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m6ATEpre:Predicting YTHDF1-mediated mRNA Translation Efficiency Regulated by m6A Sites via Multi-omics Data Integration 认领 引用
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作者 ZHANG Teng ZHANG Ming +1 位作者 ZHANG Shao-Wu LIU Lian 《生物化学与生物物理进展》 SCIE CAS CSCD 北大核心 2026年第4期1087-1102,共16页
Objective The most prevalent mRNA modification,N6-methyladenosine(m6A)plays an important role in various RNA metabolism,including gene expression and translation.By recruiting different“reader”proteins and their ... Objective The most prevalent mRNA modification,N6-methyladenosine(m6A)plays an important role in various RNA metabolism,including gene expression and translation.By recruiting different“reader”proteins and their cofactors,m6A modification can affect messenger RNA(mRNA)degradation,splicing,nuclear export and translation.However,the selective mechanism by which m6A sites regulate mRNA translation through m6A reader YTHDF1 binding remains poorly understood,due to a lack of computational methods for identifying context-specific m6A sites that regulate translation.To address this,we developed a novel computational framework named m6ATEpre,the first tool designed to predict cell-specific m6A sites that regulate translation efficiency.Methods m6ATEpre integrates multi-omics data,introduces a novel feature representation strategy for m6A site sequences,and employs an autoencoder to effectively capture embedded feature representations.Specifically,m6ATEpre first integrated MeRIP-seq data and PAR-CLIP data through overlapping m6A sites with YTHDF1 binding sites and identified YTHDF1-mediated m6A sites.Then,m6ATEpre detected the translation gene by analyzing the Ribo-seq data under YTHDF1 knockdown vs control condition.Genes whose translation is mediated by YTHDF1 in an m6A-dependent manner were identified by a significant decrease in translation efficiency upon YTHDF1 knockdown.Next,we proposed a binary vector indicating the presence or absence of YTHDF1 binding motifs to characterize each m6A site sequence.This represents a novel feature representation strategy for m6A sites.m6ATEpre utilized the autoencoder to extract the potentially important feature representations and constructed a multilayer perceptron neural networks model to predict potential m6A sites that regulating translation efficiency.Results A comprehensive evaluation of m6ATEpre was conducted through a series of experiments.We compared its performance against that of a similar prediction task model,as well as other classifiers.The results indicate that m6ATEpre achieved the best prediction performance.In addition,we analyzed different feature representation strategies and performed ablation experiments to validate the rationality of the model design.The results demonstrate that our proposed feature representation strategy has a greater advantage in improving prediction performance.In the HeLa cell line,bioinformatic analysis of the metagene distribution and sequence minimum free energy of m6A sites regulating translation efficiency(m6A-reg-TE sites)revealed their specific properties in translation regulation.Functional enrichment analysis indicated that m6A-reg-TE genes are associated with specific biological processes and KEGG pathways.By integrating the binding sites of YTHDF1 co-factors with m6A-reg-TE sites,we revealed that YTHDF1-mediated and m6A-dependent translation efficiency regulation requires the cooperation of multiple translation-regulatory RNA-binding proteins among its co-factors in the HeLa cell line.Furthermore,we extended our predictions to the dataset of the HEK293T cell line.Similarly,bioinformatic analysis of the metagene distribution and functional enrichment revealed the cell-specific characteristic of these predicted m6A-reg-TE sites in HEK293T cells.Likewise,integrated analysis of multiple YTHDF1 co-factors and m6A-reg-TE sites predicted in the HEK293T cell line reveals their m6A-dependent cooperation in regulating translation efficiency.Conclusion m6ATEpre is a timely tool that will advance our understanding of the mechanisms of m6A regulation in translation efficiency.The source code and datasets used in this work can be downloaded from http://gffzz6591ccda58324448sn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/s/bAZZFr. 展开更多
关键词 m6A modification YTHDF1-mediated translation efficiency multi-omics data integration feature representation
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An Optimized Ensemble Learning Framework for Energy Efficiency Assessment in Low-Voltage Distribution Networks Using Multi-Source Data Integration 认领 引用
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作者 Yujie Shi Guoxing Wu +2 位作者 Qingwei Wang Xieli Fu Wenfeng Yang 《Energy Engineering》 EI 2026年第9期350-375,共26页
This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from ... This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from smart meters,SCADA systems,meteorological stations,and network topology databases,employing advanced feature engineering to extract 89 essential predictors from 147 initial features.Three gradient boosting algorithms-Random Forest,XGBoost,and LightGBM-are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization.The stacking ensemble achieved superior performance with an MAE of 118.4 kWh,an RMSE of 164.2 kWh,an MAPE of 3.98%,and an R2of 0.952,representing 16.8%improvement over individual models.SHAP analysis provided model interpretability,identifying temperature,historical consumption,and temporal features as the primary drivers of efficiency.The framework demonstrated robust performance under data quality degradation and successfully generalized across diverse network configurations.Field implementation yielded an 8.3%reduction in distribution losses(95%CI:7.2%-9.4%,p<0.0001),34%decrease in transformer failure rates(95%CI:28%-40%,p=0.003),and 12%-15%operational cost reduction.The framework's ability to provide accurate predictions from 15 min to 24 h ahead while maintaining computational efficiency enables proactive distribution network management,supporting the transition toward efficient and sustainable power systems. 展开更多
关键词 Ensemble learning energy efficiency assessment low-voltage distribution networks multi-source data integration SHAP analysis
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Discussion on the Application of Geotechnical Survey Data Integration Technology in Geotechnical Engineering 认领 引用
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作者 XIN Nan 《外文科技期刊数据库（文摘版）工程技术》 2026年第3期011-015,共5页
Geotechnical engineering serves as an indispensable pillar in national economic development, imposing stringent and meticulous requirements for project safety assurance and construction quality. Traditional methods of... Geotechnical engineering serves as an indispensable pillar in national economic development, imposing stringent and meticulous requirements for project safety assurance and construction quality. Traditional methods of geotechnical survey data collection and management have proven outdated, with fragmented data sources and isolated information silos that hinder comprehensive analysis and utilization. To address these challenges, a specialized data integration technology has been developed to resolve practical issues in geotechnical projects, ensuring systematic organization and rational allocation of data for collaborative use. The study thoroughly examines critical issues arising during data formatting, management, and transmission, proposing a technical solution centered on data standardization and information integration. This approach emphasizes unified data formats and standardized processing procedures, significantly enhancing seamless information exchange across platforms and departments. A detailed management framework covers the entire workflow—from data collection and preliminary processing to centralized storage—ensuring orderly execution at every stage. Practical applications demonstrate remarkable effectiveness: the integrated technology improves data management efficiency, reduces error rates, and provides reliable decision-support data for field operations. Research confirms its exceptional performance in geotechnical projects, effectively supporting quality control and risk mitigation while driving advancements in engineering informatization. This study significantly enriches the theoretical framework for geotechnical engineering data management. In practical applications, it demonstrates substantial potential for widespread adoption, effectively enhancing overall project quality while markedly improving construction safety—delivering profound significance and value. 展开更多
关键词 geotechnical investigation data integration technology geotechnical engineering data standardization information sharing
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A Low-Code Orchestration Middleware for Secure and Transparent IoT-Blockchain Integration 认领 引用 被引量：1
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作者 Jesús Rosa-Bilbao 《Computers, Materials & Continua》 SCIE EI 2026年第7期1967-1979,共13页
The integration of Internet of Things(IoT)infrastructures with Distributed Ledger Technologies(DLT)remains challenging due to the reliance on complex,tightly coupled back-end systems or centralized oracle services tha... The integration of Internet of Things(IoT)infrastructures with Distributed Ledger Technologies(DLT)remains challenging due to the reliance on complex,tightly coupled back-end systems or centralized oracle services that hinder scalability,maintainability,and trust.This paper introduces a lightweight middleware architecture based on a Low-Code Development Platform(LCDP)that enables flexible and secure IoT-to-blockchain orchestration.We develop a custom workflow extension for the n8n platform that supports direct interaction with smart contracts,thereby removing the need for third-party oracle intermediaries.The proposed system was evaluated in a real-world deployment involving a network of Netatmo environmental sensors and the Alastria consortium blockchain.Experimental results show that the middleware can process 12 concurrent sensor data streams with an average end-to-end latency of 37.4 s,a delay dominated by the blockchain consensus time rather than middleware overhead,while ensuring the generation of immutable and verifiable audit trails.These findings demonstrate that low-code orchestration can deliver an effective,scalable,and fault-tolerant alternative for integrating IoT infrastructures with blockchain in Industry 4.0 environments. 展开更多
关键词 Low-code development platform IoT-blockchain integration distributed ledger technologies workflow orchestration smart-contract interaction data integrity
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Predicting Tropical Cyclone Genesis Location Using STAG-Net:A Spatio-Temporal Attention-Gated Network 认领 引用
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作者 Kalim Sattar Malik Muhammad Saad Missen +5 位作者 Syeda Zoupash Zahra Najia Saher Rab Nawaz Bashir Oumaima Saidani Shahid Kamal Muhammad I.Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1112-1135,共24页
Tropical Cyclone(TC)genesis forecasting is an important aspect of early warning systems,as it allows the adoption of early warnings and mitigation plans.However,existing methods often rely on binary classification or ... Tropical Cyclone(TC)genesis forecasting is an important aspect of early warning systems,as it allows the adoption of early warnings and mitigation plans.However,existing methods often rely on binary classification or fail to capture the complex spatio-temporal dependencies that govern TC formation.To address this limitation,this study introduces STAG-Net,a novel Spatio-Temporal Attention-Gated Network designed to directly predict the geographical coordinates of TC genesis.The model uses multivariate variables of meteorological factors such as u-wind,v-wind,relative humidity,temperature,and large-scale dynamic features using a Convolutional Neural Network(CNN),Gated Recurrent Units(GRUs),and a channel-wise attention mechanism in identifying both spatial and temporal characteristics.The methodology takes the initial tropical disturbance data as an input and obtains spatial features in the ERA5 reanalysis dataset that covers 37 isobaric pressure levels.The study also investigates the effect of grid resolution on prediction performance,as four grid sizes were compared,namely 10 x 10,20×20,30×30,and 40×40.The experimental results demonstrate that STAG-Net significantly outperforms existing baselines such as the Dynamic Spatio-temporal model(DST),Spatial Attention Fusing Network(Saf-Net),and a temporal-only model.Notably,the model achieves an average MAE of 2.67°,MSE of 13.24,RMSE of 3.45,and R2 of 0.87045,corresponding to performance improvements of 9.75%,26.25%,12.92%,and 4.27%,respectively,over the baseline model.The results also indicate that the 30×30 grid configuration was found to be the most effective.The results highlight the significance of the proposed approach for the TC genesis location prediction task. 展开更多
关键词 Tropical cyclone genesis atmospheric dynamics spatio-temporal analysis deep learning reanalysis data
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Blockchain-Based Transparent Certificateless Data Integrity Auditing with Enhanced Tag Security 认领 引用
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作者 Chao Zhang Weidong Zhong +6 位作者 Xu An Wang Weiwei Jiang Ziteng Wang Miao Tian Jianhong Ling Hangjiang Du Yunhui Duan 《Computers, Materials & Continua》 SCIE EI 2026年第8期948-976,共29页
The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing(RDIA)technologies.However,traditional schemes rely on trusted third-party auditors(TPAs),lea... The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing(RDIA)technologies.However,traditional schemes rely on trusted third-party auditors(TPAs),leading to potential collusion and single-point failure vulnerabilities.The integration of blockchain alleviates these issues through decentralization and transparency,yet existing blockchain-based certificateless auditing schemes still suffer from security flaws in the tag generation phase.Addressing the tag forgery vulnerability in Miao et al.’s scheme,which stems from the absence of random parameters in the hash function input,this paper proposes a lightweight enhancement mechanism:incorporating a random factor into the hash input during tag generation to ensure dynamic unforgeability of tags.While retaining the efficiency advantages of the original framework,the improved scheme achieves resistance against tag forgery,proof forgery,and collusion attacks under the Computational Diffie-Hellman(CDH)and Discrete Logarithm(DL)hardness assumptions,validated through rigorous formal proofs.Experimental performance analysis demonstrates that the proposed enhanced scheme introduces negligible computational overhead,providing a secure,practical,and transparent auditing solution for multi-cloud storage environments. 展开更多
关键词 Blockchain tag security certificateless cryptography data integrity auditing cloud storage
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Localization of False Data Injection Attacks in Power Grid Based on Adaptive Neighborhood Selection and Spatio-Temporal Feature Fusion 认领 引用
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作者 Zehui Qi Sixing Wu Jianbin Li 《Computers, Materials & Continua》 SCIE EI 2025年第11期3739-3766,共28页
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail... False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model. 展开更多
关键词 Power grid security adaptive neighborhood selection spatio-temporal correlation false data injection attacks localization
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Energy data space ensuring reliable interaction of virtuality and reality in integrated energy system:A survey 认领 引用
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作者 Haoran Li Chenghui Zhang +2 位作者 Peide Liu Bo Sun Haoran Zhao 《Cyber-Physical Energy Systems》 2026年第1期1-12,共12页
With the deep integration of energy and information networks,the low-carbon,efficient,and economical oper-ation and management of integrated energy systems are increasingly driven by digital twins and large artificial... With the deep integration of energy and information networks,the low-carbon,efficient,and economical oper-ation and management of integrated energy systems are increasingly driven by digital twins and large artificial intelligence models,which rely heavily on the robust support of the Internet of Things and big data.However,the data interaction process within energy systems faces issues such as varying privacy protection demands and conflicts of interest among participating entities,leading to development bottlenecks like prominent data silos,a lack of secure sharing mechanisms,and low collaborative efficiency.As a novel infrastructure for data resource circulation,the energy data space integrates various software and hardware encryption methods to construct a trusted execution environment,providing solutions for the secure sharing and value realization of energy data re-sources.Based on a decentralized yet adjustable underlying architecture,the energy data space can be adaptively designed according to the coupling characteristics of energy and information flows,meeting the real-time,effi-ciency,and scalability requirements of cross-entity systems and large-scale businesses.This paper investigates,analyzes,and summarizes technical routes through which the energy data space can facilitate the digital and intelligent transformation of integrated energy systems.It also elaborates on the supporting role of the energy data space in the conceptual architecture,technical support,application scenarios,and value creation of energy industry upgrading. 展开更多
关键词 Energy data resources Energy data space Integrated energy system Digital energy network Reliable interaction
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Optimal Scheduling of Data Centre Integrated Energy Systems Considering Electric-Thermal Demand Response Under Icing Uncertainty 认领 引用
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作者 Yan Wang Hui Hou +4 位作者 Zhengguo Wang Wenzhe Zheng Zhengtian Li Xiangning Lin Wu Chen 《Energy Internet》 2026年第2期142-154,共13页
Extreme icing disasters increasingly undermine the reliability of integrated power and heat networks by causing line outages,supply shortages and sharp thermal load fluctuations.To address these challenges,this paper ... Extreme icing disasters increasingly undermine the reliability of integrated power and heat networks by causing line outages,supply shortages and sharp thermal load fluctuations.To address these challenges,this paper proposes a comprehensive optimisation framework that exploits the spatiotemporal flexibility of data centres for coordinated electric–thermal demand response under uncertain icing disasters.An improved spatiotemporal inverse distance weighting method combined with a Gaussian copula is first developed to reconstruct the joint spatial temporal dependence of icing variables,whereas a cellular automaton is introduced to capture icing-driven fault propagation and generate representative stochastic scenarios.Based on these scenarios,an integrated electric–thermal demand response model is formulated,jointly leveraging data centre load migration and waste heat recovery,with an objective function incorporating operating cost,response benefits,icing-related damage and social penalties.A case study in Chun'an County,Zhejiang Province,validates the proposed framework.Compared with fixed icing assumptions and decoupled power–heat scheduling,the method reduces residential electricity load shedding to 36.34%,increases data centre task migration utilisation to 67.21%and improves waste heat recovery efficiency to 73.45%.The results demonstrate that data centre flexibility can significantly enhance the resilience and reliability of multienergy systems under extreme icing disasters. 展开更多
关键词 data centre electric-thermal demand response icing disasters icing uncertainty modelling integrated energy system
Holistic Hierarchical Predictive-Integration Theory (HHPIT): An Exploration of AI-Empowered Innovation and Empirical Research in Traditional Chinese Medicine Meridian Theory 认领 引用
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作者 Jiren Zhang Yang Zhang +3 位作者 Bingna Hao Junjie Hao Leiming Wang Fengtian Lao 《Journal of Clinical and Nursing Research》 2026年第2期278-285,共8页
By 2025,research on Traditional Chinese Medicine(TCM)meridians has generated 12-15 macro-level theories and over 20 specific hypotheses,manifesting a highly fragmented research landscape.Objective:This paper proposes ... By 2025,research on Traditional Chinese Medicine(TCM)meridians has generated 12-15 macro-level theories and over 20 specific hypotheses,manifesting a highly fragmented research landscape.Objective:This paper proposes the“Holistic Hierarchical Predictive-Integration Hypothesis”(HHPIT)to construct a unified theoretical framework that integrates the rational components of existing meridian hypotheses.Methods:The HHPIT hypothesis systematically reviews current meridian theories,employs interdisciplinary methodologies,integrates artificial intelligence technology,and establishes a three-tier architecture encompassing structural,functional,and systemic layers.Results:HHPIT successfully integrates diverse meridian theories,proposes a computable algorithmic pipeline,and provides specific application protocols for chronic disease treatment,anti-aging,and enhancement of Zang-fu organ functions.Conclusion:HHPIT offers a novel,computable,and verifiable research paradigm for meridian studies,promoting the modernization and internationalization of TCM theory. 展开更多
关键词 Holographic hierarchical prediction integration Meridian research Artificial intelligence Modernization of Traditional Chinese Medicine Multimodal data fusion
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IDCE:Integrated Data Compression and Encryption for Enhanced Security and Efficiency 认领 引用 被引量：1
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作者 Muhammad Usama Arshad Aziz +2 位作者 Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期1029-1048,共20页
Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive da... Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field. 展开更多
关键词 Chaotic maps security data compression data encryption integrated compression and encryption
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Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data 认领 引用 被引量：7
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作者 ZHANG Li LEI Jun +3 位作者 WANG Changjian WANG Fei GENG Zhifei ZHOU Xiaoli 《Journal of Geographical Sciences》 SCIE CSCD 2022年第10期1886-1910,共25页
This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic... This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,China.Then,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique.Results reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various regions.Total carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions.The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial differentiation.This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps. 展开更多
关键词 carbon emissions nighttime light data spatio-temporal variations influencing factors Xinjiang
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tsRNADisease:a manually curated database of tsRNAs associated with human disease 认领 引用 被引量：1
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作者 Hui Yang Shaoying Zhu +5 位作者 Huijun Wei Wei Huang Qi Chen Yungang He Kun Lv Zhen Yang 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第3期537-543,共7页
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f... tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://gffzz9c504e06f78b4edahn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn. 展开更多
关键词 tsRNA Disease Cancer Data integration Database
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Fog-IBDIS:Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems 认领 引用 被引量：5
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作者 Junliang Wang Peng Zheng +2 位作者 Youlong Lv Jingsong Bao Jie Zhang 《Engineering》 SCIE EI CAS 2019年第4期662-670,共9页
Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is p... Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data. 展开更多
关键词 Fog computing Industrial big data Integration Manufacturing system
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A Deep Web Data Integration System for Job Search 认领 引用 被引量：9
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作者 LIU Wei LI Xian +2 位作者 LING Yanyan ZHANG Xiaoyu MENG Xiaofeng 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1197-1201,共5页
With the rapid development of Web, there are more and more Web databases available for users to access. At the same time, job searchers often have difficulties in first finding the right sources and then querying over... With the rapid development of Web, there are more and more Web databases available for users to access. At the same time, job searchers often have difficulties in first finding the right sources and then querying over them, providing such an integrated job search system over Web databases has become a Web application in high demand. Based on such consideration, we build a deep Web data integration system that supports unified access for users to multiple job Web sites as a job meta-search engine. In this paper, the architecture of the system is given first, and the key components in the system are introduced. 展开更多
关键词 Web database Web data integration, job Website
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An Arrhythmia Intelligent Recognition Method Based on a Multimodal Information and Spatio-Temporal Hybrid Neural Network Model 认领 引用
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作者 Xinchao Han Aojun Zhang +6 位作者 Runchuan Li Shengya Shen Di Zhang Bo Jin Longfei Mao Linqi Yang Shuqin Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第2期3443-3465,共23页
Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to... Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness. 展开更多
关键词 Multimodal learning spatio-temporal hybrid graph convolutional network data imbalance ECG classification
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Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China 认领 引用 被引量：3
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作者 TAO Jian-bin LIU Wen-bin +2 位作者 TAN Wen-xia KONG Xiang-bing XU Meng 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2019年第10期2393-2407,共15页
Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role... Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties. 展开更多
关键词 winter rape,spatio-temporal dynamics,time-series MODIS data artificial neural network
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Integral experiment on slabnatPb using D-T and D-D neutron sources to validate evaluated nuclear data 认领 引用
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作者 Kuo-Zhi Xu Yang-Bo Nie +6 位作者 Chang-Lin Lan Yan-Yan Ding Shi-Yu Zhang Qi Zhao Xin-Yi Pan Jie Ren Xi-Chao Ruan 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第3期119-133,共15页
Lead(Pb)plays a significant role in the nuclear industry and is extensively used in radiation shielding,radiation protection,neutron moderation,radiation measurements,and various other critical functions.Consequently,... Lead(Pb)plays a significant role in the nuclear industry and is extensively used in radiation shielding,radiation protection,neutron moderation,radiation measurements,and various other critical functions.Consequently,the measurement and evaluation of Pb nuclear data are highly regarded in nuclear scientific research,emphasizing its crucial role in the field.Using the time-of-flight(ToF)method,the neutron leakage spectra from threenatPb samples were measured at 60°and 120°based on the neutronics integral experimental facility at the China Institute of Atomic Energy(CIAE).ThenatPb sample sizes were30 cm×30 cm×5 cm,30 cm×30 cm×10 cm,and 30 cm×30 cm×15 cm.Neutron sources were generated by the Cockcroft-Walton accelerator,producing approximately 14.5 MeV and 3.5 MeV neutrons through the T(d,n)4He and D(d,n)3He reactions,respectively.Leakage neutron spectra were also calculated by employing the Monte Carlo code of MCNP-4C,and the nuclear data of Pb isotopes from four libraries:CENDL-3.2,JEFF-3.3,JENDL-5,and ENDF/B-Ⅷ.0 were used individually.By comparing the simulation and experimental results,improvements and deficiencies in the evaluated nuclear data of the Pb isotopes were analyzed.Most of the calculated results were consistent with the experimental results;however,a few areas did not fit well.In the(n,el)energy range,the simulated results from CENDL-3.2 were significantly overestimated;in the(n,inl)D and the(n,inl)C energy regions,the results from CENDL-3.2 and ENDF/B-Ⅷ.0 were significantly overestimated at 120°,and the results from JENDL-5 and JEFF-3.3 are underestimated at 60°in the(n,inl)D energy region.The calculated spectra were analyzed by comparing them with the experimental spectra in terms of the neutron spectrum shape and C/E values.The results indicate that the theoretical simulations,using different data libraries,overestimated or underestimated the measured values in certain energy ranges.Secondary neutron energies and angular distributions in the data files have been presented to explain these discrepancies. 展开更多
关键词 Integral experiment Neutron leakage spectra natPb D-T and D-D neutron sources Evaluated nuclear data
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Multi-omics data integration provides insights into the post-harvest biology of a long shelf-life tomato landrace 认领 引用 被引量：5
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作者 Riccardo Aiese Cigliano Riccardo Aversano +18 位作者 Antonio Di Matteo Samuela Palombieri Pasquale Termolino Claudia Angelini Hamed Bostan Maria Cammareri Federica Maria Consiglio Floriana Della Ragione Rosa Paparo Vladimir Totev Valkov Antonella Vitiello Domenico Carputo Maria Luisa Chiusano Maurizio D’Esposito Silvana Grandillo Maria Rosaria Matarazzo Luigi Frusciante Nunzio D’Agostino Clara Conicella 《Horticulture Research》 SCIE CSCD 2022年第1期882-896,共15页
In this study we investigated the transcriptome and epigenome dynamics of the tomato fruit during post-harvest in a landrace belonging to a group of tomatoes(Solanum lycopersicum L.)collectively known as“Piennolo del... In this study we investigated the transcriptome and epigenome dynamics of the tomato fruit during post-harvest in a landrace belonging to a group of tomatoes(Solanum lycopersicum L.)collectively known as“Piennolo del Vesuvio”,all characterized by a long shelflife.Expression of protein-coding genes andmicroRNAs aswell as DNAmethylation patterns and histonemodificationswere analysed in distinct post-harvest phases.Multi-omics data integration contributed to the elucidation of the molecularmechanisms underlying processes leading to long shelf-life.We unveiled global changes in transcriptome and epigenome.DNA methylation increased and the repressive histone mark H3K27me3 was lost as the fruit progressed from red ripe to 150 days post-harvest.Thousands of genes were differentially expressed,about half of which were potentially epi-regulated as they were engaged in at least one epi-mark change in addition to being microRNA targets in~5%of cases.Down-regulation of the ripening regulator MADS-RIN and of genes involved in ethylene response and cell wall degradation was consistent with the delayed fruit softening.Large-scale epigenome reprogramming that occurred in the fruit during post-harvest likely contributed to delayed fruit senescence. 展开更多
关键词 epigenome transcriptome post harvest biology long shelf life transcriptome epigenome dynamics multi omics data integration piennolo del vesuvio tomato landrace
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A systematic data-driven modelling framework for nonlinear distillation processes incorporating data intervals clustering and new integrated learning algorithm 认领 引用
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作者 Zhe Wang Renchu He Jian Long 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期182-199,共18页
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie... The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation. 展开更多
关键词 Integrated learning algorithm Data intervals clustering Feature selection Application of artificial intelligence in distillation industry Data-driven modelling
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