Search Results (4,846)

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental disorder characterized by social, communication, and behavioral challenges. Complementary and Alternative Medicine (CAM) is widely used by parents worldwide, yet research exploring parents’ experiences, particularly in Arab countries, is limited. This study explored mothers’ perceptions and experiences of CAM use for children with ASD, information-seeking behaviors and challenges encountered. Methods: A qualitative study using semi-structured interviews was conducted among twenty mothers at Autism Academy of Jordan in 2024. Inclusion criteria were mothers with children diagnosed with ASD for at least six months and those who had used at least one CAM therapy. Interviews were conducted via Skype, transcribed verbatim, and analyzed using NVivo 12 with inductive thematic analysis. Results: Three major themes emerged in this qualitative study: (1) mothers’ experiences with CAM and perceptions of benefit; (2) sources of information and decision-making processes; and (3) main challenges in selecting and implementing CAM. Mothers reported using therapies such as honey, black seed, camel milk, Hujama, olive oil, supplements, and region-specific programs like Andalosiah. Faith, cultural beliefs, and the desire for natural, safe interventions strongly influenced CAM selection. Internet searches and social media groups were primary information sources. Challenges included financial, logistical, emotional burdens, and lack of trustworthy, Arabic-language information sources. Conclusions: Mothers in Arab countries navigate CAM use for their children with ASD through culturally and religiously informed practices. Interventions should focus on developing evidence-based guidance, culturally sensitive counseling, and accessible information to support families in safe, informed CAM use. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

Background: Language barriers remain a major obstacle to equitable healthcare access for immigrants and refugees across Europe. Greece, as both a transit and host country, faces persistent challenges in providing linguistically and culturally appropriate care. Methods: This study presents a narrative literature review synthesizing international, European, and Greek evidence on the effects of limited language proficiency, professional interpretation, and intercultural mediation on healthcare access, patient safety, satisfaction, and clinical outcomes. Peer-reviewed studies and selected grey literature were identified through searches of PubMed, Scopus, Web of Science, and CINAHL. Results: The evidence consistently demonstrates that the absence of professional interpretation is associated with substantially higher rates of clinically significant communication errors, longer hospital stays, increased readmissions, and higher healthcare costs. In contrast, the use of trained medical interpreters and intercultural mediators improves comprehension, shared decision-making, patient satisfaction, and clinical outcomes. Comparative European data from Italy, Spain, Germany, and Sweden show that institutionalized interpretation systems outperform Greece’s fragmented, NGO-dependent approach. Greek studies further reveal that limited proficiency in Greek is associated with reduced service utilization, longer waiting times, and lower patient satisfaction. Conclusions: This narrative review highlights the urgent need for Greece to adopt a coordinated, professionally staffed interpretation and intercultural mediation framework. Strengthening linguistic support within the healthcare system is essential for improving patient safety, equity, efficiency, and the integration of migrant and refugee populations. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

This study aims to establish an integrated landscape planning model and explore its applicability through the convergence of digital twin technology. The primary goal is to address the fragmented implementation of landscape policies and to provide a systematic framework that enhances efficiency and visualization in the planning process. To this end, text-mining analysis was conducted to extract relevant laws, statutory plans, and project data, thereby identifying key factors for model construction. The resulting model integrates conservation-oriented and recreation-oriented modules, presenting a practical approach for landscape management. Furthermore, by utilizing Blender 3D and OpenStreetMap, this study demonstrates the process through which a digital twin visualizes and simulates the spatial characteristics of the actual target site, thereby validating its utility in decision-making and stakeholder communication. The results indicate that the landscape planning model was reconfigured and integrated into 6 detailed implementation measures and 41 specific indicators. Moreover, the model visually linked 36 laws and approximately 70 plans and projects. Ultimately, the study confirms that the proposed approach provides a dynamic, data-driven platform for sustainable landscape management. Full article

Freshwater ecosystems are increasingly threatened by pollution, hydromorphological alteration, invasive species, and loss of ecological connectivity, complicating the monitoring and conservation of native fish communities. Environmental DNA (eDNA) has emerged as a sensitive, non-invasive, and cost-effective tool for detecting species, including rare or low-abundance taxa, overcoming several limitations of traditional methods. However, its rapid expansion has generated methodological dispersion and heterogeneity in protocols. This systematic review and bibliometric analysis synthesize 131 articles published between 2020 and 2025 on the use of eDNA in freshwater fish conservation. Due to the strong methodological heterogeneity among studies, the evidence was synthesized through a structured qualitative approach under PRISMA standards. Results show rapid growth in scientific output since 2023. eDNA has proven highly effective in identifying key ecological patterns such as migration and spawning, detecting critical habitats, and supporting temporal and spatial assessments. It has also facilitated early detection of invasive species including Oreochromis niloticus, Oncorhynchus gorbuscha, and Chitala ornata, and improved monitoring of threatened native species, reinforcing conservation decision-making. Despite advances, challenges persist, including variability in eDNA persistence and transport, gaps in genetic reference databases, and a lack of methodological standardization. Future perspectives include detecting parasites, advancing trophic analyses, and integrating eDNA with ecological modeling and remote sensing. Full article

Background/Objectives: Birth preparedness and complication readiness (BPCR) is a cornerstone of maternal health strategies designed to minimize the “three delays” in seeking, reaching, and receiving skilled care. In Ethiopia, uptake of BPCR remains insufficient, and little evidence exists on how individual- and community-level factors interact to shape preparedness. This study assessed the determinants of BPCR among women of reproductive age in Hawela Lida district, Sidama Region. Methods: A community-based cross-sectional study was conducted among 3540 women using a multistage sampling technique. Data were analyzed with multilevel mixed-effect negative binomial regression to account for clustering at the community level. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were reported to identify determinants of BPCR. Model fitness was assessed using Akaike’s Information Criterion (AIC), the Bayesian Information Criterion (BIC), and log-likelihood statistics. Results: At the individual level, women employed in government positions had over three times higher expected BPCR scores compared with farmers (AIRR = 3.11; 95% CI: 1.89–5.77). Women with planned pregnancies demonstrated higher BPCR preparedness (AIRR = 1.66; 95% CI: 1.15–3.22), as did those who participated in model family training (AIRR = 2.53; 95% CI: 1.76–4.99) and women exercising decision-making autonomy (AIRR = 2.34; 95% CI: 1.97–5.93). At the community level, residing in urban areas (AIRR = 2.78; 95% CI: 1.81–4.77) and in communities with higher women’s literacy (AIRR = 4.92; 95% CI: 2.32–8.48) was associated with higher expected BPCR scores. These findings indicate that both personal empowerment and supportive community contexts play pivotal roles in enhancing maternal birth preparedness and readiness for potential complications. Random-effects analysis showed that 19.4% of the variance in BPCR was attributable to kebele-level clustering (ICC = 0.194). The final multilevel model demonstrated superior fit (AIC = 2915.15, BIC = 3003.33, log-likelihood = −1402.44). Conclusions: Both individual- and community-level factors strongly influence BPCR practice in southern Ethiopia. Interventions should prioritize women’s empowerment and pregnancy planning, scale-up of model family training, and address structural barriers such as rural access and community literacy gaps. Targeted, multilevel strategies are essential to accelerate progress toward improving maternal preparedness and reducing maternal morbidity and mortality. Full article

With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article

Background: Effective preoperative patient counseling is essential to shared decision-making. In thyroid surgery, patient communication can be complicated by the complex anatomy and variable surgical approaches, which may not be fully conveyed through conventional verbal explanations or schematic drawings. Virtual three-dimensional (3D) thyroid models may provide an intuitive tool to enhance patient comprehension. Methods: We conducted a randomized controlled trial at Peking Union Medical College Hospital with 94 newly-diagnosed thyroid cancer patients scheduled for thyroidectomy. Participants were assigned to either the control group (n = 47), which received preoperative drawing-based counseling, or the intervention group (n = 47), which utilized a virtual 3D model for communication. The Thyroid Navigator app, developed by Kuma Hospital, was used to provide dynamic 3D representation of the thyroid gland, surrounding structures, and potential surgical procedures. After standardized preoperative consultations, patients were surveyed to assess their understanding in pertinent anatomy and postoperative complications. Results: Patients in the 3D model group demonstrated similar correct response rates in lesion localization (p = 0.536) or parathyroid gland recognition (p = 0.071), but significantly higher accuracy in identifying the recurrent laryngeal nerve and the extent of lymph node dissection compared with the control group (p < 0.05). Moreover, comprehension of the causes of major postoperative complications—including hoarseness (recurrent laryngeal nerve injury, p = 0.004), hypocalcemia (parathyroid gland impairment, p = 0.015), and bleeding (inadequate hemostasis, p = 0.008)—was significantly improved in the 3D model group. Conclusions: Use of a virtual 3D thyroid model significantly improves patient comprehension of thyroid anatomy, surgical procedures, and potential complications, thereby enhancing clinician–patient communication. Virtual 3D models represent a practical and cost-effective supplement to conventional counseling in thyroid surgery, offering clear benefits in patient education and shared decision-making. Full article

As global agriculture seeks to reconcile the dual imperatives of food security and environmental sustainability, this study examines the role of Internet access in promoting green agricultural production, specifically by reducing fertilizer and pesticide use. Using a panel dataset from 16 rural fixed observation points in Henan Province from 2009 to 2022, we find that Internet access significantly lowers per-unit farmland expenditures on fertilizers and pesticides by 6.0% and 7.3%, respectively. Mechanism analysis reveals that these positive effects operate through three main channels: improved information accessibility delivers timely agricultural data and guides input decisions; enhanced technical learning efficiency reduces barriers to adopting green technologies; and stronger market connectivity via e-commerce platforms shortens supply chains and provides price incentives. Heterogeneity analysis further identifies more pronounced effects among farmers with higher human capital (higher education, better health, younger age), higher production capital (greater mechanization, larger farmland, stronger decision-making capacity), lower livelihood capital (lower income, lower consumption, less communication expenditure), and higher spatial capital (residing in urban suburbs, poverty registration villages, and traditional villages). This study provides micro evidence for digital technology to empower sustainable agricultural development and provides policy implications for building a sustainable agri-food system. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures. First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

Background/Objective: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have received widespread attention as effective obesity treatments. However, limited research has examined the perspectives of patients contemplating GLP-1RAs. This study explored perceptions, motivations, and barriers among individuals considering GLP-1RA therapy for obesity treatment, with the goal of informing patient-centered care and enhancing clinician engagement. Methods: Adults completed surveys and interviews between June and November 2025. In this pragmatic mixed-methods study, both survey and interview questions explored perceived benefits, barriers, and decision-making processes. Qualitative data, describing themes based on the Health Belief Model, were analyzed using Dedoose (version 9.0.107), and quantitative data were analyzed using SAS (version 9.4). Participant characteristics included marital status, income, educational attainment, employment status, insurance status, age, race/ethnicity, and sex. Anticipated length on GLP-1RA medication and selected self-reported health conditions (depression, anxiety, hypertension, heart disease, back pain, joint pain), reported physical activity level, and perceived weight loss competency were also recorded. Results: Among the 31 non-diabetic participants who were considering GLP-1RA medication for weight loss, cost emerged as the most significant barrier. Life course events, particularly (peri)menopause among women over 44, were commonly cited as contributors to weight gain. Participants expressed uncertainty about eligibility, long-term safety, and treatment expectations. Communication gaps were evident, as few participants initiated discussions and clinician outreach was rare, reflecting limited awareness and discomfort around the topic. Conclusions: Findings highlight that individuals considering GLP-1RA therapy face multifaceted emotional, financial, and informational barriers. Proactive, empathetic clinician engagement, through validation of prior efforts, clear communication of risks and benefits, and correction of misconceptions, can support informed decision-making and align treatment with patient goals. Full article

This study aims to assess the perceived quality of telecommunication services in Portugal and examine how governance practices influence organizational performance, addressing the lack of empirical evidence on service quality gaps in the Portuguese telecommunications sector. Specifically, it investigates the alignment between customers’ expectations and perceptions of service delivery among major telecommunications providers in northern Portugal. A convenience sample of 119 subscribers was collected through an online questionnaire disseminated via social media and email. The survey measured service quality across the five SERVQUAL dimensions (tangibles, reliability, responsiveness, assurance, and empathy), and sociodemographic variables (gender, age, and education) were recorded to explore their influence on customer satisfaction and perceived quality. Results reveal a consistent gap between expectations (6.51) and perceptions (5.54), particularly in reliability and responsiveness, despite generally positive evaluations of tangibility and assurance. Sociodemographic factors significantly influenced satisfaction levels and perceptions of service quality, highlighting the importance of tailored governance strategies. These findings demonstrate that effective governance and quality management are interdependent drivers of sustainable competitiveness in technology-intensive sectors. By identifying specific quality gaps and their drivers, this study provides actionable insights for improving service delivery. Enhancing organizational reliability, responsiveness, and empathy—supported by transparent communication and data-driven decision-making—is essential for improving customer trust, operational resilience, and long-term performance. By integrating continuous quality assessment into administrative strategy, telecommunications firms can enhance service excellence and contribute to broader goals of sustainable economic development and digital transformation. Full article