Research and Education: Traditions and Innovations: Proceedings of the 19th International Conference on Global Research and Education (Inter-Academia 2021) (Lecture Notes in Networks and Systems) 9789811903786, 9789811903793, 9811903786

Table of contents :
Preface
Contents
About the Authors
Bio- and Environmental Engineering
Investigating Urban Sustainability by Emphasizing on the Approaches for Reducing Fuel Consumption
1 Introduction
2 Sustainability
2.1 Sustainable Development
3 Urban Environmental Quality
4 Ecological Factor for Achieving Environmental Sustainability Goals
4.1 Ecological Development
4.2 Introducing a Conducted Study
4.3 Ecological Urban Dimensions and Features
5 Transportation System
5.1 Transportation Problems as a Serious Threat to Urban Sustainability
6 Research Suggestion
6.1 Intelligent Transportation System (ITS)
6.2 To Estimate Fuel Consumption Using GPS Data
6.3 To Reduce Fuel Consumption by Using ITS
6.4 Emissions of Pollutants Using ITS
6.5 Path Optimization
6.6 Traffic Data Collection Techniques Using Intelligent Transportation System
7 Conclusion
References
Environmental Risk Management by Achieving Sustainable Development Goals in Architecture and Urban Engineering
1 Introduction
1.1 Risk
1.2 William Fine Method
1.3 Risk Level Evaluation and Classification by William Fine’s Method.
1.4 Suggested Measures for Risk Management By William Fine Method
2 Sustainable Development
2.1 The Conducted Studies on Sustainability
2.2 Introducing a Practical Example of Sustainability
2.3 Tips for Achieving Sustainable Building
3 Conclusions and Final Suggestions
References
Application of Additive Technology to Create Universal Carriers of Cellular Structures
1 Introduction
2 Materials and Methods
3 Results and Discussions
4 Conclusion
References
Comparison and Choice of the Greenhouse Gas Accounting Method for a Model Region in Germany
1 Introduction
2 Methodology
3 Results
3.1 Sectors
3.2 Quality of the Data
4 Conclusion
References
Evaluation of Heavy Metal Contamination in Mytilus sp. Shells
1 Introduction
2 Materials and Methods
3 Results
4 Discussions
5 Conclusion
References
Electric and Electronic Engineering
A Fluorescent Protein-Based OR Gate for Photon-Coupled Protein Logic
1 Introduction
2 Concept and Operational Principle
2.1 Fluorescent Protein-Based OR Gate
2.2 Reversibly Photoswitchable Fluorescent Protein-Based Inverting Gate
2.3 NOR Gate
3 Real-Life Examples
3.1 An mTFP0.7-based OR Gate
3.2 A Dronpa-Based Inverting Gate
3.3 mTFP0.7-Dronpa NOR Gate
4 Discussion
5 Conclusion
References
3D Fluorescence Spectroscopy of Liquid Media via Internal Reference Method
1 Introduction
2 Literary Overview
3 Problem Statement and Description
4 Experiment Description and Conclusion
References
High-Performance and Low-Voltage Current Sense-Amplifier Using GAA-CNTFET with Different Chirality and Channel
1 Introduction
2 Carbon Nanotube Field-Effect Transistor
3 Memories
4 Sense Amplifier
5 Current Sense Amplifier (CSA)
6 Simulation Result
7 Conclusion
References
An Angle-Sensitive, Nickel Bolometer-Based, Adaptive Infrared Pixel Antenna
1 Introduction
2 Proposed Structure
3 Results and Discussion
4 Conclusion
References
Intelligent and Soft Computing Techniques
Intelligent Pantry: A Low Cost Smart Storage Manager for Food Spoilage Prevention
1 Introduction
2 The Architecture of the Intelligent Pantry
3 The iPantry Manager
3.1 System Database
3.2 Stock Quantity Watcher
3.3 Extra Functionalities and Hardware
4 Conclusion
References
A Parallelization of Instance Methods of a .NET Application that Search for Required Structured Data Stored in a Skip List
1 Introduction
2 A Simple List and a Skip List
3 Parallel Programming in the .NET Framework
4 C# .NET Application with Serial, Threaded, and Parallel Instance Methods
5 Experiment, its Results and their Brief Analysis
6 Conclusion
References
Deep Learning Applications for COVID-19: A Brief Review
1 Introduction
2 Materials and Methods
2.1 COVID-19
2.2 Computer Vision in Medical Imaging
2.3 Deep Learning
3 Deep Learning Medical Image Analysis Techniques in COVID-19
4 Discussion
5 Conclusion
References
Manufacturing Technology
Effects of Process Parameters on the Dissimilar Friction Stir Welded Joints Between Aluminum Alloy and Polycarbonate
1 Introduction
2 Experiments
3 Results and Discussion
3.1 Tensile Strength Analysis
3.2 Hardness Profiles
4 Conclusions
References
Material Science and Technology, Smart Materials
Investigation of the Convection Effect on the Inclusion Motion in Thermally Stressed Crystals
1 Introduction
2 Analysis of Approaches
3 Results and Discussion
3.1 Determination of the Parameters of the Viscous Incompressible Liquid Layer, at which Convection Can Occur
3.2 Basic Equations Describing Convection in Layers of Viscous Incompressible Liquid Bounded by Perfectly Heat-Conducting Solid Medium
3.3 Influence of Convective Mass Transfer on the Velocity of Inclusion Motion
3.4 Two-Level Model of Induced Transitions of Crystal Atoms into Solution and Back
4 Conclusions
References
Digital Materials Science: Numerical Characterization of Steel Microstructure
1 Introduction
2 Experimental Part
3 Image Processing
4 Results and Discussion
5 Conclusions
References
Structure and Optical Properties of a-C Coatings Doped with Nitrogen and Silicon
1 Introduction
2 Deposition Technique
3 The Results and Discussion
4 Conclusion
References
Polyaniline-Based Food Quality Markers
1 Introduction
2 Materials and Methods
3 Results
4 Discussions
5 Conclusion
References
Vacuum Coatings Based on Miramistin and Their Biological Properties
1 Introduction
2 Materials and Methods
3 Results and Discussion
4 Conclusions
References
Structure and Properties of Metal-Carbon a-C Coatings Alloyed with Ti, Zr and Al with a High Concentration
1 Introduction
2 Deposition Technique
3 Results and Discussion
4 Conclusion
References
Impact of Modeling Method on Geometry and Mechanical Properties of Samples with TPMS Structure
1 Introduction
2 Modeling of Samples
3 Measurement of Surface Curvature
4 Finite Element Analysis
5 Fabrication of Samples
6 Mechanical Testing
7 Conclusion
References
Characteristics of Nanocomposite Sol-Gel Films on Black Silicon Surface
1 Introduction
2 Experimental
3 Results and Discussion
4 Conclusion
References
NiO and NiO:Al Films for Solar Cells: A Compromise Between Electrical Conductivity and Transparency
1 Introduction
2 Preparation of Materials
3 Results and Discussion
4 Conclusion
References
Photoactive and Structure Properties of ZnO:XMgO Nanocomposite Sol–gel Films on the Surface of Silicon
1 Introduction
2 Experimental
3 Results and Discussion
4 Conclusions
References
Synthesis of Scintillating Sodium Boron Glass-Ceramic Materials Containing YNbO4:Tb3+ Crystallites
1 Introduction
2 Experimental
3 Results and Discussion
4 Conclusion
References
Development of Sapphire-Like Glass by Sol-gel Technology
1 Introduction
2 Preparation of Materials
3 Results and Discussion
4 Conclusion
References
Sol-gel Synthesis TiO2 Nanotubes Based on ZnO Nanorods, for Use in Solar Cells
1 Introduction
2 Preparation of Materials
2.1 First Stage
2.2 Second Stage
2.3 The third Stage
3 Results and Discussion
4 Conclusion
References
Metamaterials and Metasurfaces
Production and Experimental Study of a Weakly Reflecting Absorbing Metamaterial Based on Planar Spirals in the Microwave Range
1 Introduction
2 Theory
3 Simulation
4 Results of the Experiment
5 Conclusions
References
Modeling and Diagnostics
Characterization of Laser Welding of Steel 30XГCH2A by Combining Artificial Neural Networks and Finite Element Method
1 Introduction
2 Finite Element Analysis
3 The Use of Artificial Neural Networks
4 Conclusions
References
Investigation of Acoustic Wave Propagation in Complex Geometry
1 Introduction
2 Computational Details
3 Results and Discussion
4 Conclusion
References
Development of Material for 3d Printing Based on Thermoplastic Elastomer
1 Introduction
2 Materials and Methods
3 Results
4 Conclusions
References
Investigation of the Effect of Observation Window on the Sensitivity Enhancement in the Multi-Pass Cell Outside the Expansion Wave Tube Chamber
1 Introduction
2 Measurement Method
2.1 Laser Absorption Spectroscopy (LAS)
2.2 Multi-pass Cell
3 Calculation Method
3.1 Making Multi-pass Cell
3.2 Inserting Windows
3.3 Calculation Conditions
4 Result and Discussion
5 Conclusion
References
Modeling and Optimization of a Microgrid for a Midrise Apartment and Industry
1 Introduction
1.1 Microgrid
1.2 Component of Microgrid
1.3 The Market Size of Microgrid
2 Microgrid Model and Its Components
2.1 Microgrid Architecture
2.2 Classification of Microgrid Modeling
2.3 Microgrid Modeling
2.4 HOMER Optimization of the Microgrid
2.5 Results
3 Conclusion
References
Nanotechnology and Nanometrology
Raman Investigation of Multiferroic Bi1-xSmXFeO3 Materials Synthesized by the Sol–gel Method
1 Introduction
2 Experimental
3 Results and Discussion
4 Conclusion
References
Piezoelectric Properties of SrBi2(TaxNb1−x)2O9 Thin Films Synthesized by Sol–gel Method
1 Introduction
2 Experimental
3 Results and Discussion
4 Conclusion
References
Plasma Physics
The Physicochemical/Electrical Properties of Plasma Activated Medium by Dielectric Barrier Discharge Microplasma
1 Introduction
1.1 Materials and Methods
2 Results and Discussion
2.1 Temperature Rise and Evaporation of PAM During Treatment
2.2 UV–VIS Spectroscopy
2.3 Resistivity of PAM
3 Conclusion
References
A Young Researcher
Spatial Resolution of X-ray Imaging Using 80 µm Pitch TlBr Detector
1 Introduction
2 Structure of the Imager
3 Experimental Results
3.1 Imaging Result
4 Polarization and Recovery Effect
5 Conclusion
References
Partial Two-Particle Equations in the Relativistic Configurational Representation for Scattering p-States in the Case of a Superposition of Two -Function Potentials
1 Introduction
2 Relativistic Partial Green Functions
3 Solution of the Equations in Case of Superposition of Two -Function Potentials
4 Conclusion
References
Analysis of the Spatial Distribution of the Second-Harmonic Radiation Generated in a Thin Surface Layer of a Spheroidal Dielectric Particle
1 Introduction
2 Problem Statement
3 Solution of the Problem
4 Graphical Analysis
5 Conclusion
References
Maxima of the Power Density of the Second–Harmonic Generation from a Linear Structure of Long Cylindrical Dielectric Particles
1 Introduction
2 One Long Cylindrical Particle
3 Linear Structure of Long Cylindrical Particles
3.1 Problem Statement
3.2 Solution
3.3 Analysis of the SHG Power Density
4 Conclusion
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 422

Sergei Khakhomov Igor Semchenko Oleg Demidenko Dmitry Kovalenko   Editors

Research and Education: Traditions and Innovations Proceedings of the 19th International Conference on Global Research and Education (Inter-Academia 2021)

Lecture Notes in Networks and Systems Volume 422

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

Sergei Khakhomov Igor Semchenko Oleg Demidenko Dmitry Kovalenko •

•

•

Editors

Research and Education: Traditions and Innovations Proceedings of the 19th International Conference on Global Research and Education (Inter-Academia 2021)

123

Editors Sergei Khakhomov Francisk Skorina Gomel State University Gomel, Belarus Oleg Demidenko Francisk Skorina Gomel State University Gomel, Belarus

Igor Semchenko Francisk Skorina Gomel State University Gomel, Belarus Dmitry Kovalenko Dean of the Faculty of Physics and Information Technologies Francisk Skorina Gomel State University Gomel, Belarus

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-19-0378-6 ISBN 978-981-19-0379-3 (eBook) https://doi.org/10.1007/978-981-19-0379-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The twenty-first century is prominent for technological progress. Many innovations were found in the twentieth century, but the new era is even more progressive in this sense. The modern world is developing so fast, and keeping track of all its changes deals with the importance of not finding the right answer but asking the right questions. Such questions are conventionally posed at the International Conference on Global Research and Education occurring annually since 2002. The IA conferences have always provided a sound basis for international academic and social interaction between professors, researchers, and students of affiliated universities from Asia and Europe. They are definitely a landmark and unforgettable event for all the participants and contribute to the further development of partnership in advanced fields of science and technology, becoming an encouraging factor on the way to science for new generations. The 19th Inter-Academia conference was organized in Gomel, Belarus. It is the continuation of the thriving event series organized by the Inter-Academia community bringing together 15 universities worldwide: Japan, Germany, Hungary, Romania, Poland, Czech Republic, Slovakia, Latvia, Belarus, Ukraine, Bulgaria, Russia, Lithuania, and Moldova. It is Shizuoka University (Japan) that initiated the establishment of this community. The conference topics were represented by a wide range of scientific, technical, and educational areas from physics and engineering to multimedia and E-learning techniques and materials. This book is the outcome of the 19th Inter-Academia conference hosted by Francisk Skorina Gomel State University on October 20–22, 2021, and run by the physics and information technology department. It comprises 39 papers thoroughly reviewed and addresses recent advancements in bio- and environmental engineering; electric and electronic engineering; intelligent and soft computing techniques; internet-based education, distance learning; manufacturing technology; material science and technology, smart materials; measurement, identification, and control; metamaterials and metasurfaces; modeling and diagnostics; multimedia and E-learning techniques and materials; nanotechnology and nanometrology; photonics; plasma physics. v

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Preface

We would like to express our gratitude to the authors for their hard work and the opportunity to gain new experience being at the forefront of scientific trends. We are also grateful to all the reviewers for the time and effort in evaluating the papers. Our thanks to all those who have contributed to the conference and this book. Gomel, Belarus

Sergei Khakhomov Igor Semchenko Oleg Demidenko Dmitry Kovalenko

Contents

Bio- and Environmental Engineering Investigating Urban Sustainability by Emphasizing on the Approaches for Reducing Fuel Consumption . . . . . . . . . . . . . . . Leila Abdolalizadeh and Annamaria R. Varkonyi Koczy

3

Environmental Risk Management by Achieving Sustainable Development Goals in Architecture and Urban Engineering . . . . . . . . . Leila Abdolalizadeh and Annamaria R. Varkonyi Koczy

15

Application of Additive Technology to Create Universal Carriers of Cellular Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. V. Avdeeva, Ye. M. Dovydenko, K. V. Laznev, A. V. Petkevich, A. A. Rogachev, and V. E. Agabekov Comparison and Choice of the Greenhouse Gas Accounting Method for a Model Region in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatiana N. Fedosenko-Becker, Michael Becker, and Michael Berger Evaluation of Heavy Metal Contamination in Mytilus sp. Shells . . . . . . Iuliana Motrescu, Mihai Alexandru Ciolan, Anca Elena Calistru, Gerard Jitareanu, and Liviu Dan Miron

29

35 41

Electric and Electronic Engineering A Fluorescent Protein-Based OR Gate for Photon-Coupled Protein Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Balázs Rakos

53

3D Fluorescence Spectroscopy of Liquid Media via Internal Reference Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. R. Khajisvili, N. Kh. Gomidze, and J. J. Shainidze

59

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High-Performance and Low-Voltage Current Sense-Amplifier Using GAA-CNTFET with Different Chirality and Channel . . . . . . . . . Singh Rohitkumar Shailendra, Pragya Sharma, M. Aarthy, and Hidenori Mimura An Angle-Sensitive, Nickel Bolometer-Based, Adaptive Infrared Pixel Antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mustafa Shubbar and Balázs Rakos

73

83

Intelligent and Soft Computing Techniques Intelligent Pantry: A Low Cost Smart Storage Manager for Food Spoilage Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Tusor, J. T. Tóth, and A. R. Várkonyi-Kóczy

91

A Parallelization of Instance Methods of a .NET Application that Search for Required Structured Data Stored in a Skip List . . . . . . . . . . 103 Igor Košt’ál Deep Learning Applications for COVID-19: A Brief Review . . . . . . . . . 117 Hamed Tabrizchi, Jafar Razmara, Amir Mosavi, and Annamaria R. Varkonyi-Koczy Manufacturing Technology Effects of Process Parameters on the Dissimilar Friction Stir Welded Joints Between Aluminum Alloy and Polycarbonate . . . . . . . . . 133 A. Azhagar, K. Sakai, H. Shizuka, and K. Hayakawa Material Science and Technology, Smart Materials Investigation of the Convection Effect on the Inclusion Motion in Thermally Stressed Crystals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Oleksandr P. Kulyk, Victor I. Tkachenko, Oksana L. Andrieieva, Oksana V. Podshyvalova, Volodymyr A. Gnatyuk, and Toru Aoki Digital Materials Science: Numerical Characterization of Steel Microstructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 M. M. Sychov, A. G. Chekuryaev, S. P. Bogdanov, and P. A. Kuznetsov Structure and Optical Properties of a-C Coatings Doped with Nitrogen and Silicon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 D. G. Piliptsou, A. S. Pobiyaha, A. V. Rogachev, and A. S. Rudenkov Polyaniline-Based Food Quality Markers . . . . . . . . . . . . . . . . . . . . . . . . 181 A. E. Shumskaya, Kh. A. Novik, T. V. Zhidko, L. N. Filippovich, Zh. V. Ihnatovich, V. Ye. Agabekov, and A. A. Rogachev

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Vacuum Coatings Based on Miramistin and Their Biological Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Yiming Liu, I. I. Kontsevaya, A. A. Rogachev, Xiaohong Jiang, and M. A. Yarmolenko Structure and Properties of Metal-Carbon a-C Coatings Alloyed with Ti, Zr and Al with a High Concentration . . . . . . . . . . . . . . . . . . . . 195 J. Fang, D. G. Piliptsou, A. V. Rogachev, X.-H. Jiang, N. N. Fedosenko, and E. A. Kulesh Impact of Modeling Method on Geometry and Mechanical Properties of Samples with TPMS Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 A. I. Makogon, S. V. Balabanov, K. S. Koshevaya, and M. M. Sychov Characteristics of Nanocomposite Sol-Gel Films on Black Silicon Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 A. V. Semchenko, V. V. Sidsky, O. I. Tyulenkova, V. E. Gaishun, D. L. Kovalenko, G. Y. Ayvazyan, and L. A. Hakhoyan NiO and NiO:Al Films for Solar Cells: A Compromise Between Electrical Conductivity and Transparency . . . . . . . . . . . . . . . . . . . . . . . 221 V. V. Filippov, D. L. Kovalenko, I. A. Kashko, A. K. Tuchkovskii, V. V. Vaskevich, and V. E. Gaishun Photoactive and Structure Properties of ZnO:XMgO Nanocomposite Sol–gel Films on the Surface of Silicon . . . . . . . . . . . . . . . . . . . . . . . . . . 227 V. V. Sidsky, V. V. Malyutina-Bronskaya, S. A. Soroka, K. D. Danilchenko, A. V. Semchenko, and V. A. Pilipenko Synthesis of Scintillating Sodium Boron Glass-Ceramic Materials Containing YNbO4:Tb3+ Crystallites . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 M. I. Moskvichyov, V. V. Vaskevich, A. V. Semchenko, V. V. Sidsky, V. A. Kravets, V. V. Tkachenko, and V. B. Zalessky Development of Sapphire-Like Glass by Sol-gel Technology . . . . . . . . . 243 D. L. Kovalenko, V. E. Gaishun, Ya. A. Kosenok, O. I. Tyulenkova, V. V. Vaskevich, M. I. Moskvichyov, N. A. Aleshkevich, O. V. Ignatenko, and E. G. Zemtsova Sol-gel Synthesis TiO2 Nanotubes Based on ZnO Nanorods, for Use in Solar Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 D. L. Kovalenko, M. Dobromir, V. V. Vaskevich, A. V. Semchenko, D. Luca, V. V. Sidski, O. I. Tyulenkova, and Y. A. Kosenok Metamaterials and Metasurfaces Production and Experimental Study of a Weakly Reflecting Absorbing Metamaterial Based on Planar Spirals in the Microwave Range . . . . . . 261 I. Semchenko, A. Samofalov, A. Kravchenko, and S. Khakhomov

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Modeling and Diagnostics Characterization of Laser Welding of Steel 30XГCH2A by Combining Artificial Neural Networks and Finite Element Method . . . . . . . . . . . . . 273 Yuri Nikitjuk, Georgy Bayevich, Victor Myshkovets, Alexander Maximenko, and Igor Aushev Investigation of Acoustic Wave Propagation in Complex Geometry . . . . 281 M. Yu. Arsentev, E. I. Sysoev, and A. I. Makogon Development of Material for 3d Printing Based on Thermoplastic Elastomer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 M. V. Timoshenko, S. V. Balabanov, M. M. Sychov, and D. I. Nikiforov Investigation of the Effect of Observation Window on the Sensitivity Enhancement in the Multi-Pass Cell Outside the Expansion Wave Tube Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Ryuji Kobayashi, Hiroaki Sakai, and Makoto Matsui Modeling and Optimization of a Microgrid for a Midrise Apartment and Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Rituraj Rituraj and Annamaria R. Varkonyi-Koczy Nanotechnology and Nanometrology Raman Investigation of Multiferroic Bi1-xSmXFeO3 Materials Synthesized by the Sol–gel Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 O. Fesenko, T. Tsebriienko, A. Yaremkevych, V. V. Sidsky, A. V. Semchenko, V. E. Gaishun, D. L. Kovalenko, and S. A. Khakhomov Piezoelectric Properties of SrBi2(TaxNb1−x)2O9 Thin Films Synthesized by Sol–gel Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 A. V. Semchenko, V. V. Sidsky, A. Yu. Shepelina, D. A. Kiselev, and V. A. Pilipenko Plasma Physics The Physicochemical/Electrical Properties of Plasma Activated Medium by Dielectric Barrier Discharge Microplasma . . . . . . . . . . . . . 335 A. G. Yahaya, T. Okuyama, J. Kristof, M. G. Blajan, and K. Shimizu A Young Researcher Spatial Resolution of X-ray Imaging Using 80 µm Pitch TlBr Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Kohei Toyoda, Katsuyuki Takagi, Hiroki Kase, Toshiyuki Takagi, Kento Tabata, Tsuyoshi Terao, Hisashi Morii, AKifumi Koike, Toru Aoki, Mitsuhiro Nogami, and Keitaro Hitomi

Contents

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Partial Two-Particle Equations in the Relativistic Configurational Representation for Scattering p-States in the Case of a Superposition of Two d-Function Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Valery Kapshai and Anastasia Grishechkina Analysis of the Spatial Distribution of the Second-Harmonic Radiation Generated in a Thin Surface Layer of a Spheroidal Dielectric Particle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Valery Kapshai, Anton Shamyna, and Anton Talkachov Maxima of the Power Density of the Second–Harmonic Generation from a Linear Structure of Long Cylindrical Dielectric Particles . . . . . . 369 Valery Kapshai, Anton Talkachov, and Anton Shamyna Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377

About the Authors

Sergei Khakhomov graduated from Francisk Skorina Gomel State University in 1991 with a degree in Physics. After completing postgraduate studies, Sergei A. Khakhomov has been working at Gomel State University: in 1995—became Assistant of the Department of General Physics, in 1997—took up Senior Lecturer, in 1998—became Associate Professor, from 2003 to 2009—Head of the Department of Optics, from 2009 to 2010—Dean of the Faculty of Physics, from 2010 to 2014—Vice-rector for academic affairs, from September 2014 to June 2016 —First Vice-rector, since June 15, 2016—Rector of Francisk Skorina Gomel State University and since May 2018—Professor of Department of Optics. He earned a PhD in Physics and Mathematics in 1997 at the Belarusian State University in Minsk. In 2018, he was awarded the degree of Doctor of Physics and Mathematics (Higher Doctorate) at the Institute of Physics of the National Academy of Sciences of Belarus in Minsk. He was Executive and Supervisor of more than 20 international projects. He is Scientific Supervisor of one PhD student and Author of more than 340 scientific and scientific-methodical works, including three monographs, 15 chapters in books, 25 educational–methodical and reference books, over 90 scientific articles in journals, more than 80 scientific and methodical articles in conference proceedings, and eight patents. Igor Semchenko graduated from Gomel State University in 1981 with a degree in Physics. He earned a PhD in Physics and Mathematics in 1984 at the Institute of Physics of the National Academy of Sciences of Belarus in Minsk. In 1997, he was awarded the degree of Doctor of Physics and Mathematics (Higher Doctorate) at the Institute of Physics of the National Academy of Sciences of Belarus in Minsk. In 1999, he got Full Professor at the Higher Attestation Commission of the Republic of Belarus in Minsk. He is Corresponding Member of the National Academy of Sciences of Belarus. His research interests are focused on the field of electrodynamics of artificial anisotropic structures and metamaterials. Professor Semchenko was Scientific Supervisor of six PhD students and one Doctor of Science. He was

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About the Authors

Supervisor of more than 25 international scientific projects. He is Co-author of more than 350 scientific papers. His works include four monographs, 13 chapters in collective monographs, more than 110 articles in journals, more than 120 articles in conference proceedings, nine patents, five preprints, as well as 60 abstracts. Oleg Demidenko graduated from Gomel State University in 1982 with a major in “Organization of mechanized processing of economic information.” In 1991, he completed a PhD course at Moscow Institute of Civil Aviation Engineers and defended the thesis “Development of methods and means of analyzing processes of functioning of informational and calculating networks.” In 2004, he defended a doctoral thesis (Higher Doctorate) “Techniques and means of research and adapting a calculating process to operating load of a local area network” at Belarusian State University. In 2004, he was awarded Full Professor. He worked as Head of Department of Automated Systems of Data Processing for 14 years. Since 2007, he has been Vice-rector for research of Francisk Skorina Gomel State University. He is Scientific Supervisor of six PhD students and Author of six monographs and more than 150 scientific publications. Dmitry Kovalenko PhD, is Associate Professor, Dean of the Faculty of Physics and IT, and Leading Researcher of the Laboratory of Advanced Materials of Francisk Skorina Gomel State University. He has been engaged in scientific work since 1997. Currently, the area of scientific interests of Dmitry Kovalenko includes the development of thin-film materials by the sol–gel method for use in optics and microelectronics. He is Scientific Supervisor in the state research programs “Photonics, optoelectronics and microelectronics” and “Physical materials science, new materials and technologies.” He was invited to carry out collaborative research at the Institute of Low Temperatures and Structural Research of the Polish Academy of Sciences (Wroclaw, Poland), Alexandru Ioan Cuza University (Iasi, Romania), the University of Palermo (Palermo, Italy), the University of Aveiro (Aveiro, Portugal), etc.

Bio- and Environmental Engineering

Investigating Urban Sustainability by Emphasizing on the Approaches for Reducing Fuel Consumption Leila Abdolalizadeh and Annamaria R. Varkonyi Koczy

Abstract In the current situation, urbanization is the most desirable way of life, which has led to an increase in urban population, disproportionate expansion of cities, and environmental problems, including increased pollution from transportation, reduced fuel consumption, and consequently reduced emissions are the most serious threats. Sustainable urban development is a common way to cover some of these threats and can improve the economic, social, and environmental conditions of the city. The main aim of the study is to investigate the methods of reducing fuel consumption in the field of transportation. In this regard, various approaches and methods have been proposed by researchers. But with the advent of information and communication technology and intelligent transportation systems, these techniques and approaches have taken a new direction. Today, solutions to reduce fuel consumption using intelligent transportation systems have become one of the most popular fields of science. The aim of this study was to evaluate the dimensions of urban ecology to achieve the quality of a sustainable urban environment and to provide suggestions for reducing the harmful effects on the environment by the transportation system. Keywords Sustainability · Urban environment quality · Ecological development · Intelligent transportation system (ITS) · GPS data

L. Abdolalizadeh (B) Urban Development Department, Faculty of Art and Architecture, Science and Research Branch, Islamic Azad University, Tehran, Iran A. R. V. Koczy John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary Department of Informatics, J. Selye University, 94501 Komarno, Slovakia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_1

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1 Introduction Nowadays, transportation issues such as environmental pollution, depletion of energy resources, increased material and intellectual damages caused by accidents, supervision and management problems in suburban transportation, increased wasted time and rapid growth in demand for transportation and, especially during peak hours in cities around the world has become a serious problem [1, 2]. Looking at history, we will find out how nature is used in all walks of life. Human life became more and more distant from nature over time until its vision of nature became merely a source of energy. The growing demand for energy resources and the increased hazards posed by human intervention in nature have led to increased attention to reducing pressure on natural ecosystems. To address these hazards, sustainable design has provided solutions to achieve its goals. Sustainability has been a pervasive issue whose main concern is environmental issues [3]. Sustainable development strives to create a good physical and economic base in the environment with minimal negative impact on the environment [4, 5]. Sustainability is one of the new trends and approaches of architecture that has attracted the attention of many contemporary designers and architects in recent years. This architecture, derived from the concepts of sustainable development, seeks to adapt to the environment and climate is one of the basic human needs in today’s world [5–7]. The main goal of sustainable development is to meet the basic needs, improve and promote everyone’s standard of living, maintain and manage better ecosystems and a safer and happier future. Other sustainable development goals include reducing waste and energy distribution to the environment, reducing pollutant production, using renewable materials in the natural cycle, and utilizing renewable energy sources such as solar wind energy and geothermal energy [8, 9]. In the meantime, ecological development as an important goal of current research aims to enable human beings to flourish in harmony with nature and achieve sustainable development by fully understanding the interaction between the environment, the economy, politics and socio-cultural factors based on ecological principles [10, 11]. This paper attempts to introduce some basic design principles with a sustainable development approach and interact with nature, and provide design approaches with low energy requirements, and fairness of transportation to reduce urban traffic.

2 Sustainability 2.1 Sustainable Development Moughtin and Shirley have presented Fbased on the components of sustainable development. Their integrated form and model bring the three elements together; Economic activity is subordinated to society and community because it is one of the

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many forms of social activity. Social life is then confined to the environment as all activities take place in a specific environment [12].

3 Urban Environmental Quality Environmental quality is an abstract concept that operates as a result of human and natural works on different spatial scales. Quality is also the manner of the characteristics of anything or phenomena that has a certain emotional and intellectual effect on human beings. Van kam considers the environmental quality as a pervasive concept and believes that different theories about different environmental perspectives have multidimensional meaning [13]. The issue of environmental quality has attracted much attention in the economic sciences, along with theoretical and empirical perspectives. It has increasingly advocated the use of a multidimensional approach that examines social and political change and, and in addition to consumption, may also affect one’s satisfaction (Table 1). Table 1 Factors, components, and effective indicators of environmental quality from the scientists’ viewpoint Urban planners

Theorist

Factors Affecting Environmental Quality

Kevin Lynch

Vitality, meaning, proportion, accessibility, supervision and authority, efficiency, justice

Ian Bentley (1985)

Permeability, diversity, readability, flexibility, visual fitness, sensory richness, color attribution, resource efficiency, cleanliness, biological support

Matthew Carmona (2003)

The physical component, the perceptual or semantic component, the social component, the visual component, temporal component

Panther And Carmona (1997) Environmental sustainability, urban landscape, views, city form, buildings form, public arena (continued)

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Table 1 (continued) South worth (1989)

Structure and readability, form, comfort and convenience, accessibility, health and safety, historical protection, vitality, environmental protection, diversity, adaptability, openness, social interdependence, equality, preservation, adaptability, meaning, supervision and authority

Appleyard & Jacobs (1987)

Access to opportunities, imagination and happiness, livability, identity and control, community and public life, authenticity and meaning, public liability or pervasive environment, urban self-sufficiency

Roger Trancik (1986)

Maintain sequence of motions, confinement, edge joining, axis control and perspective, mixing inside and outside spaces

Haughton & Hunter (1994)

Diversity, concentration, democracy, penetrability, security, proper scale, organic design, economics and its useful tools, creative relationships, flexibility, consultation and users’ participation in projects

Shirvani (1981)

Compatibility, external effects, architectural themes

Coleman (1987)

Historic preservation and urban restoration, design for pedestrians, vitality and diversity of use of cultural environment and context, natural environment and context, attention to architectural values of the environment

Green (1992)

Performance, order, identity, attractiveness

Brian Goodey

Vitality, harmony with existing context, diversity, human scale, permeability, possibility of measured and controlled transformation, richness

Social science scholars Jane Jacobs (1961)

Margaret Mead

Proper activities, visual order of the environment, mixed use, attention to the element of the street, permeability, social mix and flexible spaces Compatible neighbors, neighborhood Sense, a sense of continuity„ awareness of the biosphere and the sense of common fate, ecological conservation, diversity, anonymity, mobility, location selection, avoiding social separation, and the possibility of social bonding

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Table 2 Ecological development dimensions Ecological development dimensions Meaning Ecological security

Clean air, safe and reliable water resources, food, healthy living and working environments, urban services and natural disaster protection

Ecological health

Economic efficiency and efficacy of ecological engineering for recycling and treatment of human waste, gray water of all waste

Ecological industrial metabolism

Protecting resources and the environment through the industrial transition, emphasizing material reuse, creating a renewable energy life cycle, efficient transportation and responding to human needs

Ecological landscape

To organize built structures, open spaces such as parks and plages, communication elements such as streets and bridges, and natural effects such as land topography and waterways in a way that maximize biodiversity and access to urban environments for all citizens and conserve resources and energy. Similarly, it minimizes problems such as car accidents, air pollution, degradation of natural water currents, heat islands and global warming

Ecological awareness

To help people find their place and cultural identity in nature. So, they can change their consumption behavior and enhance their ability to protect high quality urban ecosystems

4 Ecological Factor for Achieving Environmental Sustainability Goals 4.1 Ecological Development Ecological development seeks to enable humans to thrive in harmony with nature and achieve sustainable development by fully understand the interplay between environment, economy, politics, and cultural-social factors based on ecological principles. Ecological development links citizenship’s decisions, public management, ecologically efficient industries, people’s needs and expectations, culture and natural landscape, so that nature and the man-made environment can be integrated together in a useful and practical way.

4.2 Introducing a Conducted Study Epstein et al., in a study in 2015, claimed that the literature on institutional fit is developing rapidly within the broader discourse on sustainability which has resulted

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in a diverse, but sometimes inconsistent program of research. Therefore, we review the recent literature on the fit between institutions and social–ecological systems to identify three general types of fit: namely, ecological fit, social fit and social– ecological system fit [14].

4.3 Ecological Urban Dimensions and Features According to Tachi Wong, the ecological urban can be described with the following features: 1. 2. 3.

4.

5.

6.

7.

8.

9. 10.

A compact, mixed-use urban that protects the natural environment and biodiversity and areas of agricultural production. The natural environment infiltrates the urban space and affects the urban, while the urban and its coast provide a major portion of its food needs. Road infrastructures and freeways focus more on the transportation of pedestrian cycling, with a particular focus on rail transport and the use of motorcycles and cars. In the field of water, energy and waste management, there is a wide range of environmental technologies. Urban life support systems changed into closed loop systems. The central city and sub-centers of the city are human centers that emphasize accessibility and mobility by means of vehicles other than cars and attract a large part of employment’s growth and residents. The city has a high quality of public and social culture, equality of rights and good governance. The general territory encompasses the entire transportation system and all related environments. The physical structure and urban design of a city, especially its public environment, must be clear, influential, powerful, diverse, wealthy and have appropriate visual effects and meet the needs of citizens. Economic efficiency and job creation are enhanced through innovation, creativity, the unity of the environment, culture, local history, and high environmental and social quality of the city’s public environment. Planning for the future of the city is an impractical debate, and the decisionmaking process is not a computerized process that can predict. All decisions are based on sustainability, Social, economic, environmental and cultural integration as well as a tendency for urban transportation principles. Such decision-making process is a democratic one involves empowerment and creating hope.

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5 Transportation System 5.1 Transportation Problems as a Serious Threat to Urban Sustainability Nowadays, transportation issues such as environmental pollution, depletion of energy resources, increased material and intellectual damages caused by accidents, supervision and management problems in suburban transportation, increased wasted time and rapid growth in demand for transportation and, especially during peak hours in cities around the world has become a serious problem (Kashfi, 2018). According to a report released by the International Union for Conservation of Nature and Natural Resources (IUCN) in 2018, greenhouse gas emissions have doubled over the past two years and will increase 14 times by 2050. According to a report by the US Environmental Protection Agency (EPA), carbon emissions from fossil fuels have increased significantly since 1900. As shown in figure below, greenhouse gas emissions have increased by about 90% since 1970. The share of pollution from fossil fuel consumption and industrial processes accounts for 72% of total greenhouse gas emissions, which increased from 1970 to 2010. Agriculture, deforestation and other land use changes are the second largest producers of these gases [15] (See Fig. 1). The largest sources of greenhouse gas emissions are the use of fossil fuels for human activities such as heat, agriculture, transportation and electricity. These resources fall into six categories: heat and electricity production, agriculture, industry, transportation, construction and other energies.

Fig. 1 Global graph of carbon emissions from fossil fuels [16]

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6 Research Suggestion 6.1 Intelligent Transportation System (ITS) Intelligent based systems have a deep penetration in technology development [17]. Nowadays, transportation issues such as environmental pollution, depletion of energy resources, increased material and intellectual damages caused by accidents, supervision and management problems in suburban transportation, increased wasted time and rapid growth in demand for transportation and, especially during peak hours in cities around the world has become a serious problem. Solutions to reduce fuel consumption proposed by this study will be investigated using the intelligent transportation system. Energy consumption and emission of pollutants affect human health and sustainability of the city. Analyzing large volumes of GPS data collected from vehicles can provide useful insights into the amount and distribution of energy consumption and pollutants. Monitoring stations are one of the oldest methods of estimating pollution. These stations are located in specific areas of the city. The data collected from these stations is used to evaluate atmospheric conditions based on clean air standards. While the data from these stations are reliable, they are expensive to install and maintain [18] and therefore, few of these monitoring stations are usually installed in one city [19].

6.2 To Estimate Fuel Consumption Using GPS Data The rapid development of data collection has created storage, and environmental networks that encompasses a great deal of information and infiltrates many aspects of society and technology. GPS routing information is widely used as one of the elements of this huge amount of data due to its wide coverage, good connectivity, and low cost. Vehicle paths contain rich information about driving and traffic conditions, which can be used to accurately measure greenhouse gas emissions [20]. The authors estimated the emission levels of pollutants macroscopically at the city scale by initial studies of vehicle routing data. They calculated NOX emission rates using GPS taxi data and average-based estimation model of speed.

6.3 To Reduce Fuel Consumption by Using ITS There are many activities today in various areas of intelligent transportation, such as communications, control, automotive information technology and highway structure. The primary goals of the intelligent transportation system are to increase safety and transport efficiency. In addition, in recent years the ITS has been recognized as one of the solutions to reduce environmental impact. The environmental impacts of energy

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consumption, emission of pollutants and greenhouse gases lead to a decrease in air quality. Many applications of the ITS are designed to reduce environmental impact. Many of these applications use automotive networks to connect vehicles with infrastructure. The results show that energy consumption decreased by about 5% to 15%.

6.4 Emissions of Pollutants Using ITS Top-down and bottom-up approaches are generally used to monitor the emission of pollutants [21, 22]. The top-down approach estimates the amount of CO2 emissions from all fuel data consumed on a large scale at time and place (for example, annual country data). Greenhouse gas emissions are estimated manually from social and economic data over a precise period of time (for example, daily emissions in a city). A bottom-up approach (inside 13) has been proposed based on ITS technologies. Traffic data from the ITS is fully used for the evaluation and measurement of driving patterns and vehicle technology data. These data are collected through the detection tools available on the paths, GPS of vehicles and digital elevation model (DEM). In this way, the technology of vehicles and driving patterns are measured over a short period of time. Accordingly, the emission of real-time pollutants from road transport can be estimated by IVE software. The IVE software was designed in 2007 with the support of the US Environmental Protection Agency. The main purpose of this software was to provide a suitable model for estimating the emission values according to the situation of developing countries. Release coefficients of this software have been achieved for different vehicles based on the models of COPERT IV, MOBIE6, and EMFAC2007. IVE software has many advantages over similar and popular software such as COPERT and MOBILE. First, the software has more features types over similar software. Also, the estimation of release coefficients in this software is based on instantaneous speeds, not average speeds, which is one of the great advantages of this software.

6.5 Path Optimization Due to advances in technology, ITS technology, and traveling costs, increased fuel consumption and emission of pollutants, financial reasons and environmental concerns are considered as important goals and factors of path optimization. Optimization is a way in which options with considerable and viable performance are discovered by investing in essential factors and not paying attention to unnecessary and unwanted items. Optimizing the solutions of routing problems is known as path optimization. It is the process of finding the most cost-effective path for a set of stops. There is general and sufficient evidence to suggest that path optimization has been merely an individual argument in the past, and that security was an

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important factor for path optimization [23, 24]. Today, given the daily needs and time constraints, the amount of fuel consumed or the pollutants emitted are considered as the economic constraints of the various criteria. Researchers and scientists have come up with different methods and techniques to discover the optimal path. Vehicles optimization path is known as environmental routing with respect to fuel consumption. In many studies, the road network is first simulated to a positive-weight graph in such a way that the nodes and edges represent intersections and roads, respectively and the weight of the edge, depending on different criteria, can represent the length of the segment of the path, the amount of fuel consumption, the time traveling the path, etc. Then, the path is calculated by running the algorithm of finding the shortest path on the graph. Dijkstra’s algorithm, the basic algorithm of the problem, is the shortest path obtaining the shortest path from the origin to all vertices of the graph by scrolling through the positive-weighted weighted graph. It is one of the algorithms used to compute the single-source shortest path and is similar to the Primer algorithm. If the edge graph has a negative weight, this algorithm is not working properly and we should use other algorithms such as the Bellman Ford algorithm which their time complexity is greater.

6.6 Traffic Data Collection Techniques Using Intelligent Transportation System As mentioned, increasing pollution and fuel consumption are one of the major challenges in the world. Today, there are many ways to reduce fuel consumption and emissions. Techniques, methods, and algorithms are discussed in this article, but it should be noted that the main inputs of these algorithms are traffic data. So, collecting traffic information from different urban areas and making this information available to drivers and cars is one of the other issues and challenges that must be addressed. In collaborative traffic information systems, information is collected by moving vehicles. Due to the management, control and analysis center, these systems are grouped into two categories: centralized and decentralized systems. In centralized collaborative information systems, data and traffic information collected by roadside equipment is sent to the unit control and central management. The results of the processing of this information are then transmitted to the vehicles via the communication network. These types of systems are called centralized collaborative traffic information systems because of the need for unit control and central management [25]. Decentralized collaborative traffic information systems are systems in which operations of collecting, distributing, analyzing, storing and searching traffic information are performed by moving vehicles. These systems utilize vehicles wireless networks, infrastructure networks, or a combination of both networks. Collaborative traffic information systems typically utilize a peer-to-peer network on the physical network

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to perform a more efficient collection, storage and retrieval of traffic information. Such a layer can be applied to vehicles, infrastructure nodes, or both.

7 Conclusion The main goal of this study is to provide sustainable development, meet basic needs, improve the quality of life for all, maintain and manage better ecosystems and a safer and more prosperous future. Other goals of sustainable development include reducing energy loss and diffusion to the environment, reducing pollutant production, using renewable materials in the natural cycle, and utilizing renewable energy sources such as solar wind energy and geothermal energy. Ecological development in environmental protection is inseparable from the development process. In the field of development and environmental protection, meeting the needs of the current generation without compromising the resources needed for the next generation depends on achieving sustainable development. In the meantime, in order to achieve the goals of sustainable development and due to the increasing population growth and increasing pollution caused by transport, strategies were examined in the research proposals to reduce fuel consumption in different areas. In the past few decades, many intelligent transport system applications have been implemented to reduce crowd, improve safety, reduce fuel consumption and reduce emissions. GPS data collection, fuel consumption modeling, path optimization using Dijkstra algorithm, A* and ALT searches, etc. were the methods that were studied in this paper. It should be borne in mind that data played an important role in these methods. Therefore, in a separate section, data collection techniques were investigated using the Intelligent Transportation System. As future work, it is suggested to consider travel time reduction plans and methods using the intelligent transportation system.

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Environmental Risk Management by Achieving Sustainable Development Goals in Architecture and Urban Engineering Leila Abdolalizadeh and Annamaria R. Varkonyi Koczy

Abstract In today’s world, the increasing progress of technology and urban construction has increased the variety of unknown hazards to the environment. This has led organizations to find a way to reduce these risks. In this regard, environmental risk assessment and sustainable development in architecture and urban engineering can be a good tool for achieving sustainable development goals. Risk Management is a new branch of management science that found its new place in a variety of trends including finance and capital, investment, trade, insurance, industrial, civil, military and safety projects, health and environmental management has found its place. Therefore, the purpose of the study is to review environmental risk management by introducing the performance of William Fine and PHA approaches in Environmental Risk Assessment and to achieve Sustainable development goals in architecture and urban engineering. It helps us to promote sustainable development in harmony with nature while reducing the level of environmental risks. Keywords Risk management · Environmental sustainability · William Fine · Pearl River Tower · Green way

1 Introduction With the expansion of cities, population growth, and the lack of adequate measures to control pollution, the harmful effects on the environment and human beings are becoming increasingly apparent [1, 2]. As the industries progress, these contaminants are critical threats to public health [3, 4]. Environmental management is employing the best available techniques to control human activities that have significant impacts L. Abdolalizadeh (B) Urban Development Department, Faculty of Art and Architecture, Islamic Azad University, Science and Research Branch, Tehran, Iran A. R. V. Koczy John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary Department of Informatics, J. Selye University, 94501 Komarno, Slovakia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_2

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on the environment [5]. Environmental risk assessment (ERA) is the process of qualitatively analyzing the risk potentials and actual coefficients of potential risks in the project as well as the sensitivity or vulnerability of the peripheral environment. Accordingly, in addition to analyzing and reviewing different aspects of risk with a full understanding of the environment of the area, the level of affected environmental sensitivity and the environmental values of the area are used in risk analysis [6]. Risk assessment is a systematic approach to identifying and assessing risks to rank decisions to reduce risk to an acceptable level [7]. Risk assessment is done with the aim of accepting risk or not accepting risk. If the risk is not acceptable, the risk is reduced by other options. Risk acceptance involves making decisions about which risks are acceptable to different authorities. In general, there are many different ways of assessing risks, some of which are more applicable and others more specific. By the way, these methods have evolved from the past to the present. Environmental risk assessment specialists can make a change in common methods of risk assessment by relying on their own risk experience and knowledge, the nature and characteristics of the project, ecological conditions of the ecosystem, or intended site-specific conditions [8, 9]. Considering the variety of environmental threats and endangering human life cycle in today’s societies, one of the major problems and causes of environmental threat is the increase of urban constructions which has caused various unknown risks to the environment. The approach of sustainable urban design allows architects and urban engineering planners to create dynamic spaces that maximize the interaction between humans and their ecosystems. The idea of sustainable urban design actually provides the means to minimize the impact of urban development on the environment [5, 10]. The purpose of this type of design is to minimize environmental damage, minimize energy consumption and maximize harmony with nature [11]. In other words, the design philosophy of sustainability, which nowadays supports and promotes sustainability, has been recognized with regard to sustainability issues in construction in the construction industry and is due to its impact on the natural environment (Second Step). 4) Welfare and Sustainable Design in Architecture was also approved by the Environmental Monitoring Institute in 2014 of the Well Building Standard.

1.1 Risk Risk is describing the consequences of a potential risk by stating the probability of its occurrence. In other words, risk is the possibility of occurring an injury resulted from a hazard. According to the above definitions, it can be concluded that risk is a combination of the probability of an event occurring and its consequence or outcome. Risk = the probability of hazard × hazard

(1)

In the OHSAS standard, risk is the probability and consequences of an occurring specified hazardous event. The UNDP defines risk as: “The risk is the possibility of

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a crisis and loss of life, health and property in an accident and in a particular area at a given time.” Three notable risks are. There are three cases in dealing with risk [12]: 1. 2. 3.

The magnitude of a hazard or accident The probability of occurrence The importance of the danger of risk for the community or intended groups [12].

1.2 William Fine Method The William Fine Risk Assessment Method is an organized and systematic way to identify and evaluate potential hazards and to estimate hazard levels in order to manage hazard and reduce it to an acceptable level. In this way, it is imperative to pay attention to the severity of the effects of human, environmental and workplace hazard exposure. This method is one of the quantitative methods. This quantitative evaluation can identify the existing hazard factors and factors and can prevent or control them by taking preventive and control measures. This method helps managers to move forward by prioritizing hazard and accident control programs, and by establishing urgency and control planning to accelerate clear goals [13, 14]. – It provides a simple way to evaluate a variety of hazards and controls for review and decision making. – This method helps managers to move forward by prioritizing hazard and accident control programs, and by establishing urgency and control planning to accelerate clear goals. They can investigate the needed cost to control damages using a risk factor. The purpose of using this method is to determine a method for deciding on the necessity and justification of risk elimination costs as well as the necessity to implement risk management programs as quickly as possible [15]. In this technique, considering the costs, corrective measures for hazard control are suggested. One of the characteristics of this method is the inclusion of costs to check the justifiability of the controls. Factors used in this method contains Consequence (C), Probability (P), Exposure (E) and Risk Ranking (R) = C × P × E. Consequence The intensity of hazard is related to the seriousness of the “potential risk effect” on individuals and the environment. The score for this parameter is determined according to Table 1. Probability The probability of a hazard occurring determines the probability of hazard occurrence. It is only by eliminating or reducing the mechanisms of any hazard that one can hope to reduce the number of occurrences. The score for this parameter is determined according to Table 2.

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L. Abdolalizadeh and A. R. V. Koczy

Table 1 Ranking consequence (B) in William Fine method [15] with 5% over permitted level Rank Description of consequence

Row

10

Death of some people, irreparable long-term environmental damage, financial 1 damage (over $ 5 million), international impact on organization reputation, excessive use of resources and energy, excessive pollutant concentration (50% over permitted level)

8

One person deaths, injuries resulting in permanent disability of more than one person, medium-term irreparable environmental damage, financial losses between 100 and 150 million Tomans, international impact on the reputation of the organization, relatively high consumption of resources and energy, relatively high pollutant concentration (30% over permitted level)

2

6

Damage resulting in permanent disability of more than one person, short-term environmental irreparable damage, financial loss between 50 and 100 million Tomans, impact on the reputation of the organization locally, high resource and energy consumption, pollutant concentration 10% over permitted level

3

5

Long-term damage without permanent disability, long-term environmental damage 4 compensation, financial loss of between 5 and 50 million Tomans, impact on the reputation of the organization locally, average consumption of resources and energy, contaminant concentration 5% over permitted level

4

Temporary damage, short-term environmental impacts, financial loss of less than 5 5 million Tomans, impact on the reputation of the organization internally, low resource consumption, pollutant concentration less than 5% over permitted level

2

Minor damage, requiring first aid (three days and less treatment). Damage of less than 1 million Tomans, impact on reputation of the organization in a single unit, very low resource consumption, pollutant concentration at standard level

6

1

No need for further reviews, cost effective financial loss, no impact on the reputation of the organization, no environmental damage

7

Table 2 Ranking of the probability of occurrence of risk (A) in William Fine’s technique [14, 15] Row Description of probability of occurrence

Ranking

1

Most likely, it happens daily or weekly, they are uncontrollable

10

2

The chance is 50–50. It can happen, it happens monthly, it needs to control the power out of power

6

3

It can happen by chance, the chance of occurring is 50%, it occurs several times 4 throughout the year and can be controlled at the surface

4

It probably won’t happen until a few years after the impact, but it can happen, it 2 rarely happens, and it can be controlled at the origin

5

They have actually impossible occurring or never will happen

1

People Exposure to Risk The number of times a safety, health and environmental hazard occurs has a direct impact on the degree of hazard impact. The rate of exposure determines the sequence of occurring the hazard. The contamination rage parameter is used instead of this parameter in relation to environmental risk. The meaning of contamination range is

Environmental Risk Management by Achieving Sustainable Development Goals … Table 3 Contamination range in William Fine’s technique [15]

19

Row

Contamination range

Ranking

1

Out of organizational area

7

2

The whole organization

6

3

Large part of organization

5

4

At the work unit

4

5

Part or section

3

6

Range around the person or equipment

2

7

No pollution

1

the range and extent of contamination spread over the studied environmental area. Table 3 shows how to divide it.

1.3 Risk Level Evaluation and Classification by William Fine’s Method. After calculating the risk rating, the following formula is used to justify the cost. J = R/(C F × DC)

(2)

J = Cost J usti f ication V alue

(3)

C F = Cost Factor : The cost factor is the allocation for eliminating programs and reducing hazards. DC = Degr ee o f Corr ection V alue: Degree of Correction determines the degree of hazards that we can reduce. If J > 10, the cost is acceptable and if J < 10, the hazard elimination costs will be unacceptable. The cost value and degree of correction are determined by Tables 4 and 5. By applying this technique, finally, by using obtained factors for intensity, the probability and the amount of risk will be obtained. By using the above mentioned coefficients, its economic justification is investigated.

1.4 Suggested Measures for Risk Management By William Fine Method In the proposed stage of corrective action considering the importance of risks and risk level classification, first the hazards are identified with higher and unacceptable risks and the necessary goals and programs are determined to eliminate, reduce and control

20 Table 4 Costs factor

Table 5 Degree of Correction [16]

L. Abdolalizadeh and A. R. V. Koczy Cost Factor (CF): The cost factor is the allocation for eliminating programs and reducing hazards Rate

Classification

10

More than 800 million tomans

6

Between 80 and 800 million tomans

4

Between 8 and 800 million tomans

3

Between 800 and 80 million tomans

2

Between 100 and 800 thousand tomans

1

Under 100 thousand tomans

0.5

Without any cost

Degree of Correction (DC) Rate

Classification

1

The hazard is completely eliminated (100%)

2

At least 75% hazard is eliminated

3

75 to 50% hazard is eliminated

4

35 to 50% hazard is eliminated

5

Less than 25% hazard is eliminated

them in order to reach the Medium (M) risk level or Low (L) risk levels. Control plans are then provided for Medium (M)or abnormal risks in order to promote to normal (L) by applying control and continuous monitoring techniques [16].

2 Sustainable Development Louise Chawla (2015) explores the various ways related to nature that can be contributed to the health and welfare of children. This research seeks to change the research approaches by using the human development capabilities approach for a broad definition of State Welfare investigating different aspects of health. Research has been evaluated since the 1970s. The global interest in environmental issues is one of the reasons for the emergence of new ideas that have led to the emergence of buildings in balance with the environment called “sustainable buildings” [5, 10, 17]. Accordingly, the idea of

Environmental Risk Management by Achieving Sustainable Development Goals …

21

dynamic building is based on the adaptation to environment [18]. Sustainable development tries to create a good physical and economic base in the environment with the least negative impact on the environment [19]. Environmental sustainability and sustainable buildings as well as WELL are related to an affluent population.

2.1 The Conducted Studies on Sustainability Börjesson and Rubensson (2019) examined customer satisfaction among transportation passengers in Stockholm at 15 years. The study analyzed the satisfaction and importance of many of their characteristics and time trends. In today’s world, increasing progress of industries, industrial technology, and urban construction has increased variety of unknown hazards to the environment. This has led organizations to find a way to reduce these risks. To achieve sustainable development goals, preserve environmental considerations is a requirement in the country. In this regard, environmental risk assessment and sustainability development in architecture and urban engineering can be a good tool for achieving sustainable development goals. Risk Management is a new branch of management science that found its new place in a variety of trends including finance and capital, investment, trade, insurance, industrial, civil, military and safety projects, health and environmental management has found its place. Therefore, our aim in the present study is to review the environmental risk management by introducing the performance of William Fine and PHA approaches in Environmental Risk Assessment and to achieve Sustainable development goals in architecture and urban engineering. It helps us to promote sustainable development in harmony with nature, while reducing our environmental risk levels [20]. Local governments around the world are facing external and internal pressures to adopt climate change reduction strategies. Canada’s British Columbia legislation has recently required all municipalities to meet their greenhouse gas emission targets. The lack of specificity in the law makes it possible that even if the rules are universal, it can lead to the minimum emissions [21].

2.2 Introducing a Practical Example of Sustainability Pearl River Tower The architect’s idea for this tower is to use high-performance aerodynamic structure. The tower consists of four pits with solar panels on its body for entrances to generate wind energy. The tower is intended for office use and has been partly shown by China National Tobacco Company. This tower, with 214,000 m2 height, green technology and engineering advances, creates a new definition of sustainable design [18] and contributes

22 Table 6 Description of the Pearl River Tower

L. Abdolalizadeh and A. R. V. Koczy Name of building

Pearl river tower

Location

Guangzhou, china

Team work

Gordon gill, Som (Skidmore, Owings and Mirrill

Implementation period From December 2006 to march 2011

significantly to a healthier environmental cycle. It should be noted that the sustainable tower consists of 81 floors. The area of the apartments ranges from 124 m2 to the apartments with an area of 1200 m (including parking inside the apartment and tower with height of 420 m). Sustainable technology can be clearly seen in the body of the tower to direct the wind to a pair of mechanical openings and the winds are producing energy. Other elements of integration in the tower include: Solar panels, a narrow dual layer wall, cooling roof system, and underfloor ventilation that all contribute to energy efficiency in the tower (Table 6). To Acquire Direct Aerodynamic Solar Energy of Tower The direct acquisition of solar energy depends on the shining aerodynamic shape of the tower and is designed through an accurate understanding of the solar patterns around the site. The correct method of this design determines the path of the sun by correctly optimizing and utilizing this valuable energy for the benefit of the building. On the west side, three layers with exterior curtains are located on cast iron gaps and the interior facade with two-way interior ventilation to the north and south. Both curtains are incorporated automatically and play an important role in building energy efficiency (Official Dynamic Arch, 2010).

2.3 Tips for Achieving Sustainable Building To optimize and access an integrated system, some exceptions must be taken into account to transform frozen, lifeless architecture into an integrated structure that interacts with the surrounding environment. In this regard, the following points can be mentioned by adopting the Pearl River Tower [22]: – Ability of dynamic building to keep pace with surrounding areas from a cultural and social point of view. – Ability to prevent solar energy damage. – Ability to retain motor elements. – Ability to prevent wind turbine damage

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23

3 Conclusions and Final Suggestions Given the inevitability of the detrimental effects of industries and industrial technology and the increase in urban construction on the environment, it seems necessary to provide strategies to control the risks posed by industries or to have a risk management system in place. One of the main goals of management is to control risks and assess their risks. There are several methods of risk assessment, one of the simplest and most comprehensive being the William Fine method. The William Fine’s Risk Assessment Approach is an organized and systematic approach to identify and assess potential hazards and estimate hazard levels in order to manage and reduce them to an acceptable level. In this way, it is imperative to pay attention to the severity of human exposure to hazards, environment and workplace. This method is one of the quantitative methods and since quantitative evaluation can identify the existing hazard factors and risk factors and take action by adopting preventive and control measures to eliminate or control them. Considering the variety of environmental threats, one of the major problems and causes of the environmental threat is the increase of urban construction which has increased the various unknown hazards to the environment. The sustainable urban design approach allows architects and urban planners to create dynamic spaces that maximize the interaction between humans and their ecosystems. The idea of sustainable urban design actually provides the means to minimize the impact of urban development on the environment. The purpose of this type of design is to minimize environmental damage and energy consumption and interact with nature. In other words, the philosophy of sustainable design, support and incentives today in the sense of sustainability, has been recognized in the construction industry by considering sustainability issues in construction because of its impact on the natural environment. In the present study, considering the variety of environmental hazards, we have considered the hazards of “urban construction” and introduced “sustainability” as a preventive strategy to reduce these irreparable hazards and by introducing the Pearl River Tower, we made this description clearer. Rapid population growth, along with uncontrolled expansion of cities, unreasonable increases in land prices, air and soil pollution, threaten the existence of green space. On the other hand, community needs and awareness of the need for contact with natural and quasi-natural green spaces have increased, so both urban parks and green spaces close to cities are under intense population pressure. By increasing the threat of climate change, pollution and depletion of natural resources and biodiversity, a change in lifestyle and environmental benefits is essential. Green Way The green way is a response to the irregular development of cities and human interference with nature that has disrupted the balance between the natural environment and man-made environments, and it is designed in order to achieve sustainable development goals in the city, which drive development as protection goes along. Investigating the dimensions, components and aspects of the green way, and indeed its

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components as a subsystem of the city, is one of the primary goals for achieving sustainable development. The idea of green way was first put forward by an American landscape architect named Frederick Law Olmsted. In the mid-nineteenth century, he proposed the creation of a parkway to connect between parks in New York and Boston. The primary parkways consist of routes to pass carriage and amusement parks. After these routes became unfavorable due to heavy traffic, urban planners turned to parkways in which traffic was prohibited. Charles Little’s book in 1991 was a review of 16 green ways that describes the author, William White as the first person to use the name Green Way in the US Outdoor Safety Research. Related topics of international green way in the US published 26 articles by Arne and Fabes in 1996 published in a book entitled “Green Ways: The Beginning of an International Movement”. Green Infrastructure The green infrastructure network is considered as an ecological network due to the environmental benefits and protection of natural open spaces. It encompasses a wide range of open spaces and ecosystems from micro to macro scale, all of which are located in an interconnected network of cores, junctions and enclosures (Table 7). Green Architecture By looking at history, we will learn how to use nature in all areas of past livings. Human life became more and more distant from nature over time until its vision of nature became merely a source of energy. The growing demand for energy resources and the increased hazards posed by human intervention in nature have led to increased attention to reducing pressure on natural ecosystems. To address these hazards, sustainable design has provided solutions such as green design with the aim of using renewable energies for indoor heating comfort. Green architecture is one of the new subdivisions of contemporary architecture that has attracted the attention of many contemporary designers and architects over the last decade. This trend in architecture seeks to adapt to the environment. Increasing temperatures on earth, piercing of the ozone layer, increased environmental pollution and the destruction of biodiversity have made the need for ecology and environmental considerations foreseeable for the future. In addition to the physical needs of humans, green architecture also satisfies their spiritual needs. Green architecture is in fact systems that express sustainable development at the community level based on human health, productivity and prosperity. Biophilic Design Biophilic design is part of an innovative vision of urban planning that focuses on human nature and life. In other words, a habitable and livable building is created. In a biophilic city, desires, limitations, and mutual respect between human and the environment are central issues. Note that plant life plays a decisive role in the environment. Not only plants provide useful food and products, they also play a vital role

Local and city scale

Pattern

– Continuity – Spatial – hierarchy Connectivity – Network – Safety – linear, – Flexibility longituBeauty dinal, – Sustainable strip - Integrated - Coherent – Creating Continuity

Quantity Quality

Source

–Transportation – Accessibility – Outdoor and green space – Region systems – Growing spots – City ecosystem – Physical environment requirement Landscape Rural, city and suburbs space

Instrument

Table 7 Dimension of physical green way Method

Place

Time

Agent

– Beautifying – Sustainable – Topography – Integrating During (meaning - Physical Communinatural long term) pressures of cating or artithe city – DisplaceConnecting ficial – Coordinating ment factors - Protecting capacity of - town – Creating the city and System village – Physical Control – Forming

Humans - other creatures

Actor –

Environmental Risk Management by Achieving Sustainable Development Goals … 25

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L. Abdolalizadeh and A. R. V. Koczy

in balancing nature. The environment of a biophilic city consists of numerous vegetation (such as trees, shrubs, medicinal plants, grasses and ferns) and plants (such as mosses, lichen, algae and fungus). These species grow together under special environmental conditions. These natural processes play an important role in regulating the Earth’s climate. The fact is that plants adapt themselves to higher temperatures and different species live together. As evidenced by the green architectural descriptions, architecture that is in harmony with nature will lead to environmental sustainability and will stand in the face of the crises of the nature cycle, as environmental crises are one of the manifestations of a departure from justice, especially it has presented itself in the contemporary world and made the environment unfit for the human yard to continue. This crisis is caused by a number of factors, such as promoting an inaccurate attitude towards the environment and consequently adopting inappropriate, extravagant and unbalanced approaches to the exploitation of natural resources, and neglecting the just rules governing the universe’s relation to the components and elements of the world. This has disrupted many natural equilibrium and, consequently, made the environment unfit for human life. To resolve such crises, identifying human coordinates, nature and its relationship with one another, and the rights that other human beings and even nature have to adhere to, is the primary necessity of which Islamic teachings are rooted [23]. When it comes to architecture, green municipal and environmental sustainability, hundreds of design approaches can be mentioned together. For example, the common way to reduce the negative effects of air pollution is the sustainable roofing method, known as green roofs. It means that how green roofs can directly and indirectly help reduce carbon emissions in urban areas [24]. Green construction can somewhat be effective in environmental sustainability. It can be argued that the negative impact of construction on the environment substantially extends the concept of green building, commonly referred to as buildings compatible with environment. So all over the world, green buildings have gained many ratings tools over the past few decades [25]. The future perspective can also be followed by the use of intelligent and machine learning based technologies. This field of the science is particularly employed by different sciences [26].

References 1. Hossain, N., Mahlia, T., Saidur, R.: Latest development in microalgae-biofuel production with nano-additives. Biotechnol. Biofuels 12(1), 125 (2019) 2. González-Fernández, C., et al.: Impact of microalgae characteristics on their conversion to biofuel. Part II: focus on biomethane production. Biofuels Bioprod. Biorefin. 6(2), 205–218 (2012) 3. Hernando, M.D., et al.: Environmental risk assessment of pharmaceutical residues in wastewater effluents, surface waters and sediments. Talanta 69(2), 334–342 (2006) 4. Van der Oost, R., et al.: Fish bioaccumulation and biomarkers in environmental risk assessment: a review. Environ. Toxicol. Pharmacol. 13(2), 57–149 (2003)

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5. Nosratabadi, S., et al.: State of the art survey of deep learning and machine learning models for smart cities and urban sustainability. In: International Conference on Global Research and Education. Springer (2019) 6. Muhlbauer, W.K.: Pipeline Risk Management Manual, vol. 35. Elsevier, Amsterdam (1996) 7. Lindhe, A.: Risk assessment and decision support for managing drinking water systems. Chalmers University of Technology (2010) 8. Dai, F., Lee, C., Ngai, Y.Y.: Landslide risk assessment and management: an overview. Eng. Geol. 64(1), 65–87 (2002) 9. Henley, E.J., Kumamoto, H.: Probabilistic Risk Assessment and Management for Engineers and Scientists, 2nd edn. IEEE Press (1996) 10. Artur, S.M., et al.: Sustainable Solutions for the Global South in a Post-Pandemic World. Institute for Advanced Sustainability Studies (IASS) (2021) 11. Nosratabadi, S., et al.: Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture 11(5), 408 (2021) 12. Calow, P.P.: Handbook of Environmental Risk Assessment and Management. Wiley, Hoboken (2009) 13. Jouzi, S.A., Kabzadeh, S., Irankhahi, M.: Safety, Health & Environmental risk assessment and management of Ahwaz Pipe Manufacturing Company via “William Fine” method (2010) 14. Jafari, N.R., et al.: Risk assessment of Ilam gas refinery based on William Fine method in 2012. Sid. (2014) 15. Navaie, A.Z., Omidvari, M.: Safety risk assessment in motor vehicle industries using William Fine and ANP-DEMATEL. Sid. (2017) 16. Soury Laky, M., et al.: Safety and Occupational health Risk assessment in a metal industry using Job Safety Analysis and William Fine methods. Rahavard Salamat J. 2(2), 18–31 (2016) 17. Elliott, J.: An Introduction to Sustainable Development. Routledge, Milton Park (2012) 18. Elkhateeb, A.M., Fikry, M.A., Mansour, A.D.: Dynamic building and its impact on sustainable development. Alexandria Eng. J. 57(4), 4145–4155 (2018) 19. Bahadure, S., Kotharkar, R.: Framework for measuring sustainability of neighbourhoods in Nagpur, India. Build. Environ. 127, 86–97 (2018) 20. Börjesson, M., Rubensson, I.: Satisfaction with crowding and other attributes in public transport. Transp. Policy 79, 213–222 (2019) 21. Senbel, M., et al.: Local responses to regional mandates: assessing municipal greenhouse gas emissions reduction targets in British Columbia. Sustain. Sci. Pract. Policy 9(1), 28–41 (2013) 22. Burgman, M.: Risks and Decisions for Conservation and Environmental Management. Cambridge University Press, Cambridge (2005) 23. Knox, P.L., McCarthy, L.: Urbanization: An Introduction to Urban Geography. Pearson Prentice Hall, Upper Saddle River (2005) 24. Shafique, M., et al.: An overview of carbon sequestration of green roofs in urban areas. Urban Forest. Urban Green. 47, 126515 (2020) 25. Ding, Z., et al.: Green building evaluation system implementation. Build. Environ. 133, 32–40 (2018) 26. Meiabadi, M.S., et al.: Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication. Polymers 13(19), 3219 (2021)

Application of Additive Technology to Create Universal Carriers of Cellular Structures E. V. Avdeeva, Ye. M. Dovydenko, K. V. Laznev, A. V. Petkevich, A. A. Rogachev, and V. E. Agabekov

Abstract In order to obtain biocompatible carriers with mechanical properties close to living tissues, 3D-printing was carried out on a modified 3D-printer created on the basis of the Wanhao Duplicator 4S (China) by installing a special extrusion head - a syringe extruder for the possibility of printing sodium alginate and chitosan hydrogels. The optimal compositions of hydrogels for 3D-printing using “supporting” hydrogels based on agar or gelatin, which allow forming print objects, have been determined. Stable structures were obtained in the form of a Menger cube with a satisfactory shape and size. Optimal speeds of movement of the extruder-syringe for 3D-printing with sodium alginate hydrogel into agar “supporting” gel – 9–11 mm/s, for sodium alginate into gelatinous “supporting” gel – 2 mm/s, for chitosan into “supporting” gel with the addition of (NH4 )2 HPO4 – 6–8 mm/s. Keywords 3D-printing · Hydrogel · Chitosan · Sodium alginate

1 Introduction Additive technologies, including 3D-printing, are an effective way to create highly structured, interconnected and porous structures compared to conventional casting, electrospinning, etc. [1]. 3D printing is actively used to create highly porous hydrogel scaffolds for cell cultures, as biomimetic microchips for studying diseases, creating artificial heterogeneous tissues in regenerative medicine, and reconstructing models and organs with high geometric accuracy [2]. 3D-hydrogels are also used to create conductive composites for flexible bioelectronics [3]. According to medical images, tomography of organs, blood vessels and other elements of the body, using the method of 3D-printing, structures from hydrogels, including inhabited cell cultures, can be reconstructed. E. V. Avdeeva (B) · Ye. M. Dovydenko · K. V. Laznev · A. V. Petkevich · A. A. Rogachev · V. E. Agabekov State Scientific Institution, Institute of Chemistry of New Materials of the NAS of Belarus, Fr. Skoriny 36, 220141 Minsk, Republic of Belarus © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_3

29

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E. V. Avdeeva et al.

Living tissues usually have high crash resistance to withstand significant internal and external mechanical stress, compression and tension [4]. The high tissue strength is forcing researchers to create hydrogels capable of achieving strength similar to withstand physiological mechanical stress. Despite recent success in the development of strong hydrogels [5], some of them contain toxic chemicals and/or could be aggressive that limit the ability to encapsulate cells. In addition, it is desirable to manufacture embedded hydrogels with a macroporous architecture that promote the formation of complex tissue. An example of such a macroporous hydrogel is a polyvinyl alcohol-based hydrogel used for microsurgical reconstruction of the spinal cord as a growth guide for nerve fibers [6]. Similar compositions are biocompatible materials of sodium alginate, as well as its composition with polyethylene glycol (PEG) [7] and chitosan [8]. When crosslinked, such hydrogels show high crack resistance and allow cell encapsulation. The technology of layer-by-layer construction of an object using 3D-printing inevitably imposes restrictions on the size of elements and their shape. When selecting the parameters for 3D-printing, an important problem remains to obtain a product that would replicate dimensions of a computer model. The accuracy of reproduction of model is greatly influenced by the principle of operation of the extrusion heads of a 3D-printer [9]. The work is devoted to obtaining biocompatible carriers with mechanical properties similar to living tissues by extrusion 3D-printing. Printing of models was carried out with biocompatible hydrogels of sodium alginate and chitosan in order to determine the optimal concentrations of components and process parameters.

2 Materials and Methods For printing, an upgraded 3D-printer was used, created on the basis of the Wanhao Duplicator 4S (PRC) by installing a special extrusion head - a syringe extruder. Applied syringe extruder is designed with adaptation to work on a printer without changing the method of feeding the material according to [10]. The hydrogel in the process of 3D printing was in a syringe, the piston of which was pushed by a hairpin. The rotation of the hairpin was carried out by means of a gear transmission of the stepper motor wheel and the main wheel of the syringe extruder. The original models were built in the form of a Menger sponge (a fractal cube with an edge length of 1.6 cm and hole sizes of 0.1 × 0.1; 0.3 × 0.3 and 0.5 × 0.5 mm2 ) using the program KOMPAS-3D. Solutions of sodium alginate in distilled water and solutions of chitosan in acetic acid were used as the extruded material. The printing process from sodium alginate or chitosan was carried out in a volume of “supporting” gels from agar or gelatin with the addition of CaCl2 for sodium alginate and (NH4 )2 HPO4 for chitosan (see Fig. 1). The possibility of models printing with a small hole size (up to 1 mm2 ) was tested using the Menger sponge (see Fig. 2), a fractal cube with a self-similarity property and having a zero volume, but an infinite face area. The Menger sponge is topologically

Application of Additive Technology …

31

sodium alginate in a syringe extruder

To extract the object from the "supporng" gel, apply a change in pH, t 0

"supporng" gel (agar + CaCl2 or gelan + CaCl2 ) alginate (so dendric colloids Ca2+ ) Monolayer prinng

3D object aer removing the "supporng" gel

Sequenal layer-by-layer prinng, with "supporng" of layers

Fig. 1 The process of obtaining objects from a hydrogel by 3D-printing

Fig. 2 Computer simulation of three-dimensional porous samples in the form of a Menger sponge

characterized as a one-dimensional locally connected metrizable compact set that has no locally dividing points and nonempty open subsets embeddable into the plane. Printing parameters were set using the KISSlicer program: layer height 0.2 mm, filling density 100%, printing speed 2–11 mm/s, nozzle diameter 0.2 mm. The conditions for printing three-dimensional objects depended on the composition of the components, so for samples based on sodium alginate in a “supporting” gel of agar or gelatin, the table temperature was 25 °C, the ambient temperature was 25 °C, the temperature of the gels for extrusion and the “supporting” layer was also 25 °C. For three-dimensional printing of samples based on thermo-gelling chitosan in an agar “supporting” gel, the table temperature was 52 °C, the ambient temperature was 25 °C, the temperature of the extrusion gels was 28 °C, and the temperature of the “supporting” gels was 40 °C. 3D-printing was performed for the following compositions: sodium alginate (1–2 wt% in an aqueous solution), agar (0.3–0.4 wt% in 0.15 wt% in a CaCl2 solution),

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gelatin (1, 0–2.0 wt% in CaCl2 solution), CaCl2 (0.15 wt%); chitosan (1.5–3 wt.% in 0.5% acetic acid solution) in a “supporting” gel (NH4 )2 HPO4 (20 g/l). Resulting compositions had a Bingham rheology, thanks to which the extruded hydrogel was securely fixed in the “supporting” volume of the hydrogel.

3 Results and Discussions Print quality is determined by the stability and fidelity of the printed model. The accuracy of the model and the form of the structure of the print object is greatly influenced by the speed of extrusion of the material and the movement of the extrudersyringe. Using the KISSlicer software for the modified 3D-printer, various speeds of the extruder-syringe were adjusted: when printing the outer perimeter of the model, when printing its inner filling and feeding material. The determination of the operating speeds of the 3D-printer was carried out by applying a monolayer of the model with a hydrogel in the volume of a “supporting” gel (see Fig. 3a). The speed of material feeding during the formation of the inner filling of the model, as well as when printing the outer perimeter, affects the reliability of fixing the material inside the volume of the “supporting” suspension. Crosslinking of the extrusive sodium alginate hydrogel during 3D-printing occurred due to ionotropic gelation with calcium ions. Crosslinking of the “thermal-sensitive” extrusion solution of chitosan during 3Dprinting was associated with ionotropic gelation with phosphate ions. It should be noted that the crosslinking of chitosan depends on the concentration of ammonium hydrogen phosphate, the concentration of the chitosan solution, and the concentration of an aqueous solution of acetic acid. The addition of ammonium hydrogen phosphate rapidly increases the pH of the acidic chitosan solution to nearly neutral pH and allows the chitosan to remain in solution even at neutral pH (~pH 7.0–7.2). These almost neutral chitosan/(NH4 )2 HPO4 aqueous solutions well gel quickly when heated. With an increase in the degree of chitosan deacetylation, the temperature required for gelling decreases. The selection of a suitable hydrogel extrusion rate when printing the model was carried out by applying several layers of hydrogel at different speeds in the volume “supporting” hydrogel (see Fig. 3d). The best reproduction of the model (see Fig. 3b) was observed with the following print parameters (Table 1). The print parameters of the objects depended on the composition of the components and the “supporting” hydrogel. Thus, for sodium alginate in an agar “supporting” hydrogel, the printing speed was 9–11 mm/s. These printing modes allow obtaining objects with accurate reproduction and fixation inside the “supporting” hydrogel. For sodium alginate in a “supporting” gelatin hydrogel, the print speed was reduced to 2 mm/s. This is due to the elastic properties of gelatin. As the print speed is increased, grooves are formed that alter the outer edges of the object. For “thermal-sensitive” chitosan, these rates were selected taking into account the time of thermal crosslinking between layers.

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33

a

b Fig. 3 Computer modeling a and 3D printing b of three-dimensional porous samples using sodium alginate in a “supporting” agar gel Table 1 Optimal printing parameters for hydrogels Extrusive hydrogel composition

Maintenance gel composition

Print speed of internal filling of the model, mm/s

Print speed of the outer perimeter of the model, mm/s

Hydrogel extrusion rate, ml/s

Sodium alginate

agar + CaCl2

9–11

9–11

1,3

Sodium alginate

gelatin + CaCl2

2

2

1,3

Chitosan

agar + (NH4 )2 HPO4

6–8

6–8

1,3

34

E. V. Avdeeva et al.

4 Conclusion The possibility of using a water-based sodium alginate hydrogel and chitosan in an aqueous solution of acetic acid for 3D-printing is presented. Three-dimensional models in the form of Menger cubes were printed from the listed pairs of substances. Optimal speeds of movement of the extruder-syringe based on Wanhao Duplicator 4S have been determined for 3D-printing with sodium alginate hydrogel in agar “supporting” gel – 9–11 mm/s, for sodium alginate in gelatinous “supporting” gel – 2 mm/s, for chitosan in “supporting” gel with the addition of (NH4 )2 HPO4 – 6– 8 mm/s. The presented technological approaches can be used for 3D-printing of volumetric objects for biomedical applications.

References 1. Wang, J., Lu, T., Yang, M., Sun, D., Xia, Y., Wang, T.: Hydrogel 3D printing with the capacitor edge effect. Sci. Adv. 5, eaau8769 (2019) 2. Williams, A.H., Roh, S., Jacob, A.R., Stoyanov, S.D., Hsiao L., Velev, O.D.: Printable homocomposite hydrogels with synergistically reinforced molecular-colloidal networks. Nat. Commun. 14, 283 (2021) 3. Han, D., et al.: Soft robotic manipulation and locomotion with a 3d printed electroactive hydrogel. ACS Appl. Mater. Interfaces 10(21), 17512–17518 (2018) 4. Taylor, D., O’Mara, N., Ryan, E., Takaza, M., Simms, C.: The fracture toughness of soft tissues. J. Mech. Behav. Biomed. Mater. 6, 139–147 (2012) 5. Lin, S., Liu, Ji., Liu, X., Zhao, X.: Muscle-like fatigue-resistant hydrogels by mechanical training. Proc. Nat. Acad. Sci. 116(21), 10244–10249 (2019) 6. Dekopov, A.V., Tomskiy, A.A., Artyukhov, A.A., Mnikhovich, M.V., Paskhin, D.L., Salova, E.M.: Method for microscurgeric reconstruction of the spinal cord on an animal model by using polyvinyl alcohol biodegradated hydrogel. Application filed by Federal State Autonomous Institution “National Medical Research Center of Neurosurgery named after Academician N.N.Burdenko” of the Ministry of Health of the Russian Federation (FSAU “NMIC of Neurosurgery named after Academician N.N.Burdenko” Ministry of Health of Russia Date of publication: 08.02.2018 Bull. № 4. Priority to RU2016150195A, Application granted 2018-02-08 7. Hong, S., et al.: 3D printing of highly stretchable and tough hydrogels into complex, cellularized structures. Adv. Mater. 27(27), 4035–4040 (2015) 8. Foley, P.L., et al.: A chitosan thermogel for delivery of ropivacaine in regional musculoskeletal anesthesia. J. Biomater. 34, 2539–2546 (2013) 9. Dovydenko, Y.M., Agabekov, V.Y., Chizhik, S.A.: Optimization of 3D printing parameters with sodium alginate hydrogel. Proc. Nat. Acad. Sci. Belarus. Phys. Tech. Ser. 64(1), 7–13 (2019) 10. Hinton, T.J., et al.: Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels. Sci. Adv. 1(9), e1500758 (2015). https://doi. org/10.1126/sciadv.1500758

Comparison and Choice of the Greenhouse Gas Accounting Method for a Model Region in Germany Tatiana N. Fedosenko-Becker, Michael Becker, and Michael Berger

Abstract Following the sustainable development trends, Germany is steadily moving towards an energy transformation. This complex process requires serious planning and an interdisciplinary approach. Within the framework of the German project “WESTKUESTE100” the Roadmaps to complete CO2-neutrality in a model region will be offered. The first step to creating realistic scenarios is to analyze existing accounting methods of greenhouse gas emissions and to adapt the choosen methods for the specific purposes. This work describes an approach to the selection and adaptation of the CO2-equivalent emission accounting method for a model region in order to create scenarios for the energy transformation. Keywords Sustainable development · Accounting methods · Greenhouse gas emissions

1 Introduction The problem of climate change is getting more serious from year to year and there is still no clear defined realistic approach. One of the most iconic visualisations of the climate change in the world is the Mauna Loa continuous record of carbon dioxide concentration [1] (see Fig. 1). The complexity of the problem is justified by its multidimensionality, therefore, it is only possible to slow down the climate change by interdisciplinary scientific approaches: technical, social and political. The European Union (EU) is going to take on a pioneering role with regard to the existential global challenges and to become the first CO2-neutral continent by 2050. The “European Green Deal” – a new growth strategy, presented by the EU Commission in December T. N. Fedosenko-Becker (B) · M. Berger Institute for the Transformation of the Energy System (ITE), West Coast University of Applied Sciences, Markt 18, 25746 Heide, Germany e-mail: [email protected] M. Becker Eco. Income Engineering GmbH, Billwerder Neuer Deich 12, 20539 Hamburg, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_4

35

36

T. N. Fedosenko-Becker et al.

Fig. 1 Atmospheric CO2 at Mauna Loa Observatory from 1959 to 2020 [1]. The data shows an increase of CO2 concentration in the atmosphere of almost 31%

2019, should help the EU to achieve a modern, resource-efficient and competitive economy [2]. Germany has recognized that its long-term competitiveness and future viability can only be secured through economic activity geared towards innovation, sustainability, environmental protection and social needs, therefore it is essential to transform the existing energy systems fundamentally.

2 Methodology The German federal government with its energy and climate concepts has given a significant impetus in the direction of energy transformation and, among other things, has set the central goals of reducing greenhouse gas emissions by at least 80% by 2050 compared to 1990 and providing 60% of gross final energy consumption from the renewable energies by 2050 [3] and [4]. According to the presentation of the problem in the description of the project “WESTKUESTE100”, these goals can only be achieved in case of a successful promotion of the energy system decarbonization across all sectors and in particular addressing the associated issues of a temporally, regionally and materially independent use of renewable energies. The new German climate protection targets open up a number of options of combining non-fossil and low-fossil technologies and process management. A big step to the sustainability in heating, industrial processes, as well as construction and transport sectors can be made by modelling a regional economy on an industrial scale [5]. Energy efficiency potentials and their implementation play a major role here. For this, among many other things, several realistic scenarios, which consider how a fluctuating electricity supply can be used economically for CO2-reduced production, should be developed. The development of roadmaps up to complete CO2-neutrality in a model region is one of the tasks of the German project “WESTKUESTE100”. The development of realistic scenarios requires the methodological basics of accounting for greenhouse gas emissions. On this stage various international and

Comparison and Choice of the Greenhouse Gas Accounting Method ...

37

national accounting tools and regulations with different scopes should be examined and compared. [6–9]. In this work only the accounting methods suitable for the chosen model region (Schleswig–Holstein in Germany) were examined in detail. The main focus is on the following methods: BISKO (accounting system for municipalities) which is published by the German”Institut fuer Energie- und Umweltforschung” and the GPC (Greenhouse Gas Protocol). In contrast to the Intergovernmental Panel on Climate Change or IPCC [10] which uses territorially based carbon accounting method for its guidelines, BISKO and the GPC use a modification of this accounting method.

3 Results A modification, which must always be considered comparing different balances, lies in the approach of the underlying accounting principle. On the one hand, the approach of the territorial balance is used, which balances all the greenhouse gas emissions that arise within a territory. Both the BISKO and the GPC standards are defined as a final energy-based territorial carbon accounting [11, 12], so the overall consumption of final energy is considered. Afterwards the carbon emissions are calculated trough the specific emission factors. This method can lead to differences of up to 20% compared to the national greenhouse gas inventories accounted by the IPCC standard [11]. However, for the grid distributed energy types, like electricity through the electrical grid, or heat through district heating pipes, the method is adjusted. Especially in these cases BISKO as well as GPC switch to a consumption-based approach. An exception in this regard is the sector of transportation, especially in the categories on road and railroad transportation. BISKO uses a weighted approach, considering the utilization of the transportation network. Because of the consumption-based approach, there is a risk of neglecting or double-counting greenhouse gas emissions. If, for example, a territorial balance is carried out in the territory A using an end-of-line energy-based approach and in the territory B an approach based on a source balance, an incorrect accounting of the emission values is inevitable. This problem can be illustrated with the following simple thought experiment. A factory produces a product in territory A, which is then delivered to territory B and used there. In territory A, the greenhouse gas emissions that arise during the manufacturing of the product are accounted for using the polluter balance. As a result, greenhouse gas emissions are not allocated to territory A, as the product is used in territory B. However, if a territorial balance is carried out in territory B, the greenhouse gas emissions are not balanced in territory B either, since those did not arise within territory B, but within territory A. As a result, greenhouse gas emissions may be shown neither in the balance sheet for territory A, nor in the balance sheet for territory B. Therefore these emissions are not taken into account, what may be quite comfortable to both territories. In the opposite case, the greenhouse gas emissions would be balanced twice.

38 Table 1 Consideration of different sectors from BISKO and GPC

T. N. Fedosenko-Becker et al. Sector

BISKO

GPC

Stationary energy

X

X

Transportation

X

X

Agriculture

X

Industrial processes

X

Waste

X

3.1 Sectors For a detailed presentation and the identification of key areas, a subdivision into different sectors is made in both accounting methods. Both BISKO and GPC cover stationary emissions from energy consumption as well as emissions from transport. In the sector of stationary non-energy based emissions, BISKO just covers the emissions from processing oil, gas and coal, while the GPC also considers emissions from other industrial processes. Another major difference is the consideration of agriculture emissions as well as the emissions from the waste sector, which BISKO does not take into account. Table 1 shows which sectors are suggested to be covered by BISKO and GPC. It is obvious that the GPC proposes a much wider approach in regard of sectors compared to BISKO. BISKO mainly focus on the stationary energy sector and transportation, while GPC also takes additional sectors into account.

3.2 Quality of the Data In order to ensure the comparison of two different greenhouse gas balances, it is necessary to determine the data quality. Data quality helps to estimate a greenhouse gas balance for its reliability as a basis for the development of scenarios for the reduction of greenhouse gas emissions. For both BISKO and the GPC, it is recommended to define the data quality. However, while the GPC does not provide any further information on determining the data quality, BISKO provides an explicit method. The BISKO method is based on 4 factors, which assign a value between 0 and 1 to each data source. The allocation of the respective factor is based on the regional reference of the data source. Table 2 shows the values and the data classes of the BISKO method. Based on Table 2 Definition of the factors for the data quality based on the BISKO method

Data class

Factor

Regional primary data

1.0

Extrapolation of regional primary data

0.75

Regional parameters and statistics

0.25

Nationwide parameters and statistics

0.0

Comparison and Choice of the Greenhouse Gas Accounting Method ...

39

these factors, the final data quality can be calculated by G=

j 

E i Fi

(1)

i=1

where G is the value of the data quality, E is the share in the greenhouse gas balance, F is the factor determined and i is the number of the sector.

4 Conclusion This investigation shows that the accounting methods follow different approaches when considering individual sectors, as well as when applying the ‘polluter pays’ principle or territorial principle within the individual sectors. A uniform and thus comparable accounting methodology is of course indispensable for the definition of applicable scenarios. A problem here is the intention to achieve the most complete and precise coverage of the respective sectors for the development of meaningful scenarios on the one hand, as well as the limited availability of the data sources and the required data quality for this purpose on the other hand. A review of the available data shows that the availability is not guaranteed, especially for the sectors of industry-related emissions, as well as agriculture and waste. For other sectors such as final energy-based stationary emissions, data are generally available, but the quality of the data is limited. This can impair the informative value of the accounting and thus the meaningfulness of the scenarios and even the goals designed on the basis of this accounting. For this reason, a uniform method for determining the data quality of the accounting has to be established.

References 1. Dlugokencky, Tans, P. (eds.): NOAA/GML. https://gml.noaa.gov/ccgg/trends/ 2. Ministerium fu¨r Energiewende, Landwirtschaft, Umwelt, Natur und Digitalisierung. Bericht der Landesregierung: Energiewende und Klimaschutz in Schleswig-Holstein - Ziele, Maßnahmen und Monitoring 2020. SchleswigHolsteinischer Landtag, 19. Wahlperiode, Drucksache 19/2291, 06/2020, pp. 9–1 (2020)0. (in German). 3. Bundesministerium fu¨r Wirtschaft und Energie. Energiekonzept fu¨r eine umweltschonende, zuverla¨ssige und bezahlbare Energieversorgung (2010). https://www.bmwi.de/Redaktion/DE/ Downloads/E/energiekonzept-2010.html, (in German) 4. Bundesministerium fu¨r Umwelt, Naturschutz nukleare Sicherheit. Der Klimaschutzplan 2050 – Die deutshe Klimaschutzlangfriststrategie. https://www.bmu.de/themen/klima-energie/ klimaschutz/nationale-klimapolitik/klimaschutzplan-2050/#c8418, (in German) 5. Bioenergy International. Westku¨ste 100 renewable hydrogen project secures EUR 30 million in funding. https://bioenergyinternational.com/heat-power/westkuste-100-renewable-hydrogenproject-secures-eur-30-million-in-funding

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6. Lombardi, M., Laiola, E., Tricase, C., Rana, R.: Assessing the urban carbon footprint: an overview. Environ. Impact Assess. Rev. 66, 43–52 (2017). https://doi.org/10.1016/j.eiar.2017. 06.005 7. ICLEIHEAT—Harmonized Emissions Analysis Tool. https://ledsgp.org/wp-content/uploads/ 2015/09/HEATplusbrochure.pdf 8. ICLEI Clean Air Climate Protection Software. http://www.4cleanair.org/Oldmembers/mem bers/committee/ozone/webcastSIslides.pdf 9. Hermannsson, K., McIntyre, S.G.: Local consumption and territorial based accounting for CO2 emissions. Ecol. Econ. 104, 1–11 (2014). https://doi.org/10.1016/j.ecolecon.2014.04.020 10. IPCC Guidelines for National Greenhouse Gas Inventories Volume 1 General Guidance and Reporting. IPCC, Geneva, Switzerland (2006). https://www.ipcc-nggip.iges.or.jp/public/200 6gl/index.html 11. Institut fu¨r Energie und Umweltforschung Heidelberg. BISKO Bilanzierungssytematik Kommunal: Empfehlung zur Methodik der Kommunalen Treibhausgasbilanzierung fu¨r den Energie- und Verkehrssektor in Deutschland, November 2019. https://www.ifeu.de/fileadmin/ uploads/BISKOMethodenpapierkurzifeuNov19.pdf 12. World Resources Institute. Grenhouse Gas Protocol: Global Protocol for Comunity-Scale Greenhouse Gas EmissionInventories (2014). https://ghgprotocol.org/sites/default/files/standa rds/GHGPGPC0.pdf

Evaluation of Heavy Metal Contamination in Mytilus sp. Shells Iuliana Motrescu , Mihai Alexandru Ciolan , Anca Elena Calistru , Gerard Jitareanu , and Liviu Dan Miron

Abstract Shellfish consumption and cultivation has been increasing, partly own to their health benefits: they are rich in lean proteins, healthy fats, minerals and vitamins. On the other hand, some shellfish such as bivalve shells are filter feeders, accumulating heavy metals from the environment, both in the consumed part and the shell in different proportions. Because the commonly analysis methods involve complicated procedures, we focused on the possibility of evaluating the heavy metal content in the shells using physical techniques such as environmental scanning electron microscopy (ESEM) coupled with energy dispersive X-ray spectroscopy (EDAX). Based on these methods we mapped the structure and compositions of the shells and discussed the impact of the environment both on the biomineralization process and the consumers health, the latter done by estimating the concentrations of heavy metals in the consumed parts. Keywords Heavy metal contamination · Mytilus · Biosafety

I. Motrescu (B) Sciences Department, Iasi University of Life Sciences, Iasi, Romania e-mail: [email protected] I. Motrescu · A. E. Calistru · G. Jitareanu Research Institute for Agriculture and Environment, Iasi University of Life Sciences, Iasi, Romania M. A. Ciolan Research Center on Advanced Materials and Technologies, Science Department, Institute of Interdisciplinary Research, Alexandru Ioan Cuza University of Iasi, 11 Carol I bld, Iasi, Romania A. E. Calistru · G. Jitareanu Pedotechnics Department, Iasi University of Life Sciences, Iasi, Romania L. D. Miron Clinics Department, Iasi University of Life Sciences, 3 Mihail Sadoveanu Alley, 700490 Iasi, Romania © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_5

41

42

I. Motrescu et al.

1 Introduction According to the Food and Agriculture Organization of the United Nation reports, the consumption of shellfish has been increasing in the past decades own not only to the health benefits of seafood, but also to the technical developments and rising incomes [1]. In the past decade the ratio between captured fisheries and aquaculture reversed, the latter having a steep increasing trend. The shellfish proteins are essential nutritional components of the diet in many countries, while the healthy fats, minerals, vitamins, and other nutrients contained in seafood seem to promote the health of important organs in the human body and boost the immune system [2–6]. On the other hand, toxic concentrations of chemical contaminants can be present in some of the shellfish, as a result of bioaccumulation from the water during their life span, bivalves being filter feeders. The contamination of marine waters usually occurs from anthropogenic sources - industrialization, urbanization, and agricultural activities, its spreading being strongly influenced by different transport and biogeochemical processes [7–11]. Heavy metals are some of these contaminants. Elements such as copper, iron, zinc, selenium are essential macro-nutrients for the life of these organisms, having biochemical function; however due to pollution, they can be found in the marine environments in high concentrations along with some other nonessential and toxic elements such as cadmium, chromium, lead, arsenic, mercury [7–13]. The bioaccumulation process of these trace metallic elements mirrors the conditions of a specific marine environment and the degree of the heavy metal pollution, as well as the presence of these elements in the edible part [9–15]. For the latter, researchers found correspondences between the concentrations inside the shell where the bioaccumulation takes place, the soft tissue, and the marine environment where the samples developed [16–22]. In our previous work we have been studying the biomineralization and bioaccumulation processes in Mytilus sp. shells, such as to use these bivalve mollusks as environmental monitors [23]. Based on the about mentioned premises in this work we focus on determining the distribution of some heavy metal elements inside the shells using environmental scanning electron microscopy (ESEM) coupled with energy dispersive X-ray spectroscopy (EDAX). The data was compared with the values determined using High Resolution X-Ray Fluorescence Spectroscopy (XRF). Simple detection of heavy metal concentrations in the shells could be a rapid indication of heavy metal presence in dangerous concentrations inside the consumed part and could aid to rising alerts for consumers.

2 Materials and Methods For the experiments presented here, Mytilus galloprovincialis (Mediterranean mussel or blue mussel) samples originating from three aquaculture locations in Europe (Fig. 1) were commercially procured. The shells were cleaned with pure water,

Evaluation of Heavy Metal Contamination in Mytilus sp. Shells

43

Fig. 1 Provenience locations for the Mytilus galloprovincialis culture samples

then ethanol. For each ESEM analysis three samples were randomly used. EDAX mapping of the elemental distribution was performed for several areas on the outer side of the shell (periostacum), inner side (nacre), and in between on fractured shells. ESEM imaging was performed with FEI-Quanta 450, Thermo Fisher Sci. coupled with the EDAX detector. The ESEM system allows the analysis of the shells as they are, with no necessity of prior preparation such as coating with conductive materials. The samples were fixed inside the ESEM chamber on aluminum stubs using carbon tape. For the analysis 5 × 5 mm parts of the shells are fractured, so they have the same exposure towards the detector, the analyzed area being closed to flat. The stage is kept at 10 mm for best imaging and elemental analysis and so that small surface defects should not affect the results. Imaging and elemental mapping was performed under high vacuum conditions, at a working pressure of about 4*10–4 Pa, and with accelerated electron beam energy of 15 keV. For the EDAX analysis, before measurement the calibration is checked with a standard AlCu sample. DPP-II Shell software collects a spectrum, the size of AlKα and CuKα peaks is checked, the corresponding energy for each, the number of counts and amplification time are set. Then the software makes adjustments for different amplification times for fine calibration using an automatic procedure. The noise level is also adjusted after the previously mentioned procedure. The TEAM version V4.1 (from EDAX Inc.) is used to operate the EDAX and extract the elemental information. In the map analysis mode, an image of the sample is first obtained, then the area of the interest is selected. The spot size is increased, usually up to 6, to have enough counts for the spectrum. The software measures a spectrum for the respective area. The operator selects the elements to be mapped on the selected area of the sample. For our data, an average number of 50 k frames was used with a dwell of 200 μs. Before the reconstruction, we first process the spectrum by manually adjusting the background so that the selected elements best fit the spectrum; we only fitted elements for which we observed clear peaks. We also kept (for better comparison) all detected elements the same for all the samples – for small or non-existing peaks in a spectrum, the software automatically gives a quantification value close to zero. The quantification of the elements is automatically performed. The EDAX maps can be reconstructed

44

I. Motrescu et al.

by the same software (TEAMS), and exported as an analysis report along with the quantification results. The wavelength dispersive X-ray fluorescence system used is Tiger S8 from Bruker, Germany. For this analysis ten shells from each location were randomly chosen, then grinded using high energy ball mill PM100 from Retsch. The powder was mixed with wax and the mixture was built into pellets using a 20-t hydraulic laboratory pellet presser.

3 Results Mytilus galloprovincialis lives on the Atlantic coast of Europe and Mediterranean and Black seas. For the results presented in this work, the shells were in similar ontogenetic state; the size of the shells was as follows: 1–6.8 ± 0.5 (cm), 2–6.77 ± 0.45 (cm), and 3–6.82 ± 0.4 (cm), respectively. To study the elemental distribution in different parts of the shells, ESEM analysis coupled with EDAX was performed. First, an average spectrum was obtained to identify in each case the relative presence of different elements. Subsequently, different areas on the analyzed surfaces were mapped to image the distribution of specific elements. Figure 2 shows an elemental mapping for a surface on the outer part of a shell from sampling location 1 with separate distribution of some elements (C, O, N, Ca, Pb). The exposed outer layers show the calcium carbonate and organic matrix, nitrogen being

Fig. 2 EDAX elemental mapping for an area on the periostacum of a sample collected from location 1

Evaluation of Heavy Metal Contamination in Mytilus sp. Shells

45

Fig. 3 EDAX elemental mapping for an area on the a nacre and b fractured side of a sample collected from location 1

an indicator of the latter. Lead appears uniformly distributed without any preference for the organic part or carbonate crystalline structure. A similar behavior was also found for other trace metals (e.g. Fe, Cd, Cu, Zn) (data not shown here). In the inner part of the shells, we can clearly see the aragonite tablets with mineral bridges in between (Fig. 3(a)) organized in a prismatic structure (Fig. 3(b)). There is less organic matter in the exposed nacre structure compared to the inner prismatic layers (Fig. 3) and periostacum (Fig. 2). Lead elemental concentration was insignificant, seemingly in this case being fixed more towards the exterior of the shells. An opposite trend was found for Al which seems to be found in much smaller quantities towards the outer part than the nacre. Figure 4 shows the elemental distribution for the sample 2 on the periostacum (Fig. 4(a)), and nacre (Fig. 4(b)), respectively. There is a similar distribution of the organic and calcium carbonate components with higher organic concentration on the outside and decreasing towards the inner part. The lead concentration is higher than for the samples from location 1 but has a similar distribution. Significant concentrations of other elements are found as well. The statistic of the components for all the analyzed samples is shown in Table 1. The distribution inside the shell looks not homogenous for all studied elements, and shows that some are found in higher concentration on the periostacum, the part most exposed to the environment, than on the inside in the nacre. Also, different environmental conditions determine the differences found between the three analyzed samples. WDXRF high resolution data is presented in Table 2. Because the values are obtained for a batch of shells from the same sampling location mixed together, they

46

I. Motrescu et al.

Fig. 4 EDAX elemental mapping for an area on the a periostacum and b nacre of a sample collected from location 2 Table 1 Elemental composition of samples estimated from the EDAX measurements (values denoted with “d” – detected were very small, under 0.1%, while “n.d.” – means a not detected element in the analysis) Element

Elemental weight (%) Sample 1

Sample 2

Sample 3

Out

In

Out

In

Out

In

C

54.4

28.4

57.4

26.3

48.7

29.1

O

18.3

42.2

17.3

45.6

19.6

52.8

N

24.2

3.6

22.5

14.2

24.2

9.6

Ca

0.5

21.8

1.5

11.5

10.2

6.1

F

1

0.4

0.8

0.9

0.6

1

Na

0.5

0.5

d

0.6

n.d

n.d

Mg

d

0.8

d

d

n.d

0.2

Al

1

0.7

d

d

0.7

0.3

As

d

d

d

d

d

d

P

d

0.3

d

d

n.d

n.d

S

0.2

0.2

d

d

0.2

n.d

Pb

d

d

n.d

d

0.2

d

U

n.d

d

d

d

n.d

n.d

Ba

d

d

d

d

0.1

0.2

Fe

d

0.1

d

d

d

0.3

Ni

d

0.3

d

d

n.d

0.2

Co

n.d

n.d

0.3

0.1

d

d

Cu

d

d

d

d

d

d

Zn

d

d

d

d

d

d

Evaluation of Heavy Metal Contamination in Mytilus sp. Shells

47

Table 2 Elemental composition of the samples from WDXRF measurements Sample no.

Elemental weight (μg/g) Ca

O

Fe

Al

1

509,800

205,000

2700

15,600

2

431,500

174,000

3000

16,000

3

502,400

202,000

2900

15,600

3

Co

Ni

Cu

Zn

As

Sr

Ba

Pb

U

0

4

6

8

3

872

2

13

12

27

4

9

9

5

1059

4

14

14

6

10

8

2

1149

22

4

15

give a general idea about the trace elements fixed in the shells and, thus, of the marine environment from which the samples come from.

4 Discussions The ESEM micro-imaging of the samples and EDAX do not need any complicated prior processing of the samples (e.g. covering with conductive layer) and there is no need for special conservation methods as in the case of the soft tissue of the mussels. The samples can be kept for long time and without the need of a refrigerator. The distribution of the elements inside the shell is far from uniform. On the outer side of the sells the organic components are predominant. That is why on the outside there is not so much Ca found. We can see the differences by checking the percentages for the inside and fractured areas. Form the EDAX analysis, the elemental mapping indicates that the trace elements are distributed relatively uniform on an analyzed surface, only variating with depth from one side of the shell to the other. No preference towards organic components or calcium carbonate crystals was detected. In the same time some elements are found with predilection on one side of the shell: Al, Fe, Ni in the nacre, Co, Pb in the periostacum. Differences can be also spotted in the appearance of the nacre with aragonitic tablets stacked in layers (Fig. 3) and aragonitic prisms (Fig. 4(b)). The crystal growth seems to be mostly influenced by environmental conditions such as water temperature, salinity, contaminants, and, to a smaller extent, by biological factors that can also limit the amount of element biomineralization [25, 26]. Comparing the values found for the analyzed surfaces (Table 1) and general averages (Table 2), we found good correlation of the results, despite the reduced resolution relative determinations of the EDAX which proves that the method can be used for the fast detection of trace element presence in the shells. Moreover, using several shells for preparing the pellets for EDAX analysis, we can get a general view regarding the batch of shells with very good resolution of the elemental composition and in relatively low time, without complicated chemical procedures like in the case of other technologies that have sophisticated protocols, use toxic chemicals, thus being time consuming, less sensitive, and less operator and environmentally friendly.

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When comparing the general pattern for trace metals accumulation with the one reported in the literature Zn > Cu > Ni > Pb which is assumed to be determined by some accumulation limitation mechanisms [27], we find good correlation for samples 1 and 2 ith Zn > Cu > Ni, and higher lead content, and higher Cu content for sample 3. Relative to the existing literature, the values (Table 2) are in the ranges reported for Mytilus alloprovincialis in the analyzed locations, except for lead [28]. It looks like the areas 1 and 2 have higher Pb concentrations indicating a higher pollution of the respective areas with lead. Studying the heavy metal concentrations in the sediments, shell, and soft tissue, correlations have been found for different elements [24–32]. The ratio between the concentrations of metals in the shell and soft tissue is about 1.2 for Cu, 2.1 for Pb, 0.6 for Fe, and 0.4 for Zn [29]. Moreover, lead is preferentially incorporated into aragonite than calcite, meaning in the nacre [30]. Therefore, in our case the expected values in the soft tissue might have been above the limit regulated by EU directive 2001/22/EC at least for the samples 1 and 2. Considering the bioaccumulation factors which is the ratio between the metal in the shell relative to the metal in the sediments, these results can also give indication about the bioavailability of these elements in the sediments corresponding to the three locations. Fe concentrations seem to be more than ten times higher in the sediment than in the shells, Ni 5 times while Zn is lower in the sediments by the same factor [31]. Thus, analyzing the elemental components in the shells we can draw conclusions both related to values in the consumed part, and the corresponding values in the sediments for environmental monitoring.

5 Conclusion The structure, composition and element distribution in Mytilus galloprovincialis shells originating from three locations was analyzed using environmental scanning electron microscopy coupled with energy dispersive X-ray spectroscopy without any sophisticated preparation of the samples. The results show that trace elements are not uniformly distributed in the shells, and there was no clear association between a trace metal and organic/inorganic crystalline structure. The high-resolution X-ray fluorescence spectroscopy measurements confirmed the EDAX results. Regarding the environmental quality of the areas where the samples originated, samples 1 and 2 shown higher lead accumulations indicating a higher lead bioavailability, while samples 2 and 3 shown higher Cu concentrations. The contamination also mirrors the heavy metals in the consumable part, so based on simple analyses samples could be imaged and selected for further investigations that would establish their consumption safety. Funding This research was partially supported by the project IUCN 04–4-1142–2021/2025 – 46.

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References 1. http://www.fao.org/ 2. Souci, S.W., Fachman, W., Krant, H.: Food Composition and Nutritional Tables, 7th edn., p. 1300. Medpharm Scientific, Stuttgart (2000) 3. Venugopal, V., Gopakumar, K.: Shellfish: nutritive value, health benefits, and consumer safety. Compr. Rev. Food Sci. Food Saf. 16, 1219–1242 (2017) 4. Harnedy, P., FitzGerald, R.: Bioactive peptides from marine processing waste and shellfish: a review. J. Funct. Foods 4, 6–24 (2012) 5. Khan, B.M., Liu, Y.: Marine mollusks: food with benefits. Compr. Rev. Food. Sci. Food Saf. 18, 548–564 (2019) 6. Cheung, R.C.F., Ng, T.B., Wong, J.H.: Marine peptides: bioactivities and applications. Mar. Drugs 13, 4006–4043 (2015) 7. Millward, G., Turner, A., He, X.: Metal pollution. In: Cochran, J.K., Bokuniewicz, H.J., Yager, P.L. (eds.) Encyclopedia of Ocean Sciences, 3rd edn, vol. 6, pp. 342–349. Academic Press, Elsevier (2019) 8. Bashir, I., Lone, F.A., Bhat, R.A., Mir, S.A., Dar, Z.A., Dar, S.A.: Concerns and threats of contamination on aquatic ecosystems. In: Hakeem, K., Bhat, R., Qadri, H. (eds.) Bioremediation and Biotechnology, pp. 1–26. Springer, Cham (2020) 9. Pandiyan, J., Mahboob, S., Govindarajan, M., Al-Ghanim, K.A., Ahmed, Z., Al-Mulhm, N., Jagadheesan, R., Krishnappa, K.: An assessment of level of heavy metals pollution in water, sediment and aquatic organisms: a perspective of tackling environmental threats for food security. Saudi J. Biol. Sci. 28(2), 1218–1225 (2021) 10. Abdallah, M.A.M.: Bioaccumulation of heavy metals in mollusca species and assessment of potential risks to human health. Bull. Environ. Contam. Toxicol. 90, 552–557 (2013) 11. Chandurvelan, R., Marsden, I., Glover, C.N., Gaw, S.: Assessment of a mussel as a metal bioindicator of costal contamination: relationships between metal bioaccumulation and multiple biomarker responses. Sci. Total Environ. 511, 663–675 (2015) 12. Stankovic, S., Jovic, M., Stankovic, A.R., Katsikas, L.: Heavy metals in seafood mussels. risks for human health. In: Lichtfouse, E., Schwarzbauer, J., Robert, D. (eds.) Environmental Chemistry for a Sustainable World: Volume 1: Nanotechnology and Health Risk, pp. 311–373. Springer, Dordrecht (2012) 13. Tanaskovski, B., Jovic, M., Mandic, M., Pezo, L., Degetto, S., Stankovic, S.: Elemental analysis of mussels and possible health risks arising from their consumption as food: the case of Boka Kotorska Bay, Adriatic sea. Ecotoxicol. Environ. Saf. 130, 65–73 (2016) 14. Milenkovic, B., Stajic, J.M., Stojic, N., Pucarevic, M., Strbac, S.: Evaluation of heavy metals and radionuclides in fish and seafood products. Chemosphere 229, 324–331 (2019) 15. Saher, N.U., Kanwal, N.: Assessment of some heavy metal accumulation and nutritional quality of shellfish with reference to human health and cancer risk assessment: a seafood safety approach. Environ. Sci. Pollut. Res. 26, 5189–5201 (2019) 16. Szefer, P., Frelek, K., Lee, C.B., Kim, B.S., Warzocha, J., Zdrojewska, I., Ciesielski, T.: Distribution and relationship of trace metals in soft tissue, byssus and shells of Mytilus edulis trossulus from the southern Baltic. Environ. Pollut. 120, 423–444 (2002) 17. Biligin, M., Uluturhan-Suzer, E.: Assessment of trace metal concentrations and human health risk in clam (Tapes decussatus) and mussel (Mytilus galloprovincialis) from the Homa Lagoon (Eastern Aegean Sea). Environ. Sci. Pollut. Res. 24, 4174–4184 (2017) 18. Jokssimovic, D., Tomic, I., Stankovic, A.R., Jovic, M., Stankovic, S.: Trace metal concentrations in Mediterranean blue mussel and surface sediments and evaluation of the mussels quality and possible risks of high human consumption. Food Chem. 127, 632–637 (2011) 19. Wang, W.X., Fisher, N.S., Luoma, S.N.: Kinetic determinations of trace element bioaccumulation in the mussel. Mytilus edulis 140, 91–113 (1996) 20. Shilla, D., Qadah, D., Kalibbala, M.: Distribution of heavy metals in dissolved, particulate and biota in Scheldt estuary, Belgium. Chem. Ecol. 24, 61–74 (2008)

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21. Irato, P., Santovito, G., Cassini, A., Piccinni, E., Albergoni, V.: Metal accumulation and binding protein induction in Mytilus galloprovincialis, Scapharca inaequivalvis, and tapes philippinarum from the Lagoon of Venice. Arch. Environ. Contam. Toxicol. 44, 0476–0484 (2003) 22. Catsiki, V.A., Florou, H.: Study of the behavior of the heavy metals Cu, Cr, Ni, Zn, Fe, Mn and 137 Cs in an estuarine ecosystem using Mytilus galloprovincialis as a bioindicator species: the case of Thermakos gulf, Greecer. J. Environ. Radioactivity 86, 31–44 (2006) 23. Motrescu, I., Calistru, A.E., Jitareanu, G., Miron, L.D.: Monitoring the environmental quality of marine waters through the analysis of biomineralization in bivalve shells. In: Varkonyi-Koczy, A. (ed.) Engineering for Sustainable Future, vol. 101, pp. 269–277. Springer, Cham (2020) 24. El-Sikaily, A., Khaled, A., Nemr, A.E.: Heavy metals monitoring using bivalves from mediterranean sea and red sea. Environ. Monit. Assess. 98, 41–58 (2004) 25. Checa, A.G.: Physical and biological determinants of the fabrication of molluscan shell microstructures. Front. Mar. Sci. 5, 353 (2018) 26. Nikolayev, D., Lychagina, T., Pakhnevich, A.: Experimental neutron pole figures of minerals composing the bivalve mollusc shells. SN Appl. Sci. 1, 344 (2019) 27. Chanduvelan, R., Marsden, I.D., Glover, C.N., Gaw, S.: Assessment of a mussel as a metal bioindicator of costal contamination: relationships between metal bioaccumulation and multiple biomarker responses. Sci. Total Environ. 511, 663–675 (2015) 28. Okay, O.S., Ozmen, M., Gungordu, A., Yilmaz, A., Yakan, S.D., Karacik, B., Tutak, B., Schramm, K.-W.: Heavy metal pollution in sediments and mussels: assessment by using pollution indices and metallothionein levels. Environ. Monit. Assess. 188, 352 (2016) 29. Szefer, P., Szefer, K.: Metals in molluscs and associated bottom sediments of the southern Baltic. Helgol. Meeresunters 44, 411–424 (1990) 30. Vander Putten, E., Dehairs, F., Keppens, E., Baeyens, W.: High resolution distribution of trace elements in the calcite shell layer of modern Mytilus edulis: environmental and biological controls. Geochim. Cosmochim. Acta 64, 997–1011 (2000) 31. Szefer, P., Frelek, K., Lee, C.-B., Kim, B.-S., Warzocha, J., Zdrojewska, I., Ciesielski, T.: Distribution and relationships of trace metals in soft tissue, byssus and shells of Mytilus trossulus from the southern Baltic. Environ. Pollut. 120, 423–444 (2002) 32. Joksimovic, D., Tomic, I., Stankovic, A.R., Jovic, M., Stankovic, S.: Trace metal concentrations in Mediterranean blue mussel and surface sediments and evaluation of the mussels quality and possible risks of high human consumption. Food Chem. 127, 632–637 (2011)

Electric and Electronic Engineering

A Fluorescent Protein-Based OR Gate for Photon-Coupled Protein Logic Balázs Rakos

Abstract We propose a fluorescent protein OR gate, which can serve as nanometerscale building block in photon-coupled, protein-based logic circuits. The structure has numerous advantages with respect to previously introduced photoswitchable protein logic gates, such as its simplicity and faster response time. Furthermore, it can be accomplished with only a single molecule, and fluorescence is the sole requirement, which broadens the choices in contrast to photoswitchable proteins. Besides its operational principle, readily available protein OR gates are introduced, and their integration into photon-coupled protein circuits is discussed, as well. We believe that the present work brings the realization of terahertz-speed molecular circuits yet another step closer. Keywords Fluorescent protein · Reversibly photoswitchable fluorescent protein · Molecular electronics · Molecular computing · Protein computing · Logic gates · Logic circuits

1 Introduction The continuous demand for improvements of computing power, combined with the need for sustainable development stimulates the creation of faster, smaller, lower power consuming and dissipating electronic elements in logic circuits, and processors. The application of proteins in such architectures is a potentially promising way due to their numerous advantages [1–4], and they may lead to the development of terahertz-speed computing circuits. Reversibly photoswitchable fluorescent proteins (RSFPs) [5–7] are particularly favorable in this respect [8–10]. RSFPs are a subgroup of proteins, which can be switched between different forms by light with well-defined frequencies, and at least one of their forms has fluorescent properties. Due to the robustness of photon coupling, logic circuits based on B. Rakos (B) Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_6

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photon-coupled RSFPs promise an outstanding stability besides other advantageous properties [8–10]. In previous works, the operation of logic gates in photon-coupled RSFP architectures was based on photoswitching combined with fluorescence [8, 9]. Although such arrangements can be made very fast (terahertz frequencies), each of the two processes (photoswitching and fluorescence) contribute to the speed constraints. This is particularly true in the case of presently existing RSFPs, where the photoswitching process is relatively slow. In this study, we focus on an OR gate, based exclusively on fluorescence. Since the operation of the proposed gate relies on only one process, faster response is expected, therefore its application in photon-coupled RSFP arrangements can further improve the computing speed.

2 Concept and Operational Principle 2.1 Fluorescent Protein-Based OR Gate When a fluorescent protein (FP) is subjected to radiation with a well-defined wavelength, λex , it emits photons with another specific wavelength, λem . The excitation and emitted frequencies are characteristics of the particular molecule. Since in the case of FPs, the only speed-limiting factor is fluorescence lifetime (can be on the order of picoseconds), their incorporation into photon-coupled RSFP protein architectures can boost the speed of the logic circuit. Furthermore, it improves the choice of readily-available proteins as building blocks of such arrangements. Let us assume that the absence of radiation is treated as logic ‘0’, and its existence means logic ‘1’. If the sources of the incident exciting radiation on the FP serve as inputs, and the emitted radiation is the output, the molecule operates as a binary OR gate. In the absence of input photons, the FP doesn’t emit fluorescent radiation, therefore the output is logic ‘0’. However, if any of the input sources emit photons with the excitation frequency, the protein demonstrates fluorescence, therefore its output becomes logic ‘1’.

2.2 Reversibly Photoswitchable Fluorescent Protein-Based Inverting Gate If RSFP-based inverting gates are applied in the circuit besides the FP-based OR gates, universal binary logic architectures can be designed. In this case the NOR gate can be also realized, which is a universal logic gate. In the following, we intoduce the concept and operational principle of the RSFP inverting gate. RSFPs usually have two states corresponding to two different conformations. One of the states (state 1) is the baseline, resting state, which can be switched to the

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other state (state 2) by light with a well-defined wavelength, λ12 . The protein can be reversibly switched back to state 1 by another light pulse with λ21 . One of the states is fluorescent, it emits light with λem wavelength upon excitation with a λex wavelength light pulse. The other state is usually non-fluorescent. An RSFP with a fluorescent resting state (state 1), and a non-fluorescent state 2 behaves as an inverting logic gate. If the protein is continuously irradiated by light with λex wavelength, it emits an output radiation with λem (output logic ‘1’) if it is in state 1. However, if an input radiation with λ12 (input logic ‘1’) is incident on the molecule, it switches to state 2, which is non-fluorescent, therefore the output radiation ceases to exist (output logic ‘0’). After the input radiation is terminated (input logic ‘0’), the protein relaxes back to its baseline, fluorescent state (state 1), thereby emitting light with λem (output logic ‘1’) again.

2.3 NOR Gate The NOR gate can be realized by placing an FP-based OR gate and an RSFP-based inverting gate next to each other. The gates must have the appropriate properties in order to operate properly. In the following, we describe the requirements and operational principle of the NOR gate. The FP-RSFP NOR gate can work properly if the wavelength of the fluorescent radiation of the FP, λem is equal to the wavelength, λ12 , required to switch the RSFPbased inverting gate to state 2. When the output of the FP-based OR gate is logic ‘0’ (no emitted radiation), the RSFP-based inverting gate emits light (logic ‘1’). However, if the output of the OR gate is logic ‘1’, it emits radiation with λem . Since λem = λ12 , the RSFP inverting gate switches to state 2, which is non-fluorescent, therefore its output radiation is absent (logic ‘0’).

3 Real-Life Examples In the following, we illustrate the operation of the previously described logic gates with the aid of readily-available, real-life proteins. The building blocks of the OR, inverting and NOR logic gates are the mTFP0.7 [7] and Dronpa [5] RSFPs.

3.1 An mTFP0.7-based OR Gate Although the OR gate could be realized using FPs only, in the present example we utilize an RSFP, mTFP0.7 for this purpose. Its properties are displayed in Table 1, based on [11]. Its resting state is the fluorescent one.

56 Table 1 Properties of mTFP0.7 based on [11]

Table 2 Properties of dronpa based on [11]

B. Rakos Protein

λex

λem

mTFP0.7

453 nm

488 nm

Protein

λex

λem

λ12

λ21

Dronpa

503 nm

517 nm

488 nm

405 nm

In the absence of an input excitation radiation (input logic ‘0’) there is no output emitted fluorescent radiation (output logic ‘0’). However, if any of the input radiation sources emit photons with λex = 453 nm wavelength (input logic ‘1’), then the molecule emits light with λem = 488 nm wavelength (output logic ‘1’). This behavior realizes an OR gate.

3.2 A Dronpa-Based Inverting Gate The operation of the inverting gate is demonstrated with the aid of Dronpa. Its properties are displayed in Table 2. The data were taken from [11]. The resting state (state 1) of Dronpa is the fluorescent one. We assume that during operation the Dronpa-based inverting gate is continuously irradiated by light with λex = 503 nm wavelength. Please note that in this case the excitation radiation is not the input radiation, it serves only for the proper operation of the gate. If the input radiation is absent (input logic ‘0’) the molecule emits fluorescent light with λem = 517 nm wavelength (output logic ‘1’). On the other hand, if there is an input radiation with λ12 = 488 nm wavelength (input logic ‘1’), the protein switches to its state 2, which is non-fluorescent, therefore it won’t emit radiation anymore (output logic ‘0’). After the λ12 = 488 nm input radiation stops, the molecule relaxes back to its baseline, fluorescent state, thereby emitting fluorescent radiation again (output logic ‘1’). This behavior corresponds to that of an inverting gate.

3.3 mTFP0.7-Dronpa NOR Gate If the mTFP0.7-based OR gate and the Dronpa-based inverting gate are placed next to each other, we get a NOR gate (see Fig. 1). The two molecules fulfill the requirement λem = λ12 , stated in Sect. 2.3, since λem = λ12 = 488 nm (see Tables 1 and 2). If the output of the mTFP0.7-based OR gate is logic ‘0’ (no emitted light), the Dronpa-based inverting gate emits fluorescent radiation (output logic ‘1’). On the other hand, if the output of the OR gate is logic ‘1’, it emits radiation with λem =

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Fig. 1 The mTFP0.7-Dronpa NOR gate arrangement. The proteins were depicted by VMD [12] with the aid of their 3D structural data obtained from the Protein Data Bank [13]

488 nm. Since λem = λ12 , the inverting gate switches to its non-fluorescent state 2, which results in the cessation of its output radiation (output logic ‘0’).

4 Discussion The proposed FP-based OR gate has several advantages over the previously introduced RSFP-based one. There are significantly more FPs to choose from. It is a very simple, single-molecule gate. Furthermore, FPs are faster, because switching is not involved in their operation. Since in more complicated logic circuits there are numerous OR gates, this can improve their overall speed considerably. If the FP-based OR gate is combined with an RSFP-based inverting gate, a NOR gate can be realized, which permits the design of universal logic circuits. However, there is another reason why RSFPs are still indispensable in logic architectures besides FPs. If the circuit would consist of FPs only, the signal would deteriorate rapidly, since in the case of these gates, due to losses, the output power is always lower than the input one. On the other hand, in the case of RSFPs the output power is determined by the radiation stimulating fluorescence, the input radiation affect the switching effect only. In the previous section we showed that there are existing, readily-available molecules, which can realize both the proposed OR and NOR gates. Although, due to the presently limited choice of already existing RSFPs, the response time of the readily-available NOR gate is slow (on the order of milliseconds) [14], in our opinion these example-proteins clearly demonstrate the potential of the proposed concept.

5 Conclusion In this work, we introduced a fluorescent protein-based OR gate, which can be incorporated into photon-coupled, reversibly photoswitchable fluorescent protein logic architectures. Its two major potential advantages are the improved speed and improved choice of elementary building blocks of such circuits. Besides the OR gate, we proposed a reversibly photoswitchable fluorescent protein-based inverting gate.

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The combination of the two logic gates leads to the NOR gate, which enables one to design universal logic architectures. Moreover, readily-available proteins suitable for such purposes have been introduced in order to demonstrate the potential of the proposed concept. Acknowledgements The research reported in this paper and carried out at the Budapest University of Technology and Economics has been supported by the National Research Development and Innovation Fund based on the charter of bolster issued by the National Research and Innovation Office under the auspices of the Ministry for Innovation and Technology.

References 1. Rakos, B.: Simulation of Coulomb-coupled, protein-based logic. J. Auto. Mob. Robot. Intell. Syst. 3(4), 46–48 (2009) 2. Rakos, B.: Modeling of dipole–dipole-coupled, electric field-driven, protein-based computing architectures. Int. J. Circ. Theor. App. 43(1), 60–72 (2015) 3. Rakos, B.: Pulse-driven, photon-coupled, protein-based logic circuits. Adv. Intel. Syst. Comput. 519, 123–127 (2016) 4. Rakos, B.: Multiple-valued computing by dipole–dipole coupled proteins. Int. J. Circ. Theor. App. 47(8), 1357–1369 (2019) 5. Ando, R., Mizuno, H., Miyawaki, A.: Regulated fast nucleocytoplasmic shuttling observed by reversible protein highlighting. Science 306, 1370–1373 (2004) 6. Stiel, A.C., Trowitzsch, S., Weber, G., Andresen, M., Eggeling, C., et al.: A bright-state structure of the reversibly switchable fluorescent protein Dronpa guides the generation of fast switching variants. Biochem. J. 402, 35–42 (2007) 7. Henderson, J.N., Ai, H.W., Campbell, R.E., Remington, S.J.: Structural basis for reversible photobleaching of a green fluorescent protein homologue. Proc. Natl. Acad. Sci. U. S. A. 104, 6672–6677 (2007) 8. Rakos, B.: Pulse-driven, photon-coupled, protein-based logic circuits. Adv. Intel. Syst. Comput. 519, 123–127 (2017) 9. Rakos, B.: Photon-coupled, photoswitchable protein-based OR, NOR logic gates. Adv. Intel. Syst. Comput. 660, 99–103 (2017) 10. Rakos, B.: Modeling and simulation of photon-coupled, fluorescent photoswitchable protein logic. Int. J. Circ. Theor. App. 48, 2130–2140 (2020) 11. Bourgeois, D., Adam, V.: Reversible photoswitching in fluorescent proteins: a mechanistic view. IUBMB Life 64(6), 482–491 (2012) 12. Humphrey, W., Dalke, A., Schulten, K.: VMD - visual molecular dynamics. J. Molec. Graph. 14(1), 33–38 (1996) 13. www.rcsb.org 14. Habuchi, S., et al.: Reversible single-molecule photoswitching in the GFP-like fluorescent protein Dronpa. Proc. Nat. Acad. Sci. 102(27), 9511–9516 (2005)

3D Fluorescence Spectroscopy of Liquid Media via Internal Reference Method M. R. Khajisvili , N. Kh. Gomidze , and J. J. Shainidze

Abstract Common methods of analyzing the chemical composition of a substance include many existing optical methods. Selecting an effective and optimal method from numerous optical methods and adapting it to the properties of the investigated substance is one of the most urgent tasks of modernity. Scientists are trying to study the substances – bio-nano-agents that surround humanity and develop methods and approaches that are used in one way or another to study materials. The properties of materials are determined by considering both, the main components and the compounds. In addition, often the properties of the materials depend on the distribution of the compounds or components in its volume. Keywords Spectroscopy · Fluorescence · Bio-nano-agents

1 Introduction The diversity of the structure of the study objects determines the multiplicity of optical spectroscopy methods. To choose an effective optical method, it is necessary to determine the following: 1. What is the object of analysis? In the simplest case, it can be a separate chemical substance, the composition of which must be determined. However, if we are dealing with a substance of complex composition - technological material, alloy, air, food or drink - it is necessary to solve the problem of how to take a sample, so that the sample fully reflects the object of research. 2. What information should we get as a result of the analysis? It is important to know, is it necessary to determine the composition of the sample as a whole, if it is sufficient to determine its surface structure? Is it necessary to conduct a complete analysis of the sample or/and if it is sufficient to limit the analysis of any of its compounds? 3. What is the analysis for? For example, an analysis is performed to determine the concentration of substances that are marginally permissible, or for other purposes.

M. R. Khajisvili (B) · N. Kh. Gomidze · J. J. Shainidze Batumi Shota Rustaveli State University, 6010 Batumi, Georgia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_7

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In a word, the analyst should not only possess the technology of processing the analysis methodology, but also be equipped with the necessary and specialized knowledge of the production of the analysis itself. For which methods are: • not aggressive; • measurements can be taken from distance without direct physical contact with the sample; • The object of study is easily analyzed; • quantitative research of liquid, solid and gaseous samples can be easily performed regardless of optical properties; • spectroscopic methods by concentration range include all other analytical methods. Classification of spectroscopic methods is performed according to: • electromagnetic radiation; • the interaction of the radiation with the substance: absorption spectroscopy, emission spectroscopy, combination scattering/Raman spectroscopy and reflection spectroscopy; • the objects to be studied: atomic and molecular structure of the object; • spectrum registration: visual, photographic and photoelectric. Atomic spectroscopy methods are based on the phenomenon of light absorption and radiation by free atoms, as well as their luminescence. The valence electrons of atoms are excited in the ultraviolet and visible spectrum of light, the internal electrons are excited and give the X-ray spectrum. Spectral lines are caused by the excitation processes of free atoms and single-atom ions. The number of spectral lines is determined by the number of possible electronic transitions in the atoms. The energy state of atoms is characterized by total quantum numbers (L - total orbital quantum number, S = S1 + S2 - total spin quantum number). The terminologies singlet and triplet state are the result of the spin quantum number. Electron spinning around its own axis generates a local small magnet with a spin. Singlet and triplet states depend on the quantum number of spin of the electrons. According to the Pauli Exclusion Principle, two electrons within a defined orbital cannot have four equal quantum numbers and so differ in spin number. Two spin numbers are attributed to an electron, and—therefore, two electrons that belong to the same orbital will have opposite spins [12]. A parameter called the multiplicity (M) is: M = |S1 + S2 | + 1 If the spins are parallel (M = 1), we have a singlet state S, if the spins are antiparallel (M = 3), we have a triplet state T . The orbital and spin numbers make the Russell–Saunders connections: J =L±S

3D Fluorescence Spectroscopy of Liquid Media ... Light source

Monochromator/spectromet

61 Detector CCD

Fig. 1 Schematic diagram of an optical spectroscope

Orbital transitions between states are subject to certain restrictions. According to the selection rule for permissible and prohibited transitions: 1. 2.

Transitions S ↔ P ↔ D ↔ F are allowed (L = ±1); Transitions between states are allowed for M = 1. This rule can be violated in the spectra of atoms whose nuclei are characterized by high charge, but the lines responsible for forbidden transitions are still weak with respect to the lines responsible for permissible transitions.

Impulse and phase-modulated (harmonic) methods are used for spectroscopic analysis of the aquatic environment. Each method has advantages in certain directions. For example, in the impulsed method, the sample is irradiated with short pulses of light and the dependence of the fluorescence intensity on time is measured. In modern spectroscopy, the source of radiation is pico-second pulse lasers. In periodically repeated pulses, fluorescence measurements are recorded either by the stroboscopic method or by the photon counting method. The main components of optical spectroscopy are: light source, monochromator (spectrometer) and detector - recording device (Fig. 1). Sources. At the present stage of development of technology, the following light sources can be used: – – – – –

Different types of incandescent lamp Halogen lamp Light emitting diode (diode lasers) Arc discharge mercury or xenon lamp Calibrated lamp with argon or other inert gas substitute

Monochromator/Spectrometer. There are two types of monochromators available today. It is a prism-based monochromator and a monochromator based on diffraction lattice. All of them are made from the following components: – – – – –

Inlet hole - it separates the narrow stream from the beam of incident light; Lens system - convex mirror to receive a parallel beams of light; Dispersible element - prism or diffraction lattice; Focusing device - is used to collect the flow at the output hole; Output hole - from which a stream of light of the desired spectral width is received.

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Detectors. Two types of receiver detectors are widely used: – Photographic emulsion - on which radiation causes chemical changes; – A photoelectric device - that converts a light signal into an electrical or/and thermal signal. In the case of small intensities of radiation falling on the detector, the photon counting method can be used. Photoelectric devices respond to photons that fall on the receiving device. The quantum yeld of such a device is the ratio of the generated photoelectrons to the number of absorbed photons. Photovoltaic devices are divided into two parts: – Devices with external photo effects; – Devices with internal photo effect. The photocell, a semiconductor device with an internal photo effect is n-type semiconductor material, on which at first i-layer material with its own conductivity is layered, and then a thin layer with p-type semiconductor. Photodiodes differ from each other in working spectral range and sensitivity. Photodiodes of silicon (Si), germanium (Ge) and indium-gallium-arsenide (InGaAs) are relatively common. These LEDs are designed for the ultraviolet range from near-infrared to 0.2 to 2.3 μm. InAs and InSb photodiodes can be used in the relatively long wavelength range (1.5–5.5 μm). The need to create adaptive photodetectors led to the creation of multi-element semiconductor solid-state detectors. We separate the detectors according to the method of signal processing: – CID – Charge Injection Device; – CCD – Charge-Coupled Device. Both detectors are based on silicon, which is equipped with photosensitive elements - “pixels” (Fig. 2).

Fig. 2 Schematic diagram of a semiconductor photo detector. 1. Photons of light, 2. Micro-lens; 3. Red LED, 4. Semi-crystalline transparent silicon electrode, 5. Isolator (silicon oxide); 6. n-type silicon layer. (Internal photo effect zone); 7. A potential well zone where electrons are collected from the charge carrier generation zone; 8. P-type silicon base

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Optical Spectroscopy Methods. Two direct methods are used for measuring signal velocity as a result of exposure to light. There are phase and pulse spectrometers respectively. It should be noted that pulse spectrometers are simple tools for determining the life expectancy of fluorescence. The sample is lit by a pulsed light source. The duration of the pulse is less than the lifetime of the fluorescence. Fluorescence damping and fading is registered on an oscilloscope or on a personal computer via electronics consisting of analog-to-digital converters. The principle of measurement is similar to the principle of measuring phosphorescence and slow fluorescence (with a lifespan of several milliseconds). However, of course, at frequencies corresponding to the angular velocity of the fluorescence damping and fading, it is technically impossible to generate excitation light pulses. Two methods are used for this purpose. In one method, the photomultiplier operates in pulse mode, the time of pulse mode of which is shorter than the life of the fluorescence. The pulse source and the photomultiplier are switched on with a frequency of several thousand iterations per second, but with some delay. Fluorescence curves are obtained by measuring the delay time between excitation and registration. An amplified signal is received from the photomultiplier through the detector and is recorded as a delay time function. The pulsed light source used in this method should have a pulse duration of less than the fluorescence signal, so it is convenient to use hydrogen pulsed lamps as the light source. In the second method, the photomultiplier operates in pulse mode and has a high sensitivity. In this case, the life-time of excitation light pulses is several times longer than the life-time of fluorescence. The signal from the photomultiplier is registered through the detector and can be visualized. Usually, to get the intensity that is enough to obtain a photographic image, it is necessary to repeat this operation several thousand times.

2 Literary Overview Today, the growing interest in laser fluorescence spectroscopy is due to its practical application in laser communication, ecology, and medicine. Medical imaging, for example, is one of the major problems in medicine. A number of works have already been performed to increase the characteristics of the detected signal. For example, spectra of emission radiation intensity of blood plasma were measured as a part of Revaz Turmanidze’s master thesis [1], where a partially coherent laser beam fell on the blood plasma placed in the cuvette and the intensity of the scattered laser radiation was also intensified. In another work [2] was discussed the relationship of stochastic shifts in solutions and gases to the wavelength of the absorbed light. Scattering from water molecules (Raman scattering) has been studied in the paper [3]. Fluorescence characteristics such as fluorescence attenuation and damping dependence on wavelength, quantum yeld, and fluorescence damping time are studied [4] in the paper. Fluorescence anisotropy and fluorescent energy transfer conditions have been studied [5] in the paper.

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In the paper [6] it has been shown that partial spatial coherence of the source reduces scintillation. The partial coherence function of a partially coherent Gaussian source is studied in [7] papers. In Kakha Makharadze’s doctoral dissertation [8] the characteristics of the Gaussian beam are studied, such as average intensity, beam width, phase front radius of curvature, wave front coherence radius. Analysis of radius of the partially spatial coherence beam is presented in [9]. Under conditions of weak and strong turbulence, an analytical image of the partially spatial coherence beam intensity correlation function was obtained in the paper [10]. Rittov’s method in an atmosphere with low turbulence is used in work [11]. [12] paper investigates the scintillation effect for a partially temporal coherence wave. Within grant project №FR-152-9-240-14 [8, 13, 14] was taken spectra using spectrometer StellarNet Black-Comet (190–900 nm) which captures of laser beams scattered from an optically dense random phase screen. The difficulty is otherwise - to evaluate the statistical characteristics of the signal detected through a heterogeneous environment and to visualize the spectral image. These difficulties are caused by environmental statistics. For example, the detection of fluorescence signals in complex environments and the visualization of spectral images are associated with significant difficulties because the complex composition of the environment (petroleum, petroleum products, organic compounds, etc.) determines the overlap of spectra and reduces the ability to decipher useful information. It becomes important to increase the quality of the signal registered on the detector and to determine the factors that are responsible for this quality. This is the subject of this work.

3 Problem Statement and Description Suppose (x1 , z 1 ) the coordinates of a beam of light for an optical system are known. After passing through the phase screen, the coordinates of the beam are transformed by the matrix equation: 

    z2 z1 A B = C D x1 x2

where x1 = εθ , ε - is the refractive index of the media at the point where the beam falls, θ is the angle between the direction of propagation of the beam and the optical axis. The determinant of the beam transmission matrix is the ratio of the refractive index to the input and output of the optical system. Such an approach allows us to study dependence of scintillation index o a random phase screen on the correlation radius of the diffuser and the distance from the diffuser. Capturing and then visualizing the detected signal spectrum is one of the most important tasks in medical physics. The realization of visualization is carried out

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Fig. 3 General scheme of medical image visualization

using appropriate mathematical methods. The object of study can be considered as a linear system that determines the transformation of the primary signal. Important part is the computation of the matrix of this transformation by mathematical methods. In principle, formation of the visual image involves receiving a medical signal, forming it using reconstruction techniques, then analyzing and evaluating the medical image (Fig. 3). As can be seen from the diagram, five components are important: the patient, the image formation system, the operator system, the image, and the observer. According to the task, we currently have different systems, such as: X-ray radiology, radioisotope radiation formation systems, magnetic resonance, ultrasound systems. In each method it is possible to control different physical parameters by the operator, for example: change of potential in the X-ray tube, controls of the concentration and type of radioisotopes, control of the magnetic field gradient and characteristic times, etc. There are different methods for creating or registering a medical image. For example: photographic plate coverage, gamma camera, digital detectors and others. In the end, the image is analyzed by the observer to obtain structural (anatomical) or in some cases functional (physiological) information. Of course, it is up to the observer to detect a number of pathologies, however the latter depends on three factors at the same time: the quality of the image, the condition of the observation, the physical characteristics of the observation. The image itself depends on 5 factors: contrast, resolution (spatial resolution), background noise, artifacts and distortions. It is these factors that contribute to the high quality of the image in the process of its formation. Different radiations have different scales of resolution, for example: the range of vision for gamma radiation is in the range of 10–3 mm, 5–2 mm for ultrasound,

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3–1 mm for magnetic resonance, 2–0.8 mm for tomography, fluoroscopy 1–0.4 mm, conventional radiography 0.5–0.1 mm, mammography below 0.05 mm. Suppose experimental measurements are carried out on the object under examination by the selected method. Assume that some physical characteristic parameters were taken into account when performing N independent measurements: x1 , x2 , x3 , . . . , xi , . . . , x N , to each xi measured value Corresponds only integer number. The main characteristics of these data are their sum and average size: ≡

N 

xi , xe ≡

i=1

 N

The distribution of sets xi can be represented by the distribution function F(x), which is the relative frequency at which x takes values from the database described above. Frequency allocation is automatically normalized to 1: ∞ 

F(x) = 1.

x=0

Table 1 below shows a randomly generated signal from 10 experiments in the form of numbers from 0 to 9. The frequency of these numbers and the frequency distribution numbers is: 10 statistics for each experiment are given. The sum of the generated  50 x = 50, the average of the generated numbers is: x ≡ = = 5. i e i=1 N 10 Table 1 Distribution of randomly generated signal and signal frequency #

Randomly generated signal xi

Frequency: F(xi )

Distribution frequency of the Signal

1

1

2

F(1) = 2/10 = 0.2

2

2

3

F(2) = 3/10 = 0.3

3

9

2

F(3) = 0/10 = 0

4

8

1

F(4) = 0/10 = 0

5

2

3

F(5) = 0/10 = 0

6

9

1

F(6) = 0/10 = 0

7

2

3

F(7) = 1/10 = 0.1

8

7

1

F(8) = 1/10 = 0.1

9

9

2

F(9) = 3/10 = 0.3

10

1

2

F(0) = 0/10 = 0

10  i=1

xi = 50

xe ≡

 N

=

50 10

=5

10  x=0

F(x) = 1

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Let us considered that process of detection is binary. We can have only two outcomes: “success” or “failure”. The probability of success can be conditionally denoted by P, which we consider to be constant for all experimental trials. The number of experiments can usually be denoted by N . Under certain conditions we might predict a distribution function that describes a process. Let us distinguish three well-known statistical models: 1. Binomial distribution - one of the most general and usable in computational calculations; 2. Poisson distribution - a relatively simplified model when P is small and N is large; 3. Gaussian or Normal Distribution - an even more simplified model when the average probability of success is relatively high. By binomial distribution the probability of x success in N experiments can be expressed by the formula: P(x) =

x N! p (1 − p) N −x (N − x)!x!

where: N 

P(x) = 1,

x=0

x=

N 

x P(x) = pN

x=0

σ2 ≡

n 

(x − x)2 P(x) = pn(1 − p) = x(1 − p)

x=0

σ is a dispersion. When x  1, the Poisson distribution can then be approximated by the Gaussian (normal) distribution: P(x) = √

1 2π σ

e−

(x−x)2 2σ 2

where σ 2 = x is practically a boundary condition. The Gaussian or normal distribution is most often found in the continuous distribution of probabilities of true random x. In our case the photons fall on the detector. Suppose v is the average speed of photon detection. r d t is the differential probability that an event will occur in d t time, assuming that the photon occurred at time t = 0. The differential probability that the following photon will occur in d t time can be calculated by the formula: I1 (t)dt = P(0)r dt Where P(0) is the probability of no photon being generated generated between 0 and t. It can be expressed by the Poisson distribution:

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Fig. 4 Probability of time interval distribution between two subsequent detected photons for different average velocities.

P(0) =

(r t)0 e−r t = e−r t 0!

The differential probability that the following photon will occur in the detector dt time will be: I1 (t)dt = r e−r t dt Probability of time interval distribution between two subsequent detected photons for different average velocities is shown in Fig. 4.

4 Experiment Description and Conclusion Figure 5 demonstrates the principal the principal scheme of a phase spectrofluorometer. As a light source we used high-sensitivity impulsive nitrogen laser NL-100 (frequency 0–25 Hz). In order to reduce the “electronic noise” caused by the relaxation processes, we used a multichannel analyzer that is compatible with computercompatible electronic devices such as ADC (analog–digital conversion), DAC (digital analog conversion) and electronic circuits. Fluorescence excitation in phase fluorometers occurs with a light beam that is modulated by high frequency. Fluorescence phase, i.e., the degree of modulation will be compared to the light phase. In the case of exponentially fading fluorescence, the shift ψ between the fluorescent radiation and excitation light phases is depicted with the expression: ωτ = tgψ, where τ is fluorescence life expectancy, and ω is an angular velocity of the modulation. The signal from the source and the fluorescent signal excited on the sample hit the detector that registers the phase shifts among these signals. For example, Bailey and Rolfson measured the phase shifts between these two signals, whereas the two output signals coincide with the phase, the detector registered a minimum signal at the output. There are other methods of measuring

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Fig. 5 Principal scheme of phase fluorometer. 1 – light source, 2 – optical modulator; 3 – optical filter, 4 – light splitter; 5 – cuvette with rotation system; 6 – optical fiber; 7 – cuvette holder; 8 – correlator of coherence beams; 9 – scattering plate; 10 – photomultiplier; 11 – phase meter; 12 – computer; M1 – stationary mirror; M2 – moving mirror; L – converging lens; CCD – monochromator

the ψ phase shifts based on the determination of the quantity of these two signals. It is obvious that the longer is the life expectancy of fluorescence, the smaller is the size of its modulation. The quality of modulation (m s ) in the light beam and fluorescence signal modulation quality (m f ) is associated with the ψ phase shifts m with the ratio of: m sf = cosψ. The phase shifts measured by these two methods coincide with each other with great accuracy in the case of exponentially fading fluorescence. The distinction is observed only in case of non-exponential fading of fluorescence. One of the main drawbacks of the phase fluorometer in principle is the difficulty in the interpretation of the results which arise in case of non-exponential fading of the fluorescence, but Birks, Dyson and Munro have used this method even in case of non-exponential fading of the fluorescence. In the first phase fluorometers, polarizing exciting light and Kerr effect were used for light beam modulation. In this case, the high-frequency field acts on the fluid or crystal electronic-optical effect. Then, for light modulation, the light diffraction was studied on the ultrasonic waves in the fluid that formed the quartz crystal. The light beam with the constant intensity falls onto the diffraction grating. Among the easiest methods a high-frequency pipe is used, in which the modular light is obtained directly [16]. A relatively convenient method is to use a hydrogen lamp that is powered by a modulated signal. Thus, the phase fluorometer allows us to determine the duration of the received signal through the phase screen with a well-known modulation signal. It is the iterative methods that have been developed and widely applied in the tasks of synthesis of diffractive optical elements [17]. The main advantage of these methods is that iteration algorithms are more accurate than other algorithms. On the other hand, the realization of the algorithm of synthesis of optical elements on the computer requires significant computing costs. The disadvantage of these algorithms is the use of approximation to describe the laser beam distribution.

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Fig. 6 Sample of the 3D spectrum of fluorescence. The excitation wavelength is 200–800 nm

With the method offered by us, the lifespan of fluorescence can be determined indirectly, from the attempts of damping fluorescence [18]. It is Important to know the polarization of the source in defining the phase screen regulations (viscosity, temperature, structure) [19]. Figure 6 demonstrates a sample of the 3D spectrum of fluorescence when the excitation wavelength is 200–800 nm, when the fluorescence wavelength is placed in the interval (a) 250–620 nm – short and (b) 620–800 nm long wavelengths respectively. As a spectrofluorometer the Black-Comet (190–1100 nm) from company StellarNet was used. Quantitative characteristics of spectra such as Base, Power Spectral Density (PSD) and Width allow us to collect data that is the basis for capturing high quality threedimensional spectra. Based on the shape of the spectrum and the tabular data, it is possible to analyze the spectral composition of a random-inhomogeneous media.

References 1. Turmanidze, R.: Evaluation of the correlation function of the fluctuation of the laser beam intensity in the blood. Master thesis. Batumi (2017). (in Georgian) 2. Stokes, G.G.: Über die Veränderung der Brechbarkeit des Lichts. Annalen der Physik, B. 163(11), 480–490 (1852) 3. Berlman, I.B.: Handbook of Fluorescence Spectra of Aromatic Molecules, 2nd edn. Academic Press, New York (1971) 4. Korotkova, O., Andrews, L.C.: Speckle propagation through atmospheric turbulence: effects of partial coherence of the target. In: SPIE Proccedings (2002) 5. Bernard, V.: Molecular Fluorescence: Principles and Applications. Wiley-VCH Verlag GmbH (2001)

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6. Banakh, V.A., Buldakov, V.M., Mironov, V.L.: Intensity fluctuations of a partially coherent light beam in a turbulent atmosphere. Opt. Spectrosk. 54, 1054–1059 (1983) 7. Fante, L.: Intensity fluctuations of an optical wave in a turbulent medium, effect of source coherence. Opt. Acta 28, 1203–1207 (1981) 8. Gomidze, N.Kh., Shashikadze, Z.Kh., Makharadze, K.A., Khajishvili, M.R.: About fluorescence excitation spectrums. In: 6th International Conference on Advanced Optoelectronics and Lasers. Conference Proceedings, Sudak, Ukraine, 9–13 September 2013, pp. 317–319 (2013) 9. Belenkii, M.S., Kon, A.I., Mironov, V.L.: Turbulent distortions of the spatial coherence of a laser beam. Kvantovaya Electron. (Moscow) 4, 517–523 (1977) 10. Leader, J.C.: Intensity fluctuations resulting from a spatially partially coherent light propagating through atmospheric turbulence. J. Opt. Soc. Am. A 69(1), 73–84 (1979) 11. Mandel, L., Wolf, E.: Optical Coherence and Quantum Optics. Cambridge University Press, Cambridge (1995) 12. Joseph, R., Lakowic, Z.: Principles of Fluorescence Spectroscopy, p. 960. Springer Science, Heidelberg (2006) 13. Gomidze, N.Kh., Khajishvili, M.R., Makharadze, K.A., Jabnidze, I.N., Surmanidze, Z.J.: About statistical moments of scattered laser radiation from random phase screen. Int. J. Emerg. Technol. Adv. Eng. 6(4), 237–245 (2016). ISSN 2250–2459 (ISO 9001:2008 Certified) 14. Gomidze, N.Kh., Makharadze, K.A., Khajishvili, M.R., Shashikadze, Z.Kh.: About numerical analyses of sea water with laser spectroscopy method. In: 2011 XXXth URSI General Assembly and Scientific Symposium, 30TH 2011, vol. 5, pp. 1620–1624 (2011). ISBN 978–1–4244–5117– 3 15. Gomidze, N., Shainidze, J., Shengelia, G., Turmanidze, R.: To the problems of fluorescence excitation spectrums. Int. Sci. J. “Mach. Technol. Materi.” 279–282 (2018). web ISSN 1314– 507X; print ISSN: 1313–0226 16. Misievich, S.K., Skidanova, R.V.: J. Comput. Opt. Nanophoton., 269–281 (2017) 17. Gomidze, N. Kh., Khajisvili, M.R., Jabnidze, I.N., Makharadze, K.A., Surmanidze, Z.J.: To the problems of detecting signals passing through a random phase screen. J. Recent Adv. Technol. Res. Educ., 177–184 (2018). Print ISBN: 978–3–319–99833–6, Electronic ISBN: 978–3–319–99834–3 18. Gomidze, N., Makharadze, K., Jabnidze, I., Kalandadze, L., Khajishvili, M., Nakashidze, O.: To the diagnostics of optically dense media via phase screen model. In: Conference Proceedings. 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL 2019), pp. 71–74. IEEE Explore Publisher (2019). ISBN: 978–1–7281–1813–0 19. Gomidze, N.Kh., Shainidze, J.J., Jabnidze, I.N., Makharadze, K.A., Khajishvili M.R., Kalandadze, L.G., Nakashidze, O, Mshkhaladze, E.N.: Estimation of scintillation index for a Gaussian laser beam propagating through a random phase screen. J. Biol. Phys. Chem. 20, 104–110 (2020). https://doi.org/10.4024/27GO19A.jbpc.20.03

High-Performance and Low-Voltage Current Sense-Amplifier Using GAA-CNTFET with Different Chirality and Channel Singh Rohitkumar Shailendra, Pragya Sharma, M. Aarthy, and Hidenori Mimura

Abstract This paper represents the Current sense amplifier based on Gate all around Carbon nanotube field-effect transistor (GAA-CNTFET). Every 18 months, the number of transistors in an integrated circuit double according to Moore’s law. This increased number of transistors means the size of transistors will decrease as a result of which the circuit size decreases. As the size of the transistor decreases below 10 nm, the traditional MOSFETs show multiple limitations, thereby leading to the development of CNTFETs to replace these MOSFETs. Parametric analysis has been efficiently performed on parameters like Power, Delay, and Power delay product by varying chirality vectors (n, m). Four unique configurations have been used as Single-channel single chirality (SCSC), double channel single chirality (DCSC), Single channel double chirality (SCDC), Double channel double chirality (DCDC) in this work. In this paper, it is studied that the nano-scale domain CNTFETs devices are better than MOSFETs due to less delay and power. It is observed that the delay of the Current sense amplifier decreases when increasing the chirality and number of CNT in the channel. Delay, Power, PDP analysis has been performed by varying tox and Kox . Keywords Current-sense amplifier · CNT · CNTFET · Chirality · SRAM

S. R. Shailendra (B) Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Japan e-mail: [email protected] S. R. Shailendra · H. Mimura Research Institute of Electronics, Shizuoka University, Hamamatsu, Japan P. Sharma · M. Aarthy School of Electronics Engineering, VIT, Vellore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_8

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1 Introduction The semiconductor industry gives the power of enabling revolutionary technology for digital devices. The semiconductor industries focus on compact, low power and highperformance devices. As forecasted by International Technology Roadmap Semiconductors (ITRS), Silicon transistors will reach their limit in 2020 (Moore’s law). When the MOSFET channel length is below 10 nm, it will suffer many issues such as short channel effect, drain induced barrier lowering, velocity saturation, mobility degradation, punch through effect, hot carrier injection, parasitic capacitance and resistance, random dopants distribution [1]. Nowadays, several devices such as FINFET, FDSOI, PD-SOI, SET, and CNTFET are introduced [2]. From all the nano-devices CNTFET is an excellent alternative that shows good performance across all the matrices like delay, power, PDP. CNTFET is pronounced lower technology because of its predetermined properties such as ballistic transport, the low threshold voltage requirement, different variety of wall, ambipolar conduction and good Ion /Ioff ratio. The sense amplifier is considered to be an important part of memory devices. Performance can be enhanced by scaling down the technology node of the current sense amplifier. CNTFET allows users to scale down technology nodes from 32 nm to a range of 10 nm [3, 4]. The current sense amplifier is a special-purpose amplifier that amplifies low voltage from the output node directly proportional to current flows from the rail of SRAM. In CSA, the output voltage is directly proportional to the current flowing from the output rail. The current sense amplifier utilizes a “currentsense resistor” to convert the load current in the power rail to a small voltage, which is then amplified by a circuit. Section 2 describes the CNT, its types and properties, Sect. 3 deals with memories and their types. Section 4 deals with a comprehensive description of the sense amplifier, Sect. 5 describes CSA, Sect. 6 shows simulation results and Sect. 7 conclude the results of CSA.

2 Carbon Nanotube Field-Effect Transistor Carbon nanotube field-effect transistor uses Carbon Nanotube (CNT) as the channel material in place of bulk Silicon (Si) as in the traditional MOSFET. Carbon Nanotubes are hollow cylinders having a diameter of one to tens of nanometers and length up to centimetres. CNTs have sp2 hybridization, the same structure as graphite. Indeed, a CNT is a rolled sheet of graphene [5]. The behaviour of a CNT can be either semiconductor or metallic depending on its chirality (n, m). If n-m = 3 K (K ∈ Z) and n = m, the CNT is metallic, otherwise, it is a semiconductor and can be utilized as the channel region of the CNT-based transistor [6]. CNTFET has different types such as suspended CNTFET, top-gated CNTFET, back gated CNTFET, and GAA-CNTFET. The structure of the GAA-CNTFET is shown in Fig. 1. In this device, the channel region is surrounded by the gate and gate oxide. The CNTs are placed on a thick layer of an insulator to overcome the

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Fig. 1 Diagram of Carbon nanotube Field-effect transistor (CNTFET)

body effect; hence the body terminal can be ignored. Performance of GAA-CNTFET improved by improving the Ion and that will improve the Ion /Ioff ratio. When m = 0 and n can be divided into np and nn . In this paper (nn ,0) will represent the n-type GAA-CNTFET, and (np ,0) will represent the p-type GAA-CNTFET.

3 Memories As the brain works for the human body same thing semiconductor memory works for computer devices. Semiconductor memory is a memory device used for storing data. Data can be stored with the help of instructions. It’s a space in the computer where the data is processed by applying particular instruction sets [7]. Memory is built by combining several parts; each part is called a cell having its unique address. Memory can be divided into two parts i.e., Volatile and non-volatile memory. SRAM and DRAM are coming in the category of volatile memory. Static random-access memory (SRAM) stores the data or retain the data as long as power remains on; otherwise, it gets lost. SRAM can be designed by using six transistors and without having any capacitors. The sense amplifier is used in SRAM for reading the data. The advantage of using a sense amplifier is low leakage power, and another advantage is since we are not using a capacitor therefore, we don’t need to refresh it every time [8, 9].

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4 Sense Amplifier As the name suggests, sense amplifier means interpretation or determination. The sense amplifier is one of the elements which use in-memory circuitry during the read cycle. The sense amplifier is used for amplifying the low power signal and convert it into a determined value of either logic 0 or 1. As in the other amplifier, most of the time the data got changed due to propagation or noise in the memory cell, therefore it does not provide the actual value to the peripheral device. In this paper, we are using a current sense amplifier to sense the data from SRAM [10].

5 Current Sense Amplifier (CSA) In CSA, the output voltage is directly proportional to the current flowing from the output rail. The current sense amplifier amplifies the differential current. CSA can be built by combining two circuits i.e. Current transporting circuitry and current sensing circuitry [11]. Current transporting circuitry consists of precharge circuitry made of 3 PMOS transistors with positive feedback and 2 PMOS transistors which are used to connect bit line and bit line bar to the sensing circuitry [12]. The current sensing circuit is built by cross-coupled two pairs of inverters having a high gain. CSA will work in two different phases either in the precharge phase or the evaluation phase. In the precharge phase, precharge circuitry will charge all the transistors in the same voltage, this will prevent latch-up phenomena and provide an equal delay. Applying biasing voltages in Bl or Blq and applying a low voltage to precharge circuitry denoted by Pch . In the evaluation phase, enable the Ysel then apply high SAen voltage, so that n1 and n2 get enabled. n1 and n2 will receive a biasing current but after a delay, due to the presence of two pairs of inverters that enables the transistor p12 which in turn will enable both the cross-coupled inverter and this will work as an amplifier. The output will be provided at Q which can be either logic 1 or logic 0. CSA does not depend upon the bit line capacitance so the amplification is independent of large bit line capacitance discharging as shown in Figs. 2 and 3.

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Fig. 2 Circuit diagram of a current sense amplifier

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Fig. 3 Output representation of current sense amplifier

6 Simulation Result In CSA, parametric variation has been done by varying the parameters like diameter, dielectric constant, thickness, with the different number of channels. Simulation has been done by using virtuoso cadence at 11 nm technology node, which is based on compatible Verilog-A model i.e. compatible in hspice developed at Stanford University. In this paper, both single chirality and double chirality have been discussed and designed by using both P-type and N-type of CNTFET [13]. For delay calculation, we are using a transient graph with 50% rising edge and 50% falling edge and for power, PSF file has been used for power calculation and PDP is simply a product of power and delay. Power, delay and PDP have been analysed with single chirality and double chirality having a single channel and double channel by using a transient graph as shown in Fig. 3. We are performing the parametric study with different parameters on the current sense amplifier. Chirality vectors in carbon nanotube can be represented by nn and np for n-type and p-type (nn , np ,0). When we are increasing the chirality vector, the diameter of CNT increases and this will improve the delay. If we need to reduce more delay, double-channel can be used as shown in Fig. 4. By parametric analysis, we conclude that single-chirality for both single-channel and double-channel (20,20,0) gives the optimized result whereas dual chirality for both single-channel and dualchannel (20,16,0) gives the optimized result. Delay calculations for different Kox have been done for all SCSC, SCDC, DCSC, DCDC as shown in Figs. 5 and 6. Delay calculations for different tox have been done for all SCSC, SCDC, DCSC, DCDC as shown in Figs. 7 and 8.

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Fig. 4 Graphical representation of delay analysis of different chirality with single and double channel

Fig. 5 Delay analysis of different chirality with single-channel having the different Kox

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Fig. 6 Delay analysis of different chirality with the double-channel having the different values of kox

7 Conclusion CNTFET is one of the promising unique nanodevices which can be developed as a new generation for the semiconductor industry. In this paper, Current sense amplifier with 11 nm technology is analysed in the virtuoso tool having 0.8 V. From the simulation results of the current sense amplifier, it can be concluded that by varying the chirality vectors diameter of CNTFET changes gradually, which will affect the delay, power and PDP. For single chirality single channel (SCSC) (20,20,0) will give an optimized result like the delay of 49.57 ps, 2.2064 µW power, and PDP is 109.37125 aJ. For single chirality double channel (SCDC) (20,20,0) gives a delay of 24.66 ps and power is 3.5904 µW, PDP is 88.539264 aJ. For double chirality single channel (DCSC) (20,16,0) gives optimised results such as delay 54.61 ps, power 1.3808 µW, PDP 75.405 aJ. For double chirality double channel (DCDC) (20,16,0) gives a delay of 27.67 ps, 1.3808 µW power and PDP is 38.192928 aJ. Based upon the applications, the combination of chirality vectors, thickness oxide and dielectric oxide values should be chosen wisely to obtain optimal performance.

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Fig. 7 Delay analysis of different chirality with single-channel having the different values of tox

Fig. 8 Delay analysis of different chirality with double-channel having the different values of tox

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References 1. Iijima, S.: Helical microtubules of graphitic carbon. Nature 354(6348), 56–58 (1991) 2. Geim, A.K., Novoselov, K.S.: The rise of graphene. Nat. Mater. 66, 183-191Y (2007) 3. Zeitzoff, P.M.: MOSFET scaling trends and challenges through the end of the roadmap. In: Proceedings of the IEEE 2004 Custom Integrated Circuits Conference (IEEE Cat. No. 04CH37571). IEEE (2004) 4. Dang, T., Anghel, L., Leveugle, R.: CNTFET basics and simulation. In: International Conference on Design and Test of Integrated Systems in Nanoscale Technology. IEEE (2006) 5. Iijima, S.: Helical microtubules of graphitic carbon. Nature 354, 56–58 (1991) 6. Hoenlein, W., Kreupl, F., Duesberg, G.S., Graham, A.P., Liebau, M., Seidel, R., Unger, E.: Carbon nanotubes for microelectronics: status and future prospects. Mater. Sci. Eng. C 23(6–8), 663–669 (2003) 7. Cho, G., Lombardi, F.: Design and process variation analysis of CNTFET-based ternary memory cells. Integration 54, 97–108 (2016) 8. Divya, M.K., Singh, G.: “Performance analysis of CNFET based 6T SRAM. Structure 12, 13 (2018) 9. Kumar, H., Srivastava, S., Singh, B.: Low power, high-performance reversible logic enabled CNTFET SRAM cell with improved stability. Mater. Today Proc. 42, 1617–1623 (2021) 10. Marani, R., Perri, A.G.: A comparison of CNTFET models through the design of a SRAM cell. ECS J. Solid-State Sci. Technol. 5(10), M118 (2016) 11. Tao, Y.-P., Hu, W-P.: Design of sense amplifier in the high-speed SRAM. In: 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. IEEE (2015) 12. Liu, Z., et al.: Design of a novel ternary SRAM sense amplifier using CNFET. In: 2017 IEEE 12th International Conference on ASIC (ASICON). IEEE (2017) 13. Wong, H.-S.P., et al.: Carbon nanotube device modelling and circuit simulation. In: Carbon Nanotube Electronics, pp. 133–162. Springer, Boston (2009)

An Angle-Sensitive, Nickel Bolometer-Based, Adaptive Infrared Pixel Antenna Mustafa Shubbar and Balázs Rakos

Abstract The present study proposes an infrared pixel antenna combined with readily-available, angle-sensitive, nickel bolometers. The antenna can automatically optimize itself for the direction of the incident mid-infrared radiation. The structure is expected to improve the performance of infrared sensors used in several areas, such as medicine, telecommunication and military. Furthermore, it can boost the efficiency of infrared energy harvesting applications, such as rectenna (antenna-rectifier) systems. We present through simulations that the structure operates in the mid-infrared band. The antenna automatically adapts itself to the incoming radiation from perpendicular, east, and west directions. This can provide a more than 10 dB enhancement regarding the perpendicular direction, and more than 5 dB average enhancement for west and east directions with 3 dB difference between them. The Sonnet Professional 17.57 was used as a simulation environment. Keywords Infrared antenna · Pixel antenna · Bolometer switches · Self-adapting antenna

1 Introduction The development of infrared (IR) sensors faces several challenges related to the cooling requirements, spectral response, responsibility, sensor size and the control of frequency and spatial domains [1]. Their integration with antennas is a potential solution for such limitations, which offers good control of the spatial and frequency domains. Furthermore, the antenna-coupled structures promise spatially compact sensors and large collection areas [2]. K. Kim et al. [3] reports a detectivity seven times higher than that of conventional microbolometers when the sensors are integrated together with 3D feed horn antennas. G. Jayaswal [4] and A. M. A. M. Shubbar (B) · B. Rakos Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_9

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Sabaawi [5] introduce a bowtie antenna for IR field capture, and they propose a metal–insulator-metal (MIM) diode for rectification of high-frequency currents. Recently, reconfigurable antennas have gained considerable research interest in order to manipulate the antenna characteristics, suppress excessive noise, and avoid jamming signals, hence maximizing the power transfer [6]. One of the most attractive reconfigurable antenna type is the pixel antenna [7, 8]. It operates in a way similar to an LCD screen, which can display any shape [8]. Such antennas consist of a twodimensional grid, composed of small metallic patches (pixels), interconnected by switches. By activating or deactivating these switches, the surface, shape, physical length of the antenna can be rearranged, and its current distribution can be altered, which results in the modification of its frequency and radiation characteristics. This reconfiguration principle provides high flexibility and reconfigurability [3–5]. Due to its numerous advantages, in this work, we propose a pixel antenna design. Another important part of our present angle-sensitive antenna design is the bolometer. Bolometers change their resistance if they are irradiated, therefore, they can be applied as radiation-controlled switches. Mohammedi et al. [11] propose an angle-sensitive bolometer design, based on a nickel-metal thin film. Nickel has one of the highest temperature coefficients of resistance (TCR) among available metals (α = 0.006 K−1 . The low resistance of the device is in the range of few tens of Ohms in the absence of radiation. The resistance of the structure depends on the angle of incidence of the radiation besides its intensity.

2 Proposed Structure The proposed pixel antenna is composed of a 3 × 3 grid of square pixels with w = 1.5 μm side length, separated by a 0.8 μm gap (see Fig. 1). The grid structure is located on a SiO2 layer with an optimized thickness of 0.8 μm. The antenna is fed by a 50  microstrip line with 0.4 μm width, located on the same side of the substrate. The feed line is backed by a rectangular ground plane with dimensions of 4.9 × 2 μm, placed on the other side of the SiO2 layer. Both the ground plane and the SiO2 layer are supported by a 600 μm thick silicon substrate. The nickel bolometer described in [11] with its dimensions of 0.4 × 0.8 μm is used as a switch to interconnect any two adjacent pixels in the grid. Six of them are placed in the horizontal direction, and the other six bolometers are placed in the vertical direction (see Fig. 1a). The proposed structure has several different states that can be used for frequency hopping and radiation pattern steering. To simplify this structure and for easier investigation, Sc1 and Sc2 are set as permanent connections, therefore they aren’t affected by the incident wave. The switches numbered S1, S2, S3, S4, S9 and S10 are assumed to be in high resistance mode (open switch, the connections are removed) and the switches numbered by S5, S6, S7 and S8 are the controllable switches that can be affected by the incident radiation, thereby altering the antenna characteristic (see Fig. 1b).

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(c) Fig. 1 Antenna geometry a, Simplified structure b and substrate arrangement c

Micrometer-dimension walls are proposed to be installed at the right edge of the S5 and S7 switches. In contrast, the same type of barriers are placed at the left edge of the S6 and S8 switches. The walls serve to block incident radiation from certain directions. If the radiation comes from the east, S5 and S7 will not be affected due to the shadow effect of the micro-wall, which results in a radiation characteristic directed towards the east direction (see the following section). Similarly, when the radiation comes from the westward direction, S6 and S8 will be shaded by the microwalls, keeping them in their ON state. Thereby, the radiation characteristics of the antenna is directed towards the west direction. We will show through simulations that the proposed antenna operates in the midinfrared band. Furthermore, the simulated characteristics confirm that the structure automatically adapts itself to the direction of the incident radiation, and some of the switched states show evidence of gain enhancement with respect to the case when all of the pixels are connected.

3 Results and Discussion The frequency-dependent reflection coefficient for the three switching states obtained through simulation is displayed in Fig. 2. when Sc1 = Sc2 = ON (black), Sc1 =

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Fig. 2 Antenna frequency response when the incident radiation comes from the perpendicular (black), east (blue) and west (red) directions

(a)

(b)

Fig. 3 E-plane a and H-plane b antenna charactristics when all the switches are irradiated (green), and all of the switches are irradiated except Sc1 and Sc2 (black) at 40 THz

Sc2 = S5 = S7 = ON (blue) and Sc1 = Sc2 = S6 = S8 = ON (red). Since the reflection coefficient of the antenna is low at a frequency range between 36 and 44 THz, the structure operates in the mid-IR band with good matching conditions in the case of the different states. The slight changes in the bandwidth are related to the changes in the physical dimensions of each state. The two symmetrical states have a semi-identical response due to the same physical length. Figure 3 shows the antenna characteristics in the case when all of the switches are irradiated simultaneously (green line) and compared to the case when Sc1 and

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(b)

Fig. 4 E-plane a and H-plane b antenna charactristics when radiation comes from east (blue), west (red) and all of the switches irradiant (black) at 40 THz

Sc2 are replaced by small connection pads (black). Both the the E-plane and Hplane characteristics in Fig. 3 shows that the antenna earns around 15 dB gain in the perpendicular direction. When the incident radiation comes from the perpendicular direction, all of the switches are irradiated simultaneously, therefore they are turned OFF except the small connection pads numbered by Sc1 and Sc2. Thereby, the antenna characteristics are directed toward the perpendicular direction and maximize the power absorption from this direction. This is the reason that guides us to set Sc1 and Sc2 in permanently connected mode. Figure 4a displays the E-plane characteristics of the antenna when the incoming radiation is incident from the east (blue) and west (red). If radiation comes from the east, S5 and S7 are not affected, they remain in the ON mode due to the effect of the sidewalls, while S6 and S8 are irradiated and turned to the OFF state. In this case, the gain difference between west and east directions is around 3 dB. In a vice versa manner, the same concept applies to the west direction as displayed in Fig. 4a (red). Although the antenna characteristics are steered to the west and east directions, the antenna preserves its 5 dB gain in perpendicular direction for both cases, as displayed in Fig. 4b.

4 Conclusion A flexible, inexpensive, easily implementable, self-adapting antenna structure was presented in this study. The arrangement is suited for mid-infrared frequencies. The proposed design shows gain enhancement towards the direction of the incident radiation. The antenna provides more than 10 dB gain in perpendicular, and 3 dB for

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the eastward and westward directions. In the future, the structure can be further optimized by enabling it to cover more directions, possibly with the aid of more sophisticated bolometer design. Acknowledgements The research reported in this paper and carried out at the Budapest University of Technology and Economics has been supported by the National Research Development and Innovation Fund based on the charter of bolster issued by the National Research and Innovation Office under the auspices of the Ministry for Innovation and Technology.

References 1. Mubarak, M., Sidek, O., Abdel-Rahman, M., Mustaffa, M., Mustapa Kamal, A., Mukras, S.: Nano-antenna coupled infrared detector design. Sensors 18, 3714 (2018). https://doi.org/10. 3390/s18113714 2. Alda, J., Boreman, G.D.: Infrared Antennas and Resonant Structures. SPIE Press, Bellingham (2017) 3. Kim, K., Park, J.-Y., Han, Y.-H., Kang, H.-K., Shin, H.-J., Moon, S., Park, J.-H.: 3D-feed horn antenna-coupled microbolometer. Sens. Actuators A 110, 196–205 (2004). https://doi.org/10. 1016/j.sna.2003.09.018 4. Jayaswal, G., Belkadi, A., Meredov, A., Pelz, B., Moddel, G., Shamim, A.: Optical rectification through an Al2O3 based MIM passive rectenna at 28.3 THz. Mater. Today Energy 7, 1–9 (2018). https://doi.org/10.1016/j.mtener.2017.11.002 5. Sabaawi, A.M.A., Tsimenidis, C.C., Sharif, B.S.: Bow-tie nano-array rectenna: design and optimization. In: 2012 6th European Conference on Antennas and Propagation (EUCAP), pp. 1975–1978. IEEE, Prague, Czech Republic (2012). https://doi.org/10.1109/EuCAP.2012. 6206439. 6. Han, L., Wang, C., Zhang, W., Ma, R., Zeng, Q.: Design of frequency- and patternreconfigurable wideband slot antenna. Int. J. Antennas Propag. 2018, 1–7 (2018). https://doi. org/10.1155/2018/3678018 7. Rodrigo, D., Cetiner, B.A., Jofre, L.: Frequency, radiation pattern and polarization reconfigurable antenna using a parasitic pixel layer. IEEE Trans. Antennas Propagat. 62, 3422–3427 (2014). https://doi.org/10.1109/TAP.2014.2314464 8. Rodrigo, D., Jofre, L.: Frequency and radiation pattern reconfigurability of a multi-size pixel antenna. IEEE Trans. Antennas Propagat. 60, 2219–2225 (2012). https://doi.org/10.1109/TAP. 2012.2189739 9. George, R., Kumar, C.R.S., Gangal, S.A.: Design of a frequency reconfigurable pixel patch antenna for cognitive radio applications. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1684–1688. IEEE, Melmaruvathur (2016). https://doi.org/ 10.1109/ICCSP.2016.7754451. 10. Tewari, M., Yadav, A., Yadav, R.P.: Frequency reconfigurable antenna: using pixel ground. In: 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–6. IEEE, Jaipur (2016). https://doi.org/10.1109/ICRAIE.2016.7939590. 11. Mohammadi, E., Behzadnezhad, B., Behdad, N.: An angle-sensing infrared detector using a two-element biomimetic antenna array. IEEE Trans. Antennas Propagat. 66, 5818–5826 (2018). https://doi.org/10.1109/TAP.2018.2863112

Intelligent and Soft Computing Techniques

Intelligent Pantry: A Low Cost Smart Storage Manager for Food Spoilage Prevention B. Tusor, J. T. Tóth, and A. R. Várkonyi-Kóczy

Abstract A home storage of various, perishable goods (e.g. food items, sanitary products, medical supplies etc.) is an integral part of any household. It gains even more importance in case of unexpected events, such as pandemics, when people tend to stock up for months ahead. However, with higher amounts of perishable items at home, the probability of food spoilage (and thus, food waste) occurring is also higher, unless each item is carefully accounted for. In this paper, a novel smart storage framework is presented for tracking the expiration date and location of stored items and providing users with information and warnings about the goods in the storage, using cheap, affordable devices. Furthermore, the system can act as an advisor about replenishing supplies by learning the consumption habits and priorities of its users. Keywords Household economics · Intelligent space · Smart storage · Raspberry pi · Single board computers · Assisted daily life · Smart home

1 Introduction In the past decades, smart home environments have become more and more affordable, giving average households more control over their home devices and appliances. Pantries are perhaps one of the most important parts of each household, storing B. Tusor (B) · J. T. Tóth · A. R. Várkonyi-Kóczy Department of Mathematics and Informatics, J. Selye University, Bratislavská Str. 3322, P.O. BOX 54. 945 01, Komárno, Slovakia e-mail: [email protected] J. T. Tóth e-mail: [email protected] A. R. Várkonyi-Kóczy e-mail: [email protected] B. Tusor · A. R. Várkonyi-Kóczy Institute of Software Design and Software Development, Óbuda University, Bécsi Str. 96/b, 1034 Budapest, Hungary © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_10

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various perishable and non-perishable goods ranging from food items, sanitary products, to medical supplies, etc. In case of unexpected events, such as pandemics, in which there is a danger of a shortage of any of these products, it is a good idea to keep a healthy amount of stocks of the basic necessities. However, with higher amounts of stored perishable items, the probability of food spoilage (and thus, food waste) occurring is also higher, unless each item is carefully accounted for. Another problem is the localization of the items in question, finding a given product among multiple storage boxes or even rooms can be a time-consuming task. There have been numerous research projects proposing so-called smart pantry frameworks that aim to help the average households in solving the problem of tracking inventory and restocking the items with low stocks. One popular way for item tracking is using weight sensors in the storage compartments [1–3], which provides a reliable way to tell the amount of items in each compartment, though it requires that the compartments are homogenous in terms of stored items. Cameras and RFID tags [4] are also sometimes used to help the system keep track of items. Another overarching feature of these projects is that they expand on the Internet of Things (IoT) paradigm (i.e., creating interconnectivity among electronic devices for higher efficiency and convenience). Various platforms have been used to achieve this, e.g. NodeMCU modules [1], Arduino Uno [2], etc. Other approaches use automatic storages: [5] have proposed a carousel-like, stepper motor driven storage that takes care of putting the items into their designated places. Most smart pantry projects that have been proposed focus on restocking, presuming that the (grocery) items will likely be consumed before their expiration date, thus, do not take spoilage into account. Another aspect of the problem is that the financial state of the users is not taken into account, as not everyone can afford a fully automated storage system, or even outfitting every single container with a weight sensor. In this paper, the Intelligent Pantry (iPantry), a novel smart storage framework is presented for tracking the expiration date and location of stored items and providing users with information and warnings about the goods in the storage (i.e. when does each item expire and spoil, and where can the user find them). The framework is an Intelligent Space (iSpace [6]) application: the central computing module of the system, the so-called iPantry Manager is based on the DIND (Distributed Intelligent Networking Device) schema, which the iSpace is built upon. The system is designed with modularity in mind, divided into different levels in order to be more affordable for a wider range of consumers. The rest of the paper is as follows. Section 2 summarizes the architecture of the iPantry system, while Sect. 3 provides a detailed description of its functionalities. The last Section concludes the paper and outlines future development plans.

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2 The Architecture of the Intelligent Pantry The basic idea behind the system is that the user has a pantry or home storage of any size or kind, be it a whole room full of various containers, or even a single closet with boxes. This storage is made into an Intelligent Space using devices based on the needs and financial situation of the user, preferably without altering the space too much. The layered architecture of the proposed iPantry Smart Storage framework is summarized in Fig. 1. Given the complexity and cost of the various functionalities of the system, the framework can be separated into different levels.

Fig. 1 The general architecture of the Intelligent Pantry framework

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At level 1 is the most cost-effective implementation, which is a smart phone application that can provide the most basic functions: keeping track of the inventory (along with expiration and spoilage, as well as presumed location (the container the user admittedly places the given item)). A virtual iPantry Manager is realized where the user can manually enter each item, take note of its expiration date and the location it has been deposited to, and thus, the system can provide warnings on the approaching expiration date, as well as help locate the items in question. The system observes the habits of the user (adding and removing items, as well as the frequency of these actions and the amount of items), in order to provide more reliable notifications about restocking (which is more flexible than simply issuing a warning when the stocks reach at an arbitrary, low number). Of course, this level has the least amount of conveniency. Furthermore, the access is also restricted to a single person (the owner of the phone). At level 2, the main processing functions and the database (the iPantry Manager module) are implemented in a suitable low-cost single board computer (SBC), like the Raspberry PI 3B or Zero W [7]. Multiple users can access it through smartphone applications, as well as optional interfaces (such as touch-based displays). As it has been mentioned earlier, the iPantry system is built on the iSpace paradigm [6], where it is a requirement that any modification of the original space (which is to be turned into an Intelligent Space) should be minimal. Placing an SBC fits into this. Intelligent Spaces are created by using DINDs, which need to consist of 3 parts: at least one sensor, a computing unit and a network unit. Modern SBCs satisfy these requirements, providing a cheap way to set up iSpace environments. Thus, the iPantry Manager can communicate with other iSpace applications in the household, such as an iSpace based smart refrigerator [8], so they can realize a more complex system that can account for perishable items inside and outside of the fridge. Level 3 is divided into two distinct parts, both of which add more functionalities (and thus, more comfort and convenience) for the user, but different types of services, so the two parts can still be used without each other. Level 3/a can provide barcode scanners and image data for easier (and faster) product registration, as well as expiration date scanners for expiration date registration. Level 3/b can provide further aid with locating given items (e.g., through RFID localization [9]), and give more information (e.g., using weight scales in the storage compartment to see if there has been any access to said compartment, and as a result, a change in the content). Thermometers and hygrometers are another very important addition to the iPantry, as many perishable items are required to be stored at certain temperatures and humidity levels, outside of which their shelf life might be significantly reduced. Furthermore, there have been many research projects aiming to create spoilage sensors [10, 11], which can help the system more accurately warn its users about food spoilage.

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3 The iPantry Manager The iPantry Manager (Fig. 2) consists of 4 main parts. The System Database contains the relevant information that is required for the operation of the system. The User Interface is used to add or remove items, or change the location of existing ones. Each of these actions is logged. The list of known containers can also be modified and customized, and read their sensory data if such is available. Remarks: A perishable item is considered “fresh” until its expiration date, then “stale” until its estimated spoilage date (which is calculated from the type of item and its expiration date). Technically, a product can still be safely consumed after its expiration date [13] for a period of time, though their quality (the taste for food and drink items, the potency for medicine, etc.) is no longer assured by the manufacturer. The length of this time period depends on the type of the given item, as well as the condition it is in, the parameters of the storage it has been stored in (e.g. temperature), etc. Thus, this period of time can only be estimated using said parameters. After this time, the item is deemed “spoiled”, unfit for human consumption. The system has two autonomous modules: the Product State Watcher that automatically checks the state of the items (i.e., have they gone stale or spoiled). This is done through storing the expiration and estimated spoilage dates in an Event List, which is checked on a daily basis in a time window (e.g. for a week or month forward in time from the current day), and issues warnings if any such event is expected to happen in the near future for any of the stored items. The other autonomous module is the Stock Quantity Watcher, which observes the actions of the user (adding or removing items from the stocks), and learns when to issue a recommendation for purchasing new units of a given item with low current stocks, as well as the recommended quantity. To achieve this, fuzzy logic [12] is used.

Fig. 2 The architecture of the iPantry Manager

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3.1 System Database The System Database consists of 6 parts: • Item Database: stores the details the various items stored in the pantry. • Logs: Keeping track of the actions of the user regarding the Item Database. • Container List: The items are stored in various physical containers in the pantry, with known location, appearance (e.g. color-coded, numbered, etc.) and if available, sensory data (weight, temperature, etc.). • Event List: storing the upcoming expiration and spoilage dates. • Warning Lists: lists for the user about upcoming expiration or spoilage of stored items, as well as recommendations for restocking the items with low quantities. • Quantity rules: the rules used to determine which items to recommend for restocking, with estimated quantities. An example for the Item Database can be seen in Table 1. For each item, an ID number, its name, type (food, water, medicine, hygienic product, etc.), subtype (painkiller, soap, etc.) are noted, as well as the ID of the container they are stored in. Furthermore, their expiration date, estimated spoilage date, quantity (Q) and current state (fresh, stale or spoiled) are marked. Similarly to the Item Database, the other data stores can be implemented using simple lists. However, for the Event List (as seen in Fig. 3) a slightly more advanced structure (multilevel linked lists) is used, so the system does not need to look through Table 1 An example for the item database (Date format: DD/MM/YY) ID

Name

Type

Subtype

Container

Exp. date

Spoilage date

Q

Current state

#1

Ibuprofen

Medicine

Painkiller

#2

12/10/20

26/11/21

1

Stale

#2

Tasty Beans

Food

Canned food

#1

10/01/22

10/01/25

2

Fresh

#3

Handsoap

Hygienic product

Soap

#2

–

–

4

–

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Aqua

Water

Mineral water

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20/11/21

10/01/24

3

Fresh

#5

Beefy Ramen

Food

Instant noodles

#1

04/05/17

20/11/21

2

Stale

#6

Ancient sandwich

Food

Sandwich

#1

01/01/18

01/01/21

1

Spoiled

Fig. 3 The structure of the Event List

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the whole list of items each time the expiration dates are needed to be checked. The main list consists of the important dates in chronological order. Each entry contains a list of the known events on these days: expiration (E) or spoilage (S) of given items, along with the ID of the corresponding item. Whenever a new item is added, its expiration date and derived spoilage date are taken and searched for in the event list. If no such day is in the system yet, then it is added. The two new events belonging to the new item are subsequently added to the date. If an item is removed, then the system deletes the events related to them from the event list (as well as the warning lists, if it was already on them). The system periodically checks the event list. If any new event will happen within a month or week that is not on the monthly or weekly warning list yet, then it is added to the corresponding one. For example, on November 14, 2021, the system will warn that item #3 will expire and item #4 will spoil in a week, and item #1 will spoil in a month.

3.2 Stock Quantity Watcher The purpose of the Stock Quantity Watcher is to check the available amount of each stored item, and issue a warning if the amount falls under a certain, adaptable threshold. In order to determine this threshold for each item, fuzzy inference is applied based on observations about the actions of the user (i.e., adding or removing items from the pantry). Let us consider that the (isosceles) triangular fuzzy set of the current amount of a given item is described with three parameters: I = (a, b, c), where c is the center of the isosceles triangle, while the two ends are a and b (a < c < b). Similarly, let the output fuzzy set described with O = (A, B, C), where A < C < B. This is illustrated in Fig. 4. The activation level of the fuzzy set belonging to the current amount of a given item:

Fig. 4 The fuzzy sets for the stock watcher subsystem. The inference process is marked with the colored lines: first, using the parameters (a, b, c) of the input fuzzy set µ(x) the fuzzy activation value µ(x 0 ) is calculated from the current amount of the given item (x 0 ), then the proposed amount of items (z0 ) (that should be purchased to restock) is calculated from µ(x 0 ) using the parameters (A, B, C) of the associated output fuzzy set (µ(z))

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⎧ ⎪ ⎨1 − µ(x0 ) = 1 − ⎪ ⎩0

i f x0 ∈ [a, c] i f x0 ∈ [c, b] other wise

c−x0 c−a x0 −c b−c

(1)

Similarly, fuzzy membership function value for the output of the fuzzy rule can be written as: ⎧ C−z ⎪ ⎨ 1 − C−A0 f z 0 ∈ [A, C] 0 −C µ(z 0 ) = 1 − zB−C (2) i f z 0 ∈ [C, B] ⎪ ⎩0 other wise The goal is to find the z0 output value belonging to the input value x 0 , (i.e., the value for which µ(x 0 ) = µ(z0 )). In Mamdani-type fuzzy inference, the centroid of the resulting fuzzy set is taken, but in this application the problem is much simpler: if the amount of stored item is lower, then more of it will be purchased to replenish the stocks. Thus, x and z are in an inverse relationship. For example, let us consider the user buying 24 bars of soap (as washing hands is very important during a global pandemic). They use it one by one, until the number of soap bars in storage is reduced to 4, in which case the user buys 15 more. From this, the system can deduce that 4 is a critically low number for these stocks. In this hypothetical scenario time goes on, and when there are only 6 bars of soap remaining, the user once again adds more, this time 18 bars of soap. It is easy to see that the user has a preference for not only a minimal amount of soap in their storage, but also a maximal number that can be roughly estimated from the observed data. Furthermore, the amount of purchased soap bars is in an inverse ratio to the currently stocked amount of soaps. Thus, since it is known that for the x0 ∈ [a, c] values the output values should be z 0 ∈ [C, B] and vice versa, for x0 ∈ [c, b] input the output should be z 0 ∈ [A, C], therefore (2) can be made simpler: ⎧ ⎪ ⎨1 − µ(z 0 ) = µ(x0 ) = 1 − ⎪ ⎩0

C−z 0 C−A z 0 −C B−C

i f x0 ∈ [c, b] i f x0 ∈ [a, c] other wise

(3)

Consequently, z0 can be easily calculated: ⎧ ⎨ (µ(x0 ) − 1)(C − A) + C i f x0 ∈ [c, b] z 0 = C − (µ(x0 ) − 1)(B − C) i f x0 ∈ [a, c] ⎩ 0 other wise

(4)

Using (4), the system can provide an estimation to the amount that should be purchased from a given item, based on the preferences of the user. Let us take the previous soap example to illustrate this procedure. The system makes observations of the actions of the user, in {old amount, newly added amount}

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Fig. 5 A practical illustration for the fuzzy inference for input value x 0 = 4

form, recording 3 observations: {0, 24}, {4, 15}, and {6, 12}. From this, the system can set up the fuzzy sets: I soap = (0, 6, 3) and Osoap = (12, 24, 18). This is shown in Fig. 5. Let us suppose that x 0 = 4. Using (1), µ(x 0 ) = 2/3 is calculated, from which the gained output (the amount of soap that the system will automatically recommend to purchase when the current stocks fall below 6) is z0 = 16.

3.3 Extra Functionalities and Hardware The iPantry Manager module does not require too much computational power for level 2, so any relatively cheap SBC is sufficient for its implementation (such as a Raspberry PI Zero W). The main functionalities of the system are utilized at the lower levels (1 and 2), although it means the Item Database needs to be manually changed by the user each time they interact with the storages (by adding, moving and removing items). Level 3 can provide additional convenience at the cost of more complexity and monetary expenditures. A camera can be used to recognize an item either by taking a photo of it and cross-referencing it with known products (similarly to Google Lens [14, 15]), by reading the barcode [16], etc.; or even read the expiration date automatically. Cameras can also be used to determine which container the user accessed. Expiration date and barcode scanners are readily available for many SBC platforms in the market. Of course, these functions may require a stronger SBC, such as the Raspberry PI 4B [17], which is used in the current implementation. Item localization is another way to improve convenience, if the user prefers to relocate objects after they first have deposited them. RFID-based localization is a popular method these days, in which cheap tags are glued to the products so they can be tracked with relative precision. Bluetooth-based localization is also an option [18].

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4 Conclusion In this paper, an iSpace-based novel smart storage framework is presented for average households, for the tracking of the expiration date and location of stored items and providing users with information and warnings about the goods in the storage using cheap, affordable devices. Various options are explored in order to create a system that can be tailored to the needs (and financial situation) of the user. Furthermore, the system can advise the user in the replenishing of the stocks, for which a fuzzy logic-based inference is used to recommend a quantity as well as the item that should be purchased to refill the stocks. This is particularly useful if the user has multiple units of the same product distributed among many containers, saving them the time and effort of manually counting them and determining how many new units should be bought. The proposed system is more flexible than other ones in literature, due to the application of fuzzy logic, and multiple levels of implementation, so the user can tailor the Intelligent Pantry storage system to fit their needs. Furthermore, the system is designed to accommodate not just perishable items, but other items (hygienic accessories, medicine, etc.) as well. In future work, more research will be put into the methods of estimating the spoilage date of perishable items, in order to enhance the capabilities of the system. The personal taste of the user can also be modeled by observing the characteristics of the stored items (e.g. if the user particularly favors pasta, strawberry-flavored snacks, or prominently buys gluten-free products, etc.). Furthermore, cybersecurity as an aspect has not been considered yet, even though online privacy is an important topic of discussion these days. Acknowledgements This publication the result of the Research & Innovation Operational Programme for the Project: “Support of research and development activities of J. Selye University in the field of Digital Slovakia and creative industry”, ITMS code: NFP313010T504, co-funded by the European Regional Development Fund. This work has also been partially sponsored by the Hungarian National Scientific Fund under contract OTKA 139418.

References 1. Manikandan, T., Selvan, D.T., Vaidhyanathan, R., Vigneshvaran, B., Nandalal, V.: Home groceries management system using IOT. Int. J. Psychosoc. Rehabil. 24(05), 2741–2746 (2020) 2. Suganya Devi, K., Sabarish, N.S., Sankra, M., Santhosh Kumar, A.: Internet of Things (IoT) based smart kitchen pantry. Irish Interdisc. J. Sci. Res. 4(3), 56–61 (2020) 3. Ahir, D.D., Sarang, M., Kokpkar, P., Jadhav, S., Pathak, S.: Smart pantry. Int. J. Sci. Dev. Res. 4(4), 482–485 (2019) 4. Chopade, S.D., Nighot, M.K.: Smart kitchen management using IOT technology. Int. J. Adv. Res. Comput. Eng. Technol. 8(7), 269–271 (2019) 5. Sobota, D., Karlovits, S.W., Khan, A.S.: Senior Project Design: A Smart Pantry System. American Society for Engineering Education (2017)

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6. Hashimoto, H.: Intelligent space: interaction and intelligence. Artif. Life Rob. 7(3), 79–85 (2003) 7. Gay, W.W.: Raspberry Pi Hardware Reference (2014). https://doi.org/10.1007/978-1-48420799-4 8. Tusor, B., Gubo, S., Kmeˇt, T., Tóth, J.T.: Augmented Smart Refrigerator—An Intelligent Space Application (2020). https://doi.org/10.1007/978-3-030-36841-8_17. 9. Huang, S., Shi, Z.H.: An application of autonomous learning multimodal system for localization in industrial warehouse storage rack. In: Proceedings of the 18th IEEE International Conference on Machine Learning and Applications, pp. 519–522 (2019) 10. Alamdari, N.E., Aksoy, B., Aksoy, M., Beck, B.H., Jiang, Z.: A novel paper-based and pHsensitive intelligent detector in meat and seafood packaging. Talanta 224, 121913 (2021) 11. Liu, S.F., Petty, A.R., Sazama, G.T., Swager, T.M.: Single-walled carbon nanotube/metalloporphyrin composites for the chemiresistive detection of amines and meat spoilage. Communication 54(22), 6554–6557 (2015) 12. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965). https://doi.org/10.1016/s0019-995 8(65)90241-x 13. Zielinska, D., Bilska, B., Marciniak-Łukasiak, K., Łepecka, A., Trzaskowska, M., NeffeSkocinska, K., Tomaszewska, M., Szydłowska, A., Kołozyn-Krajewska, D.: Consumer understanding of the date of minimum durability of food in association with quality evaluation of food products after expiration. Int. J. Environ. Res. Public Health 17(5), 1632 (2020) 14. Townsend, T.: Google Lens Is Google’s Future. Recode (2017) 15. Bilyk, Z.I., Shapovalov, Y.B., Shapovalov, V.B., Megalinska, A., Andruszkiewicz, F., Dolhanczuk-Sródka, A.: Assessment of mobile phone applications feasibility on plant recognition: comparison with Google Lens AR-app, pp. 61–78 (2020). Corpus ID: 229549001 16. Hosozawa, K., et al.: Recognition of expiration dates written on food packages with open source OCR. Int. J. Comput. Theory Eng. 10(5), 170–174 (2018) 17. Raspberry Pi (Trading) Ltd.: Raspberry Pi 4 Model B datasheet (2019). https://datasheets.ras pberrypi.org/rpi4/raspberry-pi-4-datasheet.pdf 18. Hossain, A.K.M.M., Soh, W.: A comprehensive study of bluetooth signal parameters for localization. In: 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5 (2017)

A Parallelization of Instance Methods of a .NET Application that Search for Required Structured Data Stored in a Skip List Igor Košt’ál

Abstract The skip list is a more memory efficient version of a single level linked list. Searching for the required data elements in a skip list is more efficient than in a single level linked list because a skip list allows us to skip to the searched element in it. We have created a C# .NET application that uses a skip list with structured data in its data elements. This application can perform search operations within these data elements using serial, threaded, and parallelized instance methods, and simultaneously it is able to measure the execution times of particular methods. By comparing these times, we have examined the execution efficiency of parallelized instance methods of the object of the .NET application compared to threaded and serial instance methods of the same object. The results and evaluation of this examination are listed in the paper. Keywords Parallelization · Skip list · Data element · Structured data · .NET application

1 Introduction A single level linked list (a simple list) is a dynamic data structure that is used for storing data in applications. However, there are also multi-level linked lists (called skip lists [5]) that are more complicated for creating but searching for the required data elements in them is more efficient because they allow us to skip to the searched element in them. We dealt with a comparison of execution efficiency of the use of a skip list and simple list in a C# .NET Application [2]. This application had the same structured data of students stored in the data elements of its skip and simple list. From the results of the comparison of the execution times of the search operations and the insertion operation performed by our .NET application in the skip list and execution times of I. Košt’ál (B) Faculty of Economic Informatics, University of Economics in Bratislava, Dolnozemská cesta 1, 852 35 Bratislava, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_11

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the same operations performed by the same application but in a simple list, it was obvious that the use of the skip list in this application was with a bigger number of data elements containing structured data, significantly more efficient [2]. We were interested in now, how to parallelize search operations in a skip list with the structured data of students in its data elements. We have created a C# .NET application that uses a skip list with such data elements. This application parallelizes the execution of search instance methods in its instance methods, and at the same time measures the execution times of its serial and parallel search instance methods. By comparing these times, we have examined the execution efficiency of a parallelization of search instance methods of our C# application. The results and evaluation of this examination are listed in the paper.

2 A Simple List and a Skip List A one-way linked list (a simple list) (Fig. 1a) is a set of dynamically allocated elements (called nodes), arranged in such a way that each element contains two items—the data and a reference to the next element [2]. The last node has a reference to NIL (a special node, which terminates the list). A linked list is a real dynamic data structure [1]. The number of nodes in a list is not fixed and can grow and shrink on demand [1]. Any application which has to deal with an unknown number of objects will need to use a linked list [1]. We might need to examine every node of the list when searching a simple list. If the list is stored in sorted order and every other node of the list also has a reference to the node four ahead it in the list (a skip list) (Fig. 1b), we have to examine no more than [n/4] + 2 nodes (where n is the length of the list) [5]. This data structure could be used for fast searching for required nodes. We have created a C# .NET application that uses a skip list, but with structured data of students in its data elements (Fig. 2). Using our .NET application, we have examined the execution efficiency of a parallelization of search instance methods of this application.

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Fig. 2 The internal structure of the skip list that uses our .NET application [2]

3 Parallel Programming in the .NET Framework Nowadays, many personal computers and workstations have multiple CPU cores that enable multiple threads to be executed simultaneously. To take advantage of the hardware, we can parallelize our code to distribute work across multiple processors. The Microsoft Visual Studio development environment and the .NET Framework 4 enhance support for parallel programming by providing a runtime, class library types, for example, the Task Parallel Library, and diagnostic tools. These features simplify parallel development. We can write efficient, fine-grained, and scalable parallel code in a natural idiom without having to work directly with threads or the thread pool. [3] The Task Parallel Library (TPL) is a set of public types and APIs (Application Programming Interfaces) in the System.Threading and System.Threading.Tasks namespaces. The purpose of the TPL is to make developers more productive by simplifying the process of adding parallelism and concurrency to applications [3]. We know two kinds of a parallelism in parallel programming in the .NET Framework—data and task parallelism. Data parallelism refers to scenarios in which the same operation is performed concurrently (that is, in parallel) on elements in a source collection or array. In data parallel operations, the source collection is partitioned so that multiple threads can operate on different segments concurrently. The TPL supports data parallelism through the System.Threading.Tasks.Parallel class. This class provides methodbased parallel implementations of for and foreach loops. We write the loop logic for a Parallel.For or Parallel.ForEach loop much as we would write a sequential loop. We do not have to create threads or queue work items. The TPL handles all the low-level work for us. When a parallel loop runs, the TPL partitions the data source

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so that the loop can operate on multiple parts concurrently [3]. Data parallelism with the Parallel.ForEach method is used in a parallelized code of our .NET application. The TPL is based on the concept of a task, which represents an asynchronous operation. In some ways, a task resembles a thread or ThreadPool work item, but at a higher level of abstraction. The term task parallelism refers to one or more independent tasks running concurrently [3]. Task parallelism with the Parallel.Invoke method is used in a parallelized code of our .NET application. Several overloads of the Parallel.For and Parallel.ForEach methods are used in data parallelism. We introduce a brief description of this overload of the Parallel.ForEach method from [3] that is used in a parallelized code of our .NET application. This description explains how this method operate in this code. The Parallel.ForEach method executes a foreach operation on an IEnumerable in which iterations may run in parallel. Syntax public static ForEach(IEnumerable source, Action body); Type Parameters TSource—the type of the data in the source. Parameters IEnumerable source—an enumerable data source. Action body—the delegate that is invoked once per iteration. Remarks The body delegate is invoked once for each element in the source enumerable. It is provided with the current element as a parameter. The Parallel.ForEach method does not guarantee the order of execution. Unlike a sequential ForEach loop, the incoming values are not always processed in order. We also introduce a brief description of the Parallel.Invoke method from [3] that is used in a parallelized code of our .NET application. This description explains how this method operate in this code. The Parallel.Invoke method executes each of the provided actions, possibly in parallel. Syntax public static void Invoke (params Action[] actions); Parameters Action[] actions—an array of Action to execute. Remarks This method can be used to execute a set of operations, potentially in parallel. No guarantees are made about the order in which the operations execute or whether they execute in parallel. This method does not return until each of the provided operations has completed, regardless of whether completion occurs due to normal or exceptional termination.

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4 C# .NET Application with Serial, Threaded, and Parallel Instance Methods Our .NET application was developed in the C# language in the development environment Microsoft Visual Studio 2019 Enterprise for the Microsoft .NET Framework version 4 and for the Microsoft Windows 10 operating system. The .NET application has the structured data of students stored in the data elements of a skip list. This application can perform search operations within these data elements using serial, threaded, and parallelized instance methods, simultaneously it is able to measure the execution times of particular methods, and write these times into the LogFile.txt disc file. The source code of this application is divided into two parts that are saved into files: SkipLNodeClasses.cs—the SkipList class is defined here. This class includes member methods that are able to perform all search operations such as searching for students (data elements) according to their points for accommodation, year of birth, surname, ISIC, and according to distance between their residence and university in a skip list (find_1st_pts, find_1st_y, find_1st_surname, find_1st_isic, find_1st_distanceOfResid), inserting of a new student (a data element) sorted into a skip list (insert_1st). The SkipList class also includes service member methods serving to delete particular data elements or all data elements. The SkipListNode nested class is defined within the scope of the SkipList class. The SkipListNode class object represents one data element (node) of a skip list. In the SkipList class, two structures the Student and the date are declared, by which the data part of the data element of the skip list is created. From the SkipLNodeClasses.cs source file was built (.dll) library into the SkipListNode.dll file that is the dynamic run-time component of our .NET application. SkipList_WinForm1.cs—the SkipList_WinForm1 class that is derived from the System.Windows.Forms.Form class is defined here. This system class represents a window that makes up an application’s user interface. The SkipList_WinForm1 class includes event handlers of all controls of the user interface of our .NET application and helper methods. The SerialSearchBtnClick, SearchOnMultipleThrdsBtnClick, ParallelInvokeSearchBtnClick, and ParallelForEachSearchBtnClick event handlers of four buttons contain serial, threaded, and parallelized search operations source code that allows us to search for students (data elements) in a skip list according to several required parameters, for example, according to several points for accommodation, several distances of a residence of students from the university, etc. The user inserts these several different required parameters into the requiredValueTB input text box of the .NET application. From this text box, they are read by the Parse_requiredValueTB helper member method, which is called by each of the mentioned event handlers. The SerialSearchBtnClick event handler (Fig. 4) searches for students (data elements) in the skip list according to several required parameters in serial. In the displayed part of the source code of this event handler, students are searched for according to the 4 required distances of their residence from the university. The

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event handler performs a search on one thread in serial using four calls to the SearchStudentsWithDistOfResid helper member method (Fig. 3), which performs the real search for particular students (data elements) in the skip list. The SearchOnMultipleThrdsBtnClick event handler (Fig. 5) searches for students (data elements) in the skip list according to several required parameters on multiple threads. In the displayed part of the source code of this event handler, students are searched for according to the 4 required distances of their residence from the university. The event handler performs a search on four threads by calling the SearchStudentsWithDistOfResid helper member method (Fig. 3), which performs the real search for particular students (data elements) in the skip list. private string SearchStudentsWithDistOfResid(int distance_f) { string str_result = ""; int i = 0; if (flag_stream == 1) while (i < students_count) { if (ref_arr_students[i].isic != 30000000000000001) // calling the instance method of the ‘list’ object (this is object of the ‘SkipList’ class) str_result += list.find_1st_distanceOfResid(ref_arr_students[i], distance_f); i++; }... return str_result; }

Fig. 3 The part of the source code of the SearchStudentsWithDistOfResid helper member method

private void SerialSearchBtnClick(object sender, EventArgs e) { string str_result = ""; string str_result1 = ""; string str_result2 = ""; string str_result3 = ""; string str_result4 = ""; . . . int requiredValuesCount = Parse_requiredValueTB(); // calling the helper member method ... // if the ‘residDistance_RB’ radio button is checked else if (residDistance_RB.Checked) { // if the user inserted 4 values into the 'requiredValueTB' text box if (requiredValuesCount == 4) { . . . int dist1 = numbersIn_requiredValueTB[0]; int dist2 = numbersIn_requiredValueTB[1]; int dist3 = numbersIn_requiredValueTB[2]; int dist4 = numbersIn_requiredValueTB[3]; // perform four tasks in serial on the source data str_result1 = SearchStudentsWithDistOfResid(dist1); // calling the helper member method str_result2 = SearchStudentsWithDistOfResid(dist2); str_result3 = SearchStudentsWithDistOfResid(dist3); str_result4 = SearchStudentsWithDistOfResid(dist4); ...}...}...}

Fig. 4 The part of the source code of the SerialSearchBtnClick event handler

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private void SearchOnMultipleThrdsBtnClick(object sender, EventArgs e) { string str_result = ""; string str_result1 = ""; string str_result2 = ""; string str_result3 = ""; string str_result4 = ""; . . . int requiredValuesCount = Parse_requiredValueTB(); // calling the helper member method ... else if (residDistance_RB.Checked) { // if the ‘residDistance_RB’ radio button is checked // if the user inserted 4 values into the 'requiredValueTB' text box if (requiredValuesCount == 4) { ... // create the delegate object and bind to the 'SearchStudentsWithDistOfResid' target method AsyncMCallerWF1C callerM1 = new AsyncMCallerWF1C(SearchStudentsWithDistOfResid); ... // execute 4 methods asynchronously on 4 secondary threads IAsyncResult asyncState3 = callerM1.BeginInvoke(numbersIn_requiredValueTB[3], null, null); IAsyncResult asyncState2 = callerM1.BeginInvoke(numbersIn_requiredValueTB[2], null, null); IAsyncResult asyncState1 = callerM1.BeginInvoke(numbersIn_requiredValueTB[1], null, null); IAsyncResult asyncState0 = callerM1.BeginInvoke(numbersIn_requiredValueTB[0], null, null); ... // capture all return values str_result1 = callerM1.EndInvoke(asyncState3); str_result2 = callerM1.EndInvoke(asyncState2); str_result3 = callerM1.EndInvoke(asyncState1); str_result4 = callerM1.EndInvoke(asyncState0); ...}...}...}

Fig. 5 The part of the source code of the SearchOnMultipleThrdsBtnClick event handler

The ParallelInvokeSearchBtnClick event handler (Fig. 6) searches for students (data elements) in the skip list according to several required parameters in parallel, using task parallelism. In the displayed part of the source code of this event handler, students are searched for according to the 4 required distances of their residence from the university. The event handler performs a search in parallel by calling the Parallel.Invoke method, in which task parallelism is implemented. The SearchStudentsWithDistOfResid helper member method (Fig. 3) is called in the lambda expressions of the Parallel.Invoke method. This helper method performs the real search for particular students (data elements) in the skip list. The ParallelForEachSearchBtnClick event handler (Fig. 7) searches for students (data elements) in the skip list according to several required parameters in parallel, using data parallelism. In the displayed part of the source code of this event handler, students are searched for according to the 4 required distances of their residence from the university. The event handler performs a search in parallel by calling the Parallel.ForEach method, in which data parallelism is implemented. The SearchStudentsWithDistOfResid helper member method (Fig. 3) is called in the lambda expression of the Parallel.ForEach method. This helper method performs the real search for particular students (data elements) in the skip list.

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private void ParallelInvokeSearchBtnClick(object sender, EventArgs e) { string str_result1 = ""; string str_result2 = ""; string str_result3 = ""; string str_result4 = ""; . . . int requiredValuesCount = Parse_requiredValueTB(); // calling the helper member method . . . else if (residDistance_RB.Checked) { // if the 'residDistance_RB' radio button is checked // if the user inserted 4 values into the 'requiredValueTB' text box if (requiredValuesCount == 4) { . . . int dst1 = numbersIn_requiredValueTB[0]; int dst2 = numbersIn_requiredValueTB[1]; int dst3 = numbersIn_requiredValueTB[2]; int dst4 = numbersIn_requiredValueTB[3]; // perform four tasks in parallel on the source data Parallel.Invoke( () => { str_result1 = SearchStudentsWithDistOfResid(dst1); }, //close the first Action () => { str_result2 = SearchStudentsWithDistOfResid(dst2); },//close the second Action () => { str_result3 = SearchStudentsWithDistOfResid(dst3); }, // close the third Action () => { str_result4 = SearchStudentsWithDistOfResid(dst4); } // close the fourth Action ); // close Parallel.Invoke . . . } . . . } . . . }

Fig. 6 The part of the source code of the ParallelInvokeSearchBtnClick event handler

private void ParallelForEachSearchBtnClick(object sender, EventArgs e) { . . . int requiredValuesCount = Parse_requiredValueTB(); // calling the helper member method . . . else if (residDistance_RB.Checked) { // if the 'residDistance_RB' radio button is checked if (requiredValuesCount > 0) { int k = 0; string local_str_result = ""; int[] integer_inputs = new int[requiredValuesCount]; while (k < requiredValuesCount) { integer_inputs[k] = numbersIn_requiredValueTB[k]; k++; } // the 'local_strings' object represents a thread-safe, unordered collection of objects var local_strings = new ConcurrentBag(); Parallel.ForEach(integer_inputs, currentInput => { local_strings.Add(SearchStudentsWithDistOfResid(currentInput)); }); List strings_list = local_strings.ToList(); foreach (string str in strings_list) local_str_result += str; ...}...}...}

Fig. 7 The part of the source code of the ParallelForEachSearchBtnClick event handler

The SkipList_WinForm1.cs source file was built into the SkipList_WinForm.exe file. The SkipList_WinForm.exe is a Windows application that uses the dynamic run-time component SkipListNode.dll. As we mentioned above, our .NET application can perform search operations within data elements of a skip list using serial, threaded, and parallelized instance methods, and simultaneously it is able to measure the execution times of particular methods. By comparing these times, we have examined the execution efficiency of parallel search operations in a skip list in an experiment.

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5 Experiment, its Results and their Brief Analysis We assume that parallel search operations of our .NET application searching for students (data elements) in the skip list according to several required parameters are more execution efficient than serial and threaded search operations of the same .NET application searching for the same students in the same skip list according to the same several required parameters. With an increasing number of data elements in the skip list, the execution efficiency of parallel search operations should increase compared to the execution efficiency of serial and threaded search operations. To verify these assumptions, we performed an experiment using our .NET application. During this experiment, our .NET application worked with 4 large sets of student data, which were stored in 4 disk files students_c.txt, students100c.txt, students200c.txt and students300c.txt. For each search, the .NET application always loaded data from one of these four disk files into its skip list. The first set of data after loading into the skip list of the .NET application contained 11 data elements with structured data of students. The other 3 data sets, also after loading into the .NET application skip list, contained 100, 200 and 300 data elements with structured data of students. The .NET application searched for students (data elements) in each of these data sets loaded in the skip list according to several required parameters. During the first search for students (data elements) in a skip list with 11, 100, 200 and 300 data elements according to 4 (77; 61; 69; 41) and 10 (77; 61; 69; 41; 55; 89; 65; 93; 78; 75) required points for accommodation, we gradually measured the execution times of the SerialSearchBtnClick serial instance method, the SearchOnMultipleThrdsBtnClick threaded instance method, the ParallelInvokeSearchBtnClick parallel instance method, and the ParallelForEachSearchBtnClick parallel instance method using a .NET application. During the second search for students (data elements) in a skip list with the same 11, 100, 200 and 300 data elements according to 4 (137; 65; 112; 454) and 10 (137; 65; 112; 454; 5; 101; 98; 78; 139; 6) required distances of residence of students from the university, we used a .NET application to gradually measure the execution times of the same four instance methods. The .NET application, which searched for the required data in the skip list and measured the execution times of particular searches, was running on a computer with the following basic hardware configuration: Intel Core i5-8250U Processor (6 MB Cache, 1.60 GHz, 4 GT/s, 4 Cores, 8 Threads), RAM: 8 GB. The Microsoft Windows 10 Home, 64 bit operating system and the Microsoft .NET Framework 4 were installed on this computer. One of the outputs of our .NET application displayed in its text box after searching for students according to 4 required parameters, distances between their residence and the university: 137; 65; 112; 454 [km] in the skip list with 100 data elements, is shown in Fig. 8. The same output writes the .NET application into the LogFile.txt disc file. The .NET application writes the results of other search operations into the same LogFile.txt file, too.

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surname first name (points for acc.) residence (dist. to univ.) date of the birth]

36104758538294289 36105758538295289 36106758538296289 36104278107495531 36072128769834583 36105278107496531 36073128769835583 36106278107497531 36074128769836583 36107278107498531 36075128769837583 36100427025140126 36130757022430100

Novak Peter Pelak Peter Pelak Jan Sykora Frantisek Sykora Andrej Sykora Dezider Sykora Peter Sykora Filip Sykora Roman Sykora Arpad Sykora Jakub Tell Viliam Jankovic Jan

(69) Humenne (454) 2000-03-09 (70) Humenne (454) 2000-03-10 (71) Humenne (454) 2000-03-11 (77) Dubnica-nad-Vahom (137) 1999-12-06 (77) Dubnica-nad-Vahom (137) 1999-12-05 (78) Dubnica-nad-Vahom (137) 1999-12-07 (78) Dubnica-nad-Vahom (137) 1999-12-06 (79) Dubnica-nad-Vahom (137) 1999-12-08 (79) Dubnica-nad-Vahom (137) 1999-12-07 (80) Dubnica-nad-Vahom (137) 1999-12-09 (80) Dubnica-nad-Vahom (137) 1999-12-08 (93) Sturovo (112) 2000-01-01 (75) Piestany (65) 2002-06-04

Search time: 00:00:00.0013853, 1,3853 ms

Fig. 8 The output of our .NET application displayed in its text box after searching for students according to 4 required parameters, distances between their residence and university: 137; 65; 112; 454 [km] in a skip list with 100 data elements

We calculated the speedups of every parallel instance method using p computing elements (CPUs) by the following formula Speedup( p) = Tserial /T ( p) The term T serial refers to the execution time of the serial version of the given method. The execution times of search operations performed by search instance methods of our .NET application on all four sets of skip lists and speedups of parallel search instance methods of this application are shown in the following graphs (Fig. 9). Brief Results Analysis. From the graphs showing execution times of serial, threaded, and parallel search instance methods and speedups of parallel search instance methods of the object of our .NET application in dependency on the number of data elements in a skip list, it is obvious that parallel methods have significantly shorter execution times and greater speedups at higher numbers of data elements compared to serial and threaded methods. A significant difference between these execution times can be seen at the number of data elements 200. Parallel instance methods achieve the highest speedups when searching in a skip list with 300 data elements and the smallest speedups (sometime less than 1, which is the deceleration) when searching in a skip list with 11 data elements.

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Fig. 9 The execution times of all search instance methods and speedups parallel search instance methods performed by our .NET application in the skip list

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6 Conclusion From the results of the comparison of the execution times of the serial, threaded, and parallel search instance methods and speedups of parallel search instance methods performed by our .NET application in the skip list, it is obvious that parallel instance methods of the object of this application are able to search required students (data elements) in a skip list with significantly shorter execution times at higher numbers of data elements than serial and threaded instance methods of the same object of this .NET application. From these comparison results, we can also say that for a skip list with small numbers of data elements containing structured data, e.g., 11 data elements, the execution efficiency of serial, threaded, and parallel search instance methods of the object of the .NET application is approximately the same and speedups of parallel methods are minimal, even sometime they are less than 1. However, if we use a big number of such data elements in a skip list of a .NET application, 200 or more, it is a good choice to use parallel search instance methods of the object of this application to search for required students (data elements) in a skip list.

References 1. Adamchik, V.S.: Linked Lists. http://www.cs.cmu.edu/~adamchik/15-121/lectures/Linked% 20Lists/linked%20lists.html. Accessed 17 July 2021 2. Košˇtál, I.: Comparison of execution efficiency of the use of a skip list and simple list in a .NET application. In: Laukaitis, G. (eds.) Recent Advances in Technology Research and Education Proceedings of the 17th International Conference on Global Research and Education InterAcademia – 2018. LNNS, vol. 53, pp. 252–259. Springer, Cham (2019) 3. Microsoft Corporation: Documentation. https://docs.microsoft.com. Accessed 17 July 2021 4. Niemann, T.: Sorting and Searching Algorithms. epaperpress.com (1999) 5. Pugh, W.: Skip lists: a probabilistic alternative to balanced trees. Commun. ACM 33(6), 668–676 (1990)

Deep Learning Applications for COVID-19: A Brief Review Hamed Tabrizchi , Jafar Razmara , Amir Mosavi , and Annamaria R. Varkonyi-Koczy

Abstract With the emergence of Coronavirus Disease 2019 (COVID-19) and millions of confirmed cases over the world, fast detection with low diagnosis error has become a pivotal task among the research community. Owing to image processing based methodologies in Artificial Intelligence (AI), the chest X-ray images along with Deep Learning (DL) algorithms have recently become a valid choice for early COVID-19 screening. This review has scanned well-known scientific databases based on the target intervention (DL) and the target population (COVID-19). This study retrieved 60 studies, after passing through excluding criteria, only 25 studies are considered in this brief review. Due to the need for a reference for the use of DL in the healthcare domain, this review paper has tried to provide a nutshell resource for researchers to think about the design of more effective Deep Learning models for early COVID-19 detection. Although the majority of the Deep learning methods are still in development and not tested in a clinical setting, the included studies in this literature showed that using Deep learning models can impact the detection of COVID-19 with an acceptable rate of accuracy. Keywords Deep learning · Computer vision · COVID-19

H. Tabrizchi (B) · J. Razmara Department of Computer Science, Faculty of Mathematical Sciences, University of Tabriz, Tabriz, Iran e-mail: [email protected] J. Razmara e-mail: [email protected] A. Mosavi John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary e-mail: [email protected] A. R. Varkonyi-Koczy Institute of Software Design and Software Development, Obuda University, Budapest, Hungary e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_12

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1 Introduction Most aspects of human life have been affected by COVID-19 (coronavirus disease) since December 2019. The World Health Organization (WHO) has declared COVID19 as a pandemic. Overcoming such a virus necessitates social organization, technological resolutions, and healthcare workers’ courage. As the World Health Organization suggests, molecular assays, including the RT-PCR (reverse-transcription polymerase chain reaction), must be used to confirm the entire COVID-19 diagnoses [1, 2]. In addition to medical imaging and RT-PCR, computed tomography (CT) has turned into a crucial technique to facilitate the management and diagnosis of COVID-19 patients. The radiologists’ crucial duty is providing early treatment and diagnosis to recognize the infection with COVID-19 from other situations with the same symptoms at computed tomography [3, 4]. In accordance with the changes occurred in COVID-19 radiography found in computed tomography images, artificial intelligence (AI) techniques can derive unique graphical characteristics of COVID19 and present a clinical diagnosis before the pathogenic tests, and as a result, leading to saving crucial time for control of disease [5]. One of the top praised techniques used in image-based healthcare applications is DL (Deep Learning). Thus, many studies have been suggested to develop devices with smart image-based diagnosis capability to detect COVID-19. The artificial intelligence methods equipped with DL systems based on medical imaging have been developed to extract image characteristics, such as spatial relation and shape characteristics. DL techniques have been used to determine the nature of recognized diseases through computed tomography images [6–8]. Manifestation of COVID-19 in images has been correlated with the severity of the disease, giving the means to determine its progression [9]. However, the RT-PCR result does not determine the disease stage or severity. The chest radiographs may function as a quantitative technique to determine the degree of COVID-19 involvement and assess the risk of admission to ICU (Intensive Care Unit). Given its rapid accessibility and high sensitivity, chest computed tomography has an important role in the management and diagnosis of COVID-19 and has been perceived as an imaging modality with the most sensitivity for the detection of complications [9, 10]. It should be noted that the imaging-based AI algorithms developed for the diagnosis of COVID-19 may facilitate the development of the same automatic systems for the likely future pandemics with no available RT-PCR tests. Given the above facts, the present brief review study concentrates on the improvement of image-based technological resolutions with special emphasis on deep learning.

2 Materials and Methods Using the existing literature, this section presents a brief background on the principles of deep learning, COVID-19, and an analysis of adopting deep learning in medical images processing.

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2.1 COVID-19 In December 2019, the COVID-19 index case was discovered in the city of Wuhan, Hubei province, People Republic of China. It should be noted that there are some sources show that the spread of the COVID-19 pandemic in China is likely to begin in late December 2019 [11]. It was declared and recognized as an infectious virus known as SARS CoV-2 (acute respiratory syndrome coronavirus 2). According to investigations, COVID-19 was probably originated from Huanan Seafood Market located in Wuhan. Finally, the government of People Republic of China acknowledged another 27 cases officially by December 2019 [12]. Through several experiments, the researchers realized that the wild bats could have transferred the infection. The virus is categorized as a beta-CoV (Beta-coronaviruses) that also includes SARS-CoV (SARS coronavirus) [13]. In [14], authors perform a number of tests regarding the factors in COVID-19 origin. They find that the virus originated from a Wuhan laboratory. The virus epidemic began in the spring carnival, and numerous individuals from around the world had travelled to China to take part in that event. The gathering of a huge population from various nations catalysed the virus spread not only within China but across the International borders to different nations. The WHO officials declared the epidemic as a PHEIC (Public Health Emergency of International Concern) in the last week of January. In climates with high humidity and temperatures, the COVID-19 spread is minimal. In addition, the virus spread or growth parameters are dependent on hygiene, environmental circumstances, physical contacts, and water droplets of respiration [14, 15].

2.2 Computer Vision in Medical Imaging Object localization, classification, and detection respectively refer to determining the location of available objects, the type of objects found in an image, and both location and type simultaneously. The quantity of publications employing computer vision approaches for static medical imagery has multiplied in recent years [16– 18]. Some areas, such as pathology, radiology, dermatology, and ophthalmology, have received considerable attention due to the ascending access to highly structured images, and the visual pattern-recognition capabilities required for diagnostic tasks in the above specialties [19]. The terms medical image analysis or medical imaging are employed to define a wide range of processes and techniques creating a general image of the interior parts of the body and specific tissues or organs as well. Generally, the disciplines covered by medical imaging include MRI (magnetic resonance imaging), ultrasound, X-ray radiography, thermography medical photography, endoscopy, etc. The medical image analysis is primarily aimed at increasing the efficiency of medical interventions and clinical examinations to have a look underneath bones and skin into the internal tissues and organs and figure out the origin of their problems. Firstly, medical imaging explores the inner workings of physiology and

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anatomy [20]. Secondly, the analysis of medical images contributes to diagnosis of the abnormalities and discovery of their impacts and causes. Medical image analysis is among the largest fields of healthcare in terms of data volume. This issue changes the use of intelligence resolutions into a logical decision [20, 21].

2.3 Deep Learning Artificial neural networks (ANN) and DL have received a great deal of momentum in today’s scientific studies because they are capable of learning from the context. Given their capability of adapting to multiple types of data across various domains, these methods have become popular in different applications, including image recognition, self-driven cars, prediction and classification problems, object recognition, smart homes, etc. Deep learning mimics the human brain functions when filtering data for accurate decision makings [22]. Just like the human brain, a system is trained through deep learning so that it can filter out the inputs via various layers to contribute to data classification and prediction. The above layers act as layered filters employed in ANNs within the brain in which each layer functions as a feedback to the next layer. These feedback cycles are continued until obtaining the accurate output. By assigning weights to each layer, the accurate outputs are developed, and these weights are modified throughout training to reach the precise output. The majority of deep learning techniques employ ANN for feature extraction and processing. Feedback method is utilized for the mechanism of learning in which the input data of each level is updated so that a summarized representation is formed. In deep learning, deep means the number of demanded layers for the data transformation [23]. During the above transformation procedure, a Credit Assignment Path (CAP) is employed. For feed-forward neural networks, the depth of CAP is determined via the quantity of hidden layers plus the quantity of output layers [24]. Recurrent Neural Networks (RNNs) might have multiple signals traversing frequently within a layer, and therefore, the depth of CAP is not determinable. The technique of feature extraction in CNN is automated, which is conducted throughout the training process on images making DL the most precise technique for image processing fields. CNN is the major deep learning approach used in these tasks. RNNs function in the same way as CNNs. The difference is that RNNs are predominantly employed for language computations. Recurrent Neural Network makes use of the feedback loops concept in which the output of one layer is fed as the input of the succeeding layer [25, 26]. One can use RNN for the datasets, including text, video, audio, time-series, financial data, etc. Generative Adversarial Networks (GANs) work based on the concept of the discriminator and the generator network. While the generator network creates fake data, the discriminator distinguishes the real and fake data. Both of these networks function toward devising the training procedure, and therefore, Generative Adversarial Networks are frequently employed in applications requiring the creation of images from the text [27]. One can use deep learning in various domains involving the processing of a large dataset. In smart cities, where a huge sum of data is created

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due to digitization, deep learning is of high potential. Two parameters determine the evaluation of deep learning methods: the first, the massive sum of data size to be processed and, the second, the massive computational power [28]. Also, deep learning contributes to the faster analysis of complicated medical images to render precise diagnoses. Deep learning is commonly used in the health care sector for assisting in the early diagnosis of diseases, broad data interpretations, and thus, decreasing the manual workload [28, 29]. In sum, given the above-cited issues, one can conclude that the use of a deep learning framework to diagnose COVID-19 via medical images may result in designing computer-aided diagnosis instruments for COVID-19 in clinical conditions. The following section provides more details on the models proposed on the basis of deep learning presented in recent years.

3 Deep Learning Medical Image Analysis Techniques in COVID-19 Generating precise models that can localize, classify, and detect various objects in a single image has remained a fundamental challenge in computer vision [30]. Given the recent progressions in computer vision models and deep learning, it is easier to develop medical applications of image detection than ever before. Such models pave the way for localization and recognition of various objects in an image, extracting the features and learning to distinguish the object type instances [31]. Chest imaging has a crucial function in determining the extent of infection and follow-up necessities in COVID-19 disease diagnosis. The COVID-19 patients are typically distinguished due to the diffused or patchy asymmetric opaqueness of their airspace [32]. Also, the computed tomography images and the indicators reflect the bilateral lung involvement. While patients with severe symptoms in the Intensive Care Unit show a consolidative pattern, the non-ICU ones have ground-glass patterns in their reports. In fact, ground-glass opacity is a finding seen on chest x-ray or CT imaging of the lungs that is defined as an area of hazy opacification (x-ray images) or increased attenuation (CT images) due to air displacement by fluid, airway collapse, fibrosis, or a neoplastic process. In contrast, the chest images in MERS and SARS diseases have presented unilateral indicators [33]. However, sometimes the chest images and primary X-ray have shown normalcy for the patients who had been already infected with the disease. This finding reflects the necessity of further validation via employing deep learning-based techniques or physical tests. To acquire computed tomography images, an X-ray captures the images of a particular part from various angles while rotating, which are saved in a computer and analyzed further to generate a new image eliminating the whole overlapping. Such images assist doctors in understanding the internal structures with improved clarity and having an idea of the structure, density, size, shape, and texture. Therefore, as a diagnostic method, a CT scan is more effective compared to an X-ray. The chest X-ray or computed tomography cannot

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distinguish the symptoms of COVID-19 from other symptoms associated with cold. The chest X-ray or CT often uncover the existence of an infection, which may also result from some other diseases [33, 34]. The conventional RT-PCR technique can detect the disease with high accuracy; however, it has a longer detection time and requires reagents. Pandemic crises with their ever-growing population of patients are in dire need of faster disease detection via spending minimal resources. To satisfy this demand, deep learning algorithms functioning on the basis of image processing have a prominent role in discarding the incredible crowd of those undergoing the swab test and the relevant ones [35]. Figure 1 shows the images of X-ray and CT scans for COVID-19 patients. While the disease is infecting millions of people, we are still faced with the lack of large datasets available for public use particularly concentrated on the tests lacking infections. The precision of a deep learning model is dependent on the data availability, and this is a great concern for COVID-19. Given the use of inadequate data, the model accuracy for deep learning becomes controversial. In addition, the access to X-ray and CT images of greater populations is necessity; however, collecting them

(a)

(b)

(c)

(d)

Fig. 1 a and b indicate chest X-ray images of COVID-19 patients, c and d indicate chest CT scan images of COVID-19 patients

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Table 1 A summary of DL for medical image processing in COVID-19 Authors

Title

Dataset

Methods

Ozturk et al. [36]

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Chest X-ray

CNN, DarkNet, DarkCovidNet

Jangam et al. [46]

Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking

CT scan, chest A stacked ensemble of X-Ray pre-trained computer vision models, VGG 19, ResNet 101, DenseNet 169 and WideResNet 50

Asmaa Abbas et al. [37] Classification of chest X-rays COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

CNN, ImageNet and ResNet and Decompose, Transfer, and Compose (DeTraC),

Che Azemin, Hassan, Mohd Tamrin, and Md Ali [56]

COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

Chest X-Ray

CNN, ResNet-101

Zebin and Rezvy [45]

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Chest X-ray

VGG-16, ResNet50, and EfficientNetB0 pre-trained on ImageNet dataset

Alam et al. [42]

COVID-19 Detection Chest X-ray from Chest X-ray Images Using Feature Fusion and Deep Learning

CNN, VGGNet

Hwang et al. [40]

COVID-19 pneumonia on Chest X-rays, chest X-rays: chest CT Performance of a deep learning-based computer-aided detection system

Deep learning-based CAD

Al-Waisy et al. [54]

COVID-CheXNet: hybrid Chest X-ray deep learning framework for identifying COVID-19 virus in chest X-rays images

COVID-CheXNet

(continued)

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Table 1 (continued) Authors

Title

Wang, Lin, and Wong [60]

COVID-Net: a tailored Chest X-ray deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Dataset

Methods COVID-Net

Singh, Pandey, and Babu [52]

COVIDScreen: Chest X-ray explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Base learners: VGG-16, ResNet-50, DenseNet-121, and DenseNet-169 Meta-learner: Naive Bayes

Umer et al. [51]

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images

Chest X-ray

CNN

Mahmud, Rahman, and CovXNet: A Fattah [53] multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization

Chest X-ray

CovXNets

Ouchicha, Ammor, and CVDNet: A novel deep Meknassi [59] learning architecture for detection of coronavirus (Covid-19) from chest x-ray images

Chest x-ray

CNN, CVDNet

Ismael and Sengür ¸ [44] Deep learning approaches Chest X-ray for COVID-19 detection based on chest X-ray images

ResNet18, ResNet50, ResNet101, VGG16, and VGG19

Yoo et al. [58]

Deep Learning-Based Chest X-ray Decision-Tree Classifier for COVID-19 Diagnosis from Chest X-ray Imaging

Deep learning-based decision-tree classifier

S. A. B. P and Annavarapu [41]

Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification

CNN, ResNet50

Chest X-ray

(continued)

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Table 1 (continued) Authors

Title

Shervin Minaee et al. [38]

Deep-COVID: Predicting Chest X-rays COVID-19 from chest X-ray images using deep transfer learning

CNN, ResNet18, ResNet50, SqueezeNet, and DenseNet-121

Tang et al. [49]

EDL-COVID: Ensemble Deep Learning for COVID-19 Case Detection From Chest X-Ray Images

COVID-Net, EDL-COVID

Rahaman et al. [57]

Identification of Chest X-Ray COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Calderon-Ramirez et al. Improving Uncertainty [47] Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images

Dataset

Chest X-ray

Chest X-ray

Methods

Pre-trained CNN, transfer learning,

MixMatch, estimation methods like softmax, MCD and DUQ

Gupta, Anjum, Gupta, and Katarya [43]

InstaCovNet-19: A deep Chest X-ray learning classification model for the detection of COVID-19 patients using Chest X-ray

Integrated Stacking InstaCovNet-19

Rajaramanet al. [55]

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays

Chest X-rays

A custom CNN and a pre-trained ImageNet model

Xu, Lam, and Jia [39]

MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images

Chest X-ray

MANet, CNN, UNet with ResNet

Mukherjee et al. [50]

Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays

Chest X-ray

Shallow CNN

Madaan et al. [48]

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

Chest X-ray

CNN, XCOVNet

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from different healthcare organizations seems to be an impossible task. As the paper mentioned before, different studies are being carried out rigorously to overcome these limitations and get transparency in diagnosis of COVID-19. To ensure that the greatest number of papers are found in the field of detection of COVID19 considering the image-based deep learning methods, the present study aims to provide a systematic literature review method constituting the following steps. The presented study considers a multidisciplinary perspective. Moerover, it was not limited to any journals, conferences, or any specific category of them. In the current study, at the first step, 3,250 papers were found through initial searches in Google Scholar, Thomson Reuters Web-of-Science (WoS), and Elsevier Scopus databases. The keywords of “deep learning”, “COVID-19”, “Computer Vision”, “X-ray”, and “Coronavirus” were searched simultaneously in the title, abstract, and keywords of these papers. At the last step, the duplicate papers which were found in both databases were deleted, and the full text of 60 papers was carefully read, and finally, 25 papers were included in the present study. After a critical review of the evolving literature, it becomes clear that deep learning and medical image processing have a crucial role in fighting the COVID-19 pandemic. Table 1 summarizes the selected references.

4 Discussion After a critical review of the literature, it becomes clear that deep learning and medical image processing have a crucial role in fighting the COVID-19 pandemic by means of some promising applications such as coronavirus diagnosis. Table 1 summarizes this brief survey and the selected references. In fact, the use of deep learning in medical image analysis effectively supports prediction of disease in gigantic datasets acquired from accessible sources, including healthcare institutes and health organizations. The applications of deep learning concentrate on medical imaging emerged as a promising approach. The applications of deep learning are employed to analyze and process medical imaging data to assist doctors and radiologists in improving the diagnosis accuracy (around 94 to 98% accuracy). Deep learning using medical images also has the capability of identifying the potential targets of a suitable COVID-19 vaccine. Numerous studies have been carried out on COVID-19 to underline the automated diagnosis of COVID-19 through deep learning systems via medical imaging datasets.

5 Conclusion It is undeniable that deploying DL-based detection models on computers with limited image processing power, lack of image datasets with sufficient instances, lowresolution images is a difficult task. Our presented brief review study summarizes the recent efforts on COVID19 detection considering deep learning. Regardless of promising results, the success in applying deep learning for processing COVID-19

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medical images necessitates considerable effort, time and close cooperation between various parties from industry, academia, and government. In accordance with the whole studies mentioned above, we assume that the use of image processing and deep learning techniques and also various technologies, including mobile communications, data science, and biomedicine, can present practical solutions to overcome the COVID19 outbreak. Furthermore, from the analysis, it must be mentioned that most X-ray or CT image datasets do not represent the real world, and this affects the accuracy of the model deploying in the real world and dynamic environments. The main reason is the fact that they are few X-ray or CT image datasets publicly available for training models to detect COVID-19. In the end, we wish that our study becomes a valuable reference and induces numerous novel studies on deep learning and processing of medical images in the battle against the COVID-19 virus outbreak.

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30. Ibrahim, M.R., Haworth, J., Cheng, T.: Understanding cities with machine eyes: a review of deep computer vision in urban analytics. Cities 96, 102481 (2020). https://doi.org/10.1016/j. cities.2019.102481 31. Tulbure, A.-A., Tulbure, A.-A., Dulf, E.-H.: A review on modern defect detection models using DCNNs – deep convolutional neural networks. J. Adv. Res. (2021). https://doi.org/10.1016/j. jare.2021.03.015 32. Carlicchi, E., Gemma, P., Poerio, A., Caminati, A., Vanzulli, A., Zompatori, M.: Chest-CT mimics of COVID-19 pneumonia—a review article. Emerg. Radiol. 28(3), 507–518 (2021). https://doi.org/10.1007/s10140-021-01919-0 33. Zhang, N., et al.: Clinical characteristics and chest CT imaging features of critically ill COVID19 patients. Eur. Radiol. 30(11), 6151–6160 (2020). https://doi.org/10.1007/s00330-020-069 55-x 34. Karthik, R., Menaka, R., Hariharan, M.: Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Appl. Soft Comput. 99, 106744 (2021). https:// doi.org/10.1016/j.asoc.2020.106744 35. Ravi, N., Cortade, D.L., Ng, E., Wang, S.X.: Diagnostics for SARS-CoV-2 detection: a comprehensive review of the FDA-EUA COVID-19 testing landscape. Biosens. Bioelectron. 165, 112454 (2020). https://doi.org/10.1016/j.bios.2020.112454 36. Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Rajendra Acharya, U.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020). https://doi.org/10.1016/j.compbiomed.2020.103792 37. Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell. 51(2), 854–864 (2020). https://doi.org/10.1007/s10489-020-01829-7 38. Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Jamalipour Soufi, G.: Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med. Image Anal. 65, 101794 (2020). https://doi.org/10.1016/j.media.2020.101794 39. Xu, Y., Lam, H.-K., Jia, G.: MANet: a two-stage deep learning method for classification of COVID-19 from chest X-ray images. Neurocomputing 443, 96–105 (2021). https://doi.org/10. 1016/j.neucom.2021.03.034 40. Hwang, E.J., et al.: COVID-19 pneumonia on chest X-rays: performance of a deep learningbased computer-aided detection system. PLoS ONE 16(6), e0252440 (2021). https://doi.org/ 10.1371/journal.pone.0252440 41. Babu P., S.A., Annavarapu, C.S.R.: Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification. Appl. Intell. 51(5), 3104–3120 (2021). https:// doi.org/10.1007/s10489-021-02199-4 42. Alam, N.-A.-A., Ahsan, M., Based, Md.A., Haider, J., Kowalski, M.: COVID-19 detection from chest X-ray images using feature fusion and deep learning. Sensors 21(4), 1480 (2021). https://doi.org/10.3390/s21041480 43. Gupta, A., Anjum, Gupta, S., Katarya, R.: InstaCovNet-19: a deep learning classification model for the detection of COVID-19 patients using Chest X-ray. Appl. Soft Comput. 99, 106859 (2021). https://doi.org/10.1016/j.asoc.2020.106859 44. Ismael, A.M., Sengür, ¸ A.: Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst. Appl. 164, 114054 (2021). https://doi.org/10.1016/j.eswa.2020. 114054 45. Zebin, T., Rezvy, S.: COVID-19 detection and disease progression visualization: deep learning on chest X-rays for classification and coarse localization. Appl. Intell. 51(2), 1010–1021 (2020). https://doi.org/10.1007/s10489-020-01867-1 46. Jangam, E., Barreto, A.A.D., Annavarapu, C.S.R.: Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking. Appl. Intell. (2021). https://doi.org/10.1007/s10489-021-02393-4 47. Calderon-Ramirez, S., et al.: Improving uncertainty estimation with semi-supervised deep learning for COVID-19 detection using chest X-ray images. IEEE Access 9, 85442–85454 (2021). https://doi.org/10.1109/access.2021.3085418

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Manufacturing Technology

Effects of Process Parameters on the Dissimilar Friction Stir Welded Joints Between Aluminum Alloy and Polycarbonate A. Azhagar, K. Sakai, H. Shizuka, and K. Hayakawa

Abstract Effects of processing parameters on weld interface and material flow during friction stir welding of dissimilar materials were investigated. The tapered cylindrical tool pin made of SKD11 was used to improve the in-process material flow behaviors during the process. In the experiments 3 mm thick plates of AA2017 aluminum alloy and polycarbonate were used as materials for welding at a tool rotation speed of 1320 or 1760 rpm, travel speed of 60 mm/min, and tool offset of 2, 3 or 4 mm toward AA2017. From the experiments, it was found the suitable process parameters for the butt welded joint, between the AA2017 and polycarbonate. Keywords Friction stir welding · Aluminum alloy · Polycarbonate

1 Introduction Friction stir welding (FSW) is a solid-state joining process that uses a nonconsumable tool to join two facing workpieces without melting the workpiece material. In this process, heat is generated from the friction between the rotating welding tool (including both the shoulder and the probe) and the material being welded, causing the material to soften at a temperature lower than its melting point. The softened material underneath the shoulder is further subjected to deformation by the

A. Azhagar (B) · K. Hayakawa Graduate School of Science and Technology, Shizuoka University, Hamamatsu 432-8011, Japan e-mail: [email protected] K. Hayakawa e-mail: [email protected] K. Sakai · H. Shizuka Department of Mechanical Engineering, Shizuoka University, Hamamatsu 432-8561, Japan e-mail: [email protected] H. Shizuka e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_13

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rotational and transverse movements of the tool. Accordingly, the tool geometry has a considerable influence on the weld properties. Derazkola et al. studied dissimilar joining of AA5058 and polycarbonate along with combination of tapered cylindrical tool pin geometry of FSW tool with a tilt angle of 2° is performed [1]. Moshwan et al. has performed the dissimilar joint between polycarbonate and AA7075 with tapered cylindrical tool pin profile [2]. Derazkola et al. Have studied the dissimilar FSWeld joints on AA5058 and PMMA along with a combination of tapered cylindrical tool pin with tilt angle of 2° was performed [3]. Huang et al. reviewed various studies on dissimilar joints and their applications for FSW of polymers and metal materials [4]. In this paper, the effects of process parameters on the dissimilar friction stir welded joints between aluminum alloy and polycarbonate were investigated to clarify the suitable condition for sufficient joint efficiency.

2 Experiments Aluminum alloy AA2017 (Al) and polycarbonate (PC) with dimensions of 100 × 50 × 3 mm shown in Fig. 1(b) were joined via FSW in a butt configuration. Tapered cylindrical tool pin made of SKD11 tool steel with shoulder diameter of 18 mm and tool pin height of 2.7 mm was used as shown in Fig. 1(a). Tool rotation speed of 1320 or 1760 rpm, travel speed of 60 mm/min and plunge depth of 2.74 mm was adopted. The tool offset towards Al was varied as 2, 3 and 4 mm. The tensile tests were carried out after welding to evaluate joint efficiency. The specimens for the tests were prepared from three regions of the welded material

b

Fig. 1 Geometries of tool and workpiece a Geometries of Tool, b Geometries of work-piece

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Table 1 FSW process parameters with an ultimate tensile strength of joint Experimental condition

Tool offset (mm)

Rotational speed (rpm)

(a)

4

1760

4.49

3.70

3.80

1320

4.20

3.80

3.60

1760

5.60

4.60

4.40

(b) (c)

3

(d) (e) (f)

2

Ultimate tensile strength (MPa) (Start) test 1

(Middle) test 2

(End) test 3

1320

4.49

5.00

4.60

1760

33.10

4.80

4.49

1320

5.80

33.40

4.80

(start, middle and end), according to the size and procedure mentioned in ASTM E8 as shown in Fig. 3.

3 Results and Discussion 3.1 Tensile Strength Analysis Table 1 shows the resulting ultimate tensile strength of joint to the process parameters of tool offset and rotational speed. It is observed that the tool offset of 3 and 4 mm leads to much lower tensile strength than the tool offset of 2 mm. Figure 2 shows the material flow at the joint interface between Al and PC, where (a) to (f) show the results by the experimental conditions (a) to (f) in Table 1. The highest and lowest ultimate tensile strengths of 33.4 MPa and 3.60 MPa were obtained from experimental conditions (f) and (b) in which the tool offset of 2 and 4 mm was used. The tool pin offset of 2 mm enhanced material flow along with the weld interface. From Fig. 2 (e) and (f), non-uniform material flow along the weld zone due to high rotational speed and highly deformed material flow is observed. On the other hand, Fig. 2(a) and (b), show the shoulder influenced weld formation, due to the improper material fusion between the two dissimilar materials and reduced tool pin interaction at the weld zone, which has led to lower tensile strength. Figure 2(c) has exhibited a tunnel like appearance throughout the weld joint due to tool pin progress. In Fig. 2(d), regular weld formation is observed.

3.2 Hardness Profiles Vickers hardness was measured at an applied load of 0.5 N with a dwell time of 10 s. The hardness profile of weld cross section fabricated by different process parameters of 1320 and 1760 rpm with tool pin offset positions is shown in Fig. 4. The PC side

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Fig. 2 Material flow at joint interface. (a) to (f) correspond to experimental condition (a) to (f) in Table 1

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Fig. 3 Geometry of welded specimen used for tensile tests (Standard size per ASTM E-8)

a Tapered Cylindrical Tool Pin 1320 rpm

160

140 120 100

16.2

80

60 12.6 40 20

107

Al Side

Tool Pin Offset 2mm Tool Pin Offset 3mm Tool Pin Offset 4mm

0 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Distance from weld center (mm)

Micro Hardness, HV 0.5N

Micro Hardness, HV 0.5N

160

PC Side

Tapered Cylindrical Tool Pin 1760 rpm

b

153

155

140 120 100 PC Side

60 18.2

99.5

Al Side

80

40 10.8 20

Tool Pin Offset 2mm Tool Pin Offset 3mm Tool Pin Offset 4mm

0 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Distance from weld center (mm)

Fig. 4 Hardness profile of the joints fabricated by different tool pin offset positions

is at a distance of −1 to −7 mm from the weld centerline, whereas the Al side is at a distance from 0.75 to 7 mm from the welding centerline. The drop-in hardness profile is observed at the stir zone or pin influence zone. For the value of Vickers hardness of the PC, the value increases as the distance from the welding centerline increases. In case of rotation speed of 1320 rpm, the lowest value of 12.6 HV0.5 N is obtained at 1 mm of PC side and then the value increases gradually to 16.2 HV0.5 N , as shown in Fig. 4(a). In case of 1760 rpm, the lowest value of 10.8 HV0.5 N is obtained at 1 mm of PC side and then the value gradually increases to 18.2 HV0.5 N , as shown in Fig. 4(b). For the value of Vickers hardness of Al, the value tends to increases as the distance from the welding centerline increases. In case of rotation speed of 1320 rpm, the lowest hardness value of 107 HV0.5 N was obtained at 0.75 mm of Al side and then the value increases to 153 HV0.5 N , as shown in Fig. 4(a). In case of rotation speed of 1760 rpm, the lowest hardness value is 99.5 HV0.5 N at 0.75 mm of Al side and then gradually increased to 155 HV0.5 N .

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From the hardness profile plots, variations in hardness are observed in the stir zone due to the change in tool rotational speed. An increase in tool rotational speed has reduced the hardness at the weld interface. The hardness reduction was noticed in the welded area of the PC side, caused by thermal degradation due to the tool stirring and frictional heat that softened the material and decreased its hardness.

4 Conclusions The effect of tool pin offset on joint efficiency was evaluated based on the tensile strength of the welded samples. The results showed that the FSW with a tool pin offset of 2 mm, the rotational speed of 1320 rpm and travel speed of 60 mm/min recorded a higher tensile strength. Since the tool shoulder and pin enhanced the heat transfer, the decrease of tool pin offset which in turn enhanced a smaller number of voids and defects in the weld surface and in terms increase weld strength. Tensile strength and hardness of the weld joints have lower values compared to the base materials owing to the presence of voids at the weld interface and highly influenced by welding parameters. The findings of this study can be used to develop an improved tool pins profile and along with optimized process parameters that can produce superior dissimilar friction stir welds.

References 1. Derazkola, H.A., Elyasi, M.: The influence of process parameters in friction stir welding of Al-Mg alloy and polycardonate. J. Manuf. Process. 35, 88–98 (2018) 2. Moshwan, R., Sahifulddin, M., Yusof, F.: Dissimilar friction stir welding between polycarbonate and AA7075 aluminum alloy. Int. J. Mater. Res 105, 1–9 (2014) 3. Derazkola, H.A., Fard, R.K., Khodabakshi, F.: Effects of processing parameters on the characteristics of dissimilar friction-stir-welded joints between AA5058 aluminum alloy and PMMA Polymer. Inst. Welding 62, 117–130 (2018) 4. Huang, Y., Meng, X., Xie, Y., Wan, L., Lv, Z., Cao, J., Feng, J.: Friction stir welding/processing of polymers and polymer matrix. Compos. Part A Appl. Sci. Manuf. 105, 235–257 (2018)

Material Science and Technology, Smart Materials

Investigation of the Convection Effect on the Inclusion Motion in Thermally Stressed Crystals Oleksandr P. Kulyk , Victor I. Tkachenko , Oksana L. Andrieieva , Oksana V. Podshyvalova , Volodymyr A. Gnatyuk , and Toru Aoki

Abstract The conditions for occurrence of convective mass transfer in a liquid inclusion located in a thermally stressed crystal have been investigated. An estimated calculation of the Rayleigh number for a convective cell with solid boundaries has been performed. It is shown that the calculated value of the Rayleigh number can exceed the critical value. For such conditions, we have obtained analytical expressions for the perturbed velocity and temperature of a cylindrical convective cell with solid boundaries that exist in the inclusion. The theoretical model of induced transitions of atoms of the crystal matrix into solution and back is proposed, taking into account convective mass transfer. Based on the proposed model, the analytical expressions for the dependence of the inclusions velocity on their size at low and high temperature gradients have been obtained. A good quantitative correspondence between the theoretical model and the experimental data is shown in both cases. Keywords Crystallization · Solution · Thermally stressed crystal · Liquid inclusions · Convective mass transfer

O. P. Kulyk · V. I. Tkachenko · O. L. Andrieieva School of Physics and Energy, V.N. Karazin Kharkiv National University, 4 Svobody Sq., Kharkiv 61022, Ukraine V. I. Tkachenko · O. L. Andrieieva National Science Center “Kharkiv Institute of Physics and Technology” of the National Academy of Sciences of Ukraine, 1 Akademichna St., Kharkiv 61108, Ukraine O. V. Podshyvalova (B) National Aerospace University “Kharkiv Aviation Institute”, 17 Chkalov St., Kharkiv 61070, Ukraine V. A. Gnatyuk V.E. Lashkaryov Institute of Semiconductor Physics of the National Academy of Sciences of Ukraine, 41 Prospekt Nauky, Kyiv 03028, Ukraine T. Aoki Research Institute of Electronics, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu 432-8011, Japan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Khakhomov et al. (eds.), Research and Education: Traditions and Innovations, Lecture Notes in Networks and Systems 422, https://doi.org/10.1007/978-981-19-0379-3_14

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1 Introduction It is known that crystallization in a fluid medium (solutions, melts, high-density gaseous media) can be accompanied by convection, when heat and/or substance are trans-ported by hydrodynamic flow. Many modern technologies related to crystal growth inside a fluid medium have problems with convective heat and mass transfer control. The quality of grown crystals determined by the perfection of their structure and stoichiometry depends on understanding the mechanisms of convection during crystallization and the possibility to control it. The problem of controlling convective flows has become particularly important in connection with the growth of protein crystals from biological solutions (see [1] and references therein). Protein crystallization plays a vital role in structural biology, protein purification, drug transport, etc. High-quality crystals are required for X-ray studies of protein molecules, which are essential for understanding protein functions. One of the methods for obtaining such crystals is the vapor-diffusion technique of protein crystallization, in which crystals are grown inside an evaporating drop on the glass plate surface. However, due to the variety of hydrodynamic processes (the capillary effect [2], surface tension induced flow [3] and “buoyancy effect” caused by density gradient [1]) in such a method, there are difficulties with its complete description and, accordingly, its wide application. Since protein solutions are, in fact, multicomponent mixtures, their thermodiffusion properties also cause many questions and are the subject of modern research. It is known that protein–ligand systems are stabilized in buffer solutions containing various salts (see [4] and references therein). To understand the microscopic mechanisms underlying thermodiffusion in such protein systems, at the first step, the thermodiffusion of aqueous solutions of potassium salts is studied [4]. A similar approach seems appropriate in this work when studying hydrodynamic effects in solutions in the presence of geometric restrictions on the liquid volume.

2 Analysis of Approaches At crystallization from the solution, near a solid crystalline surface there is a stationary boundary layer in which transfer is carried out by ordinary diffusion for structural particles or thermal conduction for temperature. Diffusion transfer in the whole volume of the mother medium can occur, for example, during crystal growth in supersaturated solid solutions, gels, or under specially created conditions that suppress liquid convection: in weightlessness (under microgravity conditions), under the influence of magnetic fields, etc. [5]. In convection, the transfer of heat and matter in a fluid medium is carried out by hydrodynamic flows. Driving forces of convective heat and mass transfer in liquid media are caused by the difference in density of crystal and liquid, inhomogeneous density of liquid itself due to temperature and concentration gradients (free or natural convection) [6]) or by existence of surface tension differences along the free liquid surface (capillary, thermocapillary convection or

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thermal and solutal Marangoni effects, see [7] and references therein). Capillary convection under conditions of traditional growth experiments is usually negligible compared to free convection or forced convection caused by liquid mixing. The effect of such convection flows usually comes to the reduction of the diffusion field extension in the mother medium. As a result, due to a decrease in the boundary layer thickness, concentration and temperature drops increase near the crystal surface, which can lead to an increase in the rate of crystallization (decrystallization). However, such an increase may occur when the processes at the crystallization front are not limiting. The “buoyancy effect”, observed in [1], is qualitatively similar to the Rayleigh convection, which was studied inside a drop of diluted NaCl aqueous solution held by two flat substrates during natural evaporation [8]. In its “pure form”, this type of convection in solutions, in the presence of geometrical restrictions on the liquid volume, is possible to study in inclusions of saturated solution in inhomogeneously stressed crystals. The enclosed and small volume of solution in inclusions allows us to create low under- and supersaturation in them. This practically excludes the influence of foreign impurities, always present in usual growth experiments, on the growth-dissolution processes. At small values of thermodynamic driving forces due to, for example, a dislocation density inhomogeneity (see [9] and references therein) or radiation point defects (see [10] and references therein), conditions for mostly diffusion mass transfer can arise in the inclusion. This is partly indicated by the invariability of the shape of experimentally observed inclusions during their motion and the conclusions of the phenomenological theory [11] based on the equations of motion and conservation of matter, taking into account elementary processes occurring at the inclusion boundaries. Investigation of the motion of liquid inclusions (as well as pores or gas-filled cavities) in the inhomogeneity caused by a temperature gradient [12, 13] is of greatest interest, both for the study of growth and dissolution processes and for applied material science. Such studies often allow us to reveal important features of growth processes, despite the simplicity of the experiments. For example, in experiments on pore motion in the temperature gradient and subsequent relaxation of their nonisometric shape under isothermal conditions, it was possible to detect and describe kinematic (shock) waves of the density of elementary steps on vicinal growth surfaces near (100) NaCl [14, 15]. Later on, the decay of shock waves described by the obtained analytical solution of the Burgers equation was interpreted for the profile of a one-dimensional echelon of elementary steps formed during the growth of a NaCl single crystal from the vapor phase at the base of a macroscopic cleavage step [16]. Investigations of the mechanisms of step bunch formation on vicinal crystal surfaces are important in connection with obtaining low-dimensional structures for various applied problems [17–20]. Due to the smallness of the driving forces, the motion of a saturated solution inclusion in a water-soluble crystal under the temperature gradient is impossible without sources of dissolution layers—steps at dislocations with a screw component of the Burgers vector [10]. The optical smoothness of the front (dissolving) surfaces enables us to consider that the motion of inclusions is limited by the emission processes of

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the structural matrix particles rather than by their diffusion through the solution and the processes of assimilation by the back surface. Crystal growth on the back surface of the inclusion occurs by motion of the steps formed in the corners between its back and side surfaces. Therefore, the large scatter of inclusion velocities depending on their size is explained solely by the difference in the morphology of dissolving faces [12]. It is considered that for inclusion sizes of 10–5 –10−4 m and temperature gradient values of 103 –104 K/m at dislocation densities typical for synthetic single crystals, simple dislocation sources in the form of pyramidal dissolution pits provide the maximum inclusion velocities. Along with that, as shown by the results of this work, convective instability may occur in the inclusion against the background of diffusion processes due to the difference in temperature and concentration of the solution near the front and back surfaces. Due to the convective flows near these surfaces, the processes of emission and assimilation of structural matrix particles can intensify, which should lead to peculiarities in the kinetics of inclusion motion. Convective heat and mass transfer can occur in viscous horizontal liquid layers located in thermally conductive media with a negative vertical temperature gradient. Such convection processes should be taken into account when studying the motion of inclusions in thermally stressed crystals. In Rayleigh’s formulation, the process of convective heat and mass transfer is considered on the example of the problem of convective heat and mass transfer through a horizontal layer of a viscous incompressible liquid heated from below [21–23]. Among the problems of convective mass transfer, the Rayleigh problem with solid boundaries is distinguished because of its practical importance. In the presence of solid boundaries, the perturbed velocity, as well as its first derivative with respect to the vertical coordinate, are equal to zero on solid horizontal boundaries. The practical significance of the problem is the ability to control the temperature of heat-conducting media and the process as a whole. Until recently, Rayleigh problems with solid boundaries were solved using numerical methods. These methods do not allow to establish physical dependences between the parameters of the problem, but allow to obtain numerical arrays, and on their basis to describe the convective mass transfer only for specific parameters of media. In [24, 25], analytical solutions of the Rayleigh problem with solid boundary conditions for cylindrical cells were obtained for the first time. These solutions made it possible to establish functional dependences between the medium parameters and to write expressions for the perturbed velocity and temperature. Moreover, analytical solutions made it possible to determine the characteristic geometric dimensions of cylindrical cells: for perfectly heat-conducting boundaries, the cell diameter in units of the layer thickness is about 2.46 [26]. The experimentally measured diameter in the same units is a value of 2.5–2.6 [27]. Inclusions in thermally stressed crystals represent a closed volume with a shape and size determined by the crystal symmetry. The crystal as a whole is a heatconducting medium with a given temperature gradient. The inclusion volume is filled with a concentrated solution of the crystal material, which is characterized by viscosity and thermal diffusivity coefficients.

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In general, the inclusion can be considered as a candidate for the formation of cylindrical Benard cells with the axis directed along the temperature gradient. A number of conditions must be met for a convective cell to appear. One of them is that, for perfectly heat-conducting boundaries, the cell diameter in units of the layer thickness should be about 2.46. If the cell diameter exceeds the layer thickness, then the cells will be exactly enough to most tightly fill its volume [28, 29]. At that, in the case of a large number of cells, their shape transforms from a cylinder to a polygonal straight prism, most often a hexagonal one. The aim of this work is to estimate the parameters of a liquid inclusion in a thermally stressed crystal and to determine the conditions of convection occurrence; to determine the physical mechanisms that affect the velocity of an inclusion containing one convective cylindrical cell.

3 Results and Discussion 3.1 Determination of the Parameters of the Viscous Incompressible Liquid Layer, at which Convection Can Occur Determining the parameters of a viscous incompressible liquid in the presence of geometric restrictions on its volume, we use the experimental data presented in the literature [30–33] and the results of our own studies [10, 12, 34]. We will consider the inclusion of a saturated solution with average concentration C(T ) (mol/m3 ), characterized by the size h in the direction of the temperature gradient of a constant value A. Under such conditions, in the gravitational field with intensity g, free convection can arise in a liquid. This fact was already noted in [30, 35] in the theoretical estimates of the liquid inclusion velocities, however, the possibility of convective flows was not taken into account. Meanwhile, the experiments performed in [36] on the motion of liquid inclusions in alkaline-halide crystals indicate that free convection plays a role in the kinetics of inclusion motion. It follows from the results of experiments that when moving inclusions with characteristic size of h ≈ 5 · 10−4 m under gradients of A ∼ 104 K/m and temperatures above 80 °C, in some cases, the velocity is higher at the horizontal temperature gradient than at the vertical one. As was shown in [22], the spectrum of normal perturbations of liquid equilibrium in a cavity of certain geometry depends on the Rayleigh number: R=

g ∗ β Ah 4 νχ

(1)

where ν = η/ρ is the kinematic viscosity of a liquid, determined by the values of its dynamic viscosity η and density ρ, χ = κ/ρc P is the thermal diffusivity of a liquid,

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which, in addition to density,  also  depends on thermal conductivity κ and specific 1 ∂ρ is the coefficient of thermal expansion, g ∗ is the heat capacity c P , β = − ρ ∂ T P “effective” acceleration acting on the element of liquid similar to the acceleration due to gravity. Let us show that for normal perturbations of liquid equilibrium in a cavity, the Rayleigh number corresponds to the one at which convection with solid boundary conditions is possible. To estimate the value of the Rayleigh number, we use the values typical for inclusions of saturated aqueous solution in KCl single crystals, at temperature T = 360 K. To calculate the values of saturation concentration, concentration gradient and density of the solution as functions of temperature, we use empirical relationships and experimental data presented in [33]. As a result of the calculation, the following values were obtained: C = 5563 mole/m3 , dC/dT = 61.3 mole/m3 ·K, ρ = 1208 kg/m3 . Using the obtained data on the change in density with temperature, we also find the value of the coefficient of thermal expansion β = 5.3 · 10−4 K−1 . We define the thermal conductivity of the saturated solution as κ = 0.92 · κH2 O (T )  0.63 W/mK [37]. Based on the temperature dependence of the specific heat capacity of aqueous solutions with different salt concentrations, we find the value of c P  3·103 J/kg·K [38]. According to the obtained values of κ, c P and ρ, we can determine the value of thermal diffusivity χ = 1.74·10−7 m2 /s. From the data on the temperature dependence of the dynamic viscosity of aqueous solutions KCl at concentration close to saturation η = 4.22 · 10−4 Pa · s [39] and density ρ we can find the value of kinematic viscosity: ν  3.5 · 10−7 m2 /s. For further calculations, it is necessary to determine the characteristic size of the inclusion in the direction of the temperature gradient and the gradient itself. Most of the experimental data refer to inclusions with characteristic sizes within h ∼ 10−4 m and a temperature gradient of A ∼ 104 K/m. At these values, the temperature difference between the front (hot) and back (cold) surfaces of the inclusion is T = A · h ≈ 1 K. The difference in solution concentrations near the front and back surfaces, caused by this temperature difference, can be estimated from the known value of the concentration gradient: C = (dC/dT ) · T  61.3 mol/m3 . Based on the results of modern studies of the effect of osmosis in concentrated salt solutions, it would be possible to calculate more accurately the values of forces to which a liquid of mass m ∼ ρ · h 3 ∼ 10−9 kg in the inclusion is subjected due to differences in temperature and solution concentration on the front and back surfaces [40]. However, for estimates, we restrict ourselves to thesimplest concepts  of osmotic pressure for ideal solutions. The result is P  R C T + T C ∼ 105 Pa. Estimating the force value as F  P · h 2 , we find that, due to the difference in osmotic pressures on the front and back inclusion surfaces, there is an own effective acceleration, which is directed along the temperature gradient: g ∗ = F/m ∼ 106 m/s2 .

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Substituting these values into the expression for the Rayleigh number, we find that the order of magnitude for the investigated conditions corresponds to the critical Rayleigh number for the occurrence of convection with solid boundary conditions R ∼ 104 [21].

3.2 Basic Equations Describing Convection in Layers of Viscous Incompressible Liquid Bounded by Perfectly Heat-Conducting Solid Medium To describe convective processes in layers of a viscous liquid heated from below, the Navier–Stokes equations in the Boussinesq approximation (NSB), written in the Cartesian coordinate system, are traditionally used. There are three types of problems, which differ in the boundary conditions imposed on the velocity of convective mass transfer: free (on two boundaries, the liquid contacts a liquid of another nature), solid (rigid, the liquid contacts a solid surface) or mixed (one boundary is free, the other is rigid) boundary conditions [21–23]. Solutions of the NSB equations with solid or mixed boundary conditions for normal perturbed velocity and temperature inside the cell do not have an analytical form, but were obtained in the Cartesian coordinate system using numerical methods [21, 22]. However, as shown in [27, 41], the cells have a cylindrical shape when they appear. Therefore, it is reasonable to look for solutions of the NSB equations in cylindrical geometry. Solutions of the linearized NSB equations for perturbations of the vertical velocity vz (r, z, t) and temperature T (r, z, t) in axially symmetric cylindrical geometry have the following form [26]: vz (r, z, t) = v(z)J0 (kr r ) exp(−λt), T (r, z, t) = ϑ(z)J0 (kr r ) exp(−λt),

(2)

where λ is the stability parameter [22]; v(z) and ϑ(z) are the amplitudes of perturbations of vertical velocity and temperature; J0 (x) are the Bessel functions of the first kind of order zero in the argument x; kr is the radial wave number. All quantities are dimensionless because they are reduced to the size, time, and temperature specific to the problem [22]. In the stationary state (λ = 0), substitution (2) in the linearized NSB equations leads to the characteristic equation [22]: 3  2 q − b = − a3 1

(3) 3

where the notation are a = (kr2 R) 3 , b = kr2 , R = gβ h is the Rayleigh number, g is νχ the acceleration of gravity, which is directed against axis z, = T2 −T1 is the temperature difference between the lower and upper planes (temperature head), ez is a unit

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vector directed along axis z, ν and χ are the coefficients of kinematic viscosity and thermal diffusivity of the liquid, respectively; β is the volumetric thermal expansion coefficient of the liquid. The roots of the characteristic Eq. (3) are defined by the following expressions: q1,2 q5,6

 √ √ = ± b − a; q3,4 = b + 0.5a(1 + i 3) = ±(X + + i X − );  √ = b + 0.5a(1 − i 3) = ±(X + − i X − ),

(4)

   21 √ ,χ = 0.5a +b, i = −1 is the imaginary where X ± = 0.5 χ 2 + 0.75a 2 ± χ unit. The vertical velocity amplitude (2) is expressed through the roots of the characteristic equation, and describes neutral perturbations. We will look for it in the form: v(z) =

6

Cm exp(qm z),

(5)

m=1

where Cm are the constants determined from the boundary conditions:   v(0) = v(1) = 0, ∂v(0) ∂z = ∂v(1) ∂z = 0, ϑ(0) = ϑ(1) = 0.

(6)

Expression (5) and boundary conditions (6) determine the values of the critical Rayleigh numbers and the wave number of neutral perturbations. Previously, their values were determined only as a result of an approximate numerical solution of the transcendental equations [22]. However, it turned out to be possible to find an analytical expression for v(z). For this, in (5) we assume,  for example, the following : C = C = A relations between the constants C m 1 2 1 2, C 3 = C 4 = C 5 = C 6 =  −A1 (4ch(z 0 X + )), where A1 is an arbitrary constant and z 0 = 0.5 is the coordinate of half of the layer thickness. In this case (5) takes the form:

v(z) = A1 ch(q1 (z − z 0 )) − cos(X − (z − z 0 ))ch(X + (z − z 0 ))ch −1 (z 0 X + ) . √ √ If we assume q1 = i a − b = inπ , and require the equality X − = a − b = nπ , then the amplitude of the vertical velocity is determined by the formula:       1 1 X + ch −1 X + sin(nπ z). v(z) = A1 1 − ch z − 2 2

(7)

It is easy to check √ that expression (7) satisfies the boundary conditions (6). The equality X − = a − b is valid at a = 8(nπ )2 and b = 7(nπ )2 . In the following, all calculations are performed for a cell with a mode number n = 1. The radial velocity in the convective cell is determined from the continuity equation for an

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incompressible liquid and is equal to: vr (r, z, 0) = −kr−1 J1 (kr r )

∂v(z) . ∂z

(8)

The amplitude of the perturbed temperature ϑ(z) is defined through v(z) from the equation [26]: d 2 ϑ(z) − bϑ(z) = −v(z). dz 2

(9)

From (9) it is easy to derive the expression for the perturbed temperature satisfying the boundary conditions: 1 ϑ(z) = − √ b

z v(ξ )sh 0

√

 √  1  √  1 sh z b √  b(ξ − z) dξ + √ v(ξ )sh b(ξ − 1) dξ. (10) b sh b 0

Since solution (7) describes the stationary state (λ = 0) of a cylindrical cell, the rigid parameters a and b should determine the point lying on the neutral curve R1 (kr ). As a result of the scale-shift transformations described in [25], the Rayleigh number rigid for solid boundary conditions can be represented as: R = R1 rigid R1

 2 3 π + (β1 · kr )μ1 = α1 , (β1 · kr )μ1

(11)

where α1 = 2.597, β1 = 0.7, μ1 = 2.085. Expression (11) has a good numerical agreement with the results given, for example, in [22]. Thus, this section describes the perturbed radial and vertical velocities and perturbed temperature of a cylindrical convective cell for mode number n = 1 and solid boundary conditions. The critical Rayleigh number for a cell  with solid rigid = boundary conditions and a perfectly heat-conducting medium is R1 m 1707.262, and the critical wave number is (kr )m = 3.116.

3.3 Influence of Convective Mass Transfer on the Velocity of Inclusion Motion Let us consider the case when in a thermally stressed crystal there is an inclusion in the form of a rectangular parallelepiped (RP). A convective cell of cylindrical shape with radius R0 and height h is inscribed in the RP. Figure 1 shows the dimensions of a cylindrical cell and indicates the temperature on the front and back surfaces of the inclusion. The arrows show the direction of liquid mass transfer in the inclusion.

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Fig. 1 Diagram of a cylindrical convective cell in the inclusion located in the temperature gradient field. The arrows show the direction of liquid flow in the cell

The assumption of the existence of a convective cell in the inclusion corresponds to the fact that the temperatures of the front z = 0 and back z = h surfaces of the inclusion are equal to: T0 (0) = T2 , T0 (h) = T1 (T2 > T1 ). To simplify our calculations, we assume that in the equilibrium state the temperature dependence on coordinate z can be represented by a linear function:  0 (z) = − ez . ∇T h

(12)

For an azimuthally symmetric convective cell, the perturbed velocity and temperature do not depend on the angle ϕ. Therefore, everywhere in the following calculations one can assume ϕ = 0. The independent variables remain r and z. The temperature gradient is directed in the opposite direction to axis z. We assume that the square-shaped front surface of the inclusion is crossed by a dislocation with a screw component of the Burgers vector. The front and back surfaces of the inclusion are washed by the radial flow (8) of the inclusion liquid. We assume that the velocity of the inclusion, which has the shape of a rectangular parallelepiped with dimensions a × a × h (a is a base edge, h is a height), is directed along axis z and is equal to V0 . To simplify the calculations, we change to a frame of reference where the inclusion is at rest. Then the velocity of the crystal matrix, and along with it the velocity of the screw dislocation, is given in the form: U (h) = V (h) − V0 , |V0 | ≥ |V (h)|, where V (h) = V (R0 + (h − h 0 )ϑ(h − h 0 )) is the inclusion velocity and V (h 0 ) = V0 is the inclusion velocity corresponding to the threshold value of the inclusion size h 0 at which U (h 0 ) = 0. Here, ϑ(x) is the unit function of the form: ϑ(x < 0) = 0; ϑ(x ≥ 0) = 1. Thus, in the chosen frame of reference, a crystal matrix with a screw dislocation either rests when h ≤ h 0 , or moves in the positive direction of axis z when h > h 0 . A diagram of the inclusion in the crystal is shown in Fig. 1. The inclusion in the crystal contains solution that helps relaxation of tension in the crystal due to accelerated diffusion and transfer of the crystal matrix material in solution from the front surface to the back. In the process of relaxation, the experimentally established fact of inclusion motion without a significant change in its shape indicates the transition of crystal atoms from the front inclusion surface into the solution, and an equivalent deposition of crystal atoms from the solution on the back inclusion surface.

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The presence of convective mass transfer in the inclusion with velocities vz (r, z, 0) and vr (r, z, 0) creates solution flows washing the front and back inclusion surfaces. The rate of atoms emission/assimilation by the crystal surfaces depends on the temperature and its gradient. Therefore, in thermally stressed crystals, the convective process can accelerate the dissolution/growth processes on the inclusion surfaces, which can affect the velocity of its motion. The processes of transitions of crystal atoms into solution and back have a probabilistic nature. Therefore, to describe such transitions, similarly to the transitions in crystals with inhomogeneous defect distribution [9], a two-level model can be used.

3.4 Two-Level Model of Induced Transitions of Crystal Atoms into Solution and Back The processes on the front and back surfaces of the inclusion are similar to those that occur when a crystal grows from a supersaturated solution (vapor or liquid), or when it dissolves in an undersaturated solution [5, 6, 42, 43]. That is why the dissolution/crystallization models developed earlier are also applicable to the study of inclusions motion in crystals, since it is based on the processes of crystal growth and dissolution. Using the results of [9], we can show that the number of atoms on the front surface n 2 (t) (the lower energy level with energy ε2 ), the number of atoms in the solution n 1 (t) (the upper energy level with energy ε1 , (ε1 > ε2 )) and the number of solution atoms N (t) obey the equations: ∂n 1 ∂n 1 ∂n 1 + Vr (r, z) + Vz (r, z) = μ12 (r, z)(n 2 − n 1 )N , ∂t ∂r ∂z ∂n 2 ∂n 2 ∂n 2 + Vr (r, z) + Vz (r, z) = −μ12 (r, z)(n 2 − n 1 )N , ∂t ∂r ∂z ∂N ∂N ∂N + Vr (r, z) + Vz (r, z) = −μ12 (r, z)(n 2 − n 1 )N , ∂t ∂r ∂z

(13)

where Vr (r, z) = vr (r, z, 0), Vz (r, z) = vz (r, z, 0); μ12 (r, z) is the probability of induced transitions of atoms between energy levels, in the general case, a function of coordinates r, z. We analyze the system of Eqs. (13) on the cell axis in the vicinity of the front surface, i.e., in the vicinity of z = 1 + z˜ , |˜z | T1 ) or gradient, when the probability of induced transitions is a linear function of z, i.e. μ(z) = μ1 + μ2 z, |μ2 | 0. Solutions of the system (13) under this condition coincide with the solutions given in [9], with the only difference that ξ = t − A˜z −1 should be used as the new variable. Without analyzing the properties of the solutions of system (15), since they correspond to those described earlier [9], we focus on the velocity of inclusion motion. For this, we proceed similarly to [9] and specify a plane of constant phase. This plane is defined by the expression: ξ (˜z , t) = t − A˜z −1 = C,

(16)

where C is a constant. It follows from Eq. (16) that for a given time t = t S and C = C S , we can determine the coordinate of the plane z˜ = z˜ S . If we fix only C = C S , then over time the coordinate of the plane z˜ S changes, i.e. it moves in space with a certain velocity VS . We determine this velocity from the expression for the total time derivative of Eq. (16): VS =

z˜ 2 d z˜ =− , dt A

(17)

where VS = ddtz˜ is the velocity of the plane of constant phase. Expression (17) is valid wherever z˜ = 0. If we assume z = h, then VS determines the velocity of the plane of constant phase of the back inclusion wall. In the laboratory frame of reference in dimensional quantities, the inclusion velocity can be represented as: V (h) = V0 −

(h − h 0 )2 = α + βh + δh 2 , Ah 20

(18)

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Fig. 2 Dependence of the maximum inclusion velocity on their size at a small temperature gradient (A  8 · 102 K/m)

Table 1 Results of statistical processing of the deviation of curve (18) from the experimental points α Value

β Standard error Value

–0.71488 0.173349

δ Standard error Value

1.18314 0.13781

Statistics Standard error Adj. R-Sqr.

–0.13313 0.01796

0.82522

where α = V0 − A1 , β = Ah2 0 , δ = Ah1 2 . 0 Expression (18) has the form of a quadratic trinomial and was used to describe experimental data on the inclusion motion in [34] in the presence of a small temperature gradient. The markers in Fig. 2 show the experimental points that determine the dependence of the maximum inclusion velocity on their sizes [34]. Only the points with the highest velocities are taken, because, in our opinion, they correspond to the perfect location of the screw dislocation in the inclusion. The curve was obtained by selecting the constants α, β, δ using the ORIGIN software package so that its deviation from the experimental points was minimal. The indicators of statistical processing of the deviation of curve (18) from the experimental points are given in Table 1. Statistical processing shows a fairly good fit of curve (18) to the experimental points, since Adj. R-Sqr. is close to one. Thus, in this section, we propose a model of induced atom transitions from crystal to solution and back to describe the inclusions motion in thermally stressed crystals with a small temperature gradient. The model is based on the assumption of the existence of convection with solid boundary conditions in a liquid inclusion located

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in a thermally stressed crystal. The obtained result can be used to determine the characteristic parameters of both the crystal itself and the inclusion motion kinetics. A Great Temperature Gradient. In the case of a large temperature head, as noted above, the probability of induced transitions is assumed to be a linear function of the coordinate z: μ(z) = μ1 + μ2 z, |μ2 |