Search Results (298)

Postsecondary institutions are seeing an increased prevalence of student mental health concerns and disabilities, highlighting the need for campus-based approaches that support student well-being. While college campuses provide many services to support students, occupational therapists are largely absent from these support systems, despite growing interest in this emerging field of practice. This program description and implementation case study examines preliminary indicators of feasibility for the WILL Thrive program, which delivered occupational therapy (OT) services on a college campus through a Level II fieldwork placement. Feasibility was examined across domains of acceptability, demand and implementation using an integrated approach combining a needs assessment, service development and process evaluation. Data sources included environmental observations, surveys, stakeholder interviews and process evaluation measures, including service delivery tracking, referral patterns, and resource utilization. Referrals and service utilization in this case were most frequently observed among students reporting neurodevelopmental and mental health-related functional challenges, providing preliminary indicators of potential service users, though a small, heterogeneous sample size limits generalizability. Referral patterns and engagement from the wellness center and accessibility staff highlight preliminary strengths of the program, including early indicators of acceptability and demand. In contrast, implementation barriers were also identified, including limited campus-wide understanding of the OT scope and role and constraints in on-campus OT supervision. Findings offer early, exploratory signals of feasibility for integrating OT services through an OT fieldwork II model and suggest that OT may complement existing campus supports by addressing participation-focused, functionally orientated needs. Results should be viewed as preliminary and inform future implementation studies that include systematic outcome measures, comparative analysis with existing services, and broader assessment across diverse higher education contexts. Full article

Peru’s recent national mental health (MH) reforms aim to decentralise care and expand access to MH services for rural populations by integrating services into primary healthcare through the expansion of Community Mental Health Centres (CMHCs). Evidence on the implementation of these reforms at the local level remains limited. This qualitative study aimed to (i) describe the structure and implementation framework of MH services, (ii) analyse local understandings of MH; and (iii) examine pathways to care and identify barriers and facilitators to MH service implementation from both the supply (service providers) and demand (users and community members) perspectives. MH services were mapped across three provinces of northern Peru using a review of national MH policies, 2 focus group discussions, and 31 semi-structured interviews. Data were analysed thematically to explore local understandings of MH, pathways to care, and health system barriers. Local understandings of MH are shaped by cultural beliefs, social norms, and economic conditions, with many individuals experiencing distress initially relying on family networks or traditional healers. Stigma and expectations of a quick recovery hinder engagement with formal services. While the expansion of CMHCs has improved geographical access to specialised care in rural areas through proximity and being patient-centred, the implementation of respectful provider interactions remains uneven. Weak referral pathways and limited coordination between primary care centres and CMHCs frequently shift the responsibility for navigating care onto users and their families. Family involvement and culturally sensitive practices foster trust and support continued engagement. Persistent challenges include the limited capacity of service providers, high staff turnover, and the follow-up mechanisms, stigma, and tensions between cultural and biomedical understandings of MH. Peru’s expansion of CMHCs represents a significant health system reform to improve equitable access for rural populations. To sustain these gains, it will be necessary to strengthen workforce stability, clarify referral processes, and integrate culturally responsive approaches within primary care systems, offering lessons for similar resource-constrained contexts. Full article

Transforming a conventional university campus into a smart campus by leveraging modern technologies aims to deliver university services efficiently, effectively, and at low cost. Modern technologies enhance campus life by providing services, such as smart classrooms and campus security, on demand. Seamless service delivery requires reliable and efficient access to the services that take into consideration the dynamic contextual attributes related to, e.g., end-device mobility, latency sensitivity, and resource constraints. University staff, students, and visitors frequently submit different types of service requests on the move, which requires a robust orchestration framework capable of managing these requests across edge-cloud environments. The orchestration framework needs to intelligently distribute the workload, taking into consideration the latency sensitivity requirements and contextual conditions, including resource constraints. Therefore, a fuzzy-logic orchestration framework for smart-campus environments in edge-cloud architecture is proposed. The framework incorporates key factors, including user speed, resource utilization, and request delay sensitivity, in the decision-making process to satisfy both service consumers and service providers. It prioritizes latency-sensitive requests while simultaneously enhancing resource utilization efficiency. Simulation-based experimental results demonstrate the effectiveness of the proposed framework compared with benchmark approaches in orchestrating incoming workloads under several user and contextual conditions. Additionally, the results show that the proposed framework improves the execution rate by 30% compared to benchmark models and achieves more than double resource utilization efficiency. Full article

Lightweight and slender footbridges exemplify sustainable, material-efficient infrastructure, yet their vibration performance is frequently governed by high dynamic sensitivity, which directly affects serviceability, user comfort, and structural durability. This paper provides a critical review of full-scale experimental investigations and validated finite element simulations addressing the dynamic behavior of various footbridges, focusing on the influence of structural typology, material solutions, and excitation characteristics on vibration-related performance within sustainability-driven design objectives. This article is organized into three core research themes: (1) standards and guidelines in bridge engineering practice, (2) dynamics of footbridges with special structural response to human-induced loading, including walking, running, crowd actions, and higher harmonic contributions, and (3) case studies, based on which the gaps in the current approach are formulated. Based on a synthesis of findings from leading research on structural dynamics and sustainable infrastructure, this paper highlights critical gaps in current vibration serviceability guidance for footbridges. Concluding remarks delineate the principal research challenges and formulate evidence-based, practical recommendations to enhance the resilience, vibration comfort compliance, and sustainability of future footbridge infrastructure. Full article

Background/Objectives: The advent of the pandemic catalysed the global adoption of digital health services. In Spain, this transition has markedly influenced eHealth accessibility, particularly through mobile-based technologies. This study compares reported real-access experiences of users of digital health at two key moments—2018 (pre-pandemic) and 2025 (post-pandemic)—to identify how access determinants have evolved. Methods: The evolution of users who have accessed digital health resources and their characterization, including the specification of who uses websites and mobile applications, was analyzed using binary logistic regression models for each year. These models incorporated sociodemographic characteristics and patterns of device usage as predictors. Results: Access to digital health services increased significantly between 2018 and 2025. The proportion of the population using such services increased from 50.2% to 85.4%. The use of mobile applications grew from 24.4% to 80.2%. In 2018, access was linked to a wider range of factors, including age, education, municipality size, self-rated health, and computer or tablet use. In contrast, the 2025 model revealed a more concentrated set of determinants. Age emerged as the primary barrier, especially >65 years, reducing the likelihood of eHealth access. Meanwhile, frequent use of digital devices (e.g., computers, smartwatches) was positively correlated with accessing eHealth. Conclusions: The study reveals an increase in access to digital health services in Spain, accompanied by shifts in the factors influencing this access. Notwithstanding technological advances, the digital divide could persist as a major impediment to access for the groups mentioned earlier. This analysis suggests the need for targeted digital inclusion measures, especially as mobile platforms are becoming the main entry point to healthcare services. Full article

To meet the energy-saving requirements of user equipment (UE) operating in Radio Resource Control idle/inactive states (RRC_IDLE/RRC_INACTIVE) in the 3rd-Generation Partnership Project (3GPP) 5G-Advanced (5G-A) networks, the New Radio (NR) downlink paging procedure relies on periodic monitoring and frequent synchronization signal block (SSB) measurements, which wastes energy when no paging arrivals occur. Meanwhile, heterogeneous Quality of Service (QoS) constraints make it difficult for fixed-parameter Idle Discontinuous Reception and Paging Early Indication mechanisms (IDRX/PEI) to balance energy, delay, and reliability. This paper develops a UAV-assisted 5G-A paging control framework that maps services into multiple QoS classes and models QoS violation risk and system energy consumption under unified accounting, including UE monitoring/reception energy and unmanned aerial vehicle (UAV) forwarding energy. We then propose a QoS-aware risk-driven paging strategy: an offline Long Short-Term Memory (LSTM) predictor is trained to estimate the time-to-next-arrival (TTNA) of paging events and produce a bounded urgency/risk signal to initialize class-dependent thresholds, while online triggering and QoS-feedback-based threshold adaptation regulate the empirical violation rate toward target constraints under varying loads, enabling a controllable energy–delay trade-off. A simulation-based evaluation is conducted to compare the proposed method with representative baselines (Enhanced Paging Monitoring (EPM), Split Paging Occasion (SPOP), and Predicted Paging Early Indication (PPEI)) and to examine the impact of SSB overhead and UAV relaying on the energy–delay–reliability trade-offs. Full article

Vehicular Edge Computing (VEC) can effectively guarantee the service experience of user vehicles, but resource-limited Roadside Units (RSUs) may face insufficient computing capacity during task peak periods. Utilizing Assisting Vehicles (AVs) with idle resources to share computing power can alleviate the pressure on RSUs. However, existing studies often fail to adequately incentivize selfish assisting vehicles to contribute resources and frequently lack a global optimization perspective from the overall system welfare. To address these challenges, this paper proposes an incentive-driven utility-balanced task offloading framework that aims to maximize social welfare while jointly optimizing resource allocation and profit pricing. Specifically, we first formulate the resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem. To solve this problem, we introduce hybrid action representation learning to VEC for the first time and propose the HyAR-PPO algorithm to jointly optimize discrete offloading decisions and continuous resource allocation. This algorithm maps heterogeneous hybrid actions to a unified latent representation space through a Variational Autoencoder for the solution. Subsequently, equilibrium prices among user vehicles, Computation Service Providers (CSPs), and assisting vehicles are determined through Nash bargaining games, satisfying individual rationality constraints and achieving Pareto-optimal fair profit distribution. Experimental results demonstrate that the proposed framework can effectively coordinate multi-party interests. Compared with mainstream methods, the approach based on hybrid action representation learning achieves a significant improvement in social welfare, with its advantages being more pronounced in medium-to-large-scale scenarios. Full article

Background: Street sex workers (SSWs) experience some of the highest levels of health inequality in the UK, yet face persistent barriers to accessing NHS healthcare. These barriers are shaped by structural disadvantage, stigma, and the complex realities of their lives. Despite significant health needs, engagement with services remains low, and existing models of care often fail to accommodate the lived experiences of this population. Aims: This study explores how SSWs access, experience, and navigate NHS healthcare. It aims to understand the barriers and enablers of access, identify areas for improvement, and offer recommendations to inform the development of more inclusive service provision. Methods: An ethnographic approach was undertaken within a South Yorkshire charitable organisation. Data collection involved participant observation and an arts-based scrapbook intended to facilitate trauma-informed, flexible engagement. Thematic analysis was used to analyse the data, organised around a dynamic, processual approach using the candidacy framework. Findings: Barriers to care were present across all stages of healthcare engagement, including minimisation of health needs, administrative exclusion, lack of continuity, and stigma from professionals. Participants frequently described systems as inaccessible. Key enablers included supportive organisational staff and consistent, trusted relationships with specific providers. Areas for Improvement and Recommendations: Findings highlight the need to simplify registration processes, provide in-person options, and reduce reliance on digital communication. Greater continuity of care and gender-sensitive, trauma-informed approaches were consistently requested. Services should not be evaluated solely by uptake but by how well they accommodate marginalised users. Healthcare settings that prioritise safety, trust, and consistency were shown to improve engagement. SWs spoke of the work of accessing care, which for many was too hard to gain. Conclusions: SSWs are not disengaged from healthcare but are routinely excluded by systems that fail to meet their needs. Service redesign must begin from the realities of those who are most marginalised, through co-production, to reduce health inequity and build meaningful access. Full article

Service recommendation aims to assist users in selecting appropriate services according to their requirements while ensuring seamless compatibility in modern cloud and edge computing environments. In dynamic multi-cloud scenarios, services are typically deployed across heterogeneous cloud platforms and are frequently reconfigured. However, most existing service recommendation approaches primarily focus on static compatibility aspects, such as service interfaces or communication protocols, while overlooking the dynamic characteristics of service interactions. However, several limitations can be identified. First, the lack of effective mechanisms for quantifying service compatibility in dynamic cloud environments often leads to degraded system efficiency. Second, the absence of dedicated multi-cloud service compatibility quantification methodologies restricts recommendation accuracy. Third, insufficient mathematical analysis with respect to uniqueness, feasibility, and correctness may result in unstable evaluation outcomes and additional computational overhead. To overcome these limitations, this paper presents McCom, a multi-cloud service recommendation framework designed to quantify service compatibility performance and address the aforementioned challenges. First, a novel Markov chain-based compatibility quantification model is developed to characterize service interactions in dynamic multi-cloud environments. By exploiting the homogeneity, irreducibility, and convergence properties of Markov chains, the proposed model enables stable and reliable compatibility assessment. Second, a multi-cloud compatibility quantification strategy is introduced to mitigate interference arising from complex service pools through refined filtering and sketching mechanisms. Third, a series of mathematical proofs are provided to rigorously demonstrate the feasibility, correctness, and uniqueness of the proposed quantification method. Extensive simulation results indicate that the proposed framework achieves significant performance improvements, including enhancements in recommendation quality (14.44% in F1 score), reductions in latency (40.68%), and increases in accuracy (50.85%), compared with existing state-of-the-art approaches. Full article

Current cloud resource configuration schemes at the Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) levels often result in frequent Quality of Service (QoS) violations, low resource utilization, and inadequate revenue assurance for Software-as-a-Service (SaaS) providers. To overcome these limitations, this paper proposes a novel two-stage, optimal collaborative configuration strategy for IaaS resources, designed explicitly to maximize SaaS providers’ expected revenue under three prevalent IaaS pricing models. In the first stage, each SaaS provider determines its initial optimal resource demand using historical user data. In the second stage, resources are dynamically reallocated collaboratively among SaaS providers experiencing resource surpluses and deficits. This strategy achieves a dual objective: maximizing the SaaS provider’s expected revenue while enabling the IaaS provider to enhance utilization through more precise resource allocation—all while ensuring zero QoS violations at the IaaS provider level and a drastically reduced probability of SaaS-to-user QoS violations. We instantiate this framework by deriving optimal collaborative configuration strategies for three prevalent IaaS pricing models: Fixed-price (OCCS_FI), Segmented-price (OCCS_SI), and Dynamic-price (OCCS_DI). Theoretical analysis and comprehensive experimental evaluations confirm the efficacy of our proposed strategies. Under conditions of stochastic user demand, our strategies ensure no QoS violations are triggered at the IaaS provider level, while seeking to maximize the expected revenue for SaaS providers and maintain high resource utilization. This is achieved by determining an optimal initial resource purchase that accounts for demand uncertainty, followed by a collaborative reallocation mechanism that mitigates shortages. Combined, these measures reduce the probability and impact of SaaS-to-user QoS violations to a negligible level. Full article

In the digital tourism marketplace, online reviews have become a decisive source of information for travelers who cannot directly assess the quality of hospitality services before purchase. However, it remains unclear how service-related and sustainability-related cues interact to shape guest perceptions in sustainable hotel contexts. This study investigates how intrinsic and extrinsic cues are reflected in online user reviews of sustainable hotels. Drawing on Cue Utilization Theory, a longitudinal content analysis was conducted on a worldwide online travel platform’s (OTP) reviews of hotels certified for their sustainability practices (2004–2024) by user selection. A coding framework was developed deductively based on prior studies on hotel service quality and sustainability practices, and data were analyzed using MAXQDA, Leximancer, and VADER sentiment analysis. Findings indicated that reviews primarily emphasize service quality attributes—particularly staff efficiency, food quality, and accommodation facilities—while sustainability themes appear less frequently, often in nature-related contexts. Sentiment analysis revealed a predominantly positive emotional tone shaped by service quality experiences. Overall, intrinsic cues play a central role in forming guest perceptions, highlighting the need for emotionally engaging sustainability communication strategies. Full article

Background and Objectives: Telemedicine has become an essential component of chronic Inflammatory Bowel Disease (IBD) care, yet the factors that shape patient satisfaction with remote consultations remain only partially understood. This study aimed to assess satisfaction with institutional telemedicine services among Italian patients with ulcerative colitis (UC) and Crohn’s disease (CD), and to identify sociodemographic, clinical and organisational predictors to inform more person-centred telehealth models. Materials and Methods: We conducted a prospective, multi-centre, cross-sectional study in three IBD units in northern, central and southern Italy between June and October 2024. Consecutive adult patients who had completed a scheduled, non-emergency telemedicine visit were invited within 24–48 h to complete an online questionnaire including the Italian Telemedicine Satisfaction Questionnaire (I-TSQ), sociodemographic items, IBD-related variables, and telemedicine process indicators (accessibility, technology usability, technical support, time saved). Data were analysed descriptively and with multivariable linear regression to determine independent predictors of satisfaction, adjusting for recruiting centre. Results: A total of 705 patients participated (54.9% UC; 55.3% disease duration > 10 years). Overall, telemedicine satisfaction was high (mean I-TSQ total 57.5 ± 4.9; range 35–70), and all respondents reported reduced indirect costs compared with in-person visits. Greater ease of technology use, more frequent contact with the care team, male sex, older age, and employment were independently associated with higher satisfaction scores. Conversely, first-ever teleconsultations, CD, subcutaneous therapies, more difficult platform access, and the need for technical support were linked to lower satisfaction. Model fit was modest (R² up to 0.20), suggesting the presence of additional unmeasured relational and contextual factors. Conclusions: Telemedicine for IBD is widely accepted in Italy, but satisfaction is strongly conditioned by digital usability, previous experience, and clinical complexity. Tailored telehealth pathways that incorporate user-friendly platforms, proactive technical support, and attention to vulnerable subgroups are needed to translate high satisfaction into sustained, equitable remote care. Full article

Social media has the potential to serve beneficial purposes. The abundance of uploaded content and responses from the public generates various opinions, allowing them to be identified as positive or negative regarding the portrayal of Bandung Regency. This research aims to analyse the classification and frequency of words for each sentiment expressed by X (Twitter) users regarding Bandung Regency. The research employs the Support Vector Machine (SVM) method. We expect the results to aid in formulating governmental programmes for Bandung Regency. The research revealed that the SVM model, which uses the Sigmoid kernel function with parameters $C$ = 10 and gamma ($\gamma$) = 1, is the most optimal sentiment classification model for handling an imbalanced dataset. This model achieved an 83.01% negative recall value. Furthermore, frequent words appearing in both classes indicate that several positive opinions about Bandung Regency exhibit similar dominance, except for football dominance in negative opinions. This research pertains to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice, and Strong Institutions). The suggested technique facilitates evidence-based policy reviews, transparent governance, and enhanced responsive public services by analysing public sentiment regarding local government performance. The results illustrate how social media analytics can aid local governments in assessing popular sentiment and pinpointing areas for policy response. Full article

The exponential proliferation of the Internet of Things (IoT) has catalyzed a paradigm shift in industrial automation and smart city infrastructure. However, this rapid expansion has engendered significant heterogeneity in communication protocols, creating critical barriers to seamless data integration and interoperability. Conventional gateway solutions frequently exhibit limited flexibility in supporting diverse protocol stacks simultaneously and often lack granular user controllability. To mitigate these deficiencies, this paper proposes a novel, modular IoT gateway architecture, designated as MIGS (Modular IoT Gateway System). The proposed architecture comprises four distinct components: a Management Component, a Southbound Component, a Northbound Component, and a Cache Component. Specifically, the Southbound Component employs instance-based isolation and independent task threading to manage heterogeneous field devices utilizing protocols such as Modbus, MQTT, and OPC UA. The Northbound Component facilitates reliable bidirectional data transmission with cloud platforms. A dedicated Cache Component is integrated to decouple data acquisition from transmission, ensuring data integrity during network latency. Furthermore, a web-based Control Service Module affords comprehensive runtime management. We explicate the data transmission methodology and formulate a theoretical latency model to quantify the impact of the Python Global Interpreter Lock (GIL) and serialization overhead. Functional validation and theoretical analysis confirm the system’s efficacy in concurrent multi-protocol communication, robust data forwarding, and operational flexibility. The MIGS framework significantly enhances interoperability within heterogeneous IoT environments, offering a scalable solution for next-generation industrial applications. Full article

Communication infrastructure in remote areas struggles to deliver stable, high-quality services for power systems. Low Earth Orbit (LEO) satellite networks offer an effective solution through their low latency and extensive coverage. Nevertheless, the high orbital velocity of LEO satellites combined with massive user access frequently leads to signaling congestion and degradation of service quality. To address these challenges, this paper proposes a LEO satellite handover strategy based on Quality of Service (QoS)-constrained K-Means clustering and Deep Q-Network (DQN) learning. The proposed framework first partitions users into groups via the K-Means algorithm and then imposes an intra-group QoS fairness constraint to refine clustering and designate a cluster head for each group. These cluster heads act as proxies that execute unified DQN-driven handover decisions on behalf of all group members, thereby enabling coordinated multi-user handover. Simulation results demonstrate that, compared with conventional handover schemes, the proposed strategy achieves an optimal balance between performance and signaling overhead, significantly enhances system scalability while ensuring long-term QoS gains, and provides an efficient solution for mobility management in future large-scale LEO satellite networks. Full article