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5th International Conference on Technology and Vocational Teachers, ICTVT 2019

作者： Mao, W.-L. Fathurrahman, H.I.K. Lee, Y. Chang, T.W. Department of Electrical Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu64002 Taiwan Bachelor Program in Interdisciplinary Studies College of Future National Yunlin University of Science and Technology Taiwan Department of Digital Media Design National Yunlin University of Science and Technology Taiwan

This paper proposes a simple Convolutional Neural Network (CNN) program to classify epileptic seizure. The diagnosis of epileptic seizure involves the identification and different characteristic of the Electroencephalography (EEG) signal. As such, it needs a method for identifying and classifying epileptic seizure. Deep learning is part of a neural network that has the ability and pattern to identify and classify epileptic seizure. CNN has been demonstrated high performance on image classification and pattern detection. In this paper, we combine the continuous wavelet transform (CWT) and CNN to classify epileptic seizure. This experiment uses the wavelet transform to convert signal data of EEG to time-frequency domain images. The output of the wavelet transform is an image that will classify into five attributes. In this experiment, we develop a simple program that will compare with other CNN approach (AlexNet and GoogleNet). The results of this experiment are two kinds of data, accuracy, and loss. The resulting accuracy is 72.49%, and the loss is 0.576. This result has a better learning time than GoogleNet and smaller loss result than AlexNet. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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Role of the Ionomer in Supporting Electrolyte-Fed Anion Exchange Membrane Water Electrolyzers

ACS electrochemistry

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ACS electrochemistry 2025年第2期1卷 239-248页

作者： Emily K Volk Arielle L Clauser Melissa E Kreider Diego D Soetrisno Sunilkumar Khandavalli Joshua D Sugar Stephanie Kwon Shaun M Alia Advanced Energy Systems Graduate Program Colorado School of Mines Golden Colorado 80401 United States. Sandia National Laboratories Livermore California 94550 United States. Chemistry and Nanoscience Center National Renewable Energy Laboratory Golden Colorado 80401 United States. Materials Science Center National Renewable Energy Laboratory Golden Colorado 80401 United States. Department of Chemical and Biological Engineering Colorado School of Mines Golden Colorado 80401 United States.

While anion exchange membrane water electrolyzers (AEMWEs) have achieved significant performance advances in recent decades, overpotentials remain high relative to their proton exchange membrane water electrolyzer (PEMWE) counterparts, requiring AEMWE-specific catalyst layer design strategies to further advance this technology. In this work, the role of the ionomer in catalyst layer structure and quality, catalyst layer stability, and ion conduction for supporting electrolyte-fed AEMWEs is assessed for catalyst layers composed of NiFeO and PiperION TP85 from Versogen at variable ionomer contents (0-30 wt %) for tests up to 200 h. The results reveal that, for supporting electrolyte-fed AEM devices, the ionomer is not required for ion conduction through the catalyst layer. Instead, the ionomer is found to play a critical role in catalyst layer structure and stability, where intermediate ionomer contents lead to the lowest overpotentials, highest effective surface areas, and lowest catalyst layer resistances. Catalyst layer stability is found to be a function of both catalyst adhesion and ionomer loss. These results show that an ionomer may be selected which is not of the same chemistry as the anion exchange membrane, mitigating ionomer stability concerns throughout the catalyst layer and offering a pathway towards highly active and stable AEMWEs.

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High Magnetoresistance Ratio on hBN Boron-Vacancy/Graphene Magnetic Tunnel Junction

arXiv

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arXiv 2023年

作者： Harfah, Halimah Wicaksono, Yusuf Sunnardianto, Gagus Ketut Majidi, Muhammad Aziz Kusakabe, Koichi Graduate School of Engineering Science Osaka University 1-3 Machikaneyama-Cho Osaka Toyonaka560-0043 Japan Computational Condensed Matter Physics Laboratory RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Tangerang Selatan Banten15314 Indonesia Research Collaboration Center for Quantum Technology 2.0 Bandung40132 Indonesia Department of Physics Faculty of Mathematics and Natural Science Universitas Indonesia Kampus UI Depok Depok16424 Indonesia School of Science Graduate School of Science University of Hyogo 3-2-1 Kouto Kamigori-cho Ako-gun Hyogo678-1297 Japan

We presents a new strategy to create a van der Waals-based magnetic tunnel junction (MTJ) that consists of a three-atom layer thickness of graphene (Gr) sandwiched with hexagonal boron nitride (hBN) by introducing a monoatomic Boron vacancy in both hBN layers. The magnetic properties and electronic structure of the system were investigated using density functional theory (DFT), while the transmission probability of the MTJ was investigated using the Landauer-Büttiker formalism within the non-equilibrium Green function method. The Stoner gap was found to be created between the spin-majority channel and the spin-minority channel on LDOS of the hBN monoatomic boron-vacancy (VB) near the vicinity of Fermi energy, creating a possible control of the spin valve by considering two different magnetic allignment of hBN(VB) layers, anti-parallel and parallel configuration. The results of the transmission probability calculation showed a high electron transmission in the parallel configuration of the hBN(VB) layers and a low transmission when the antiparallel configuration was considered. A high TMR ratio of approximately 400% was observed when comparing the antiparallel and parallel configuration of hBN(VB) layers in the hBN (VB)/Gr/hBN(VB), giving the highest TMR for the thinnest MTJ system. © 2023, CC BY.

关键词： III-V semiconductors

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Emulating Complex Synapses Using Interlinked Proton Conductors

arXiv

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arXiv 2024年

作者： Zhang, Lifu Li, Ji-An Hu, Yang Jiang, Jie Lai, Rongjie Benna, Marcus K. Shi, Jian Department of Materials Science and Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Neurosciences Graduate Program University of California San Diego La Jolla CA92093 United States Department of Mathematics Purdue University West LafayetteIN47907 United States Department of Neurobiology School of Biological Sciences University of California at San Diego La Jolla CA92093 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

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16-Hydroxybriaranes from the octocoral Briareum stechei

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Phytochemistry Letters 2023年 57卷 92-95页

作者： Liu, Chia-Jung Wang, Chien-Chiao Chang, Chun-Feng Zhang, Mingzi M. Tsou, Lun Kelvin Wen, Zhi-Hong Hwang, Tsong-Long Chen, You-Ying Sung, Ping-Jyun Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung 804201 Taiwan National Museum of Marine Biology and Aquarium Pingtung 944401 Taiwan Graduate Institute of Natural Products College of Medicine Chang Gung University Taoyuan 333323 Taiwan Graduate Institute of Biomedical Sciences College of Medicine Chang Gung University Taoyuan 333323 Taiwan Division of Urology Department of Surgery Kaohsiung Armed Forces General Hospital Kaohsiung 802301 Taiwan Institute of Molecular and Genomic Medicine National Health Research Institutes Miaoli Zhunan 350401 Taiwan Institute of Biotechnology and Pharmaceutical Research National Health Research Institutes Miaoli Zhunan 350401 Taiwan Department of Anesthesiology Chang Gung Memorial Hospital Taoyuan 333008 Taiwan Research Center for Chinese Herbal Medicine Graduate Institute of Healthy Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan 333324 Taiwan Department of Chemical Engineering Ming Chi University of Technology New Taipei 243303 Taiwan Chinese Medicine Research and Development Center China Medical University Hospital Taichung 404394 Taiwan Graduate Institute of Natural Products Kaohsiung Medical University Kaohsiung 807378 Taiwan Ph.D. Program in Pharmaceutical Biotechnology Fu Jen Catholic University New Taipei City 242062 Taiwan

Two new 16-hydroxybriaranes, 11-dehydroxybriavioid B (1) and 12-epi-briastecholide L (2), have been isolated from an algal-containing octocoral identified as Briareum stechei. The structures of metabolites 1 and 2 were corroborated by extensive 2D-NMR studies and comparison with literature data. Briarane 2 was found to reduce the release of inducible nitric oxide synthase (iNOS) to 87.92 %, at a concentration of 10 μM. © 2023 Phytochemical Society of Europe

关键词： Briarane Briareum stechei Briastecholide Briavioid iNOS

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Impact of absence of lactose on the dynamic sensory profile of yogurt: A multiple-intake TDS approach

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LWT 2022年 162卷

作者： Souza Olegario, Lary González-Mohino, Alberto Estévez, Mario Madruga, Marta Suely Ventanas, Sonia Post-Graduate Program in Food Science and Technology Department of Food Engineering Technology Centre Federal University of Paraiba Paraíba João Pessoa58051-900 Brazil IPROCAR Research Institute TECAL Research Group Universidad de Extremadura Cáceres10003 Spain

This paper aims to evaluate the impact of lactose free yogurt version on dynamic sensory perception and consumer acceptability. A total of 46 participants evaluated classical (SNY) and lactose-free (SLF-Y) commercial version of yogurts using the multiple-intake TDS technique, providing information about the dominant sequence of six flavor and texture sensory attributes. Moreover, information regarding the overall liking of the evaluated products was obtained. During the first intake, in SLF-Y samples, sourness presented a higher dominance rate compared to SNY. A similar dominance profile was obtained during the second intake between the two yogurts. During the third intake, SNY consumption was dominated by dairy attribute whereas during SLF-Y intake, fermented and creaminess were the dominance attributes. Yogurt version and the numbers of intakes presented a significant impact on temporal perception. Moreover, similar liking scores were reported for both evaluated yogurts. The present study revealed that absence of lactose showed an impact of temporal sensory perception of yogurt and the potential modifications on sensory perception can be revealed by applying the multiple intake TDS approach. © 2022 The Authors

关键词： Sensory perception

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Deep potentials for materials science

材料展望（英文）

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材料展望（英文） 2022年第2期 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople's Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople's Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China International Digital Economy Academy(IDEA) ShenzhenPeople's Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

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A biomimetic micropillar wound dressing with flavone and polyphenol control release in vitro and in vivo

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Journal of the Taiwan Institute of Chemical Engineers 2024年 160卷

作者： Cheng, Tien-Hsin Lin, Ruei-Hong Cheng, Yu-Shen Shih, Pin-Keng Show, Pau Loke Chen, Hsiao-Yi Nakmee, Pattarawadee Sumthong Chang, Jui-Jen Huang, Dong-Ming Wang, Hui-Min David Department of Chemical and Materials Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Xingda Rd. South Dist. Taichung402 China Department of Neurological Institute Taichung Veterans General Hospital Taichung China Doctoral Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung China Department of Surgery China Medical University Hospital Taichung Taiwan School of Medicine China Medical University Taichung Taiwan Department of Chemical Engineering Khalifa University Abu Dhabi United Arab Emirates Faculty of Science at Sriracha Kasetsart University Sriracha Campus 199 Moo 6 Sukhumvit Rd. Chonburi Sriracha20230 Thailand Department of Medical Research China Medical University Hospital China Medical University Taichung China Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes Miaoli County China Regenerative Medicine and Cell Therapy Research Center Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Laboratory Science and Biotechnology China Medical University Taichung China

Background: Wound healing is a complex and dynamic process involving the replacement of devitalized and missing cellular structures and tissue layers. Methods: This study involved developing a drug-loaded biomimetic micropillar wound dressing based upon inspiration from witnessing the adhesive ability in a gecko's footpad. The conformal adhesion was set up by applying a soft elastomer thin film on the developed wound dressing surfaces. The results showed that the developed surfaces prepared based on polydimethylsiloxane (PDMS) had low surface free energy, high chemical stability, and good plasticity. Astaxanthin (AST) and curcumin were applied as the patch's bioactive compounds to help accelerate the wound healing process. Results: The results showed that the developed surfaces prepared based on polydimethylsiloxane (PDMS) had low surface free energy, high chemical stability, and good plasticity. Astaxanthin (AST) and curcumin were applied as the patch's bioactive compounds to help accelerate the wound healing process. The results indicate that the developed biomimetic micropillar patches could release the loaded drugs and significantly increase cell viability and collagen content to 20 % ∼ 25 % in vitro and in vivo. Conclusion: In summation, the primary purpose of this study has been to design a novel encapsulated carrier (micropillar patch) for use in adjustable drug release, while combining with the biomimetic microstructure to reach wound healing through percutaneous absorption. © 2024 Taiwan Institute of Chemical Engineers

关键词： Chemical stability

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

arXiv

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arXiv 2023年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The International Audio Laboratories Erlangen Germany The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Portugal The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2023, CC BY.

关键词： Dynamic programming

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