Search Results (5,011)

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a periodic $M/M\left(t\right)/1$ queue whose service rate follows from a signal-to-noise-ratio (SNR)-to-rate map and construct an edge-resident controller that exploits this periodic structure for real-time quality-of-service (QoS) control. From a harmonic-balance (Fourier–Galerkin) solution of the periodic regime, the controller derives backlog and tail-probability indicators and uses them to drive admission, redundancy and handover decisions on the device. The method rests on a stability criterion and a quantitative error bound for the spectral truncation, under stated regularity and stability conditions, and is validated against Monte Carlo simulation along a ∼650 km geo-anchored corridor: on the periodic backbone, the solver matches simulation to within about $1.6\%$, and a coefficient-driven admission rule lowers the 99th-percentile delay by about $28\%$ relative to a reactive baseline at high load. On the full map-derived profile with aperiodic coverage gaps, the proposed proactive controller—spectral backbone admission combined with a radio-map look-ahead—attains the lowest mean and tail delay, about $27\%$ and $21\%$ below the reactive baseline and $54\%$ and $42\%$ below uncontrolled DropTail, with buffer overflow cut from $2.2\%$ to $0.1\%$, at a deliberate admitted-load cost (goodput ≈0.84 vs. $0.94$). An operation-count analysis indicates compatibility with sub-$100$ms control deadlines on a Cortex-A55-class system-on-chip. The controller runs on the device itself, without cloud or GPU, and the architecture is realised in a granted patent; end-to-end hardware benchmarking and an extension to non-Poisson traffic are left for future work. Full article

Background: Missed and zero-dose children remain major barriers to polio eradication in settings affected by insecurity, poverty, weak health systems, and social exclusion. This narrative review synthesized evidence on how gender norms and related social determinants influence childhood polio vaccination in Afghanistan, Nigeria, Pakistan, and Sudan. Afghanistan and Pakistan remain the only countries with endemic wild poliovirus transmission, while northern Nigeria, especially areas affected by insurgency, and war-affected Sudan continue to report substantial numbers of polio zero-dose and under-immunized children, together with occasional reports of circulating vaccine-derived poliovirus. Methods: A comprehensive search of peer-reviewed and institutional literature was conducted in major databases and relevant grey literature sources. Eligible studies examined childhood immunization or polio vaccination among children under five and reported gender-related determinants of vaccination uptake. Findings were synthesized thematically. Results: Forty-one studies were included. Five interconnected domains emerged: women’s empowerment, autonomy, and household decision-making; male involvement, household gender norms, and family power relations; socioeconomic and structural gender-related barriers; education, information access, community perceptions, and health system responsiveness; and war, insurgency and their gendered impact on childhood polio immunization. Across countries, limited maternal autonomy, restricted mobility, dependence on male decision-makers, poverty, conflict, displacement, misinformation, and weak health services reduced access to vaccination. Supportive male engagement, female health workers, trusted community leadership, and culturally responsive outreach facilitated vaccine acceptance. Conclusions: Gender influences childhood polio vaccination through intersecting household, community, structural, and health-system pathways. Family power extends beyond male decision-making, with women’s education and influence within households shaping vaccination decisions. Strengthening women’s agency, constructively engaging men (e.g., through husband schools), and delivering culturally responsive services are essential for reaching missed and zero-dose children and accelerating polio eradication. Full article

Artificial intelligence, digital payments and platform-based services are reshaping the operating conditions of Africa’s informal economy and micro-enterprise sector. While mobile money, digital marketplaces, app-mediated logistics and algorithmic scoring systems are commonly presented as instruments of financial inclusion and enterprise modernisation, their effects on informal traders remain uneven, conditional and under-governed. This systematic review synthesises recent peer-reviewed evidence on AI-mediated digitalisation pathways for informal and micro-enterprises in Africa, with particular attention to mobile money, platform payments, app-based logistics, digital credit, algorithmic management and platform governance. Following PRISMA-informed systematic review procedures, this review analyses 60 peer-reviewed, DOI-bearing articles published between April 2022 and June 2026 through a mechanism–outcome synthesis approach. The final corpus was selected through database searching, duplicate removal, title-and-abstract screening, full-text eligibility assessment, quality appraisal and mechanism–outcome coding. The findings show that digitalisation can expand market access, reduce cash-handling risks, create transaction histories, strengthen customer reach, improve operational continuity and support household resilience. However, the same digital infrastructures may also intensify livelihood vulnerability through opaque scoring, unexplained account freezes, fee shocks, exclusionary verification procedures, algorithmic ranking losses, data extraction and weak dispute-resolution mechanisms. The review therefore argues that informal enterprise digitalisation should not be understood only as a technology adoption issue, but as a socio-technical systems governance challenge. The article contributes a governance-and-risk framework linking local infrastructure, platform and payment design, AI (artificial intelligence) mediation, adoption conditions and livelihood outcomes. It further identifies minimum policy and design protections required for inclusive and resilient participation, including transparent fees, proportionate verification, human appeal channels, explainable restrictions, data-use consent, timely settlement and contingency mechanisms during digital outages or erroneous flags. The review concludes that sustainable digital inclusion for African informal micro-enterprises depends not merely on access to digital tools, but on the fairness, transparency, recoverability and accountability of the systems through which traders participate. Full article

Mobile edge computing (MEC) enables resource-constrained user devices (UDs) to obtain low-latency computing services by offloading computational tasks to the network edge. Non-orthogonal multiple access-enabled mobile edge computing (NOMA-MEC) systems feature asymmetric states across UDs, dynamic task arrivals, and competition for wireless and edge computing resources. Under these conditions, offloading decisions affect device energy consumption, task delay, and edge computing resource allocation, making long-term system optimization difficult. This study jointly optimizes task offloading and system resource scheduling to minimize the long-term delay–energy cost. The problem is formulated as a partially observable Markov decision process (POMDP) and addressed using a master-refined multi-agent proximal policy optimization (MR-MAPPO) algorithm. MR-MAPPO combines continuous action relaxation, master action refinement, and a behavior cloning auxiliary term to learn policies in a hybrid discrete–continuous action space. A marginal congestion delay term is also introduced to capture the impact of newly admitted tasks on existing edge workloads. Simulation results show that MR-MAPPO outperforms the considered baselines, while ablation studies verify the effects of its key components. Under the main experimental setting, MR-MAPPO reduces the system cost by 17.9% and 22.9% relative to standard MAPPO and particle swarm optimization (PSO), respectively. Full article

Background: Family-based interventions are effective in mitigating the risk for relapse of schizophrenia. However, the accessibility of these interventions is scarce in many clinical settings. A group-based brief cognitive behavioural intervention facilitated by a therapist using videoconferencing may help address this practice gap and improve the high treatment disengagement that occurs in interventions delivered in self-paced web-based forums or mobile applications. Objective: To examine the feasibility, acceptability, and safety of an online group-based cognitive behavioural family intervention for dyads of individuals with schizophrenia-spectrum disorders and caregivers. Methods: This feasibility study adopted a parallel-group, assessor-blind randomised controlled trial with a twelve-week post-intervention follow-up as well as individual semi-structured interviews. Participants were randomly assigned to the intervention group [i.e., tele-group cognitive behavioural family intervention (tgCBFI) group] and the treatment-as-usual group. Both groups also received biweekly brief telephone support. The feasibility and acceptability were assessed by the recruitment rate, intervention completion rate, retention rate and participants’ service satisfaction. Safety was measured by the number of adverse events. Results: Most intervention group participants (85.7%) attended all six online group sessions (six service user-caregiver dyads, corresponding to 12 participants), while 100% of participants attended the per-protocol number of sessions (≥four sessions). The study recruitment rate was 16.4%, while the study retention rate for follow-up assessments was 95.8%. No adverse events were reported throughout the study. Five themes were generated to illustrate the benefits of and recommendations for the tgCBFI programme to complement the quantitative findings. Conclusions: The preliminary results suggested that the use of videoconferencing to deliver group-based cognitive behavioural intervention was partially feasible and provided exploratory estimates suggesting possible improvement in psychiatric symptoms for individuals with schizophrenia-spectrum disorders, warranting a fully powered trial. Trial Registration: prospectively registered at ClinicalTrials.gov NCT05808244. Full article

In smart cities, crowding in transportation hubs, large event venues, and commercial/tourist districts can rapidly escalate from service congestion to public-safety incidents. Real-world operations are constrained by heterogeneous sensing coverage, delayed statistics, privacy requirements, and the need for accountable multi-agency decisions. Following a rigorous PRISMA protocol, we synthesized 107 primary empirical studies (2020–2026) to systematically review context-aware crowd technologies. Moving beyond isolated algorithmic benchmarks, we organized these advances into a mathematically formalized closed-loop framework (Sensing–Prediction–Intervention–Feedback). Crowd sensing has evolved toward edge-based computer vision, passive mobile signaling, and multimodal fusion to balance operational trade-offs among density applicability, environmental robustness, privacy burdens, and end-to-end latency. Prediction architectures—converging on Spatiotemporal Graph Neural Networks (ST-GNNs) and simulation-augmented digital twins—are critically evaluated against constraints in predictive horizon, computational overhead, and explainability. To bridge theory and practical deployment, we deduce a multidimensional evaluation taxonomy and a hierarchical trigger-and-escalation matrix, tailoring control philosophies (e.g., spatiotemporal capacity synchronization and dynamic demand reshaping) to the three heterogeneous scenarios. Finally, we propose four strategic directions to chart a deployment-oriented roadmap for the integrated Urban Brain: edge-based privacy-preserving fusion, cross-scenario generalization, accountable Explainable Artificial Intelligence (XAI) with human-in-the-loop synergy, and end-to-end outcome-oriented empirical validation. Full article

Co-production plays an important role in improving community safety worldwide. However, in China’s governance system, where governmental authority remains strong, enterprises and the public have traditionally been passive participants, creating multiple challenges for the development of co-production. To address these challenges, this study examines the behavioral patterns and strategic choices of grassroots governments, property service enterprises (PSEs), and community residents in co-producing community safety. A tripartite evolutionary game model is constructed as the analytical framework, and the “Property Deliberation Council” (PDC) practice in Changsha is used to calibrate the parameters and conduct simulation analysis. The study explores the interest interactions and stability mechanisms among the three actors in community safety co-production. The results show that, under the benchmark parameter settings, the system can evolve toward an ideal stable state characterized by guided cooperation from grassroots governments, active cooperation from PSEs, and active participation from community residents. Moderate government incentives can increase the participation benefits of enterprises and residents, whereas excessive incentives may increase the cost burden on governments. Information transparency and sanction intensity generate a synergistic constraint effect. Social capital can reduce cooperation costs, suppress opportunistic behavior, and promote the transition of co-production from external mobilization to endogenous stability. This study argues that community safety co-production in the Chinese context is neither simple administrative mobilization nor citizen-led co-production. Rather, it is a governance process jointly shaped by government guidance, the embedded role of PSEs, and resident participation. The findings provide theoretical explanations and policy implications for improving community safety governance platforms, strengthening information disclosure and accountability mechanisms, cultivating community social capital, and promoting sustainable community safety co-production. Full article

Background/Objectives: Transportation insecurity and chronic health conditions can substantially influence healthcare access and alternative in-person healthcare utilization in urban areas. Alternative healthcare delivery models, such as telehealth services and professional homecare visits, may help address mobility-related barriers. This study examines how transportation insecurity, chronic health status, and healthcare access challenges are associated with homecare visits and telehealth utilization, while also assessing differences in health and well-being perceptions among individuals with and without chronic health conditions in Dallas, Texas, and Detroit, Michigan. Methods: A cross-sectional survey design was employed among 1649 respondents using a questionnaire. Binary logistic regression analyses were conducted to examine associations between transportation insecurity, chronic health status, healthcare access challenges, homecare visits, and telehealth utilization. Mann–Whitney U tests were used to compare perceived physical health and mental health between participants with and without chronic health conditions. Results: Transportation insecurity emerged as having a significant relationship with homecare visits, with transportation-insecure respondents being approximately 4.7 times more likely to receive homecare services than transportation-secure respondents. Transportation insecurity and healthcare access challenges were also significantly associated with greater telehealth utilization in Detroit and both cities combined. Participants without chronic health conditions reported significantly more favorable perceptions of physical and mental health than those with chronic conditions. Transportation affordability, lack of available rides from family or friends, and long waiting times were identified as the most common healthcare transportation barriers. Conclusions: Healthcare utilization and well-being outcomes are significantly associated with self-reported chronic health status and transportation insecurity. Expanding transportation assistance programs, strengthening homecare services, and improving telehealth availability may enhance healthcare access and support better health outcomes among transportation-disadvantaged and chronically ill populations. Full article

The telecommunications sector continues to experience exponential growth in demand, accompanied by a corresponding increase in customer complaints regarding service quality. To effectively address these challenges, many telecom companies rely on customer feedback to assess and improve their network and services. This case study focused on a Lebanese telecom company, investigating the application of machine learning algorithms, particularly Artificial Neural Networks. The analysis performed compares the effectiveness of various optimizers and activation functions to identify the most suitable approach for our specific context. Utilizing a sample database comprising 10,000 mobile market subscribers, this study incorporates variables such as gender, age, device manufacturer, service quality, and complaint status. The results of this case study emphasize that, across various metrics, and despite its complexity, Artificial Neural Networks outperform other algorithms in terms of prediction performance. Additionally, we propose a segmented prediction model based on time intervals and customer groups to enhance prediction accuracy and practical utility. The segmentation will involve examining customer groups based on their characteristics. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article

Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, but they often require repeated numerical solving and are usually designed for a specific objective. Learning-based approaches can reduce online decision time after training; however, their effectiveness depends strongly on the reward design and the selected operating objective. In response to these challenges, we propose a Deep Hybrid Intelligent (DHI) architecture designed to evaluate dynamic uplink power management within cell-free massive MIMO environments. The framework uses Soft Actor-Critic (SAC) learning to generate continuous uplink transmit-power decisions and evaluates objective-specific configurations for fairness, signal-to-interference-plus-noise ratio (SINR) improvement, and spectral-efficiency enhancement. In addition, three optimization-based strategies, namely max-min fairness, max-product SINR optimization, and max-sum-rate maximization, are incorporated to analyze the trade-off among fairness, signal quality, throughput, and computational cost. Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) optimization is employed for the max-product and max-sum-rate objectives, while the max-min strategy is evaluated through a fairness-oriented feasibility procedure. Simulation results show that the fairness-oriented configuration achieves the highest Jain’s fairness index, reaching 0.989 at 120 access points, whereas the sum-rate-oriented configuration provides stronger SINR and user-rate performance. The results also indicate execution-time reductions of 51.6%, 83.7%, and 85.0% for the evaluated max-min, max-product, and max-sum-rate strategies, respectively, compared with conventional optimization-based implementations. These execution-time gains are accompanied by a clear performance trade-off: the max-min strategy provides the strongest fairness behavior, the max-sum-rate strategy improves total spectral efficiency and user-rate performance, and the max-product strategy offers a balanced operating point between collective SINR improvement and user-service balance. Therefore, the proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions. These results indicate that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks. Full article

Satellite–terrestrial integrated networks (STINs) can extend remote sensor telemetry, remote Internet of Things (IoT), and emergency communication services beyond terrestrial coverage, but their evaluation is complicated by heterogeneous mobility, channel, resource, and control-plane dynamics. This study presents a software-based modular testbed and performance-emulation framework for STINs. The framework integrates scenario generation, model-driven data processing, replaceable algorithm engines, scheduler-based execution control, and a Kafka-style message interface. It models terrestrial, unmanned aerial vehicle, and low-Earth-orbit satellite entities and provides link-budget abstraction, access control, mobility-aware handover, traffic generation, scheduling, load balancing, adaptive routing, and multi-mode transmission for mixed sensing and communication traffic. The representative strategies are evaluated using a lightweight emulation model parameterized by standards-informed NTN and link-budget assumptions. Representative results reveal tradeoffs between access, handover, routing, and scheduling strategies, together with sensitivity to workload, mobility, outage, demand, and selected model parameters. The proposed framework therefore supports traceable STIN strategy evaluation for remote sensor networks, sensing-data backhaul, and remote-IoT service scenarios under explicit emulation assumptions. Full article

Background: Migrant agricultural workers in Italy often experience social and health vulnerabilities, including unstable housing and limited access to primary care. In Southern Italy, many live in informal settlements and seek care through outreach services. This study describes patterns of pharmacological treatment in this population and examines how they relate to the clinical conditions managed in mobile clinics. Methods: We analyzed routinely collected data from 2928 unique patients (8547 clinical encounters; 8965 treatment occurrences) managed by Doctors with Africa CUAMM mobile clinics in 12 informal settlements in Apulia, Italy, between 2017 and 2026. Diagnoses were grouped into clinical categories, and pharmacological treatments were classified by therapeutic class. We conducted a descriptive analysis of the distribution of diagnostic categories and associated treatments. Results: The population was predominantly male (96.5%), young (81% <45 years), and largely excluded from regular primary care (93.5% without a General Practitioner). Musculoskeletal disorders and fatigue were the leading diagnostic category (34.0%), followed by gastrointestinal (13.5%) and respiratory conditions (13.0%). Non-steroidal anti-inflammatory drugs (NSAIDs) and analgesics were the most frequently recorded treatments (32.6% of treatment occurrences). Among musculoskeletal presentations, NSAIDs were used in 76% of cases. Gastroprotective agents were documented in 52% of encounters with gastrointestinal symptoms. Among cardiovascular presentations, 87.7% of treatment occurrences involved chronic management with antihypertensives or beta-blockers. Conclusions: In this outreach setting, medication use provides a descriptive picture of common health problems and treatment responses among migrant agricultural workers living in informal settlements. The prominent use of symptomatic pharmacological relief, particularly NSAIDs in musculoskeletal conditions, suggests that care is often focused on managing pain and acute complaints in a population facing barriers to continuous primary care. These findings support the need for stronger inclusion of migrant workers in the National Health Service and for policies that address underlying social and structural determinants of health. Full article

Passenger satisfaction is a key component of sustainable urban mobility, influencing public transport use, customer loyalty, and the attractiveness of railway systems. However, evidence from Latin American commuter rail services remains limited. This study evaluates the satisfaction of frequent users of the Limache–Puerto railway service operated by EFE Valparaíso in Greater Valparaíso, Chile, based on measurements conducted between 2024 and 2025. Using 400 surveys administered on station platforms and onboard trains, the analysis assessed overall satisfaction, Net Promoter Score (NPS), and experience attributes related to safety, predictability, cleanliness, information provision, comfort, and crowding. A repeated cross-sectional quantitative design with quota sampling was employed to compare successive measurement waves. Results indicate that passengers highly value the service’s speed and efficiency. Although NPS experienced a temporary decline during 2025, the final measurement remained comparable to the 2024 baseline. Key concerns included perceived platform safety, peak-hour crowding, onboard environmental conditions (temperature and odors), and service frequency in critical segments. Mentions of informal vendors decreased, whereas concerns regarding criminal incidents and passenger flow difficulties increased. The findings highlight the need for targeted interventions in safety, crowding management, environmental quality, and communication during disruptions. This pilot operational monitoring study proposes a practical framework for evaluating passenger satisfaction and NPS through repeated measurements, providing evidence to support service management and sustainable railway planning. Full article

Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches. Full article