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Integrating local energetics into Maxwell-Calladine constraint counting to design mechanical metamaterials

Physical Review E 2024年第2期110卷 025002-025002页

作者： Jason W. Rocks Pankaj Mehta Department of Physics Biological Design Center Faculty of Computing and Data Science

The Maxwell-Calladine index theorem plays a central role in our current understanding of the mechanical rigidity of discrete materials. By considering the geometric constraints each material component imposes on a set of underlying degrees of freedom, the theorem relates the emergence of rigidity to constraint counting arguments. However, the Maxwell-Calladine paradigm is significantly limited—its exclusive reliance on the geometric relationships between constraints and degrees of freedom completely neglects the actual energetic costs of deforming individual components. To address this limitation, we derive a generalization of the Maxwell-Calladine index theorem based on susceptibilities that naturally incorporate local energetic properties such as stiffness and prestress. Using this extended framework, we investigate how local energetics modify the classical constraint counting picture to capture the relationship between deformations and external forces. We then combine this formalism with group representation theory to design mechanical metamaterials where differences in symmetry between local energy costs and structural geometry are exploited to control responses to external forces.

关键词： Mechanical deformation Mechanical metamaterials Discrete symmetries Group theory

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Optimal Decision Support System Model for Breast Cancer Diagnosis 4

Optimal Decision Support System Model for Breast Cancer Diag...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Saadah, Hanin Owda, Amani Yousef Stergioulas, Lampros Owda, Majdi Arab American University Faculty of Graduate Studies Department of Natural Engineering and Technology Sciences West Bank Ramallah Palestine The Hague University of Applied Sciences Faculty of It and Design Unesco Chair on Ai and Data Science The Hague Netherlands Arab American University Faculty of Data Science Unesco Chair on Data Science for Sustainable Development Ramallah Palestine

ISBN: (纸本)9798350376111

Breast cancer is a common cancer among women worldwide, and early diagnosis is crucial for improving the chances of successful treatment. Decision support systems (DSS) are computer-based systems that help and guide users in making decisions. This paper discusses the characteristics of an optimal DSS for diagnosing breast cancer, including accuracy and reliability. This paper also introduces an optimal DSS for diagnosing breast cancer that should be able to provide an accurate, reliable, and comprehensive guide to users while being up-to-date, and flexible. The results obtained from the R-squared in the model succeeded in explaining 70% of the variation in the outputs. In contrast, the initial value of R-squared was 0.705091558 and then improved to 0.700018844. Such a system has the potential to improve the early diagnosis and treatment of breast cancer significantly and could ultimately save lives. © 2024 IEEE.

关键词： Lung cancer

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CONSTRUCTING ERGODIC NETWORKS USING DISCRETE MARKOV CHAINS 51

CONSTRUCTING ERGODIC NETWORKS USING DISCRETE MARKOV CHAINS

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51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Ohba, H. Sadakuni, S. Matsuda, T. Mizuno, S. Faculty of Health Data Science Juntendo University Chiba Japan Faculty of Information Technology Kanagawa Institute of Technology Kanagawa Japan Department of Information Design Shizuoka Institute of Science and Technology Shizuoka Japan

In a region's tourism strategy, connecting famous locations to surrounding areas can stimulate activity throughout the region. Connections between locations can be established by identifying the central locations in the area and creating routes from them to other locations. This study proposes an algorithm to obtain ergodicity in discrete Markov chains by utilizing network centrality measures. The number of calculations to obtain ergodicity was compared using several definitions of network centrality, enabling the selection of an appropriate network centrality measure for the target network. The proposed method identifies the central locations within the network and determines the necessary adjustments to transition probabilities between locations to achieve ergodicity. If the location network holds ergodicity, a stationary distribution of the Markov chain exists and optimization applications are expected. Numerical calculations were used to demonstrate the method's applicability to various networks. Furthermore, using a Wi-Fi log, we observed that the proposed method can reconstruct a network with ergodicity for networks that use access points as locations, indicating the social applicability of the proposed method. The proposed method can be used to analyze various networks in modern society, such as facility operations and tourism stimulation. In facility operations, this method can be applied to manage human traffic and analyze stay duration. To promote tourism, by considering tourist sites as network locations, the method clarifies which routes should be prioritized to construct an uninterrupted tourist network, making the construction and operation of an extensive tourist network possible. © 2024 Computers and Industrial Engineering. All rights reserved.

关键词： Equivalence classes

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Forecasting Solar Power Generation Using Extreme Gradient Boosting: A Machine Learning Approach 4

Forecasting Solar Power Generation Using Extreme Gradient Bo...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Abumohsen, Mobarak Owda, Amani Yousef Owda, Majdi Abumihsan, Ahmad Stergioulas, Lampros Arab American University Faculty of Graduate Studies Department of Natural Engineering and Technology Sciences West Bank Ramallah Palestine Arab American University Faculty of Data Science Unesco Chair in Data Science for Sustainable Development West Bank Ramallah Palestine Arab American University Faculty of Data Science RamallahP600 Palestine The Hague University of Applied Sciences Faculty of It and Design The Hague Netherlands

ISBN: (纸本)9798350376111

The swift expansion of solar photovoltaic (PV) technology has introduced significant challenges for those overseeing electricity distributions due to its reliance on weather conditions. In this study, predictive modeling techniques including Multilayer Perceptron (MLP) Regression, Gradient Boosting Regression, and Extreme Gradient Boosting (XGB) Regression were employed to enhance solar PV output forecasting. Comparative analysis utilizing performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-square revealed that the XGB model outperforms others. Specifically, the XGB model demonstrated an MAE of 0.0653, RMSE of 0.10461, and R-squared of 88.66%, showcasing its superior accuracy and efficiency in solar power management. This synergy between machine learning and energy efficiency heralds a promising direction towards achieving a more sustainable, decarbonized, and digitalized energy sector. © 2024 IEEE.

关键词： forecasting machine learning power generation solar power

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The Impact of 5G Technology on the Functionality and Performance of Internet of Things (IoT) Devices 8

The Impact of 5G Technology on the Functionality and Perform...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Yesankar, Prajyot Gourshettiwar, Palash Gote, Pradnyawant Jiet, Moses Makuei Gadkari, Ayush Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350376425

The design of wireless mobile devices of the next generation 5G promises to address the demands of complex IOT designs in terms of connectivity technologies. This The study illuminates the architecture, benefits, and needs that5G can satisfy. drawbacks of the 5G network. Numerous sites throughout the world have conducted extensive analyses of IOT devices with 5G capabilities and research on this kind of network. A new era of communication has begun with the introduction of 5G technology, which promises unheard of velocity, low latency, and enormous capacity. The significant effects of 5G on the operation and efficiency of connected Internet of Things (IoT) gadgets are examined in this research. IoT devices stand to gain from improved communication capabilities as 5G networks proliferate, opening the door for creative applications in a variety of industries. The first section of the article gives a summary of the main characteristics of 5G technology, highlighting how it has the potential to completely change how Internet of Things devices interact and communicate. Real-time data transfer is made possible by 5G networks far lower latency and higher capacity, which open up possibilities for faster and smooth interactions between IoT *** report also explores how 5G may affect how much electricity Internet of Things devices use. 5G has the ability to solve a long-standing issue in the IoT ecosystem by extending the life of the batteries of IoT devices with the adoption of cost-effective communication protocols and increased network efficiency. A solution that can efficiently handle enormous volumes of information being transferred at very high capacity is required as the Internet of Things gains popularity One of the main demands or goals that need to be reached in the near future, or the next generation of IOT devices, is more capacity, a faster data transfer speed and less delay. © 2024 IEEE.

关键词： 5G mobile communication systems

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Brain Connectivity Using Deep Learning Algorithms 1

Brain Connectivity Using Deep Learning Algorithms

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Gajanan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions. © 2023 IEEE.

关键词： Learning algorithms

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Detection of DDoS Attack on Software-Defined Networking Controller Using Convolutional Neural Networks 4

Detection of DDoS Attack on Software-Defined Networking Cont...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Abumihsan, Ahmad Owda, Majdi Owda, Amani Yousef Abumohsen, Mobarak Stergioulas, Lampros Arab American University Faculty of Data Science RamallahP600 Palestine Arab American University Faculty of Data Science Unesco Chair in Data Science for Sustainable Development West Bank Ramallah Palestine Arab American University Faculty of Graduate Studies Department of Natural Engineering and Technology Sciences West Bank Ramallah Palestine Arab American University Department of Natural Engineering and Technology Sciences RamallahP600 Palestine The Hague University of Applied Sciences Faculty of It and Design The Hague Netherlands

ISBN: (纸本)9798350376111

Software-Defined Networking (SDN) embodies an advanced network architecture, developed to transform traditional networks and adapt them to fulfill contemporary needs. SDN distinguishes itself by decoupling the control and data planes, allowing for more effective monitoring, management, and optimization of network resources. However, the centralized structure of SDN makes it prone to multiple types of cyber-attacks. In particular, Distributed Denial of Service (DDoS) attacks pose a severe threat to the SDN controller. This paper introduces a method for identifying DDoS attacks on SDN controllers using a 1D Convolutional Neural Network (CNN) combined with a unique feature selection strategy termed Integrated Features Selection (IFS). IFS employs four different filter-based feature selection techniques: mutual information, chisquared, ReliefF, and analysis of variance (ANOVA). Our proposed model has demonstrated outstanding performance, achieving a detection accuracy rate of 97.42%, a precision rate of 96.34%, a false positive rate (FPR) of 2.18%, an F- measure of 96.6%, and a detection time of 0.697 seconds. The exceptional effectiveness of our model is attributed to the strength of its innovative feature selection method. © 2024 IEEE.

关键词： DDoS deep learning feature selection SDN

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A System for Real-Time Shoulder Angle Measurement Using Computer Vision 4

A System for Real-Time Shoulder Angle Measurement Using Comp...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Langote, Meher Saratkar, Saniya Puri, Chetan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Computer Science Design Maharashtra Wardha India

ISBN: (纸本)9798331529635

Recently, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. This article presents a real-time shoulder angle measurement system utilizing computer vision and machine learning techniques, specifically designed for the rehabilitation of frozen shoulder and related conditions. The system leverages MediaPipe, an open-source framework, to capture and analyze shoulder movements accurately. With its modular architecture, the system processes video feeds in real time, detecting key body points and calculating shoulder angles, thereby providing immediate feedback to patients and therapists. The study evaluates the system's effectiveness in clinical and home-based environments, highlighting its potential to enhance physiotherapy by enabling precise, data-driven treatment plans. This research study also identifies current research gaps, proposes solutions, and explores future enhancements, including environmental adaptation, mobile integration, and the extension to other joints. This study has attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI), Computer vision, and a machine learning mode. © 2024 IEEE.

关键词： Goniometers

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless Systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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Game theory in network security for digital twins in industry

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Digital Communications and Networks 2024年第4期10卷 1068-1078页

作者： Hailin Feng Dongliang Chen Haibin Lv Zhihan Lv School of Information Engineering Zhejiang A&F UniversityHangzhou311300China School of Data Science and Software Engineering Qingdao UniversityQingdao266000China North China Sea Offshore Engineering Survey Institute Ministry of Natural Resources North Sea BureauQingdao266000China Department of Game Design Faculty of ArtsUppsala UniversitySweden

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game *** the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game *** evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is *** this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and *** the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy *** y0?0.9,X converges rapidly in a relatively short *** y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to *** is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the *** greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability *** the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of *** study can provide an effective solution to protect the network security

关键词： Digital twins Industrial internet of things Network security Game theory Attack and defense

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