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Big data analysis of waterflood performance in mature conventional oilfields in Eastern China 认领 引用
1
作者 Tianrui Ye Zhiqiang Chen Cheng Dai 《Energy Geoscience》 EI CAS CSCD 2026年第2期17-28,共12页
Most conventional oilfields in Eastern China with waterflood operations have reached ultra-high water cut in recent decade.The high water injection demand and produced water treatment cost pose significant environment... Most conventional oilfields in Eastern China with waterflood operations have reached ultra-high water cut in recent decade.The high water injection demand and produced water treatment cost pose significant environmental threats.Therefore,optimizing waterflood performance is key to improving production efficiency.This study performs data analysis on waterflood operations of all the oilfields operated by Sinopec across Eastern China.The production mechanisms and most effective operations for different reservoir types at diverse production stages are identified using data-driven methods.Random Forest models(RFMs)are constructed and integrated with Shapley Additive exPlanations(SHAP)analysis to quantify the weights and patterns of key geological and engineering features.A comparison of the estimated ultimate recovery factors for different blocks shows that geological factors play dominant roles in medium-to-high permeability reservoirs while development parameters are more critical for low-permeability reservoirs.The analysis of temporal data regarding field development and production history is conducted to select oil production-increasing operations in blocks.The results show that the most influential field operations vary for the diverse production stages,and well patterns should be carefully designed to improve production efficiency and reduce ineffective water circulation. 展开更多
关键词 Random Forest model(RFM) Big data analysis Waterflood performance Key factor analysis
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Multidimensional Analysis of Urban Vitality Centers Using Multisource Data:A Case Study of Changchun,China 认领 引用
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作者 GAO He LIANG Shilong +2 位作者 ZHANG Peiqing JIANG Xue LIU Jibin 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第6期959-973,共15页
As a core city in the old industrial base of Northeast China,Changchun urgently needs to identify the core hubs and structural deficiencies of its urban vitality center system.This is essential for addressing spatial ... As a core city in the old industrial base of Northeast China,Changchun urgently needs to identify the core hubs and structural deficiencies of its urban vitality center system.This is essential for addressing spatial imbalance,enhancing urban efficiency,and revitalizing demographic vitality in a period of urban transition.By integrating multi-source data with social network analysis(SNA),this study examines the spatial structure and network characteristics of urban vitality centers in the central urban area of Changchun in 2024.The results reveal three main findings.First,67 vitality centers were identified and organized into a three-tier hierarchical system composed of core-level,sub-core-level,and node-level centers.Core-level centers are mainly dominated by commercial and consumption functions,whereas sub-core-level and node-level centers perform more differentiated and complementary service roles.Second,the spatial pattern of vitality exhibits strong central agglomeration and clear directional expansion.High-vitality areas are concentrated in the historical urban core,while secondary centers extend along major development corridors,forming a clear contrast between central concentration and peripheral weakness.Third,the vitality center network is characterized by low density but relatively high connectedness and efficiency.A limited number of core nodes dominate resource transmission,while peripheral nodes participate only weakly in the overall network.Blockmodel analysis further shows that cross-block linkages are more significant than intra-block cohesion,although weak internal cohesion in key intermediary blocks constrains the overall transmission efficiency of the network.Based on the integrated perspective of structure,function,and space,this study proposes hierarchical coordination,core-area quality enhancement,and peripheral service supplementation as key pathways for optimizing Changchun’s vitality center network and promoting more balanced and resilient urban development in old industrial cities undergoing transition. 展开更多
关键词 urban vitality centers vitality center network social network analysis(SNA) multi-source data fusion Changchun,China
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A Resilient BIRCH-Based Smart Framework for Real-Time IoT Data Clustering 认领 引用
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作者 Prabhat Das Dibya Jyoti Bora +2 位作者 Sajal Saha Cheng-Chi Lee Hirak Mazumdar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期864-898,共35页
Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing high... Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing highfrequency,continuous IoT data streams due to limited adaptability and high computational overhead.To address these challenges,this study proposes a resilient adaptation of the BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies)algorithm,tailored specifically for streaming IoT data.The enhanced approach dynamically recalculates clusters and determines the optimal number of clusters using the KneeLocator method.Unlike the original batchoriented BIRCH,the modified version processes data incrementally,enabling continuous adaptation to changing data distributions.The proposed method was validated on benchmark IoT datasets and compared against K-Means,DBSCAN,standard BIRCH,and other state-of-the-art streaming-based clustering algorithms.Results consistently show that the modified BIRCH outperforms existing approaches in execution speed,memory efficiency,scalability,and clustering accuracy.In addition,the algorithm has been deployed within a web-based application featuring interactive visualization and anomaly detection,highlighting its practical relevance for smart city and industrial IoT scenarios.To promote reproducibility and future research,the complete framework and source code have been made publicly available. 展开更多
关键词 IoT applications clustering algorithms smart city applications real-time data processing industrial IoT real-time clustering
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Spatio-Temporal Earthquake Analysis via Data Warehousing for Big Data-Driven Decision Systems 认领 引用
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作者 Georgia Garani George Pramantiotis Francisco Javier Moreno Arboleda 《Computers, Materials & Continua》 SCIE EI 2026年第3期1963-1988,共26页
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei... Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management. 展开更多
关键词 Data warehouse data analysis big data decision systems seismology data visualization
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GranuSAS:Software of rapid particle size distribution analysis from small angle scattering data 认领 引用
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作者 Qiaoyu Guo Fei Xie +3 位作者 Xuefei Feng Zhe Sun Changda Wang Xuechen Jiao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期216-225,共10页
Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces th... Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization. 展开更多
关键词 small angle x-ray scattering data analysis software particle size distribution inverse problem
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Network-based hierarchical heterogeneity analysis and applications to cancer omics data 认领 引用
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作者 Ruiyue Wang Sanguo Zhang Shuangge Ma 《Science China Mathematics》 SCIE CSCD 2026年第8期2181-2194,共14页
In the study of complex diseases,heterogeneity analysis has been routinely conducted.A series of recent studies have suggested that network(graph)-based heterogeneity analysis can take a system perspective and be more... In the study of complex diseases,heterogeneity analysis has been routinely conducted.A series of recent studies have suggested that network(graph)-based heterogeneity analysis can take a system perspective and be more informative than that based on simpler statistics such as mean and variance.In this article,we conduct Gaussian graphical model(GGM)-based heterogeneity analysis.Significantly advancing from the existing literature,we consider the scenario,where measurements can be decomposed into two parts,with the first and second parts for a rough grouping and a refined subgrouping,respectively.Additionally,the groups and subgroups have a nested structure,which enhances interpretability.A penalization approach is developed for simultaneous sparse estimation,grouping and subgrouping,and achieving the hierarchical structure.Its theoretical properties are rigorously established,and an effective computational algorithm is developed.Simulation demonstrates its competitive empirical performance.The analysis of data from the PanCancer Analysis of Whole Genomes(PCAWG)further demonstrates its practical utility and leads to sensible findings. 展开更多
关键词 heterogeneity analysis network analysis Gaussian graphical model hierarchy cancer omics data
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Tongue Image Analysis and Clinical Data Fusion:A Novel Approach for Non-invasive Diagnosis of Metabolic Dysfunction-associated Fatty Liver Disease 认领 引用
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作者 Chen-Xia Lu Chuan-Xi Tian +13 位作者 Yi-Bo Jiao Hui Zhu Hai-Yan Yu Zi-Xin Shu Ling-Han Zhang Jia Zhang Lan Wang Qi Hao Wen-Bin Zou Ming-Zhong Xiao Cheng-Hai Liu Qiu-Yang He Bee Luan Khoo Xiao-Dong Li 《Journal of Clinical and Translational Hepatology》 SCIE CSCD 2026年第4期416-429,共14页
Background and Aims:Metabolic dysfunction-associated fatty liver disease(MAFLD)represents a predominant cause of chronic liver disease,underscoring the demand for accessible,non-invasive diagnostic tools.Tongue diagno... Background and Aims:Metabolic dysfunction-associated fatty liver disease(MAFLD)represents a predominant cause of chronic liver disease,underscoring the demand for accessible,non-invasive diagnostic tools.Tongue diagnosis in Traditional Chinese Medicine provides a distinctive perspective on systemic health,though it remains largely subjective.This study aimed to develop an interpretable multimodal deep learning model for MAFLD screening by integrating quantitative tongue image features with routine clinical data.Methods:From 904 screened candidates,477 subjects(157 healthy,320 MAFLD)were included and randomly allocated to training,validation,and test sets in an 8:1:1 ratio.All participants underwent standardized tongue imaging(International Commission on Illumination L*a*b color features)and comprehensive clinical evaluation.We constructed a dual-stream deep learning model,combining a ConvNeXt-Tiny network for tongue images and a multilayer perceptron for clinical variables.Feature fusion was achieved via a Dynamic Affine Feature Transformation module,and the model was trained using weighted cross-entropy loss.Results:MAFLD patients showed significant metabolic abnormalities compared to healthy controls.A progressive decrease in tongue yellowness(b* value)was observed with advancing fibrosis.On an independent test set(n=48),the multimodal model achieved 97.92%accuracy,Quadratic Weighted Kappa of 0.9538,and 96.88%sensitivity,and 100%specificity,outperforming single-modality and serological models.Interpretability analyses confirmed the model’s focus on clinically relevant tongue regions and key metabolic drivers.Conclusions:We developed an accurate and interpretable multimodal model that synergizes tongue image features with metabolic indicators for MAFLD screening.This approach presents a promising,low-cost tool potentially well-suited for resource-limited settings. 展开更多
关键词 Metabolic dysfunction-associated fatty liver disease Tongue image analysis Non-invasive prediction Multi-modal data fusion Deep learning ConvNeXt-Tiny network Non-invasive Diagnosis.
Exploration of Curriculum Reform in Data Management and Analysis for Medical Research 认领 引用
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作者 Ci Song You Zhang +5 位作者 Qiufen Sun Caiwang Yan Xia Zhu Yi Yang Lijun Bian Meng Zhu 《Journal of Contemporary Educational Research》 2026年第5期159-164,共6页
Objective:To explore the effectiveness of integrating scenario-based learning(SBL)with case-based learning(CBL)in teaching data management and analysis in medical research.Methods:Students in the preventive medicine p... Objective:To explore the effectiveness of integrating scenario-based learning(SBL)with case-based learning(CBL)in teaching data management and analysis in medical research.Methods:Students in the preventive medicine program at Nanjing Medical University were divided into two cohorts:the 2020 cohort received CBL-only instruction,while the 2021 cohort underwent combined SBL-CBL teaching.Teaching effectiveness was evaluated through process assessment and final skill examination.Results:The SBL-CBL group achieved significantly higher final comprehensive scores than the CBL group(86.33±6.37 vs.79.50±14.38).All question-type scores improved.In process assessment,the CBL group scored higher overall(17.19±1.89 vs.16.31±2.04),a difference mainly attributed to peer evaluations.Conclusion:The integration of SBL and CBL has significantly enhanced students’comprehensive data analysis skills,providing empirical evidence for teaching reform in the field of public health. 展开更多
关键词 Scenario-based learning Case-based learning Data analysis Process assessment
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Research on industry requirements for software testing engineers based on data analysis 认领 引用
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作者 Jingdong Jia Zishen Wang 《计算机教育》 2026年第6期259-266,共8页
It is widely recognized that universities should cultivate professional engineers needed by the marketplace.Thus,it is necessary to know what are industry requirements for software testing engineers,which is crucial f... It is widely recognized that universities should cultivate professional engineers needed by the marketplace.Thus,it is necessary to know what are industry requirements for software testing engineers,which is crucial for the teaching of software testing courses.This paper analyzed testing job ads to study employers’requirements.We first obtained lots of job ads for testers from a recruitment website.Then,the regular expression matching method was used to extract the relevant texts from job ads,and the original dataset was built after data preprocessing.Next,the fine-tuned bBidirectional eEncoder rRepresentations from tTransformers(BERT)model was used to classify the job requirement data into three categories:knowledge,skills,and dispositions.Finally,through the word frequency statistics method,the common requirements of each category for testers were extracted.In terms of knowledge,the mastery of testing tools,the mastery of common testing method,and proficiency in programming languages are the top three requirements.In respect to skills,test cases design,testing requirement analysis,and test plan formulation are highlighted.As for dispositions,communication skills,teamwork,and responsibility are emphasized.In addition,we also explored the common industry requirements of each category for four tester subroles:function,performance,automated testing,and system testing,and compared the results with the overall requirements.Our findings provide the direction not only for the teaching of software testing course but also for the career planning of testers. 展开更多
关键词 Industry requirements Software testing Job advertisements BERT model Data analysis
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Sparse Canonical Correlation Analysis with L2,1-Norm for Functional Data 认领 引用
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作者 Zhang Zejiang Yang Zhixia +1 位作者 Ye Junyou Wang Yulan 《新疆大学学报（自然科学版中英文）》 CAS 2026年第3期305-323,共19页
Functional canonical correlation analysis is a key method in multivariate statistics for identifying optimal linear correlations between two functional datasets.However,some functions within these datasets may exhibit... Functional canonical correlation analysis is a key method in multivariate statistics for identifying optimal linear correlations between two functional datasets.However,some functions within these datasets may exhibit anomalies such as sudden changes or fluctuations that deviate from the overall trend,resulting to inaccurate results.To address this,we propose an improved method:Sparse functional canonical correlation analysis based on the L2,1-norm.This approach reduces outliers by optimizing the selection of orthogonal basis functions,thereby enhancing the accuracy and reliability of the analysis.Numerical experiments show that the L2,1-norm-based method significantly outperforms traditional methods. 展开更多
关键词 functional canonical correlation analysis L2,1-norm functional data outliers orthogonal basis function
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Current Situation of Application and Development Prospects of the Statistical Analysis of Big Data 认领 引用
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作者 Zhuoran LI 《Meteorological and Environmental Research》 2026年第1期45-47,共3页
With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues su... With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues such as computer memory capacity and distributed storage of data across different locations,and are unable to directly apply to large-scale data sets.Therefore,in the context of big data,designing efficient and theoretically guaranteed statistical learning and inference algorithms has become a key issue that the current field of statistics urgently needs to address.In this paper,the application status of statistical analysis methods in the big data environment was systematically reviewed,and its future development directions were analyzed to provide reference and support for the further development of theory and methods of the statistical analysis of big data. 展开更多
关键词 Big data Statistical analysis Current status Development prospects
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Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements 认领 引用 被引量：4
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作者 Jinsong Li Hao Liu +2 位作者 Wenzhuo Li Tianshu Bi Mingyang Zhao 《Global Energy Interconnection》 EI CSCD 2022年第2期131-142,共12页
The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information ... The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests. 展开更多
关键词 Power system Data network Wide-frequency information Real-time system Traffic analysis Optimization strategy
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Research on Innovative Paths and Practical Optimization of Big Data Analysis and Processing in Geological Survey Engineering 认领 引用
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作者 GAO Xingda 《外文科技期刊数据库（文摘版）工程技术》 2026年第7期113-117,共5页
Geological survey engineering is advancing toward intelligence, refinement and full-domain coverage. Traditional survey data processing methods can no longer meet the demands of processing massive, heterogeneous multi... Geological survey engineering is advancing toward intelligence, refinement and full-domain coverage. Traditional survey data processing methods can no longer meet the demands of processing massive, heterogeneous multi-source survey data, making big data technology a vital support for technological innovation and efficiency improvement in geological survey engineering. Centered on the full-process data application of geological survey engineering, this paper focuses on innovative approaches to big data analysis and processing technologies, system construction methods and engineering application improvement strategies. Relying on the full-chain application scenarios of survey data, it explores the deep integration mode of digital-intelligent technologies and traditional geological surveys, and establishes a big data processing system compatible with modern geological survey engineering. By integrating multi-source heterogeneous geological data, innovating intelligent analysis algorithms, optimizing engineering workflows and building an integrated data platform, geological survey data has transformed from decentralized storage and single-dimensional analysis to centralized governance, intelligent mining and precise application. This transformation significantly improves data utilization efficiency, analysis accuracy and engineering service capacity, providing technical support and operational references for high-quality development in mineral exploration, geological disaster prevention, engineering construction and other fields. 展开更多
关键词 Geological Survey Engineering Big Data Analysis Data Processing Innovative Paths Practical Optimization Digital-Intelligent Application
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Research on User Behavior Analysis Based on Big Data Technology 认领 引用
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作者 Yunzhe Dai 《Journal of Electronic Research and Application》 2026年第3期101-107,共7页
Big data technology refers to the ability to efficiently extract high-value information from multiple sources and massive amounts of data.It is an important achievement in the development of information technology and... Big data technology refers to the ability to efficiently extract high-value information from multiple sources and massive amounts of data.It is an important achievement in the development of information technology and has significant application value in the field of user behavior analysis.Against the backdrop of rapid development of the digital economy and industry transformation,the role of e-commerce in the market system is increasingly prominent,and the scale of platform users continues to expand.In order to promote high-quality and sustainable development of the e-commerce industry,e-commerce platforms urgently need to use precise marketing methods to provide personalized products and services according to user needs,thereby improving user conversion rates and platform operating efficiency.This article takes e-commerce users as the research object.Firstly,it elaborates on the data characteristics and types of e-commerce user behavior.Secondly,it summarizes the relationship between big data and user behavior analysis,as well as the application value of big data technology in e-commerce user behavior analysis.Finally,it proposes scientific and effective application strategies,aiming to provide reference for e-commerce platforms to achieve accurate recommendations,optimize service strategies,enhance user experience and market competitiveness by mining user consumption preferences,potential needs and behavioral characteristics. 展开更多
关键词 Big data technology E-commerce users Behavioral analysis Precision marketing
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Research on the Application of Integrating Engineering Thermodynamics and Heat Transfer Knowledge with Diesel Engine Knowledge in Military Academy Teaching Based on Data Analysis 认领 引用
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作者 Fan Zeng Wenjie Wang Zuo Zhou 《Journal of Contemporary Educational Research》 2026年第1期68-74,共7页
Aiming at the characteristics of naval power engineering students in military academies who have a relatively weak theoretical foundation but focus on skill development,this study integrates the essential fundamental ... Aiming at the characteristics of naval power engineering students in military academies who have a relatively weak theoretical foundation but focus on skill development,this study integrates the essential fundamental theoretical knowledge of engineering thermodynamics and heat transfer with the corresponding professional knowledge of diesel engines.It quantifies their correlation through SPSS data analysis and proposes a teaching model“guided by fault phenomena and maintenance cases,supported by visualization and simulation.”The article elaborates on the knowledge point connection method based on data analysis,including descriptive statistics,correlation analysis,and regression modeling,and applies it in combination with the trinity teaching process of“case guidance,project assessment,and practical operation reinforcement.”The results show that this integrated method can effectively reduce the difficulty of theoretical learning,stimulate students’interest,enhance fault diagnosis and practical abilities,and provide data support and an effective path for cultivating high-quality technical and skilled marine power engineering talents. 展开更多
关键词 Higher vocational teaching Engineering thermodynamics Heat transfer Diesel engine Data analysis Teaching application
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Bibliometric analysis of papers on inflammation in glaucoma from 2000 to 2025 认领 引用
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作者 Wen-Li Chen Xue Wu Li-Xia Zhang 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第3期590-599,共10页
AIM:To perform a bibliometric analysis of publications focusing on inflammatory mechanisms in glaucoma,thereby comprehensively understanding the current research status and identifying potential frontier directions fo... AIM:To perform a bibliometric analysis of publications focusing on inflammatory mechanisms in glaucoma,thereby comprehensively understanding the current research status and identifying potential frontier directions for future studies.METHODS:A systematic search was conducted in the Web of Science Core Collection(WoSCC)database to retrieve relevant literature published from January 1,2000,to August 31,2025(data accessed on September 12,2025).Multiple data visualization tools were employed to conduct in-depth analyses of the included publications,covering aspects such as publication quantity and quality,evolutionary trends of research hotspots,keyword cooccurrence networks,and collaborative patterns among countriesegions,institutions,and authors.RESULTS:A total of 3381 articles related to glaucoma inflammation were extracted from WoSCC.The analysis showed that the USA had the highest research output in this field(29.04%,n=982),followed by China(18.40%,n=622)and UK(6.01%,n=203).Based on citation frequency and burst intensity,the USA also ranked as the most influential country.Baudouin C and Sun X were identified as the most productive authors,while Journal of Glaucoma and Investigative Ophthalmology&Visual Science were the journals with the highest number of published relevant articles.Additionally,keyword analysis revealed that“neuroinflammation”,“retinal ganglion cells(RGCs)”,“pathophysiology”,and“traditional Chinese medicine”are emerging research hotspots in the field of immuneinflammatory responses in glaucoma.CONCLUSION:This study presents a comprehensive bibliometric overview of research on glaucoma-related inflammation,indicating that this field has received extensive scientific attention with a steady upward trend in research activity.Furthermore,it establishes a theoretical basis for the development of neuroinflammation-targeted therapeutic strategies for glaucoma and emphasizes the necessity of strengthening interdisciplinary collaboration to promote the clinical translation of research findings. 展开更多
关键词 glaucoma inflammatory mechanism bibliometric analysis data visualization research hotspot neuroinflammation
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Real-Time Monitoring and Intelligent Analysis Platform for Carbon Emission in Smart Power Plants 认领 引用
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作者 Jie Gao Tiejun Lin Zhannan Ma 《Journal of Architectural Research and Development》 2025年第5期96-100,共5页
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla... As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends. 展开更多
关键词 Smart power plant Real-time carbon emission monitoring Intelligent analysis platform Internet of Things perception
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Commentary on:Intensity modifies the association between continuous bouts of physical activity and risk of mortality:A prospective UK Biobank cohort analysis 认领 引用
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作者 Barbara E.Ainsworth Zhenghua Cai 《Journal of Sport and Health Science》 SCIE CAS CSCD 2026年第2期77-79,共3页
Rowlands et al.1present an analysis of accelerometer data from the UK Biobank cohort,examining variations in the duration,intensity,and accumulation of moderate-intensity physical activity(MPA)and vigorous-intensity p... Rowlands et al.1present an analysis of accelerometer data from the UK Biobank cohort,examining variations in the duration,intensity,and accumulation of moderate-intensity physical activity(MPA)and vigorous-intensity physical activity(VPA)sufficient to reduce the risk of all-cause mortality.In this study,the authors questioned if shorter durations(i.e.,1,2,3,4,5,10,15,and 20 min/day)of MPA and VPA performed continuously or accumulated throughout the day would equally reduce the risks of all-cause mortality as longer duration MPA and VPA recommended in the physical activity(PA)guidelines. 展开更多
关键词 intensity accelerometer mortality association risk prospective cohort analysis accelerometer data UK Biobank
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Enhancing IoT Resilience at the Edge:A Resource-Efficient Framework for Real-Time Anomaly Detection in Streaming Data 认领 引用 被引量：1
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作者 Kirubavathi G. Arjun Pulliyasseri +5 位作者 Aswathi Rajesh Amal Ajayan Sultan Alfarhood Mejdl Safran Meshal Alfarhood Jungpil Shin 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期3005-3031,共27页
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability... The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices. 展开更多
关键词 Anomaly detection streaming data IoT IIoT TMoT real-time lightweight modeling
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Extraction of effective response for controlled-source electromagnetic data based on clustering analysis 认领 引用
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作者 Cong Zhou Zhan-zi Qin +2 位作者 Liang Yang Tara P.Banjade Xiao-fei Zhou 《Applied Geophysics》 SCIE CSCD 2025年第4期1297-1312,1499,共16页
The issue of strong noise has increasingly become a bottleneck restricting the precision and application space of electromagnetic exploration methods.Noise suppression and extraction of effective electromagnetic respo... The issue of strong noise has increasingly become a bottleneck restricting the precision and application space of electromagnetic exploration methods.Noise suppression and extraction of effective electromagnetic response information under a strong noise background is a crucial scientific task to be addressed.To solve the noise suppression problem of the controlled-source electromagnetic method in strong interference areas,we propose an approach based on complex-plane 2D k-means clustering for data processing.Based on the stability of the controlled-source signal response,clustering analysis is applied to classify the spectra of different sources and noises in multiple time segments.By identifying the power spectra with controlled-source characteristics,it helps to improve the quality of the controlled-source response extraction.This paper presents the principle and workflow of the proposed algorithm,and demonstrates feasibility and effectiveness of the new algorithm through synthetic and real data examples.The results show that,compared with the conventional Robust denoising method,the clustering algorithm has a stronger suppression effect on common noise,can identify high-quality signals,and improve the preprocessing data quality of the controlledsource electromagnetic method. 展开更多
关键词 controlled-source electromagnetic method Data processing Cluster analysis Noise
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