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Numerical electric field analysis of capacitance and potential in space for estimation of permittivity distribution

IEEJ Transactions on Fundamentals and Materials 2018年第4期138卷 119-126页

作者： Yamanobe, Hiroshi Ohchi, Masashi Master's Program in Electrical Electronics and Computer Engineering Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tsudanuma Narashino275-0016 Japan Department of Electrical and Electronic Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tsudanuma Narashino275-0016 Japan

We have been studying on measuring capacitance and potential using long distance electrode for estimating a permittivity distribution. The estimating method of permittivity distribution was proposed using Finite Element Method (FEM) and Genetic Algorithm (GA). However, the measurement accuracy was not reviewed. In this paper, we carried out numerical analysis using FEM in case of change of diameter, position and permittivity of a rod of dielectric and discussed the measurement accuracy of capacitance and potential. © 2018 The Institute of Electrical Engineers of Japan.

关键词： Finite element method

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Decoding Transition between Kinematics Stages for Brain-Machine Interface*

Decoding Transition between Kinematics Stages for Brain-Mach...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuhang Chen Xiang Zhang Xiang Shen Yifan Huang Yiwen Wang Program of Bioengineering Hong Kong University of Science and Technology department of Electronic and Computer Engineering Hong Kong University of Science and Technology department of Chemical and Biological Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781728145709

Brain-machine interfaces (BMIs) translate the neural activity into digital command to control external devices in accomplishing movement task, which could involve multiple stages of behaviors in sequence. Previous work generally discriminates the stage labels using a classifier and uses a combination of sub-decoders designed respectively for each stage. Without considering the time dynamics of neural activity, the classifier often introduces noisy estimation in stage prediction. Brain-controlling neuro-prosthesis requires the decoder to continuously output the kinematics interpretation on brain state for each time instance, including when to start or end each stage within the task smoothly and accurately. We propose to decode the kinematic states within stages and during stage transition in one paradigm. The kinematics state vector is extended, and the time dynamics is modelled in the state-observation framework. We validate our approach on a brain-controlled lever discrimination task. The rats need to adjust the neural activity to press the correct virtual lever and drives the brain state to trigger the start of the next trial. Compared with the existing method whose mean square error is 0.8038, our results show smoother transition prediction and better decoding accuracy with less mean square error which is 0.7922. And the correction rate of holding lever and rest is above 90%. These results help the subjects do the task with even less response time (2.81s) and shorter inter-trial duration (4.5s).

关键词： Decoding Kinematics Pressing Neural activity Task analysis Rats Human factors

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HER2 amplification in colorectal cancer with brain metastasis: A propensity score matching study

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European journal of cancer (Oxford, England : 1990) 2023年 181卷 62-69页

作者： Po-Chuan Chen Yu-Min Yeh Chun-Ting Chu Pei-Fang Su Pin-Hsuan Chiu Bo-Wen Lin Shang-Hung Chen Peng-Chan Lin Chung-Ta Lee Helen H W Chen Chien-Chin Chen Division of Colorectal Surgery Department of Surgery National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Department of Oncology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Division of Colorectal Surgery Department of Surgery Ditmanson Medical Foundation Chia-Yi Christian Hospital Chiayi 600 Taiwan. Department of Statistics National Cheng Kung University Tainan 701 Taiwan. The Center of Quantitative Sciences Clinical Medicine Research Center National Cheng Kung University Hospital Tainan 704 Taiwan. Department of Oncology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan National Institute of Cancer Research National Health Research Institute Tainan 704 Taiwan. Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science National Cheng Kung University Tainan 704 Taiwan. Department of Pathology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Department of Oncology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan 704 Taiwan. Electronic address: helen@mail.ncku.edu.tw. Department of Pathology Ditmanson Medical Foundation Chia-Yi Christian Hospital Chiayi 600 Taiwan Department of Cosmetic Science Chia Nan University of Pharmacy and Science Tainan 717 Taiwan Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan 701 Taiwan Ph.D. Program in Translational Medicine Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung 40227 Taiwan. Electronic address: hlmarkc@gmail.c

BACKGROUND:The association between human epidermal growth factor receptor-2 (HER2) amplification and brain metastasis (BM) in patients having colorectal cancer (CRC) has been suggested but not yet established. This study investigated the expression patterns of HER2, its association with BM, and its prognostic value in patients having CRC. METHODS:We retrospectively identified 99 patients having metastatic CRC (mCRC) and BM (the BM cohort) and compared them with a cohort of 249 patients having mCRC and without BM (the stage IV cohort) by propensity score matching. Immunohistochemical studies of HER2 on all available paraffin-embedded tumour samples, either from the primary tumour, the metastasis (brain and/or extracranial sites) or both, were performed and analysed. HER2 fluorescent in situ hybridisation was applied when necessary. The expression of HER2 was compared and correlated with survival. RESULTS:HER2 amplifications were detected in 16 (18.4%) of 87 and 9 (3.6%) of 249 patients who had specimens available in the BM and stage IV cohorts, respectively (P < .001). After propensity score matching, HER2 amplification was significantly associated with BM (odds ratio: 5.38, P = .003). HER2 heterogeneity was frequently observed not only at the single tumour level but also in paired tumour samples. A marginally significant longer survival since BM was found in patients having HER2-amplified mCRC than in those without (P = .07). CONCLUSIONS:HER2 amplification was significantly associated with BM in patients having mCRC and might have prognostic value for survival since BM. Given the heterogeneity of HER2 expression, the testing of HER2 status on available tissues from both primary and metastatic tumours should be encouraged.

关键词： Brain metastasis Colorectal cancer HER2 amplification Heterogeneity Propensity score matching

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The Use of Large Language Models and Their Association With Enhanced Impact in Biomedical Research and Beyond

MedComm - Future Medicine

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MedComm - Future Medicine 2025年第2期4卷

作者： Cheng, Huzi Shi, Wen Sheng, Bin Lee, Aaron Y. Car, Josip Chaudhary, Varun Atanasov, Atanas G. Liu, Nan Qiu, Yue Chen, Qingyu Wong, Tien Yin Tham, Yih-Chung Zheng, Ying-Feng Department of Psychological and Brain Sciences Indiana University Bloomington IN United States Department of Ophthalmology the First Affiliated Hospital of Guangzhou Medical University Guangzhou China Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology University of Washington School of Medicine Seattle WA United States Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Department of Primary Care and Public Health School of Public Health Imperial College London London United Kingdom Department Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Surgery McMaster University Hamilton ON Canada Ludwig Boltzmann Institute Digital Health and Patient Safety Medical University of Vienna Vienna Austria Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences Jastrzebiec Poland Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Tsinghua Medicine Tsinghua University Beijing China Yale School of Medicine Yale University United States Singapore Eye Research Institute Singapore National Eye Centre Singapore School of Clinical Medicine Beijing Tsinghua Changgung Hospital Beijing China Ophthalmology and Visual Sciences Academic Clinical Program Duke-NUS Medical School Singapore Centre for Innovation and Precision Eye Health and Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China

The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines. © 2025 The Author(s). MedComm - Future Medicine published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： academic writing chatGPT large language models LLM

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PV System Power Model Application in Smart Cities

PV System Power Model Application in Smart Cities

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Chiou-Jye Huang Min-Chan Hwang Hsin-Chuan Chen Ping-Huan Kuo School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China School of Computer Engineering University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University Pingtung Taiwan

ISBN: (数字)9781728101101

ISBN: (纸本)9781728101231

The photovoltaic (PV) system energy generation is closely related to climate conditions, installation site, and system design. In this paper, a simple energy generation model is proposed and established by means of module temperature and solar irradiation, module temperature coefficient, module rated power, number of modules, and three correction coefficients including temperature derating coefficient, inverter conversion loss coefficient, and other loss coefficients. The experiment results show that the estimation model proposed in this paper has high accuracy using the Mean Absolute Percentage Error (MAPE) model as the evaluation index, the estimation model MAPE is 6.75%, while the PV SYST software simulation has MAPE of 11.90%. According to international MAPE evaluation standards, the method proposed in this paper has higher accuracy level than PV SYST software. This model can be used for the estimation of energy generation when installing solar PV systems, and can be used for the reasonable assessment of PV system long-term power generation, to predict system power generation anomalies, and reduce system abnormalities caused by downtime losses, and increase investment efficiency.

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Graphene-polymer nanofibers enable optically induced electrical responses in stem cell-derived electrically excitable cells and brain organoids

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Biomaterials 2025年 323卷 123430页

作者： Erin LaMontagne Alex Savchenko Gisselle Gonzalez Ritwik Vatsyayan Blanca Martin-Burgos Francesca Puppo Diogo Biagi Fabio Papes Shadi A Dayeh Alysson R Muotri Adam J Engler Shu Chien-Gene Lay Department of Bioengineering La Jolla CA 92093 USA. Nanotools Bioscience La Jolla CA 92093 USA. Department of Electrical and Computer Engineering La Jolla CA 92093 USA. Neurosciences Graduate Program La Jolla CA 92093 USA. Department of Pediatrics La Jolla CA 92093 USA. Neurosciences Graduate Program La Jolla CA 92093 USA Department of Pediatrics La Jolla CA 92093 USA Departmentof Cellular and Molecular Medicine University of California San Diego La Jolla CA 92093 USA Sanford Stem Cell Institute La Jolla CA 92037 USA. Electronic address: muotri@ucsd.edu. Shu Chien-Gene Lay Department of Bioengineering La Jolla CA 92093 USA Sanford Stem Cell Institute La Jolla CA 92037 USA. Electronic address: aengler@ucsd.edu.

Human pluripotent stem cell (hPSC)-derived electrically excitable cells provide a unique window into development, but they remain electrically immature partially due to the lack of chronic stimulation. Here, we fabricated electrospun polymer nanofibers containing light-reactive reduced graphene oxide (rGO) as part of a new classes of on-demand, electrically active biomaterials to enhance cell function. Fiber size, stiffness, and electrical conductivity varied with rGO concentration, which impacted hPSC-derived cardiomyocyte and neuron responses; with acute light stimulation, cardiomyocytes exhibited increased, synchronous calcium handling. Long-term, daily nanofiber light stimulation improves brain organoid electrical activity and activates photoreceptor pathways. This work outlines a tunable method where electrical cell functions can be titrated with rGO fibers and light stimulation, and it suggests that repetitive light stimulation may provide a novel method for retinal differentiation.

关键词： Brain organoids Electrospun nanofibers Light stimulation Reduced graphene oxide (rGO)

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Corrigendum to "Novel model of multiple sclerosis induced by EBV-like virus generate a unique B cell population" [Journal of Neuroimmunology volume 394 (2024) 578408]

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Journal of neuroimmunology 2025年 404卷 578625页

作者： J L Deffenbaugh K-J Jung S P Murphy Y Liu C N Rau C Petersen-Cherubini P L Collins D Chung A E Lovett-Racke Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA. Department of Computer Science and Engineering The Ohio State University USA. Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA Neuroscience Graduate Program The Ohio State University USA. Department of Biomedical Informatics The Ohio State University Wexner Medical Center USA Pelotonia Institute for Immuno-Oncology The Ohio State University Wexner Medical Center USA. Department of Neuroscience The Ohio State University Wexner Medical Center USA. Electronic address: amylovettracke@***.

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Roadmap on Nonlocality in Photonic Materials and Metamaterials

arXiv

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arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

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Machine Learning-Based Statistical Hypothesis Testing for Fault Detection

Machine Learning-Based Statistical Hypothesis Testing for Fa...

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Conference on Control and Fault-Tolerant Systems (SysTol)

作者： Radhia Fazai Majdi Mansouri Kamal Abodayeh Mohamed Trabelsi Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematical Sciences Prince Sultan University Riyadh Saudi Arabia Department of Electronic and Communications Engineering Kuwait College of Science and Technology Kuwait Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (纸本)9781728103815

This paper elaborates the development of machine learning approach merged with statistical hypothesis testing aimed at enhancing the operation of photovoltaic (PV) systems by developing intelligent PV fault detection framework. Fault detection in PV systems is important to ensure optimal energy harvesting and reliable power production because PV systems usually operate in a harsh outdoor environment and tend to suffer various faults. In this paper, therefore, special attention is paid to detection of various faults during different modes of operation. The proposed approach merges the benefits of machine learning technique (MLT) with statistical hypothesis testing to enhance the fault detection and monitoring of PV systems. The proposed technique will be effective in monitoring the PV faults under both normal and abnormal conditions. For the framework developed, the modeling phase is addressed using MLT and the faults are detected using the generalized likelihood ratio test (GLRT) chart. The MLT is used to compute the residuals monitored and the GLRT chart is applied to the monitored residuals evaluated for fault detection purposes. The developed MLT-based GLRT algorithm is implemented and validated using both simulated and real PV data. The results are evaluated in terms of false alarm rates (FAR), missed detection rates (MDR) and computation time.

关键词： Ground penetrating radar Monitoring Circuit faults Testing Photovoltaic systems Fault detection

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Considerations for the development of LMR-based optical fiber sensors for gas sensing applications

Considerations for the development of LMR-based optical fibe...

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Bragg Gratings, Photosensitivity and Poling in Glass Waveguides and Materials, BGPPM 2018

作者： Dreyer, Uilian José Ozcariz, Aritz Zubiate, Pablo Martelli, Cicero da Silva, Jean C.C. Zamarreño, Carlos Ruiz Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba80230-901 Brazil Electric and Electronic Engineering Department Public University of Navarra Campus Arrosadia Pamplona31006 Spain

This work presents a preliminary study of a LMR-based gas sensor. Results of the device subjected to annealing process show a stable and repetitive response that is required for the utilization in gas sensing applicat... 详细信息

ISBN: (纸本)9781943580439

关键词： Gas detectors

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