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High-throughput study of the anomalous Hall effect

npj Computational Materials 2023年第1期9卷 767-774页

作者： JakubŽelezný Yuta Yahagi Carles Gomez-Olivella Yang Zhang Yan Sun Institute of Physics Czech Academy of SciencesCukrovarnická1016200 Praha 6Czech Republic Department of Applied Physics Tohoku UniversitySendaiMiyagiJapan Department of Physics and Astronomy University of Tennessee KnoxvilleKnoxvilleTN 37996USA Min H.Kao Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China

Despite its long history,the anomalous Hall continues to attract attention due to its complex origins,its connection to topology,and its use as a probe of magnetic *** this work we investigate the anomalous Hall effect in 2871 ferromagnetic materials using an automatic high-throughput calculation *** analyze general properties of the effect,such as its reliance on spin-orbit coupling strength and *** materials with the largest anomalous Hall effect,we find that symmetry-protected band degeneracies in the non-relativistic electronic structure,such as mirror symmetry-protected nodal lines,are typically responsible for the large ***,we examine the dependence of the anomalous Hall effect on magnetization direction and demonstrate deviations from the commonly assumed expression j^(AHE)~M×E.

关键词： effect symmetry protected

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IoT Based Sport Healthcare Monitoring System 7

IoT Based Sport Healthcare Monitoring System

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7th International Conference on Software Engineering and computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Samsuddin, Muhammad Naim Mohd Raffei, Anis Farihan Mat Rahman, Nur Shamsiah Abdul Universiti Malaysia Multimedia Computing Computer Vision Faculty of Computing College of Computing and Applied Sciences Pahang26600 Malaysia

ISBN: (纸本)9781665414074

The Internet of Things is considered now as one of the feasible solutions for any remote value tracking especially in the field of health monitoring. Monitoring the athlete in aspect of fitness and training progress performances are very important to avoid accident happened during training. The proposed system developed the IoT based sport healthcare monitoring system using Arduino and several sensors. A fuzzy logic algorithm is also developed to provide decision for the athlete health condition. A web-based application is also developed to allow the instructor to store and retrieve the athlete information as well as monitor the fitness training performance of the athlete. © 2021 IEEE.

关键词： Internet of things

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems 20

Robust and Reliable Liveness Detection Models for Facial Rec...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Anjum, Haris Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Siddique, Abu Bakar Imad, Muhammad Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

ISBN: (纸本)9798350395785

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model's accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system. © 2023 IEEE.

关键词： Feature extraction

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Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network 8

Optimized Smart Agricultural Production Systems using Enhanc...

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

作者： Meena, S. Gomathi Goyal, Himanshu Rai Lavanya Muthukumar, E. Megha, Mamtaben Kamleshbhai Parikh, Swapnil Peri College of Arts and Science Pg Department of Computer Science and Computer Applications Tamil Nadu Mannivakkam600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India Nehru College of Management Department of Mba Coimbatore Tamil Nadu Thirumalayampalayam641105 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Vallabh Vidyanagar388120 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350376425

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). This system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced Parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. The EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). These results highlight the efficiency of the approach and its potential for further advancement in smart agriculture. © 2024 IEEE.

关键词： Emperor Penguin Optimizer (EPO) Enhanced Parallel Convolutional Neural Network (EPar-CNN) Green Anaconda Optimization (GAO) Internet of Things (IoT) Smart Agricultural Systems

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Fraction of Data in a Secure Client-Server Communication Method Using Key Exchange

Fraction of Data in a Secure Client-Server Communication Met...

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2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022

作者： Ahamed, B. Bazeer Krishnamoorthy, Murugan University of Technology and Applied Sciences Department of IT Al Masnaah Oman Vellore Institute of Technology School of Computer Science and Engieering Vellore632014 India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

Secure communication is an interesting issue nowadays. While the fame of chat applications expands, this notoriety carries some security problems with it. An assortment of authentication mechanisms and encryption strategies recommended and applied between to limit the expanding security issues. Numerous analysts pick the best they found and use it in various mix to give security to the chat application. On the comparable terms, we have utilized a combination of key exchange calculation mixed with an encryption algorithm. This blend is alluded to as 'Three-way system' since it guarantees all the three-protection plan of verification, data security and authentication, simultaneously. In this paper, we actualize a best in class client server hybrid model encryption framework utilizing DH algorithm combined with (AES) Advanced Encryption Standard encryption algorithm. Regardless of whether the key in transmission is cracked, the concept of Diffie Helman key exchange makes it futile, since key in transmission is of no utilization without client's private key, which is kept uniquely to the real client. This proposed design of three-way component makes it difficult for hackers to exploit the security framework, thereby securing the chat application. © 2022 IEEE.

关键词： Personal computing

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Enhancing Urban Air Quality with Smart Vehicle Exhaust Management

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Tyagi, Vaishali Dubey, Roshan Singh, Suraj Ram, Mangey Department of Applied Sciences & Humanities ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science & Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical, or biological agent that modifies the natural characteristics of the atmosphere. Burning gasoline and diesel fuel creates harmful byproducts like nitrogen dioxide, carbon monoxide, hydrocarbons, benzene, and formaldehyde. This study proposes a sensor-based model to control vehicular pollution by monitoring emission levels and preventing vehicles from operating if their pollution exceeds government standards. The proposed model consists of a smoke sensor, particulate sensors, carbon monoxide sensors, along with Arduino based microcontroller, data logger, LCD display, and GPRS modem. Using the block diagram, a mathematical model has been developed and some differential equations by using Markov process have been generated. This Markov-based model gives the information about the failure causes and working of the proposed system. Some reliability matrices like availability, mean time between failure (MTBF), and sensitivity analysis have been evaluated with the help of Laplace transform and illustrated with the help of graphs. Also, the failure cases of the system and its cost analysis have been done along with all the numerical calculations required for the successful operation of the model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Air pollution Cost analysis Failure cases Markov process Reliability Sensor system

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Optical Solitons with Parabolic and Weakly Nonlocal Law of Self-Phase Modulation by Laplace-Adomian Decomposition Method

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computer Modeling in Engineering & sciences 2025年第3期142卷 2513-2525页

作者： Oswaldo González-Gaxiola Anjan Biswas Ahmed H.Arnous Yakup Yildirim Applied Mathematics and Systems Department Universidad Autonoma Metropolitana-CuajimalpaVasco de Quiroga 4871Mexico City05348Mexico Department of Mathematics and Physics Grambling State UniversityGramblingLA 71245-2715USA Department of Applied Sciences Cross-Border Faculty of HumanitiesEconomics and EngineeringDunarea de Jos University of GalatiGalati800201Romania Department of Mathematics and Applied Mathematics Sefako Makgatho Health Sciences UniversityPretoria0204South Africa Department of Physics and Engineering Mathematics Higher Institute of EngineeringEl-Shorouk AcademyCairo11837Egypt Department of Computer Engineering Biruni UniversityIstanbul34010Turkey Mathematics Research Center Near East UniversityNicosia99138Cyprus

Computational modeling plays a vital role in advancing our understanding and application of soliton *** allows researchers to both simulate and analyze complex soliton phenomena and discover new types of soliton *** the present study,we computationally derive the bright and dark optical solitons for a Schrödinger equation that contains a specific type of *** nonlinearity in the model is the result of the combination of the parabolic law and the non-local law of self-phase modulation *** numerical simulation is accomplished through the application of an algorithm that integrates the classical Adomian method with the Laplace *** results obtained have not been previously reported for this type of ***,for the purpose of comparison,the numerical examination has taken into account some scenarios with fixed parameter ***,the numerical derivation of solitons without the assistance of an exact solution is an exceptional take-home lesson fromthis ***,the proposed approach is demonstrated to possess optimal computational accuracy in the results presentation,which includes error tables and *** is important tomention that themethodology employed in this study does not involve any form of linearization,discretization,or ***,the physical nature of the problem to be solved remains unaltered,which is one of the main advantages.

关键词： Soliton solutions parabolic law nonlinearity weakly nonlocal Schrödinger equation laplace-adomian decomposition method

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Simulation of the Flow near a Rotating Propeller on Adaptive Unstructured Meshes Using the Immersed Boundary Method

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Mathematical Models and computer Simulations 2022年第2期14卷 224-240页

作者： Tsvetkova, V.O. Abalakin, I.V. Bobkov, V.G. Zhdanova, N.S. Kozubskaya, T.K. Kudryavtseva, L.N. Keldysh Institute of Applied Mathematics Russian Academy of Sciences Moscow Russian Federation Dorodnitsyn Computing Center Federal Research Center “Computer Science and Control” Russian Academy of Sciences Moscow Russian Federation

Abstract: A method is presented for modeling the flow of a viscous compressible gas around a moving body on a simply connected unstructured mesh, which is possible when using the immersed boundary method, and the features of its application for calculating the flow around a rotating propeller. An important part of the method is the adaptation of a mobile mesh that retains its topology to the surface of the streamlined body. The position and shape of the body are specified by an interpolation mesh, which stores the distance to the body surface and the normal to the surface. The method is used to calculate the flow around a propeller rotating in a flow. The problem is considered in a two-dimensional setting. The shape of the propeller has areas of high curvature that require a special approach when adapting to the boundaries. The results of calculating the problem of the flow around a rotating propeller and a number of auxiliary problems considered in order to verify the developed method are presented. © 2022, Pleiades Publishing, Ltd.

关键词： computational gas dynamics immersed boundary method interpolation mesh mesh adaptation movable mesh rotating propeller unstructured mesh

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A novel augmented reality-based simulator for enhancing orthopedic surgical training

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computers in Biology and Medicine 2025年 185卷 109536-109536页

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for instructors. Methods: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann–Whitney U tests. Results: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22±1.32mm with LoA −2.37 to 2.81 mm for distance deviation, 0.94±2.41 degrees with LoA −3.78 to 5.66 degrees for yaw deviation, −0.34±1.30 degrees with LoA −2.90 to 2.22 degrees for pitch deviation) and p-values of Mann–Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of 0.05. Conclusion: This work sh

关键词： Augmented reality

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A Hybrid Model for Botnet Detection using Machine Learning 2

A Hybrid Model for Botnet Detection using Machine Learning

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Zaheer, Anam Tahir, Sidra Almufareh, Maram Fahaad Hamid, Bushra University of Arid Agriculture University Institute of Information Technology Rawalpindi Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350335644

Botnet attacks are becoming a growing threat to the security of computer networks, and there is a need for effective and efficient methods for detecting these attacks. In this study, we proposed a hybrid machine-learning model for detecting botnet attacks in network traffic. The approach combines three machine learning algorithms: k-means, rule- based system, and decision tree. The experiment was conducted using the CTU-13 dataset and features extracted from the Barnacles Mating Optimizer for network traffic flow. The experiment results showed that the proposed hybrid machine learning approach can achieve high accuracy levels in detecting botnet attacks, with a computed accuracy of 99%. The combination of different machine learning algorithms can improve the accuracy of the system, making it more robust to different types of botnet attacks. The use of real-world data, such as the CTU-13 dataset, and incorporating features from the Barnacles Mating Optimizer, make the results of this experiment more representative of real-world botnet attack detection computed accuracy 99.32 for kmean, 99.11% for decision tree and 97.14% for rule-based system. Precision for kmean 98.93%, decision tree 98.37% and rule-based 95.93% computed. © 2023 IEEE.

关键词： Decision trees

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