Search Results (1,112)

This study examines how two forms of financial literacy—objective financial literacy (OFL; demonstrated knowledge of interest rates, inflation, and diversification) and perceived financial literacy (PFL; self-assessed confidence in financial matters)—relate to financial wellbeing through distinct capability pathways, and whether self-regulation conditions these links. We use three nationally representative cross-sections from Portugal (2015, 2020, 2023; N = 3648), a European setting marked by declining objective literacy and constrained market participation. Guided by capability theory, we propose a dual-lane model in which OFL operates through behavioural capability (BC; enacted saving, investing, and planning behaviours) to shape objective financial wellbeing (OFW; resilience, assets, and saving), while PFL operates through perceived capability (PC; financial self-efficacy and perceived control) to shape subjective financial wellbeing (SFW; perceived security, satisfaction, and freedom from financial stress). We also test whether non-impulsive, future-oriented behaviour (NIB) strengthens the associations along the objective lane. Structural equation models provide partial support for the dual-lane model, revealing three asymmetries with implications for European policy: (1) the link between behavioural capability and objective financial wellbeing weakens in 2023, suggesting that macroeconomic conditions can undercut even prudent financial behaviour; (2) perceived financial literacy directly predicts subjective financial wellbeing, but perceived capability does not mediate this association, indicating that financial confidence shapes wellbeing independently of self-efficacy; and (3) non-impulsive, future-oriented behaviour amplifies the association between objective literacy and objective wellbeing in 2015 and 2023 but not in 2020, showing that the benefits of self-regulation are context-dependent. The findings inform financial education and policy across Europe by distinguishing intervention levers for objective versus subjective outcomes and identifying conditions under which behavioural interventions are most effective. Full article

Rapid population ageing in China urgently demands improved attention to elderly friendly community green space design. Despite national efforts toward community renovation and urban regeneration, existing projects often overlook the systematic optimisation of green spaces explicitly tailored to elderly residents, leading to environments that inadequately support their physical, psychological, and social needs. Given that home-based care remains the predominant preference for elderly populations in China, creating optimised community green spaces is essential to facilitate healthy ageing-in-place effectively. This study systematically investigates the discrepancies between elders’ observed usage patterns and their stated landscape design preferences in two residential communities in Suzhou, China. By integrating year-round observational data with subjective interviews, the research identifies critical mismatches between elderly individuals’ actual behaviours and expressed preferences, highlighting significant deficiencies in current landscape designs. Comparative analyses reveal that prioritising microclimate comfort, accessible pathways, and targeted seating arrangements significantly enhances elderly usage frequency and satisfaction. Ultimately, this study provides practical, policy-aligned recommendations for designing climate-adaptive, elderly centric community green spaces, effectively contributing to sustainable urban renewal and the Healthy China 2030 initiative. Full article

Despite the widespread adoption of model predictive control (MPC) in railway research, the integration of intelligent learning mechanisms into train control systems operating under moving block signaling remains limited, particularly in approaches that preserve constraint satisfaction and industrial feasibility. To address this gap, this paper presents a novel learning-based model predictive control (LMPC) framework for high-speed train control under the moving block signaling principle. Moving block signaling dynamically enforces safe inter-train separation based on the absolute braking distance, imposing stringent safety, comfort, and performance constraints on train operation. The proposed LMPC exploits the repetitive nature of railway operations by progressively improving its control policy through the incorporation of historical operational data into the terminal set of the optimization problem. This learning capability enables the controller to optimize train behavior on a given line while pursuing different control objectives, namely maximum-speed operation for leading trains and minimum safe inter-train separation for following trains, in full compliance with signaling requirements, speed limits, actuator constraints, and comfort-related jerk bounds. Simulation results on a representative high-speed line show that, compared with a conventional non-learning MPC, the proposed LMPC achieves a measurable reduction in traction-related energy consumption while maintaining comparable speed profiles, travel times, and strict constraint satisfaction. These improvements are achieved through a single software-level modification of the train control algorithm, without requiring additional onboard hardware or infrastructure upgrades, positioning the proposed LMPC as a promising and practically viable solution for energy-efficient deployment in high-speed railway operations. Full article

This paper presents a decision-support framework for analysing the trade-off between total operational cost and customer service level in multi echelon inventory systems. The model integrates fixed-order-quantity replenishment policies, lead-time dynamics and multi objective optimisation to generate a detailed Pareto frontier of efficient solutions. A real multi echelon distribution network is used to demonstrate the model’s applicability and managerial relevance. The results indicate that raising the service level from 46% to the sector standard of 96% increases total cost by approximately 19%, while achieving full demand satisfaction requires an additional 5% cost increase for only marginal service improvement. This pattern reveals a clear cost–service turning point around the 96% service level, beyond which additional gains exhibit sharply diminishing returns. The framework, therefore, provides a transparent and analytical mechanism for identifying replenishment strategies that balance cost efficiency with service performance. By decomposing total cost into ordering, holding, transport and lost-sales components, the model enhances managerial visibility and supports targeted policy adjustments. The paper also discusses limitations of the current formulation and outlines avenues for future research, including alternative replenishment policies, multi-product extensions and richer uncertainty modelling. Full article

How people perceive their urban environment is often more closely related to well-being than the environment’s objective characteristics. Yet the cognitive antecedents of environmental perception remain underexplored. This study examined whether residents’ awareness of Shenzhen’s national Forest City designation is associated with subjective well-being (SWB) through perceived naturalness. A cross-sectional survey of 308 Shenzhen residents measured Forest City recognition, perceived naturalness, life satisfaction, and positive and negative affect. Residents who recognized the Forest City designation reported higher perceived naturalness, life satisfaction, positive affect, and overall SWB than those with lower recognition; the two groups did not differ in negative affect or affect balance. Structural equation modeling indicated that the association between recognition and SWB operated indirectly through perceived naturalness, with the direct path nonsignificant and the model accounting for 71% of the variance in SWB. Multiple regression confirmed that Forest City recognition was the strongest predictor of perceived naturalness after controlling for sociodemographic and behavioral covariates. These findings suggest that policy-related knowledge may serve as a cognitive antecedent of environmental perception and that the well-being outcomes of urban greening may depend partly on whether residents are aware of their city’s green identity. The results are relevant to SDG 3 and SDG 11, indicating that inclusive sustainability communication may help distribute well-being benefits equitably across urban populations. Full article

Background: The retention of Clinical Research Associates (CRAs) is critical for the integrity and sustainability of clinical trials in South Africa, an emerging hub for global clinical research. High CRA turnover threatens trial quality, data continuity, and site relationships, yet the context-specific drivers of turnover within the South African clinical research landscape remain poorly understood. This study explores the factors influencing job satisfaction and turnover intentions among CRAs to inform targeted retention strategies. Methods: A qualitative, interpretivist study was conducted using semi-structured interviews. Twelve CRAs with experience in South African Contract Research Organizations (CROs) were sampled on LinkedIn using purposive sampling. Data were analyzed iteratively using thematic analysis within Atlas.ti 26.0.1.33961 software, guided by Herzberg’s Two-Factor Theory and Mobley’s Turnover Model. Results: The analysis revealed a complex model of turnover drivers. Compensation was the most salient factor, operating not only as a hygiene factor but also as a direct motivator for job mobility in a competitive market. Unsustainable workload and a culture stigmatizing discussions of overload were key push factors. Intrinsic motivators were equally decisive: misalignment with therapeutic area preferences caused profound dissatisfaction, while alignment fostered engagement. Career growth manifested dual pathways: ambition for vertical progression and a redefined search for horizontal growth into roles offering greater work-life flexibility. Conclusions: CRA turnover is driven by an interplay of extrinsic pressures and intrinsic motivational deficits. To enhance retention, managers must adopt a multi-pronged strategy: implement market-competitive, well-being-oriented compensation; foster a culture that supports open workload dialogue; create transparent career architectures with dual progression tracks; and facilitate internal mobility across therapeutic areas. This study provides a foundational framework for developing context-sensitive retention policies, thereby contributing to the stability and quality of clinical research in South Africa. Full article

Background: Translating urban environmental health evidence into actionable policies remains challenging in South-Eastern Europe, where environmental epidemiology has yet to reach maturity and institutional capacity and cross-sector coordination are suboptimal. This study assessed stakeholders’ awareness, perceived roles, and prioritization of urban health challenges, alongside the barriers and evidence needs related to healthy and sustainable urban development. Methods: A cross-sectional online survey was conducted between March and May 2025 among 108 stakeholders identified through a collaborative evaluation process. Participants represented national institutions, municipal actors, academia, non-governmental organizations, business, and citizens. They reported on their role and influence, and were asked to identify priority urban health problems, relevant policies and actions, perceived barriers to decision-making, and expected benefits of addressing priority problems. Results: Most respondents reported limited or moderate influence on urban decision-making. Priority problems clustered around air pollution, traffic, and land-use pressures, with climate change and heat also frequently cited. Dominant barriers included lack of coordination and policy continuity, insufficient political support, and limited funding and institutional capacity. Anticipated gains centered on improved public health, cleaner air, and citizen satisfaction, with broader quality-of-life and economic co-benefits also identified. Conclusions: Prioritized urban environmental problems are largely consistent with scientific evidence on their health impacts, though certain risk factors remain underestimated. Access to specific, actionable scientific evidence and the co-production of solutions with broad stakeholder representation are essential prerequisites for effective urban health policy and practice. Full article

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have emerged as a promising architecture for beyond-5G wireless networks because they can provide user-centric coverage, improved spectral efficiency, and reduced cell-boundary limitations. However, dynamic downlink power allocation remains challenging due to user mobility, time-varying channel conditions, interference coupling, and the need to maintain Quality of Service (QoS) under practical transmit-power constraints. This paper proposes a Deep Hybrid Intelligent (DHI) framework for dynamic downlink power allocation in CF-mMIMO systems. The proposed framework integrates Soft Actor–Critic (SAC) reinforcement learning with three power-control strategies: DHI-Max-Min, DHI-Max-Product, and DHI-Max-Sum-Rate. The SAC agent learns adaptive power-allocation policies from the network state, while L-BFGS-B refinement is applied to the Max-Product and Max-Sum-Rate strategies to improve the power-allocation decisions under bounded transmit power. The framework is evaluated using a CF-mMIMO scenario with 64 access points and 32 pieces of user equipment distributed over a 1000 × 1000 m² area. The simulation results show that DHI-Max-Sum-Rate achieves the highest sum spectral efficiency, while DHI-Max-Min provides the strongest QoS-oriented performance with a QoS satisfaction rate of 93.75%. In addition, DHI-Max-Product and DHI-Max-Sum-Rate achieve mean computational times of 0.0690 s and 0.0696 s, respectively, compared with 0.63 s for the DDPG benchmark. These results demonstrate that the proposed DHI framework provides an adaptive and computationally efficient solution for QoS-aware downlink power allocation in dynamic CF-mMIMO networks. Full article

Effective emergency relief allocation in dynamic post-disaster environments depends critically on accurate and timely demand information. From a sustainability perspective, improving allocation accuracy is essential for using scarce rescue resources efficiently and supporting resilient disaster response. However, existing demand forecasting approaches frequently exhibit systematic bias, leading to resource misallocation and diminished rescue outcomes. Although deploying on-site assessment teams can partially mitigate this limitation, a unified framework that systematically embeds field assessment feedback into operational allocation processes remains lacking. To bridge this gap, this study proposes a multi-agent joint assessment-allocation model that facilitates coordinated operations between demand assessment and resource distribution activities. The sequential decision-making process is formulated as a Markov Decision Process (MDP), and deep reinforcement learning is employed to coordinate the actions of assessment and allocation teams, enabling allocation policies to be continuously refined through real-time field feedback. By improving the match between actual demand and material supply, the proposed model aims to support more resource-efficient disaster response under demand uncertainty. An empirical case study based on the 2025 Dingri County earthquake in Tibet is conducted to validate the proposed framework. Results demonstrate that integrating assessment feedback substantially improves resource allocation performance: in multi-site rescue scenarios, the framework increases the number of rescued individuals, reduces mission completion time, and enhances overall demand satisfaction. Further sensitivity analysis reveals that a moderate increase in team size strengthens cross-site coordination, whereas excessive team deployment yields diminishing returns and may generate operational redundancy. These findings suggest that sustainable emergency management depends not only on the availability of relief resources, but also on the efficient coordination of real-time information acquisition and material allocation. The proposed framework offers a generalizable approach for integrating real-time information acquisition with dynamic relief allocation. It improves the efficient utilization of scarce rescue resources, reduces avoidable operational redundancy, and strengthens the resilience of emergency response systems, thereby contributing to sustainable disaster risk reduction. Full article

Background: Research on psychological well-being has traditionally focused on individual characteristics such as personality traits, values, and beliefs. However, comparatively less attention has been paid to the sociocultural contexts in which individuals are embedded and that influence how the individual characteristics are expressed, evaluated, and rewarded. One theoretical framework that captures this interaction is person–culture match (PCM), defined as the alignment between individual traits, values, or beliefs and those prevalent within the surrounding culture. Objectives: This narrative review synthesizes conceptual and empirical research on PCM and discusses its implications for psychological well-being and broader societal consequences. Methods: A narrative review of the literature was conducted to identify key theoretical contributions and empirical studies on PCM. The reviewed literature includes cross-cultural research examining the alignment between personal characteristics and corresponding cultural characteristics, as well as its implications for well-being and broader societal processes. Results: Across a wide range of studies, individuals tend to report higher well-being when their personal traits, values, or beliefs align with characteristics prevalent within their sociocultural context. This pattern has been documented across multiple characteristics, including personality traits, religiosity, political ideology, and personal values. Higher PCM has been associated with higher life satisfaction, greater positive affect, stronger self-esteem, and lower levels of stress and depressive symptoms. Conclusions: The literature suggests that well-being is shaped not only by individual characteristics but also by their alignment with one’s sociocultural contexts. Future research is needed to clarify the mechanisms underlying these effects and to explore the broader societal consequences of PCM. Considering cultural alignment may therefore be valuable for both advancing research and informing public health strategies and policy interventions aimed at enhancing well-being and social cohesion. Full article

The rapid integration of artificial intelligence in higher education positively affects student satisfaction, engagement, and learning outcomes. However, students frequently report ethical unease, guilt, and concerns about dependency. The current literature offers a limited explanation for their coexistence, as both have been treated as parallel or independent outcomes. Hence, this review extends and integrates existing theories by reconceptualising cognitive and moral dissonance as a central psychological process that explains how student satisfaction with AI-mediated learning is produced, negotiated, and sustained. Following PRISMA 2020 guidelines, we adopted a two-layer explanatory review design, synthesising 40 Scopus-indexed studies (Layer 1 = 15 studies; Layer 2 = 25 studies) from 2016 to 2025. Layer 1 studies explicitly define dissonance-related explanatory mechanisms that influence satisfaction and continued AI use across contexts such as dissertation writing, programming education, and problem-based learning. Layer 2 encompasses satisfaction-based studies that report ethical or affective concerns in parallel without theorising their interaction. The findings suggest a recurring satisfaction–dissonance paradox, in which students often experience genuine or conditional satisfaction from performance gains while simultaneously managing their psychological discomfort through one or more regulation mechanisms. Further, persistent and escalated dissonance leads to withdrawal or full or partial adaptive behaviour. We propose these dynamics as a testable Dual-Process Satisfaction–Dissonance Framework (DPSDF), which includes five dissonance triggers, five regulation strategies, three feedback loops, and four behavioural outcomes. Further, five domain experts’ suggestions have been taken to provide specific practical implications. This framework extends understanding of AI-mediated learning and provides foundations for future theory and policy development in higher education. Full article

Role-Based Access Control (RBAC) is the de-facto mechanism for preserving Kubernetes and other cloud-native container platforms, however real deployments occasionally drift away from the principle of least privilege as clusters, teams, and services improve. This paper introduces an automated RBAC hardening framework that formulates least-privilege policy design as a limited optimization problem over RoleBindings and ClusterRoleBindings. The objective combines (i) a permission-risk score for namespaced and cluster-scoped actions with (ii) an operational complexity term that discourages overly large binding sets. Solid limitations encode functional requirements as well as practical security policies, which includes namespace allowlists, role scoping rules, administrative restrictions on cluster-wide bindings, binding budgets, and separation-of-duty requirements expressed by utilizing capability classes. To allow optimizer-agnostic search while protecting Kubernetes RBAC semantics, we analyze candidate policies by utilizing a unified penalty-based fitness function that compines risk, complexity, and constraint violations into a single scalar value. We utilized ten metaheuristic as a benchmark including baseline search paths on a Kubernetes-inspired instance and report feasibility and least-privilege quality metrics (precision, recall, F1, and over-privilege ratio) parallel to RB/CRB counts and excess risk as a structural indicators. Outcomes present that feasibility is the prime challenge, and is restricted to a subset of optimizers reliably arrives to entirely feasible and compact arrangements within the exact budget, indicating the practicality of metaheuristic enhancement for systematic RBAC reduction in containerized cloud computing environments. Full article

Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switching rules, tabular states, and ad hoc training schedules. This paper proposes RL-JSO, a hybrid framework in which a dueling double deep Q-network with prioritized experience replay adaptively selects among the drift, passive, and active phases of a jellyfish search optimizer, replacing the deterministic time-control rule with a learned policy. The framework integrates a five-layer hierarchical safety control mechanism, a mastery-gated nine-stage curriculum, and a shared reward module that architecturally enforces fairness between RL-JSO and a paired RL-PSO counterpart. Evaluation across four progressive campaigns with 160 independent runs per algorithm shows that, within the evaluated JSO/PSO family, RL-JSO is the only method that sustains a 100% collision-free rate across all four progressive difficulty campaigns, its Cliff’s delta over standard JSO grows monotonically with difficulty from medium to large, and under a composite cooperation metric its coordination score remains nearly invariant while comparators degrade by 17–23%. A paired inference-time ablation on the trained checkpoint provides controlled inference-time evidence that adaptive phase switching is a principal contributor to the observed test-time performance within the trained system, rather than the heuristic fallback layers. Full article

As a multi-functional systemic carrier, rural tourism integrates diverse rural resources and serves as a key endogenous driver for sustainable rural development and the enhancement of rural residents’ livelihoods. However, excessive tourism development may lead to environmental pressures and exacerbate inequities in benefit distribution, rendering well-being gains uncertain. This study aims to explore the multidimensional mechanisms through which rural tourism influences rural residents’ well-being by utilizing national data from the 2020 China Rural Revitalization Survey (CRRS). The results indicate that village-level tourism development exerts a positive effect on material and psychological well-being. Effects are particularly strong in eastern and hilly regions and in villages where the party secretary also serves as committee director. Further analysis identifies four channels through which rural tourism enhances well-being: fostering digital financial inclusion, advancing empowerment reforms, reallocating resources, and optimizing governance frameworks. Additionally, tourism development leads to improvements in indicators such as road quality, living environment, and satisfaction with village committee performance—while highlighting policy attention to social security, housing, and income satisfaction. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article