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.

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.

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.

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.

Spatial Equity Evaluation of Rural Eldercare Service Resources Based on Accessibility: A Case Study of Huanxian County of Gansu Province, China

Safeguarding the elderly population in the countryside is an inherent requirement for alleviating social conflicts in rural areas and effectively carrying out China’s Rural Revitalization.The existing spatial mismatch between rural elderly service facilities and pop-ulation distribution in China aggravates the imbalance of facility resource space,how to improve the accuracy of demand and supply results and better measuring the spatial fairness of elderly service is significant to realize the optimal allocation of rural elderly service.Based on the above,this study pays attention to the refined needs of the elderly population,and focuses on the spatial equity of the rural elderly facilities,to make up for the short boards of the basic public services in the countryside and realize the all-round rural revitalization.This study takes Huanxian County in the hilly loess area of Longdong,China as an example and explores the spatial equity of rural elderly services by using the Urban Spatial Network Analysis(UNA)to measure the accessible quantity and supply capacity of elderly services.The results found that there were 553 rural eldercare service facilities in Huanxian County,including 285 eldercare facilities and 268 medical and health facilities,and the spatial distribution is characterized by a southward shift in the east and uneven regional distribution.Overall,the supply capacity of rural pension services was generally good,with the township center as the gathering point and spreading out in a faceted manner,with nearby townships forming a contiguous area;however,there were strong contrasts and obvious differences in the strength of supply capacity.However,the spatial equity of rural eldercare service was poor.In 42.05%of the eld-erly population grid cells,eldercare service spatial equity was extremely low.The spatial distribution also presented a northern preference over the southern,a western preference over the eastern,and a concentration along the highway.The study explored the realization of spatial equity in the optimal allocation of rural elderly services based on paying attention to the refined needs of the rural elderly pop-ulation,a vulnerable group,to provide a reference for solving the shortcomings of basic public services in rural areas.The fair allocation of rural elderly services demands that the spatial optimization of facility layout be organically coordinated with policy,management,and post-maintenance,and all elements in the rural territorial system be fully mobilized to achieve the basic guarantee of China’s rural elderly problems.

Multi-Objective Optimization of the Ultrasonic Scalpel Rod and Tip with Improved Performance:Vibration Frequency,Amplitude,and Service Life

Ultrasonic scalpel design for minimally invasive surgical procedures is mainly focused on optimizing cutting performance.However,an important issue is the low fatigue life of traditional ultrasonic scalpels,which affects their long-term reliability and effectiveness and creates hidden dangers for surgery.In this study,a multi-objective optimal design for the cutting performance and fatigue life of ultrasonic scalpels was proposed using finite element analysis and fatigue simulation.The optimal design parameters of resonance frequency and amplitude were determined.By setting the transition fillet and keeping the gain structure away from the node position to enable the scalpel to have a high service life with excellent cutting performance.The frequency modulation method of setting the vibration node bosses at the node position and setting the vibration antinode grooves at the antinode position was compared.Then,the mechanism of the influence of various design elements,such as tip,shank,node position,and antinode position,on the resonance frequency,amplitude,and fatigue life of the ultrasonic scalpel was analyzed,and the optimal design principles of the ultrasonic scalpel were obtained.The proposed ultrasonic scalpel design was confirmed by simulations,impedance measurements,and liver tissue cutting experiments,demonstrating its feasibility and enhanced performance.This research introduces innovative design strategies to improve the fatigue life and performance of ultrasonic scalpels to address an important issue in minimally invasive surgery.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Design and pricing of maintenance service contract based on Nash non-cooperative game approach

Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.

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.

Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

Influence of varied drought types on soil conservation service within the framework of climate change:insights from the Jinghe River Basin,China

Severe soil erosion and drought are the two main factors affecting the ecological security of the Loess Plateau,China.Investigating the influence of drought on soil conservation service is of great importance to regional environmental protection and sustainable development.However,there is little research on the coupling relationship between them.In this study,focusing on the Jinghe River Basin,China as a case study,we conducted a quantitative evaluation on meteorological,hydrological,and agricultural droughts(represented by the Standardized Precipitation Index(SPI),Standardized Runoff Index(SRI),and Standardized Soil Moisture Index(SSMI),respectively)using the Variable Infiltration Capacity(VIC)model,and quantified the soil conservation service using the Revised Universal Soil Loss Equation(RUSLE)in the historical period(2000-2019)and future period(2026-2060)under two Representative Concentration Pathways(RCPs)(RCP4.5 and RCP8.5).We further examined the influence of the three types of drought on soil conservation service at annual and seasonal scales.The NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP)dataset was used to predict and model the hydrometeorological elements in the future period under the RCP4.5 and RCP8.5 scenarios.The results showed that in the historical period,annual-scale meteorological drought exhibited the highest intensity,while seasonal-scale drought was generally weakest in autumn and most severe in summer.Drought intensity of all three types of drought will increase over the next 40 years,with a greater increase under the RCP4.5 scenario than under the RCP8.5 scenario.Furthermore,the intra-annual variation in the drought intensity of the three types of drought becomes smaller under the two future scenarios relative to the historical period(2000-2019).Soil conservation service exhibits a distribution pattern characterized by high levels in the southwest and southeast and lower levels in the north,and this pattern has remained consistent both in the historical and future periods.Over the past 20 years,the intra-annual variation indicated peak soil conservation service in summer and lowest level in winter;the total soil conservation of the Jinghe River Basin displayed an upward trend,with the total soil conservation in 2019 being 1.14 times higher than that in 2000.The most substantial impact on soil conservation service arises from annual-scale meteorological drought,which remains consistent both in the historical and future periods.Additionally,at the seasonal scale,meteorological drought exerts the highest influence on soil conservation service in winter and autumn,particularly under the RCP4.5 and RCP8.5 scenarios.Compared to the historical period,the soil conservation service in the Jinghe River Basin will be significantly more affected by drought in the future period in terms of both the affected area and the magnitude of impact.This study conducted beneficial attempts to evaluate and predict the dynamic characteristics of watershed drought and soil conservation service,as well as the response of soil conservation service to different types of drought.Clarifying the interrelationship between the two is the foundation for achieving sustainable development in a relatively arid and severely eroded area such as the Jinghe River Basin.

A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.

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.

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.

Multi-scenario Simulation of the Impact of Land Use Change on the Ecosystem Service Value in the Suzhou-Wuxi-Changzhou Metropolitan Area,China

As the most economically developed metropolitan area in China’s Yangtze River Delta,the rapid changing land use patterns of Suzhou-Wuxi-Changzhou(Su-Xi-Chang) metropolitan area have profound impacts on the ecosystem service value(ESV).Based on the patterns of land use change and the ESV change in Su-Xi-Chang metropolitan area from 2000 to 2020,we set up four scenarios:natural development scenario,urban development scenario,arable land protection scenario and ecological protection scenario,and simulated the impact of land use changes on the ESV in these scenarios.The results showed that:1) the area of built-up land in the Su-XiChang metropolitan area increased significantly from 2000 to 2020,and the area of other types of land decreased.Arable land underwent the highest transfer-out area,and was primarily converted into built-up land.The total ESV of Su-Xi-Chang metropolitan area increased initially then declined from 2000–2020,and the value of almost all individual ecosystem services decreased.2) Population density,GDP per area,night lighting intensity,and road network density can negatively impact the ESV.3) The total ESV loss under the natural development and urban development scenarios was high,and the expansion of the built-up land and the drastic shrinkage of the arable land contributed to the ESV decline under both scenarios.The total ESV under arable land protection and ecological protection scenarios increases,and therefore these scenarios are suitable for future land use optimization in Su-Xi-Chang.This study could provide a certain reference for land use planning and allocation,and offer guidance for the rational allocation of land resources.

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.

Effect analysis of applying high-quality service model to surgical nursing

BACKGROUND Surgical care of the hand plays a crucial role in the medical field,as problems with the hand can profoundly affect a patient's quality of life and function.In order to meet the needs of patients,improve patient satisfaction and improve treatment outcomes,high-quality service models have been introduced in the field of nursing.AIM To explore the effect analysis of applying high-quality service model to surgical nursing.METHODS We conducted a retrospective study of patients who underwent hand surgery at our hospital between 2019 and 2022,using a quality service model that included improved patient education,pain management,care team collaboration,and effective communication.Another group of patients received traditional care as a control group.We compared postoperative recovery,satisfaction,complication rate,and length of hospital stay between the two groups.Inferential statistics were used to compare the difference between the two groups by independent sample t test,Chi-square test and other methods to evaluate the effect of intervention measures.RESULTS Postoperative recovery time decreased from 17.8±2.3 d to 14.5±2.1 d,pain score decreased from 4.7±1.9 to 3.2±1.4,and hand function score increased from 78.4±7.1 to 88.5±6.2.In terms of patient satisfaction,the quality service model group scored 87.3±5.6 points,which was significantly higher than that of the traditional care group(74.6±6.3 points).At the same time,patients'understanding of medical information also improved from 6.9±1.4 to 8.6±1.2.In terms of postoperative complications,the application of the quality service model reduced the incidence of postoperative complications from 26%to 10%,the incidence of infection from 12%to 5%,and the incidence of bleeding from 10%to 3%.The reduction in these data indicates that the quality service model plays a positive role in reducing the risk of complications.In addition,the average hospital stay of patients in the quality service model group was shortened from 6.8±1.5 d to 5.2±1.3 d,and the hospitalization cost was also reduced from 2800±600 yuan to 2500±500 yuan.CONCLUSION Applying a quality service model to hand surgery care can significantly improve patient clinical outcomes,including faster recovery,less pain,greater satisfaction,and reduced complication rates.

A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple Machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.