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Terahertz science and Applications, TSA_2019

作者： Noe, G.T. Makihara, T. Li, X. Hayashida, K. Marquez Peraca, N. Tian, K. Yuan, N. Zhang, J. Jin, Z. Ren, W. Ma, G. Cao, S. Katayama, I. Takeda, J. Turchinovich, D. Nojiri, H. Bamba, M. Kono, J. Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Physics and Astronomy Rice University HoustonTX United States Department of Physics International Center of Quantum and Molecular Structures Materials Genome Institute Shanghai University Shanghai200444 China Department of Physics Graduate School of Engineering Yokohama National University Yokohama Japan Fakultät für Physik Universität Duisburg-Essen Lotharstr. 1 Duisburg47057 Germany Institute for Materials Research Tohoku University Sendai Japan Department of Materials Engineering Science Osaka University Osaka560-8531 Japan PRESTO Japan Science and Technology Agency Saitama332-0012 Japan Department of Materials Science and NanoEngineering Rice University HoustonTX United States

Terahertz time-domain spectroscopy measurements performed in pulsed, high magnetic fields reveal ultrastrong coupling between quasi-antiferromagnetic and quasi-ferromagnetic magnon modes in the canted antiferromagnet,... 详细信息

ISBN: (纸本)9781943580729

关键词： Magnetic fields

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Quasielectrostatic Wave Propagation Beyond the Delay-Bandwidth Limit in Switched Networks

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Physical Review X 2019年第3期9卷 031015-031015页

作者： Mykhailo Tymchenko Dimitrios Sounas Aravind Nagulu Harish Krishnaswamy Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin 1 University Station C0803 Austin Texas 78712 USA Department of Electrical and Computer Engineering Wayne State University Detroit Michigan 48202 USA Department of Electrical Engineering Columbia University 1300 South West Mudd 500 West 120th Street New York New York 10027 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10026 USA Department of Electrical Engineering City College of New York New York New York 10031 USA

The delay-bandwidth limit implies a stringent trade-off between the time delay, bandwidth, and propagation distance of an electromagnetic signal. Here, we show that temporal modulation can overcome this constraint, enabling extremely broadband wave propagation with close-to-zero group velocity dispersion in switched multipath electronic networks. Contrary to time-invariant waveguides, in which wave propagation implies a delicate balance between electric and magnetic stored energies, in such modulated networks the stored energy is largely electrostatic in nature. We show that in this case the phase and group velocities become independent of the properties of their constituent elements, and they are controlled only by the modulation scheme. Based on these findings, we provide practical designs of deeply subwavelength CMOS-compatible reciprocal and nonreciprocal microwave components, such as delay lines, phase shifters, couplers, and circulators. The obtained results also explicitly show that temporally modulated systems are not bound by constraints of time-invariant systems and can achieve arbitrarily large delay-bandwidth products.

关键词： Metamaterials

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Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor 17

Design and Implementation of OLED Driving and OPD Readout Ci...

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17th IEEE SENSORS Conference, SENSORS 2018

作者： Wu, Yi-Cheng Kao, Yung-Hua Chao, Paul C.-P. Wey, Chin-Long Sauter, Thilo Eka, Fitrah P. Pandey, Rajeev Department of Electrical Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Chiao Tung University Hsinchu City Taiwan EECS International Graduate Program National Chiao Tung University Hsinchu City Taiwan Department for Integrated Sensor Systems Danube University Krems Austria Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781538647073

This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 μm CMOS process, the chip area is 1.012 mm2. The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz1.5 kHz, and resulting in high SNR at frequencies under 60 Hz. © 2018 IEEE.

关键词： Electric power utilization

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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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Success Factors of the Blockchain Adoption for Smart Manufacture

Success Factors of the Blockchain Adoption for Smart Manufac...

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2018 international Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2018

作者： Surjandy Fernando, Erick Meyliana Kosala, Raymond Warnars, Harco Leslie Hendric Spits Abdurachman, Edi Supangkat, Suhono Harso Computer Science Department BINUS Graduate Program - Doctor of Computer Science Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538674222

Currently, the discussion about smart city becomes a trend topic in which the development of the Internet of Thing (IoT) is used in various sectors including in the manufacturing sector where the Indonesian government is intensively increasing the capacity and production quality. The use of IoT in both smart manufacture and smart city become an interesting thing topic for research and the combination of the new technology Blockchain, may proof the reliability of data. Trust, transparent, secure, real time online process as a new phenomenon to merge IoT with Blockchain technology into smart manufacturing technology. However, for maintaining quality and sustainability, required technology governance so the achievement of implementation can be done well. This research uses the method called Systematic Literature Review (SLR) find the Blockchain adoption factors for Smart Manufacturing Technology Governance and the research conducted on several reputable publications. The result found, there are 35 adoption success factors found from the adoption of blockchain technology into smart manufacture industry. The influence of the technology governance process in smart manufacture industry increase sustainability, realtime and accurate decision making for the industry. © 2018 IEEE.

关键词： Manufacture

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Design of a Trustworthy Cloud-Native National Digital Health Information Infrastructure for Secure Data Management and Use

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Oxford open digital health 2024年第2期2卷 oqae043页

作者： John Kar-Kin Zao Joseph Tsung-Shu Wu Kennedy Kanyimbo Florian Delizy Tchin-Tze Gan Hung-I Kuo Chih-Hao Hsia Chuan-Hsi Lo Shang-Hui Yang Clément Jean Alexandre Richard Billy Rajab Maganizo Monawe Blessings Kamanga Nikotelhe Mtambalika Kwong-Leung Joseph Yu Chih-Feng Chou Choo-Aun Neoh Joseph Gallagher John O'Donoghue Rebecca Mtegha Hsin-Yi Lee Alinafe Mbewe FiduciaEdge Technologies Co. Ltd. Hsinchu City Taiwan R.O.C. Digital Health Division (DHD) Malawi Ministry of Health Lilongwe Malawi. Luke International Nøtterøy Norway. Pingtung Christian Hospital Pingtung City Taiwan R.O.C. College of Electrical Engineering and Computer Science (EECS) National Yang Ming Chiao Tung University Hsinchu City Taiwan R.O.C. Graduate School of Engineering Katahira Campus Tohoku University Sendai Japan. WiAdvance Technology Co. Ltd. New Taipei City Taiwan R.O.C. University College Dublin Dublin Ireland. University College Cork Cork Ireland. Malawi eHealth Research Center Mzuzu University Mzuzu Malawi.

Since 2022, Malawi Ministry of Health (MoH) designated the development of a National Digital Health Information System (NDHIS) as one of the most important pillars of its national health strategy. This system is built upon a distributed computing infrastructure employing the following state-of-art technologies: (i) digital healthcare devices to capture medical data; (ii) Kubernetes-based Cloud-Native Computing architecture to simplify system management and service deployment; (iii) Zero-Trust Secure Communication to protect confidentiality, integrity and access rights of medical data transported over the Internet; (iv) Trusted Computing to allow medical data to be processed by certified software without compromising data privacy and sovereignty. Trustworthiness, including reliability, security, privacy and business integrity, of this system was ensured by a peer-to-peer network of trusted medical information guards deployed as the gatekeepers of the computing facility on this system. This NDHIS can facilitate Malawi to attain universal health coverage by 2030 through its scalability and operation efficiency. It shall improve medical data quality and security by adopting a paperless approach. It will also enable MoH to offer data rental services to healthcare researchers and AI model developers around the world. This project is spearheaded by the Digital Health Division (DHD) under MoH. The trustworthy computing infrastructure was designed by a taskforce assembled by the DHD in collaboration with Luke international in Norway, and a consortium of hardware and software solution providers in Taiwan. A prototype that can connect community clinics with a district hospital has been tested at Taiwan Pingtung Christian Hospital.

关键词： Internet of Medical Things Iso27001 National Digital Health Information System Trusted Computing Zero-Trust Architecture cloud-native computing health information security

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Genomic Surveillance of SARS-CoV-2 in Taiwan: A Perspective on Evolutionary Data Interpretation and Sequencing Issues

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Biomedical journal 2024年 100820页

作者： Yu-Nong Gong Nai-Yu Kuo Ting-Syuan Yeh Shin-Ru Shih Guang-Wu Chen Research Center for Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan International Master Degree Program for Molecular Medicine in Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. Medical Education Department Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan. Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Medical Biotechnology and Laboratory Science College of Medicine Chang Gung University Taoyuan Taiwan Research Center for Chinese Herbal Medicine Research Center for Food and Cosmetic Safety and Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan. Department of Computer Science and Information Engineering College of Engineering Chang Gung University Taoyuan Taiwan. Electronic address: gwchen@mail.cgu.edu.tw.

This review presents a comprehensive perspective on the genomic surveillance of SARS-CoV-2 in Taiwan, with a focus on next-generation sequencing and phylogenetic interpretation. This article aimed to explore how Taiwan has utilized genomic sequencing technologies and surveillance to monitor and mitigate the spread of COVID-19. We examined databases and sources of genomic sequences and highlighted the role of data science methodologies in the explanation and analyses of evolutionary data. This review addressed the challenges and limitations inherent in genomic surveillance, such as concerns regarding data quality and the necessity for interdisciplinary expertise for accurate data interpretation. Special attention was given to the unique challenges faced by Taiwan, including its high population density and major transit destination for international travelers. We underscored the far-reaching implications of genomic surveillance data for public health policy, particularly in influencing decisions regarding travel restrictions, vaccine administration, and public health decision-making. Studies were examined to demonstrate the effectiveness of using genomic data to implement public health measures. Future research should prioritize the integration of methodologies and technologies in evolutionary data science, particularly focusing on phylodynamic analytics. This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.

关键词： SARS-CoV-2 data science genomic surveillance next-generation sequencing phylodynamics

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Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

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Toward Quantification and Visualization of Active Stress Waves for Myocardial Biomechanical Function Assessment

Toward Quantification and Visualization of Active Stress Wav...

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computers in Cardiology (CinC)

作者： Niels F Otani Dylan Dang Christopher Beam Fariba Mohammadi Brian Wentz S M Kamrul Hasan Suzanne M Shontz Karl Q Schwarz Sabu Thomas Cristian A Linte School of Mathematical Sciences Rochester Institute of Technology Rochester NY Mechanical Engineering University of Kansas Lawrence KS Bioengineering Graduate Program University of Kansas Lawrence KS Center for Imaging Science Rochester Institute of Technology Rochester NY Electrical Engineering and Computer Science University of Kansas Lawrence KS Division of Cardiology University of Rochester Medical Center Rochester NY

ISBN: (数字)9781728169361

ISBN: (纸本)9781728159423

Estimating and visualizing myocardial active stress wave patterns is crucial to understanding the mechanical activity of the heart and provides a potential non-invasive method to assess myocardial function. These patterns can be reconstructed by analyzing 2D and/or 3D tissue displacement data acquired using medical imaging. Here we describe an application that utilizes a 3D finite element formulation to reconstruct active stress from displacement data. As a proof of concept, a simple cubic mesh was used to represent a myocardial tissue "sample" consisting of a 10 x 10 x 10 lattice of nodes featuring different fiber directions that rotate with depth, mimicking cardiac transverse isotropy. In the forward model, tissue deformation was generated using a test wave with active stresses that mimic the myocardial contractile forces. The generated deformation field was used as input to an inverse model designed to reconstruct the original active stress distribution. We numerically simulated malfunctioning tissue regions (experiencing limited contractility and hence active stress) within the healthy tissue. We also assessed model sensitivity by adding noise to the deformation field generated using the forward model. The difference image between the original and reconstructed active stress distribution suggests that the model accurately estimates active stress from tissue deformation data with a high signal-to-noise ratio.

关键词： Stress Myocardium Strain Biomechanics Image reconstruction Imaging Deformable models

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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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