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arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Correction: Multivariate clustering for maximizing the small cell users’ performance based on the dynamic interference alignment

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Wireless Networks 2023年第1期30卷 593-593页

作者： Dakshinamoorthy, Prabakar Vaitilingam, Saminadan Sundar, Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

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Advancements of microalgal upstream technologies: Bioengineering and application aspects in the paradigm of circular bioeconomy

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Chemosphere 2023年第1期339卷 139699页

作者： Leong, Wai Hong Rawindran, Hemamalini Ameen, Fuad Alam, Mohammad Mahtab Chai, Yee Ho Ho, Yeek Chia Lam, Man Kee Lim, Jun Wei Tong, Woei-Yenn Bashir, Mohammed J.K. Ravindran, Balasubramani Alsufi, Nizar Abdallah HICoE-Centre for Biofuel and Biochemical Research Institute of Self-Sustainable Building Department of Fundamental and Applied Sciences Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar32610 Malaysia Algal Bio Co. Ltd Todai-Kashiwa Venture Plaza 5-4-19 Kashiwanoha Chiba Kashiwa277-0082 Japan Department of Botany and Microbiology College of Science King Saud University Riyadh11451 Saudi Arabia Department of Basic Medical Sciences College of Applied Medical Science King Khalid University Abha61421 Saudi Arabia HICoE-Centre for Biofuel and Biochemical Research Institute of Self-Sustainable Building Department of Chemical Engineering Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar32610 Malaysia Centre for Urban Resource Sustainability Institute of Self-Sustainable Building Department of Civil and Environmental Engineering Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar32610 Malaysia Universiti Kuala Lumpur Institute of Medical Science Technology A1-1 Jalan TKS 1 Taman Kajang Sentral Selangor Kajang43000 Malaysia Department of Environmental Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Jalan Universiti Bandar Barat Perak Kampar31900 Malaysia Department of Environmental Energy & Engineering Kyonggi University Gyeonggi-do Suwon-si16227 Korea Republic of Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Management Information System and Production Management College of Business & Economics Qassim University P.O. BOX 6666 Buraydah51452 Saudi Arabia

Sustainable energy transition has brought the attention towards microalgae utilization as potential feedstock due to its tremendous capabilities over its predecessors for generating more energy with reduced carbon footprint. However, the commercialization of microalgae feedstock remains debatable due to the various factors and considerations taken into scaling-up the conventional microalgal upstream processes. This review provides a state-of-the-art assessment over the recent developments of available and existing microalgal upstream cultivation systems catered for maximum biomass production. The key growth parameters and main cultivation modes necessary for optimized microalgal growth conditions along with the fundamental aspects were also reviewed and evaluated comprehensively. In addition, the advancements and strategies towards potential scale-up of the microalgal cultivation technologies were highlighted to provide insights for further development into the upstream processes aimed at sustainable circular bioeconomy. © 2023 Elsevier Ltd

关键词： Microalgae

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Is the advertisement policy for dual-channel profitable for retailing and consumer service of a retail management system under emissions-controlled flexible production system?

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Journal of Retailing and Consumer Services 2024年 78卷

作者： Mridha, Bablu Sarkar, Biswajit Cárdenas-Barrón, Leopoldo Eduardo Ramana, G.V. Yang, Liu Department of Mathematics & Statistics Banasthali Vidyapith Rajasthan Banasthali 304022 India Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seodaemun-gu Seoul 03722 South Korea Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Science Saveetha University Tamil Nadu Chennai 600077 India Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Tecnológico de Monterrey School of Engineering and Sciences E. Garza Sada 2501 Sur Nuevo León Monterrey C.P. 64849 Mexico Yonsei Frontier Lab Yonsei University Seoul 03722 South Korea Department of Civil Engineering Indian Institute of Technology Delhi Hauz Khas Delhi New Delhi 110016 India Department of Management Marketing & Information System College of Business Administration Sam Houston University Huntsville 77340 TX United States

Presently, by reducing carbon emissions, biorefineries are very much careful to build a green environment. Several rules and regulations are imposed by the government to provide a comparatively better eco-friendly environment. To control the carbon emission rate, the cap-and-trade policy is intimated in this flexible biofuel production model. To reduce per unit carbon emission, manufacturers impose the advanced green technology. As online and offline selling is crucial to any industry for increasing customer demand, manufacturers sell their products by dual-channel and advertise to promote their products through online channel to make their biofuel products more popular. Keeping these in mind, a study on a sustainable flexible production of biofuel is considered here by imposing an investment in green technology cap-and-tax policy, and advertising for biofuels. A smart inspection, known as autonomation policy, is considered such that the quantity of impure biofuel is reduced. An offline selling price, online selling price, and online advertisement of biofuel-depended demand are considered for each manufacturer. This study is divided into two cases: with and without investment in green technology. The main goal of this research is to manage a dual-channel retailing for biofuel production systems under multiple manufacturers for gaining more profit. To get global optimality of profit function and decision variables, a classical optimization technique is applied. In addition, numerical experiments are carried out along with sensitivity analysis and graphical representation to show the applicability and optimality of this study. Numerical experiments prove that industries achieve 4.37% more profit when green technology is invested. The flexible production system shows 7.09% more profit than the traditional production system. In case of advertisement policy for dual-channel investment, the proposed model provides 2.55% more profit than the without investment. Numerical r

关键词： Biofuels Emission reduction Flexible production Retail management Supply chain management

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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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A second-scale periodicity in an active repeating fast radio burst source

arXiv

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arXiv 2025年

作者： Du, Chen Huang, Yong-Feng Geng, Jin-Jun Gao, Hao-Xuan Zhang, Li Deng, Chen Cui, Lang Liao, Jie Jiang, Peng-Fei Zhang, Liang Wang, Pei Hu, Chen-Ran Dong, Xiao-Fei Xu, Fan Li, Liang Zou, Ze-Cheng Kurban, Abdusattar School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Jiangsu Nanjing210023 China Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Xinjiang Urumqi830011 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences Beijing100101 China Xinjiang Key Laboratory of Radio Astrophysics Xinjiang Urumqi830011 China College of Astronomy and Space Science University of Chinese Academy of Sciences Beijing101408 China Guizhou Vocational College of Economics and Business Guizhou Duyun558022 China CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Physics Anhui Normal University Anhui Wuhu241002 China Institute of Fundamental Physics and Quantum Technology Ningbo University Zhejiang Ningbo315211 China Department of Physics School of Physical Science and Technology Ningbo University Zhejiang Ningbo315211 China INAF-Osservatorio Astronomico d’Abruzzo Teramo64100 Italy

Fast radio bursts (FRBs) are fierce radio flashes from the deep sky. Abundant observations have indicated that highly magnetized neutron stars might be involved in these energetic bursts, but the underlying trigger mechanism is still enigmatic. Especially, the widely expected periodicity connected to the spin of the central engine has never been discovered, which leads to further debates on the nature of FRBs. Here we report the first discovery of a ∼ 1.7 s period in the repeating source of FRB 20201124A. This is an active repeater, from which more than 2800 FRBs have been observed on a total of 49 days. The phase-folding method is adopted to analyze the bursts on each day separately. While no periodical signal is found in all other datasets, a clear periodicity does appear on two specific days, i.e. a period of 1.706015(2) s on MJD 59310, and a slightly larger period of 1.707972(1) s on MJD 59347. A period derivative of 6.14 × 10−10 s s−1 can be derived from these two periods, which further implies a surface magnetic field strength of 1.04 × 1015 G and a spin-down age of 44 years for the central engine. It is thus concluded that FRB 20201124A should be associated with a young magnetar. © 2025, CC BY.

关键词： Magnetoplasma

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Results of the Ontology Alignment Evaluation Initiative 2022 17

Results of the Ontology Alignment Evaluation Initiative 2022

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17th International Workshop on Ontology Matching, OM 2022

作者： Pour, Mina Abd Nikooie Algergawy, Alsayed Buche, Patrice Castro, Leyla J. Chen, Jiaoyan Dong, Hang Fallatah, Omaima Faria, Daniel Fundulaki, Irini Hertling, Sven He, Yuan Horrocks, Ian Huschka, Martin Ibanescu, Liliana Jiménez-Ruiz, Ernesto Karam, Naouel Laadhar, Amir Lambrix, Patrick Li, Huanyu Li, Ying Michel, Franck Nasr, Engy Paulheim, Heiko Pesquita, Catia Saveta, Tzanina Shvaiko, Pavel Trojahn, Cassia Verhey, Chantelle Wu, Mingfang Yaman, Beyza Zamazal, Ondrej Zhou, Lu Linköping University Swedish E-Science Research Center Linköping Sweden Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany UMR IATE INRAE University of Montpellier France ZB MED Information Centre for Life Sciences Germany Department of Computer Science The University of Manchester United Kingdom Department of Computer Science University of Oxford United Kingdom Information School The University of Sheffield Sheffield United Kingdom University of Lisbon Portugal Institute of Computer Science-FORTH Heraklion Greece Data and Web Science Group University of Mannheim Germany Fraunhofer Institute for High-Speed Dynamics Ernst-Mach-Institut EMI Germany Université Paris-Saclay INRAE AgroParisTech UMR MIA Paris-Saclay France City University of London United Kingdom Fraunhofer FOKUS Institute for Applied Informatics University of Leipzig Germany University of Stuttgart Germany University of Gävle Sweden University Côte d'Azur CNRS Inria France Albert Ludwig University of Freiburg Germany LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Trentino Digitale SpA Trento Italy Institut de Recherche en Informatique de Toulouse France World Data System International Technology Office United States Australian Research Data Commons Australia ADAPT Centre Trinity College Dublin Ireland Prague University of Economics and Business Czech Republic TigerGraph Inc. United States SIRIUS University of Oslo Norway

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Yongyi Xie Kang Lai Xiao Geng Yijun Song Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine Tongji University School of Mechanical & Aerospace Engineering Nanyang Technological University Orthopaedic Department Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Dongfang College Zhejiang University of Finance & Economics Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

关键词： Adaptation models Input variables Biological system modeling High performance computing Noise Genomics Medical services data models Robustness Bioinformatics

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Blockchain based electronic educational document management with role-based access control using machine learning model

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Scientific reports 2025年第1期15卷 18828页

作者： P Chinnasamy B Subashini Ramesh Kumar Ayyasamy Ajmeera Kiran Binay Kumar Pandey Digvijay Pandey Mesfin Esayas Lelisho Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Engineering Srivilliputtur India. Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India. Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India. Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttrarakhand India. Department of Technical Education (Government of Uttar Pradesh) Kanpur Uttar Pradesh India. Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Ethiopia. mesfinesayas@mtu.edu.et.

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%.

关键词： Blockchain Electronic educational document management Remora swarm optimization Role-based access Temporal neural network

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