With the rise of Internet health, online medical platforms play the key role of information bridge and convenient channel in medical service selection. Users tend to rely on doctor images and electronic word-of-mouth evaluations displayed on online medical platforms when selecting medical services. This study aims to explore the impact of facial features displayed on online platforms on users’ offline conversion rate. It will help us understand the psychological mechanism behind users’ decisions from the perspective of the first impression effect. Python was used to collect pictures and information of 7547 doctors from “haodf.com”, a well-known online medical platform in China. Then, we used commercial software to calculate their facial feature values. The results showed that: Appearance attractiveness, smile, and gender of facial displays on online platforms have a significant positive relationship on users’ offline conversion rate. Satisfaction evaluations do not significantly regulate the relationship between appearance attractiveness and smile on users’ offline conversion rate, but have a significant negative moderating effect on the effect of gender. This study emphasizes the importance of the eWOM platform in image management. It also provides a new perspective for optimizing resource allocation and improving service efficiency. It has important theoretical and practical value for further promoting the innovation of online service systems and realizing the seamless connection between online and offline.

In 2017, the service classification of vocational rehabilitation (LBR) became mandatory for vocational rehabilitation centres as part of the German Pension Insurance's (DRV) quality assurance process. This joint study aims to determine the quality of LBR data by analysing routine data on conformance, completeness and plausibility, using vocational training services in vocational retraining centers as an example. This analysis is based on a scientific use file 'Completed rehabilitation in the insurance history 2015-2022', as well as data on the LBR and the assignment to rehabilitation centres. Following the selection of vocational training services (i. e., qualification and integration measures in vocational retraining centres), 35,878 measures from rehabilitants who ended their rehabilitation from 2018 to 2022 were evaluated. The documented LBR codes were analysed descriptively, as well as using logistic and linear regression models. Regarding the conformance of the LBR, the analyses showed that the documentation met the structural and content-related requirements. A review of the completeness of the LBR documentation revealed that the documentation increased over the years, but to date (2022), approximately 25% of the measures were still undocumented. The decision regarding documentation depends largely on the rehabilitation centre. Plausibility analyses revealed that the documented duration varied considerably between institutions. Furthermore, it was revealed that expectations regarding the association between personal characteristics (e. g., age, sex) or measure (e. g., termination of measure, scope of rehabilitative assistance) and the documented duration of LBR were only confirmed in part in an exemplarily selected subset of the data (with services relating to health literacy and therapeutic services). Overall, the results of the analyses allow a positive conclusion to be drawn about the quality of the LBR data with regard to conformance and completeness. In terms of plausibility, however, the results are inconclusive. It has not yet been possible to take any external evaluation standards into account. These should be developed in the next step.

With the development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, smart wearable devices (SWDs) are regarded as a promising approach for sports training monitoring. However, such monitoring generates a large number of computation-intensive tasks, which SWDs struggle to process in real-time due to limited battery capacity and computing power. Fortunately, the emergence of multi-access edge computing (MEC) offers an effective solution, allowing smart wearable devices to offload tasks to edge servers at the network edge for low-latency, energy-efficient processing. This paper investigates the dynamic task offloading problem in MEC-assisted wearable device systems. By jointly optimizing the offloading decisions, CPU frequency, offloading power of SWDs, and the CPU frequency of edge servers, we aim to minimize the total energy consumption of SWDs while maintaining the queue backlog. Through the Lyapunov drift-plus-penalty optimization framework, the long-term stochastic optimization problem is decoupled into a series of deterministic single-slot subproblems. A lightweight server selection algorithm is proposed to enable adaptive switching to alternative servers during overloads with negligible computational and signaling costs. Then, the problem is further decomposed into multiple sub-problems that can be solved in parallel. Based on this, we propose the Sports Training Monitoring (STM) algorithm to achieve efficient online solutions. Theoretical analysis and experiments indicate that STM can effectively reduce the energy consumption of SWDs while maintaining system performance.

Participatory Systems Modelling (PSM) offers an opportunity to deliver robust, contextually relevant, and suitable models for informing strategic responses to complex public health challenges. For mental health care systems, PSM benefits from multi-sectoral participants' social and emotional capital for building consensus on priorities and policy objectives that can influence decision-making. In Bogotá, Colombia, we evaluated a PSM process that developed an interactive decision support tool that can inform policymaking on the selection of programs and services to mitigate social, economic, and health system drivers of youth mental health outcomes including suicidal behaviour. Using the CHaRL Framework, we explored changes in perceptions or beliefs of participants on the functionality of the (mental health) system and its drivers. Multi-sectoral participants including youth with lived experience participated in three workshops to collectively inform the building of the model. Pre-post-tests, a survey and observations of the PSM process were conducted. Through the participatory process, participants increased knowledge and understanding of the mental health care system, its drivers and constraints of performance for youth in Bogotá. Due to active engagement, a credible and comprehensive tool was created, reflective of the complexity of the system, and focused on what participants and evidence show are high priority interventions. Participants' concerns around trust in the model shifted positively over the course of the workshops. Skepticism in policymakers' capacity to use the model increased participant recognition of the need to advocate for its use. Participatory processes help to overcome stakeholder hesitancy by offering an important starting point for building trust and partnership for effecting change in health systems. Consideration of participant bias at the start of the process could strength the validity and use of the results in the longer term.

The landscape of modern computing systems is shifting towards architectures built by combining available services under the “everything as a service” paradigm. These architectures are deployed on distributed cloud-edge infrastructures, aiming to provide innovative services to a wide range of users. However, it is crucial for these systems to address environmental sustainability concerns. This poses challenges in operating such systems in open, dynamic, and uncertain environments while minimizing their energy consumption. To tackle these challenges, we propose a decentralized service assembly approach that ensures the assembly is energetically self-sustainable by relying on locally harvested and stored energy. In our contribution, we introduce a general service selection template that enables the derivation of different selection policies. These policies guide the construction and maintenance of the service assembly. To evaluate their effectiveness in meeting the sustainability requirements, we conduct a comprehensive set of simulation experiments, providing valuable insights.

The development of digital technology is facilitating the smart emergence of stroke emergency care. Deep learning MCDE techniques and algorithms based on QoS have been used in this paper to create an integrated digital platform to improve the effectiveness and efficiency of acute ischemic stroke emergency response. The MCDE method learns various heterogeneous medical knowledge to create a stroke medical knowledge graph, which serves as a repository of resources to rapidly query. Together with a QoS-based service selection algorithm, it computes ideal emergency medical intervention combinations. With its application to acute ischemic stroke treatment, the combined platform recorded DTP, DTT, and DNT performance rates of 6.29 +1.03, 20.48+3.74, and 17.35+5.12 respectively. Efficacy of thrombolytic treatment was 91.86%. Integrated digital platform-assisted emergency care allows quicker and more accurate emergency response compared to the conventional emergency interventions.

Previous studies in logistics and freight transportation have largely focused on service quality, customer satisfaction, or operational efficiency, but limited attention has been given to understanding how key service attributes influence customers’ behavioral intentions and final decisions in selecting freight forwarding services, particularly in the context of LCL shipping. This indicates a research gap in the existing literature on freight forwarding service selection. This study aims to develop a decision-making model for selecting freight forwarding services in Less-than-Container-Load (LCL) shipping by examining the influence of delivery time, price, and brand image on customers’ intention to use and decision to use freight forwarding services. A quantitative research approach was employed with data collected from 200 exporters and SMEs in Semarang, Surabaya, and Jakarta that utilize LCL shipping services. The data were analyzed using Structural Equation Modeling (SEM). The results indicate that delivery time, price, and brand image have significant positive effects on both intention to use and decision to use freight forwarding services. Among the independent variables, brand image shows the strongest influence on intention to use, while delivery time has the strongest impact on decision to use. Furthermore, intention to use significantly mediates the relationships between delivery time, price, brand image, and the decision to use freight forwarding services. This study contributes to the logistics and supply chain management literature by providing an integrated decision-making model for freight forwarding service selection in LCL shipping and offering practical insights for logistics providers to improve service strategies that enhance customer intention and decision to use their services.

The Smart Mobility vision calls for dynamic resource and service discovery to cope with the intrinsic topology volatility of Internet of Things (IoT) platforms without sacrificing the required business continuity and service flexibility. For an extended automation of collaboration within and across enterprise boundaries, trust management is equally important, granting security, reliability and scalability at the same time. To tackle the above challenges, this paper proposes the integration of a semantic-based service management layer in an IoT infrastructure grounded on the Hyperledger Sawtooth blockchain. Every service in the outlined framework is annotated with reference to a domain ontology, so that smart contracts can exploit knowledge representation and non-standard reasoning for service registration, discovery, outcomes explanation and service selection. A case study on power management of Plug-in Electric Vehicles (PEVs) is proposed to clarify the benefits of the proposal. Early performance evaluation results support the feasibility and sustainability of the approach. • Blockchain platform integrating smart contracts for semantics-enhanced resource discovery. • Prototype implementation in a service-oriented smart mobility case study. • Experiments support feasibility and sustainability of the approach.

Business leadership increasingly requires a deeper understanding of the drivers of sustainable consumption as environmentally friendly products gain strategic relevance in contemporary markets. Despite the growing demand for environmentally friendly products, sustainable consumption still faces many barriers, particularly with respect to price sensitivity and income. This study examines how income levels and price sensitivity influence sustainable consumer behavior in Slovakia. The research focuses on two hypotheses: (H1) a significant relationship exists between income and the selection of eco-friendly products or services, and (H2) the high price of green products delays the adoption of sustainable consumption habits. A questionnaire survey was conducted in the spring of 2024 to explore participants’ views. The questionnaire was distributed online, receiving 228 responses, of which 212 were retained for analysis. SPSS software was used to evaluate the results. The findings indicate that while income correlates with a greater emphasis on sustainability, wealthier consumers are less likely to purchase eco-friendly products (χ² = 9.037; p = 0.029; gamma = -0.263), contradicting previous studies that suggest a positive link between income and sustainable consumption. Additionally, the study found that price sensitivity does not affect the adoption of green purchasing habits (χ² = 5.696; p = 0.127), aligning with research indicating that consumers are willing to pay more for environmentally friendly products. The results offer relevant insights for business leadership by clarifying how income and price sensitivity shape sustainable purchasing behavior and sustainability-oriented strategic decision-making.

The rapid adoption of multi-cloud strategies by enterprises has introduced significant challenges in software architecture design, including increased system complexity, heterogeneous cloud services, interoperability issues, security risks, and performance optimization across distributed environments. Traditional architecture design approaches are often manual, time-consuming, and insufficient to dynamically adapt to evolving business and technological requirements. To address these challenges, this study proposes an AI-assisted software architecture design framework tailored for multi-cloud enterprise environments. The proposed approach leverages artificial intelligence techniques such as machine learning, knowledge-based systems, and architectural pattern recognition to support automated decision-making in cloud service selection, workload distribution, scalability planning, fault tolerance, and security enforcement. By analyzing historical system data, architectural constraints, and real-time operational metrics, the framework provides intelligent recommendations for optimal architectural configurations while ensuring compliance, resilience, and cost efficiency. The AI-assisted model enables architects to evaluate multiple design alternatives, predict performance bottlenecks, and proactively mitigate risks before deployment. Experimental evaluation and scenario-based analysis demonstrate that the proposed framework significantly improves design accuracy, reduces architectural complexity, and enhances system performance compared to conventional design methods. This research highlights the potential of AI-driven architectural intelligence to support adaptive, scalable, and robust software systems in complex multi-cloud enterprise ecosystems.

Abstract We study a multi-server strategic queueing model in which customers choose among servers that differ in service rates and service valuations. Customers can either commit to a specific server or adopt a flexible strategy by joining multiple queues simultaneously. Service is received from the server that completes the request first, after which redundant requests are canceled. This flexibility option presents a clear trade-off: customers benefit from potentially shorter waiting times, but risk receiving lower-quality service from less desirable servers. Rational customers take into account that their welfare depends not only on their own decisions but also on the decisions of other customers, thereby engaging in a noncooperative game. We characterize the Nash equilibrium outcomes of this strategic queueing game, highlighting how customers balance waiting time reduction against the uncertainty in service quality. Our analysis demonstrates that introducing the flexibility option leads to higher overall social welfare, even when customers act to maximize their own welfare. We further examine the socially optimal allocation and the revenue maximizing pricing strategy when flexibility is offered at a price.

To assess health and socio-economic impacts of climate-related displacement in North-East Bangladesh chars and examine links between non-governmental services, disease burden and migration. We analysed a household survey of 480 women aged 15-55 from nine intervention and three comparison chars, collected between March and June 2022. Using a quasi-experimental framework and estimators of the average treatment effect, we compared displaced and non-displaced households and households in chars with and without Friendship health and education services. We constructed indices of disease burden, migration and socio-economic conditions, each scaled 0-100. Displaced households had lower disease burden scores than non-displaced households after adjusting for socio-economic covariates. This pattern is consistent with improved access to services among some displaced groups, but may also reflect reporting differences and selection into the observed displaced population. Migration intensity was higher in chars where Friendship operates than in comparison chars, suggesting programme placement in areas with stronger migration pressures. Climate-related displacement interacts with service access, vulnerability and selection in complex ways; targeted interventions can reduce disease burden but do not necessarily lower migration pressures.

The world is currently experiencing a knowledge boom and renaissance that has led us to refer to this period as the "time of knowledge". ‎Science has been made possible by the human mind over time, and this has led to the development of practical and scientific methods for ‎acquiring, understanding, applying, and benefiting from knowledge. As a result, throughout time, organizations seek to provide high-‎quality e-services that will ensure that they have a better understanding of both themselves and their surroundings and the ability to coexist ‎and adapt to them. Companies, particularly in developed nations, are constantly working to create the best practices to fulfill this goal. This ‎calls attention to cloud services as a crucial component of the strategic use of IT. Cloud services offer a range of characteristics and ‎advantages for the business process that are utilized by different models of online services. These features can be used in a variety of ways ‎to support organizations objectives and introduce technology into business settings. Several business objectives can be served by ‎cloud technologies, including minimizing fixed costs in IT infrastructure, remaining flexible, enhancing collaboration, improving data ‎management and facilitating innovation. This paper, which focuses on cloud services, argues why using them is crucial to organizations ‎in this era and highlights the need for additional studies in this area, as well as active participation in the evaluation, selection, and integration ‎of services‎.

Multi-game optimization strategy for virtual power plants considering EV user service selection

ABSTRACT Digital platforms are reshaping travel decisions by enabling electronic word-of-mouth networks through online reviews, which are critical for airline service selection. In this competitive tourism sector, customer satisfaction is governed by nonlinear interactions among heterogeneous tourism service attributes, yet these dynamics remain inadequately captured by traditional attribution methods. An approach based on interpretable machine learning (IML) is proposed to address this gap. Partial customer evaluations are utilized as inputs, with overall satisfaction set as the target variable. Multiple machine learning classifiers are constructed and rigorously evaluated, identifying the gradient boosting decision tree (XGBoost) as the optimal predictor due to its superior discriminative performance. Subsequently, the main and interactive effects of tangible and intangible service factors are quantified via SHAP (Shapley Additive exPlanations), guided by a novel SHAP-IPA (Importance-Polarity Analysis) framework for satisfaction driver classification. Model robustness is confirmed through rigorous testing. The results demonstrate that actionable drivers of service satisfaction are uncovered, providing aviation practitioners with a transparent decision-support framework for strategic resource reallocation and optimizing the configuration of service portfolios under operational constraints.

For the complete implementation of an electronic prescription system, it is inevitable to consider the stakeholders' viewpoints of the system. Given that every system should be evaluated, this study was conducted aimed to explain the experiences of stakeholders in designing evaluation criteria for the electronic prescribing system in Iran: a qualitative study. This study is a qualitative study using directed content analysis. Interviews were conducted with 36 stakeholders of the Electronic prescription system (13 physicians, 11 pharmacists, 7 managers and experts of insurance organizations, and 5 patients) who were selected by purposive sampling. For data analysis, the criteria of Graneheim and Lundman (2004) were used, and MAXQDA 10 was used to manage the data. From the viewpoint of the participants, the criteria considered for evaluating the Electronic prescription system were "infrastructure", "transparency and accountability", "access to patient data", "reminder, renewals, and monitoring", "selection of drug and para-clinical services", "viewing patient records", "warning prescribers and users to support the decision", "security and confidentiality" and "data transfer and storage". The study results can help the policymakers, managers, and decision-makers in the field of Electronic prescription systems to compare different electronic prescribing systems, the result will lead to the improvement and improvement of the electronic prescribing system in Iran.

In low-altitude Internet of Things (IoT) systems, uncrewed aerial vehicles (UAVs) are increasingly deployed as agile and distributed platforms. In scenarios where infrastructure support is limited or unavailable, UAVs can form ad hoc networks that enable flexible and self-organizing communication, making them well-suited for delivering real-time edge intelligence. A key challenge in such UAV ad hoc networks lies in edge server placement to ensure low-latency data transmission while balancing the computation load among servers. In this paper, we provide insights into the design of UAV ad hoc network-assisted IoT systems by characterizing the trade-off between data transmission latency and computational load. To this end, we formulate a bi-objective optimization problem that jointly minimizes the worst-case transmission latency and the load imbalance, subject to constraints on edge UAV server selection and data assignment. To solve this problem, we propose a directed-evolution non-dominated sorting genetic algorithm (DNSGA) that removes conventional crossover operations and incorporates two problem-specific heuristics: (i) a K-means-based task assignment module to reduce latency, and (ii) an efficiency-driven load migration strategy to balance server-side workloads. Simulation results verify that the proposed DNSGA converges significantly faster than non-dominated sorting genetic algorithm II (NSGA-II), while providing a more diverse set of non-dominated solutions with up to 13.3% broader result range. Compared to multi-objective particle swarm optimization (MOPSO), DNSGA achieves up to 18.8% reduction in worst-case latency, and compared to NSGA-II, it reduces the load imbalance by up to 41.2%. These advantages highlight its effectiveness for high-efficiency UAV ad hoc network-assisted IoT systems.

Additive manufacturing (AM) is transforming sectors such as automotive, defense, medical, and footwear by enabling toolless production, high design flexibility, rapid customisation, and on-demand supply chain management. Yet widespread adoption remains constrained by capital intensity, limited in-house expertise, and complex material-machine ecosystems. As a result, firms increasingly rely on AM service providers (AMSPs). However, understanding the activities (processes) these providers perform, the services they offer, and selecting the appropriate service remains a challenge. This study addresses these gaps by first identifying seven core activities carried out by AMSPs. Second, these activities are organised into eight service categories. Third, a decision-support framework is introduced that combines a knowledge-based expert system (KBES), an adjusted scoring method, and the neutrosophic best–worst method (NBWM) to recommend the most compatible service for a customer. The framework was validated through two industrial cases: a European medical-refrigeration firm and an Indian edible-oil machinery firm, and four robustness tests. Findings demonstrate that the framework enhances decision transparency, reduces evaluation complexity, and provides actionable guidance for both customers and AMSPs, while demonstrating scalability as a software-as-a-service (SaaS) decision tool. Policy implications include designing targeted incentives to accelerate the adoption of AM for AMSPs and companies.

Shipping services play a crucial role in ensuring smooth operational processes and cost efficiency. This is particularly relevant for one of the manufacturing companies in Riau, a company engaged in the production of precast concrete using a make-to-order system. However, the selection of shipping services has so far been based solely on the experience of the purchasing department, without the application of clear criteria or prioritization systems. This has led to delays in material delivery, resulting in increased operational costs and failure to meet production targets. This study aims to provide the company with a method and relevant information for selecting an efficient and timely shipping service. This research will be using hybrid methods with Interpretive Structural Modeling (ISM) and the Simple Multi Attribute Rating Technique (SMART). Through the ISM method, four key influential criteria were identified: cost, availability of delivery units (trucks), timeliness, and delivery coverage area. These criteria were then used in the SMART method to assess 11 shipping service providers. The analysis shows that the most recommended shipping service is Lancar Jaya, with a score of 97.09, while the least recommended is Cakra, with a score of 40.15. These findings are expected to serve as a reference for one of the manufacturing companies in Riau in determining effective shipping service providers.

The Web of Things (WoT) is a set of standards established by the World Wide Web Consortium (W3C) to enable interoperability across various Internet of Things (IoT) platforms. These standards facilitate seamless device-to-device interactions and application-to-application communication across heterogeneous environments. To identify and utilize resources, whether data or services, offered by Web-connected devices and applications, these resources must be described using an open, shared, and dynamic knowledge representation capable of supporting both syntactic and semantic interoperability. In this paper, we present WoR+, a Web of Resources ontology based on a modular and unified vocabulary for describing Web resources (Web services and Web data). WoR+ offers several advantages: (a) it supports the discovery, selection, and composition of data and services provided by Web-connected devices and applications; (b) it provides reasoning capabilities for inferring new knowledge; and (c) it supports extensibility and adaptability to emerging domain requirements. Experimental evaluation shows that WoR+ ontology achieves high effectiveness, strong performance, and good clarity and consistency.