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Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

Light(science & Applications) 2023年第7期12卷 1401-1414页

作者： Cheng Xu Zhihao Ren Hong Zhou Jingkai Zhou Chong Pei Ho Nan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Inteligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore institute of Microelectronics(IME) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore NUS Graduate School for Integrative Science and Engineering Program(ISEP) National University of SingaporeSingapore 117456Singapore

Circular dichroism(CD)spectroscopy has been widely demonstrated for detecting chiral ***,the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging *** solve this problem,we report an enhanced vibrational circular dichroism(VCD)sensing platform based on plasmonic chiral metamaterials,which presents a 6-magnitude signal enhancement with a selectivity of chiral *** by coupled-mode theory,we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process,which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss,resulting in improved chiral light-matter interaction and enhanced molecular VCD ***,we demonstrate the thin-film sensing process of BSA andβ-lactoglobulin proteins,which contain secondary structures a-helix andβ-sheet and achieve a limit of detection down to zeptomole ***,we also,for the first time,explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures,where the mixing ratio can be successfully differentiated with our proposed chiral *** findings improve the sensing signal of molecules and expand the extractable information,paving the way toward label-free,compact,small-volume chiral molecule detection for stereochemical and clinical diagnosisapplications.

关键词： chiral vibrational circular

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Recent Constraints and Challenges in Battery Management System of EVs - A Brief Review 14

Recent Constraints and Challenges in Battery Management Syst...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Zahid, Faizan Abro, Ghulam E Mustafa Boudville, Rozan Talpur, Noureen Rajput, Summaiya University of Catania Department of Electrical Electronics and Computer Engineering Italy King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Aviation and Space Exploration Dhahran Saudi Arabia Universiti Teknologi MARA Electrical Engineering Studies College of Electrical Engineering Permatang Pauh Pulau Pinang13500 Malaysia Computer Information Science Department Seri Iskandar32610 Malaysia Mehran University of Engineering and Technology Department of Telecommunications Engineering Jamshoro Pakistan

ISBN: (纸本)9798350364507

As world embark on the revolution in electric vehicles, battery management systems (BMS) represent the center of technological innovation. Through a thorough analysis of academic publications from the prestigious Scopus database, our study reveals several critical issues that BMS technology faces. Our findings provide light on the significant challenges faced, from enhanced data security and accurate temperature control to real-time state estimates. Nonetheless, concealed within these difficulties are the keys to unleashing the whole possibilities of electric cars: smooth charging, enhanced longevity estimates, and stable power availability. In the midst of inventiveness gone wild, sustainability lurks, raising the difficult problem of battery disposal. This study provides an in-depth exploration of the complexities inside BMS, serving as a beacon of guidance. By taking immediate action to address these issues, we clear the path for an era in which electric cars - driven by their superior performance and increased efficiency - will predominate. © 2024 IEEE.

关键词： Battery management systems

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Effect of electrolyte anions on the cycle life of a polymer electrode in aqueous batteries

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escience 2022年第1期2卷 110-115页

作者： Ye Zhang Lihong Zhao Yanliang Liang Xiaojun Wang Yan Yao Department of Electrical and Computer Engineering Materials Science and Engineering ProgramUniversity of HoustonHoustonTX77204USA Texas Center for Superconductivity at the University of Houston HoustonTX77204USA

Redox polymers are a class of high-capacity, low-cost electrode materials for electrochemical energy storage, butthe mechanisms governing their cycling stability are not well understood. Here we investigate the effect of anionson the longevity of a p-dopable polymer through comparing two aqueous zinc-based electrolytes. Galvanostaticcycling studies reveal the polymer has better capacity retention in the presence of triflate anions than that withsulfate anions. Based on electrode microstructural analysis and evolution profiles of the cell stacking pressure, theorigin of capacity decay is ascribed to mechanical fractures induced by volume change of the polymer activematerials during repeated cycling. The volume change of the polymer with the triflate anion is 61% less than thatwith the sulfate anion, resulting in fewer cracks in the electrodes. The difference is related to the different anionsolvation structures—the triflate anion has fewer solvated water molecules compared with the sulfate anion,leading to smaller volume expansion. This work highlights that anions with low solvation degree are preferablefor long-term cycling.

关键词： Aqueous batteries Polymer electrode Anions Volume chang

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Adversarial environment design via regret-guided diffusion models 24

Adversarial environment design via regret-guided diffusion m...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hojun Chung Junseo Lee Minsoo Kim Dohyeong Kim Songhwai Oh Interdisciplinary Program in Artificial Intelligence and ASRI Seoul National University Department of Electrical and Computer Engineering and ASRI Seoul National University Interdisciplinary Program in Artificial Intelligence and ASRI Seoul National University and Department of Electrical and Computer Engineering and ASRI Seoul National University

ISBN: (纸本)9798331314385

Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generating a set of training environments tailored to the agent's capabilities. While prior works demonstrate that UED has the potential to learn a robust policy, their performance is constrained by the capabilities of the environment generation. To this end, we propose a novel UED algorithm, adversarial environment design via regret-guided diffusion models (ADD). The proposed method guides the diffusion-based environment generator with the regret of the agent to produce environments that the agent finds challenging but conducive to further improvement. By exploiting the representation power of diffusion models, ADD can directly generate adversarial environments while maintaining the diversity of training environments, enabling the agent to effectively learn a robust policy. Our experimental results demonstrate that the proposed method successfully generates an instructive curriculum of environments, outperforming UED baselines in zero-shot generalization across novel, out-of-distribution environments. Project page: https://***/projects/ADD

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Geometric discord of tripartite quantum systems

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Chinese Physics B 2023年第11期32卷 184-190页

作者：熊春河齐文韬缪茂可吴明晖 Interdisciplinary Center for Quantum Information School of PhysicsZhejiang UniversityHangzhou 310027China School of Computer and Computing Science Hangzhou City UniversityHangzhou 310015China School of Mathematical Sciences Zhejiang UniversityHangzhou 310027China School of information and electrical engineering Hangzhou City UniversityHangzhou 310015China

We study the quantification of geometric discord for tripartite quantum ***,we obtain the analytic formula of geometric discord for tripartite pure *** is already known that the geometric discord of pure states reduces to the geometric entanglement in bipartite systems,the results presented here show that this property is no longer true in tripartite ***,we provide an operational meaning for tripartite geometric discord by linking it to quantum state discrimination,that is,we prove that the geometric discord of tripartite states is equal to the minimum error probability to discriminate a set of quantum states with von Neumann ***,we calculate the geometric discord of three-qubit Bell diagonal states and then investigate the dynamic behavior of tripartite geometric discord under local *** is interesting that the frozen phenomenon exists for geometric discord in this scenario.

关键词： geometric discord tripartite quantum systems quantum state discriminations frozen discord

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Impact of High-k Materials and Tube Numbers on CNFET Gate's Performances 5

Impact of High-k Materials and Tube Numbers on CNFET Gate's ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Yalung, Christofer Tantraviwat, Doldet Uatrongjit, Sermsak Yamwong, Wittawat Doctor of Philosophy Program in Nanoscience and Nanotechnology International Program/Interdisciplinary Center of Excellence in Materials Science and Technology Faculty of Science Chiang Mai University Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand Chachoengsao Thailand

ISBN: (纸本)9781665474672

Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and contact materials. In this study, the dielectric constant and the number of carbon nanotubes (CNTs) were emphasized to analyze the propagation delay and average power dissipation of basic logic gates. Zirconium dioxide (ZrO2), hafnium oxide (HfO2), and silicon dioxide (SiO2) were used as dielectric materials. In this experiment, the Stanford CNFET model was utilized, and variable factors such as the number of CNTs and the dielectric constant (Kox) accumulated on the nanotube gate's surface were diversified. The correlation coefficients of the delay, the average power, and the CNT numbers were also presented. Furthermore, the multi-objective genetic algorithm and the Rmethod were utilized to find the optimal Kox and the number of CNTs for minimizing the average dissipation power and the propagation delay of some logic gates designed with CNFET. For all the gates considered, the optimal number of CNTs is 4 while the dielectric constant is within the ranges of 19 to 24. © 2023 IEEE.

关键词： Carbon nanotubes

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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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An Explainable Machine Learning-Based Phishing Website Detection using Gradient Boosting

An Explainable Machine Learning-Based Phishing Website Detec...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Prayogo, Rizal Dwi Alfisyahrin, Alvin Rizqi Gambetta, Windy Karimah, Siti Amatullah Nambo, Hidetaka Kanazawa University Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa Japan Telkom University School of Computing Bandung Indonesia Kanazawa University Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa Japan Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350376210

As the need for achieving optimal performance in prediction models, several recent and complex models have been developed. However, many of these models operate as black boxes, providing little insight into their predictive results. In recent years, three gradient-boosting methods based on Decision Trees have been recommended, i.e., XGBoost, CatBoost, and LightGBM. These methods have proven to deliver competitive performance with fast training times. Nevertheless, in critical domains like security, there is a growing need for increased transparency among stakeholders. One pressing concern in the security field is the proliferation of phishing websites. To address this issue, we propose an explainable machine learning-based approach using gradient-boosting methods with hyperparameter optimization on three phishing website datasets. Our best methods surpass the state-of-the-art phishing website detection methods, achieving accuracy rates of 97.45%, 99.16%, and 97.85% for the UCI (2015), Mendeley (2018), and Mendeley (2020) datasets, respectively. Subsequently, we implement posthoc explainability using SHAP and LIME for the selected dataset. The experimental results indicate that three features, i.e., length_url, directory_length, and time_domain_activation, are consistently identified as the most influential features in the dataset. Moreover, our proposed approach demonstrates promising results for detecting phishing websites with both high accuracy and explainability. © 2024 IEEE.

关键词： Phishing

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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