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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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GMD-based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter-wave massive MIMO

arXiv

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arXiv 2020年

作者： Ying, Keke Gao, Zhen Lyu, Shanxiang Wu, Yongpeng Wang, Hua Alouini, Mohamed-Slim Beijing100081 China College of Cyber Security Jinan University Guangzhou510632 China State Key Laboratory of Cryptology P.O.Box 5159 Beijing100878 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China Electrical Engineering Program Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Reconfigurable intelligent surface (RIS) is considered to be an energy-efficient approach to reshape the wireless environment for improved throughput. Its passive feature greatly reduces the energy consumption, which makes RIS a promising technique for enabling the future smart city. Existing beamforming designs for RIS mainly focus on optimizing the spectral efficiency for single carrier systems. To avoid the complicated bit allocation on different spatial domain subchannels in MIMO systems, in this paper, we propose a geometric mean decomposition-based beamforming for RIS-assisted millimeter wave (mmWave) hybrid MIMO systems so that multiple parallel data streams in the spatial domain can be considered to have the same channel gain. Moreover, by exploiting the common angulardomain sparsity of mmWave massive MIMO channels over different subcarriers, a simultaneous orthogonal match pursuit algorithm is utilized to obtain the optimal multiple beams from an oversampling 2D-DFT codebook. Besides, by only leveraging the angle of arrival and angle of departure associated with the line of sight (LoS) channels, we further design the phase shifters for RIS by maximizing the array gain for LoS channel. Simulation results show that the proposed scheme can achieve better BER performance than conventional approaches. Our work is an initial attempt to discuss the broadband hybrid beamforming for RISassisted mmWave hybrid MIMO systems. Copyright © 2020, The Authors. All rights reserved.

关键词： MIMO systems

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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Simulation of hybridized nanofluids flowing and heat transfer enhancement via 3-D vertical heated plate using finite element technique

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Scientific reports 2022年第1期12卷 11658页

作者： Muhammad Bilal Hafeez Marek Krawczuk Hasan Shahzad Amjad Ali Pasha Mohammad Adil Faculty of Mechanical Engineering and Ship Technology Institute of Mechanics and Machine Design Gdansk University of Technology Narutowicza 11/12 80-233 Gdańsk Poland. Muhammad.bilal.hafeez@pg.edu.pl. Faculty of Mechanical Engineering and Ship Technology Institute of Mechanics and Machine Design Gdansk University of Technology Narutowicza 11/12 80-233 Gdańsk Poland. Faculty of Materials and Manufacturing College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing China. Aerospace Engineering Department King Abdulaziz University Jeddah 21589 Saudi Arabia. Mechanical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia. KAUST Clean Combustion Research Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia.

The present study probed the creation of heat energy and concentrating into Newtonian liquids across vertical 3D-heated plates. The role of the Soret and Dufour theories in concentrating and energy formulas is discussed. The role of hybrid nanoparticles is introduced to illustrate particle efficiency in terms of solute and thermal energy. It is removed a viscous dissipation process and a changing magnetic field. The proposed approach is motivated by the need to maximize solute and thermal energy uses in biological and industrial domains. The constructed system of (partial differential equations) PDEs includes concentration, momentum, and thermal energy equations within various thermal characteristics. Transformations are used to formulate the system of (ordinary differential equations) ODEs for solution. To assess various features vs various variables, a Galerkin finite element approach is used. Motion into nanoscale components is shown to be smaller than motion into hybrid nanoparticles. Furthermore, fluctuations in heat energy and solute particle counts are seen in relation to changes in Soret, Eckert, magnetic, and Dufour numbers. The basic finding is that the generation of thermal energy for hybridized nanomaterials is much higher.

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Local ultra-densification of single-walled carbon nanotube films: modeling and experiment

arXiv

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arXiv 2022年

作者： Grebenko, Artem K. Drozdov, Grigorii Gladush, Yuriy G. Ostanin, Igor Zhukov, Sergey S. Melentyev, Aleksandr V. Khabushev, Eldar M. Tsapenko, Alexey P. Krasnikov, Dmitry V. Afinogenov, Boris Temiryazev, Alexei G. Dremov, Viacheslav V. Dumitricã, Traian Li, Mengjun Hijazi, Hussein Podzorov, Vitaly Feldman, Leonard C. Nasibulin, Albert G. Skolkovo Institute of Science and Technology Nobel str. 3 Moscow121205 Russia Center for Photonics and 2D Materials Moscow Institute of Physics and Technology Institute Lane 9 Dolgoprudny Russia Scientific Computation Program University of Minnesota Minneapolis55455 United States University of Twente PO Box 217 Enschede7500 AE Netherlands Aalto University Department of Applied Physics Puumiehenkuja 2 EspooFI-00076 Finland Faculty of Physics Lomonosov Moscow State University Leninskie gory 1 Moscow Russia Kotel'nikov Institute of Radioengineering and Electronics of RAS Fryazino Branch Vvedensky Sq. 1 Fryazino Russia Russian Quantum Center Bolshoy Boulevard Moscow30 s1 Russia Department of Mechanical Engineering University of Minnesota Minneapolis55455 United States Department of Physics Rutgers University PiscatawayNJ08854 United States Intel Corporation AlbuquerqueNM United States Aalto University P.O. Box 16100 AaltoFI-00076 Finland

Fabrication of nanostructured metasurfaces poses a significant technological and fundamental challenge. Despite developing novel systems that support reversible elongation and distortion, their nanoscale patterning and control of optical properties remain an open problem. Herein we report the atomic force microscope lithography (AFML) application for nanoscale patterning of single-walled carbon nanotube films and the associated reflection coefficient tuning. We present models of bundling reorganization, formed-pattern stability, and energy distribution describing mechanical behavior with mesoscopic distinct element method (MDEM). All observed and calculated phenomena support each other and present a platform for developing AFML patterned optical devices using meshy nanostructured matter. Copyright © 2022, The Authors. All rights reserved.

关键词： Carbon films

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Local Ultra-Densification of Single-Walled Carbon Nanotube Films: Experiment and Mesoscopic Modeling

SSRN

引用

SSRN 2022年

作者： Grebenko, Artem K. Drozdov, Grigorii Gladush, Yuriy G. Ostanin, Igor Zhukov, Sergey S. Melentyev, Aleksandr V. Khabushev, Eldar M. Tsapenko, Alexey P. Krasnikov, Dmitry V. Afinogenov, Boris Temiryazev, Alexei G. Dremov, Viacheslav V. Dumitricã, Traian Li, Mengjun Hijazi, Hussein Podzorov, Vitaly Feldman, Leonard C. Nasibulin, Albert G. Skolkovo Institute of Science and Technology Nobel str. 3 Moscow121205 Russia Center for Photonics and 2D Materials Moscow Institute of Physics and Technology Institute Lane 9 Dolgoprudny Russia Scientific Computation Program University of Minnesota Minneapolis55455 United States University of Twente PO Box 217 Enschede7500 AE Netherlands Aalto University Department of Applied Physics Puumiehenkuja 2 EspooFI-00076 Finland Faculty of Physics Lomonosov Moscow State University Leninskie gory 1 Moscow Russia Kotel'nikov Institute of Radioengineering and Electronics of RAS Fryazino Branch Vvedensky Sq. 1 Fryazino Russia Russian Quantum Center Moscow121205 Russia Department of Mechanical Engineering University of Minnesota Minneapolis55455 United States Department of Physics Rutgers University PiscatawayNJ08854 United States Aalto University P.O. Box 16100 AaltoFI-00076 Finland Intel Corporation AlbuquerqueNM United States

Fabrication of nanostructured metasurfaces poses a significant technological and fundamental challenge. Despite developing novel material systems that support reversible elongation and distortion, their nanoscale patterning and control of optical properties remain an open problem. Herein we report the atomic force microscope lithography (AFML) application for nanoscale patterning of single-walled carbon nanotube films and the associated optical reflection coefficient tuning. Large scale mesoscopic distinct element method (MDEM) atomic force nanoindentation simulations of single-walled carbon nanotube samples comprising entangled dendritic nanotube bundles with branches extending down to individual tubes explain the mesoscale mechanism of local irreversible densification and further predict its impact on mechanical properties. All observed and calculated phenomena support each other and present a platform for developing AFML patterned optical devices using nanofibrous matter. © 2022, The Authors. All rights reserved.

关键词： Carbon films

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A biometric based medical information system for the identification of comorbidity in Covid-19 patients

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AIP Conference Proceedings 2021年第1期2387卷

作者： V. Kumaresan K. Punitha S. Ragupathy B. Priestly Shan T. Gunasekaran 1Department of Electrical and Electronics Engineering Kongu Engineering College Perundurai Tamilnadu India 2School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India 3Department of Electrical and Electronics Engineering SSM College of Engineering Komarapalayam Namakkal Tamil Nadu India 4Professor and Dean - Academic Affairs Chandigarh University Mohali Punjab India 5Program Director - Telecommunications University of Technology and Applied Sciences Higher College of Technology Muscat Sultanate of Oman

Medical information System provides the best way to collect patient’s medical information. It helps the doctors to retrieve the patient’s previous medical history very faster and able to provide suitable treatment quickly. To keep medical information of a patient securely is a difficult task. Many MQTT algorithms have been developed to provide security for the patients information. The sensitive medical information of a patient can also be protected by using biometric technology. Internet of Thing (IoT) technology used to send the medical information to the doctor like blood group, BP value, cholesterol level and heart rate level. The proposed system helps to provide the comorbidity details of the Covid-19 infected patient’s to doctors mobile phone. It also takes the current health details of the patients like temperature, respiratory rate and heart beat rate. By considering the past and present medical data of the Covid-19 patients , this system recommends the type of treatment to be given for Covid-19 infected patients.

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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Influence of Magnetic Fields on Electrochemical Reactions of Redox Cofactor Solutions

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Angewandte Chemie 2021年第33期133卷

作者： Jimin Park Florian Koehler Georgios Varnavides Marc-Joseph Antonini Prof. Dr. Polina Anikeeva Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA Research Laboratory of Electronics and McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA Harvard/MIT Health Science & Technology Graduate Program Cambridge MA 02139 USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA

Redox cofactors mediate many enzymatic processes and are increasingly employed in biomedical and energy applications. Exploring the influence of external magnetic fields on redox cofactor chemistry can enhance our understanding of magnetic-field-sensitive biological processes and allow the application of magnetic fields to modulate redox reactions involving cofactors. Through a combination of experiments and modeling, we investigate the influence of magnetic fields on electrochemical reactions in redox cofactor solutions. By employing flavin mononucleotide (FMN) cofactor as a model system, we characterize magnetically induced changes in Faradaic currents. We find that radical pair intermediates have negligible influence on current increases in FMN solution upon application of a magnetic field. The dominant mechanism underlying the observed current increases is the magneto-hydrodynamic effect. We extend our analyses to other diffusion-limited electrochemical reactions of redox cofactor solutions and arrive at similar conclusions, highlighting the opportunity to use this framework in redox cofactor chemistry.

关键词： electrochemistry magnetic field effects magneto-hydrodynamic effect radicals redox cofactors

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Need Analysis of Interactive Multimedia Development for Virtual Learning in Pandemic

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Journal of Physics: Conference Series 2021年第1期2111卷

作者： J F Lempas S Soenarto A D Barahama Instructional Technology Post-Graduate Program Universitas Negeri Yogyakarta Jalan Colombo 01 Daerah Istimewa Yogyakarta 55281 Indonesia Department of Electronics and Informatics Engineering Education Post-Graduate Program Universitas Negeri Yogyakarta Jalan Colombo 01 Daerah Istimewa Yogyakarta 55281 Indonesia

The pandemic that has not ended has an impact on changing the educational paradigm. Existing conditions demand to continue to use virtual learning. During the epidemic, this study will examine students' demand for interactive multimedia in virtual learning. The interviews reveal that the media that teachers provide in virtual learning during this pandemic has limits. Data was gathered through interviews and questionnaires given to teachers and students. The student requirements questionnaire reveals that interactive multimedia is the most effective instructional medium for pupils. This study is part of a larger investigation of the evolution of multimedia learning during a pandemic.

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