & Seelig, G., (2018). A molecular multi-gene classifier for disease diagnostics. Nature Chemistry, 10(7), 746–754. Lv, H., Li, Q., Shi, J., Fan, C., & Wang, F., (2021). Biocomputing based on DNA strand displacement reactions. ChemPhysChem, 22(12), 1151–1166. Lyu, Y., Wu, C., Heinke, C., Han, D., Cai, R., Teng, I. T., & Tan, W., (2018). Constructing smart protocells with built-in DNA computational core to eliminate exogenous challenge. Journal of the American Chemical Society, 140(22), 6912–6920. Ma, Q., Zhang, C., Zhang, M., Han, D., & Tan, W., (2021). DNA Computing: Principle, construction, and applications in intelligent diagnostics. Small Structures, 2(11), 2100051. Miao, P., & Tang, Y., (2021). Cascade strand displacement and bipedal walking-based DNA logic system for miRNA diagnostics. ACS Central Science, 7(6), 1036–1044. Morihiro, K., Ankenbruck, N., Lukasak, B., & Deiters, A., (2017). Small molecule release and activation through DNA computing. Journal of the American Chemical Society, 139(39), 13909–13915. Okamoto, A., Tanaka, K., & Saito, I., (2004). DNA logic gates. Journal of the American Chemical Society, 126(30), 9458–9463. Orbach, R., Willner, B., & Willner, I., (2015). Catalytic nucleic acids (DNAzymes) as functional units for logic gates and computing circuits: From basic principles to practical applications. Chemical Communications, 51(20), 4144–4160. Patwardhan, J., (2006). Architectures for Nanoscale Devices (Vol. 68, No. 7). Paun, G., Rozenberg, G., & Salomaa, A., (2005). DNA Computing: New Computing Paradigms. Springer Science & Business Media. Pei, R., Matamoros, E., Liu, M., Stefanovic, D., & Stojanovic, M. N., (2010). Training a molecular automaton to play a game. Nature Nanotechnology, 5(11), 773–777.

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48. Penchovsky, R., (2005). Biol., 2012, 1, 471; (b) Penchovsky, R., & Breaker, R. R., Nat. Biotechnol, 23, 1424. 49. Peng, R., Zheng, X., Lyu, Y., Xu, L., Zhang, X., Ke, G., & Tan, W., (2018). Engineering a 3D DNA-logic gate nanomachine for bispecific recognition and computing on target cell surfaces. Journal of the American Chemical Society, 140(31), 9793–9796. 50. Phillips, A., & Cardelli, L., (2009). A programming language for composable DNA circuits. Journal of the Royal Society Interface, 6(suppl_4), S419–S436. 51. Prokup, A., Hemphill, J., & Deiters, A., (2012). DNA computation: A photochemically controlled AND gate. Journal of the American Chemical Society, 134(8), 3810–3815. 52. Qian, L., & Winfree, E., (2011). Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332(6034), 1196– 1201. 53. Qian, L., Winfree, E., & Bruck, J., (2011). Neural network computation with DNA strand displacement cascades. Nature, 475(7356), 368–372. 54. Qu, X., Wang, S., Ge, Z., Wang, J., Yao, G., Li, J., & Fan, C., (2017). Programming cell adhesion for on-chip sequential Boolean logic functions. Journal of the American Chemical Society, 139(30), 10176– 10179. 55. Seelig, G., Soloveichik, D., Zhang, D. Y., & Winfree, E., (2006). Enzyme-free nucleic acid logic circuits. Science, 314(5805), 1585– 1588. 56. Shah, S., Yang, M., Song, T., & Reif, J., (2022). Molecular computation via polymerase strand displacement reactions. In: Handbook of Unconventional Computing: Implementations (Vol. 2, pp. 165–179). 57. Song, T., Shah, S., Bui, H., Garg, S., Eshra, A., Fu, D., & Reif, J., (2019). Programming DNA-based biomolecular reaction networks on cancer cell membranes. Journal of the American Chemical Society, 141(42), 16539–16543. 58. Stojanović, M. N., & Stefanović, D., (2003). Deoxyribozyme-based half-adder. Journal of the American Chemical Society, 125(22), 6673– 6676. 59. Stojanovic, M. N., Mitchell, T. E., & Stefanovic, D., (2002). Deoxyribozyme-based logic gates. Journal of the American Chemical Society, 124(14), 3555–3561.

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60. Su, H., Xu, J., Wang, Q., Wang, F., & Zhou, X., (2019). High-efficiency and integrable DNA arithmetic and logic system based on strand displacement synthesis. Nature communications, 10(1), 1–8. 61. Tsaftaris, S. A., Katsaggelos, A. K., Pappas, T. N., & Papoutsakis, E. T., (2004). How can DNA computing be applied to digital signal processing?. IEEE Signal Processing Magazine, 21(6), 57–61. 62. Vijayakumar, P., & Macdonald, J., (2017). A DNA logic gate automaton for detection of rabies and other lyssaviruses. ChemPhysChem, 18(13), 1735–1741. 63. Wang, H., Peng, P., Wang, Q., Du, Y., Tian, Z., & Li, T., (2020). Environment‐recognizing DNA‐computation circuits for the intracellular transport of molecular payloads for mRNA imaging. Angewandte Chemie International Edition, 59(15), 6099–6107. 64. Wang, X., Liu, L., Wu, H., Wu, Z., Tang, L. J., & Jiang, J. H., (2021). Programming DNA cascade circuits on live cell membranes for accurate cancer cell recognition and gene silencing. Chemical Communications, 57(31), 3816–3819. 65. Watada, J., (2008). DNA computing and its application. In: Computational Intelligence: A Compendium (pp. 1065–1089). Springer, Berlin, Heidelberg. 66. Xu, J., & Tan, G., (2007). A review on DNA computing models. Journal of Computational and Theoretical Nanoscience, 4(7, 8), 1219–1230. 67. Xu, J., & Zhang, L., (2003). DNA computer principle, advances and difficulties (I): Biological computing system and its applications to graph theory. Chinese Journal of Computers-Chinese Edition, 26(1), 1–11. 68. Yang, L., Zhao, Y., Xu, X., Xu, K., Zhang, M., Huang, K., & Han, D., (2020). An intelligent DNA nanorobot for autonomous anticoagulation. Angewandte Chemie, 132(40), 17850–17857. 69. You, M., Lyu, Y., Han, D., Qiu, L., Liu, Q., Chen, T., & Tan, W., (2017). DNA probes for monitoring dynamic and transient molecular encounters on live cell membranes. Nature Nanotechnology, 12(5), 453–459. 70. You, M., Peng, L., Shao, N., Zhang, L., Qiu, L., Cui, C., & Tan, W., (2014). DNA “nano-claw”: Logic-based autonomous cancer targeting and therapy. Journal of the American Chemical Society, 136(4), 1256– 1259.

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71. Zhang, C., Ge, L., Zhuang, Y., Shen, Z., Zhong, Z., Zhang, Z., & You, X., (2019). DNA computing for combinational logic. Science China Information Sciences, 62(6), 1–16. 72. Zhang, D. Y., & Seelig, G., (2011). Dynamic DNA nanotechnology using strand-displacement reactions. Nature Chemistry, 3(2), 103–113. 73. Zhang, D. Y., & Winfree, E., (2009). Control of DNA strand displacement kinetics using toehold exchange. Journal of the American Chemical Society, 131(47), 17303–17314. 74. Zhang, J., & Lu, Y., (2018). Biocomputing for portable, resettable, and quantitative point‐of‐care diagnostics: Making the glucose meter a logic‐gate responsive device for measuring many clinically relevant targets. Angewandte Chemie International Edition, 57(31), 9702–9706. 75. Zhang, L., Zhang, Y. M., Liang, R. P., & Qiu, J. D., (2013). Colorimetric logic gates based on ion-dependent DNAzymes. The Journal of Physical Chemistry C, 117(23), 12352–12357.

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CHAPTER

STOCHASTIC COMPUTING PRINCIPLES

CONTENTS

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3.1. Introduction....................................................................................... 70 3.2. Stochastic Thinking............................................................................ 72 3.3. Fundamentals of Stochastic Computing............................................. 75 3.4. Stochastic Computing Techniques...................................................... 80 3.5. Optimization Methods For Stochastic Systems................................... 84 3.6. Technology and Design...................................................................... 86 3.7. Stochastic Computing Applications and Potential Research Areas...... 88 3.8. Summary........................................................................................... 92 References ............................................................................................... 93

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3.1. INTRODUCTION The semiconductor and integrated circuit (IC) industry are confronted with the problem of excessive energy usage. Processing systems and modern computers are built on Von Neumann’s architecture and the Turing machine, which were developed in the 1920s. This chapter was primarily concerned with the design of systems that exhibit deterministic behavior. Stochastic computing was developed to address issues such as energy consumption and system dependability. The purpose of this chapter is to examine and investigate the ideas that underpin stochastic computing as well as the strategies used to achieve it. When it comes to building arithmetic units, we may obtain smaller area sizes and improved energy efficiency by applying stochastic computing. In addition, we hope to make the use of stochastic systems in the design of neuromorphic and futuristic BLSI systems more widely accepted. The computer’s obsession with perfection is finally coming to an end. A worry about power usage is moving computer designers to a design philosophy in which faults are either permitted to exist and ignored or rectified only when absolutely required (Lammers, 2010). The conventional techniques of repairing errors, which entail going back to the beginning to rectify problems after they have been found, are time-consuming and energy-intensive. In addition, typical design topologies demand that designed switches have lower threshold voltages and greater operating frequencies than their counterparts. Essentially, decreasing the voltage while simultaneously raising the clock frequency in the integrated circuits results in an increase in error rate in our systems, as seen in Figure 1. The outcome is not optimal when using typical computer methods. Stochastic computation, which makes use of the statistical character of application-level performance measurements of new applications and aligns it to the statistical properties of the underlying device and circuit fabrics (Shanbhag et al., 2010), is a technique for increasing the speed of calculation. The writings of John von Neumann may be used to trace the beginnings of stochastic computing back to its inception. Stochastic computing circuits can perform arithmetic operations with only a small number of logic gates. This is accomplished using numerical values encoded inside the statistics of random binary systems (also known as encoding). Stochastic computing is a new type of computation that is being explored and is regarded to be promising for efficient probability calculation. The AND gate and multiplexer (MUX) are used to execute the basic accumulation

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and multiplication operations in stochastic computing, as opposed to the deterministic approach used in the conventional computation. As a result, stochastic computing has higher energy efficiency and a smaller hardware footprint than traditional deterministic computation. As a result, stochastic computing makes it considerably easier to implement a very large-scale combination on a single chip than it is to understand it using traditional fixed-point hardware (Gaudet et al., 2019). Over time, several applications for stochastic computing have been developed. The topic of error corrective decoding (Agrawal, 1974) is a particularly intriguing use of stochastic computing. Image processing is another use of stochastic computing. Complicated mathematical operations must be executed in parallel within every pixel in an image for image processing activities to be completed. Stochastic computing circuits provide the possibility of implementing these tasks at a minimal hardware cost (Gaudet et al., 2019). Spiking Neural Networks (SNNs) was created because of the use of stochastic computing in Artificial Neural Networks. Their goal is to simulate the stochastic activity of biological neurons. We can accomplish Brainware Large-Scale Integration using stochastic neurons, stochastic computing, and their associated systems. These BLSI systems are intended to replicate and calculate some functions like those performed by the brain. Furthermore, we may create Neuromorphic circuits using a mix of stochastic computing and advancements in device technology. Designing Neuromorphic chips lays the groundwork for implementing NNs and BLSI systems on these chips, allowing for energy efficiency and more processing power (Law et al., 1996). The following is a summary of the chapter. The second section provides an Unpredictive Nondeterminism perspective on our surroundings. We go on to part III after analyzing stochastic computing from a data science perspective. In this part, we contrasted deterministic computing to stochastic computing, which is a more traditional computing paradigm. We also discussed the many components and characteristics of stochastic methods. The most prevalent strategies and methods for implementing stochastic systems are summarized in Section IV. Stochastic BitStream computing, Stochastic Sensor Network on Chip, Algorithmic Noise Tolerance (ANT), and Brain-inspired computing are the methodologies in question. The major goal of section V is to offer strategies for latency optimization in stochastic systems. We discussed the implications of device design for stochastic

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computing in section VI. We also went over the Invertible logic briefly. The possible uses of stochastic computing are discussed in Section VII. Finally, part VIII brings this work to a close with some conclusions and recommendations for the future (Von et al., 1956).

3.2. STOCHASTIC THINKING Uncertainty is one of the most significant notions in our world, as well as one of its fundamental principles. Daily, we are confronted with uncertainty in a variety of domains. Computer science, often known as data science or data computation, is one of these topics in computer science. Instead of dealing with uncertainty, we choose to deal with assurance and predictable phenomena. For example, we like predictable functions, which means that we obtain the same outcome every time we provide the same input as before. Trying to tackle a problem or phenomenon by applying causal determinism to it is not a good use of time or resources. Now, we are attempting to boost the computational ladder to aid us in better understanding the world. And unless we consider unpredictability, this aim will be impossible to achieve (Najafi et al., 2017). No of whether the world is essentially unpredictable or not, the reality that we will never have a perfect understanding of the world says that we should handle it as if it is inherently uncertain. As a result of this assertion, we might refer to it as Unpredictive Nondeterminism. We will describe a few ideas in the following sections to assist you in better comprehending the subject.

3.2.1. Stochastic Process A constant process in which the next state may be influenced by both the past states and certain random factors is defined as follows: Because today’s computers are unable of creating random numbers and data, they rely on techniques to produce pseudorandom numbers, which makes it difficult to grasp the consequences of random occurrences in a stochastic procedure while simulating and calculating their probability. Thus, these statistics are not derived from chance in nature (Abdallah & Shanbhag, 2010).

3.2.2. Simulation Models A description of computations that provide useful information about the expected behaviors of the system being modeled: when we talk about

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possible behaviors, we’re specifically interested in stochastic behavior and stochastic systems. Simulation In the sense that they describe possible results but do not tell us how to get there, models are descriptive instead of prescriptive.

3.2.3. Optimization Models The modeled system from a prescriptive perspective. These optimization models show us how to accomplish a goal. For instance, how do you find the quickest route from point A to point B? In contrast to optimization models, the simulation model demonstrates what will happen next if we take a specific step. However, it does not provide instructions on how to make anything happen. Regardless of how these two models operate, they are both critical in the design of stochastic systems (Varatkar et al., 2008). Now it’s time to clarify what probabilistic Computing entails. All the technological advancements and changes that have taken place in recent years, as well as the ever-increasing volume of data and user expectations, indicate the direction that computers must go to comprehend and act on all the data. To achieve this aim, we require computer systems that are efficient of inductive assumption, which means that they must be able to monitor their environment and deduce its fundamental causes. Because they are created to address scientific issues, most of the computers that we use today are not fit for these types of tasks. They are unable to make sense of facts based on this basis. In a nutshell, conventional computers follow a set of guidelines that tell them how to convert inputs into outputs in a specific way. The two most common ways in which computers are utilized to analyze or comprehend data are as follows: through Inference and through Simulation (Kratyuk et al., 2008). In the Simulation, the machine begins with some basic assumptions, accepts inputs in the form of a world configuration, and then creates output as well as an observable path. In this approach, the computer performs a set of preprogrammed instructions in a similar direction as the instructions are received. The inference problem is the inverse of the preceding problem. This approach uses the same underlying assumptions as of the previous one, but it takes as input a trajectory seen in the real world and provides as output a structure of the world that explains the trajectory. The orientation of this strategy is from the facts to the most likely reasons for those facts. An issue

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that frequently arises in Inference is the fact that there are several possible explanations/configurations for a given outcome. There is a great deal of ambiguity and unpredictability regarding which configuration or path is the best one to take. Because of this uncertain nature, we cannot anticipate definitive answers; nevertheless, we may strive for solid approximations and acceptable predictions about the data, which will result in fewer needless complicated designs (Samarah & Carusone, 2013). Probabilistic Computing is built on the principles that have been covered thus far. Managing and measuring ambiguity about fundamental explanations is at the heart of what we do (Figure 3.1).

Figure 3.1. (a) and (b) Both demonstrate the operation of probabilistic Computing. For both simulation and inference issues, the program is the same as the background assumptions. However, observations are the system’s input in the inference issue. Source: https://www.oreilly.com/content/probabilistic-programming/.

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3.3. FUNDAMENTALS OF STOCHASTIC COMPUTING Efficiency in energy use and system dependability are two of the most critical concerns facing the engineering community today. All the device scientists, system engineers, and subject matter experts in the area are working together to build the next generation of processing and communication technologies that are as energy-efficient and reliable as they possibly can be (Onizawa et al., 2019). The development of energy-efficient gadgets will result in the development of energy-efficient systems. The other point to note is that dependability at the system level does not necessarily imply dependability at the device level structure since we may create reliable systems out of defective devices. At the system level, the connection between energy dependability and efficiency is quite near to a tie. To put it another way, there is a trade-off between system dependability and energy efficiency. When it comes to achieving both energy reliability and efficiency, stochastic computing believes that one must experiment with the link between energy reliability and efficiency at all levels of the design in order to achieve both goals (Onizawa et al., 2019). The nature of Computing’s foundations is deterministic in nature. Turing’s deterministic finite state machine provides the basis for the creation of most machines and computers that we currently employ. This design architecture is based on the architecture developed by Von Neumann. Deterministic calculations are handled by computers and machines, which is why they are designed in this way. It is possible to see that these devices have stochastic physics and causes if we examine the sources of the device features in further detail. Therefore, building energy-efficient systems is such a difficult task, and we now have a problem known as the power wall in the semiconductor and integrated circuit industries. This means that reducing energy usage is extremely difficult and time-consuming at this stage and that other approaches for creating new devices must be investigated (Baek et al., 2018). Rather than building the computational method on the Von Neumann architecture and Turing machine, we should start with Shannon’s foundations to address deterministic computing challenges and meet the demands of industry for more energy-efficient and dependable devices. Shannon demonstrated that by employing statistical estimate and detection

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methods, information might be sent over a noisy channel with a randomly low likelihood of error (Shannon – Hartley Theorem). According to stochastic computing, it should be viewed as a problem of transmitting data across a noisy channel, with estimation and detection used to correct for mistakes. With this new perspective, we may infer that flawless and ideal switches are no longer required. They can, therefore, exhibit nondeterminism traits. This nondeterminism should, hopefully, result in significant energy savings. Since stochastic computation produces the same outputs as deterministic computation with far less energy usage, and consumers won’t be able to tell the difference in higher-level applications, this new technique was a huge step forward in system-level architecture (Baek et al., 2018).

Figure 3.2. Deterministic Computing in many setups, as well as metrics conversion from deterministic to stochastic computing. Source: https://arxiv.org/ftp/arxiv/papers/2011/2011.05153.pdf.

The approach of error by Von Neumann was similarly inadequate, as he noticed (Von Neumann, 1986), and as a result, the error should be considered as information rather than as an error. To chart a road from deterministic computing to stochastic computing, we must first categorize deterministic computing into two categories. Deterministic computing may be divided into three groups: (i) one to one; (ii) many-to-one; and (iii) many-to-many. Figure 3.2 better reviews these configurations.

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3.3.1. One-to-One It makes no difference how many outputs and inputs there are. For each input and output, there is only one connection. In other words, one input is connected to just one output, and vice versa.

3.3.2. Many-to-One We can link several inputs to a single output in this category. However, this is not the case. In other words, a single input can only be connected to a single output.

3.3.3. Many-to-Many One input can link to several outputs in this category. In other words, the system is in a “don’t care” condition. Logic minimization entails mapping a many-to-many outline to a many-to-one configuration by selecting the logic that results in the shortest configuration or logic netlist. Stochastic Computing, in contrast, can only be designed as a probabilistic many-to-many system.

3.2.3.1. Probabilistic Many-to-Many In addition to the Deterministic many-to-many setup, in this configuration, we may link one input to several outputs at the same time. Each link has a likelihood value associated with it that is unique to that relationship. The probability of the other outbound connections from the same node is complementary to one another (Hsu, 2014). As a result, the challenge of dependability in stochastic situations boils down to taking the outputs and analyzing them using error statistics, after which we construct the final output by employing estimate and detection techniques.

3.2.3.2. Error Statistics Voltage over scaling is one of the approaches employed in stochastic programming to determine error statistics (VOS). Because we’re working with current systems, the best method to introduce error is to lower the voltage while keeping the clock frequency constant (Chen et al., 2018).

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For the same throughput, this approach consumes less electricity. Furthermore, we may use this approach to monitor output logic mistakes and then determine the probability distribution of errors (Figure 3.3).

Figure 3.3. VOS method explained using a simple block. Various probability distributions of error are caused by the same clock frequency and different voltage levels. Source:https://scholar.google.com/citations?view_op=view_ citation&hl= En&user=ZhHOYKcAAAAJ&citation_for_ view=ZhHOYKcAAAAJ:d1gkVwhDpl0C.

3.2.3.3. Statistical Estimation and Detection Techniques such as estimation and detection are divided into two broad categories, in which we are always provided a collection of data to work with. During the estimated issue, we will be looking at yields, and we will be attempting to determine what the proper output was that caused these outputs. Ideally, we would only have accurate output values; however, due to faults, these output values are migrating and obviating away from the correct values, resulting in an undesirable situation. We want to make an educated guess about the proper result depending on the number of observations (Li & Lilja, 2011). The detection problem, on the other hand, is approached in the other direction. In other words, we have an idea that the proper output is one of a finite set of possibilities. For instance, when faced with several options, we may utilize observation to discover which option corresponds to which observation. In other words, we are attempting to determine which observation corresponds to which instance of a limited collection of observations. Figure 3.4 shows the context of the detection block and statistical estimation.

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Figure 3.4. Basic structure of detection block and statistical estimation. Source: https://en.x-mol.com/paper/article/1326757843988291584.

Stochastic computing is the process of aligning application-level metrics to the statistics of nanoscale textiles (Figure 3.5).

Figure 3.5. Statistical branching of stochastic computing. Source: https://arxiv.org/abs/2011.05153.

Skew tolerance is one of the numerous characteristics of stochastic systems.

3.2.3.4. Skew Tolerance When there is a delay in our system, that is, when input data arrive at various times, the right value is computed even if the inputs are briefly mismatched (Lilja, 2011)

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Figure 3.6 demonstrates the multiplication of two input data with differing arrival timings.

Figure 3.6. Using an AND with an unsynchronized bitstream to perform stochastic multiplication. Source: https://arxiv.org/abs/2011.05153.

3.4. STOCHASTIC COMPUTING TECHNIQUES Over the years, there have been several advancements in stochastic system implementation approaches. We’ll go through four of these tactics and techniques in this chapter: (i) algorithmic noise tolerance (ANT); (ii) stochastic sensor network on chip (SSC); (iii) stochastic bitstream computing; and (iv) spintronic approach for stochastic solutions. In the subsequent, we summarize each of these methods one by one (Shanbhag et al., 2010).

3.4.1. Algorithmic Noise Tolerance The function of ANT systems is determined by the interaction of two major building pieces. Following a review of statistical ideas and the foundations of stochastic computing, we allow the main block to generate mistakes and operate in a noisy environment before proceeding. We will be able to use the block at extremely high levels of energy efficiency thanks to this technology. In comparison to the main block, the estimator should be simpler to implement, resulting in less circuitry and area requirements. It should also be noted that mistakes are not welcome in the estimator block. In other words, the estimator should only make tiny mistakes and should not interact with the huge errors produced by the main block (Abdallah & Shanbhag, 2010).

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Figure 3.7 reveals the basic block of ANT systems. The estimator is a computer block with a modest level of complexity that generates a statistical estimate of the right main PE output, i.e., ya = yo + η ye = yo + e where ya represents the real main block output, yo represents the error-free main block output, is the hardware error, ye represents the estimator output, and e represents the estimation error. The following decision rule is used to acquire the final/corrected output of an ANT-based system:

Figure 3.7. (a) Framework of the ANT system. (b) error distributions. Source: https://en.x-mol.com/paper/article/1326757843988291584.

3.4.2. Stochastic Sensor Network on Chip (SSC) SSC computes by combining the outputs of numerous estimators or sensors, allowing for hardware faults, and then fusing them to give the final corrected result (Varatkar et al., 2008).

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The main framework of the SSNoC system is depicted in Figure 3.8. We divide any computing block into subblocks in SSC. Every subblock is referred to as a sensor. There are hardware and estimated faults in each of the sensors and subblocks. Our developed system determines the final output for each sensor using Detection and Estimation methods as well as the probability distribution of error after breaking our computing function into subblocks. Finally, all the outputs are fused to provide the final adjusted output (Kratyuk et al., 2008). The output of the ith sensor is given as: yei = yo + ei + ηi where i and ei are the ith estimator’s hardware and estimation errors, respectively, simulations show an 800 percent increase in detection probability while saving up to 40% on electricity. Soft-Input Soft-Output computation, Soft NMR, Stochastic Computation with Error Statistics, and Probability Processing are a few additional communication-inspired stochastic approaches along with SSNoC and ANT. During our investigation, we should investigate the efficacy and general approach of these strategies (Samarah & Carusone, 2013).

Figure 3.8. The framework of the SSNoC system. Source: https://en.x-mol.com/paper/article/1326757843988291584.

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3.4.3. Stochastic Bitstream Computing It is necessary to produce stochastic bitstreams to perform stochastic computing (SC). Bitstreams are streams of randomly produced 1s and 0s in which the probability of a single bit being 1 is p, and the probability of an individual bit being 0 is 1 p. These bitstreams are used to represent the outputs of stochastic circuits, intermediate values, and inputs, among other things. Many stochastic computing techniques demand that the input and transitional value bitstreams be as truly random as feasible, to the degree that this is achievable (Gaudet et al., 2019). Figure 3.9 displays a block schematic of a frequently employed circuit for generating a stochastic bitstream, which may be found in Figure 3.10. An RNG (random number generator) generates a sequence of N-bit binary values, one of which is produced for each clock cycle of the generator. The random values are then supplied into a comparator, which compares them to an N-bit binary number, B, to determine which is greater. A 0 or a 1 is created in the stochastic bitstream based on the result of the comparison. It is common to employ a counter to convert a stochastic bitstream back to its usual binary image. This works as follows: on each clock cycle, if the stochastic bitstream includes a 1, the counter is increased by one.

Figure 3.9. (a) is a block diagram of a Random Number Generator (RNG). (b) block diagram of a 3-bit Linear feedback shift register (LFSR). S o u rc e : h t t p s : / / s c h o l a r. g o o g l e . c o m / c i t a t i o n s ? v i e w _ o p = v i e w _ citation&hl=En&user=ZhHOYKcAAAAJ&citation_for_ view=ZhHOYKcAAAAJ:d1gkVwhDpl0C.

If the counter keeps value C after L clock cycles with a length-L bitstream, the value represented by the bitstream is C/L, which is expectedly in the (0: 1) range.

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Sequence generation is the name given to this technology, and the most common applications for Low Discrepancy Sequences and random number generators are linear-feedback Shift registers (Gaudet et al., 2019).

3.4.4. Spintronic Approach for Stochastic Solutions With the use of spintronic devices, it is possible to achieve efficient sampling operations that outperform inference efficiency with respect to power, area, and speed. To build a stochastic number generator, it is necessary to make use of the inherent unpredictability included in the switching process of a spintronic device. When it comes to stochastic computing, bit-wise operations using stochastic bitstreams that are created by an RNG (random number generator) and comparator are the most common methods of implementation. Spintronic devices, like the MTJ (magnetic tunnel junction), which switch in a stochastic manner, provide a high-quality entropy source for random number generators. It is possible to generate actual random bitstreams by using specific circuit designs that are based on fundamentally unpredictable physical phenomena (Gaudet et al., 2019). Depending on the intrinsic stochastic performance of the MTJ device, it is possible to quickly construct a TRNG, which may then be utilized for stochastic number generator applications (SNG). To regulate the process of reading and writing, only a pair of transistors is required (Gaudet et al., 2019).

3.5. OPTIMIZATION METHODS FOR STOCHASTIC SYSTEMS We’ve gone through the basics of stochastic systems and design methodologies so far. Even though stochastic computing uses smaller hardware for complicated processes and has a greater noise tolerance than standard deterministic systems, stochastic systems have substantial delay and latency. Different optimization strategies should be considered to solve this challenge. As previously stated, stochastic systems feature Skew tolerance, which indicates that they can still calculate the proper production value even if the inputs arrive at various times. Our systems benefit from synchronization since it simplifies the design process and ensures performance. However, this benefit comes at a high price. To synchronize our systems, we require clock distribution networks

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(CDN). Unfortunately, the CDN costs area uses a lot of energy and slows down the system. A solution known as “Poly synchronous clocking” was proposed to address this problem and reduce the excessive potential of stochastic systems in one research (Najafi et al., 2017). This technique can be implanted using one of two alternative ways: • Or

Use a low-cost local clock to synchronize each domain. This approach eliminates the need for a costly global CDN.

•

Maintain the global CDN while loosening the clock skew constraints. This strategy enables you to work at a greater frequency. The experimental findings in indicate that both Poly synchronous clocking approaches greatly reduce potential in stochastic systems, indicating that they are effective. In terms of delay, the first technique (removal of the local CDN) results in significantly lower energy use than the second way. In terms of area savings, the first option (removing the CDN) saves more space for large-scale systems than the second way (retaining the CDN). But for smaller systems, the second strategy (slowing down the clock) is preferable (Najafi et al., 2017). A synchronized clock with the external data, or, in other words, a clock that is synced with the sender’s clock, is required in serial communications and sequential calculations to achieve fault-free and/or Latent fault-free communication between the sender and the receiver CPUs. The PhaseLocked Loop (PLL) is one of the approaches that may be utilized for this purpose (PLL). PLL is a closed-loop control system) that ensures that the produced signal at the receiver remains synchronized with the reference signal in the absence of a reference signal. Analog PLLs necessitate the use of massive on-chip capacitors, the leaking of which can significantly reduce the jitter performance of the PLL. Thus, digital PLLs have a limited number of benefits over analog PLLs. In prior studies, we devised and implemented the ADPP protocol for this purpose (Kratyuk et al., 2008). It is recommended that the mixture of the suggested ADPLL and the stochastic time digital converter (TDC) be investigated further. To now, there is no guarantee that this strategy will be helpful in reducing latency in terms of enhancing performance. Further research on this approach is required.

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3.6. TECHNOLOGY AND DESIGN Implementing stochastic computing on the device level offers new problems on device architectures and manufacturing processes, as stochastic computing is a new field in creating energy-efficient and dependable systems. Important ideas in device design for energy efficiency in stochastic computing are discussed below.

3.6.1. Non-Deterministic Device Behavior We’re used to nondeterminism at the system level; why should we compel gadgets to work in a deterministic manner, given their non-deterministic nature (physics)? We should accept the non-deterministic nature of gadgets while developing them.

3.6.2. Low SNR Switches It is allowed in stochastic systems if we design and fabricate switches with a narrower gap between 1 and 0 logics. In the output, we may utilize estimation and detection to decode it and determine what the proper output is.

3.6.3. Multi-State Switches Conventional switches have two states: the on state and the off state, which are mutually exclusive. Energy is used during the shift from one state to another. Because the energy difference between the two steps is big in twostate switches, we have higher energy consumption in these switches. Using multi-state switches, we may profit from a smaller energy gap among two different states. This results in a reduction in the amount of energy consumed during the transition from one state to another. Furthermore, multi-state switches provide for greater design freedom.

3.6.4. Invertible Logics Invertible logic, as opposed to traditional binary logic, has lately been proposed for offering the capacity of forwarding and backward operations. It is built on Boltzmann machines and probabilistic magneto resistive device models (p-bits) as the foundation (Onizawa et al., 2019). Figure 3.10(a) depicts an invertible logic notion implemented using a Boltzmann machine and probabilistic bits (p-bits). Forward and/or backward operation is possible with invertible logic circuits (Onizawa et al., 2019).

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Figure 3.10. (a) Concept of invertible logic, (b) simple invertible AND. Source: https://en.x-mol.com/paper/article/1326757843988291584.

Invertible logic design is a crucial step in realizing stochastic systems. Further research into the design and execution of invertible logic using FinFET and CMOS technologies is needed. Figure 3.11 shows the 5-bit ReLU function and realization of Hamiltonian Full Adder.

Figure 3.11. Realization of Hamiltonian Full Adder. Source:https://scholar.google.com/citations?view_op=view_ citation&hl= En&user=ZhHOYKcAAAAJ&citation_for_ view=ZhHOYKcAAAAJ:d1gkVwhDpl0C.

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3.7. STOCHASTIC COMPUTING APPLICATIONS AND POTENTIAL RESEARCH AREAS Now that we’ve looked at stochastic computing and the methods for implementing it, it’s time to look at the possibilities of stochastic systems in new fields.

3.7.1. Neuromorphic Computing Neuromorphic computing is the process of designing and developing computer chips that employ the same physics of computation as our brain’s nervous system. Artificial Neural Networks are fundamentally different from this form of computation. An ANN is computer software that replicates the logic of the human brain. Neuromorphic Computing Hardware version Artificial Neural Networks Software version Artificial neural networks and neuromorphic chips can collaborate because advancements in both domains, particularly in neuromorphic computing, will make it possible to run ANNs on neuromorphic hardware in the future. Computers that use binary thinking are still in use today. The architecture of Von Neumann was responsible for their design. Neuromorphic computing, on the other hand, is very adaptable. Rather than utilizing electrical impulses to correspond to the numbers one and zero, the makers of these new chips want to have their computer’s neurons interact with one another in the same manner as real neurons do. Biological neurons communicate with one another using a precise electrical current that travels across a synaptic gap (Samarah & Carusone, 2013). This capacity to transfer a gradient of perception from neuron to neuron, as well as the ability to have them all functioning together at the same time, suggests that neuromorphic chips may someday be more energy efficient than our traditional computers, particularly when doing complex tasks. This is an area in which stochastic computing has the potential to make a significant contribution to the realization of neuron-to-neuron interactions. We will need new materials to do this since the ones we are now utilizing in our computers will not suffice. This feature of silicon makes it more difficult to manage the current flow between artificial neurons due to

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its physical properties. Studies on Tantalum Oxide and single-crystalline silicon have been conducted to build devices that can regulate the current flow with pinpoint accuracy. The scientists at the University of Manchester have devised and built a neuromorphic computing system that is based on standard computer architecture. This system is known as SpiNNaker, and it is comprised of typical digital components, such as cores and routers, that are connected and communicate with one another in novel and creative ways (Baek et al., 2018). Neuromorphic computers allow for increased speed and complexity while using less energy. Figure 3.12 is the design of IBM’s neuromorphic chip called “Truenorth” (Hsu, 2014).

Figure 3.12. (a) IBM’s original Truenorth chip layout, (b) Based on IBM’s Truenorth chip, neuromorphic computing. Source:https://scholar.google.com/citations?view_op=view_ citation&hl= En&user=ZhHOYKcAAAAJ&citation_for_ view=ZhHOYKcAAAAJ:d1gkVwhDpl0C.

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3.7.2. Brain-Inspired Computing The product of brain-inspired Computing depending on stochastic computation is BLSI (Brainware Large-Scale Integration). As opposed to more typical binary techniques, stochastic computing uses random bitstreams to realize area-efficient hardware for difficult operations like multiplication and tanh. Brainware computing necessitates complex functions that can be achieved with stochastic computing in a space-efficient manner. Because human brains can operate well under extreme noise and mistakes, stochastic computing was chosen for BLSI (Gaudet et al., 2019). BLSI systems can be used to model or emulate the human cortex’s function. For instance, in the BLSI system created for the execution of the Simple Cell of Primary Visual Cortex (Gaudet et al., 2019). One of the fascinating study fields that might bring potential for numerous future applications is the mixture of Stochastic Computing, Neuromorphic devices, and Brainware Large-Scale Integration (Figure 3.13).

Figure 3.13. Application of stochastic computation in BLSI. (a) Various blocks of Vision chip, (b) Fundamental structure of Equivalent-to-Stochastic converter. Source: https://en.x-mol.com/paper/article/1326757843988291584.

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3.7.3. Invertible Logics and Stochastic Computing Devices As we previously noted, invertible logic for bidirectional processes utilizing the p-bits and Boltzmann machine was recently introduced. Designing more practical and economic invertible logic in terms of latency, energy consumption, and dependability is one of the most promising research subjects. Different technologies, including FinFET, CMOS, and possibly Gate-AllAround FET, should be studied in future research for invertible logic designs (Chen et al., 2018).

3.7.4. Machine Learning Deep neural networks (DNNs) and Machine Learning have supplanted traditional approaches for practically all recognition and detection applications in recent years. Hardware complexity and energy consumption are the most prevalent difficulties in building and implementing Artificial Neural Networks. In a Neural Network, as the number of efforts and hidden layers grows, the hardware complexity grows exponentially, and the built hardware takes up more space and consumes more power. In the design of stochastic systems, we have previously demonstrated that stochastic computing will provide the desired balance between energy efficiency and dependability. Image processing techniques were developed using stochastic computing in one research (Li & Lilja, 2011). The result of the developed image processing algorithm is compared to the output of the Stochastic Computing and Conventional Computing techniques in Figure 3.14.

Figure 3.14. A comparison of the fault tolerance characteristics of several image processing algorithm hardware implementations. A traditional implementation is used to create the graphics in the first row. A stochastic implementation is used to create the pictures in the second row. Soft mistakes are inserted at a rate of one per second(a) 0%; (b) 1%; (c) 2%; (d) 5%; (e) 10%; (f) 15%; (g) 30%. Source: https://arxiv.org/abs/2011.05153.

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3.8. SUMMARY We briefly discussed stochastic thinking from the perspective of data science in this chapter. We taught the principles and basic notions of stochastic computing after contemplating a stochastic perspective of the universe. We can observe that stochastic bit stream spintronic and Computing methods are the most promising strategies for creating future stochastic systems after analyzing different implementation methodologies (Morro et al., 2015) By creating energy-efficient and dependable devices, stochastic computing, as an evolving systems design, can solve the power wall issue in the semiconductor and IC industries. In this area, more study is required. Hopefully, the study and efforts in this field will result in state-of-the-art system construction, with the possibility of realizing a long-term objective of applying neuromorphic processors with processing capacity comparable to that of the human brain and building more complicated BLSI systems.

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We might think about it for future work in this area: • • •

Different invertible logic designs. Designing BLSI systems centered on specific brain functions. Designing neuromorphic chips.

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44. Mondal, A., & Srivastava, A., (2019). Energy-efficient design of MTJbased neural networks with stochastic computing. ACM Journal on Emerging Technologies in Computing Systems (JETC), 16(1), 1–27. 45. Morro, A., Canals, V., Oliver, A., Alomar, M. L., & Rossello, J. L., (2015). Ultra-fast data-mining hardware architecture based on stochastic computing. PloS One, 10(5), e0124176. 46. Najafi, M. H., Lilja, D. J., Riedel, M. D., & Bazargan, K., (2017). Polysynchronous clocking: Exploiting the skew tolerance of stochastic circuits. IEEE Transactions on Computers, 66(10), 1734–1746. 47. Nerode, A., (1958). Linear automaton transformations. Proceedings of the American Mathematical Society, 9(4), 541–544. 48. Onizawa, N., Koshita, S., Sakamoto, S., Abe, M., Kawamata, M., & Hanyu, T., (2017). Area/energy-efficient gammatone filters based on stochastic computation. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(10), 2724–2735. 49. Onizawa, N., Nishino, K., Smithson, S., Meyer, B., Gross, W., Yamagata, H., & Hanyu, T., (2019). A design framework for invertible logic. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1, 312–316. 50. Onizawa, N., Smithson, S. C., Meyer, B. H., Gross, W. J., & Hanyu, T., (2019). In-hardware training chip based on CMOS invertible logic for machine learning. IEEE Transactions on Circuits and Systems I: Regular Papers, 67(5), 1541–1550. 51. Pervaiz, A. Z., Sutton, B. M., Ghantasala, L. A., & Camsari, K. Y., (2018). Weighted $ p $-bits for FPGA implementation of probabilistic circuits. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1920–1926. 52. Prediger, S., (2008). Do you want me to do it with probability or with my normal thinking? Horizontal and vertical views on the formation of stochastic conceptions. International Electronic Journal of Mathematics Education, 3(3), 126–154. 53. Rahman, S., (2008). A polynomial dimensional decomposition for stochastic computing. International Journal for Numerical Methods in Engineering, 76(13), 2091–2116. 54. Ren, A., Li, Z., Ding, C., Qiu, Q., Wang, Y., Li, J., & Yuan, B., (2017). SC-DCNN: Highly-scalable deep convolutional neural network using stochastic computing. ACM SIGPLAN Notices, 52(4), 405–418.

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55. Samarah, A., & Carusone, A. C., (2013). A digital phase-locked loop with calibrated coarse and stochastic fine TDC. IEEE Journal of SolidState Circuits, 48(8), 1829–1841. 56. Sartori, J., & Kumar, R., (2011). Stochastic computing. Foundations and Trends® in Electronic Design Automation, 5(3), 153–210. 57. Shanbhag, N. R., Abdallah, R. A., Kumar, R., & Jones, D. L., (2010). Stochastic computation. In: Proceedings of the 47th Design Automation Conference (Vol. 3, pp. 859–864). 58. Sheynin, O., (1998). Stochastic thinking in the bible and the Talmud. Annals of Science, 55(2), 185–198. 59. Sim, H., & Lee, J., (2019). Cost-effective stochastic MAC circuits for deep neural networks. Neural Networks, 117, 152–162. 60. Smithson, S. C., Onizawa, N., Meyer, B. H., Gross, W. J., & Hanyu, T., (2019). Efficient CMOS invertible logic using stochastic computing. IEEE Transactions on Circuits and Systems I: Regular Papers, 66(6), 2263–2274. 61. Toral, S. L., Quero, J. M., Ortega, J. G., & Franquelo, L. G., (1999). Stochastic A/D sigma-delta converter on FPGA. In: 42nd Midwest Symposium on Circuits and Systems (Cat. No. 99CH36356) (Vol. 1, pp. 35–38). 62. Varatkar, G. V., Narayanan, S., Shanbhag, N. R., & Jones, D. L., (2008). Trends in energy-efficiency and robustness using stochastic sensor network-on-a-chip. In: Proceedings of the 18th ACM Great Lakes symposium on VLSI (Vol. 2, No. 1, pp. 351–354). 63. Von, N. J., (1956). Probabilistic logics and the synthesis of reliable organisms from unreliable components. Automata Studies, 34(34), 43–98. 64. Wang, A., Afshar, P., & Wang, H., (2008). Complex stochastic systems modelling and control via iterative machine learning. Neurocomputing, 71(13–15), 2685–2692. 65. Wang, Z., & Ierapetritou, M., (2018). Constrained optimization of black-box stochastic systems using a novel feasibility enhanced Kriging-based method. Computers & Chemical Engineering, 118, 210–223. 66. Yuan, B., Wang, Y., & Wang, Z., (2016). Area-efficient error-resilient discrete Fourier transformation design using stochastic computing. In:

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Proceedings of the 26th Edition on Great Lakes Symposium on VLSI (Vol. 2, No. 1, pp. 33–38). 67. Yuan, B., Wang, Y., & Wang, Z., (2016). Area-efficient scaling-free DFT/FFT design using stochastic computing. IEEE Transactions on Circuits and Systems II: Express Briefs, 63(12), 1131–1135. 68. Zhang, Q., (2019). Performance enhanced Kalman filter design for non-Gaussian stochastic systems with data-based minimum entropy optimization. AIMS Electronics and Electrical Engineering, 3(4), 382–396. 69. Zhang, Q., Wang, Z., & Wang, H., (2016). Parametric covariance assignment using a reduced-order closed-form covariance model. Systems Science & Control Engineering, 4(1), 78–86.

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4

CHAPTER

PRINCIPLES AND APPLICATIONS OF SOCIAL COMPUTING

CONTENTS

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4.1. Introduction..................................................................................... 102 4.2. The Nature of Social Computing...................................................... 104 4.3. Challenges....................................................................................... 107 4.4. Approach......................................................................................... 109 4.5. Summary......................................................................................... 111 References.............................................................................................. 113

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4.1. INTRODUCTION In this chapter, social computing is defined as the calculation of social dependency between autonomous individuals. Several distinct types of applications, like social networks, multiparty commercial procedures, and online debate, are unified by the concepts discussed in the following section. It is believed that recent software engineering practices fall far short of what is needed to develop efficient applications (DeRemer & Kron, 1976). Computation’s nature is evolving. It’s transitioning from activity and data-driven to interaction-driven. The transition may be seen in social cloud, social networks, virtual organizations, e-business, etc. Autonomous social actors engage in these apps to exchange information and services. Software engineering, on the other hand, hasn’t kept up with the continuous transformation. It is still entrenched in lower-level control flow abstractions and a logically centralized vision of systems that dates back to its early times. Social computing is defined as the collaborative computation of several independent agents, such as individuals, organizations, or their software surrogates. The term “joint” relates to their relationships and the social ties that result from them, not to collaboration, integration, or any other type of logical centralization. The computation might be used to perform a multiparty business transaction, plan a meeting, loan a book to a friend, create consensus on a problem through reasoning, or even worldwide distributed software development directly. The programs that do social calculations are known as social applications (Garlan et al., 2004) (Figure 4.2).

Figure 4.1. Different paradigms of social computing. Source: https://hcis-journal.springeropen.com/articles/10.1186/s13673–018– 0131-z.

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Modern techniques see systems from a logically centralized perspective: to develop a system is to construct a computer. The computer can be modular and dispersed internally, but it will efficiently show only 1 locus of control: that of its stakeholders. Even more liberal approaches to software development like the programming in the large (PiL), which allowed independently created components, did not allow autonomous actors to be included as components. To rewire elements, recent research in architecturebased adaptation depends on centralized adaptation managers (DeRemer & Kron, 1976). Requirements engineering (RE), according to Zave and Jackson (1997), shows the significance of modeling and evaluating the environment while developing system specifications. The idea of a system like a computer, on the other hand, is still alive and well in RE. The methods of agent-based software engineering are also conceptually centralized. For instance, Jennings (2000) illustrates a scheduling issue that is solved by spreading it across intelligent agents. In summary, existing concepts like OS, transaction managers, databases, and flight control systems have proven to be useful in the development of controllers. They fall short, although when it comes to developing social applications that need interactions between social actors. Even with existing software engineering methodologies, it is clear that tasks are executed to construct social apps. Online banking, for instance, is a social app that allows a consumer to engage with one or more banks to make payments, transfers, and deposits. To the extent that writers and viewers voice their views and debate them on blogging, they are social applications. Facebook and LinkedIn, for example, allow users to engage with one another. Furthermore, only because social apps may be built and does not imply it is the best approach to do it. The goal of software engineering study is to develop building programs simpler, and the largest jumps are frequently achieved through novel abstractions (DeRemer & Kron, 1976). Our main argument is that existing software engineering methodologies lack the social abstractions needed to construct social applications systematically. In reality, such programs’ social components are recently handled offline. The connecting principals, for instance, understand the contractual connections that exist in a commercial contract; however, does not the software that supports their interaction. Every program is constructed from the bottom up from lower-level abstractions dependent upon control flow, as there are no social abstractions. This reduces utilization and leads to complicated software code and models, which causes software management issues. If there are the right social abstractions, apparently different social apps like

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software development, commercial transactions, social networks, and blogs might all be developed around the same core concepts and operate on the same platform which expresses the concepts (Desai et al., 2009).

4.2. THE NATURE OF SOCIAL COMPUTING Social business procedures, sociotechnical systems, service-oriented computing, e-government, virtual organizations, and other elements of social apps are researched under different guises in computer science, based upon the specific aspects stressed. Earlier, three main but distinct groups (not simple examples) of these apps were addressed, and it was then demonstrated that there is more similarity amongst them than may appear. The main goal is to use this similarity to inform the development of all social apps (Singh, 2011) (Figure 4.2).

Figure 4.2. Social computing architecture. Source: https://hcis-journal.springeropen.com/articles/10.1186/s13673–018– 0131-z.

4.2.1. Multiparty Business Processes On the Internet, more commercial transactions are taking place. Formerly, only the most resourceful firms were capable to establish e-business systems; but, with developments in Web technology and the introduction of online markets like eBay, even people with minimal sources had been capable to engage in commercial transactions with one another. A standard set of abstractions, platforms, and protocols for facilitating these multilateral

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business procedures and transactions was the fundamental inspiration for service-oriented computing (Singh, 2011).

4.2.2. Social Networks Another orthogonal but more current path in the history of the Web is that of social networks, in which players are the network’s nodes and social interactions among actors are the connections between the nodes. Consumers are highly based upon social networks to coordinate business and social activities, rather than simply to broadcast content, according to a recent study. Social networks are being utilized in a variety of ways, including book sharing, money lending, carpooling, and assisting tourists in finding hosts to stay with, among many other things (Robertson et al., 2016) (Figure 4.3).

Figure 4.3. Structural model for social computing. Source: https://www.researchgate.net/figure/Structural-Model-for-Social-Computing-This-Structural-Model-gave-us-a-broader-insight_fig1_315766318.

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4.2.3. Online Discourse Through online forums and blogs, the Web facilitates semi-structured social interactions amongst actors (including microblogs, like Twitter). Commenting on blog news articles and posts is becoming more usual these days. People frequently fight fiercely for their perspective in response to an article. Blogs are progressively being used by organizations, including governments, to gather ideas and thoughts from their audiences. To record such relationships in a more relevant and systematic way, researchers are progressively turning to principles from discourse theory, particularly argumentation. IBIS (issue-based information systems) is a conventional computer science application field that aims to use argumentation to capture information in software development lifecycles (Chopra et al., 2013). Many of such social apps are developed mostly on top of the web. The Web, on the other hand, was conceived as a resource-oriented, distributed, hyperlinked database. As a result, it only provides database abstractions at a basic level (the HTTP primitives). It lacks higher degree social abstractions, making it difficult to create social apps. Certain social apps are built on a specific layer that sits on top of the Web. For example, commercial applications are commonly created on top of the WS-* platform. Moreover, there are no social abstractions in WS-*. Consider that business processes, which are a common approach to developing these systems, are action and flow-centric rather than actor-centric. Although LinkedIn and Facebook are theoretically comparable, they do not share any basic abstractions, application programming interface (API), or infrastructure other than at the Web level (DeRemer & Kron, 1975). Furthermore, even though a LinkedIn acquaintance may very well be a Facebook friend, the two networks remain incompatible. Naturally, the question of whether or not enterprises seek interoperability arises. But let’s assume they desired it. Verify the platform’s accessibility to ensure its compatibility. The importance of online discourse has only lately begun to be recognized. Even though some recent attempts, like debategraph.org, have started to accommodate a deeper argumentation structure, the major focus is now on the modest structure beyond comment trees and posts (comments to comments, etc.). What does it mean to be social? What conceptually connects such applications? All social applications might be developed from similar social abstractions, according to our core understanding. All social applications are based on the concept of social reliance amongst actors. For components, manufacturers rely on suppliers. Patients rely upon laboratories to provide

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them with reliable test outcomes. Whenever a bidder wins an eBay auction, the seller relies upon him to complete the transaction. When a person invites friends to a party at his house on Facebook, the guests rely on him to host the event. Whenever a person agrees to host a tourist, the visitor is reliant upon that person for lodging. When someone says in an online forum that worldwide glacier volume is declining at a five per cent yearly rate, or when a stakeholder asserts that a specific requirement should be implemented, they are both accountable to certain other participants for the validity of their assertions (regardless of whether the others believe them) (Prieto-Diaz & Neighbors). In circumstances involving interactions amongst actors, social interdependences emerge spontaneously. In reality, because it is based upon connection, the reliance is social. Social reliance is a basic unifying concept, a semantic concept that may be applied to a variety of situations. Social computing is the actors’ collaborative computation, not in terms of basic objectives or intentions, although in terms of the growth of the social network interdependence amongst them. When a supplier supplies parts, for instance, the manufacturer’s reliance upon the supplier is met, but the supplier can now be reliant upon the manufacturer for payment. Popular social networks emphasize application-specific and, in that regard, arbitrary connections like family, colleague, friend, collaborator, etc. However, these interactions are said to be based on social dependency (Bridge & Thompson, 1976). For instance, one relies upon coauthors to share collaborative work, grandparents to babysit the kids, and friends not to transmit information and images that they have access to, like addresses and phone numbers. Consequently, the concept of social reliance leads to the concept of social networks, in which social interdependences are the essential linkages between actors. Dependencies may theoretically be created on top of other types of connections, like relationships.

4.3. CHALLENGES To put it another way, developing every social application today is similar to writing Emacs in programming banking apps or microprocessor assembly language through IP/TCP connections. There are no universal social abstractions, languages, systematic procedures, or infrastructure for constructing and operating them. As a result of the observation that a merger of what was previously deemed various kinds of applications,

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this deficiency would only become more apparent and its impacts would grow more significant. For instance, the new field of social cloud aims to leverage social networks to deliver corporate services; social networks are progressively being utilized to perform political debate, and socio-technical systems are being used to manage autonomous groups (Hoare, 1975). Social dependency networks will rationally and uniformly reflect all types of social applications which are now recognized and addressed in a variety of ways, including those outlined above. It’s simpler to develop a social platform that supports interoperability and a related application programming interface that makes programming social apps far simpler than it is today, similar to how TCP/IP encouraged interoperability between computers and enabled programming network applications simpler through a socket-dependent application programming interface (Hoare, 1975). The following obstacles in developing a principled approach for social applications are identified.

4.3.1. Identify and Formalize the Social Abstractions What does it mean to be socially dependent? What is the best way to express and calculate social dependence? What will the basic patterns of social reliance be, from which compound patterns may be constructed? The above questions emphasize not only technical rigor but also generalization through social dependencies. It ought to be capable to capture and reasoning about a wide range of patterns, from simple ones like those that arise in scheduling an appointment to more complex ones like those that arise in argumentation and business contracts (Oreizy et al., 1999) (Figure 4.4).

Figure 4.4. How abstraction, formalization, and implementation contribute to the next stage of modeling and simulation. Source: https://link.springer.com/chapter/10.1007/978–3-030–17164–3_2.

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4.3.2. Devise a Social Application Language The language will describe an application’s design in terms of functions and social abstractions. What properties must a language have to be descriptive enough for a variety of applications? Simple business contracts, service level agreements, delegation, quality limitations, temporal limits on events, compensation and timeouts, for instance, must all be encoded. Is it possible to represent all of this utilizing only dependencies rather than low-level abstractions?

4.3.3. Identify the Software Engineering Principles What are the most important social application’s specification artifacts? What are the concepts and strategies for creating these artifacts in software engineering?

4.3.4. Build a Social Middleware and API Similarly, how socket apps operate over IP/TCP and Web services operate HTTP (or WS-*), the middleware describes the social layer, a platform upon which social applications run. The middleware, on the other hand, understands social abstractions, unlike both of them. What are the fundamental platform features? What are the procedures for delivering such services? How does the platform handle dependencies and preserve actor interoperability? What is the most fundamental API for accessing platform services? How may API extensibility be assured so that higher-level patterns may be programmed more easily (Salehie & Tahvildari, 2009)? These questions need to be answered.

4.4. APPROACH Social applications are inextricably linked to the actual world, throughout the sense that actors receive their autonomy from their principals’ autonomy, and social dependencies are actual-world relationships that emerge as a result of actor communication. Social commitment and social trust are 2 interesting possibilities for encapsulating the concept of reliance (Singh, 2008; Desai et al., 2009). When a supplier promises to provide components to a manufacturer, for instance, it signifies the producer may rely on the supplier for components. A commitment is a fundamental contract. Modeling the substance of online conversation may be done using the concept of

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dialectical commitment, which is a type of social commitment. Despite cognitive trust, the idea of social trust focuses on the architectural definition of social apps by encapsulating trust connections among roles. Consider the possibility that social applications are defined mainly in terms of actor trust connections; yet, trusted relationships may require to be supported by social commitments to be practical. For instance, a healthcare system design can state that people trust the local government with arranging doctor appointments; yet, patients will want this confidence to be supported by proper promises and agreements from all involved parties. For social applications, it is necessary to re-learn key software engineering principles like abstraction, modularity, encapsulation, and segregation of responsibilities. Consider the concept of modularity. Actors will be the fundamental unit of modularity since they are independent. When expressed as such, that might not seem shocking, but examine how serviceoriented techniques that depend upon business procedures entirely disregard this concept. Consider the consequences of actor modularity for internally complicated actors like organizations: since organizations are made up of actors who can be complicated themselves, one will have no choice but to model the relationships between the actors down (Robertson et al., 2016). Application-level protocols (as opposed to middleware protocols) would be the foundation of our method as interaction specifications. In our method, Facebook, for instance, will be 1 application-level protocol, whereas eBay will be another. Conventional protocol specification languages, such as UML interactions state charts and diagrams, are too lower-level for social applications. Rather, protocols should be defined in terms of how communications influence social dependencies; in other terms, communications should be mapped to the middleware’s social abstractions (Chopra et al., 2013). The application-level protocols are operational in the perspective of the fundamental protocols by the middleware. Enacting the basic protocols will develop the social interdependence between actors. Maintaining interoperability is a crucial task for middleware. The middleware will also provide protocols for finding actors, such as finding auctioneers with a strong reputation or specialists on a specific subject. The approaches that are anticipated from referral networks are crucial in this case. The middleware will run on open-source infrastructures like enterprise services busses (ESBs) or HTTP. It will be necessary to construct prototype apps that include argumentation, business interactions, and social networks to illustrate the

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benefits of our method. Software project management (visualizing software development like an interactive activity amongst stakeholders) and personal data software that enables consumers to keep a record of their obligations is 2 of those that we’re particularly interested in (similar to a calendar, only far more resourceful) (Bridge & Thompson, 1976).

4.5. SUMMARY Certain current concepts in this field depart significantly from our view of social computing. Certain contemporary manifestos and financing initiatives highlight the concept of strong social computers that may engage people (for instance, through crowd computing) as problem-solving components while also taking laws and social conventions into consideration (Robertson et al., 2016). The concept of social computing is complemented by social computing. The social computer in our model is a single actor that operates on top of the social middleware. Consider a social computer network. The application that the computers collectively show will require our idea of social computing (through the social language) (for instance, a virtual organization for resource sharing). The network will get teeth as a result of our vision (through the middleware). Our objective is to make it simple to program social computers (via the social API) (Gerard & Singh, 2013). It is important to note that the term “social computing” does not relate to social techniques to answering questions like collaborative tagging and filtering. While input from various actors is obtained, a centralized computer mines user inputs to answer questions; there is no direct contact between actors in such systems. A single social computer might be made up of the algorithms for collaborative filtering and labeling. Running Google’s PageRank is not social computing for a similar reason. The difference between social computing and social computer is similar to that between interaction and algorithm. Social computing is not about actors’ shared aims or intents, abstractions that are utilized to record actors’ mental states and have proven useful in distributed AI (particularly through speech act theory) and RE. In reality, we make no judgment about actor status, rationale, or goals in our concept of social computing. The public, the observable, is at the heart of social computing; at any one time, the network of social interdependence represents the application’s observable state. As a result, mentalist conceptions may not be utilized to express social interdependence (Singh, 1998).

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A fundamental shift in how it thinks about, models, and engineers social apps, and a completely distinct platform upon which to execute such applications are the two main achievements from our perspective. Unlike other platforms, the social platform will be able to comprehend (and calculate) higher-level social abstractions and patterns. The databases and operating systems had been the first platforms for developing apps; IP/ TCP became the platform for network applications; and today, there are enormous developing apps on top of the Web, but there is no one social platform on which to operate social applications. Yes, there are social applications, however, they are currently only available on the Web and not on any social network. This mismatch would be addressed by the suggested social platform. The platform has the potential to be as crucial for the future of social apps as the Web is for information delivery. People and small enterprises have found Web 2.0 to be empowering. A strong conceptual grasp of social apps, as well as an architecture that greatly facilitates their development, would prove to be far more empowering. The accomplishment of this vision’s goals would lead to shared conceptual foundations for fields like socio-technical systems, serviceoriented computing, and social networks, as well as fundamentally novel research directions.

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25. Labrou, Y., Finin, T., & Peng, Y., (1999). Agent communication languages: The current landscape. IEEE Intelligent Systems and Their Applications, 14(2), 45–52. 26. Magee, J., Kramer, J., & Sloman, M., (1989). Constructing distributed systems in conic. IEEE Transactions on Software Engineering, 15(6), 663–675. 27. Mallya, A. U., & Singh, M. P., (2007). An algebra for commitment protocols. Autonomous Agents and Multi-Agent Systems, 14(2), 143– 163. 28. Marengo, E., Baldoni, M., Baroglio, C., Chopra, A. K., Patti, V., & Singh, M. P., (2011). Commitments with regulations: Reasoning about safety and control in REGULA. In: 10th International Conference on Autonomous Agents and Multiagent Systems, 2, 467–474. 29. Maudet, N., & Chaib-Draa, B., (2002). Commitment-based and dialogue-game-based protocols: New trends in agent communication languages. The Knowledge Engineering Review, 17(2), 157–179. 30. Murray-Rust, D., Papapanagiotou, P., & Robertson, D., (2015). Softening electronic institutions to support natural interaction. Human Computation, 2(2), 1–20. 31. O’Hara, K., Contractor, N. S., Hall, W., Hendler, J. A., & Shadbolt, N., (2013). Web science: Understanding the emergence of macrolevel features on the world wide web. Foundations and Trends in Web Science, 4(2, 3), 103–267. 32. Oreizy, P., Gorlick, M. M., Taylor, R. N., Heimhigner, D., Johnson, G., Medvidovic, N., & Wolf, A. L., (1999). An architecture-based approach to self-adaptive software. IEEE Intelligent Systems and Their Applications, 14(3), 54–62. 33. Parnas, D. L., & Madey, J., (1995). Functional documents for computer systems. Science of Computer Programming, 25(1), 41–61. 34. Pitt, J., & Mamdani, A., (1999). A protocol-based semantics for an agent communication language. In: IJCAI (Vol. 99, pp. 486–491). 35. Prieto-Diaz, R., & Neighbors, J. M., (1986). Module interconnection languages. Journal of Systems and Software, 6(4), 307–334. 36. Robertson, D., Giunchiglia, F., Pavis, S., Turra, E., Bella, G., Elliot, E., & Parsons, M., (2016). Healthcare data safe havens: Towards a logical architecture and experiment automation. The Journal of Engineering, 2016(11), 431–440.

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37. Rodriguez, S., Julián, V., Bajo, J., Carrascosa, C., Botti, V., & Corchado, J. M., (2011). Agent-based virtual organization architecture. Engineering Applications of Artificial Intelligence, 24(5), 895–910. 38. Salehie, M., & Tahvildari, L., (2009). Self-adaptive software: Landscape and research challenges. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 4(2), 1–42. 39. Singh, M. P., (1998). Agent communication languages: Rethinking the principles. Computer, 31(12), 40–47. 40. Singh, M. P., (2008). Semantical considerations on dialectical and practical commitments. In: AAAI (Vol. 8, pp. 176–181). 41. Singh, M. P., (2011). Trust as dependence: A logical approach. In: The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume, 2, 63–870. 42. Singh, M. P., Chopra, A. K., & Desai, N., (2009). Commitment-based service-oriented architecture. Computer, 42(11), 72–79. 43. Van, L. A., Darimont, R., & Letier, E., (1998). Managing conflicts in goal-driven requirements engineering. IEEE Transactions on Software Engineering, 24(11), 908–926. 44. Wooldridge, M., & Jennings, N. R., (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2), 115–152. 45. Wooldridge, M., (1997). Agent-based software engineering. IEE Proceedings-Software Engineering, 144(1), 26–37. 46. Wooldridge, M., (2000). Semantic issues in the verification of agent communication languages. Autonomous Agents and Multi-Agent Systems, 3(1), 9–31. 47. Wooldridge, M., Jennings, N. R., & Kinny, D., (2000). The Gaia methodology for agent-oriented analysis and design. Autonomous Agents and Multi-Agent Systems, 3(3), 285–312. 48. Zave, P., & Jackson, M., (1997). Four dark corners of requirements engineering. ACM transactions on Software Engineering and Methodology (TOSEM), 6(1), 1–30. 49. Zeng, D., Wang, F. Y., & Carley, K. M., (2007). Guest editors’ introduction: Social computing. IEEE Intelligent Systems, 22(5), 20– 22.

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CHAPTER

COMPUTATIONAL PRINCIPLES IN MEMORY STORAGE

CONTENTS

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5.1. Introduction..................................................................................... 118 5.2. Creating Persistence From Memory-Less Components...................... 122 5.3. Robustness to Noise......................................................................... 130 5.4. Memory Capacity............................................................................ 133 5.5. Model Mechanisms: Tests and Questions......................................... 140 5.6. Biological Versus Computer Memory............................................... 142 References.............................................................................................. 145

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5.1. INTRODUCTION Integrating, learning, generalizing, predicting, and inferring are all adaptive actions that depend on the capacity to retain and use knowledge further down the road. Throughout this review, we will look at theoretical ideas that can let the brain create permanent states for memory storage. Our research outlines the needs that a memory system should meet and then evaluates current models and postulated biological substrates in the context of these needs. We also point out outstanding topics, theoretical issues, and problems that are shared by computer science and information theory, among other things. The term “memory” refers to any of the wide range of changes in the connectivity or activity of neural systems that are activated by brain states or stimuli and then endure for a period longer than the length of the stimuli or brain states that prompted the changes. When an agent has memory, he or she may learn from experience (Carew et al., 1981; Pavlov, 2010), generalize more quickly, recall past information to better infer or guess with incomplete data, and generalize more quickly (Bialek et al., 2001). Other calculations that rely on the accumulation of knowledge over time, such as integration search and decision-making, are also performed (Gold & Shadlen, 2007) (Figure 5.1).

Figure 5.1. Illustration of in-memory computation system. Source: https://hazelcast.com/glossary/in-memory-computation/.

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Even while many of these tasks need the use of elements other than memory, memory—defined and explored in this chapter as the persistence of states through time—is a necessary component. In this section, we give a look into some of the concepts, methods, and biological substrates that are thought to constitute the building blocks of brain memory from a computational and theoretical viewpoint. We will be concentrating on the issue of memory preservation while also examining other issues that have perplexed us. Short-term and long-term memory has traditionally been divided into several kinds, with varied views of what constitutes a “border” (James, 1890). For example, short-term memory (STM) is described as lasting seconds to tens of seconds, but long-term memory (LTM) is characterized as lasting hours to decades. Furthermore, STM refers to persistent variations in activity, whereas LTM refers to changes in the existence of connections and the strength of synapses between neurons. The dynamical boundary between LTM and STM is unclear, and a few of the central computational trials involved in preserving states over time are the same regardless of whether ‘time’ refers to decades or seconds and whether ‘state’ refers to activity or structure, according to some researchers (Bailey et al., 2015). What characteristics could be desired in a memory storage system in general? First and foremost, the system should have states that can persist throughout time, according to its specification. Secondly, it should have adequate “capacity” or number of states, with the ability scaling up in an acceptable (and efficient) manner in relation to the amount of energy expended. For STM and LTM, this capacity restriction may be significantly different from one another. Third, the persistence of multiple memory states should be triggered by separate inputs that are to be recalled. Fourth, the states should be resistant to noise, and any simultaneously stored memories should not cause significant interference with the states. Finally, the memories that have been saved should be appropriately retrievable when given the right indications to do so (Tetzlaff et al., 2012). Why is it so difficult to build up memory states? If not revived, biophysical variables have inherent timeframes during which they degrade to some baseline level. Postsynaptic potentials go on tens to hundreds of milliseconds, and some simplification processes can take several hundred milliseconds. Membrane time constants range from milliseconds to tens of

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milliseconds (Koch et al., 1996). Behavioral timelines for STM, on the other hand, range from tens of seconds to minutes. When it comes to structural and other types of LTM, molecules have average protein times of days, while memories can last for years. As a result, in both STM and LTM, the brain must create states that endure utilizing, as far as we know, memoryless substrates. Noise is also present in the brain. Signals are sent across synapses, which have a chance of failing (Hodgkin & Huxley, 1952). Neurons receive varying inputs and spike in a random manner. In individual spines and boutons, copy counts of essential ionic and proteins species can be low, allowing for substantial variations. Even in constant states, noise can cause the system to enter a non-persistent state or the incorrect constant state (Figure 5.4). Finally, the desires of memory are diametrically opposed; attempting to fulfill them all at the same time necessitates compromises. Different memory conditions should be well-separated for a robust recovery so that states with noise can be transferred to the right memory. However, resilience enforces a capacity limit: a fixed representational space can only hold so many wellseparated states (Figure 5.5). Alternatively, improved error correction, as used in communications theory, can make memory both resilient and highcapacity, but this necessitates complicated encoding and decoding (Figure 5.6b), which may be neutrally improbable. Slow biophysical processes can produce lengthy endurance durations with less circuit finetuning, but they also make the system less sensitive to inputs (Sreenivasan & Fiete, 2011; Pereira & Wang, 2015). Quick overwriting of old memories are caused by plasticity robust enough for rapid learning of new information (Lahiri & Ganguli, 2013). To properly comprehend the imperatives of neuronal memory, we need to know where the brain stands on these choices. We explore the processes that generate constant states, their resilience and capacity, the genetic proof for these processes, and the key distinctions in memory layouts between von Neumann and neural computers.

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Figure 5.2. Stable states from positive feedback. Source: https://scite.ai/reports/computational-principles-of-memory-ymdNXx.

Note: (a) When positive feedback surpasses fundamental decay (gray), total activation is backed up(right)ward, and vice versa when deterioration prevails in a system with sigmoid-shaped positive feedback (black). White circles indicate attractors or stable states. (b) An exponentially decaying unit with saturating response and a self-excitation can generate the dynamics shown in a. (c) Bistability having a nonlinearity that is not linear. The response is modest when the input is weak. Activity grows linearly over a threshold before saturating. Nonlinear feedback with early acceleration and then saturation can create biostability in general. (d) As in b, but with

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a homogenous population in place of the unit. The activation space or state space of all units is represented by a shaded cube. The activation level of unit 1 is represented by r1, and so on. All units’ activation vectors are points in state space. White circles represent high and low activity levels. (e,f) Feedback is calibrated to cancel decay over a wide range of activations, resulting in a continuum of stable states as illustrated in f. (g,h) A variant of e in which the state continuum creates a ring (shown in h) (Fuster & Alexander, 1971). In this illustration, neurons organized in a ring stimulate their close neighbors (top, green) while inhibiting all other neurons (orange). One neuron’s synapses are depicted. The steady activity profile is a bump, and all bump movements around the ring are bumps.

5.2. CREATING PERSISTENCE FROM MEMORY-LESS COMPONENTS The majority of STM models depend on some type of ongoing activity. Through the interruption periods of a variety of memory tasks, constant activity has been seen in several cortical locations, and it corresponds with memory performance and task load, which includes that permits the considered item to be decrypted. According to the core canon of neuroscience, which dates to Ramón y Cajal and others the foundation of LTM is permanent or enduring synaptic change. In tests, LTM is linked to persistent changes in synapse structure and weights, and preventing long-term plasticity impairs memory learning (LTP) (Kandel et al., 2014; Takeuchi et al., 2014). How can long-lasting states of neuronal memory be achieved? States must be stabilized by circuit interactions to remain ahead of the time factors of the fundamental elements. Circuit methods might include molecular interactions in a synapse (LTM) or across-neuron signaling (STM) and can result in a variety of constant responses, such as stable states, dynamic trajectories, and slowly decaying states, as we’ll see in the next section.

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Figure 5.3. Depending on how the memory is used for computing data, four main in-memory computing approaches can be defined. Source: https://pubmed.ncbi.nlm.nih.gov/26906506/.

Note: (A) Computation-near-Memory (CnM): By lowering the length of the interconnections, 3D-integration technologies may bring compute and storage closer together. Logic and storage remain two distinct things. (B) Computation-in-Memory (CiM): The conventional memory structure remains unaffected, and data processing is handled by peripheral hardware. (C) Computation-with-Memory (CwM): To access pre-computed results, memory is employed as a Look-Up Table. (D) Logic-in-Memory (Lim): Simple logic is added to each memory cell to perform data computation directly inside the memory.

5.2.1. Positive Feedback Positive feedback is a broad idea that may be used to create persistent states from variables that are intrinsically non-persistent (Figure 5.2). As in the ‘reverberating activity loops’ postulated by Lorente de Nó and Hebb, units

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in a circuit that stimulate one another permit excitement to last longer than the length of the stimulating stimulus (Hebb, 1955).

5.2.1.1. Discrete Memory States Non-persistent, nonlinear units that powerfully excite themselves can sustain a high activity once activated (Figs. 5.2 and 5.4d), but the ‘downstate can stay stable even if the initial activity is modest; input pulses can take transitions among these stable states. In engineering, this is how leaky capacitive components are used to make flip-flops or switches and it is also the basis for static random-access memory (Dirik & Jacob, 2009). Strong synaptic contacts between neurons, as well as autocatalytic chemical events, are two types of positive feedback that have been postulated to promote bistable control dynamics (Wilson & Cowan, 1972).

Figure 5.4. Long-term synapse size maintenance. Green hexagons in both images depict synapse-produced molecules that catch centrally transported resources for synapse upkeep. Source: https://www.nature.com/articles/nn.4237

Note: (a) The state of a bistable molecular switch (orange) controls how many seizing molecules (green) are created and, therefore, the synapse size; the state of the switch regulates however many capturing molecules (green) are generated, and hence the synapse size. (b) The synapse produces capturing molecules (green) indirectly proportionate to its size in this illustration. These molecules seizure synaptic maintenance resources in proportion to their number; consequently, at any given size, the synapse may acquire resources in proportion to its size. The soma can simply combine feed forward

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signals from synapses to compute how several resource molecules replicate in both a and b. Since somatic changes alone could account for synaptic distinctiveness, the preservation signal, or the permanent determination of synapse size, must be present in the synapse. The soma would need at least 2K persistence states to be actively engaged in creating a synapsespecific persistent maintenance signal, which is a physiologically untenable situation. Strong feedback via excitatory connections may sustain numerous distinct stable states, each with a different dispersed design of activity transversely the neurons (Figure 5.4c). Surprisingly, despite the discrete attractor Hopfield network’s ubiquitous effect on how we think about memory in neurological systems, finding uncontentious instances of distinct attractors in the brain that survive in the nonappearance of inputs is difficult (Destexhe & Sejnowski, 2009).

5.2.1.2. A Continuum of Memory States Positive feedback, when correctly tuned, can either provide a variety of stable states or a single stable state (Little, 1974). If the excitatory take since positive feedback is sufficient to counteract the intrinsic decaying of the state, and if this stability can be maintained for enough states, the system may provide a variety of states and utilize them to store an analog variable, as seen in Figure 5.4 (Burak & Fiete, 2009). Continuous attractors are often observed just when circuit interfaces exhibit some degree of equilibrium or other finetuning, which is rare. Regard as a ring of neurons, each of which has a similar strong excitatory drive to its instant neighbors and the same strong inhibitory drive to the remainder of the neuronal network (Figure 5.4e) (Ben-Yishai et al., 1995). The stable state on the ring is defined by an activity bump and all its translations, which together define a one-dimensional continuous attractor with one dimension. The potential of neural plasticity to generate and rectify these structured interfaces, and the ability of intrinsic biostability to stable these networks, are both possible, although in most cases, considerable finetuning is required. The topic of how the brain might be able to address this difficulty is still an unresolved theoretical and experimental one.

5.2.1.3. Persistent States from Inhibitory Interactions Mutual inhibition, rather than excitement, can provide positive feedback: units that prevent one another from successfully disinhibiting themselves.

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Thus, switching dynamics may be produced by coupling an excitatory feedforward drive with reciprocal inhibition61 (Figure 5.4d). Circuits with all-inhibitory recurrent connections, on the other hand, can produce continuous attractors.

5.2.1.4. LTM maintenance Because of that, protein lifespans are generally on the order of days; how is a long-term memory preserved across months and years? LTM stability may result from synaptic stability, according to popular belief (Crick, 1984). Positive feedback is frequently addressed in the context of STM; however, the ideas are universal and applicable to LTM. Autocatalytic processes or inhibiting molecular interactions or mutually exciting can sustain inherently non-persistent chemical states6, permitting them to function as a synaptic preservation signal (Figure 5.5). A synapse could only have two sizes if synaptic strength was only determined by the state of a single bistable molecular switch, without diverse copies of the molecules being compartmentalized and self-reliantly switchable, or if multiple switches involving multiple molecular species with staggered switch thresholds exist. The synapse in the latter two examples might have numerous distinct levels. Protein kinase M (PKM) and Calmodulin-dependent protein kinase II (CaMKII) are two major possibilities for sustaining synaptic states through positive feedback in experiments. CaMKII autophosphorylation operates as a positive feedback loop, making it possible for it to function as a bistable switch (Lisman et al., 2015). After LTP development, inhibiting CaMKII’s association with the NMDA receptor disturbs potentiation in a way that lasts even when the inhibitor is eliminated, and altering CaMKII-NMDAR centers has long-term impacts on three-dimensional learning (Zhou et al., 2007). Furthermore, CaMKII and the translation factor CPEB70 may form a bistable switch. However, not all CaMKII inhibitors impair LTP maintenance in the processes tested (Chen et al., 2001). PKM, a constitutively active protein kinase, impedes a protein that inhibits its own translation. The PKM inhibitor ZIP interrupts both LTP and memory protection, and models indicate that once PKM is highly expressed, it can maintain its state (Jalil et al., 2015). ZIP, on the other hand, is not specific to PKM, and intact LTP maintenance and memory in PKM knockout mice confuse PKM’s role in memory preservation, though the latter outcomes may not pertain to wild-type mice (Lee et al., 2013).

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Obviously, both putative processes have a case to make. Feedback, like STM, might produce an analog molecular maintenance signal. When a synapse creates a set number of molecules in proportion to its present size and these molecules absorb resources for synaptic preservation, the mechanism is called tuned positive feedback. Fine-tuning is required for producing permanent analog states with positive feedback, as detailed below. The states can drift over time, even if well-tuned, and far more so if not, which is problematic over the long duration of LTM, implying that preservation is more likely to rely on discrete attractors. As a result, synapse strengths can be discretized. Results from electron microscopy that demonstrate distinct synapses between the same dendrite and axon have basically the same volume appear to be compatible with a finite set of sizes; however, considerably more research is needed to answer this topic (Bartol et al., 2015).

5.2.2. Negative Derivative Feedback Memory states can be generated by a system with strong inhibitory feedback, high excitatory coupling, and slower excitatory than inhibitory networks. (Boerlin et al., 2013). Negative feedback control is a generic idea employed by biological systems. Fast-acting inhibitory connections prevent state transitions, allowing the network to stay in the states it has been assigned to. Negative derivative feedback systems, like positive feedback networks, can have steady states or extremely lengthy transients, are sensitive to inputs despite their extended perseverance durations, and are in some respects more durable. It’s uncertain if continuous attractors found in experiments rely on negative derivative feedback, positive feedback, or a mixture of both.

5.2.3. Long transients, Feedforward Structures, and Chaotic States When well-tuned and free of noise, the preceding methods can theoretically allow memory to last an indefinite amount of time. There are even additional mechanisms for temporary memory. Attractors are created by sufficiently strong positive feedback with specific topologies, although positive feedback, in general, creates slow styles,’ which display activity decline at times slower than the biophysical time factors of the constituent elements (Figure 5.4b). These gradually declining traces can be utilized to collect and decode inputs from the system’s immediate output (White et al., 2004).

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Postpone lines, which were employed to build memory in the early days of computing, are another approach to delay decay. A delay line in the brain can be made up of neurons connected in a feedforward architecture, with one activity flowing into the following or a series of subcellular chemical reactions (Eckert, 1953; Abeles et al., 1993). The signal decays quickly at each node, but it is transferred up the chain (Figure 5.4f), prolonging the signal lifespan according to the number of nodes. Negative derivative feedback networks and other so-called ‘balanced’ networks are examples of ‘non-normal’ networks, which also incorporate delay lines. In contrast to the desiderata for memory networks, generally balanced networks prefer to provide quick transient reactions (Goldman, 2009). Large normal and non-normal networks may exhibit complicated constant dynamical replies, such as oscillations, when they are firmly coupled (Sprott, 2008). theoretically, whether these complex dynamics may consistently function as STM remains an open subject. Long stimulus-dependent trajectories have been reported in different brain systems in experiments. Although it’s vague if these trajectories serve memory or just transfer the system from one state to one more for other reasons (Laurent, 2002).

5.2.4. Slow Biophysical or Stable Processes The notion is that memory is constructed from components that have a low requirement for memory. The construction of LTM and STM from fundamentally continual or slowly decomposing elements raises several questions in terms of computation.

5.2.4.1. Slow Synaptic Dynamics for STM Maintenance Several biophysical periods in the brain are short in comparison to STM persistence times; nevertheless, longer time factors at the synaptic and cellular levels, such as synaptic facilitation, post-tetanic potentiation, and calcium-sensitive cation currents, could help to stabilize persistent neural activity states (Murphy & Miller, 2009). However, unlike positive feedback models with quicker parts, STM models based on slow biological timescales have the disadvantage of being sluggish to react to input changes. A unit-strength input administered for t seconds results in a change in the durable state relative to t/; to stimulate a unit change in state, the input must be applied for t seconds. Positive

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feedback–stabilized states with rapid components, on the other hand, allow for reasonably quick state transitions, even if the efficient network time constant for memory is very divergent or long. The oculomotor integrator’s stability under perturbation and the sensitivity of positive feedback– stabilized circuits of rapid components, on the other hand, suggest that slower biophysical timescales may be relevant in STM preservation.

5.2.5. Intrinsic Biochemical Multistability for LTM Maintenance Slow states, such as inherent molecular multistability, might be involved in LTM maintenance at synapses. If the energy blockade among the states is high enough, a molecule having two low-energy states can remain in either state for extremely long periods; these states can act as indicators of a potentiated synapse (Goldman, 2009).

Figure 5.5. Robustness of persistent activity architectures. Source: https://www.researchgate.net/publication/295833452_Computational_ principles_of_memory.

Note: Robustness of persistent activity architectures. (a) States in the neighbors of attractors decompose to their attractors in discrete attractor networks, resulting in automated correction. This can be thought of as incomplete trigger memory retrieval. If the brain picture is an attractor, a noisy response

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causes the network to recover the undamaged image. The network fails to retrieve the entire picture if there is enough noise and accumulates to another memory state (first image) (b) As shown in the top illustration, an incessant attractor adjusts noise that is parallel to the attractor manifold, but not noise that is parallel to the exhaust system, because this noise induces the system to enter a different permissible state, which corresponds to a different value of the represented variable (Abeles et al., 1993). Sensitivity to structural noise is at the bottom. The collection of stable states is continuous with architectural symmetries or finetuning, but tiny perturbations to network design split the continuum into a series of closely spaced and hence quasicontinuous fixed points. (c) In a non-persistent method, the initial gap among states decays with time when coding with extended transients (converging blue traces). As a result, the capacity of a given noise level to obscure two states improves over time. (d) In balanced networks, complex paths can be encircled by a tiny zone that draws states back onto the curve. With system size nevertheless, these areas appear to be vanishingly tiny (Ganguli et al., 2008). The prion-like CPEB (cytoplasmatic polyadenylation element-binding protein) has been postulated as the synaptic preservation signal, which is consistent with the latter theory. CPEB, which controls protein translation in dendrites and is triggered by NMDA signaling, occurs in two stable folded morphologies, one of which forms identity multimers. CPEB mutations affect cerebellar learning, and shutting down CPEB following memory establishment alters earlier stable hippocampus memory (Maass et al., 2002).

5.3. ROBUSTNESS TO NOISE Given the obvious omnipresence of noise in the brain, the resilience of proposed memory systems is critical to their credibility. We investigate the impact of noise in both the state variables and the underlying system’s parameters.

5.3.1. Discrete Attractors Are Robust In the presence of uncorrelated noise, bistable switches with strong positive responses have an exponentially low likelihood of spontaneous switching

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(Mannozzi et al., 2018). As a result, the states have a high noise resistance and can restore the attractor from a disrupted network state. Robust positive feedback tends to push reactions into the saturated activation regime; hence bistable switches are similarly resistant to changes in coupling strength. Small changes in coupling strength will not destroy or even significantly affect bistable states if the connection is strong enough. Other discrete attractor states are similarly strong to noise that is less than half the distance among any pair of attraction states (Figure 5.6a). The system cannot be moved to another attractor by this noise, and the subtleties revert to the new attractor (Alon et al., 1999).

5.3.2. Continuous Attractors Are Partially Robust Continuous attractors are a mathematical idealization in which minor perturbations in network topology cause the continuum to break up into a string of stable points (Figure 5.6b), much as a narrow stream of water is susceptible to necking off into intimately spaced droplets (Figure 5.6a). Nevertheless, because closely spaced fixed points resemble a continuum, we will refer to both ‘really’ constant attractors and quasi-continuous attractors created from fixed points as constant attractors from here on. Constant attractors are not completely impervious to continuous noise: disturbances of the attractor various dissipate fast and are thus rectified by the attractor itself (Figure 5.6b). Nevertheless, sections of noise throughout the manifold cause the state to shift to another stable location on the manifold, causing the state to become unstable (Figure 5.6b). The attractor dimension is proportional to the size of the network (Liddell & Sherrington, 1924). In the presence of low-amplitude noise, nearly constant attractors with a degree of distinctness along the attractor might resist the diffusion of the exemplified variable, but at the expense of a rounding-off or discretization error in the represented variable (Boerlin et al., 2013). The drawback is that they do not permit for the combination of tiny, gradually variable inputs, and, at the same time, they are not as resilient as discrete attractors with well-separated segments.

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Figure 5.6. The tradeoff between robustness and capacity. Source: https://www.proquest.com/docview/1767659366.

Note: (a) A decoder may reliably recover the state from a reasonably significant quantity of noise if the memory states, either continuous or discrete, are well-separated. This is because the neighbors of each memory state are vast (b). The neighbors of each memory state must obviously shrink as more memory states are packed into the fixed state space volume of a certain number of neurons. A modest bit of noise causes the state to shift into the vicinity of another memory state. As a result, larger capacity equates to lower noise tolerance.

5.3.3. Robustness of Negative Derivative Feedback Networks Positive feedback systems are more susceptible to slight changes in contact intensity than negative derivative feedback systems. Starting with

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a system tuned to have 100 times the determination of the biophysical time constant, a 5% reduction in excitative feedback causes a fivefold decline in the retention time of positive feedback networks, just a 5% change in the persistence time of negative derivative feedback networks. This occurs because, in well-tuned positive feedback networks, it is divided by a value near to zero; a slight change in feedback pushes the denominator away from zero, immediately reducing the determination time. Negative derived feedback networks multiply by a high amount, minor changes in excitatory intensity result in modest percentage shifts in this multiplier, and therefore the time constant (Lim & Goldman, 2013).

5.3.4. Noise Tolerance in Networks with Long Transients Network responses converge toward zero over time in positive feedback networks that encapsulate information with long, decomposing transients (Figure 5.6c); thereby, for spatial and temporal punctate responses and continuing noise, primary states can begin well-separated, only developed more easily confused over time due to noise. As a result, noise, in addition to intrinsic activity decay, adds to information loss. The quantity of information rises in percentage to network size and diminishes with time, at best as the reverse of elapsed time, in networks with feedforward or concealed feedforward form, like synaptic chains. The attractive dynamics of networks that create complicated trajectories can remedy tiny perturbations (Figure 5.6d). The attractive zone, on the other hand, is tiny in these statistically homogenous random networks and declines with network size (Bartol et al., 2015).

5.4. MEMORY CAPACITY How much can data be saved as a function of network size, given a computational model of memory storage? This is a problem with memory capacity. What are the constraints on LTM and STM capacity in actual neural systems?

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Figure 5.7. Intricacy cost of storing a constant variable in a set of well-separated distinct attractors. Source: https://www.semanticscholar.org/paper/Computational-principles-ofmemory-Chaudhuri-Fiete/6964c1c35445b628081b8d90698de58c9a47ed86.

Note: (a) If we take a continuous circular adaptable, it is possible to map values of the variable continuously and naturally onto a (quasi)continuous attracter of the same topology and dimension while still maintaining metric relationships among different values of the variable. According to the change in the variable value, a distinct storage state for each different value is selected, making the encoding very straightforward. (b) To encode a constant variable in a series of well-separated distinct stable states in another coding aspect, first, discretize the variable (first set of arrows), then choose how to transfer the discrete values into the attractors (second set of arrows). There is no metric-preserving mapping in general, and the encoding problem is quite difficult (Bourne & Harris, 2011). A lot of variables, as we’ll see below, restrict memory capacity. If the memory states aren’t durable over time, one of them is decay. Another factor is noise, which can produce the recorded memory state to switch (Figure 5.6), and enhancing noise tolerance through well-separated memory states lowers capacity (Figure 5.7). The third factor is intervention: even if current memory states can be maintained over time and are resistant to noise if the number of inputs entered memory throughout an organism’s lifespan exceeds the memory space available, accepting new memories demands overwriting old ones.

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Figure 5.8. Applications of cloud computing technology. Source: https://www.educba.com/cloud-computing-technology/.

5.4.1. How Much LTM Is Enough? Only particular combinations of elements may produce meaningful scenes and events in the world; however, if the number of elements is vast, this can soon lead to a combinative explosion. Even if the number of synapses and neurons is enormous, LTM capacity can be overloaded if it increases linearly with them. Theoretical LTM capacity estimates for the brain range from 10 to 10 bits. Human LTM for development stimuli is large, according to empirical evidence (Harris et al., 2015).

Figure 5.9. Types of long term memory. Source: https://www.simplypsychology.org/long-term-memory.html.

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5.4.2. Capacity in LTM Models 5.4.2.1. Hopfield Networks The Hopfield system is a well-known model of’ auto-associative and LTM storage retrieval based on memory-free units. Its synaptic weights can hold the memory of a collection of input designs. For retrieval, the brain dynamics flow to a stable activity state matching to the nearest stored design, employing the learned weights and beginning with incomplete or noisy inputs (as in Figure 5.6a). Since the tag to recover a state is the state itself, the network is dubbed auto-associative, and it accurately recovers memory from and denoises noisy cues (Volk et al., 2013). A Hopfield network with N neurons may hold N arbitrary stable patterns in its weights, according to general arguments. This scaling is achieved using specific learning rules, including the associatory Hopfield learning rule, which strengthens a weight whenever post- and presynaptic neurons display associated activity (Ogasawara & Kawato, 2010). Learning new inputs after capacity has been achieved, on the other hand, undermines the stability of all current states, making them unrecoverable. Adding boundaries to the range of each synapse, for example, permits the network to operate in a ‘palimpsest’ manner, in which old memories slowly fade as novel ones are learned, avoiding devastating removal from interfering but lowering capacity.

Figure 5.10. A generalized representation of Hopfield network. Source: https://en.wikipedia.org/wiki/Hopfield_network.

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5.4.2.2. Palimpsest LTM Networks The palimpsest method forces a choice between maintaining memories and putting in new data with great fidelity, rapidly overwriting earlier synaptic strengths. In the palimpsest mode, several research looks at the combined restriction on the duration and fidelity of memories. These findings concern information stored directly in synaptic weights; it is presently unknown how the boundaries might alter in Hopfield networks for credible memory retrieval.

5.4.2.3. High-Capacity LTM Systems Information theory demonstrates how to design sets of states of length N (referred to as ‘codes’) that can represent exponentially numerous states (eN, where 0 eN = 1) with high reliability. The theory of error-correcting rules, on the other hand, does not take into consideration the expenses of encoding and decoding (i.e., denoising). Typically, excellent denoising codes entail a high level of complexity, as well as substantial costs in terms of either space or time. In a neurological system, neurons must do all the encoding, storing, and denoising functions and they must do so with the same sorts of resources. The topic is whether the brain can encode, store, and denoise many states utilizing just a small number of neurons, which is close to exponentially many (Kwapis & Helmstetter, 2014). It is interesting to note that Hopfield systems with bistable switches or disjoint cliques can contain an almost exponentially large number of stable states that are denoised by network changes in volume (Pastalkova et al., 2006). These specifically designed states, on the other hand, do not correlate to random input patterns. The mapping of any patterns onto these structured states (in this perspective, the inputs are saved by ‘hashing’ them to the durable states) may make it possible to store arbitrary patterns; nevertheless, this encoding may be computationally difficult, as seen in Figure 5.12b. It entails mapping an exponentially large number of inputs, some of which may not be well-separated, to an equal number of different well-separated memory states, all deprived of a structure compatible with the inputs characterized. It is yet unclear if the nature of natural inputs allows for natural functions of this type, which would allow for resilient exponential or, at the very least, superliner storage to be achieved (Meyer et al., 2014).

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Figure 5.11. According to the Atkinson-Shiffrin model of memory, information passes through three distinct stages in order for it to be stored in long-term memory. Source: https://courses.lumenlearning.com/wmopen-psychology/chapter/reading-storage/.

5.4.3. How Much STM Is Enough? According to behavioral research, human STM capability is < 10 objects. This is unexpected, given the significant link between general intelligence and STM implementation. It’s uncertain if the strict capacity restriction is a design element or the result of constraints like limited resources, deterioration, or interference. If the former, the question of which limitations are the limiting circumstances remains unanswered. If the latter is the case, STM may be optimized for simple encoding and retrieval, and deleting earlier inputs preserves the buffer clutter-free for quick access, albeit this goal could be best provided by selective deletion of superfluous items rather than generic refreshing (Boerlin et al., 2013).

5.4.4. Capacity in STM Models Because the Hopfield network’s learned patterns represent stable states of the dynamics, it also acts as an STM. For continuous attractor networks, the same is true. STM is encoded in both circumstances by initiating a stable

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state and consisting of that state’s perseverance throughout time. In this case, the Hopfield network’s capacity is N, which is linear in the number of neurons in the system. It is impossible to enumerate states in continuous attractor networks to assess capacity. Nonetheless, the capacity of such networks is determined by the range of the depicted varying divided by the decoded error at time T after encoding. The capacity scales linearly with N and inversely with T when defined in this manner. The deterioration with time is an extra penalty imposed by continuous attractor networks, yet it is consistent with Hopfield networks (Aslam et al., 2009). Instead of fixed-point dynamics, items in a series of input can be reconstructed from the immediate states of systems with lengthy transients. In linear networks, the capacity of such networks rises linearly with N and is broadly defined as the cumulative memory of past inputs that can be reconstructed from the present state. The finest linear networks for sequence memory with continuous neural noise are structured in a single, maximum long chain, according to the same criteria. A single input can be stored in a network with transient dynamics. Again, performance enhances linearly as N increases.

5.4.5. Discrete Versus Continuous Attractors for Memory? Given the capacity and resilience benefits of well-separated distinct memory stages, why could the brain employ (quasi)continuous memory states in the first place? It is possible to maintain metric information by mapping an analog variable onto such a continuous variation of matching measurement, with adjacent values of the variable translated to surrounding brain states (Figure 5.12). An input representing the time-derivative of an external analog variable can be used to directly update the neural state to the new value of the external analog variable when the neural interpretation maintains the metric of the exterior analog variable (Figure 5.12a). As a matter of fact, many systems represented as continuous attractors are established or postulated as “integrators.” (Zhou et al., 2007).

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Figure 5.12. The cost of storing a variable in a series of discrete attractors is complex. Source: https://www.nature.com/articles/nn.4237

Note: (a) Think about a constant circular variable. The variable’s values can be continuously and naturally plotted onto a (quasi)continuous attractor with the same topology and dimension, conserving metric relationships among various values of the variable. The encoding is straightforward, with each value being assigned to a different storage state dependent on the difference in the variable value. (b) To encode a continuous variable in a series of wellseparated finite stable states in another coding component, first discretize the variable (first set of arrows), then choose how to transfer the distinct values into the attractors (second set of arrows). There is no measurable statistic mapping in general, and the encoding problem is quite difficult.

5.5. MODEL MECHANISMS: TESTS AND QUESTIONS Given the computational concepts discussed here, it is evident that much more research is needed to determine which systems in the brain are really employed for STM and LTM.

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5.5.1. Questions and Tests for STM Is STM based on sustained activity, and if so, what role does the circuit against slow subcellular processes play? After some seconds of aberrant synchronized brain activity139, some patients with ‘absence’ (petit mal) seizures can understand queries and resume discussions. This finding could, in theory, refute STM processes based on short biophysical time constants and circuit feedback–stabilized activity, but the evidence isn’t conclusive because seizures are frequently localized and may not disrupt activity in the relevant areas. Blocking activity in relevant areas for varying lengths of time (e.g., optogenetically) and then measuring how well memory recovers could help to solve this problem (Song & Wang, 2005). During the latency period, neurons thought to be interested in STM can show changing shapes of activation (140). These results could indicate that the circuit uses feedforward structures or transitory dynamics (Figure 5.4b,f); otherwise, they could indicate that the circuit is stable, with the whole state freely flowing adjacent to the various while a lower-dimensional projection of the system activity stays stable and maintains the memory. To uncover directions that disturb memory preservation, an experimental investigation might apply focused perturbations (such as programmed photostimulation (Cannon et al., 1983). Does temporal decay or interference cause recall performance to deteriorate in STM psychophysics? The former denotes a constant attractor or a temporary memory system in which data is slowly lost over time. The latter connotes a palimpsest-like memory, through memories are temporally stable in the absence of an exterior drive but are overwritten by fresh inputs. Adjusting delay periods as keeping memory load constant, and vice versa, as well as quantifying performance, can aid in resolving the fundamental mechanisms.

5.5.2. Tests and Questions for LTM The relationship between synaptic alteration and LTM is becoming clearer. Despite this, fundamental concerns concerning LTM’s synapse and circuitlevel substrates remain unsolved. In synapses, what chemicals make up the LTM maintenance signal? The maintenance signal will determine whether the method is an inherently stable molecular state or a self-propagating molecular state (with no turnover and so potential aging problems).

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Is LTM a circuit-level trait with flowing single-synapse influences, or does it have a basis of individually stable synapses? Synapses in vitro brain slices vary strength and turnover over days, suggesting the possibility of in vivo turnover. Memory, in this approach, would be a low-dimensional projection of synaptic states, with various possible connectivity and strength combinations resulting in a similar projection, akin to a concept for STM described above. Individual synapses would vary as they moved between various synaptic states, but the memory would stay constant. To make this conceivable, a mechanism must constantly drive the system to maintain the same lower-dimensional memory while synapses change. Reconsolidation based on recollection could be one of these processes, but if the recall is impulsive, the consequence is a positive-feedback method that favors some powerful memories over the rest and deletes the rest (Ferrell et al., 2002). To test if memory can be preserved by the long-term strength of its substrata or if it needs to be re-instantiated like RAM in computers (see below) The number is significant, yet it is also exceedingly tough. A bluesky experiment would follow synaptic configurations over time while also evaluating behavioral memory retention to see if there is a subspace of synaptic patterns that correlate to a steady memory. Measurement of retention, on the other hand, causes recall, which affects the stored memory (Wimmer et al., 2014). It is also unlikely that new memories will be acquired over this time, complicating the characterization of invariant subspaces for a given collection of memories. Does LTM work in a palimpsest mode, and how does fresh information intermingle with old? It’s possible to figure out how quickly neural memories are rewritten by stimulating new learning and then watching how memory declines and neural interpretations alter. For example, how do hippocampus representations of common habitats develop after training the animal on multiple new environments vs. just retesting representations in the normal place after the same amount of time? Using a shared set of synapses, laser stimulation experiments in vivo might establish many new memories while assessing how old memories fade away.

5.6. BIOLOGICAL VERSUS COMPUTER MEMORY There are several differences between biological and computer memory; we will emphasize three of them. First and foremost, computer hardware is distinct from memory, and software is distinct from the computation.

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However, in the brain, the relationship between structure and activity is intricately interwoven. Activity patterns that underpin a computation are spontaneously encoded in LTM via activity-dependent plasticity, which is a phenomenon that occurs in all mammals. These memories, on the other hand, have an impact on the calculations that a system can do. Recovery in the brain can remodel memory, in contrast to computer memory, which cannot be changed even after being retrieved repeatedly (Taube & Bassett, 2003). To further explain the difference between the two systems, computer memory is accessed via abstract indices that are unrelated to memory content, whereas the brain is thought to work with evidenced long-term memory, in which pieces from a stored item can trigger recall of the full memory by having completed incomplete associations with other pieces from a stored item. Third, computers assign discrete places to separate memories, but memory storage in the brain is overlapping, distributed, and parallel: a set of synapses is assumed to be involved in numerous memorials, and a particular memory is dispersed throughout a network of synapses. Robustness and interference issues in the brain can arise because of these differences; however, combining memory and computation in a single location may allow for faster, more flexible calculation at a lower energy cost and deprive of the long wait times to contact memory that is a major limitation of von Neumann computer designs. Nonetheless, there are several fundamental theoretical assumptions that both biological and artificial memory share. The contrast between LTM and STM may be analogous to the divide between primary and secondary memory in computer systems. A circuit through which information may be frequently and swiftly entered and recovered is required by both STM and RAM, and the processes for accomplishing this necessitate a continual outlay of energy. Computers can either continuously refresh the crumbling states of capacitors, as inactive RAM, or employ switches stabilized by positive response among decaying components, as in static RAM, to maintain their state over time. The rehearsal mechanisms in working memory and the positive feedback pathways hypothesized to sustain persistent activity in biological memory have some parallels in terms of function. To offset decay in either scheme, the rate of impaling must be greater than the inverse biophysical time constant of the synapses or neurons being used. Strangely enough, there are quick RAM-like technologies for computer memory that

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do not require electricity for maintenance, such as flash memory, which is quite inexpensive. The actual gadget, on the other hand, degrades with time (Koulakov et al., 2002). Because it does not need neuronal spiking like STM (and hence requires less energy to sustain), LTM based on synaptic stability may be slower to persuade and may rely on more complicated encoding and decoding systems to defend against mistakes, just like hard drives and RAM do. However, unlike hard drive magnetization, sustaining a persistent synaptic chemical signal is not energy efficient. Synapses themselves need the energy to preserve their function (Goldman et al., 2003).

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77. Rosenblueth, A., Wiener, N., & Bigelow, J., (1943). Behavior, purpose and teleology. Philosophy of Science, 10(1), 18–24. 78. Sacktor, T. C., (2011). How does PKMζ maintain long-term memory?. Nature Reviews Neuroscience, 12(1), 9–15. 79. Sanhueza, M., Fernandez-Villalobos, G., Stein, I. S., Kasumova, G., Zhang, P., Bayer, K. U., & Lisman, J., (2011). Role of the CaMKII/ NMDA receptor complex in the maintenance of synaptic strength. Journal of Neuroscience, 31(25), 9170–9178. 80. Schultz, W., Dayan, P., & Montague, P. R., (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599. 81. Scoville, W. B., & Milner, B., (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20(1), 11. 82. Seung, H. S., (1996). How the brain keeps the eyes still. Proceedings of the National Academy of Sciences, 93(23), 13339–13344. 83. Shadlen, M. N., & Newsome, W. T., (1998). The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. Journal of Neuroscience, 18(10), 3870–3896. 84. Shannon, C. E., (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. 85. Softky, W. R., & Koch, C., (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. Journal of Neuroscience, 13(1), 334–350. 86. Sompolinsky, H., Crisanti, A., & Sommers, H. J., (1988). Chaos in random neural networks. Physical review Letters, 61(3), 259. 87. Song, P., & Wang, X. J., (2005). Angular path integration by moving “hill of activity”: A spiking neuron model without recurrent excitation of the head-direction system. Journal of Neuroscience, 25(4), 1002– 1014. 88. Sontag, E. D., & Boyd, S. P., (1995). Mathematical control theory: Deterministic finite-dimensional systems. IEEE Transactions on Automatic Control, 40(3), 563–563. 89. Sprott, J. C., (2008). Chaotic dynamics on large networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18(2), 023135. 90. Sreenivasan, S., & Fiete, I., (2011). Grid cells generate an analog errorcorrecting code for singularly precise neural computation. Nature Neuroscience, 14(10), 1330–1337.

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91. Takeuchi, T., Duszkiewicz, A. J., & Morris, R. G., (2014). The synaptic plasticity and memory hypothesis: Encoding, storage and persistence. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1633), 20130288. 92. Taube, J. S., & Bassett, J. P., (2003). Persistent neural activity in head direction cells. Cerebral Cortex, 13(11), 1162–1172. 93. Tetzlaff, C., Kolodziejski, C., Markelic, I., & Wörgötter, F., (2012). Time scales of memory, learning, and plasticity. Biological Cybernetics, 106(11), 715–726. 94. Van, V. C., & Sompolinsky, H., (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274(5293), 1724–1726. 95. Vidyasagar, M., (2005). Convergence of empirical means with alphamixing input sequences, and an application to PAC learning. In: Proceedings of the 44th IEEE Conference on Decision and Control, 2(1), 560–565. 96. Volk, L. J., Bachman, J. L., Johnson, R., Yu, Y., & Huganir, R. L., (2013). PKM-ζ is not required for hippocampal synaptic plasticity, learning and memory. Nature, 493(7432), 420–423. 97. White, O. L., Lee, D. D., & Sompolinsky, H., (2004). Short-term memory in orthogonal neural networks. Physical Review Letters, 92(14), 148102. 98. Widloski, J., & Fiete, I. R., (2014). A model of grid cell development through spatial exploration and spike time-dependent plasticity. Neuron, 83(2), 481–495. 99. Wilson, H. R., & Cowan, J. D., (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12(1), 1–24. 100. Wimmer, K., Nykamp, D. Q., Constantinidis, C., & Compte, A., (2014). Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nature Neuroscience, 17(3), 431–439. 101. Yoon, K., Buice, M. A., Barry, C., Hayman, R., Burgess, N., & Fiete, I. R., (2013). Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nature Neuroscience, 16(8), 1077–1084.

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102. Zhang, K., (1996). Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory. Journal of Neuroscience, 16(6), 2112–2126. 103. Zhou, Y., Takahashi, E., Li, W., Halt, A., Wiltgen, B., Ehninger, D., & Silva, A. J., (2007). Interactions between the NR2B receptor and CaMKII modulate synaptic plasticity and spatial learning. Journal of Neuroscience, 27(50), 13843–13853. 104. Zucker, R. S., & Regehr, W. G., (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64(1), 355–405.

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6

CHAPTER

APPLICATION OF COMPUTATIONAL MODELS IN CLINICAL APPLICATIONS

CONTENTS

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6.1. Introduction..................................................................................... 156 6.2. Modeling Approaches for Clinical Applications in Personalized Medicine.................................................................. 159 6.3. Models in Clinical Research for Discovery, Diagnosis, and Therapy.................................................................................. 169 6.4. Challenges and Recommendations.................................................. 177 References.............................................................................................. 184

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6.1. INTRODUCTION Personalized medicine’s future development is depended on a massive interchange of data from many sources, and also a harmonized integrated examination of wide range medical fitness and set of data. Computationalmodeling methods are important for analyzing the basic molecular procedures and paths which characterize biology of humans, but these result to a more fundamental insight into the procedures and aspects which affect diseases, allowing for personalized treatment strategies influenced by core clinical situation. Regardless of the rising acceptance of computationalmodeling techniques across many stakeholder groups, there are indeed numerous obstacles to overcome before they may be used in clinical practice. Integrating heterogeneous data from many sources and kinds, in particular, is a difficult undertaking that requires clear principles that must also adhere to great moral and legal values (d’Alessandro et al., 2015). In this section, we go through the most important computational models for personalized medication which may be used as finest-practice recommendations in clinical treatment. We explain particular issues and offer relevant procedures and suggestions for design of study, data gathering, and operation, and also validation of model, medical translation, and other study topics (Figure 6.1).

Figure 6.1. Computational methods are being used to generate and investigate complicated biological processes. Source: https://www.nibib.nih.gov/science-education/science-topics/computational-modeling.

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The quantity of individualized data in today’s medicine is growing all the time, and at the particular patient level, it has considerable potential for both treatment and diagnosis. In the midst of computational modeling, which are based on these complicated and heterogeneous data volumes, aid in the operational knowledge of the processes and causes that result particular illnesses. Similarly, they enable the development of individual dealing plans in reaction to key medical problems (d’Alessandro et al., 2015). Thus, computational models are equipped with the capability to transform in vitro, pre-clinical, and clinical outcomes (and the uncertainties associated with them) into explanatory or prescriptive expressions. Over the last few decades, the research groups along with the supervisory bodies like the European Medicines Agency (EMA) or the US Food and Drug Administration (FDA), have increasingly recognized the added worth of these models, also known as digital evidence, in pharmacology and medicine—regardless of their ultimate use or application (Kirschner et al., 2015). Computational models are increasingly used in a variety of sectors in drug and medicine development, varying from sickness model and biomarker investigation to therapeutic effectiveness and safety evaluation. Clinical measures may be processed and interpreted in silico in two ways: theory-based or datadriven. Both notions, however, are very complimentary, and they have the same basic data standards and data documentation criteria. To understand the functional importance of the given system, mechanistic models strive for a structural show of the major physiological procedures in the model calculations. Methods of data-driven, which are deep learning (DL) and machine learning (ML), that utilize models and algorithms to simulate (human) intellect and are normally mentioned as artificial intelligence (AI), aim to discover information in big data by the means of multidimensional regression analysis (Dimiduk et al., 2018). As a result, mechanistic models necessitate a structural knowledge of a process, but data availability can be limited. Machine learning concepts are essentially founded on large data sets, but such models do not require any previous functional understanding. These modeling approaches, when used in personalized medicine, allow for the stratification of patients into precise collections having alike features, which is required for innovative analysis, directed therapies, and prevention approaches. The appropriate modeling methods for clinical purposes in personalized medicine will be concisely presented in the following section (Figure 6.2).

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Figure 6.2. Computational theories for individual stratification in personalized medicine. Source: https://www.mdpi.com/2075–4426/12/2/166/htm.

Note: The modeling procedure begins with data gathering from several ways. Mechanistic models (theory-based) along with machine learning (ML) are the two basic modeling tools. Model analysis results in either a structural reorganization of disease-causing physiological systems or the recognition of patterns in large data sets. Such methods’ information may be utilized to produce information for stratifying patients into definite subgroups, simplifying detection, analysis, and treatment in personalized medicine.

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6.2. MODELING APPROACHES FOR CLINICAL APPLICATIONS IN PERSONALIZED MEDICINE 6.2.1. Mechanistic Models A mechanistic model’s objective is to functionally comprehend, investigate, and forecast the developing features of the particular parts of a biological mechanism and their coupling. Additionally, it predicts the complicated nonlinear patterns of system characteristics and models the fundamental dysregulations in method of transforming the healthy condition into a particular illness. Numerous systems medicine and systems biology techniques have been established to address this complexity. By combining biochemical, physiological, and environmental connections, these techniques have been effective in clarifying the counterintuitive activity of biological systems, most notably the human body (Wolkenhauer et al., 2013) (Figure 6.3).

Figure 6.3. Integrated model of precision medicine. Physicians and patients are both active participants in the integrated procedures. Source: https://www.researchgate.net/figure/Precision-medicine-integratedmodel-Medical-doctors-and-patients-are-active-parts-of-the_fig2_320899822.

Models are built based on existing information about the physical/ biochemical connections between classes, and kinetic models are extracted from the experimental data to simulate biological reality. Following calibration, simulations are run to develop fresh hypotheses for the purpose of

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designing further experiments. Validating model-based hypotheses generates new information that may be utilized to build another model, progressively enhancing functional understanding. Thus, the method of systems medicine and systems biology restates via data-driven modeling and model-driven experiments. Because models are nonconcrete representations of realism, their validity and utility are context- and assumption-dependent. Different modeling techniques have varying degrees of abstraction, predictive capability, benefits, and drawbacks. Previously known reviews cover the range from stable molecular connection maps and constraint-constructed modeling to qualitatively logic-based modeling and more sophisticated quantitative kinetic models. The model formalism used is determined by the accessibility of information, the nature of the study issue, and the system’s structure and size. The next section discusses the most pertinent mechanistic models (Morrison, 2016).

6.2.1.1. Molecular Interaction Maps Molecular interaction maps (MIMs) are stable ways to describe biological species’ physical and causal connections as networks (Kitano et al., 2005). These act as an information repository, including knowledge on many routes and regulation components implicated in diseases like Parkinson’s and cancer signaling (Figure 6.4).

Figure 6.4. An example of molecular interaction maps (MIM) diagram. Source: https://www.semanticscholar.org/paper/A-Process-Calculusfor-Molecular-Interaction-Maps-Barbuti-Maggiolo-Schettini/ac78002b471374f3a3fbc2d9b768f66ab544f94f/figure/2.

MIMs may be computationally studied by the use of graph-theory ideas to reveal static features of network such as (i) determining the most significant nodes, (ii) detecting communities using a grouping technique, and (iii) link forecasting for the detection of unknown linkages. Moreover, these drawings act as visualization apparatus for the action state of regulation and

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their targeting of recognized disease indicators when overlaid on expression data, providing the most basic mechanistic representation of data.

6.2.1.2. Constraint-Based Models Constraint-based models, like GEnome-scale Metabolic models (GEM), give a mathematical model for understanding a cell’s metabolic capabilities, allowing examination of whole system of genetic perturbations, investigation of metabolic diseases, and identification of important enzymatic reactions and drug targets (Kuperstein et al., 2015). GEMs have gotten a lot of interest, and several studies have been conducted on their applicability in various fields of medicine. This modeling strategy has been employed in a variety of domains, including obesity, cancer, and Alzheimer disease (Uhlen et al., 2017) (Figure 6.5).

Figure 6.5. Prespecified vs. constraint-based process models. Source: https://link.springer.com/chapter/10.1007/978–3-642–30409–5_12.

6.2.1.3. Boolean Models Boolean modeling (BM) is the most basic kind of model of logic-based, in which nodes (e.g., a, transcription factor, protein, microRNA, or gene) are defined by any of the two potential conditions: 0 (OFF, inactivation) or 1 (ON, activation) (Stempler et al., 2014). The logical operators AND, OR, and NOT are used to indicate the regulatory link between downstream nodes (targets) and upstream nodes (regulators). These models do not need comprehensive kinetic parameters for data approximation, making them suitable for use in big biological systems and simpler to calibrate or train

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using data. This strategy is often used in cancer research in the setting of systems medicine (Eduati et al., 2020).

6.2.1.4. Quantitative Models Quantitative modeling, like the technique depend on ordinary differential equations (ODEs), quantitatively examines the course of a biological process through time. This is composed of a series of differential equations including variables (that describe important values, such as the quantity of biological classes) and variables which define the system reaction to various inputs or disturbances (a large source of ODE models shows the BioModels database (Malik et al., 2020). While factor standards are fixed, they may be customized for every investigational setting or patient in order to make the model fit the provided data. Such technique thoroughly explains the dynamics of biological systems; nevertheless, it is often limited to a single route or a few reactions owing to the demand for extensive kinetic data to estimate variables. ODE models are used in personalized medicine for the finding of individual biomarkers, medication response, and individualized therapies (Hastings et al., 2020) (Figure 6.6).

Figure 6.6. Hypotheses of the quantitative model. Source: https://www.researchgate.net/figure/Hypotheses-of-the-quantitativemodel_fig2_282430262.

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6.2.1.5. Pharmacokinetic Models Pharmacokinetic models, which represent the level of a pharmaceutical substance in plasma or various organs, are a type of ODE model. Drug pharmacokinetics are typically utilized as a substitute for drug-driven reactions because these can be utilized to calculate off-target and ontargeted pharmaceutical substance experience, as well as the degree of the predicted impact. They may be characterized using either compartmental pharmacokinetic (PK) models or physiologically based PK (PBPK) modeling (Jones et al., 2015). Compartmental PK models, known as population PK (popPK) models, can be termed as top–down modeling which use plasma PK to generate an empirical model structure. Model construction normally begins at a basic one-compartment model that is then expanded with linear absorption and clearance rates. During development of model, modifications in structure of model, like peripheral compartments, may be required. PharmML (Pharmacometrics Markup Language as an interchange arrangement for the encrypting of models, related jobs, and their explanation in pharmacometrics is one of the standard arrangements of domain-specific accessible for structuring and sharing them. PBPK modeling, in comparison to compartmental PK techniques, tries to regenerate an organism’s physiology in great detail (Kuepfer et al., 2016). In a PBPK model, different organs are clearly represented and given physiological features like as, composition, volumes and blood-flow rates and surface. PBPK modeling enables for the integration of a wide range of patient-specific data, from the molecular to whole-body physiological aspects. This is due to the granularity of PBPK modeling, that could reflect physiological data at several levels of biological structure. The definition of PBPK models to reflect particular associates of patients, such as the old or ill patients from a basic reference model is a common use for individualization. This standard generally depicts an average person with average physiological parameter values (Figure 6.7).

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Figure 6.7. Flow diagram of a physiologically based pharmacokinetic (PBPK) model. Source: https://www.mdpi.com/1999–4923/11/12/649/htm.

6.2.1.6. Software Resources and Tools The subsequent section contains a collection of frequently utilized tools and resources for developing, visualizing, and simulating MIMs, which includes descriptive and analytical models as well as pharmacokinetic modeling. It is challenging to choose a single resource or tool; nevertheless, certain resources, such as Regulatory INteraction Graph (RING) and OmniPathobtain connections from many sources that may facilitate MIM building, for example. Likewise, each tool is customized to solve unique modeling issues; for example, CellNetAnalyzer (CNA) enables easy simulation of models with thousands of perturbations and inputs, but encrypting models with a graphical interface is time taking. CellCollective is a browser replication platform that enables community-driven model generation without the need to encode complicated mathematical formulae. Although the Gene Interaction Network simulation suite (GINsim) is a useful tool for attractor analysis, it may not be optimal for big models with a high number of inputs and perturbations (Helikar et al., 2012).

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Table 6.1. Tools and Resources Used to Develop MIMs, Pharmacokinetic Models and Qualitative and Quantitative Models (Li et al., 2010; Seaver et al., 2021) Research Field

Resources

Methods

Molecular interaction maps

Atlas of Cancer Signalling Network (ACSN), SIGnaling Network Open Resource (SIGNOR), SignaLink, Kyoto Encyclopedia of Genes and Genomes (KEGG), InnateDB, RING,OmniPath, WikiPathways, Reactome

Molecular Interaction NEtwoRks VisuAlization (MINERVA), Cytoscape & plugins, NaviCell, Newt, CellDesigner

Quantitative models

BioModels, Physiome Model Repository, Java Web Simulation (JWS)

CellDesigner, JWS, COmplex PAthway Simulator (COPASI)

Constrained-based models

Human metabolic atlas, BioModels Virtual Metabolic Human, BiGG (Biochemical, Genetic and Genomic knowledge base)

COBRApy, ModelSEED, Sybil package, COnstraintbased Reconstruction and Analysis (COBRA) toolbox

Boolean models

CellCollective, BioModels, GINsim, PyBoolNet repository, CellNetAnalyzer (CNA)

BoolNet, CNA, GINsim, SQUAD-Boolsim, Markovian Boolean Stochastic Simulator (MaBoSS), CellCollective, Genetic Network Analyzer (GNA), CellNOpt

Pharmacokinetic models

Open Systems Pharmacology, PharmML (Pharmacometrics Markup Language

GastroPlus®, Monolix, SimCypTM, PK-Sim®

Workflows for model building, simulation, and dissemination may involve a variety of technologies and resources. A model may, for example, be built in Cell Designer, modeled in JWS Online, and then posted in BioModels (Müssel et al., 2010). Community-driven harmonization activities resulted in various specifications and forms that facilitate tool and resource interoperability. Different, partly overlapping communities have formed throughout time, each responsible for the upkeep and creation of the

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specifications and forms. The community of the Computational Modeling in Biology Network (COMBINE) creates standards for storing and exchanging computational models. The Systems Biology Markup Language (SBML) he Systems Biology Graphical Notation (SBGN) and CellML are notable examples. Numerous techniques for MIMs and quantitative modeling’s support their standards. The Illness Maps Project is a huge group dedicated to improving disease knowledge. Their activities are focused on MIMs, including the production of maps and tools. CoLoMoTo (http://colomoto. org) is a Boolean model collaboration that promotes model representation and interchange standards, particularly SBML Qual (Chaouiya et al., 2013).

6.2.2. Deep Learning and Machine Learning Data-driven techniques assume that the causal technique is unidentified and attempt to design an operation that works on input of big data to anticipate the result, irrespective of the physiological methods that are not known. Because the methods by which systems operate such as that components collectively take out results, are regarded too difficult to identify, machine learning and deep learning models are sometimes called as black-box. As a result, the logic for the created result is impenetrable not just to specialists, but even to the designer who design them. Great efforts are utilized to make data-driven systems rational or relatable by giving details on the underlying modeling processes and the variables influencing predictions. This necessitates an analysis of the ideas of explanandum, explainee and explanans, between others, in the domain of particular medicine. For instance, the explainee, or the individual to whom an elaboration is given, such as a patient, physician, or investigator, would choose what makes an adequate elaboration of model estimation (Thorsen et al., 2020). The explanandum aim is not the history of the patient, but the prediction and the explanans which describes the forecast may be complicated mathematics in and of itself. There is no indication of causation, even if the explanations make logical understanding to the explainees. For instance, models which give post-hoc functionality representation frequently reveal that age or pre-existing sickness are significant predictors of a bad result. Though it does not convey common knowledge, the explainee “delivers” the common connection in their own perception of the description. There is no evident causal knowledge presented, even though data-driven models might generate hypotheses and so provide hints to knowledge. While knowledge is an epistemologically difficult concept, an explainee (for instance, a scientist or a physician) might be considered to know when

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they’re in a situation to proceed. For instance, a post-hoc model that visualizes the elements influencing a medical forecast for a patient might provide a clinician with enough information to proceed with proposing a medical plan of activity to her patient (Thorsen et al., 2020). When a biological region is found that appears to repeat in model-prediction drivers of feature, a biologist may be said to comprehend; or when a bioinformatician reapplies the model’s code and examines result, a bioinformatician could be said to understand (Figure 6.8).

Figure 6.8. Difference between machine learning and deep learning. Source: https://lawtomated.com/a-i-technical-machine-vs-deep-learning/.

The accuracy of these black-box models’ predictions, which are validated in a number of methods, is used to determine their quality. As a subclass of AI, machine learning operates in a cyclical fashion and learns from experience. It is mostly utilized for estimation techniques and pattern matching (Figure 6.8). DL is a subset of machine learning that is used to integrate complicated data sets like omics and clinical data. Deep Neural Networks are capable of extracting and processing information from provided data in Deep Learning. By linking many artificial neurons in densely linked layers, DL simulates the human brain (Kim et al., 2017). Machine learning/deep learning technologies enable us to shift away from interpretative efforts to relate group-level connections and in its place anticipate individual patient reactions, so allowing more customized therapy. In contrast to individuallevel predictions, group-level connections are understood in a mostly unstructured manner for the purpose of treating a person and might not give the ideal dealing method for that patient. Thereby, typical medical researches based on group and machine learning/deep learning strategies show two

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separate paradigms: the first gives data about associations at the group level, whereas the second, the machine learning/deep learning approach, builds a prediction at the individual patient level (Weissler et al., 2021). Machine learning seeks ever more precise grouping or categorization with a high degree of accuracy and confidence. Different forms of machine learning are used as suitable based on the input data and the analytical objective, such as the medical question. Thru the procedure of inference, model learning or fitting from cases, machine learning systems automatically study the model from the data (Brazma et al., 2001). Machine learning may be performed in a controlled, uncontrolled, or semi-supervised way. Unmonitored knowledge has the ability to incorporate all structures, to reduce dimensionality, to enable feature elicitation, and to visualize large amounts of data, all of which contribute to a better understanding of large amounts of clinical data and the components underlying illness beginning and development. Unsupervised knowledge enables the discovery of new information that may be utilized to improve individual patient outcome prediction. In supervised learning, the strategy is fed labeled input qualities which are used to predict a specific result from novel information, either via regression (for constant outcomes, i.e., months before illness onset) or organization (for distinct outcomes, i.e., survival or death or organization of image). Supervised machine learning clinical decision support technologies enable machine learning to personalize prediction to the unique characteristics of each patient. Semi-supervised learning is used to train on both unlabeled and labeled data. All of these machine learning methodologies need model validation, and the correctness of machine learning findings is tested utilizing independent assessment sets (Weissler et al., 2002). While machine learning best practices have been established, these are either not implemented at all or are functional partly to specific features of the designs, resulting in inconstant or inadequate reporting and documentation, along with the sharing and evaluation of machine learning models. When machine learning algorithms are reused, this problem frequently results in ambiguous and unpredictable decision making, trying to make it tough to recreate the findings with numerous uncertainties (Stupple et al., 2019). It is critical, especially in medical research and application, to design and publish machine learning/deep learning-derived models in accordance with established criteria in order to build confidence in the ensuing decisionmaking. In this context, it is critical to provide reporting rules for the appropriate models and their validation, as well as a list of characteristics and components to adequately define them. The CONSORT-AI and

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SPIRIT-AI criteria for writing medical trials including machine learning algorithms are recent examples. The AIMe registry for artificial intelligence in biomedical study (https://aimeregistry.org) was lately launched as a community-driven policy for registering biomedical artificial intelligence systems (Collin et al., 2022). The AIMe registry features a web interface that monitors writers of novel machine learning algorithms thru the freshly established AIMe standard, a general minimal data standard which enables the writing of several biomedical artificial intelligence system. The AIMe standard is separated into 5 chapters: metadata, purpose, data, method, and reproducibility.

6.3. MODELS IN CLINICAL RESEARCH FOR DISCOVERY, DIAGNOSIS, AND THERAPY Patients are possible to get great advantage in future from advancements which provide customized drug with prediction capacity to examine clinically important topics in silico. There are now a variety of computational-modeling techniques in pre-clinical and medical research that may answer these concerns in more depth and so perform a key part in future advancement of customized medicine. The next segment provides the example of effective computational analysis in clinical research discovery, diagnosis, and treatment (Figure 6.9).

Figure 6.9. Flowchart for planning and conducting clinical research. Source: https://www.cureus.com/articles/17094-planning-and-conductingclinical-research-the-whole-process.

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6.3.1. Discovery Because model uses in finding are generally hypothesis driven, they are typically mechanism-based, like MIMs, GEMs, BMs, and ODEs. That’s because the data available at this stage is often insufficient for data-driven studies. Mechanistic models are critical in the finding process because they may be used to address a broad variety of therapeutically relevant topics, from representing disease processes to identifying drug goals or simulating disease-particular characteristics. (summarized in Table 6.2). Table 6.2. Examples for Mechanistic Modeling in Discovery (Väremo et al., 2017; Singh et al., 2018)

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Research Field

Content

Molecular interaction maps Inflammation

Knowledge-base, disease mechanisms, data interpretation

Neurodegenerative disease

Knowledge-base, disease mechanisms, data interpretation

Cancer

Knowledge-base, disease mechanisms, data interpretation

Rheumatoid Arthritis

Knowledge-base, critical nodes (drug targets)

Asthma

Disease mechanisms

Atherosclerosis

Disease mechanisms, data interpretation, critical nodes (drug targets)

Boolean models Cancer

Disease mechanism, patient stratification

Type 2 diabetes

Disease mechanism, patient stratification

Obesity

Disease mechanism, patient stratification

Non-alcoholic fatty liver disease

Disease mechanism, patient stratification

Genome-scale metabolic models Cancer

Disease markers, drug targets, patient stratification

Auto-Immune diseases

Target identification, biomarkers, patient stratification

Cancer

Personalized combination therapy

Cancer

Disease signature, drug targets, patient stratification

Cancer

Disease markers, drug targets, patient stratification

Auto-Immune diseases

Target identification, biomarkers, patient stratification

Cancer

Personalized combination therapy

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A newly released plan on inflammatory determination enables for the visualization of Omics data and the generation of hypotheses about the involvement of related molecules in disease phenotypes (Collin et al., 2022). The Parkinson’s sickness map is another famous MIM example. Such maps act as the informative platform, displaying the disease’s processes in a consistent format. As a result, they arrange the field’s developing knowledge in an understandable way. The dataset of the cancer signaling network, which illustrates precisely the molecular pathways involved in cancer, is another noteworthy example of MIM. To undertake functional analysis and discover dysregulated pathways, high-throughput data may be shown on the map. Mardinoglu et al. (2014) created an extensive molecular connection map for rheumatoid arthritis, which included extensive molecular pathways of the processes in individuals with the disease. Topological characteristics of the map were examined in order to identify diagnostic and treatment indicators for rheumatoid arthritis (Mardinoglu et al., 2014). In metabolism-related illnesses such as obesity, Alzheimer’s disease (AD), non-alcoholic fatty liver disease (NAFLD), type 2 diabetes, and cancer, disease-specific GEMs were utilized to identify biomarkers and drug approaches. Khan et al. (2017) used the challenge integrated networking inference for tissues (tINIT) technique to create GEMs of 17 forms of cancer by combining transcriptome information in a system of human breakdown. They not only predicted tumor growth driver genes, but also showed substantial metabolic variability in various patients, underlining the need of customized therapy in cancer treatment. Varemo et al. (2017) created manually selected GEMs to detect diabetic muscle names. They proposed a gene signature that correctly identified individual samples’ illness progression. Väremo et al. (2017) recreated an adipocyte-specific GEM and found that obese participants’ androsterone and ganglioside GM2 metabolic functions elevated while their mitochondrial metabolic functions reduced. By evaluating the reconstituted iHepatocytes, they discovered that heparan sulfates and chondroitin are excellent indicators for the performance of NAFLD. Fey et al. (2015) recreated an AD-specific GEM in 2014 and forecast many breakdown indicators of Alzheimer’s disease development, such as prostaglandin D2 and succinate Patient-specific information has been used in Boolean models to reproduce patient diseases phenotypes, detect disease biomarkers and therapeutic targets, and subgroups of respondents and non-respondents to medication therapy, among other things. Another recent work employed Boolean models to incorporate patent information to create a tailored

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prototype that might simulate and assess the gain-of-function or lossof-function of mutant genes in certain disorders. The model predicted differences in the PI3K-AKT pathway, which caused in pancreas cancer patient heterogeneity. Models were also effective in simulating the recognized dynamics of invasive cancers of the breast and bladder, as well as predicting disease signatures and potential treatment targets for reverting invasive to non-invasive phenotypes (Khan et al., 2017). In addition, model-predicted signatures were utilized to divide patients into groups with elongated as well as short survival times. ODE simulations have been utilized to model clinical trials as well as also to suggest personalized analytic or drug targets, like extremely varied neuroblastoma prognostic indicators and patient categorization into poor and huge survival depend on model predictions (Fey et al., 2015). They were also utilized to forecast patient ‘s reaction to apoptosis-inducing medications, and inter-individual responses were shown to be considerably diverse.

6.3.2. Diagnosis The quantity of individualized data available in diagnosis is typically adequate to enable the use of ML/DL techniques. Furthermore, the data types presented are often excessively varied, making a structural representation of the controlling processes difficult because of an imperfect functional comprehension. ML/DL models may make findings by examining huge amounts of input information to detect designs and relationships that are important to the desired conclusion. These computational models may be used as clinical choice support schemes to help doctors with analysis and treatment (Mason et al.. 2021). Effective instances of patient data analysis using ML/DL ideas are presented in Figure 6.10.

Figure 6.10. Model of the medical diagnostic process. Source: https://www.researchgate.net/figure/Model-of-the-medical-diagnosticprocess_fig1_239796277

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In a broad range of medical as well as biological fields, machine learning has been proved to be a useful analytical tool. The use of machine learning models may result in a greater knowledge of disorders and the mechanism of action of medications, as well as in the development of accurate medicine. Mason et al. (2021) demonstrated as by using machine learning to predetermined antigen receptor specificity, optimal therapeutic antibodies may be identified. It enables the integration of data from multiple receptor sites into the particular pan-receptor model (Mason et al.. 2021). Mason et al. (2021) utilized Self-Organizing Maps (SOM) to organize bacterial metabolic reactions as well as thus identified a reaction metabolome in metabolomics. Thorsen-Meyer et al. (2020) demonstrated that a time-sensitive machine learning prototype may aid in the prediction of 90-day death in patients admitted to the intensive care unit. Additionally, they were capable to emphasize characteristics that contributed to a specific forecast at any time point, enabling a physician to adjust therapy accordingly. Another significant utilization of machine learning and deep learning in healthcare settings is image analysis. In the clinic, machine learning image examination is most often utilized for radiographic evaluation and decision – making process, and the models utilized may be classified as classification activities or regression models. Designs make choices regarding classification end-points including a disease scoring system or if a physician should examine an X-ray scan more closely in the most frequently used classification tasks. The purpose of regression models is to forecast a continuous variable, such as a patient’s survival time, which is often not classified into different segments. Zhang et al. (2019) unveiled the first FDA-approved autonomous AI analytical model in medicine in 2018 (Abràmoff et al., 2018). This development was significant because previously, machine learning models were used to assist doctors in making analytical results. Zhang et al. (2019) built a convolutional neural network(CNN) that has a sensibility of 87.2 percent and a specificity of 90.7 percent for properly classifying diabetic retinopathy. In current months, machine learning (ML) has been extensively employed to identify and identify COVID-19-induced lung pneumonia in computed tomography (CT) pictures (i.e., new COVID pneumonia (NCP)). Damask et al. (2020) devised a multi-scale technique which is capable of not only differentiating NCP from other types of lung lesions but it too forecasting a patient’s development to critical disease (101). Additionally, they were able to correlate the segmented lung scan features with clinical parameters such as serum C-reactive protein (CRP) and albumin levels.

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Additional significant case of an implementation of machine learning samples in clinical practice is the development of polygenic risk scores (PRS) for prevalent illnesses based on the results of genome-wide association studies (GWAS). PRS are assessed to forecast an individual’s chance of contracting an illness based on his or her genetic makeup. GWAS, which examine the genome of thousands of individuals, are an extremely powerful tool for identifying the many genetic risk factors for illness that are utilized to estimate the PRS. PRS has the potential to aid in disease early detection and prevention, and also precision medicine, resulting in improved health results for persons. There are presently several methods being established for estimating PRS, each of which may provide some benefit over the others. PRS research on schizophrenia, instructive achievement, diabetes, depression, coronary artery disease, hypertension, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer are examples of PRS studies. Table 6.3 summarizes the demonstrating methods discussed as well as their usage in clinical diagnosis (Márquez‐Luna et al., 2017). Table 6.3. Examples for the Application of Machine Learning and Deep Learning Algorithms in Diagnosis (Okser et al., 2014; Paré et al., 2017)

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Research Field

Content

Deep Learning and Convolutional Neural Network Models Radiology

DL-based model that is able to detect COVID-19-induced pneumonia on chest X-ray images

Ophthalmology

The first FDA-authorized autonomous AI system for the detection of diabetic retinopathy

Ophthalmology

A DL model for the diagnosis of glaucoma based upon images and domain knowledge features

Imaging flow cytometry

Automated image de-blurring of out-of-focus cells in imaging flow cytometry

Pathology

Assistance to pathologists for improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review

Ophthalmology

Two models for quality assurance and diagnosis of diabetic retinopathy on retinal images

Oncology

Automated detection of oral cancer on hyperspectral images

Deep Learning and Deconvolutional Neural Network Models Intensive care

ML analysis of time-series data in intensive care units led to an improvement in the prediction of 90-day mortality

Antibody engineering

Prediction of antigen specificity via DL, which leads to optimized antibody variants for therapeutic purposes

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Proteomics

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Neural network that is able to predict signal peptides (SP) from amino-acid sequences and distinguish between three groups of prokaryotic SPs

Deep Learning, Machine Learning, Random Forest, and Deconvolutional Neural Network Models Neurology

A study with the aim to differentiate between cognitive normal people and patients with Alzheimer’s disease using various ML/DL techniques on blood metabolite levels

Psychiatry

A model that detects autism spectrum disorder risk for newborns with up to 95.62% from electronic medical records

Machine Learning and Polygenic Risk Score Models Coronary artery disease

Patients with high genome-wide PRS for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial

Coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Use of PRS to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies

Machine Learning, Self-Organizing Maps, Random Forest, K-Nearest Neighbors, Support Vector Machines, Self-Operating Maps Metabolomics

SOM analysis of response metabolites detected by mass-spectroscopy leads to the identification of similar responses (ML/SOM)

Endocrinology

Prediction of diabetes based on several blood values and other patient indices (ML/SVM, RF)

Radiology

Classification of COVID-19 and non-COVID-19 patients based on features extracted from chest X-ray images (ML/KNN)

Imaging flow cytometry

An open-source toolbox for the analysis of imaging flow cytometry images (ML/RF)

Metabolomics

SOM analysis of response metabolites detected by mass-spectroscopy leads to the identification of similar responses (ML/Self, Organizing Maps (SOM))

Additional technical details about commonly used machine learning/ deep learning methodological approaches (for example random forests (RF), convolutional neural networks (CNN), K-nearest neighbors (KNN) support vector machines (SVM), and) and present best-practice references, particularly for biological uses, have been studied and justified in detail elsewhere. Additionally, the International Common Disease Alliance’s Polygenic Risk Score Task Force has released a full overview of PRS, including its present use and the issues associated with its increased usage in clinical practice (Polasek et al., 2018).

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6.3.3. Therapy Applications in treatment are mostly mechanism-based, since a functional knowledge of the resultant computer simulations is required for comprehensive risk assessment, as is the case with clinical trial simulations. Additionally, extrapolation across patient cohorts, treatment regimens, and even species is a typical necessity for pharmacological development. The following section discusses and summarizes illustrative uses of mechanistic modeling in treatment in Table 6.4. Table 6.4. Examples for Mechanistic Modeling in Therapy (Rasool et al., 2021) Research Field

Content

Mechanistic Models Geriatrics

Geriatric extrapolation

Pediatrics

Pediatric extrapolation

Disease models

Prediction of drug PK in cirrhotic patients

Pharmaco-genomics

Prediction of the incidence rates of myopathy in different genotypes

MIPD

Prediction of personalized drug exposure

MIPD: Model-informed precision dosing, PK: Pharmacokinetic. PBPK models may be utilized to aid in the development of pediatric inquiry strategies, in which PK profiles in infants, toddlers, or newborns are variate using a standard PBPK model for healthy adults. The value of this pediatric extrapolations stems from the fact that kids are not only miniature grown-ups but have a unique body arrangement (percentage of water, fat, and protein, correspondingly), which results in a variation in medication supply across distinct tissues. Similarly, the maturation of excretion (ADME), protein absorption, metabolite, and dispersion has a major influence on the pharmacokinetics of drugs in various age groups. Another well-known example of PBPK model definition is patient cohorts with hepatic or renal impairment (Shah et al., 2020). For such patient subgroups, PBPK models may be used to variate and study the impact of decreased drug clearance and the accompanying rise in antibiotic exposure, which can result in harmful actions. For cirrhotic patients, these ideas have been utilized to describe pathogenesis in context of for instance ADME gene expression, plasma protein level, or glomerular filtration rate (GFR) rates. Although pediatric extrapolations or individuals with decreased medication clearance may not constitute completely customized models, they do

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provide a significant analytical use for information-based personalization of mechanical computational models. The other pharmacological idea that is more closely related to personalized medicine is model-informed precision dosage (MIPD). MIPD is presently used on a limited number of medications which are susceptible to therapeutic drug tracing and have accessible PK models. Notably, such models include the production of virtual twins that are personalized based on data other than population demographics. A current research evaluated the impact of incorporating individual patient data to enhance PK predictions. Discrete data on I patient biometry, (ii) patient physiology, and (iii) CYP1A2 phenotypes of 48 healthy individuals contributing in a singledose clinical research were used to stepwise personalize a PBPK model of caffeine. The model’s performance was compared to that of a caffeine base model replicated using average individual variables. The analysis’s primary finding was that while including subject-specific data in personalized PBPK models may improve prediction accuracy, the degree of enhancement is increasingly based on the similarity among the patient information and the drug’s, particularly pharmacology (Debray et al., 2015).

6.4. CHALLENGES AND RECOMMENDATIONS As previously stated, computational models in customized medicine have already yielded significant outcomes. However, there are still considerable obstacles to overcome before model-based processes are fully adopted in clinical study and experimentation. Intriguingly, spite of numerous technical distinction among the computational ideas in mechanistic model and ML/ DL, both fields face challenges with data accessibility, model validation/ development, standardization, model re-use, and reporting.

6.4.1. Challenges 6.4.1.1. Data Availability and Data Harmonization The primary stage in developing a model is compiling the information which will be used in it. This job is very dependent on the data getting properly prepared and annotated. Checklists for reporting and annotating, or “minimal information guidelines,” are provided for a variety of different datatypes and may be located and downloaded through the FAIRsharing site (www.fairsharing.org). Both mechanical model and machine learning/ deep learning essentially rely on clinical measures for model creation as

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well as independent model validation. Confusing reporting is a substantial barrier to data availability, since it creates it hard to reap the full advantage of discoveries, traverse the biological literature, and develop clinically applicable results (Cohen et al. 2016). As a possible result, partial data sets may emerge, requiring certain attributes to be omitted from the research or incomplete information to be replaced. Another difficulty, especially in multicentric research, is the variety of the input information from several labs, that considerably impairs both the comparability of findings and later analysis. Metastandards, such as the recently published ISO 20691 “Biotechnology—Requirements for information structuring and explanation in the life sciences” (https://www.iso.org/standard/68848.html), assist in guiding users via the process of uniform data formatting and annotation (Figure 6.11).

Figure 6.11. Workflow and responsibilities for the iterative harmonization process in the SAIL (sample availability) method, involving multiple curation teams and facilitated by a web-based application. Source: https://www.nature.com/articles/ejhg2015165.

6.4.1.2. Model Development and Model Validation Because all computation approaches are very context particular, they could not be generalized for new situations owing to restricted extrapolability, which is a typical difficulty for model creation and validation. Validation and predicted precision are two requirements shared by all in silico models. Model-validation procedures, on the other hand, are regarded to be unique

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and type-specific. It’s critical for any method to function well on fresh data that hasn’t been utilized in training; in other words, the sample must be able to generalize to new information in the similar area (Debray et al., 2015). Model parameter ambiguity, and also inter-individual variability, are difficult to measure, and this has hampered model identification. Similarly, mistakes in fundamental model structure resulting from previous expectations, group affiliations, or pre-determined clinical associations may skew results. The model arrangement and modeling situations can be recorded in a standard way, the Simulation Experiment Description Markup Language (SED-ML), for several models and correlating formats. SED-ML was developed to capture such explanatory data needed to re-run a model, so that it could be exported from one simulation software and migrated into other (Figure 6.12).

Figure 6.12. The lifecycle of a predictive model. Source: https://ema.drwhy.ai/modelDevelopmentProcess.html.

6.4.1.3. Model Standardization, Model Re-use, and Reporting of Results Model standardization, model reusing, as well as result presentation are all greatly effect “ by the context-specific nature of the majority of models. Furthermore, several software equipments continue to lack accessible rights, lengthy maintenance, version control, and software certification standards.

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Similarly, best practices are usually not followed systematically, even when they are accessible and backed by multiple instruments, like the COMBINE simulation archive (Scharm & Waltemath, 2016). As a result, there is the dearth of practice for standardizing sample creation and the usage of community-defined formatting standards, containing machine-readable forms based on Extensible Markup Language (XML). Notable exceptions include SBML, which serves as a standardized exchange arrangement for computer samples of biological procedures; CellML, which serves as a standard arrangement for storing and exchanging recyclable, modular computer-based computational equations; and NeuroML (Neural Open Markup Language), which enables the standardization of model explanations in computational neuroscience and other fields (Figure 6.13).

Figure 6.13. Data standardization model. Source: https://www.ohdsi.org/data-standardization/.

6.4.1.4. Legal and Ethical Issues Data protection and patient data confidentiality are two legal and ethical challenges. Furthermore, in the event of a failure, problems of responsibility may emerge, which may be difficult to resolve and are a persistent source of contention (Cohen et al., 2016). The general data protection regulation (GDPR) covers most legal concerns. Data reduction, particularly in data-driven models when it is unknown which data would be required, presents computational modeling difficulties. Similarly, private parts of models and data may be of interest. Finally, it may be necessary to rule out the possibility that crucial choices are made using automated procedures, along with the right to visibility. Computer models, essentially, have to go through a public evaluation and authentication procedure, as well as if they are to be accepted by healthcare professionals, they should be evaluated via investigation and state processes

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such as randomized clinical trials (RCTs) and medical equipment regulation (MDR). Source input data must meet conventional standards for data integrity and representative, like include persons of diverse genders or races. Large volumes of information about patients outside the condition for which they seek therapy may be problematic, especially when this clearly suggests the “right not to know.” Furthermore, the rates of success for interpretation into clinical practice must be publicly stated to patients.

6.4.1.5. Study Design The investigation strategy should be specified and agreed upon before the outset of a specific research. Furthermore, it must be determined if the predicted quantity of data, on one hand, and physical understanding of the fundamental connections, contrary to this, recommend the employment of ML/DL or mechanistic models. An analytic strategy should ideally be created to fulfill particular criteria as well as, most importantly, to suit a study’s clinical demands. Such early participation of all stakeholders and, particularly, their expertise in a multidisciplinary design group is also supported by such prior conversations concerning the research design. The creation of explicit guidelines for GDPR compliance and data governance is also part of this early-project stage. Furthermore, it should be established if automated data processing by systems, including models, is permitted or prohibited, with the physician making the ultimate decision.

Figure 6.14. Basic recommendations for the use of computational models from early ideation to implementation in clinical practice. For each of the four key challenges (outer circle), a specific set of basic recommendations is given in the corresponding color. Source: https://www.mdpi.com/2075–4426/12/2/166/htm.

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6.4.1.6. Data Acquisition and Operation Multicentric studies are definitely desirable for increasing data variety and also for sample size. Furthermore, data harmonization must be acknowledged and handled at the start of a project. Patients’ informed consent must also be considered. This should be stated that information contribution is for beneficial to future patients as well as also that consenting to therapy includes agreement to the use of one’s data. Patient data collection should also comprise organized and standardized recording of all important clinical features collected, and also patient anthropometry, physiology, illness condition, and other phenotypic information, and other influential factors. This disorder and phenotype details must be shared while honoring data privacy and adhering to a standardized formulating format, like Phenopackets (http://phenopackets.org), which links phenotype explanations with disorder, patient, and genetic data, allowing clinicians, biologists, and disorder and drug investigators to create more comprehensive disease designs. For seamless data interoperability, electronic health patient data must be utilized if possible, preferably structured in established standard formats, like Fast Healthcare Interoperability Resources® (FHIR®; http://hl7.org/fhir/) of the health-care standards organization Health Level Seven International (HL7). Rather of employing chosen data based on possibly incorrect current knowledge, information should be as thorough and impartial as possible. Data should also be provided both in prepared and raw versions. Data utilized in the development and application of models must be relevant, similar, properly sourced, and rationally interpreted. The validity and reliability of data point to the state collecting standardized, harmonized data across the population and making it available as a noncommercial supply to investigate studies, that is present scheme in several countries and is made possible by exclusions to the necessity for permission in particular national and EU legislation. To get the greatest possible degree of accurate outcomes for patients collectively and individually, data entry into models and validation of the model must be completely and on a continuous base, along a high level of visibility, as is the situation with the other instruments used by physicians (Shah et al., 2020).

6.4.1.7. Model Development and Model Validation Begin with the basic structural model for model design and verification, and if the results aren’t satisfactory, add complication in modest stages. At the time of model development, one of the most important questions is either the model generalizes very well and how it reacts to unfamiliar input.

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This is similar to the standard testing and training approach. Generally, for benchmarking computing forecasts, simulation results must be compared to referred measurements. Software tools should provide description and version control in order to facilitate re-use of outcomes. Best-practice principles should be followed when reporting simulation findings. Data utilized in healthcare samples should have data about the source of information and data efficiency checking, which should be guaranteed by proper scientific testing and licensing via well-funded licensing agencies (Rasool et al., 2021). Comprehensive and widespread testing by scientific colleagues and licensing organizations, and also continual quality information by institutes that use these processes, are required for models to be utilized in clinical settings. Scientific and commercial applications of samples have distinct paths to validation and are difficult to compare, nonetheless the verification and implementation procedure should be thorough, organized, continuing, and well-funded in both cases. Licensing costs, for example, might pay the expense of ongoing commercial testing process. For the sake of adequate quality control, companies should be forced to provide both models and information to regulatory bodies, mates reviewers, and publications. Access to the information utilized in the construction and preparation of the models is required for third-party verification and duplication of outcomes. While applying to relevant authorities for approach to health-care data is hypothetically possible, preprocessing and harmonizing the information to recreate outcomes is time-taking as well as unrewarding; thus, coherent, elevated model validation necessitates the creation of systems that allow for the comeback of improved (pre-processed, harmonized) medicinal information to the state (Polasek et al., 2018).

6.4.2. Recommendations The four major problems outlined before, may be utilized to develop a series of suggestions that impose to various phases of a research study, from initial concept through implementation in clinical practice. The next sections will address the suggestions (illustrated in Figure 6.14). Inevitably, these guidelines will remain somewhat general for individual investigations, given the complimentary nature of mechanistic models and data-driven methodologies. The reader is urged, however, to compare the suggestions made here to the numerous cases addressed before. Furthermore, a few of the software products reviewed contain best practices for research design and internet materials (Table 6.2) that may be utilized for their own purposes.

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7

CHAPTER

APPLICATION OF COMPUTATIONAL MODELS IN CLIMATE ANALYSIS AND REMOTE SENSING

CONTENTS

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7.1. Introduction..................................................................................... 194 7.2. Theoretical Background................................................................... 199 7.3. Analyzing Remote Sensing and Climate Data Over Data Mining Techniques........................................................................ 202 7.4. Future Research Directions.............................................................. 203 References.............................................................................................. 205

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7.1. INTRODUCTION This chapter explains how to utilize computational simulations to evaluate and extract relevant information from remote sensing and climate data pictures in time series. This type of information has been utilized in climate change studies, as well as in enhancing agricultural crop yield predictions and promoting soil sustainability (Aggarwal, 2003). The FDASE procedure to classify correlated characteristics; a process that manages to combine basic dimension measurements through statistical analysis to monitor advancing remote sensing and climate data (Kifer et al., 2004); and the CLIPS Miner algorithm applicable to multiple time series of consecutive climate data to identify relevant and extreme patterns, all based on Fractal Theory, data streams, and time-series mining. Experiments with real data reveal that data mining is a helpful tool for agricultural businesses and the government to monitor sugar cane fields and make production more beneficial to the people and the environment (Sousa et al., 2007a) (Figure 7.1).

Figure 7.1. Climate change modeling. Source: https://www.semanticscholar.org/paper/Climate-ChangeModeling%3A-Computational-and-Wang-Post/6f88901322ca8db64e0f5d2759420b241ee795f5.

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Brazil is mostly an agricultural nation. It is the world’s top producer of maize and soybeans, as well as the world’s largest manufacturer of sugar cane and coffee (Papadimitriou et al., 2004). The agricultural sector provided 23.3% of the national GDP (Gross Domestic Product), 42% of exports, and 37% of total employment in 2007, as per official data (IBGE) from the Ministry of Agriculture. Through the agricultural zoning program, designed by the Brazilian Ministry of Agriculture, significant progress has been achieved in defining suitable regions for the development of agricultural crops in the past decade (Rossetti, 2001). Agriculture losses due to two climatic-associated hazards are the focus of the Brazilian agricultural zoning program, which tries to reduce losses caused by dry spells throughout the reproductive stage and heavy rainfall during harvesting seasons (Kleinberg, 2003; Zhu & Shasha, 2003). Based on climatic data and agrometeorological procedures, this government program establishes planting calendars for the country’s major crops, which have been estimated to attain risk rates of less than 20% in the event of a climaterelated disaster. However, after the crop has been planted, it is critical to closely monitor the yields of the crop (Aggarwal et al., 2004b; Gama et al., 2005) (Figure 7.2).

Figure 7.2. Computational intelligence modeling in remote sensing. Source: https://www.sciencedirect.com/science/article/abs/pii/ S1568494617307081.

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Production predictions that are based on reliable data are extremely useful tools for guiding farmers through planting decisions and through aiding agricultural businesses with operations and marketing decisions. They may be able to provide reliable data to assist the government in the decisionmaking method, with the goal of limiting negative effects on the economy or taking advantage of good weather and agricultural market conditions (Jin et al., 2003; Manjhi et al.,2005; Sakurai, 2007). Furthermore, crop forecasts made by agrometeorological agents in the nation seem to be an efficient strategy for protecting domestic output from foreign competition. This happens since they provide a framework that is efficient in blocking or lowering the reaction generated by speculative estimations from external actors, who are frequently owned by challenging countries in the worldwide market (Guha et al., 2003; Aggarwal et al., 2004a). When it comes to developing countries like Brazil, a well-distributed system of meteorological stations is not always the case. As a result, one of the most challenging challenges that decision-makers must overcome is keeping track of the weather conditions. Our findings revealed that the data from surface networks are insufficient for properly solving smallscale variability problems. In addition, there are large gradients in rainfall intensity, tiny cell sizes, and a short interval between precipitation cycles, all of which are features of convective rainfall, which is liable for most of the precipitation that happens in tropical climates. Furthermore, meteorologists and natural disaster specialists are growing increasingly worried about the detrimental consequences of adverse weather conditions and natural catastrophes. Employing remote sensing data as a substitute for more common methods is becoming increasingly popular since the sensors provide good geographical and sequential coverage. These devices also make it feasible to receive continuous information from the countryside, with a geographical resolution of some kilometers and a temporal resolution of a few minutes, using sensors that are mounted on tractors. It is important, however, to construct models to match the characteristics accessible in satellite spectral channels to the factors associated with the requested details since measurements collected from remote sensors are inherently indirect (FerrerTroyano et al., 2006; Aggarwal & Yu, 2008). Several satellites are being employed in this scenario to aid with land monitoring and climate forecasts. Satellites operated by the NOAA that was

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initially designed to be used as climatological satellites are now commonly employed for vegetation monitoring on both a global and a regional scale, as well as for monitoring agricultural crops, according to the NOAA. This is accomplished using the AVHRR onboard the NOAA satellites, which has two channels in the near-infrared and red spectra, which are used in vegetation studies. When using AVHRR imaging, the number of photographs taken from the same location changes from 2 to 4 per day, improving the likelihood of collecting high-quality images during the growth cycle of industrial crops (Rodrigues et al. 2008a; Rodrigues et al. 2008b). Agrometeorological models are unable to fully represent spectral variables; hence, the addition of spectral variables into agricultural monitoring models is intended to estimate factors that cannot be entirely represented by agrometeorological types. Spectral information gathered with greater frequency during the production cycle allows for a more accurate assessment of intrinsic agricultural parameter changes, like the LAI (leaf area index) and biomass, which may have a stronger association with crop output. A significant source of spectrum information comes from orbital sensors with high terrestrial resolve, which are particularly useful in this context. Sugar cane has become gradually more important in the Brazilian economy because of the substitution of fossil fuels along with renewable energy resources like ethanol, which is produced from sugar cane. rising sugar imports, spurred by Asian countries such as China, who are constantly increasing demand for these items, is another significant economic concern. As a result, we may anticipate significant downward pressure on exports in the next years, mostly due to China. Sugar cane is the principal agricultural product utilized to create ethanol in Brazil. The country is in a unique position to meet the rising global demand for anhydrous ethanol and sugar as a fuel. Brazil can sustain its global market presence all year, thanks to two primary producing areas and alternating crops. In fact, this agricultural crop is vital to the country’s economy. As a result, precise crop prediction systems are clearly needed to aid marketing strategy and production planning for both home and international markets. Sugar cane has an impact on climate change in addition to being crucial to contemporary agriculture. Temperature and precipitation should rise across the world owing to natural and human influences, as per the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC, 2007). As a result, studies have been conducted to foresee

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changes in temperature trends to develop techniques for reducing greenhouse gas emissions and adapting agricultural yields to the new circumstances of higher temperatures. Replacing fossil fuels with renewable resources is one way to minimize greenhouse gas emissions. A complex and difficult task in such a scenario is the creation of computer modeling to filter, process, integrate, or evaluate data from a wide range of diverse sources and domains. This complexity grows when various climatic and agrometeorological variables are included, as well as when climate and agricultural models are used in conjunction. Seasonal climatic variations and climate change have a significant influence on agricultural productivity in Brazil, which necessitates impact evaluation studies on climate change and seasonal climate variations. The advancements in data-collecting technologies have reduced the time interval for data collection in recent years, allowing institutions to keep massive volumes of data, like remote sensing photos and time sequence of climatic data, in their archives. For a long time, agrometeorologists have relied on climate data since ground-based climatological stations to make climate predictions. Lately, remote sensing data have been employed to increase the accuracy of classic agrometeorological forecasting systems. Nowadays, data is more readily available, and the technology (both in terms of software and hardware) for receiving, distributing, and processing lengthy time series of satellite pictures is more adequate (both in terms of software and hardware). It is important to note that the enormous volume of environmental data associated with geographic information and remote sensing images is a significant motivation for the progress of novel data mining procedures because they offer important tools for identifying patterns, relationships, and correlations that were before unknown by experts. A wide range of computer science disciplines is involved in extracting new information from data. These include artificial intelligence, databases, statistics, visualization, and machine learning. Consider datasets that combine temperature data with remote sensing photographs from sugar cane areas, for example. A quality selection method can find the dataset’s most significant features, which comprise the relevant data on agricultural yield and the relationships between them. Furthermore, knowing which traits can best estimate the values of the others is intriguing. In fact, detecting linked features in climatic datasets, as well

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as their relevance and precedent, can help enhance sugar cane agricultural monitoring in Brazil. In addition, an astonishing quantity of time series, both produced by interpolated and meteorological stations across scattered grid points, is accessible in real-world climate applications. Climate time series may be seen as developing data streams since the distribution of data in this application field typically varies over time. As a result, monitoring the behavior of changing climatic data, such as precipitation, soil water content, and air temperature may be extremely beneficial to agricultural monitoring. Another issue with climatic data time series is that they are typically made up of continuous data (Barbará & Chen, 2003), which adds to the difficulty of correctly analyzing this data to find meaningful patterns and relationships. As a result, pertinent problems to explore include: • • •

When occurrences are continuous, how do you mine patterns in time series? How can time series be quantized while keeping the temporal sense of the patterns? How can significant patterns be discovered in datasets that mix, match, and connect time series of climate data with remote sensing images?

7.2. THEORETICAL BACKGROUND Several data mining problems have been solved using fractal principles. The fundamental dimension depending on the fractal dimension, has been used for cluster algorithm, mining of sequential association rules (Barbará et al., 2004), variable selection (Traina Jr. et al., 2000), time series forecasting, and spatial data mining (Barbará et al., 2004). (Traina et al., 2001). The following are some basic ideas and definitions (Chakrabarti & Faloutsos, 2002). The symbols used in the following sections are listed in Table 7.1. The D (intrinsic dimension) indicates how much information the dataset has. A group of points dispersed along a line, for example, has an inherent dimension of one of the points are inserted in a higher-dimensional space.

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Table 7.1. Table of symbols

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Symbol

Definition

A = {a1, a2,...,aE}

A dataset A is defined by the characteristics ai.

R

real number’s domain

E

Embedding dimension

D

Intrinsic dimension

D2

Dimension of correlation fractal

r

A grid cell’s side size

pD()

Incomplete intrinsic dimension

iC()

An attribute’s maximum individual contribution

Cr, i

Count (‘occupancy’) of points in the i-th grid cell of side size r

ξ

Strength threshold of associations to be saved

ξC

Attribute set core

ξBp

Correlation base of a correlation group p

ξGp

Correlation group p

Mj(C) → aj

Mapping of attributes C

S

Time series

ei

Events of type (bi; ti)

Se

Event Sequence

Sea

Ascending event sequence

Sed

Descending event sequence

Ses

Stable event sequence

V

Design of type Valley

M

Design of type Mountain

P

Design of type Plateau

y

Time series amplitude

δ

The difference between two successive occurrences must be as little as possible.

ρ

Relevance Factor

λ

Plateau Length

n

Number of elements in a time series

A restricting the values of aj

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This means that the inherent dimensionality remains at one. (Kamel & Faloutsos, 1994) advocated the use of the inherent dimension as a method to quantify the non-uniform behavior of actual datasets, with the intrinsic dimension being defined as. The authors also conducted empirical research to demonstrate that actual data typically exhibits self-similar behavior, which is a key property of fractal objects. The D (intrinsic dimension) of a real dataset may thus be calculated by computing the fractal aspect of that dataset. The Correlation Fractal Dimension D2 may be used to determine the fractal dimension of datasets that are statistically comparable to one another. The Box Counting technique (Schroeder, 1991) is an effective method for measuring the fractal dimension of datasets contained in E-dimensional areas. This approach determines D2 as shown in Equation 1 and is a good choice for large datasets (Traina Jr. et al., 2000). In, an efficient approach for computing D2 was proposed. While the embedded dimension E sets the dataset address space, its behavior may be characterized by its inherent dimension D, which assesses the quantity of information that the data represents. Because real data seldom exhibit independence and homogeneity qualities, D is often lower than E. Because of this, D’s behavior information may be used to assist data mining jobs in a variety of ways (Faloutsos & Kamel, 1994). In a similar vein, continuous measurements of D in data stream mining can reveal broad changes in data distribution over time, allowing for the detection of relevant occurrences. Because applications in climate science and remote sensing have created continuous sequences of data over long periods of time, these data may be regarded data streams in a seamless manner, as shown in Figure 7.2. A data stream is described in this chapter as an ordered series of events c1, c2, ..., cn, in which an event cj is defined by a set of E characteristics ai, such that each cj = (a1, ..., aE), and each cj = (a1, ..., aE). Data mining techniques are a common approach to research and knowledge finding in the time series field. To detect association patterns in anomalous event sequences, Wu et al. (Wu et al., 2008) introduced the GEAM method. The MOWCATL technique was created by (Deogun & Harms, 2004) to mine numerous union rules from sequential datasets. They demonstrated a drought risk management application. Both techniques are used to discrete event sequences. A tuple of the form attribute, level>, for example, denotes an event type, where attribute is a variable including

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temperature or rain, and level is the appropriate value of the varying, like high, normal, or low. It is feasible to identify intriguing patterns using discrete events, but the challenge was simplified. For instance, an alliance pattern like “If El Nio, then low precipitation in zone X” can be identified. In this situation, data is quantified solely based on rainfall intensity, ignoring the time, which is critical for understanding the causes of the event. In fact, scientists are more interested in determining the peaks and their associated recurrence times in the El Nio time series to analyze the consequences of this phenomenon and its relationship to weather change scenarios. (Konishi & Honda, 2001) suggested an image time series mining framework. They used the approach to analyze cloud photos captured by the GMS-5 weather satellite. The proposed approach extracts information from photos and groups them into clusters based on variations in cloud mass. (Julea et al., 2006) reported implementation of the SPADE method (Zaki, 2001) to remove common developments detected on pixel-based spatial zoning. Meteosat (Meteorological Satellite) photos were used in the experiments. Feature vectors are used by the authors to depict satellite pictures or symbols associated with quantized intervals indicating satellite channel reflectance values. This method, on the other hand, does not take advantage of indexes that may be created by combining channels. The proposed methods are likewise ineffective when dealing with continuous data. They also don’t mix remote sensing and climate data, which might be a valuable source of information.

7.3. ANALYZING REMOTE SENSING AND CLIMATE DATA OVER DATA MINING TECHNIQUES Scholars in agrometeorology and agriculture primarily use predictive methods, including frequency distribution, principal component analysis, geostatistics, Fourier transform, cluster analysis, non-parametric statistics, and so on, to analyze and discover designs in earth science data and data mining data sets. Data mining approaches for climate databases, on the other hand, are still in short supply (Ghosh et al., 2011).

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Figure 7.3. Application of big data in climate change modeling. Source: https://www.frontiersin.org/articles/10.3389/fenvs.2021.619092/full.

This abundance of computer models and climate data from ground stations, combined with remote sensing data and geographic information, serves as a significant impetus for the development of novel data mining methods, as shown in Figure 7.1. In this regard, we have presented novel ways for analyzing, monitoring, and discovering patterns in time series of remote sensing and climatic data photos to enhance research on monitoring sugar cane fields, which is particularly relevant in the context of yield forecasting research (Wan et al., 2010). There are three different types of analytical techniques: A method to monitor emerging climate and remote sensing data. The CLIPS Miner system to identify the extreme and relevant designs in multiple time series of constant climate data. (i) The FDASE process to detect groups of correlated characteristics; (ii) A technique to monitor remote sensing data and emerging climate (Gholizadeh et al., 2017).

7.4. FUTURE RESEARCH DIRECTIONS In agricultural data, the knowledge discovery covers a broad range of topics that have still to be addressed, particularly in the study of climatic data coupled with distant sensor data. Techniques for detecting correlations, filling in missing data, detecting correlation patterns, and predicting should be improved. Statistical models have been used to make some advances. The increasing volume of data from meteorological stations, climate

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models, remote sensing equipment, and radars, as well as the intricacy of their analysis, presents a significant potential for data mining researchers (Bakon et al., 2020). Extreme occurrences have been researched in connection to other data, like sea surface temperature, by climate change experts. Methods for detecting correlations between several types of variables are needed. The advancement of approaches to uncover attribute associations in datasets defined by the existence of constellations will be a future focus of fractalbased correlation detection research. In real data, relationships between various clusters may differ, resulting in different sets of meaningful qualities. For example, data from remote sensing pictures and weather stations may include clusters relating to multiple regions in a large nation such as Brazil, each of which may be affected by a different climatic component. The specialists benefit from identifying these various relationships in a larger setting. To adjust climate change prediction patterns, methods for analyzing forecasting data with the goal of discovering probable model mistakes might be used. Researchers may use computational environments to simulate and model various future climate change scenarios to test the effects of global warming on agriculture. These devices can make use of information visualization methods to make manipulating and exploring time series easier for users. Another subject to explore is the link between agricultural and environmental data (namely climatic and meteorological). The development of association rule techniques aimed particularly towards agrometeorological analysis, including the production of temporal association rules, can aid in the understanding of this link. Time series forecasting may also be done using data mining techniques.

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Aggarwal, C. C., & Yu, P. S., (2008). LOCUST: An online analytical processing framework for high dimensional classification of data streams. In: Proceedings of the International Conference on Data Engineering (pp. 426–435). 2. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S., (2004). On demand classification of data streams. In: Proceedings of the ACM Knowledge Discovery & Data Mining Conference (pp. 503–508). 3. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S., (2004a). A framework for projected clustering of high dimensional data streams. In: Proceedings of the Very Large Data Base (pp. 852–863). 4. Bakon, M., Czikhardt, R., Papco, J., Barlak, J., Rovnak, M., Adamisin, P., & Perissin, D., (2020). remotIO: A sentinel-1 multi-temporal InSAR infrastructure monitoring service with automatic updates and data mining capabilities. Remote Sensing, 12(11), 1892. 5. Barbará, D., & Chen, P., (2003). Using self-similarity to cluster large data sets. Data Mining and Knowledge Discovery, 7(2), 123–152. doi: 10.1023/A:1022493416690. 6. Barbará, D., Chen, P., & Nazeri, Z., (2004). Self-similar mining of time association rules. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 3056, 86–95. 7. Chakrabarti, D., & Faloutsos, C., (2002). F4: Large-scale automated forecasting using fractals. In: Proceedings of the International Conference on Information and Knowledge Management (Vol. 1, pp. 2–9). McLean, VA-EUA. 8. Faloutsos, C., & Kamel, I., (1994). Beyond uniformity and independence: Analysis of R-trees using the concept of fractal dimension. In: Proceedings of the ACM Symposium on Principles of Database Systems (pp. 4–13). Minneapolis, MN. 9. Ferrer-Troyano, F., Aguilar-Ruiz, J. S., & Riquelme, J. C., (2006). Data streams classification by incremental rule learning with parameterized generalization. In: Proceedings of the ACM Symposium of Applied Computing (pp. 657–661). 10. Gama, J., Medas, P., & Rodrigues, P., (2005). Learning decision trees from dynamic data streams. In: Proceedings of the ACM Symposium of Applied Computing (pp. 573–577).

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11. Gholizadeh, A., Carmon, N., Klement, A., Ben-Dor, E., & Borůvka, L., (2017). Agricultural soil spectral response and properties assessment: Effects of measurement protocol and data mining technique. Remote Sensing, 9(10), 1078. 12. Ghosh, A., Mishra, N. S., & Ghosh, S., (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181(4), 699–715. 13. Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O’Callaghan, L., (2003). Clustering data streams: Theory and practice. IEEE Transactions on Knowledge and Data Engineering, 15(3), 515–528. doi: 10.1109/TKDE.2003.1198387. 14. Harms, S. K., & Deogun, J. S., (2004). Sequential association rule mining with time lags. Journal of Intelligent Information Systems, 22(1), 7–22. doi: 10.1023/A:1025824629047. 15. Honda, R., & Konishi, O., (2001). Temporal rule discovery for timeseries satellite images and integration with RDB. In: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases (pp. 204–215). Freiburg, Germany. 16. IPCC, (2007). Climate Change 2007: Synthesis Report. Retrieved from: http:// www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf (accessed on 3 August 2022). 17. Jin, C., Qian, W., Sha, C., Yu, J. X., & Zhou, A., (2003). Dynamically maintaining frequent items over a data stream. Proceedings of the ACM Conference on Information and Knowledge Management, 287–294. 18. Julea, A., Méger, N., & Trouvé, E., (2006). Sequential patterns extraction in multitemporal satellite images. In: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases (pp. 96–99). Berlin, Germany. 19. Kifer, D., Ben-David, S., & Gehrke, J., (2004). Detecting change in data streams. In: Proceedings of the Very Large Data Base (pp. 180– 191). 20. Kleinberg, J. M., (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397. doi: 10.1023/A:1024940629314. 21. Manjhi, A., Shkapenyuk, V., Dhamdhere, K., & Olston, C., (2005). Finding (recently) frequent items in distributed data streams. In:

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33. Traina, C. Jr., Traina, A. J. M., Wu, L., & Faloutsos, C., (2000). Fast feature selection using fractal dimension. Proceedings of the Brazilian Symposium on Databases (pp. 158–171). João Pessoa, PB. 34. Wan, S., Lei, T., & Chou, T., (2010). A novel data mining technique of analysis and classification for landslide problems. Natural Hazards, 52(1), 211–230. 35. Wu, T., Song, G., Ma, X., Xie, K., Gao, X., & Jin, X., (2008). Mining geographic episode association patterns of abnormal events in global earth science data. Science in China, 51, 155–164. doi: 10.1007/ s11431-008-5008-3. 36. Zaki, M. J., (2001). Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1, 2), 31–60. doi: 10.1023/A:1007652502315. 37. Zhu, Y., & Shasha, D., (2003). Efficient elastic burst detection in data streams. In: Proceedings of the ACM Knowledge Discovery & Data Mining (pp. 336–345).

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8

CHAPTER

A SOCIO-TECHNICAL PERSPECTIVE OF COMPUTATIONAL SUSTAINABILITY

CONTENTS

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8.1. Introduction..................................................................................... 210 8.2. Background of Computational Sustainability.................................... 211 8.3. Sustainability in General.................................................................. 212 8.4. Computational Sustainability........................................................... 218 References.............................................................................................. 230

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8.1. INTRODUCTION This is a comprehensive overview of computational tools for sustainability, as well as their limitations and potential. Given the worrisome growth in environmental deterioration, pollution, and other negative impacts of industrialization and urbanization, sustainability is well recognized as a concern and a topic of research and practice. Researchers in computer science from all over the world are interested in computational sustainability, which focuses on the application of efficient computational methodologies and computational models to assist in the realization of the goal of sustainability. Computational sustainability is a subfield of computer science that is growing in popularity. In this chapter, we discuss current work on computational methodologies that are applied to a variety of domains connected to sustainability, ranging from bio-surveillance to poverty mapping, renewable energy production forecasting to agricultural disease monitoring, agent-based modeling to stochastic network design. We examine some recent developments in the field of sustainable computing. Ultimately, we examine potential research areas that may be pursued to promote long-term environmental sustainability (Figure 8.1).

Figure 8.1. Computational sustainability; the multidisciplinary academic field. Source: https://www.researchgate.net/figure/Computational-Sustainability-themultidisciplinary-academic-field_fig1_266265426.

Computational sustainability and the science of sustainability (Kates, 2011; Kates et al., 2001) are two topics that are connected. In recent

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years, these vital challenges like global warming, extreme weather and its influence on human civilization, and equitable allocation of finite resources have all been addressed by sustainability (Hill, 2007; Klein et al., 1996). Computational sustainability refers to the application of computational technology and thinking to promote sustainability, as well as the associated issue of decreasing the negative environmental effects of computer technologies.

8.2. BACKGROUND OF COMPUTATIONAL SUSTAINABILITY It is critical for professionals and researchers in the field to have a deep understanding of exchanges among dissimilar systems which interrelate with a “societal level” (Ramakrishnan et al., 2012) to sustainability, with either a focus on interdisciplinary research on the topic, to achieve sustainability objectives. Computational sustainability, too, may only be effectively researched and implemented when considered as a multifaceted undertaking that includes both computer and socioeconomic components; it makes little intelligence as a merely mathematical or technical study. (Gomes, 2011) describes computational sustainability in light of this viewpoint: “a novel multidisciplinary study field aimed at developing computational models, methodologies, and tools to aid in the management of the balance among societal, economic, and environmental demands for sustainable development.” Thus, computational sustainability may refer to concerns about the sustainability in computational systems (that is, “green information technology (IT)” or “sustainable computing”), although it may also refer to computational techniques and ideas that promote sustainability (such as sustainable usage of forest sources or freshwater). In today’s modern world, there are several motives and incentives for computational sustainability. (Khanna & Speir, 2013) investigated the impact of several forms of incentives on pollution control and environmental management strategies, while (Mishra, 2008) went to great length about the motives and dangers in this subject. With the recent introduction of new and revolutionary computational techniques, it is only natural that we adapt them to long-standing environmental and sustainability issues. Ultimately, while

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computational advances and technologies are fascinating, it is also highly desired to use computational techniques to meet sustainability objectives.

Figure 8.2. These are three fundamental sustainability dimensions (Elkington 1998; Rodriguez et al. 2002; Todorov & Marinova 2011). Source: https://www.researchgate.net/figure/Dimensions-of-sustainabilityBased-on-12_fig1_50924655.

8.3. SUSTAINABILITY IN GENERAL In the following part, we’ll see a few of the most pressing issues that need to be addressed to achieve sustainability. As initially stated by Elkington (1998) and afterwards cited by Rodriguez et al., (2002), three “dimensions” or the wide range of features (shown in Figure 8.2) are taken into account the significance in sustainability analyzes: environmental, social, and economic. As shown in Figure 8.1, such three features frequently overlap. But, in contrast to general sustainability, computational sustainability is mainly associated with the environmental side of sustainability, rather than the merely economic and social perspectives (Figure 8.3).

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Figure 8.3. The Earth’s carbon cycle. Source: https://www.solarschools.net/knowledge-bank/climate-change/carboncycle.

It does, although; address these issues wherein they intersect with environmental concerns. As a result, that’s also the survey’s wide emphasis. Indicators across various areas may be utilized to measure sustainability (Intrasoft, 2018). Computing concepts can be used to exhibit and control indicators in different settings like this an indicator-based strategy (da Silva et al., 2017).

8.3.1. Sustainability Concerns Different environmental challenges and the notion of sustainability are covered in this part. This is performed to fully understand the threats to sustainability posed by diverse resources, as well as the techniques employed to address these potential issues.

8.3.1.1. Carbon Dioxide and Greenhouse Gas Emissions Greenhouse gasses can retain heat in the atmosphere of Earth, causing global warming and the greenhouse effect. Greenhouse gasses are released into the atmosphere both naturally and as a result of anthropogenic (human causing) activities, having the former serving as the predominant resource today. The Earth’s “carbon cycle,” which includes both natural and manmade compo-

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nents, is particularly noteworthy in this aspect. The carbon cycle is intricate and poorly understood (Rothman, 2015). Numerous industries, human-caused, and other activities have resulted in the worrying rise in atmospheric degrees of greenhouse gasses (GHGs) like CO2 and CH, resulting in dramatic changes in the environment. As per the scientific agreement, such variation would manifest themselves in “insignificant and real ways” (Karl & Trenberth, 2003). In Figure 8.2, we describe the most important components of this wellfamiliar cycle. The dashed lines show events that occurred on a lesser level in recent times (e.g., artificial Carbon dioxide absorption) or that occurred largely in prehistoric eras (e.g., the fossilization of organic deposits in the Earth’s crust to generate fossil fuels). The black lines depict current dominating procedures at scale, like fossil fuel and photosynthesis burning (Cuéllar-Franca & Azapagic, 2015; Wagman, 2018). Humans affect biogeochemical cycles (Mahowald et al., 2017a), for example, anthropogenic aerosol deposition impacts the environmental change cycle (Mahowald et al., 2017b), causing climatic warming and other negative consequences on marine and continental ecological processes. “Carbon neutrality” is a concept that is frequently used in the perspective of reducing greenhouse gas emissions as well as other alternatives. (Cramer et al., 2001; Six et al.,. 2004) construct a metric termed Global Warming Potential (GWP) as follows: The GWP of several GHGs is rather high. CO2e (or “CO2 equivalent”), commonly known as the “carbon footprint,” is a metric that takes into account the contributions of other Greenhouse gasses and also Carbon dioxide (Hertwich & Peters, 2009; Wiedmann & Minx, 2008). One objective of sustainability is to reduce human activity’s “carbon footprint,” which includes minimizing the amount of CO2 released indirectly or directly into the climate. It’s concerning to notice that in 2017, the concentrations of atmospheric carbon dioxide were estimated with surpassed four hundred parts per million (ppm) (Jones, 2017), a record high. It is projected to soar much higher in the future. CO2 capture and sequestration (CCS) is a related technology that may reduce CO2 emissions from industrial resources. Furthermore, this might considerably contribute to the security of food and offset emissions of fossil fuel (Lal, 2004). Another key concept in this respect is carbon trading (emissions trading with a focus on the emissions of CO2), which is a big element of overall

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emissions trading (Lohmann et al., 2006). It is a government-led and market-based strategy for reducing CO2 emissions from diverse resources (most notably, industrial resources) and mitigating the consequences of the climatic change by providing financial incentives for reducing emissions (Stavins, 2003). Carbon emissions trading are more popular recently, and it is the subject of several studies (Callon, 2009; Spash, 2010; Zakeri et al., 2015). It has been the target of considerable criticism (Caney & Hepburn 2011; Gilbertson et al. 2009). As this debate has shown, carbon and associated issues are essential to the whole topic of sustainability, and computational methods to sustainability can be seen from this viewpoint.

8.3.1.2. Ecological Issues Deforestation, habitat loss, fragmentation of habitat (Cushman, 2006), depletion of the forest, and inappropriate forest usage are all main ecological challenges that threaten sustainability. The last two difficulties are similar to the decomposition of deceased creatures’ remnants and biomass burning and are believed to have contributed significantly to the higher amounts of carbon in the atmosphere (Van der Werf et al., 2009). The vanishing of wetlands (Nicholls et al., 1999) (also known as the biological “kidneys” of our environment) is an associated issue that affects biodiversity between wetland biota (Gibbs, 2001) and results in the freshwater loss supplies. All of such factors contribute to the overall reduction of biodiversity that has immediate and extensive consequences for agriculture, agricultural production, and the total ecological environment (Garibaldi et al., 2013). As a result, conservation and associated fields are crucial to the idea of long-term viability. Climatic and weather conditions are found to be correlated to disease, as seen by phrases like “cold” for sickness induced by rhinoviruses, “malaria” (meaning “foul air”), and “underneath the weather” for feeling ill (Relman et al., 2008). This was also widely established that ecological changes and climatic change raise hazards, like flu pandemics (Curseu et al., 2010) and suppressed tropical illnesses (Garchitorena et al., 2017). Human-induced changes in ecology are likely to have produced the COVID-19 epidemic in China (Nuñez et al., 2020). Due to the excessive usage and maladministration of water, as well as water-associated disasters caused by climatic change, the scarcity of freshwater is a substantial natural source challenge in today’s world, posing threats to global health and freshwater biodiversity (Dudgeon et al.,

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2006) (that is an effective conservation priority). It necessitates long-term, sustainable methods for water conservation and the management of the freshwater ecosystem. Furthermore, depletion of groundwater is a big issue in India, the world’s biggest consumer of groundwater, which uses it primarily for agricultural and urban usage (Shah & Kulkarni, 2015). China (Hu et al., 2016), the UN (Konikow, 2015), and other countries face comparable issues. It’s also been proposed that depletion of groundwater is a reservoir of carbon dioxide in the atmosphere (Wood & Hyndman, 2017). Furthermore, ineffective management of water resources (Grigg, 2005) is a major sustainability challenge, and strategies for responsive water management (Pahl-Wostl, 2007) in the face of global climate change are critical.

8.3.1.3. Renewable Resources and Resource Usage Renewable energy sources are those that have been renewed in nature after being used responsibly. Water, Wood, animals, and other natural resources, and also wind and solar energy, are instances. Nonrenewable resources include fossil fuels like petroleum and coal (Deublein & Steinhauser, 2011), metallic ores, as well as a wide variety of other minerals. Farmland, woods, and aquatic sources are all renewable resources, but only when they are managed properly and in moderation. An essential concept in sustainability is the usage of renewable resources wherein feasible, as well as the restriction of the usage of resources in general, but notably, those that are nonrenewable, to prevent the repercussions of diminishing resources in the future. The United Nations (UN) has set the objective of “Sustainable Consumption and Production (SCP)” as an essential goal in the area of resources (UN Department of Economic and Social Affairs 2006, 2007), which might be a key step toward achieving sustainability. This comprises efficient resource utilization, with an increasing reliance on renewable resources, as well as minimization of waste. One particular objective is to eliminate the usage of fossil fuels (Heinberg, 2015). The scarcity of food supplies, along with the growing frequency of climatic disasters, is a major concern, with 815 million people being hungry every year and an extra two billion predicted to be in this situation by 2050, according to the (UN Sustainable Development, 2015). Achieving food security and transforming the global food and agricultural system are necessary to address this issue (UN Sustainable Development, 2015). Additionally, enhanced sustainable and nutrition agriculture should

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be considered as attainable objectives (United Nations Sustainable Development, 2015).

8.3.1.4. Pollution and Other Human-Induced Hazards This is a familiar and well-researched subject that encompasses a variety of issues, including the pollution of water, pollution of air, and the pollution of ground (Peirce et al., 1998). There is a lot of interest in the administration and production of toxic waste (particularly toxic waste consisting of dangerous compounds like mercury and lead) (LaGrega et al., 2010). When we consume large amounts of plastic, we generate stable and toxic waste that has negative consequences for the entire ecological system (such as the oceans, (Lamb et al., 2018)); therefore, effective administration of plastic waste is critical to maintaining ecosystem health and human well-being. Additional issues to consider include over-harvesting of land, agricultural runoff, and fragmentation of habitat, all of which have the potential to result in the loss of species and extinction in the future.

8.3.2. Other Aspects of Sustainability As previously stated, most subjects in sustainability are tied to sustainability problems, that is, environmental difficulties that constitute a danger to sustainability in general. Sustainable manufacturing is an essential factor that has a direct influence on decreasing environmental harm and is explored further down.

Figure 8.4. The life cycle of sustainable manufacture. Source: https://www.researchgate.net/figure/The-life-cycle-of-sustainablemanufacture_fig3_342601507.

By using Figure 8.3, we can see how the life cycle of sustainable manufacturing, which includes sustainable design and the well-known

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proverb “reuse and recycles,” is described (Figure 8.4). Several researchers (Sheldon, 2014; Verboekend & PérezRamírez, 2014; Webb et al., 2008) have looked at the topic of sustainable manufacturing practices. In this context, re-manufacturing is a crucial problem, and appropriate rules should be developed in this area as well (Ijomah, 2010; Ijomah et al., 2007). Researchers are looking for newer and more sustainable materials, as well as viable alternatives for current materials (Bai et al., 2018a, 2018b; Ermon et al., 2015) that may be utilized to aid in sustainability.

8.4. COMPUTATIONAL SUSTAINABILITY In the words of Gomes (2011), the purpose of computational sustainability is to “give the decision support for sustainable development strategies, with a particular emphasis on complicated issues regarding the administration of natural sources.” To make these judgments in the most optimum, or almost optimal, way possible, researchers in computers, information science, and allied disciplines would need to put in the substantial effort, and although environmental, economic, and social concerns are not often examined in those fields. According to Gomes, the most difficult problems for computer scientists to solve in the field of sustainability have been multi-objective optimization issues, which must be solved in the face of underpinning uncertainty and complicated dynamics, and which must be applied across a variety of temporal and spatial scales (Figure 8.5).

Figure 8.5. A smart building, as well as the computationally sophisticated characteristics that can help it become more sustainable. Source: https://www.mdpi.com/2071–1050/12/20/8417/htm.

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8.4.1. Computational Approaches We provide a quick overview of the different computational approaches used for sustainability in this section. Although, it’s important emphasizing that sustainability inside its broadest meaning is a wider, primarily aspiration objective, the computational techniques discussed here only touch on a few tiny parts of it instead of providing a comprehensive answer. Computational simulation; regulate, constraint reasoning, and optimization (Sheldon et al., 2012) of complicated dynamic and spatiotemporal systems (Ellner & Guckenheimer, 2006); combinational decisions; studying underneath constraints (constrictions like energy supply, management of waste, Greenhouse gasses, ecological footprint, acidification of the oceans, global equity, biodiversity, and weather); remote sensing; learning of machine, bigger data, and mining of data (Gomes, 2011); and network modeling are examples of these techniques (such as epidemic networks, transportation networks, food networks, social networks and power grid). Statistical techniques (Agrawal & Rao, 2014; Biegler et al., 1997; Hu et al., 2016; Pinedo, 2009) are frequently utilized in the chemical sector, which contributes significantly to environmental degradation as well conservation and biodiversity. Game theory and Artificial Intelligence (AI) and (Frenkel, 2009) are used more recently in the development of algorithms. Furthermore, the relatively new idea of responsive submodularity (Krause et al., 2014) might be utilized to solve the challenge of sequential decision-making under uncertainty that is a core topic in computational sustainability and has been studied extensively. In the following lines, we will discuss some of the most current developments in computational approaches as they relate to sustainability.

8.4.1.1. IoT (Internet of Things) and Related Smart systems with minimum human engagement are now conceivable because of advances in ICT (Information and Communication Technology) and IoT, and also ubiquitous computing and machine-tomachine communication. In the subsequent paragraph, we’ll look at how breakthroughs in IoT-related computational approaches and algorithms have resulted in innovations that may help the cause of sustainability. The Internet of Things (IoT) is at the heart of the development of smart systems, like smart buildings. Figure 8.5 displays the many characteristics of a sustainable “smart house” that use IoT as well as other closely related technologies.

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Stavropoulos et al. (2015) developed a new rule-based AI system that integrates heterogeneous wireless sensor networks (WSNs) underneath a Semantic Web Service middleware, to improve user comfort and reduce energy consumption. The “Green Charge” model claims to be capable to manage renewable energy in a smart building effectively and estimate future energy needs for long-term use. In a similar line, electric vehicles (EVs) are praised as a viable alternative to conventional fossil-fuel-powered cars for reducing GHG emissions and reducing fossil-fuel reliance. It is predicted that electric vehicles would create significant power storage capacity and variable demand, and also that large-scale EV adoption would alter the transportation industry’s energy resource consumption patterns. Electrical power obtained from renewable sources and distributed through a smart grid would primarily replace fossil fuels now utilized for transportation. In this context, ride-sharing is also essential, with studies concentrating on dynamic ride-sharing and attaining optimal. The main challenges in the deployment of electric vehicles in the smart grid, according to source, are the precise prediction of energy requirements of specific electric vehicle consumers, given data about their everyday tasks and requirements; prediction of accumulated energy and power needs at various network places; and comparable. Furthermore, in the context of sustainable transportation, green routing strives to give effective energy travel information and includes a variety of routing methodologies; statebased routing aims to integrate several of such approaches. Autonomous cars are another important development, with social sustainability implications ranging from human safety to increased road capacity, fuel economy, and platooning, as well as automated traffic control and automated junction administration protocol. Although, certain important downsides are recognized in current years, including health hazards.

8.4.1.1.1. Game Theory and Related We provide a brief overview of the usage of game theory, specifically relevant to what is known as “classical” game theory and associated areas, in the pursuit of sustainability. As demonstrated by fe scientist in the meaning of sustainable fashion supply chains, users of fashion items may have a suitable effect on the profitability levels of companies that start taking reasonable care to gratify ecological consequences in the planning and implementation of their supply chains, assuming that they have an appropriate degree of

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environmental awareness. Identified auctions as a favored approach for carbon trading, and auctions continue to be the topic of certain continuing studies. Although, there is also discussion regarding fairness concerns as well as how auctions are required to be constructed to be socially fair to be successful. It was stated by (Hardin, 1968) that attaining a balance between an individual’s preferences and the collective better from such an entire viewpoint is a central subject in the policy of environment. It is possible to utilize game theory to simulate numerous agent interactions and investigate the consequences of conflicting interests in this context. Similarly, in conservation efforts, such approaches have been used to provide descriptions, forecasts, and prescriptions in the setting of interactions among self-interested, strategic individuals who alter their behaviors to maximize their profits. In a conservation setting, specific instances include detecting and forecasting poaching risks in African wildlife, combating illicit fishing attempts in Indonesia, and supporting tiger conservation in Southeast Asia, among other things. In contrast to this, classical models do a poor job of describing human cognitive activity, which is susceptible to prejudice.

8.4.1.2. Data Mining and Big Data Approaches Several researchers have looked into data mining for sustainability. Optimization algorithms, scalability, integration of data, restriction logic programming, processing of the signal, mining of data, Spatio-temporal data analysis in real-time (which could be utilized to acquire information about behavioral data, for example) and also improving data analysis and understanding are among the challenges that may be confronted in this respect. Predictive ecology, management of disaster, use of climate, and preservation of natural resources are some of the frequent areas wherein mining of data is used in the context of sustainability. Mining of data is used to solve difficulties in agriculture, such as detecting the spectral properties of drought-stressed plants. Similarly, in the terms of long-term wind source assessment, analysis of data algorithms are developed for identifying the presence of disruptive events in real-time using data of frequency from energy systems. The mining of data is also used in the manufacture of sustainable industrial goods. Big data and associated methodologies, on the other hand, are the subject of research in the areas of supply chain sustainability, catastrophe resilience,

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societal development, and urban mobility. Furthermore, big data methods are critical in smart systems and urban infrastructure for real-time urban analysis, which aids in better transparency, sustainability, and efficiency in city operations.

8.4.1.3. Artificial Intelligence (AI) and Related Artificial intelligence is frequently used in research of computational sustainability: Artificial intelligence substitutes for a shortage of specialists by collecting improved information for accurate decision-making, and it has shown to be particularly successful in the terms of computational sustainability thanks to large-scale implementation and crowd-sourcing. Artificial intelligence technologies are defined as “cognitive prosthesis”, with the capacity to increase the decision-making capability of humans which is otherwise restricted, as per (Fisher, 2017), thereby generating a superior, hybrid decision-making procedure that incorporates both computer and human. In this sense, it’s worth mentioning that artificial intelligence is a large area, and the sub-domains it encompasses are frequently overlapping, making it difficult to distinguish between them. We’ve defined artificial intelligence approaches to sustainability as learning of machine and agentbased modeling methods in this debate. In terms of specific artificial intelligence utilization cases in this discipline, research has looked into artificial intelligence in conservation criminolog, bio-surveillance, biodiversity, and environmental informatics. Wildlife specialists are highly relying on artificial intelligence tools, as well as thermal imaging systems and unmanned aerial vehicles (UAVs), to assess threatened species populations over wide areas. Wild book is an accessible software platform that can follow animals in the field, providing a tool for people combating the loss of threatened species. (Reynolds et al., 2017) suggested a cost-effective dynamic conservation plan for migrating water birds based on satellite data and artificial intelligence technologies to forecast the population of birds and accessibility of wetland. “e-Bird” is another effective instance that uses an adaptive Spatio-temporal machine learning algorithm to link environmental variables to observed phenomena of bird appearances and absences. Similarly, using the “maximum entropy modeling” approach (often known as “Maxent”), the models of species distribution may be created to

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examine species and biodiversity conservation. This method is illustrated in Figure 8.6. Additionally, spatial capture-recapture methods were used to assess the species density of Ecuador’s bears. Gholami investigated Spatiotemporal models for predicting poaching risks and modeling poacher behavior. In this case, adversarial behavior modeling approaches were also used. Several renewable source allocation issues and their complicated dynamics may be modeled using Markov Decision Processes (MDPs), which can help to address the worrisome pace of depletion of resources.

Figure 8.6. How artificial intelligence methods (maximum entropy model) may be utilized in species distribution modeling for conservation of biodiversity. Source: https://www.researchgate.net/figure/How-AI-techniques-maximum-entropy-model-can-be-used-in-species-distribution-modeling_fig5_342601507.

In the light of fragmentation and habitat loss, landscape connectedness is also a top conservation priority: Several artificial intelligence methodologies are used, as well as methods such as graph theory, patch metrics, circuit theory, least-cost analysis, or individual-based models. (Xue et al., 2017) suggested a dynamic optimization strategy for determining species density and landscape connectivity dependent upon the principles of spatial capturerecapture and mixed-integer programming (MIP). Likewise, effective mitigation measures for introduced species, pollution, and transmission of infection (epidemiology) might be adopted. Stochastic network design (SND) is another related subject, particularly in the terms of conservation. SND offers a variety of uses in the terms of sustainability, including optimizing fluxes in river networks, organizing conservation efforts, and planning and preparing for catastrophes before occur. In this respect, (Wu et al., 2016) developed a technique (inspired by the river network design challenge in the context of fish conservation) that is intended to assist environmental investigators who are faced with inaccurate models and inadequate data while addressing choice difficulties.

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Techniques based on machine learning (ML) are also employed. Photovoltaic (PV) power has been predicted using regression algorithms. Additionally, the k-nearest neighbors (kNN) models are used to forecast wind power changes used regression to develop good predictors for the ecological footprint; it is often a continuous number. (Robinson et al., 2017) looked at machine learning algorithms for predicting energy usage in commercial properties, whereas the realm of urban energy analysis. Models may be built utilizing historical electrical grid data for threat prediction to avert breakdowns in a city’s electrical power system utilizing knowledge discovery and machine learning approaches. In the chemical business, machine learning (particularly, the random forest algorithm) are utilized to forecast product biodegradability. Semi-supervised learning is used by the researchers to forecast air quality in an urban setting. Probabilistic Bayesian network models is widely used in the ecological perspective for carbonrelated strategies like sequestering of carbon. Finally, in computational social science and computational sustainability, agent-based modeling (ABM) are widely utilized for modeling dynamic systems (Bonabeau, 2002). To replicate the fundamental population dynamics, Scientist employed ABM and stochastic optimization to simulate infectious illness in dynamic populations under uncertainty. The application of ABM in ecological and agricultural decision-making and proposed two techniques. The first is a hybrid ABM-LCA (life cycle analysis) method (to understand better the key factors that affect agriculturists’ decision-making and how adoption patterns may impact the LCA of switchgrass ethanol); the second is an ABM-IS (industrial symbiosis) model, that concentrates on the behavioral and cultural aspects, particularly on recognizing levels of endogenous variables (cultural cooperation, social learning capability, and so on) required for industrial symbiosis development and exploitation.

8.4.1.4. Remote Sensing (RS) The concept of sustainability revolves around remote sensing (RS) as a computational technique. In uses like monitoring of deforestation, distribution modeling of species, mapping of poverty, and natural catastrophe avoidance, remote sensing data are widely employed. Remote sensing technologies are utilized in the monitoring of agriculture (e.g., to assist avert drought and assist humanitarian operations) and in sustainable agriculture, together with geographic information systems (GIS). Researchers used RS and artificial intelligence to create a deep

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Gaussian model that predicts agricultural production. Similarly, satellite data has been utilized to train accurate learning models for agricultural disease surveillance. Wu et al., (2011) also developed a unique diseasedensity model for the “cassava” illness in Uganda, which, unlike traditional statistical methodologies, allows for a more in-depth examination of the disease-level distribution. Artificial intelligence and RS are used to develop automatic systems for predicting, detecting, and classifying plant diseases. Furthermore, RS datasets are utilized to monitor lakes and rivers on a worldwide scale; to approximate the contact area of inland freshwater bodies over time, and to identify pollutants utilizing sensors.

8.4.2. Sustainable Computing The idea of computational sustainability in a larger sense is strongly connected to the issue of sustainable computing. However, it is frequently shown as a subset of overall computational sustainability. This is not a “computational technique” per se. Furthermore, while sustainability in the computer environment is connected to sustainability in other fields in certain respects, there is still a distinction in focus and viewpoint. Whenever it comes to addressing environmental problems, IT has been described as an “enabler” (“Green by IT”). However, when IT causes environmental damage, it is considered a “producer” (“Green in IT”). Both sides of IT are important, although the terms “green IT” and “sustainable computing” are commonly used to refer to that as well. Ecologically sustainable computing (furthermore referred to as green computing or green IT are investigated in several studies, and includes, among other things, sustainable product judicious and design, environmentally conscious usage of computing resources. We merely take a cursory glance at this part. Two aspects of sustainable computing have been discussed: the efficiency of energy and the recycling of equipment. The storage of energy, reduction of energy, and energy scavenging methods are among the earliest. The 2nd is the Spatio-temporal distribution of operations, which refers to the workloads management, the procedures involved in determining when (at what time) a job should be planned and where (on which machine) a job should be designated, whereas computing workloads are being executed. The energy and other NP-hard issues are common (Agrawal & Rao, 2014).

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8.4.2.1. Primary Concerns It is vital to have a wider view of the rising complexity of computer infrastructures and to guarantee that the interconnectedness among society, information technology, and the environment is not overlooked. Inflationary cost of energy and ballooning data center expenses are among the concerns, as are constraints on energy supply and accessibility. Increasing equipment power density and greater cooling parameters are among the other concerns. Furthermore, the high concentration of extremely poisonous and beneficial materials found in information technology hardware (e.g., semiconductors), as well as their miniaturization and integration, make the reuse of electronic waste incredibly hard; properly disposing of the garbage generated by destroyed hardware is also a challenge. Furthermore, the whole procedure consumes vast quantities of energy, freshwater, and other supplies, putting a significant strain on the environment’s reserves. Among the other important challenges are energy monitoring, power estimate using modeling techniques, green measures, responsible disposal, eco-labeling, regulatory requirements, and mitigating risk associated with the environment. A technique often utilized in this regard includes chip-level methods as well as large strategies like server consolidation and dynamic allocation of resources. In contrast, according to recent research, worldwide ICT consumption of energy and carbon footprint peaked in 2016 and are less than previously estimated.

8.4.2.2. ICT (Information and Communication Technology) Several researchers have highlighted information and communications technology as playing a significant role in achieving sustainability, but certain have also expressed concern about its negative effects on the environment, owing to its large and rising requirements for resources and energy. According to, it is not clear if the capital savings and other good externalities that result from IT use are enough to outweigh the capital consumption and environmental costs that IT also entails. The “GreenSoft Model,” developed by (Naumann et al., 2011), seeks to overcome both of such issues.

8.4.2.3. Sustainable HCI (Human-Computer Interaction) Numerous researches have looked into how to make human-computer interaction more sustainable. In this respect, in a 3-way examination of the field discovered 5 unique genres of the field, which they labeled as follows:

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Certain recent breakthroughs, like biologically inspired designs, might be utilized to further explore the possibilities of environmentally friendly design. points out that the topic is still substantially unexplored and that further research is needed in this area.

8.4.2.4. Sustainable Data Centers Computer systems (servers and accompanying hardware), power supply, networking systems, management systems, and also cooling systems to eliminate heat generated by the computing devices and keep the data center at a temperature tolerable for machines and humans, are all included in data centers. In a data center, all of such systems consume energy and contribute to sustainability challenges in various ways. Companies are hesitant to completely re-engineer their programming and cooling systems in data centers to reduce the large quantities of totally avoidable wasted electricity in a secretive, risk-averse, and competitive business. As a result, balancing a data center’s dependability, cost, and the emissions of carbon is difficult a “Net-Zero” data center remains an essential but unattainable objective. Figure 8.7 depicts the many features of a data center from a sustainability standpoint. Lower server utilization is also a problem in data centers; improved usage will lead to increased optimization of data center operations and, as a result, increased energy effectiveness.

Figure 8.7. Sustainable data centers. Source: https://www.dreso.com/uk/sectors/ict-and-data-centers/sustainabledata-centers.

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Furthermore, unlike several industrial devices in other sectors, most computer servers being used nowadays really aren’t power-proportional. Smart scheduling or distributing the workload across fewer servers, upgrading hardware, and stopping idle servers, as well as operating system virtualization and centralized computing, might be used to find a solution. The mining of data might be used to improve cooling performance, such as managing possibly diverse chiller units dynamically. In this case, artificial intelligence are used: study suggested scheduling depending upon deep Q-learning; the “GreenSched” model, which is depending upon neural networks, may perform intelligent energy-aware scheduling; and (Cheung et al., 2017) suggested a “primal-dual approximation method” in the terms of scheduling issues. Exascale systems’ power utilization has become the main design restriction, and much study is done in this area. Dynamic voltage and frequency scaling (DVFS) and CPU (central processing unit) clock modification (throttling) are two common options. Furthermore, due to improved designs and lower wafer widths, the usefulness of DVFS-dependent approaches has decreased in current years. Rather, current research is focused on “getting energy-proportional computing by tackling hardwareplatform heterogeneity,” which is a major concern in today’s data centers. DPM (dynamic power management); current advancements in CMOS (complementary metal-oxide-semiconductor) technology scaling and chip design; improved DRAM (dynamic random-access memory) efficiency of power ; component-wise reserves consumption and fragmentation in power-aware systems; “power- and performance-aware network-on-chip” architectures; and wireless network-on-chip (NoC) designs using “adaptive multi-voltage scaling” (AMS). Furthermore, the efficiency of energy is extensively examined in the perspective of data centers, with suggested potential solutions such as improved server utilization via cautious scheduling and usage, as well as turning off or snoozing servers which are not in energetic usages. In this context, virtualization is an important technology, and NP-hard VM (virtual machine) placement and consolidation techniques are frequently used. (Shabeera et al., 2017) has also proposed energy-efficient techniques dependent upon Ant Colony Optimization (ACO).

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8.4.2.5. Energy Issues and Related The efficiency of energy and associated challenges are crucial to content delivery networks (CDNs) and wireless sensor networks (WSNs). The distribution of data through CDN is a field that requires a great deal of energy in terms of overall energy usage. Mirhosseini investigated ways to improve the energy efficiency of WSNs utilized in smart agriculture. Through leveraging in-network calculations, computational offloading has also been proven to reduce WSN energy usage and enhance energy balance. Other research has looked into energy-aware WSNs; particularly using machine learning approaches. Building energy-effective home energy management systems or employing networked devices that jointly regulate energy usage without a centralized unit might automate energy-efficient device utilization. The energy behavior of systems has been investigated using statistical approaches like correlation and principal component analysis (PCA).

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INDEX

A

B

Acidification 219 adaptive multi-voltage scaling\” (AMS) 228 agent-based modeling (ABM) 224 agent-based software engineering 103, 114 agricultural monitoring 197, 199 agricultural monitoring models 197 agricultural production 215, 225 Agrometeorological models 197 Algorithmic Noise Tolerance (ANT) 71 amplification 41, 46, 56, 60 AND gate 70 Ant Colony Optimization (ACO) 228 arithmetic operations 5, 11, 12, 14, 15, 22 artificial intelligence (AI) 157 Artificial Neural Networks 71, 88, 91 atmospheric carbon dioxide 214, 235 automate mathematics 4

Binary digits 7 binary number 8, 11, 12, 22 biodiversity 215, 219, 222, 223, 231 biogeochemical cycles 214 biological functions 40 biological neurons 71 biological systems 159, 161, 162 biomass 197 biomedicine 40 biomolecules 41 Boolean modeling (BM) 161 Brain-inspired computing 71 Brazilian agricultural zoning program 195

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C Calmodulin-dependent protein kinase II (CaMKII) 126 carbon footprint 214, 226, 235 CellNetAnalyzer (CNA) 164, 165 civilizations 4 climate data 194, 198, 199, 202, 203 climate-related disaster 195 climatic change 215

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CMOS (complementary metal-oxide-semiconductor) 228 computational modeling 157, 180 Computational Modeling in Biology Network (COMBINE) 166 Computational-modeling methods 156 computational social science 224 Computational sustainability 210, 211 Computation-in-Memory (CiM) 123 Computation-near-Memory (CnM) 123 Computation-with-Memory (CwM) 123 computer science 118 considerable criticism 215 contemporary culture 3 content delivery networks (CDNs) 229 COVID-19 epidemic 215 CPU (central processing unit) 228 D databases 103, 112 data processing 40 data storage 40 data streams 194, 199, 201, 205, 206, 207, 208 deep learning (DL) 157 digital circuit 41, 66 division 11 DNA-based analysis 40 DNA computation 40, 41, 59, 60, 61, 64, 66 DNA computational systems 40 DPM (dynamic power management) 228

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DRAM (dynamic random-access memory) 228 Dynamic voltage and frequency scaling (DVFS) 228 E Earth’s crust 214 e-business 102, 104 ecological environment 215 e-government 104 electric vehicles (EVs) 220 energy consumption 70, 86, 91 environmental deterioration 210 environmental informatics 222 European Medicines Agency (EMA) 157 F flight control systems 103 flip-flops 124 Food and Drug Administration (FDA) 157 fossil fuel 214 Fractal Theory 194 freshwater biodiversity 215 G Game theory 219 GDP (Gross Domestic Product) 195 gene expression 40, 43, 54, 60, 62, 64 Gene Interaction Network simulation suite (GINsim) 164 geographic information systems (GIS) 224 global equity 219 global health 215 global warming 211, 213, 231

Index

Global Warming Potential (GWP) 214 greenhouse gas emissions 214 greenhouse gasses (GHGs) 214 groundwater 216, 232, 236 H hardware 3, 4, 12, 13, 21, 22, 23, 24 hardware devices 3 hydrogen atoms 41 I ICT (Information and Communication Technology) 219, 226 individualized data 157, 172 information technology (IT) 211 information theory 118 integrated circuit (IC) 70 interdisciplinary research 211 Internet of Things (IoT) 219 J Java-based programming environment 3 L LAI (leaf area index) 197 Logic-in-Memory (Lim) 123 long-term memory (LTM) 119 long-term plasticity 122 M machine learning (ML) 157, 158, 173 Markov Decision Processes (MDPs) 223 medical translation 156 memory 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,

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239

129, 130, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 152 memory storage 118, 119, 133, 143, 149 memory system 118, 141 meteorological stations 196, 199, 203 microprocessing chips 41 mixed-integer programming (MIP) 223 modern computers 70 Molecular interaction maps (MIMs) 160 multiparty commercial procedures 102 multiplexer (MUX) 70 multiplication 11, 15, 33 N nanotechnology 40, 51, 63, 68 natural catastrophes 196 network-on-chip (NoC) 228 neural systems 118, 133 neurological systems 125 neurons 119, 122, 124, 125, 128, 132, 135, 136, 137, 139, 141, 143, 146, 150, 151, 152 novel paradigms 3 nucleic acids 41, 42, 64, 65 O offset emissions 214 Online banking 103 online debate 102 ordinary differential (ODEs) 162

equations

240

Key Principles in Computation

P pharmacokinetic (PK) models 163 photographs 197, 198 photosynthesis burning 214 Photovoltaic (PV) 224 physiologically based PK (PBPK) 163 pollution 210, 211, 217, 223 polymerase chain reaction (PCR) 40 Postpone lines 128 precipitation 196, 197, 199, 202 principal component analysis (PCA) 229 Processing systems 70 programming in the large (PiL) 103 Protein kinase M (PKM) 126 Q Quantitative modeling 162 R random binary systems 70 remote sensing 194, 195, 196, 198, 199, 201, 202, 203, 204, 206 renewable energy 210, 220, 230 Renewable energy sources 216 repairing errors 70 Requirements engineering (RE) 103 S Satellites 196 semiconductor 70, 75, 92 service-oriented computing 104, 105, 112 short-term memory (STM) 119 signal storage 41 Social business procedures 104 social cloud 102, 108

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social computing 102, 105, 111, 113, 114 social dependency 102, 107 social interactions 105, 106 social networks 102, 104, 105, 107, 108, 110, 112 social sciences 4 society 2 sociotechnical systems 104 software 3, 4, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28 software development 102, 103, 104, 106, 111 software development area 3 software engineering methodologies 103 soil sustainability 194 Spiking Neural Networks (SNNs) 71 Stochastic BitStream computing 71 Stochastic computing 70, 71, 79, 93, 95, 98 Stochastic network design (SND) 223 Stochastic Sensor Network on Chip 71, 81 stochastic systems 70, 71, 73, 79, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 98, 99 subtraction 11 Sugar cane 197 sustainability 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 230, 231, 233, 234, 235 Sustainable Consumption and Production (SCP) 216 switches 124, 126, 130, 137, 143

Index

synapses 119, 120, 122, 125, 127, 129, 135, 141, 142, 143, 145, 148 system dependability 70, 75 Systems Biology Graphical Notation (SBGN) 166 Systems Biology Markup Language (SBML) 166 T time-series mining 194 transduction speed 41 Turing machine 70, 75

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241

U unmanned aerial vehicles (UAVs) 222 V vacuum tubes 6, 7, 8, 9, 10 virtual organizations 102, 104 VM (virtual machine) 228 Von Neumann’s architecture 70 W water-associated disasters 215 weather 211, 215, 219 wireless sensor networks (WSNs) 220, 229 Wood 216

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