On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th Nation...On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and ...In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.展开更多
With the comprehensive development of modern information technology,big data technology has been integrated into various industries and has become a pillar technology supporting industrial upgrading and transformation...With the comprehensive development of modern information technology,big data technology has been integrated into various industries and has become a pillar technology supporting industrial upgrading and transformation.In enterprise human resource management,big data technology also has a broad application space and important application value.To gain higher market competitiveness and comprehensively improve the quality and efficiency of human resource management,enterprises need to rely on big data technology for comprehensive reform and optimization,thereby building an efficient,fair,open,and scientific human resource management model.This paper analyzes the problems and changes of enterprise human resource management in the era of big data,and then puts forward effective strategies for enterprise human resource management based on the era of big data.展开更多
In the era of big data,data has gradually become an important asset of enterprises,and the application of big data technology has gradually become the key to the optimization of enterprise marketing management mode.En...In the era of big data,data has gradually become an important asset of enterprises,and the application of big data technology has gradually become the key to the optimization of enterprise marketing management mode.Enterprises take the initiative to meet the development trend of the times,rely on big data technology to effectively process and analyze data,innovate decision-making methods and operation models,and achieve efficient marketing and fine management,which is an important way to improve their market competitiveness.Therefore,the author first analyzes the empowering role of big data technology on enterprise marketing management,and then discusses the difficulties faced by enterprise marketing management in the era of big data,and finally puts forward targeted improvement strategies,aiming to provide a reference for enterprises to innovate and change the marketing management mode.展开更多
With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the c...With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support.展开更多
We propose a Cross-Chain Mapping Blockchain(CCMB)for scalable data management in massive Internet of Things(IoT)networks.Specifically,CCMB aims to improve the scalability of securely storing,tracing,and transmitting I...We propose a Cross-Chain Mapping Blockchain(CCMB)for scalable data management in massive Internet of Things(IoT)networks.Specifically,CCMB aims to improve the scalability of securely storing,tracing,and transmitting IoT behavior and reputation data based on our proposed cross-mapped Behavior Chain(BChain)and Reputation Chain(RChain).To improve off-chain IoT data storage scalability,we show that our lightweight CCMB architecture efficiently utilizes available fog-cloud resources.The scalability of on-chain IoT data tracing is enhanced using our Mapping Smart Contract(MSC)and cross-chain mapping design to perform rapid Reputation-to-Behavior(R2B)traceability queries between BChain and RChain blocks.To maximize off-chain to on-chain throughput,we optimize the CCMB block settings and producers based on a general Poisson Point Process(PPP)network model.The constrained optimization problem is formulated as a Markov Decision Process(MDP),and solved using a dual-network Deep Reinforcement Learning(DRL)algorithm.Simulation results validate CCMB’s scalability advantages in storage,traceability,and throughput.In specific massive IoT scenarios,CCMB can reduce the storage footprint by 50%and traceability query time by 90%,while improving system throughput by 55%compared to existing benchmarks.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
BACKGROUND: Targeted temperature management(TTM) is a common therapeutic intervention, yet its cost-effectiveness remains uncertain. This study aimed to evaluate the real-world cost-effectiveness of TTM compared with ...BACKGROUND: Targeted temperature management(TTM) is a common therapeutic intervention, yet its cost-effectiveness remains uncertain. This study aimed to evaluate the real-world cost-effectiveness of TTM compared with that of conventional care in adult out-of-hospital cardiac arrest(OHCA) survivors using clinical patient-level data.METHODS: We conducted a retrospective cohort study at an academic medical center in the USA to assess the cost-effectiveness of TTM in adult non-traumatic OHCA survivors between 1 January, 2019 and 30 June, 2023. The primary outcome was survival to hospital discharge. Incremental cost-effectiveness ratios(ICERs) were calculated and compared with various decision makers' willingness to pay. Cost-effectiveness acceptability curves were utilized to evaluate the economic attractiveness of TTM. Uncertainty about the incremental cost and effect was explored with a 95% confidence ellipse.RESULTS: Among 925 non-traumatic OHCA survivors, only 30(3%) received TTM. After adjusting for potential confounders, the TTM group did not demonstrate a significantly lower cost(delta cost-$5,141, 95% confidence interval [95% CI]: $-35,347 to $25,065, P=0.79) and higher survival to hospital discharge(delta effect 6%, 95% CI:-11% to 23%, P=0.41). Additionally, a 95% confidence ellipse indicated uncertainty reflected by evidence that the true value of the ICER could be in any of the quadrants of the cost-effectiveness plane.CONCLUSION: Although TTM did not demonstrate a clear survival benefit in this study, its potential cost-effectiveness warrants further investigation with larger sample sizes. These findings highlight the need for additional research to optimize TTM use in OHCA care and inform resource allocation decisions.展开更多
With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of sof...With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of software-defined network(SDN)technology has brought revolutionary changes to modern network management.This paper aims to discuss the application and prospects of SDN technology in modern network management.Firstly,the basic principle and architecture of SDN are introduced,including the separation of control plane and data plane,centralized control and open programmable interface.Then,it analyzes the advantages of SDN technology in network management,such as simplifying network configuration,improving network flexibility,optimizing network resource utilization,and realizing fast fault recovery.The application examples of SDN in data center networks and WAN optimization management are analyzed.This paper also discusses the development status and trend of SDN in enterprise networks,including the integration of technologies such as cloud computing,big data,and artificial intelligence,the construction of an intelligent and automated network management platform,the improvement of network management efficiency and quality,and the openness and interoperability of network equipment.Finally,the advantages and challenges of SDN technology are summarized,and its future development direction is provided.展开更多
The Internet of Things(IoT)technology provides new impetus for the development of building intelligence.This research focuses on the intelligent design and management of buildings based on IoT engineering.It expounds ...The Internet of Things(IoT)technology provides new impetus for the development of building intelligence.This research focuses on the intelligent design and management of buildings based on IoT engineering.It expounds on the system design principles such as sensor technology,communication network technology,and data storage and analysis,and analyzes the key points of design,including design requirement analysis,equipment layout,and system integration.Through specific cases,it demonstrates the application practice of the system in buildings,and presents the application effect of intelligent system management with multi-parameter values,providing theoretical and practical references for the development of building intelligence and helping to achieve efficient,energy-saving,and safe building operation.展开更多
Purpose-The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail(HSR)catenary system.Traditional management methods struggle with challenges such as poor i...Purpose-The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail(HSR)catenary system.Traditional management methods struggle with challenges such as poor information sharing,disconnected business applications and insufficient intelligence throughout the lifecycle.This study aims to address these issues by applying building information modeling(BIM)technology to improve lifecycle management efficiency for HSR catenary systems.Design/methodology/approach-Based on the lifecycle management needs of catenary engineering,incorporating the intelligent HSR“Model-Data Driven,Axis-Plane Coordination”philosophy,this paper constructs a BIM-based lifecycle management framework for HSR catenary engineering.Findings-This study investigates the full-process lifecycle management of the catenary system across various stages of design,manufacture,construction and operation,exploring integrated BIM models and data transmission methods,along with key technologies for BIM model transmission,transformation and lightweighting.Originality/value-This study establishes a lossless information circulation and transmission system for HSR catenary lifecycle management.Multi-stage applications are verified through the construction of the Chongqing-Kunming High-Speed Railway,comprehensive advancing the intelligent promotion and highquality development of catenary engineering.展开更多
In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf ava...In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.展开更多
As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage p...As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.展开更多
Concern for individual perception is essential to enhance greenspace management.Various landscape elements are key factors affecting visitors’perception engaging in greenspaces.Targeting Belgian public greenspaces,we...Concern for individual perception is essential to enhance greenspace management.Various landscape elements are key factors affecting visitors’perception engaging in greenspaces.Targeting Belgian public greenspaces,we develop a comprehensive approach to quantify visitors’perceptions from multiple dimensions.Applying user-generated data and unsupervised machine learning approach,we identified the landscape elements and classified the greenspaces to extract perception rates and detect dominant elements.The satisfaction of every landscape element was then analyzed by the natural language process approach and standardized major axis regression to discover their contributions to overall satisfaction.Furthermore,we calculated and visualized the positive and negative interactions between elements through network analysis.Integrating the perception rates and contributions,inconsistency was observed between the dominant element and the most contributing element.The perception rate of the human element was in an overwhelmingly dominant position,with 2.46.Despite the variations among the 5 greenspace groups,multiple natural elements highly contributed to overall satisfaction,especially animal and vegetation,which achieved contributions higher than 1.2 in most of the groups.Regarding the interactions,stronger negative interactions appeared generally,reaching up to 0.496.The coexistence of natural and artificial elements has a stronger collective effect on greenspace perception,regardless of positive or negative interaction.By providing an understanding of the landscape elements,our findings can assist greenspace planners in identifying key factors of different greenspace categories from various perspectives and support explicit and effective greenspace management.展开更多
That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through...That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.展开更多
With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterp...With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.展开更多
China's marine data includes marine hydrology,marine meteorology,marine biology,marine chemistry,marine substrate,marine geophysical,seabed topography and other categories of data,the total amount of data reaches ...China's marine data includes marine hydrology,marine meteorology,marine biology,marine chemistry,marine substrate,marine geophysical,seabed topography and other categories of data,the total amount of data reaches the magnitude of PB,and the amount of data is still increasing.The safe management of these marine data storage is the basis of building a Smart Ocean.This paper discusses the current situation of security management of marine data storage in China,analyzes the problems of security management in domestic marine data storage,and puts forward suggestions.展开更多
With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspe...With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.展开更多
This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combin...This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.展开更多
文摘On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金supported by:the 2023 Basic Public Welfare Research Project of the Wenzhou Science and Technology Bureau“Research on Multi-Source Data Classification and Grading Standards and Intelligent Algorithms for Higher Education Institutions”(Project No.G2023094)Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions(Grant/Award Number:2024QN061)2023 Basic Public Welfare Research Project of Wenzhou(No.:S2023014).
文摘In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.
文摘With the comprehensive development of modern information technology,big data technology has been integrated into various industries and has become a pillar technology supporting industrial upgrading and transformation.In enterprise human resource management,big data technology also has a broad application space and important application value.To gain higher market competitiveness and comprehensively improve the quality and efficiency of human resource management,enterprises need to rely on big data technology for comprehensive reform and optimization,thereby building an efficient,fair,open,and scientific human resource management model.This paper analyzes the problems and changes of enterprise human resource management in the era of big data,and then puts forward effective strategies for enterprise human resource management based on the era of big data.
文摘In the era of big data,data has gradually become an important asset of enterprises,and the application of big data technology has gradually become the key to the optimization of enterprise marketing management mode.Enterprises take the initiative to meet the development trend of the times,rely on big data technology to effectively process and analyze data,innovate decision-making methods and operation models,and achieve efficient marketing and fine management,which is an important way to improve their market competitiveness.Therefore,the author first analyzes the empowering role of big data technology on enterprise marketing management,and then discusses the difficulties faced by enterprise marketing management in the era of big data,and finally puts forward targeted improvement strategies,aiming to provide a reference for enterprises to innovate and change the marketing management mode.
文摘With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support.
基金supported in part by the National Key Research and Development Program of China under Grant 2023YFB3106900the National Natural Science Foundation of China under Grant 62171113the China Scholarship Council under Grant 202406080100.
文摘We propose a Cross-Chain Mapping Blockchain(CCMB)for scalable data management in massive Internet of Things(IoT)networks.Specifically,CCMB aims to improve the scalability of securely storing,tracing,and transmitting IoT behavior and reputation data based on our proposed cross-mapped Behavior Chain(BChain)and Reputation Chain(RChain).To improve off-chain IoT data storage scalability,we show that our lightweight CCMB architecture efficiently utilizes available fog-cloud resources.The scalability of on-chain IoT data tracing is enhanced using our Mapping Smart Contract(MSC)and cross-chain mapping design to perform rapid Reputation-to-Behavior(R2B)traceability queries between BChain and RChain blocks.To maximize off-chain to on-chain throughput,we optimize the CCMB block settings and producers based on a general Poisson Point Process(PPP)network model.The constrained optimization problem is formulated as a Markov Decision Process(MDP),and solved using a dual-network Deep Reinforcement Learning(DRL)algorithm.Simulation results validate CCMB’s scalability advantages in storage,traceability,and throughput.In specific massive IoT scenarios,CCMB can reduce the storage footprint by 50%and traceability query time by 90%,while improving system throughput by 55%compared to existing benchmarks.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金supported by Faculty of MedicineChiang Mai University+2 种基金supported by the National Center for Advancing Translational SciencesNational Institutes of Healththrough grant number UL1 TR001860. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH。
文摘BACKGROUND: Targeted temperature management(TTM) is a common therapeutic intervention, yet its cost-effectiveness remains uncertain. This study aimed to evaluate the real-world cost-effectiveness of TTM compared with that of conventional care in adult out-of-hospital cardiac arrest(OHCA) survivors using clinical patient-level data.METHODS: We conducted a retrospective cohort study at an academic medical center in the USA to assess the cost-effectiveness of TTM in adult non-traumatic OHCA survivors between 1 January, 2019 and 30 June, 2023. The primary outcome was survival to hospital discharge. Incremental cost-effectiveness ratios(ICERs) were calculated and compared with various decision makers' willingness to pay. Cost-effectiveness acceptability curves were utilized to evaluate the economic attractiveness of TTM. Uncertainty about the incremental cost and effect was explored with a 95% confidence ellipse.RESULTS: Among 925 non-traumatic OHCA survivors, only 30(3%) received TTM. After adjusting for potential confounders, the TTM group did not demonstrate a significantly lower cost(delta cost-$5,141, 95% confidence interval [95% CI]: $-35,347 to $25,065, P=0.79) and higher survival to hospital discharge(delta effect 6%, 95% CI:-11% to 23%, P=0.41). Additionally, a 95% confidence ellipse indicated uncertainty reflected by evidence that the true value of the ICER could be in any of the quadrants of the cost-effectiveness plane.CONCLUSION: Although TTM did not demonstrate a clear survival benefit in this study, its potential cost-effectiveness warrants further investigation with larger sample sizes. These findings highlight the need for additional research to optimize TTM use in OHCA care and inform resource allocation decisions.
文摘With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of software-defined network(SDN)technology has brought revolutionary changes to modern network management.This paper aims to discuss the application and prospects of SDN technology in modern network management.Firstly,the basic principle and architecture of SDN are introduced,including the separation of control plane and data plane,centralized control and open programmable interface.Then,it analyzes the advantages of SDN technology in network management,such as simplifying network configuration,improving network flexibility,optimizing network resource utilization,and realizing fast fault recovery.The application examples of SDN in data center networks and WAN optimization management are analyzed.This paper also discusses the development status and trend of SDN in enterprise networks,including the integration of technologies such as cloud computing,big data,and artificial intelligence,the construction of an intelligent and automated network management platform,the improvement of network management efficiency and quality,and the openness and interoperability of network equipment.Finally,the advantages and challenges of SDN technology are summarized,and its future development direction is provided.
文摘The Internet of Things(IoT)technology provides new impetus for the development of building intelligence.This research focuses on the intelligent design and management of buildings based on IoT engineering.It expounds on the system design principles such as sensor technology,communication network technology,and data storage and analysis,and analyzes the key points of design,including design requirement analysis,equipment layout,and system integration.Through specific cases,it demonstrates the application practice of the system in buildings,and presents the application effect of intelligent system management with multi-parameter values,providing theoretical and practical references for the development of building intelligence and helping to achieve efficient,energy-saving,and safe building operation.
基金supported by China Academy of Railway Sciences Foundation(Research on Multi Agent Collaborative Mechanism of Intelligent High Speed Rail System Based on Complex Adaptive System Theory,Grant 2023YJ392).
文摘Purpose-The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail(HSR)catenary system.Traditional management methods struggle with challenges such as poor information sharing,disconnected business applications and insufficient intelligence throughout the lifecycle.This study aims to address these issues by applying building information modeling(BIM)technology to improve lifecycle management efficiency for HSR catenary systems.Design/methodology/approach-Based on the lifecycle management needs of catenary engineering,incorporating the intelligent HSR“Model-Data Driven,Axis-Plane Coordination”philosophy,this paper constructs a BIM-based lifecycle management framework for HSR catenary engineering.Findings-This study investigates the full-process lifecycle management of the catenary system across various stages of design,manufacture,construction and operation,exploring integrated BIM models and data transmission methods,along with key technologies for BIM model transmission,transformation and lightweighting.Originality/value-This study establishes a lossless information circulation and transmission system for HSR catenary lifecycle management.Multi-stage applications are verified through the construction of the Chongqing-Kunming High-Speed Railway,comprehensive advancing the intelligent promotion and highquality development of catenary engineering.
基金partially supported by the Foundation of State Key Laboratory of Public Big Data(No.PBD2022-01).
文摘In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22,MCIN/AEI/10.13039/501100011033by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.
基金funded by the China Scholarship Council(grant number:202004910422)
文摘Concern for individual perception is essential to enhance greenspace management.Various landscape elements are key factors affecting visitors’perception engaging in greenspaces.Targeting Belgian public greenspaces,we develop a comprehensive approach to quantify visitors’perceptions from multiple dimensions.Applying user-generated data and unsupervised machine learning approach,we identified the landscape elements and classified the greenspaces to extract perception rates and detect dominant elements.The satisfaction of every landscape element was then analyzed by the natural language process approach and standardized major axis regression to discover their contributions to overall satisfaction.Furthermore,we calculated and visualized the positive and negative interactions between elements through network analysis.Integrating the perception rates and contributions,inconsistency was observed between the dominant element and the most contributing element.The perception rate of the human element was in an overwhelmingly dominant position,with 2.46.Despite the variations among the 5 greenspace groups,multiple natural elements highly contributed to overall satisfaction,especially animal and vegetation,which achieved contributions higher than 1.2 in most of the groups.Regarding the interactions,stronger negative interactions appeared generally,reaching up to 0.496.The coexistence of natural and artificial elements has a stronger collective effect on greenspace perception,regardless of positive or negative interaction.By providing an understanding of the landscape elements,our findings can assist greenspace planners in identifying key factors of different greenspace categories from various perspectives and support explicit and effective greenspace management.
文摘That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.
文摘With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.
文摘China's marine data includes marine hydrology,marine meteorology,marine biology,marine chemistry,marine substrate,marine geophysical,seabed topography and other categories of data,the total amount of data reaches the magnitude of PB,and the amount of data is still increasing.The safe management of these marine data storage is the basis of building a Smart Ocean.This paper discusses the current situation of security management of marine data storage in China,analyzes the problems of security management in domestic marine data storage,and puts forward suggestions.
文摘With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.
文摘This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.
