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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Aibin Yan Zhelong Xu Jie Cui Zuobin Ying Zhengfeng Huang Huaguo Liang Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University / Anhui Engineering Laboratory of IoT Security Technologies Hefei China School of Electronic Science & Applied Physics Hefei University of Technology Hefei China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper presents a novel dual-interlocked storage-cell (DICE)-based double-node-upset (DNU) self-recoverable, namely DURI-FF, in the nano-scale CMOS technology. The master latch of the DURI-FF cell consists of three transmission gates (TGs) and three interlocked DICEs with three common nodes. The common nodes are connected to TGs for value initialization. The slave latch of the DURI-FF cell comprises six TGs, six inverters and three interlocked DICEs. The outputs of the inverters respectively feed the internal nodes of the slave latch. The interlocked DICEs make the master latch and the slave latch DNU self-recoverable. Simulation results validate the DNU self-recoverability of the proposed DURI-FF cell. Moreover, compared with the state-of-the-art hardened flip-flop cells, the proposed DURI-FF cell achieves roughly 43% delay reduction at the cost of moderate silicon area and power dissipation.

关键词： Latches Inverters Flip-flops Logic gates CMOS technology Simulation Reliability

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Special Issue Review: Artificial Intelligence and Machine Learning Applications in Remote Sensing

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Remote Sensing 2023年第3期15卷 569-569页

作者： Chen, Ying-Nong Fan, Kuo-Chin Chang, Yang-Lang Moriyama, Toshifumi Center for Space and Remote Sensing Research National Central University No. 300 Jhongda Rd. Jhongli Dist. Taoyuan City32001 Taiwan Department of Computer Science and Information Engineering National Central University No. 300 Jhongda Rd. Jhongli Dist. Taoyuan City32001 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Graduate School of Engineering Nagasaki University 1-14 Bunkyo-Machi Nagasaki852-8521 Japan

Remote sensing is used in an increasingly wide range of applications. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. Therefore, we organized a Special Issue on remote sensing titled "Artificial Intelligence and Machine Learning Applications in Remote Sensing." In this paper, we review nine articles included in this Special Issue, most of which report studies based on satellite data and DL, reflecting the most prevalent trends in remote sensing research, as well as how DL architecture and the functioning of DL models can be analyzed and explained is a hot topic in AI research. DL methods can outperform conventional machine learning methods in remote sensing;however, DL remains a black box and understanding the details of the mechanisms through which DL models make decisions is difficult. Therefore, researchers must continue to investigate how explainable DL methods for use in the field of remote sensing can be developed. © 2023 by the authors.

关键词： artificial intelligence machine learning deep learning remote sensing

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An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors

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Protein science 2024年第6期33卷 e5007页

作者： Lin, Tony Eight Yen, Dyan HuangFu, Wei-Chun Wu, Yi-Wen Hsu, Jui-Yi Yen, Shih-Chung Sung, Tzu-Ying Hsieh, Jui-Hua Pan, Shiow-Lin Yang, Chia-Ron Huang, Wei-Jan Hsu, Kai-Cheng Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan Warshel Institute for Computational Biology The Chinese University of Hong Kong (Shenzhen) Guangdong Shenzhen China Biomedical Translation Research Center Academia Sinica Taipei Taiwan Division of Translational Toxicology National Institute of Environmental Health Sciences National Institutes of Health Durham NC United States School of Pharmacy College of Medicine National Taiwan University Taipei Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei Taiwan Cancer Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC₅₀ value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8. © 2024 The Authors. Protein science published by Wiley Periodicals LLC on behalf of The Protein Society.

关键词： CDK8 fragment machine learning molecular docking structure-based virtual screening

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Purification and Characterization of Gamma-Aminobutyric Acid (GABA) from E. faecium BS5 and Its Antidiabetic Efficacy in Streptozotocin-Induced Diabetic Rats

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Probiotics and antimicrobial proteins 2025年 1-22页

作者： B S Sabna Ramasamy Mahendran Jeyakumar Balakrishnan Chih-Yang Huang P J Jerimon Anto Thomas Raju Eswaran P Naveen Angayarkanni Jayaraman Department of Microbial Biotechnology Bharathiar University Coimbatore T.N 641 046 India. Cardiovascular and Mitochondrial Related Disease Research Center Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Hualien Taiwan. Central Research Laboratory Vinayaka Mission's Medical College and Hospital Vinayaka Mission's Research Foundation (Deemed to be University) Karaikal Puducherry India. Department of Biological Science and Technology College of Life Sciences China Medical University Taichung Taiwan. PhD Program for Biotechnology Industry China Medical University Taichung Taiwan. Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan. Department of Biotechnology Asia University Taichung Taiwan. Center of General Education Tzu Chi University of Science and Technology Buddhist Tzu Chi Medical Foundation Hualien Taiwan. Graduate Institute of Biomedical Sciences School of Medicine China Medical University Taichung Taiwan. Department of Environmental Biotechnology Bharathidasan University Tiruchirappalli India. Department of Zoology The Madura College Madurai Tamil Nadu India. Department of Chemistry Sona College of Arts and Science Salem India. Department of Microbial Biotechnology Bharathiar University Coimbatore T.N 641 046 India. angaibiotech@buc.edu.in.

A non-proteinaceous amino acid called GABA is well-known for its physiological uses and its role as an inhibitory neurotransmitter in the brain of mammals. Apart from its neurological function, GABA has been linked to blood pressure reduction and glucose balance modulation. Since β-cell destruction and decreased insulin secretion are hallmarks of diabetes mellitus, the natural production of GABA by pancreatic β-cells increases the possibility of its therapeutic role. The aim of this study was to produce, purify, and characterize GABA from glutamic acid and assess its potential as an antidiabetic, namely to impact β-cell proliferation. GABA was produced from glutamic acid using the enzyme glutamate decarboxylase (GAD). After that, the produced GABA was purified and characterized using different analytical methods. Furthermore, in vivo studies were carried out with diabetic mice to evaluate the effect of GABA treatment on β-cell proliferation and control of insulin levels. GABA treatment improved glycaemic control in diabetic mice by increasing β-cell proliferation and regulating glucagon and insulin production. These results demonstrate the potential of GABA as an antidiabetic drug and provide a safe substitute for traditional insulin injections. Further study is needed to understand its mechanism of action and investigate its therapeutic application in the treatment of diabetes.

关键词： Diabetes mellitus GABA (gamma-aminobutyric acid) Glutamate decarboxylase (GAD) Insulin secretion Probiotics

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A Vehicle Forensics framework with Post-Quantum Cryptography and Blockchain

A Vehicle Forensics framework with Post-Quantum Cryptography...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Chien-Lung Hsu Yi-Cheih Hsu Yu-Jen Shih Cheng-Wei Wu Fu-Hau Hsu Graduate Institute of Management Chang Gung University Taiwan Taoyuan R.O.C Department of Information Management Chang Gung University Taiwan Taoyuan R.O.C Healthy Aging Research Center Chang Gung University Taiwan Taoyuan R.O.C Department of Visual Communication Design Ming-Chi University of Technology Taiwan Taoyuan R.O.C Chang-Gung Memorial Hospital Taiwan Taoyuan R.O.C Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University Taiwan Taoyuan R.O.C Computer Science& Information Engineering National Central University Taiwan Taoyuan R.O.C Information Management Chang Gung University Taiwan Taoyuan R.O.C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

With the recent rapid development of the Internet of Vehicles and autonomous driving technology, the demand for digital vehicle identification has also increased. Coupled with the gradual maturity of quantum technology, the Internet of Vehicles, which mainly uses wireless communication technology for data transmission, will face the threat of quantum computing and privacy issues. This research proposes the Vehicle Forensics Cloud System (VFCS), a framework combining post-quantum cryptography and blockchain technology to address these issues and provide a comprehensive solution for vehicle digital forensics.

关键词： Wireless communication Quantum computing Digital forensics Insurance Signal processing Blockchains Safety Autonomous vehicles Interoperability Internet of Vehicles

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Electron spin dynamics guide cell motility

arXiv

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arXiv 2025年

作者： Wang, Kai Gilmer, Gabrielle Araña, Matheus Cândia Iijima, Hirotaka Bergmann, Juliana Woollard, Antonio Mesits, Boris McGraw, Meghan Zoltowski, Brian Cappellaro, Paola Ungar, Alex Pekker, David Waldeck, David H. Saxena, Sunil Lloyd, Seth Ambrosio, Fabrisia Discovery Center for Musculoskeletal Recovery Schoen Adams Research Institute at Spaulding BostonMA United States Department of Physical Medicine & Rehabilitation Spaulding Rehabilitation Hospital BostonMA United States Department of Physical Medicine & Rehabilitation Harvard Medical School BostonMA United States Cellular and Molecular Pathology Graduate Program University of Pittsburgh PittsburghPA United States Medical Scientist Training Program School of Medicine University of Pittsburgh PittsburghPA United States École Polytechnique Institut Polytechnique de Paris Palaiseau France Department of Physics & Astronomy University of Pittsburgh PittsburghPA United States Department of Chemistry Southern Methodist University DallasTX United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry University of Pittsburgh PittsburghPA United States Department of Mechanical Engineering Massachusetts Institute of Technology BostonMA United States

Diverse organisms exploit the geomagnetic field (GMF) for migration. Migrating birds employ an intrinsically quantum mechanical mechanism for detecting the geomagnetic field: absorption of a blue photon generates a radical pair whose two electrons precess at different rates in the magnetic field, thereby sensitizing cells to the direction of the GMF. In this work, using an in vitro injury model, we discovered a quantum-based mechanism of cellular migration. Specifically, we show that migrating cells detect the GMF via an optically activated, electron spin-based mechanism. Cell injury provokes acute emission of blue photons, and these photons sensitize muscle progenitor cells to the magnetic field. We show that the magnetosensitivity of muscle progenitor cells is (a) activated by blue light, but not by green or red light, and (b) disrupted by the application of an oscillatory field at the frequency corresponding to the energy of the electron-spin/magnetic field interaction. A comprehensive analysis of protein expression reveals that the ability of blue photons to promote cell motility is mediated by activation of calmodulin calcium sensors. Collectively, these data suggest that cells possess a light-dependent magnetic compass driven by electron spin dynamics. © 2025, CC BY.

关键词： Spin dynamics

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Corrigendum to “Enhancement of Power Quality based on Dynamic Voltage Restorer Matrix Inverter - Sliding Mode Control Scheme” [Electric Power Systems Research volume 241 (2025) 111408]

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Electric Power Systems Research 2025年 242卷

作者： Abdul Hameed Soomro Abdul Sattar Larik Mukhtiar Ahmed Mahar Anwer Ali Sahito Mohsin Ali Koondhar Yun-Su Kim Zuhair Muhammed Alaas Ezzeddine Touti M.M.R. Ahmed Department of Electrical Engineering Quaid-e-Awam University of Engineering Science and Technology Sindh 67480 Pakistan Department of Electrical Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Graduate School of Energy Convergence Gwangju Institute of Science and Technology (GIST) Gwangju 61005 South Korea Department of Electrical and Electronics Engineering Faculty of Engineering and Computer Science Jazan University Jizan 45142 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Faculty of Technology and Education Helwan University Cairo Egypt

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Landau Polaritons in a Full-Dielectric Three-Dimensional Photonic-Crystal Cavity

Landau Polaritons in a Full-Dielectric Three-Dimensional Pho...

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CLEO: science and Innovations, S and I 2022

作者： Tay, Fuyang Mojibpour, Ali Liang, Shuang Baydin, Andrey Ahmadivand, Arash Peraca, Nicolas Marquez Xu, Hongjing Gardner, Geoff C. Manfra, Michael J. Hagenmüller, David Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Microsoft Quantum Purdue Purdue University West LafayetteIN47907 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Université de Strasbourg CNRS Strasbourg67000 France Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

ISBN: (纸本)9781557528209

We fabricated a full-dielectric three-dimensional photonic-crystal cavity containing an ultrahigh-mobility two-dimensional electron gas. By applying a strong perpendicular magnetic field, we created Landau polaritons originating from the ultrastrong coupling of electrons with cavity modes. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Polariton

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One City, Two Tales: Using Mobility Networks to Understand Neighborhood Resilience and Fragility during the COVID-19 Pandemic

arXiv

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arXiv 2022年

作者： Boz, Hasan Alp Bahrami, Mohsen Balcisoy, Selim Bozkaya, Burcin Mazar, Nina Nichols, Aaron Pentland, Alex Sabanci University Faculty of Engineering and Natural Sciences Istanbul34956 Turkey Massachusetts Institute of Technology Institute for Data Systems and Society CambridgeMA02139 United States New College of Florida The Graduate Program in Applied Data Science 5800 Bay Shore Rd SarasotaFL34243 United States Boston University Questrom School of Business BostonMA02215 United States

What predicts a neighborhood’s resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel application of human mobility patterns and human behavior in a network setting. We analyze mobility data in New York City over two years, from January 2019 to December 2020, and create weekly mobility networks between Census Block Groups by aggregating Point of Interest level visit patterns. Our results suggest that both the socioeconomic and geographic attributes of neighborhoods significantly predict neighborhood adaptability to the shelter-in-place policies active at that time. That is, our findings and simulation results reveal that in addition to factors such as race, education, and income, geographical attributes such as access to amenities in a neighborhood that satisfy community needs were equally important factors for predicting neighborhood adaptability and the spread of COVID-19. The results of our study provide insights that can enhance urban planning strategies that contribute to pandemic alleviation efforts, which in turn may help urban areas become more resilient to exogenous shocks such as the COVID-19 pandemic. © 2022, CC BY.

关键词： COVID-19

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