Fault tolerance(FT)schemes are intended to work on a minimized and static amount of physical resources.When a host failure occurs,the conventional FT frequently proceeds with the execution on the accessible working ho...Fault tolerance(FT)schemes are intended to work on a minimized and static amount of physical resources.When a host failure occurs,the conventional FT frequently proceeds with the execution on the accessible working hosts.This methodology saves the execution state and applications to complete without disruption.However,the dynamicity of open cloud assets is not seen when taking scheduling choices.Existing optimization techniques are intended in dealing with resource scheduling.This method will be utilized for distributing the approaching tasks to the VMs.However,the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure.The scheme prefers jobs in the activity list with the most elevated execution time on resources that can execute in a shorter timeframe,but it suffers with higher makespan;poor resource usage and unbalance load concerns.To overcome the above mentioned issue,Fault Aware Dynamic Resource Manager(FADRM)is proposed that enhances the mechanism to Multi-stage Resilience Manager at an application-level FT arrangement.Proposed FADRM method gives FT a Multi-stage Resilience Manager(MRM)in the client and application layers,and simultaneously decreases the over-head and degradations.It additionally provides safety to the application execution considering the clients,application and framework necessities.Based on experimental evaluations,Proposed Fault Aware Dynamic Resource Manager(FADRM)method 157.5 MakeSpan(MS)time,0.38 Fault Rate(FR),0.25 Failure Delay(FD)and improves 5.5 Performance Improvement Ratio(PIR)for 25,50,75 and 100 tasks and 475 MakeSpan(MS)time,0.40 Fault Rate(FR),1.30 Failure Delay(FD)and improves 6.75 improves Performance Improvement Ratio(PER)for 100,200,300 and 500 Tasks compare than existing methodologies.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 3...Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.展开更多
Enterprises are facing problems such as the dynamic matching of talents and strategies,and the construction of organizational resilience.Based on this,this paper deeply explores the significance of the research on the...Enterprises are facing problems such as the dynamic matching of talents and strategies,and the construction of organizational resilience.Based on this,this paper deeply explores the significance of the research on the“Double Helix”model of strategic human resource management in the VUCA era and the practical construction of the“Double Helix”model:the implementation path of key dimensions,aiming to achieve the coordinated progress of the two through strategies such as improving talent density,forging organizational resilience,and promoting the coordinated integration mechanism of the Double Helix,so as to provide scientific human resource management strategies for enterprises,help enterprises enhance their competitiveness in a complex and changeable environment,and achieve sustainable development.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncerta...Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand. To address these issues, this study proposes a load resource management(LRM) method to cope with the sudden power disturbances. The LRM method supports primary frequency and voltage regulation, and its integration with network dynamics minimizes the established disutility function caused by load participation. For better control performance, a non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir, reduce the frequency/voltage steady-state error, and minimize the total load control efforts. To validate the effectiveness of the proposed approach, comparative experiments were conducted with three load–resource management technologies for primary regulation auxiliary services in MATLAB/Simulink. Compared to the conventional optimal load control or using LRM alone, the improved NSGA-Ⅱ-based LRM demonstrates superior performance. It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility.展开更多
Energy security is a crucial aspect of modern societies,as it directly impacts the availability,accessibility,and reliability of energy sources.The reliance on natural resources and geopolitical factors in shaping ene...Energy security is a crucial aspect of modern societies,as it directly impacts the availability,accessibility,and reliability of energy sources.The reliance on natural resources and geopolitical factors in shaping energy security has gained significant attention in recent years.Natural resources and geopolitical risk are examined in 38 countries at risk of geopolitical conflict between 1990 and 2021 by examining CO_(2) emissions,renewable energy consumption,and foreign direct investment as controlling variables.The long-run analysis conducted in this study focused on slope heterogeneity,Westerlund cointegration,and dynamic panel data estimation.The findings indicated that the energy security index is positively associated with various determinants,including natural resources,geopolitical risk,CO_(2) emissions,and renewable energy consumption.However,foreign direct investment was found to be negatively associated with the energy security index among the selected 38 geopolitical risk countries.The role of natural resources and geopolitical risk in energy security cannot be overlooked.Natural resources provide the raw materials for generating electricity and powering our societies,while geopolitical risks can disrupt energy supply chains and threaten stability.Achieving sustainable energy security requires a comprehensive approach that addresses both aspects of energy provision.Transitioning to renewable energy sources,improving energy efficiency,diversifying energy supplies,promoting international cooperation,and conserving natural resources are essential steps towards a more sustainable and resilient energy future.展开更多
As China’s higher education transitions from extensive expansion to intensive development,the‘New Liberal Arts’concept has emerged as a pivotal strategic direction for transforming humanities disciplines.This appro...As China’s higher education transitions from extensive expansion to intensive development,the‘New Liberal Arts’concept has emerged as a pivotal strategic direction for transforming humanities disciplines.This approach emphasizes interdisciplinary integration,parallel development of theory and practice,and optimizing talent cultivation models guided by societal demands.The diversified economic structure and high-quality development trajectory of the Guangdong-Hong Kong-Macao Greater Bay Area present new demands for talent cultivation,curriculum design,and pedagogical reform within university Human Resource Management(HRM)programs.Taking Guangzhou Huashang College as the research subject,this paper analyses the core competencies required for HRM programs under the New Liberal Arts framework through policy document analysis,literature review,and field research.It identifies shortcomings in the current curriculum system regarding knowledge structure,practical pathways,and cross-disciplinary integration.Guided by Outcome-Based Education(OBE)and Competency-Based Education(CBE)frameworks,a new curriculum architecture was designed comprising four pillars:general education and humanities literacy,professional core competencies,cross-disciplinary integration,and practical innovation.Practical explorations were undertaken in areas such as university-enterprise collaboration and industry-education integration.Preliminary outcomes demonstrate that the new scheme comprehensively enhances students’data comprehension,strategic awareness,and humanistic sensitivity,providing a reference paradigm for the high-quality development of HRM programs within the new liberal arts context.展开更多
In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this neces...In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.展开更多
Morality can be defined as a series of rules-can be expressed in form of customs,beliefs and public opinions-that can be used to realign human’s thoughts,notion and behavior.In organization’s human resource manageme...Morality can be defined as a series of rules-can be expressed in form of customs,beliefs and public opinions-that can be used to realign human’s thoughts,notion and behavior.In organization’s human resource management,morality will also play important role.Type of morality in organization human resource management can be classified in different manner.Morality can be classified as mission,develop prospect,value,obligation and outlook of value and so on according to content of morality in organization human resource management,and can be classified as unconscious value,basic conscious value,and forecast conscious value,and can be classified as public morality,vocational morality,family morality and personal morality according to realigning scope of morality.Morality in organization human resource management has many characteristics,sociality,particularity,generality,and level of morality all can be deemed as characteristics of morality in organization human resource management.Function of morality in organization human resource management refers to influence and effect of morality in organization human resource management,influence and effect mainly include realign workers’thought and behavior,and realign relation among people.展开更多
The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements...The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid models.Furthermore,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G networks.Hence,6G networks rely substantially on AI methods to manage resources.This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks.Firstly,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode selection.Thereafter,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management.Finally,various open issues and potential trends related to AI-based resource management applications are presented.In summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.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
Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic...Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.展开更多
Public institutions constitute a vital component of China’s public service system,playing a significant functional role in the nation’s social development and construction.Human resource(HR)management is a vital com...Public institutions constitute a vital component of China’s public service system,playing a significant functional role in the nation’s social development and construction.Human resource(HR)management is a vital component of internal administration within these institutions.The integration of performance appraisal is crucial for enhancing the effectiveness of HR management and facilitating the smooth operation of all institutional functions.Based on this premise,this article first briefly outlines the value of applying performance appraisal in the HR management of public institutions.It then explores strategies for implementing performance appraisal within this context,aiming to provide insights for promoting the sustained and healthy development of public institutions.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless...In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.展开更多
This paper focuses on the issue of nurses’job burnout and conducts an in-depth analysis of its contributing factors from multiple dimensions,including organizational management,job characteristics,and individual attr...This paper focuses on the issue of nurses’job burnout and conducts an in-depth analysis of its contributing factors from multiple dimensions,including organizational management,job characteristics,and individual attributes.These factors include shortages in nursing human resources,lack of management support,excessive workload,and differences in coping strategies.Based on this analysis,targeted human resource management strategies are proposed,covering aspects such as optimizing human resource allocation,leadership development and organizational support,humanized management practices,and competency development with supporting systems.The aim is to alleviate nurses’job burnout,improve the quality of nursing work and nurses’professional well-being,and provide theoretical reference and practical guidance for human resource management in the nursing industry.展开更多
Amid the vigorous development of the digital economy,enterprises'innovation and entrepreneurship practices are facing unprecedented opportunities and challenges.During digital transformation,enterprises need to in...Amid the vigorous development of the digital economy,enterprises'innovation and entrepreneurship practices are facing unprecedented opportunities and challenges.During digital transformation,enterprises need to innovate human resource management(HRM)in multiple aspects[1].As a crucial link in enterprises'digital transformation,the digitalization of HRM is gradually becoming a core driving force for promoting enterprises'innovation and entrepreneurship.This paper deeply explores how the digitalization of HRM provides comprehensive support and guarantee for enterprises'innovation and entrepreneurship practices by innovating talent recruitment and selection mechanisms,optimizing talent training and development systems,improving performance management and incentive mechanisms,and constructing innovative organizational culture and team collaboration models.Combining theoretical analysis with practical cases,it reveals the important role of HRM digitalization in enhancing enterprises'innovation capabilities and stimulating employees'entrepreneurial spirit,and provides useful references for enterprises to achieve sustainable development.展开更多
Carbon dioxide is the main factor causing the greenhouse effect,and reducing carbon dioxide emissions is an important task in ecological civilization governance.The audit of outgoing officials'natural resource ass...Carbon dioxide is the main factor causing the greenhouse effect,and reducing carbon dioxide emissions is an important task in ecological civilization governance.The audit of outgoing officials'natural resource asset management is an audit of the natural resources within the jurisdiction of each province to ensure that senior officials implement policies to protect natural resources.Therefore,this paper empirically verifies the impact of audit outgoing officials'natural resource asset management on carbon emission reduction based on panel data of 297 prefecture-level cities in China from 2012 to 2021.The study finds that auditing outgoing officials'natural resource asset management can effectively reduce carbon dioxide emissions and achieve energy conservation and emission reduction.Further research finds that auditing outgoing officials'natural resource asset management can promote technological innovation and industrial structure optimization,providing a reference for the country to achieve carbon peaking and carbon neutrality goals and promote the green transformation and upgrading of social development.展开更多
Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and e...Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and exchanges model parameters to obtain an accurate model without raw data transmission.However,the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models.Moreover,the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources.In view of this,we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks(FLVN).We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles.Further,to fortify the FL reliability,we adopt the consortium blockchain to oversee the reputation informa-tion,which boasts tamper-proof and interference-resistant qualities.Finally,we formulate the resource scheduling problem by jointly optimizing the computation capability,the transmission power,and the number of local training rounds,aiming to minimize the cost of clients while guaranteeing accuracy.To this end,we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy.Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.展开更多
文摘Fault tolerance(FT)schemes are intended to work on a minimized and static amount of physical resources.When a host failure occurs,the conventional FT frequently proceeds with the execution on the accessible working hosts.This methodology saves the execution state and applications to complete without disruption.However,the dynamicity of open cloud assets is not seen when taking scheduling choices.Existing optimization techniques are intended in dealing with resource scheduling.This method will be utilized for distributing the approaching tasks to the VMs.However,the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure.The scheme prefers jobs in the activity list with the most elevated execution time on resources that can execute in a shorter timeframe,but it suffers with higher makespan;poor resource usage and unbalance load concerns.To overcome the above mentioned issue,Fault Aware Dynamic Resource Manager(FADRM)is proposed that enhances the mechanism to Multi-stage Resilience Manager at an application-level FT arrangement.Proposed FADRM method gives FT a Multi-stage Resilience Manager(MRM)in the client and application layers,and simultaneously decreases the over-head and degradations.It additionally provides safety to the application execution considering the clients,application and framework necessities.Based on experimental evaluations,Proposed Fault Aware Dynamic Resource Manager(FADRM)method 157.5 MakeSpan(MS)time,0.38 Fault Rate(FR),0.25 Failure Delay(FD)and improves 5.5 Performance Improvement Ratio(PIR)for 25,50,75 and 100 tasks and 475 MakeSpan(MS)time,0.40 Fault Rate(FR),1.30 Failure Delay(FD)and improves 6.75 improves Performance Improvement Ratio(PER)for 100,200,300 and 500 Tasks compare than existing methodologies.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
文摘Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.
基金Horizontal Topic of Suzhou Institute of Industrial Technology:Design and Optimization of Digital Salary System for Intelligent Technology Enterprises(Project No.:SIITHT2024006078)。
文摘Enterprises are facing problems such as the dynamic matching of talents and strategies,and the construction of organizational resilience.Based on this,this paper deeply explores the significance of the research on the“Double Helix”model of strategic human resource management in the VUCA era and the practical construction of the“Double Helix”model:the implementation path of key dimensions,aiming to achieve the coordinated progress of the two through strategies such as improving talent density,forging organizational resilience,and promoting the coordinated integration mechanism of the Double Helix,so as to provide scientific human resource management strategies for enterprises,help enterprises enhance their competitiveness in a complex and changeable environment,and achieve sustainable development.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
基金support of State Grid Corporation of China Project:Research on key tech-nologies of automatic generation of typical power grid operation modes and automatic calculation of section stability limits(5100-202355420A-3-2-ZN).
文摘Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand. To address these issues, this study proposes a load resource management(LRM) method to cope with the sudden power disturbances. The LRM method supports primary frequency and voltage regulation, and its integration with network dynamics minimizes the established disutility function caused by load participation. For better control performance, a non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir, reduce the frequency/voltage steady-state error, and minimize the total load control efforts. To validate the effectiveness of the proposed approach, comparative experiments were conducted with three load–resource management technologies for primary regulation auxiliary services in MATLAB/Simulink. Compared to the conventional optimal load control or using LRM alone, the improved NSGA-Ⅱ-based LRM demonstrates superior performance. It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility.
基金funded by a grant from the Interdisciplinary Research Institute in New Finance and Economics,Hubei University of Economics(No.JXZD202403).
文摘Energy security is a crucial aspect of modern societies,as it directly impacts the availability,accessibility,and reliability of energy sources.The reliance on natural resources and geopolitical factors in shaping energy security has gained significant attention in recent years.Natural resources and geopolitical risk are examined in 38 countries at risk of geopolitical conflict between 1990 and 2021 by examining CO_(2) emissions,renewable energy consumption,and foreign direct investment as controlling variables.The long-run analysis conducted in this study focused on slope heterogeneity,Westerlund cointegration,and dynamic panel data estimation.The findings indicated that the energy security index is positively associated with various determinants,including natural resources,geopolitical risk,CO_(2) emissions,and renewable energy consumption.However,foreign direct investment was found to be negatively associated with the energy security index among the selected 38 geopolitical risk countries.The role of natural resources and geopolitical risk in energy security cannot be overlooked.Natural resources provide the raw materials for generating electricity and powering our societies,while geopolitical risks can disrupt energy supply chains and threaten stability.Achieving sustainable energy security requires a comprehensive approach that addresses both aspects of energy provision.Transitioning to renewable energy sources,improving energy efficiency,diversifying energy supplies,promoting international cooperation,and conserving natural resources are essential steps towards a more sustainable and resilient energy future.
基金Guangzhou Huashang College 2024 Demonstration Major Program(Project No.:HS2024SFZY09)。
文摘As China’s higher education transitions from extensive expansion to intensive development,the‘New Liberal Arts’concept has emerged as a pivotal strategic direction for transforming humanities disciplines.This approach emphasizes interdisciplinary integration,parallel development of theory and practice,and optimizing talent cultivation models guided by societal demands.The diversified economic structure and high-quality development trajectory of the Guangdong-Hong Kong-Macao Greater Bay Area present new demands for talent cultivation,curriculum design,and pedagogical reform within university Human Resource Management(HRM)programs.Taking Guangzhou Huashang College as the research subject,this paper analyses the core competencies required for HRM programs under the New Liberal Arts framework through policy document analysis,literature review,and field research.It identifies shortcomings in the current curriculum system regarding knowledge structure,practical pathways,and cross-disciplinary integration.Guided by Outcome-Based Education(OBE)and Competency-Based Education(CBE)frameworks,a new curriculum architecture was designed comprising four pillars:general education and humanities literacy,professional core competencies,cross-disciplinary integration,and practical innovation.Practical explorations were undertaken in areas such as university-enterprise collaboration and industry-education integration.Preliminary outcomes demonstrate that the new scheme comprehensively enhances students’data comprehension,strategic awareness,and humanistic sensitivity,providing a reference paradigm for the high-quality development of HRM programs within the new liberal arts context.
基金supported by the National Key Research and Development Program of China(No.2021YFB2900504).
文摘In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.
文摘Morality can be defined as a series of rules-can be expressed in form of customs,beliefs and public opinions-that can be used to realign human’s thoughts,notion and behavior.In organization’s human resource management,morality will also play important role.Type of morality in organization human resource management can be classified in different manner.Morality can be classified as mission,develop prospect,value,obligation and outlook of value and so on according to content of morality in organization human resource management,and can be classified as unconscious value,basic conscious value,and forecast conscious value,and can be classified as public morality,vocational morality,family morality and personal morality according to realigning scope of morality.Morality in organization human resource management has many characteristics,sociality,particularity,generality,and level of morality all can be deemed as characteristics of morality in organization human resource management.Function of morality in organization human resource management refers to influence and effect of morality in organization human resource management,influence and effect mainly include realign workers’thought and behavior,and realign relation among people.
基金funded by Universiti Kebangsaan Malaysia,Fundamental Research Grant Scheme having Grant number FRGS/1/2023/ICT07/UKM/02/1Universiti Kebangsaan Malaysia Geran Universiti Penyelidikan having Grant number GUP-2024-009.
文摘The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid models.Furthermore,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G networks.Hence,6G networks rely substantially on AI methods to manage resources.This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks.Firstly,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode selection.Thereafter,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management.Finally,various open issues and potential trends related to AI-based resource management applications are presented.In summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.
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Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.
文摘Public institutions constitute a vital component of China’s public service system,playing a significant functional role in the nation’s social development and construction.Human resource(HR)management is a vital component of internal administration within these institutions.The integration of performance appraisal is crucial for enhancing the effectiveness of HR management and facilitating the smooth operation of all institutional functions.Based on this premise,this article first briefly outlines the value of applying performance appraisal in the HR management of public institutions.It then explores strategies for implementing performance appraisal within this context,aiming to provide insights for promoting the sustained and healthy development of public institutions.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
基金supported in part by the National Natural Science Foundation of China under Grant 62225103,U22B2003,U2441227,and U24A20211the Beijing Natural Science Foundation under Grant L241008+3 种基金the Defense Industrial Technology Development Program JCKY2022110C010the National Key Laboratory of Wireless Communications Foundation under Grant IFN20230201the Fundamental Research Funds for the Central Universities under Grant FRFTP-22-002C2the Xiaomi Fund of Young Scholar。
文摘In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.
基金Zhejiang Provincial Medical and Health Science and Technology Projects(Project No:2024KY1339)。
文摘This paper focuses on the issue of nurses’job burnout and conducts an in-depth analysis of its contributing factors from multiple dimensions,including organizational management,job characteristics,and individual attributes.These factors include shortages in nursing human resources,lack of management support,excessive workload,and differences in coping strategies.Based on this analysis,targeted human resource management strategies are proposed,covering aspects such as optimizing human resource allocation,leadership development and organizational support,humanized management practices,and competency development with supporting systems.The aim is to alleviate nurses’job burnout,improve the quality of nursing work and nurses’professional well-being,and provide theoretical reference and practical guidance for human resource management in the nursing industry.
基金the horizontal research project of Guangzhou College of Technology and Business entitled College Students'Entrepreneurial Intention and Realization Path under the Background of Artificial Intelligence(Project No.:KYHX2025032).
文摘Amid the vigorous development of the digital economy,enterprises'innovation and entrepreneurship practices are facing unprecedented opportunities and challenges.During digital transformation,enterprises need to innovate human resource management(HRM)in multiple aspects[1].As a crucial link in enterprises'digital transformation,the digitalization of HRM is gradually becoming a core driving force for promoting enterprises'innovation and entrepreneurship.This paper deeply explores how the digitalization of HRM provides comprehensive support and guarantee for enterprises'innovation and entrepreneurship practices by innovating talent recruitment and selection mechanisms,optimizing talent training and development systems,improving performance management and incentive mechanisms,and constructing innovative organizational culture and team collaboration models.Combining theoretical analysis with practical cases,it reveals the important role of HRM digitalization in enhancing enterprises'innovation capabilities and stimulating employees'entrepreneurial spirit,and provides useful references for enterprises to achieve sustainable development.
基金supported by the National Pre-Research Project of Hebei GEO University(Grant No.KY2024YB19)the 2023-2024 Hebei Higher Education Teaching Reform Research and Practice Project(Grant No.2023GJJG302)+4 种基金Regional System Research Center of Hebei GEO University(Grant No.QYZDYJZX202525)Smart Financial Technology Innovation Center of Hebei Province(Grant No.HBZX202401004)Hebei Geo University graduate course construction project in 2025(Grant No.YKSZ2025006)Major Research Project on Humanities and Social Sciences in Colleges and Universities of Hebei Province(Grant No.ZD202418)Teaching Case of Hebei GEO University MBA Case Center in 2025(Grant No.AL202511).
文摘Carbon dioxide is the main factor causing the greenhouse effect,and reducing carbon dioxide emissions is an important task in ecological civilization governance.The audit of outgoing officials'natural resource asset management is an audit of the natural resources within the jurisdiction of each province to ensure that senior officials implement policies to protect natural resources.Therefore,this paper empirically verifies the impact of audit outgoing officials'natural resource asset management on carbon emission reduction based on panel data of 297 prefecture-level cities in China from 2012 to 2021.The study finds that auditing outgoing officials'natural resource asset management can effectively reduce carbon dioxide emissions and achieve energy conservation and emission reduction.Further research finds that auditing outgoing officials'natural resource asset management can promote technological innovation and industrial structure optimization,providing a reference for the country to achieve carbon peaking and carbon neutrality goals and promote the green transformation and upgrading of social development.
基金supported by the National Natural Science Foundation of China (Nos.61901015,62301017).
文摘Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and exchanges model parameters to obtain an accurate model without raw data transmission.However,the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models.Moreover,the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources.In view of this,we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks(FLVN).We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles.Further,to fortify the FL reliability,we adopt the consortium blockchain to oversee the reputation informa-tion,which boasts tamper-proof and interference-resistant qualities.Finally,we formulate the resource scheduling problem by jointly optimizing the computation capability,the transmission power,and the number of local training rounds,aiming to minimize the cost of clients while guaranteeing accuracy.To this end,we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy.Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.
