Context-Aware Service IVlodes in the Cloud Computing Environment

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.

QL-CBR Hybrid Approach for Adapting Context-Aware Services

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.

Understanding Requirement Engineering for Context-Aware Service-Based Applications

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.

Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs.In addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

The primary concern of modern technology is cyber attacks targeting the Internet of Things.As it is one of the most widely used networks today and vulnerable to attacks.Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things(IoT)networks,as devices can be monitored or service isolated from them and affect users in one way or another.Securing Internet of Things networks is an important matter,as it requires the use of modern technologies and methods,and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these attacks.One of the most common types of attacks against IoT devices is Distributed Denial-of-Service(DDoS)attacks.Our paper makes a unique contribution that differs from existing studies,in that we use recent data that contains real traffic and real attacks on IoT networks.And a hybrid method for selecting relevant features,And also how to choose highly efficient algorithms.What gives the model a high ability to detect distributed denial-of-service attacks.the model proposed is based on a two-stage process:selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms logistic regression and Stochastic Gradient Descent classifier(SGD),combining these classifiers through ensemble machine learning(stacking),and optimizing parameters through Grid Search-CV to enhance system accuracy.Experiments were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 datasets.Performance evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks,achieving an accuracy of 99.965%and a detection time of 0.20 s for the CIC-IoT2023 dataset,and 99.968%accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 dataset.Furthermore,a comparative analysis with recent related works highlighted the superiority of our methodology in intrusion detection,showing improvements in accuracy,recall,and detection time.

Sharing Smart Services,Jointly Promoting Trade Development

In 2024,the China International Fair for Trade in Services (hereinafter referred to as CIFTIS) opened in the China National Convention Center and Shougang Park on September 12th.The theme of the CIFTIS this time was"global services,shared prosperity",which is not only a window for allowing the world to discover the development of China's service trade,but is also a bridge for promoting global cooperation with regards to the service trade.As one of the world-famous service trade events,the CIFTIS has become an important business card allowing China to open up.The CIFTIS has attracted the attention of the whole world and become a bridge for all parties to share development opportunities,promote industrial growth,and strengthen communication.

Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.

Residents’perceptions of ecosystem services in an urbanizing basin:A case study in the Guanting Reservoir basin,China

Understanding stakeholders’differences in perceptions of ecosystem services(ES)is crucial for guiding ecolog-ical conservation and planning.However,the variations of ES perception amongst different types of residents in urbanizing areas along an urban-rural gradient are still poorly understood.Combining a questionnaire-based survey,redundancy analysis,and statistical tests,we delineated the urban-rural gradient according to local res-idents’socio-economic characteristics,and investigated the differences in local residents’perceptions of ES and potential factors affecting them in the Guanting Reservoir basin,a rapidly urbanizing basin in China.The results showed that residents living in urban-rural transitional areas attached great importance to provisioning services of providing food and domestic water,regulating services of carbon sequestration and air purification,and cul-tural services of providing education and training,which were 0.7%-13.1%,0.7%-9.1%and 2.5%-21.2%higher than that of residents in other areas,respectively.Age and occupation were major factors affecting residents’perceptions.In terms of land-use types that deliver ES,the difference in perceptions of ES delivered by grassland was the greatest amongst residents.Our results support recommendations for policymakers to take into account the stakeholders’diverse perceptions,thus promoting residents’sense of gain on ES.

Research progress and future prospects in the service security of key blast furnace equipment

The safety and longevity of key blast furnace(BF)equipment determine the stable and low-carbon production of iron.This pa-per presents an analysis of the heat transfer characteristics of these components and the uneven distribution of cooling water in parallel pipes based on hydrodynamic principles,discusses the feasible methods for the improvement of BF cooling intensity,and reviews the pre-paration process,performance,and damage characteristics of three key equipment pieces:coolers,tuyeres,and hearth refractories.Fur-thermoere,to attain better control of these critical components under high-temperature working conditions,we propose the application of optimized technologies,such as BF operation and maintenance technology,self-repair technology,and full-lifecycle management techno-logy.Finally,we propose further researches on safety assessments and predictions for key BF equipment under new operating conditions.

Dynamical distribution of continuous service time model involving non-Maxwellian collision kernel and value functions

The distribution of continuous service time in call centers is investigated.A non-Maxwellian collision kernel combining two different value functions in the interaction rule are used to describe the evolution of continuous service time,respectively.Using the statistical mechanical and asymptotic limit methods,Fokker–Planck equations are derived from the corresponding Boltzmann-type equations with non-Maxwellian collision kernels.The steady-state solutions of the Fokker–Planck equation are obtained in exact form.Numerical experiments are provided to support our results under different parameters.

Healthcare Services Interoperability in Kenya: Challenges and Opportunities

In an attempt to assess the Kenyan healthcare system, this study looks at the current efforts that are already in place, what challenges they face, and what strategies can be put into practice to foster interoperability. By reviewing a variety of literature and using statistics, the paper ascertains notable impediments such as the absence of standard protocols, lack of adequate technological infrastructure, and weak regulatory frameworks. Resultant effects from these challenges regarding health provision target enhanced data sharing and merging for better patient outcomes and allocation of resources. It also highlights several opportunities that include the adoption of emerging technologies, and the establishment of public-private partnerships to strengthen the healthcare framework among others. In this regard, the article provides recommendations based on stakeholder views and global best practices addressed to policymakers, medical practitioners, and IT specialists concerned with achieving effective interoperability within Kenya’s health system. This research is relevant because it adds knowledge to the existing literature on how healthcare quality can be improved to make it more patient-centered especially in Kenya.

Exact Tail Asymptotics for a Queueing System with a Retrial Orbit and Batch Service

This paper discusses a queueing system with a retrial orbit and batch service, in which the quantity of customers’ rooms in the queue is finite and the space of retrial orbit is infinite. When the server starts serving, it serves all customers in the queue in a single batch, which is the so-called batch service. If a new customer or a retrial customer finds all the customers’ rooms are occupied, he will decide whether or not to join the retrial orbit. By using the censoring technique and the matrix analysis method, we first obtain the decay function of the stationary distribution for the quantity of customers in the retrial orbit and the quantity of customers in the queue. Then based on the form of decay rate function and the Karamata Tauberian theorem, we finally get the exact tail asymptotics of the stationary distribution.

Spatiotemporal variations in ecosystem services and their trade-offs and synergies against the background of the gully control and land consolidation project on the Loess Plateau,China

Studying the spatiotemporal variations in ecosystem services and their interrelationships on the Loess Plateau against the background of the gully control and land consolidation(GCLC)project has significant implications for ecological protection and quality development of the Yellow River Basin.Therefore,in this study,we took Yan'an City,Shaanxi Province of China,as the study area,selected four typical ecosystem services,including soil conservation service,water yield service,carbon storage service,and habitat quality service,and quantitatively evaluated the spatiotemporal variation characteristics and trade-offs and synergies of ecosystem services from 2010 to 2018 using the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model.We also analysed the relationship between the GCLC project and regional ecosystem service changes in various regions(including 1 city,2 districts,and 10 counties)of Yan'an City and proposed a coordinated development strategy between the GCLC project and the ecological environment.The results showed that,from 2010 to 2018,soil conservation service decreased by 7.76%,while the other three ecosystem services changed relatively little,with water yield service increasing by 0.56% and carbon storage service and habitat quality service decreasing by 0.16% and 0.14%,respectively.The ecological environment of Yan'an City developed in a balanced way between 2010 and 2018,and the four ecosystem services showed synergistic relationships,among which the synergistic relationships between soil conservation service and water yield service and between carbon storage service and habitat quality service were significant.The GCLC project had a negative impact on the ecosystem services of Yan'an City,and the impact on carbon storage service was more significant.This study provides a theoretical basis for the scientific evaluation of the ecological benefits of the GCLC project and the realization of a win-win situation between food security and ecological security.

Exploring the Spatiotemporal Changes and Driving Forces of Ecosystem Services of Zhejiang Coasts,China,Under Sustainable Development Goals

Ecosystem services(ESs)refer to the continuous provisioning of ecosystem goods and services that benefit human beings.Over recent decades,rapid urbanization has exerted significant pressure on coastal ecosystems,resulting in biodiversity and habitat loss,environmental pollution,and the depletion of natural resources.In response to these environmental challenges,the Sustainable Development Goals(SDGs)were proposed.Given the pressing need to address these issues,understanding the changes in ESs under the SDGs is crucial for formulating specific ecological strategies.In this study,we first analyzed land use and cover change in the Zhejiang coasts of China during 2000–2020.Then,we investigated the spatiotemporal configuration of ESs by integrating carbon storage(CS),soil retention(SR),habitat quality(HQ)and water yield(WY)using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model.The driving mechanisms of ESs,which varied by space and time,were also explored using the Geo-detector method.The results revealed that,over the past two decades:1)the Zhejiang coasts have experienced a significant increase of 2783.72 km^(2) in built-up land areas and a continuous decrease in farmland areas due to rapid urbanization;2)owing to higher precipitation,extensive vegetation cover,and reduced anthropogenic disturbances,forests emerge as a crucial land use type for maintaining ecosystem services such as HQ,CS,WY,and SR;3)ESs have generally declined across the entire Zhejiang coasts,with a significant decrease observed in the northern areas and an increase in the southern areas spatially;4)the expansion of built-up land areas emerged as the primary factor affecting ecosystem services,while the vegetation factor has been increasingly significant and is expected to become predominant in the near future.Our study provides insights of understanding of ecosystem service theory and emphasizing the importance of preserving biodiversity for long-term sustainable development,and valuable scientific references to support the ecological management decision-making for local governments.

Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Growing Market Demand for After-Sales Service Management

With the background of the current economic globalization and informatization,how to improve the quality of after-sales services and meet the diversified needs of consumers has become an important issue that the industry has had to face and solve.Recently,the“report on 2022-2028 after-sales service management industry market competition analysis and development strategic planning assessment forecast”(hereinafter referred to as the“Report”)released by CICC Consulting pointed out that the high penetration rate of electronic information products.

ASTM provides world-leading digitalized standards services--Interview with Hu Yanan,Operation Manager of the ASTM International China Office

With a 126-year history,ASTM International still exudes strong vitality today.It is one of the most renowned standards organizations in the world,boasting a globally leading standards development system and database.It is open,transparent,fair,diverse,inclusive,and consensus-driven,but these are just some of its characteristics.ASTM always prioritizes the development of standards and the needs of industries and users.

Regional Digital Financial Services Security Clinic for Asia Pacific Region

April 24-25, Seoul, Republic of Korea The International Telecommunication Union(ITU) is organizing the Regional Digital Financial Services Security Clinic for Asia Pacific Region on April 24-25, 2024 in Seoul, Republic of Korea. The event is being jointly held with FNSV Korea and the Korean Fintech Center.

GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.