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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ma, Wenshuo Liu, Xinru Yu, Kan Luo, Chuanwen Wang, Guopeng Liu, Xiaowu School of Artificial Intelligence and Big Data Zibo Vocational Institute Zibo China Weifang Engineering Vocational College Weifang China School of Computer Science and Engineering Macau University of Science and Technology Taipa China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information Science and Technology Beijing Forestry University Beijing China School of Computer Science Qufu Normal University Rizhao China Zhejiang Ocean University Zhoushan China The Open University of China Beijing China

ISBN: (纸本)9783031714665

data aggregation (DA) plays an important role in conserving the limited resources and extending the network lifetime of Wireless Sensor Networks (WSNs). Privacy preservation has garnered considerable attention and is considered one of the most viable schemes to address issues such as communication eavesdropping, information leakage, and unauthorized access in WSNs employing DA. In this paper, we propose an improved Secure Privacy-preserving data Aggregation (iSECPDA) mechanism. It dynamically selects cluster heads through an enhanced Stable Election Protocol (SEP), which considers the residual energy of sensor nodes to enhance network lifetime. In our proposed SEPPDA, we integrate interference information into the sensing data and ensure data transmission privacy through a proposed slicing mechanism. Theoretical analysis and simulation experiments demonstrate that iSECPDA exhibits significant performance improvements in terms of communication overhead and privacy preservation levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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Blockchain Traceability Platform Based on Green Agricultural Products 15th

Blockchain Traceability Platform Based on Green Agricultural...

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15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Tian, Xiao Yao, Yu-Ge Xu, Hui-Tong Xie, Ying-Jie Wei, Li-Jie Xie, Guo-jie Wu, Ting Gao, Yan-Wei Jining Normal University Inner Mongolia Ulanqab012000 China Baotou Big Data Research Institute National Engineering Laboratory for E-Commerce Transaction Technology Inner Mongolia Baotou014014 China Zhejiang Key Laboratory of Open Data Hangzhou310000 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China

ISBN: (纸本)9783031239014

This paper designs and completes a blockchain traceability platform based on green agricultural products. Using item coding technology, cryptography technology, blockchain, big data and other new generation information technologies, green agricultural products (especially Ulanqab potatoes, oats and other geographical indication products) from growth to harvest to circulation throughout the life cycle. The key data can be trusted and stored in the blockchain, providing consumers with real-time agricultural product traceability query services, and providing agricultural and animal husbandry departments with effective agricultural product quality and safety supervision and management mechanisms and means. With the help of QR code as the mobile Internet entrance, link online and offline, design O2O e-commerce transaction system, form a closed loop of planting, traceability and sales, digitally control the quality of authentic agricultural products, and empower the brand value of agricultural products. Realize the whole chain supervision of green products from planting source to circulation through a perfect traceability system, improve product safety and quality, enhance the market's popularity and recognition of agricultural products in the region, form effective protection of agricultural product geographical indication products, and achieve agricultural economic development "Increase quality and efficiency", and then promote the construction of smart agricultural big data projects. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Agricultural products

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A Coding Management System for Traceability of Chinese Agricultural Products Based on Blockchain 15th

A Coding Management System for Traceability of Chinese Agric...

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15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Jiao, Li-Xian Yu, Yan-Dong Liu, Yin-Bo An, Da Yao, Yu-Ge Lan, Zhu-qing Xie, Guo-jie Wu, Ting Jia, Xiao-han Jining Normal University Inner Mongolia012000 China Beijing Zhongsheng Huaan Information Technology Co. Ltd. Beijing100089 China Zhejiang Key Laboratory of Open Data Hangzhou310000 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China National Engineering Laboratory for E-Commerce Transaction Technology Baotou Big Data Research Institute Inner Mongolia014014 China

ISBN: (纸本)9783031239014

In recent years, food safety has become an increasingly serious topic around the world. Problems such as pesticides, heavy metal pollution, and chemical additives in various agricultural products have attracted widespread attention. Therefore, reliable green agricultural products are urgently demanded by consumers. To this end, this paper uses item coding technology and a new generation of information technology such as blockchain to analyze the key data of the entire life cycle of green agricultural products (especially Ulanqab potatoes, oats and other geographical indication products) from growth to harvest to circulation. Blockchain trusted certificate. Through a complete traceability supervision system, product safety and quality will be improved, the market’s visibility and recognition of agricultural products in the region will be enhanced, and the "quality and efficiency" of agricultural economic development will be achieved, thereby promoting the construction of smart agricultural big data projects. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Agricultural products

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openMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the openMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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