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Enhanced Magnetic Interaction between Ga and Fe in Two-dimensional van der Waals Ferromagnetic Crystal Fe_(3)GaTe_(2)

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Chinese Physics Letters 2023年第8期40卷 108-112页

作者：查鹤鸣李炜张高节刘文晶邓力玮姜琦叶茂武浩常海欣乔山 State Key Laboratory of Functional Materials for Informatics Center for Excellence in Superconducting ElectronicsShanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Physics and State Key Laboratory of Surfac Physics Fudan UniversityShanghai 200433China State Key Laboratory of Material Processing and Die&Mold Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Wuhan National High Magnetic Field Center and Institute for Quantum Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Center for Transformative Science ShanghaiTech UniversityShanghai 201210China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201210China Shenzhen R&D Center of Huazhong University of Science and Technology Shenzhen 518000China School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China

Fe_(3)GaTe_(2),a recently discovered van der Waals ferromagnetic crystal with the highest Curie temperature and strong perpendicular magnetic anisotropy among two-dimensional(2d)magnetic materials,has attracted significant attention and makes it a promising candidate for next-generation spintronic *** with Fe_(3)GeTe_(2),which has the similar crystal structure,the mechanism of the enhanced ferromagnetic properties in Fe_(3)GaTe_(2)is still unclear and needs to be ***,by using x-ray magnetic circular dichroism measurements,we find that both Ga and Te atoms contribute to the total magnetic moment of the system with antiferromagnetic coupling to Fe *** first-principles calculations reveal that Fe_(3)GaTe_(2)has van Hove singularities at the Fermi level in nonmagnetic state,resulting in the magnetic instability of the system and susceptibility to magnetic phase *** addition,the calculation results about the density of states in ferromagnetic states of two materials suggest that the exchange interaction between Fe atoms is strengthened by replacing Ge atoms with Ga *** findings indicate the increase of both the itinerate and local moments in Fe_(3)GaTe_(2)in view of Stoner and exchange interaction models,which results in the enhancement of the overall magnetism and a higher Curie *** work provides insight into the underlying mechanism of Fe_(3)GaTe_(2)’s remarkable magnetic properties and has important implications for searching 2d materials with expected magnetic properties in the future.

关键词： magnetic ferromagnetic mechanism

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The Future of Smart Grids: revolutionizing Energy distribution with advanced IoT, and renewable Integration 5

The Future of Smart Grids: Revolutionizing Energy Distributi...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Avinash, S. Vinitha, J.C. dharinya, S. Sree Samuthira Pandi, V. Lakshmi Priya, J. Jaishika, S. St. Joseph's College of Engineering Department of Mba Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit University School of Computer Science Engineering and Information Systems Vellore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798350376685

Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of energy distribution. Intelligent grids are expected to bring about this shift. Focusing on the role of the Internet of Things (IoT) in enabling real-time monitoring and control, as well as the incorporation of renewable energy to support a low-carbon future, this research investigates the potential for smart grids to change the energy sector. This study gives insights into how smart grids can be efficiently deployed to satisfy the growing energy needs of society by studying the technological breakthroughs, obstacles, and benefits of smart grids. Specifically, the study's focus is on smart grids. The research highlights the essential role that smart grids play in aiding the shift towards a more sustainable and resilient energy infrastructure by conducting an in-depth analysis of case studies, pilot projects, and industry trends all around the world. Providing recommendations for policymakers, industry stakeholders, and utilities to exploit the full potential of smart grids in determining the future of energy distribution, the findings contribute to the continuing discourse on the strategic development and implementation of smart grid technologies. Both of these topics are currently being discussed. © 2024 IEEE.

关键词： demand response Management distributed Generation Energy Efficiency Energy Storage Systems Grid resilience Low-Carbon Future real-Time Monitoring and Control

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Securing the digital Frontier: An Analysis of Cybersecurity Strategies and Obstacles

Securing the Digital Frontier: An Analysis of Cybersecurity ...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Saravanan, V. Indumathi, G.S. James, Sathish Kumar Pandi, V. Samuthira Shakila, J. Banumathi, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India BNY Mellon Center PittsburghPA15219 United States Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Jeppiaar Engineering College Rajiv Gandhi Salai Department of CSE Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India

ISBN: (纸本)9798331512965

In an era where digital connectivity is ubiquitous, the imperative to safeguard cyberspace has never been more pressing. This research delves into the multifaceted landscape of cybersecurity, examining the strategies employed by governments, businesses, and individuals to protect sensitive information and critical infrastructure from cyber threats. By leveraging a mixed-methods approach, which includes a review of existing literature, case studies, and expert interviews, the research provides a nuanced understanding of the cybersecurity ecosystem. Key findings reveal that while technological advancements have bolstered defense mechanisms, including sophisticated malware, advanced persistent threats (APTs), and insider threats, continues to outpace traditional security measures. Moreover, the study underscores the critical role of human factors, such as user error and inadequate training, as significant vulnerabilities. The research concludes with a set of recommendations aimed at enhancing cybersecurity posture, including the adoption of a proactive, adaptive defense strategy, increased investment in cybersecurity education and awareness, and the fostering of international cooperation to combat transnational cybercrime. The insights gleaned from this analysis are intended to inform policymakers, industry leaders, and cybersecurity professionals, equipping them with the knowledge to fortify the digital frontier against evolving threats. © 2025 IEEE.

关键词： Cyber attacks

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Synthetic Voltage and Capacity Profile regeneration Through Constant Voltage Sampling Process from Second Life Batteries Perspective

Synthetic Voltage and Capacity Profile Regeneration Through ...

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International Conference on Electrical Energy Systems (ICEES)

作者： Vignesh S Jeyraj A-L Selvaraj Hang Seng Che Tey Kok Soon HiCoE UMPEDAC Level 4 WISMA R&D Institute for Advanced Studies Universiti Malaya Kuala Lumpur Malaysia HiCoE UMPEDAC Level 4 WISMA R&D Universiti Malaya Kuala Lumpur Malaysia Department of Computer System & Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

Predicting State of Health (SoH) of Second Life Batteries (SLB) is a challenging aspect when the repurposer is often surrounded by uncertainty in historical data from Electric Vehicle (EV). The EV Original Equipment Manufacturer (OEM) hardly reveal any battery related data to public domain, it also reciprocates to the repurposer. However, SoH estimation demands minimal historic data to compare past and present state of the batteries. Thus, when repurposer examines SoH of SLB, it is mandated to adhere on either of reference Performance Test (rPT) or the one which is recommended in the cell datasheet by the cell manufacturer to compare the SLB performance with the past data. While executing this process, the repurposer often conducts multiple charge and discharge cycles to comment on SoH of the SLB cells. Hence, in order to reduce the processing time in conducting cycling experiments, and to predict the SoH of SLB, the authors proposed a novel approach on regenerating voltage and capacity profile through Constant Voltage Sampling (CVS) process considering SLB perspective. The regenerated curve is deemed as synthetic curve wherein the forward and backward prediction of SoH is carried out. raw test data is accessed from University of Michigan repository for serving as reference to the authors. The results of voltage and capacity profile are validated successfully for SoH ranges from 100% to 80%. With the help of CVS technique, discharge voltage and capacity profiles for SoH ranges from 80% to 50% were reconstructed successfully to define the health indicators governing SoH and remaining useful Life (ruL).

关键词： degradation Uncertainty Estimation Voltage Second Life discharges (electric) Batteries History Testing Synthetic data

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Accelerating deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Seongwook Kim Gwangeun Byeon Seungeui Lee Youngseok Bae Seokin Hong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Advanced R&D Center Hanwha Systems Co. Ltd. Emerging Science and Technology Directorate Agency for Defense Development

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-delta-Immediate Compression (BdI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement,

关键词： deep learning Filters Artificial neural networks Performance gain Complexity theory Hardware acceleration

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Sensorless Axial Vibration damping Control of Two-Axis Actively Positioned Bearingless Motors With Adaptive Flux Observer

Sensorless Axial Vibration Damping Control of Two-Axis Activ...

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IEEE International Conference on Electric Machines and drives (IEMdC)

作者： Masahito Miyoshi Yuichiro Nakamura Shinichi Furutani Keita Kajino Hiroya Sugimoto Advanced Technology R&D Center Mitsubishi Electric Corp Amagasaki Japan Department of Electrical and Electronic Engineering Tokyo Denki University Tokyo Japan

This paper proposes a sensorless estimation of rotor axial motions and an active control of axial vibration suppressions in two-axis actively positioned bearingless motors. Proposed adaptive flux observer effectively works for estimating the rotor axial vibration velocity that it is proportional to the error between measured and estimated stator currents. It is computationally verified that the proposed vibration suppression technique can be utilized even when continuous noises are included in current sensors.

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remote Patient Monitoring System with Wearable IoT devices and Biosensors for Vital Signs 3

Remote Patient Monitoring System with Wearable IoT Devices a...

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3rd IEEE International Conference on device Intelligence, Computing and Communication Technologies, dICCT 2025

作者： Naveen, S. Sivakumar, T.B. Supreeth, B.r. Niranjana, r. Vishwa Priya, V. Saranya, r. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences-SIMATS Saveetha University Department of Mechanical Engineering Tamil Nadu Chennai India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Ullal Bangalore India Velammal College of Engineering and Technology Department of Cse Tamil Nadu Madurai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Information Technology Tamil Nadu Chennai India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore India

ISBN: (纸本)9798331543358

due to infrequent patient monitoring and the reactive character of the traditional healthcare system, delayed diagnoses and inadequate management of chronic diseases are common outcomes. The study presents a solution to these drawbacks: a remote Patient Monitoring (rPM) system that combines advanced biosensors with wearable IoT devices to track vital signs continuously. By ensuring proactive healthcare through real-time data collecting and analysis, the entire system overcomes the drawbacks of conventional techniques. Early anomaly detection and timely medical interventions are made possible by the method's utilization of scalable cloud infrastructure and advanced machine learning (ML) methods, such as CNNs (Convolutional Neural Networks) and Long Short-Term Memory (LSTMs). The proposed system achieves a 90% early detection rate, 95% sensitivity, 97% specificity in anomaly detection, and 92% patient satisfaction, which is a substantial improvement over existing techniques, according to the results. It also exhibits better vital sign measurement accuracy than existing systems, indicating its potential to completely transform patient care through better results and economical resource utilization. © 2025 IEEE.

关键词： remote patient monitoring

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Machine Learning in recent technology: Shaping the Future of Intelligent Applications and data Analysis 5

Machine Learning in Recent Technology: Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karpagam, P. Uma Maheswari, S. Kumar, A. Pandi, V.Samuthira Jose, P. Virushabadoss, S. Dhanalakshmi College of Engineering Department of Computer Science Engineering India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

In the present day, machine learning (ML), which is a subset of artificial intelligence (AI), has emerged as a crucial technology that has revolutionized the way in which we interact with data and construct intelligent applications. The purpose of this study is to investigate the recent developments in machine learning and the profound impact that these breakthroughs have had on a variety of fields, such as the medical field, the financial sector, the transportation industry, and environmental science. Through an examination of cutting-edge machine learning techniques such as deep learning, reinforcement learning, and transfer learning, the study sheds light on the ways in which these methodologies are leading to the development of intelligent systems in the future. The purpose of this study is to investigate the function of machine learning in the process of data analysis, with a particular emphasis on its capacity to derive insights from intricate and extensive datasets. Additionally, it studies the difficulties that are linked with machine learning, such as the problem of data privacy, the interpretability of models, and the ethical implications of automated decision-making. The objective of this study is to identify major trends and future directions in machine learning by doing an exhaustive examination of the relevant literature, conducting case studies, and conducting experimental analysis. Based on the findings, it appears that the combination of machine learning with other technologies, such as edge computing and the Internet of Things (IoT), will result in intelligent systems that are more advanced and capable of adapting to their environment. The study highlights the significance of a multidisciplinary approach to machine learning, highlighting the necessity of collaboration between computer scientists, domain specialists, and ethicists in order to guarantee the development and deployment of machine learning technologies in a responsible manner. As machin

关键词： Adversarial machine learning

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Analysis of titanium coated silencer exhaust gas emission in automobile vehicle using artificial neural network

Analysis of titanium coated silencer exhaust gas emission in...

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作者： Vijayaraghavan, P. Janakiraman, V. Surulivel rajan, T. Logesh, K. rajiv Gandhi, N. Gnanavel, C. Sathi, G. Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Mechanical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

In developing countries like India, pollution is a major environmental problem. Among various factors of pollution, vehicular pollutions contribute major part in urban areas. The National Environmental Engineering research Institute has estimated that about 60–70% of total air pollution is caused by automobiles in all major *** a perfect vehicular emissions model, real world driving characteristics have to be analyzed. It is necessary to investigate the relationship between various parameters such as speed, age, power and driving behaviour of the vehicle. Vehicles particularly emit gases such as CO, CO2, HC, NOX, and O2. They are measured with five gas analyzer to find out the emission rates of four stroke two wheeler vehicles under static and dynamic condition. The factors considered are the age, power of the vehicle and driving behaviour. From the emission rates a prediction model is developed using artificial neural network and the composition of air pollutants will be calculated and then compared with emission standards. © 2022

关键词： developing countries

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The toxic effects of exposure to fibrous and fragmented microplastic in juvenile rockfish based on two omics approach

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Chemosphere 2024年 367卷 143541页

作者： Choi, Kwang-Min Mun, Seong Hee Shin, dongju Kim, Chae Hwa Kim, Tae Hee Jung, Jee-Hyun Ecological Risk Research Department Korea Institute of Ocean Science and Technology Geoje53201 Korea Republic of Ocean Science University of Science and Technology 217 Gajeong-ro Daejeon34113 Korea Republic of Department of Advanced Textile R&D Korea Institute of Industrial Technology Ansan15588 Korea Republic of

Although the hazards of environmental microplastics (MPs) are well known, it is unclear which of their characteristics have the greatest effects on organism. We investigated the toxic effects of oral administration according to physical properties, including the shape of fragmented polyethylene terephthalate (PET) (FrPET) and fibrous PET (FiPET) MPs. After 72 h of exposure, apoptosis and phagocytic activity varied significantly among juvenile rockfish (Sebastes schlegeli) exposed to both FrPET and FiPET. The levels of immune-related genes and hepatic metabolic activity also increased after exposure to both shapes of MPs, but the variation in responses was greater in fish exposed to FiPET compared with those exposed to FrPET. The transcriptomic and metabolomics analysis results indicated that the maintenance and homeostasis of immune system was affected by oral exposure to FrPET and FiPET. The amino acid metabolic processes were identified in rockfish exposed to FrPET, but the notch signaling pathway were evident in the FiPET exposure group. Metabolomics analysis revealed that oral ingestion of MP fibers led to a stronger inflammatory response and greater oxidative stress in juvenile rockfish. These results can be used to understand environmentally dominant MP toxic effects such as type, size, shapes, as well as to prioritize ecotoxicological management. © 2024 Elsevier Ltd

关键词： Invertebrates

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