This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of conte...This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of context-aware service during preparation,organization and delivery,as well as the resulting changes in service acceptance of consumers.Because of these changes,the context-aware service modes in the cloud computing environment change are intelligent,immersive,highly interactive,and real-time.According to active and responded service,and authorization and non-authorized service,the paper drew a case diagram of context-aware service in Unified Modeling Language(UML) and established four categories of context-aware service modes.展开更多
A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on su...A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study,we aim to incorporate reinforcement algorithms(Q-learning)into a context-aware system to provide relevant services based on a user’s dynamic context.To accelerate the convergence of reinforcement learning(RL)algorithms and provide the correct services in real situations,we propose a combination of the Q-learning and case-based reasoning(CBR)algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more effi-cient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.展开更多
Requirements of software systems tend to change over time. The speed of this tendency depends on the application domain the software system under consideration belongs to. If we consider novel contexts such as pervasi...Requirements of software systems tend to change over time. The speed of this tendency depends on the application domain the software system under consideration belongs to. If we consider novel contexts such as pervasive systems and systems supporting dynamic B2B interaction, requirements change so fast that the research community is studying how to build systems that are able to self-adapt on the fly to some of these changes. When this happens, the system does not need to undergo through a new development cycle thus increasing its availability and, to a certain extent, its robustness. So far, the research in the area of self-adaptive systems has been focusing on the definition of the mechanisms for supporting self-adaptation. We argue that what is missing now is a structured and robust design process associated to these mechanisms. This design process should include a Requirement Engineering (RE) phase that somewhat differs from the traditional one. However, the identification of requirements for adaptation requires a good knowledge of the context in which the system will be executed. In this work, we consider the modeling of such context as part of the RE phase and we particularly focus on Service-Based Applications (SBAs). We argue that RE activities should be supported at run-time to handle context changes and to support adaptation for SBAs. We survey the state of the art for what concerns the elicitation, modeling, and analysis of requirements and will highlight some issues and challenges in order to support adaptation for SBAs.展开更多
Robust cooperative unmanned aerial vehicle(UAV)formation in complex 3D environments is hampered by reward sparsity and inefficient collaboration.To address this,we propose context-aware relational agent learning(CORAL...Robust cooperative unmanned aerial vehicle(UAV)formation in complex 3D environments is hampered by reward sparsity and inefficient collaboration.To address this,we propose context-aware relational agent learning(CORAL),a novel multi-agent deep reinforcement learning framework.CORAL synergistically integrates two modules:(1)a novelty-based intrinsic reward module to drive efficient exploration and(2)an explicit relational learning module that allows agents to predict peer intentions and enhance coordination.Built on a multi-agent Actor-Critic architecture,CORAL enables agents to balance self-interest with group objectives.Comprehensive evaluations in a high-fidelity simulation show that our method significantly outperforms state-of-theart baselines like multi-agent deep deterministic policy gradient(MADDPG)and monotonic value function factorisation for deep multi-agent reinforcement learning(QMIX)in path planning efficiency,collision avoidance,and scalability.展开更多
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.展开更多
Saud Khan,a Pakistani physician,collected his work and residence permits at the Yangpu Government Service Centre in Danzhou City,Hainan Province,on 18 December 2025.Khan had arrived in Hainan three months earlier to p...Saud Khan,a Pakistani physician,collected his work and residence permits at the Yangpu Government Service Centre in Danzhou City,Hainan Province,on 18 December 2025.Khan had arrived in Hainan three months earlier to participate in a medical training programme at Hainan Western Central Hospital.By the time the programme concluded,he had made up his mind to stay and broaden his professional experience and long-term career prospects in Hainan.展开更多
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting s...Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.展开更多
The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despit...The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.展开更多
Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for so...Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.展开更多
Objective:To systematically summarize and evaluate the evidence on discharge preparation services for patients undergoing total knee arthroplasty,providing an evidence-based foundation for developing scientific and st...Objective:To systematically summarize and evaluate the evidence on discharge preparation services for patients undergoing total knee arthroplasty,providing an evidence-based foundation for developing scientific and standardized discharge preparation intervention programs in clinical practice.Methods:Following the“5S”evidence model,literature such as guidelines,expert consensuses,evidence summaries and randomized controlled trials related to discharge preparation services for total knee arthroplasty patients were retrieved from relevant websites and databases,both domestic and international,from database inception to August 31,2025.Two researchers independently screened the literature,conducted quality appraisals,and extracted and synthesized the evidence.Results:A total of 15 articles were included,comprising 3 guidelines,4 expert consensuses,3 evidence summaries,3 systematic reviews and 2 randomized controlled trials.Ultimately,23 pieces of evidence were summarized across five aspects.Conclusion:This study synthesizes the evidence on discharge preparation services for patients undergoing total knee arthroplasty.It is recommended that healthcare professionals apply this evidence in clinical practice,considering specific circumstances and patient needs.展开更多
With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends...With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends to develop a scientific and rational evaluation methodology and framework for assessing service quality in civil aviation airports,thereby providing a theoretical foundation and practical guidance for enhancing service standards in the aviation industry.First,the study constructs a CRITIC-bidirectional grey possibility clustering model,which uses the CRITIC method to determine the weights of indicators and integrates the forward grey possibility clustering model and the inverse grey possibility clustering model to determine possibility functions from two perspectives.Second,a service quality evaluation index system for civil airports is constructed from four dimensions,and the weights of each index within the system are subsequently calculated.Finally,the constructed model is applied to evaluate the service quality of nine domestic civil airports.Based on the clustering results,targeted countermeasures and suggestions are proposed.Empirical results demonstrate that,compared to the traditional grey possibility clustering model,the proposed model balances the objectivity of indicator weighting,the objectivity of possibility function construction,and the simplicity of the computational process,thereby possessing significant theoretical and practical implications.展开更多
Understanding the scale-dependent dynamics of ecosystem services(ESs)and their socio-ecological drivers is essential for sustainable development.While many studies rely on static or single-scale approaches,this resear...Understanding the scale-dependent dynamics of ecosystem services(ESs)and their socio-ecological drivers is essential for sustainable development.While many studies rely on static or single-scale approaches,this research employs an integrated multi-temporal(2000–2020)and multi-scale(grid,county,and landscape levels)framework to investigate China’s Central Asian frontier,a representative dryland region.We quantified six ESs:habitat quality(HQ),net primary productivity(NPP),carbon sequestration(CS),water yield(WY),soil conservation(SC),and grain production(GP).Furthermore,we explored their interrelationships and identified the drivers influencing these services across different spatial scales.Our results revealed divergent ES trajectories:the declining HQ(−0.03 a^(−1)),NPP(−0.43 t km^(−2)a^(−1)),and SC(−3.41 t ha a^(−1))contrasted with rising WY(+2.33 mm a^(−1)),GP(+0.06 t km^(−2)a^(−1)),and CS(+0.02 t km^(−2)a^(−1)).The ES relationships were predominantly synergistic,while HQ–WY exhibited a trade-off(grid:−0.03;county:−0.02;landscape:−0.03)at temporal dimension but a synergistic relationship(grid:0.45;county:0.92;landscape:0.92)at spatial dimension.As spatial scale increased,SC–CS shifted from synergy(grid:0.001)to trade-off(county:−0.01;landscape:−0.005)in the temporal dimension,while all trade-off relationships in the spatial dimension were transformed into synergies.Key drivers of ES relationships varied with spatial scale:fraction vegetation coverage(FVC)and leaf area index(LAI)at the grid scale,annual precipitation(MAP)and soil moisture(SMA)at the county scale,and population density(POP),gross domestic product(GDP),and silt content(Silt)at the landscape scale.Based on the multi-scale findings,the study divides northern Xinjiang into Grain Priority Region,Ecological Priority Region,and Desert Containment Region,and proposes tailored management recommendations,offering a flexible framework for balancing ecological and socioeconomic needs.展开更多
Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions....Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.展开更多
The Guangdong,Jiangxi and Fujian(GJF)provinces,located in the subtropical region of southeastern China,is one of the national key regions for soil erosion control and ecological restoration.This region is characterize...The Guangdong,Jiangxi and Fujian(GJF)provinces,located in the subtropical region of southeastern China,is one of the national key regions for soil erosion control and ecological restoration.This region is characterized by extensive red soil development and high rainfall erosivity,making it a representative landscape for exploring the interactions between land use change(LUC)and ecosystem services(ES).Despite the recognized importance of ES in hilly regions,comprehensive assessing the impacts of LUC on ES remain limited.This study investigates five key ES:water yield,soil conservation,carbon conservation,food supply,and habitat quality in GJF region from 2000 to 2020.By applying the InVEST model and the Geodetector method,we assessed the trade-offs,synergies,and transitions among ES,identified the natural and social drivers of ES dynamics,and quantified the contribution of LUC to ES changes using the ecosystem service contribution index.The results showed that cropland and woodland were the dominant land use types.Ecological restoration efforts positively influenced ES,with synergies intensifying and trade-offs diminishing over time.Land use conversions,particularly among woodland,grassland,and cropland,exerted significant impacts on ES.In particular,the conversion of woodland to other land uses had markedly negative effects on soil conservation,carbon conservation,and habitat quality.Forest cover was identified as a major driver of ES dynamics.These findings highlight the importance of maintaining and expanding forest and grassland cover,strengthening red soil conservation,and optimizing land use structure to achieve coordinated ecological protection and socioeconomic development in the subtropical hilly regions of southern China.展开更多
A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However...A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.展开更多
Hebei Province has incorporated targeted assistance services for people with disabilities into livelihood projects,upgrading the quality and efficiency of support services for disadvantaged groups.THE living and nursi...Hebei Province has incorporated targeted assistance services for people with disabilities into livelihood projects,upgrading the quality and efficiency of support services for disadvantaged groups.THE living and nursing allowances provided by the Chinese government for people with disabilities who are unable to work are not only important components of China’s social security system which provide for the needs of its disabled,but also show China’s ability to guarantee the basic living standard and social fairness and justice for this group of people.展开更多
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
基金the National Key Basic Research Program of China，the National Natural Science Foundation of China，the Ministry of Education of the People's Republic of China，the Fundamental Research Funds for the Central Universities of China
文摘This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of context-aware service during preparation,organization and delivery,as well as the resulting changes in service acceptance of consumers.Because of these changes,the context-aware service modes in the cloud computing environment change are intelligent,immersive,highly interactive,and real-time.According to active and responded service,and authorization and non-authorized service,the paper drew a case diagram of context-aware service in Unified Modeling Language(UML) and established four categories of context-aware service modes.
文摘A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study,we aim to incorporate reinforcement algorithms(Q-learning)into a context-aware system to provide relevant services based on a user’s dynamic context.To accelerate the convergence of reinforcement learning(RL)algorithms and provide the correct services in real situations,we propose a combination of the Q-learning and case-based reasoning(CBR)algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more effi-cient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.
文摘Requirements of software systems tend to change over time. The speed of this tendency depends on the application domain the software system under consideration belongs to. If we consider novel contexts such as pervasive systems and systems supporting dynamic B2B interaction, requirements change so fast that the research community is studying how to build systems that are able to self-adapt on the fly to some of these changes. When this happens, the system does not need to undergo through a new development cycle thus increasing its availability and, to a certain extent, its robustness. So far, the research in the area of self-adaptive systems has been focusing on the definition of the mechanisms for supporting self-adaptation. We argue that what is missing now is a structured and robust design process associated to these mechanisms. This design process should include a Requirement Engineering (RE) phase that somewhat differs from the traditional one. However, the identification of requirements for adaptation requires a good knowledge of the context in which the system will be executed. In this work, we consider the modeling of such context as part of the RE phase and we particularly focus on Service-Based Applications (SBAs). We argue that RE activities should be supported at run-time to handle context changes and to support adaptation for SBAs. We survey the state of the art for what concerns the elicitation, modeling, and analysis of requirements and will highlight some issues and challenges in order to support adaptation for SBAs.
基金supported by the STI 2030 Major Projects(No.2022ZD0208804)the National Natural Science Foundation of China(No.62473017)。
文摘Robust cooperative unmanned aerial vehicle(UAV)formation in complex 3D environments is hampered by reward sparsity and inefficient collaboration.To address this,we propose context-aware relational agent learning(CORAL),a novel multi-agent deep reinforcement learning framework.CORAL synergistically integrates two modules:(1)a novelty-based intrinsic reward module to drive efficient exploration and(2)an explicit relational learning module that allows agents to predict peer intentions and enhance coordination.Built on a multi-agent Actor-Critic architecture,CORAL enables agents to balance self-interest with group objectives.Comprehensive evaluations in a high-fidelity simulation show that our method significantly outperforms state-of-theart baselines like multi-agent deep deterministic policy gradient(MADDPG)and monotonic value function factorisation for deep multi-agent reinforcement learning(QMIX)in path planning efficiency,collision avoidance,and scalability.
基金supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
文摘Saud Khan,a Pakistani physician,collected his work and residence permits at the Yangpu Government Service Centre in Danzhou City,Hainan Province,on 18 December 2025.Khan had arrived in Hainan three months earlier to participate in a medical training programme at Hainan Western Central Hospital.By the time the programme concluded,he had made up his mind to stay and broaden his professional experience and long-term career prospects in Hainan.
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
基金supported by the National Key Research and Development Program of China under grant 2022YFF0902701the National Natural Science Foundation of China under grant U21A20468,61972043,61921003+1 种基金Zhejiang Lab under grant 2021PD0AB 02the Fundamental Research Funds for the Central Universities under grant 2020XD-A07-1.
文摘Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.
文摘The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.
文摘Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.
文摘Objective:To systematically summarize and evaluate the evidence on discharge preparation services for patients undergoing total knee arthroplasty,providing an evidence-based foundation for developing scientific and standardized discharge preparation intervention programs in clinical practice.Methods:Following the“5S”evidence model,literature such as guidelines,expert consensuses,evidence summaries and randomized controlled trials related to discharge preparation services for total knee arthroplasty patients were retrieved from relevant websites and databases,both domestic and international,from database inception to August 31,2025.Two researchers independently screened the literature,conducted quality appraisals,and extracted and synthesized the evidence.Results:A total of 15 articles were included,comprising 3 guidelines,4 expert consensuses,3 evidence summaries,3 systematic reviews and 2 randomized controlled trials.Ultimately,23 pieces of evidence were summarized across five aspects.Conclusion:This study synthesizes the evidence on discharge preparation services for patients undergoing total knee arthroplasty.It is recommended that healthcare professionals apply this evidence in clinical practice,considering specific circumstances and patient needs.
基金support supplied by the National Natural Science Foundation of China(Nos.72571136,72271120)the Ministry of Education of the People’s Republic of China Humanities and Social Science project(No.24YJA630087)。
文摘With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends to develop a scientific and rational evaluation methodology and framework for assessing service quality in civil aviation airports,thereby providing a theoretical foundation and practical guidance for enhancing service standards in the aviation industry.First,the study constructs a CRITIC-bidirectional grey possibility clustering model,which uses the CRITIC method to determine the weights of indicators and integrates the forward grey possibility clustering model and the inverse grey possibility clustering model to determine possibility functions from two perspectives.Second,a service quality evaluation index system for civil airports is constructed from four dimensions,and the weights of each index within the system are subsequently calculated.Finally,the constructed model is applied to evaluate the service quality of nine domestic civil airports.Based on the clustering results,targeted countermeasures and suggestions are proposed.Empirical results demonstrate that,compared to the traditional grey possibility clustering model,the proposed model balances the objectivity of indicator weighting,the objectivity of possibility function construction,and the simplicity of the computational process,thereby possessing significant theoretical and practical implications.
基金National Natural Science Foundation of China,No.42377302Ministry of Science and Technology of the People’s Republic of China,No.2022XJKK0904State Key Laboratory of Soil and Sustainable Agriculture,No.SKLSSA25K03。
文摘Understanding the scale-dependent dynamics of ecosystem services(ESs)and their socio-ecological drivers is essential for sustainable development.While many studies rely on static or single-scale approaches,this research employs an integrated multi-temporal(2000–2020)and multi-scale(grid,county,and landscape levels)framework to investigate China’s Central Asian frontier,a representative dryland region.We quantified six ESs:habitat quality(HQ),net primary productivity(NPP),carbon sequestration(CS),water yield(WY),soil conservation(SC),and grain production(GP).Furthermore,we explored their interrelationships and identified the drivers influencing these services across different spatial scales.Our results revealed divergent ES trajectories:the declining HQ(−0.03 a^(−1)),NPP(−0.43 t km^(−2)a^(−1)),and SC(−3.41 t ha a^(−1))contrasted with rising WY(+2.33 mm a^(−1)),GP(+0.06 t km^(−2)a^(−1)),and CS(+0.02 t km^(−2)a^(−1)).The ES relationships were predominantly synergistic,while HQ–WY exhibited a trade-off(grid:−0.03;county:−0.02;landscape:−0.03)at temporal dimension but a synergistic relationship(grid:0.45;county:0.92;landscape:0.92)at spatial dimension.As spatial scale increased,SC–CS shifted from synergy(grid:0.001)to trade-off(county:−0.01;landscape:−0.005)in the temporal dimension,while all trade-off relationships in the spatial dimension were transformed into synergies.Key drivers of ES relationships varied with spatial scale:fraction vegetation coverage(FVC)and leaf area index(LAI)at the grid scale,annual precipitation(MAP)and soil moisture(SMA)at the county scale,and population density(POP),gross domestic product(GDP),and silt content(Silt)at the landscape scale.Based on the multi-scale findings,the study divides northern Xinjiang into Grain Priority Region,Ecological Priority Region,and Desert Containment Region,and proposes tailored management recommendations,offering a flexible framework for balancing ecological and socioeconomic needs.
文摘Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.
基金funded by the National Natural Science Foundation of China(42377326 and 42201267)National Research-Development Support Plan Projects of China(Grant No.2017YFC05054)the Fujian Provincial Water Resources Department Science and Technology Project(MSK202308)。
文摘The Guangdong,Jiangxi and Fujian(GJF)provinces,located in the subtropical region of southeastern China,is one of the national key regions for soil erosion control and ecological restoration.This region is characterized by extensive red soil development and high rainfall erosivity,making it a representative landscape for exploring the interactions between land use change(LUC)and ecosystem services(ES).Despite the recognized importance of ES in hilly regions,comprehensive assessing the impacts of LUC on ES remain limited.This study investigates five key ES:water yield,soil conservation,carbon conservation,food supply,and habitat quality in GJF region from 2000 to 2020.By applying the InVEST model and the Geodetector method,we assessed the trade-offs,synergies,and transitions among ES,identified the natural and social drivers of ES dynamics,and quantified the contribution of LUC to ES changes using the ecosystem service contribution index.The results showed that cropland and woodland were the dominant land use types.Ecological restoration efforts positively influenced ES,with synergies intensifying and trade-offs diminishing over time.Land use conversions,particularly among woodland,grassland,and cropland,exerted significant impacts on ES.In particular,the conversion of woodland to other land uses had markedly negative effects on soil conservation,carbon conservation,and habitat quality.Forest cover was identified as a major driver of ES dynamics.These findings highlight the importance of maintaining and expanding forest and grassland cover,strengthening red soil conservation,and optimizing land use structure to achieve coordinated ecological protection and socioeconomic development in the subtropical hilly regions of southern China.
基金supported by the National Natural Science Foundation of China(42471336,52379021 and 42201278)the Hebei Province Backbone Talent Program,China(Returnee Platform for Overseas Study)(A20240028)+2 种基金the Hebei Province Statistical Science Research Project,China(2024HZ04)the Hebei Province Graduate Education and Teaching Reform Research Project,China(YJG2024046)the Innovation Ability Training Program for Postgraduate Students of Hebei Provincial Department of Education,China(CXZZSS2025048)。
文摘A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.
文摘Hebei Province has incorporated targeted assistance services for people with disabilities into livelihood projects,upgrading the quality and efficiency of support services for disadvantaged groups.THE living and nursing allowances provided by the Chinese government for people with disabilities who are unable to work are not only important components of China’s social security system which provide for the needs of its disabled,but also show China’s ability to guarantee the basic living standard and social fairness and justice for this group of people.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
