Search Results (843)

Background/Objectives: Psychiatric inpatient care is characterised by power asymmetries between care providers and care receivers. Despite international guidelines and policies promoting autonomy, involvement, and empowerment, these ideals remain challenging to implement in clinical practice. Psychiatric self-admission has been developed to strengthen autonomy; however, its implementation may challenge established power structures, professional roles, and responsibilities within mental healthcare settings. This study aimed to explore stakeholder perspectives on power transfer during the implementation of psychiatric self-admission in Scandinavia. Methods: A qualitative multi-source study was conducted using semi-structured interviews, focus group interviews, and document collection. Interviews were conducted with 36 participants involved in the development and implementation of self-admission in Denmark, Norway, and Sweden. Additionally, approximately 250 documents were gathered. The documentary material was analysed alongside the interviews and focus groups to provide an understanding of the implementation of self-admission models and how power transfer was represented within these processes. The analyses were inspired by methods in qualitative content analysis and document analysis. Results: Findings on stakeholder perspectives on power transfer during implementation revealed a tension reflected in two subthemes: ‘Responsive and Responsible’, where self-admission was viewed as a necessary response to needs that the healthcare system was unable to address, and ‘Unsafe and Unsound’, where self-admission was associated with uncertainties and risks related to losing control. Conclusions: Implementing psychiatric self-admission, which involved challenging traditional power relations in healthcare, was permeated by trust, distrust, and fear. The findings highlight the need to address stakeholders’ concerns related to safety and professional responsibility. Full article

Monocular 3D human pose estimation is sensitive to depth ambiguity and upstream 2D-detector error. We investigate a detector-conditioned virtual 2D pose as an auxiliary hypothesis rather than an independent physical measurement. Joint reliability combines training-subject-only monotonic confidence calibration with detector-, joint-, and confidence-specific residual risk before a compact pose lifter. A four-arm factorial design separates observed-only (O), observed plus virtual (OV), observed plus reliability (OR), and observed plus virtual plus reliability (OVR). Across three subject-disjoint splits, three seeds, two RGB detectors, and 48 subject–sequence–camera clusters, OVR improved mean per-joint position error (MPJPE) by 3.049 mm (95% cluster-aware confidence interval (CI): 0.583–4.976); reliability-only improvement was supported, whereas the conditional virtual increment and interaction were not. Validation-only sensitivity covered eight yaw/pitch hypotheses and five multi-view policies; no multi-view policy improved primary MPJPE over the locked $-{15}^{\circ }$ view. A component-level audit of two synchronized directed camera pairs favored the generator over the observed-pose baseline in all 24 stratified rows, with only two sequence clusters per row. The locked MPI-INF-3DHP Official TS1–TS6 result improved MPJPE by approximately 0.97%, below its prespecified 2% practical threshold, with a CI crossing zero. On an additional external 3DPW cohort (13,579 eligible frames, 24 sequences, YOLO11L-Pose only), OVR improved MPJPE by 1.004 mm (95% CI: −2.965–5.016), while its Procrustes-aligned MPJPE point estimate worsened by 0.640 mm; neither contrast was conclusive. Thus, calibrated reliability is the most consistently supported mechanism, whereas virtual-view dominance, official superiority, and cross-dataset superiority are not established. Full article

Frontier financial markets face a diagnostic gap in forecasting volatility: linear and single-regime GARCH fails to capture breaks, spillovers, and regime transitions. Despite the importance of these markets, there is a gap in the literature: lack of a Kenya-specific, regime-sensitive Early Warning System (EWS) that can integrate long-memory filtering of volatility with nonlinear classification models. Therefore, policymakers lack signals to anticipate systemic stress. This study constructed a multi-stage pipeline using 6703 daily observations of the NSE 20 Share Index, USD/KES exchange rate, and Brent spot prices (1997–2024). K-Means clustering, FIGARCH filtering, LSTM–XGBoost ensemble classification, a logistic threshold model, and VaR/ES back-testing were used to generate alarm signals and validate risk-management performance. Across model estimation and evaluation phases, time horizons were assessed, showing that one-day signals were reactive and ten-day forecasts diluted precision, while five-day predictions achieved the strongest balance. Across model iterations, accuracy improved from 0.86 in the initial FIGARCH–LSTM pipeline, to 0.92 in the unbalanced classification model, and 0.94 in the weighted baseline, culminating in 0.98 with the final hybrid LSTM–XGBoost ensemble. The ensemble model delivered robust detection across Calm (F1 = 0.99), Moderate (F1 = 0.82), and Stress (F1 = 0.69) regimes. Stress thresholds were developed to translate regime signals into actionable alarms. This study provides an empirical application of a hybrid framework, combining long memory modelling of volatility with the adaptability of deep learning models to deliver Basel-compliant tail-risk alarms and a state-dependent policy matrix for regulators. Full article

Accurate carbon price prediction and fluctuation analysis are essential for carbon market risk management and achieving carbon neutrality goals. However, carbon prices exhibit complex multi-scale nonlinear dynamics intertwined with time-varying external factors, hindering reliable forecasting. This study constructs a hybrid prediction framework integrating CEEMDAN, Sample Entropy (SE) reconstruction, and the Coefficient of Variation (VC) ensemble algorithm. SHapley Additive exPlanations (SHAP) quantifies scale-specific nonlinear factor contributions, while TVP-SV-VAR captures dynamic carbon price-driver correlations. Empirical results indicate that the CEEMDAN-SE preprocessing strategy significantly improves the prediction accuracy of baseline models. The CEEMDAN-SE-GRU model achieves optimal performance, with an R² of 0.96 and over 60% reductions in both MSE and MAE relative to the baseline GRU model. Meanwhile, the VC ensemble outperforms single models and alternative fusion strategies. SHAP identifies scale-heterogeneous drivers: short-run prices follow sentiment and macro outlooks, medium-run trends tie to industrial costs and global markets, long-run paths align with energy transition and global climate governance. The TVP-SV-VAR model uncovers significant time-varying spillover effects on raw carbon prices. Based on these findings, we recommend establishing multi-layered dynamic monitoring and early warning systems, constructing a differentiated and adaptive policy toolkit, refining cross-market risk isolation and buffering mechanisms, and advancing institutional improvements through gradual implementation. Full article

Artificial intelligence (AI) is reshaping financial markets—accelerating execution, automating allocation, and concentrating analytical capacity within a shrinking set of foundational models. Prior work has documented AI’s contribution to instability through algorithmic herding and high-frequency volatility, but the literature lacks a coherent classification of the mechanisms through which AI triggers catastrophic, self-reinforcing market dislocations, or “flash crashes.” This prospective review proposes a three-category taxonomy: (1) endogenous algorithmic herding crashes, driven by correlated model behavior; (2) exogenous model error cascade crashes, in which AI system failures propagate across interconnected venues; and (3) adversarial generative AI (GenAI) disinformation crashes, in which fabricated narratives trigger automated trading responses. The taxonomy is further motivated by the structural parallel between contemporary AI model homogeneity and the homogenization of Value-at-Risk (VaR) models before the 2008 crisis—a link recently formalized in the modeling literature. The analysis is extended to the emerging frontier of agentic AI—autonomous systems capable of multi-step planning and inter-agent interaction—which introduces qualitatively new systemic risks that existing regulatory frameworks are unprepared to address. The October 2025 cryptocurrency liquidation cascade, which liquidated over $19 billion within 24 h, serves as the primary empirical case study. The article concludes with policy recommendations on model-diversity mandates, real-time AI trading surveillance, adaptive circuit-breaker design, and cross-regulatory coordination on GenAI financial disinformation. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Forecasting USD/CNY exchange rate volatility is of substantial practical significance for the management of cross-border capital flows and the formulation of monetary policies. In recent years, multiple external shocks arising from China–US geopolitical tensions and sharp fluctuations in the international crude oil market have become increasingly intertwined. Traditional forecasting models are often unable to capture such sudden risk signals in a timely manner, while a single model is also insufficiently adaptable to multi-scale volatility structures. To address these challenges, this paper develops a deep-learning-based dynamic forecasting framework that integrates a China–US geopolitical risk feature system (CUGRI) with international oil price shock signals. Specifically, CUGRI is constructed from news texts published by 15 authoritative Chinese and US media outlets, combining a finance-specific language model with a geopolitical-domain sentiment lexicon to build a five-dimensional daily risk quantification feature system. At the modeling level, this paper proposes a GRU-BiLSTM-Transformer dynamic fusion model, which adaptively assigns fusion weights according to the recent forecasting performance of each sub-model. All empirical analyses are implemented under the Python programming environment with the PyTorch deep learning framework. Using nearly ten years of daily data, this paper conducts out-of-sample forecasting tests. The empirical results show that the proposed dynamic fusion model achieves an out-of-sample $R^2$ of 0.342, while reducing RMSE and MAE by 4.01% and 3.72%, respectively, relative to the best-performing baseline model. CUGRI exhibits significant incremental predictive value, with forecasting gains substantially higher during periods of elevated geopolitical risk than under normal conditions. Oil price shocks provide complementary predictive information, and the dynamic weighting mechanism further improves the accuracy of combined forecasts. In practice, the findings are relevant to cross-border firms and financial institutions when tracking changes in geopolitical and energy-market risks and adjusting foreign-exchange hedging strategies. For monetary and regulatory authorities, the model helps identify periods when external shocks may amplify USD/CNY volatility and supports exchange-rate risk surveillance and macro-financial stability analysis. Full article

Real estate market risk has long been a core policy concern for governments worldwide, as risks across the land, real estate, and financial markets are inherently deeply coupled. Accurate measurement and early warning of systemic risks in the land–real estate–financial market system constitute a critical prerequisite for forestalling major economic fluctuations. In the era of big data, the proliferation of high-frequency, large-scale, and multi-dimensional market data poses formidable challenges to conventional risk assessment frameworks. Grounded in the perspective of interlinkages among the land, real estate, and financial markets, this paper employs the BEKK-GARCH model to construct a time-varying composite risk index and further incorporates the CNN-LSTM machine learning model to provide early warning of coupled risks in the land–real estate–financial market system. The empirical results demonstrate that the constructed composite risk index exhibits strong validity and high sensitivity, and the findings remain robust under stochastic scenarios. Compared with other benchmark early-warning models, the CNN-LSTM model delivers superior overall early-warning performance. The findings of this study carry significant practical implications for dynamically monitoring and providing early warning of real estate market risks in the context of big data, curbing cross-market risk contagion, and safeguarding the sustainable and sound development of the land–real estate–finance nexus. Full article

Background: Population aging has led to an increasing proportion of older adults (aged 65 and older) acting as informal caregivers. These caregivers face risks of burden, stress, and depression. While research frequently documents negative psychological outcomes, resilience represents a crucial protective process. This systematic review synthesizes and maps the empirical evidence regarding the association between the socio-ecological resilience process and the well-being of older informal caregivers. Methods: Following Joanna Briggs Institute (JBI) mixed-methods guidelines, a convergent segregated mixed studies systematic review was conducted. A systematic search of Medline (PubMed), CINAHL, PsycINFO, and Scopus identified primary articles (2014–2025) in English and Spanish. Methodological quality was appraised using JBI critical appraisal checklists and the Mixed Methods Appraisal Tool. PROSPERO registration number: CRD420251142190. Results: Thirty-one studies were included. Elevated caregiver resilience was associated with lower reported symptoms of depression and anxiety, higher self-rated health, and positive psychological adaptation. Framed within a social ecological model, personal resources such as spirituality, hope, and self care, alongside relational assets like dyadic relationship quality and mutual coping, demonstrated a positive association with resilience and a reduction in subjective burden. Within the community context, informal peer networks and Online Health Communities functioned as essential supportive resources. On a structural level, both household wealth and the regional availability of long-term care beds acted as moderators for spousal well-being, whereas exceeding 30 weekly caregiving hours acted as a temporal threshold for positive adaptation. Conclusions: These findings suggest that resilience in older caregivers may be best conceptualized not as a static individual trait, but as a multi-level, dynamic socio-ecological process. Rather than relying on individual coping alone, public policies and clinical practice should prioritize systemic, relational, and structural environmental support, including formal respite services and long-term care infrastructure, to preserve the well-being of older spousal caregivers. Full article

Water management in Arid and Semi-Arid Regions (ASARs), specifically in the Gulf Cooperation Council (GCC) countries, has historically relied on large-scale, centralized systems that have successfully expanded potable water access. However, their high energy intensity, escalating operating costs, and limited flexibility amid increasing climate variability have raised concerns about their long-term sustainability. In this context, decentralized water systems (DWSs), including rainwater harvesting (RWH), greywater reuse (GWR), and hybrid rainwater–greywater systems (HRGSs), offer promising solutions to reduce pressure on centralized infrastructure, enhance dry-season water availability, and mitigate urban flooding risks. Despite their strategic relevance, a comprehensive sustainability assessment framework tailored to GCC conditions remains insufficiently developed. To address this gap, a systematic review of literature indexed in Scopus, Engineering Village, and Google Scholar was conducted. Thirty studies met the inclusion criteria and were critically analyzed to identify prevailing assessment approaches and recurring sustainability dimensions. Building on these findings, this study proposes a regionally tailored, multi-criteria sustainability framework designed specifically for GCC contexts. The proposed framework integrates five core dimensions, including technical, environmental, economic, social, and political–institutional, comprising 14 indicators and four sub-indicators. To refine and validate the framework, a two-round Delphi technique was conducted. A total of 102 experts from GCC member states were invited, of whom 43 participated in the first round, and 25 completed the second round. The results demonstrated strong consensus regarding the relevance and applicability of the selected indicators, with particular emphasis on technical and environmental dimensions. Notably, the lack of agreement on equal weighting in the first round justified the adoption of a ranking-based weighting approach in the second round, enabling a more realistic representation of expert consensus. The final DWS index, developed using a hierarchical multi-criteria decision analysis (MCDA) approach, integrates criterion weights, indicator weights, and performance scores into a single composite metric. The results indicate that HRGSs achieved the highest overall performance (55.00), followed closely by GWR (54.76) and RWH (54.20). Overall, the proposed framework provides a robust and context-specific tool to support sustainability assessment and inform policy development for DWSs in the GCC region. Full article

To address the overlapping risks and weak agricultural resilience in karst underdeveloped mountainous areas, this study examines 110 new-type rural collective economy projects in Hechi City, Guangxi. Grounded theory is used to qualitatively identify risk factors. The analytic hierarchy process and fuzzy comprehensive evaluation are then combined to quantify risk indicator weights and overall risk intensity, clarify how various risks constrain agricultural resilience, and construct risk control pathways to enhance resilience. Findings reveal that risks fall into four categories—systemic, external environmental, operational and management, and financial and capital risks—encompassing ten secondary dimensions. The overall risk level is moderate but clearly stratified. Market price, capital recovery, operational and sales, investment decision-making, and internal governance are relatively high-risk dimensions. Specifically, policy support risk carries the greatest relative importance weight, while natural disaster risk has the lowest current intensity. Guided by prioritizing relatively high-risk areas for targeted reinforcement and routinely consolidating moderate risks, this paper proposes a three-dimensional strategy: targeted risk control, routine foundation consolidation, and multi-dimensional safeguards. This provides theoretical support and practical reference for precise risk governance and synergistic agricultural resilience enhancement in ecologically fragile karst regions. Full article

As a potential contributor to urban sustainability, the commercialization of Urban Air Mobility depends on social license rather than mere technical feasibility. To address the limitations of the Technology Acceptance Model (TAM) in high-risk contexts, this study integrates trust, perceived risk, personal innovativeness, and governance expectation to construct an extended acceptance model tailored to China’s policy-driven institutional environment. Based on 567 valid samples analyzed via Structural Equation Modeling (SEM) and Multi-Group Analysis (MGA), the results reveal three core mechanisms. First, perceived risk positively enhances perceived usefulness (β = 0.765, p < 0.001) via risk-induced cognitive reframing, indicating that the public rationalizes threats by amplifying the technology’s functional value. Second, trust exhibits a strong compensatory effect on perceived ease of use (β = 0.885, p < 0.001) and directly drives behavioral intention (β = 0.549, p < 0.001), serving as a heuristic to reduce perceived complexity in risky decisions. Third, governance expectation directly drives behavioral intention (β = 0.225, p = 0.004) and attitude (β = 0.201, p = 0.007), confirming the primacy of institutional trust in China. The model explains 50.5% of the variance in behavioral intention (R² = 0.505) and 48.6% in attitude (R² = 0.486). Notably, the direct effects of perceived ease of use on intention were non-significant, redefining TAM boundaries where safety supersedes efficiency. Finally, gender moderates the covariance between innovativeness and governance expectation (female β = 0.765 vs. male β = 0.720, C.R. = 3.196, p = 0.001), revealing higher institutional dependency among females. These findings elucidate the risk–trust institution nexus in UAM, offering empirical evidence for differentiated sustainable transport policies and marketing strategies. Full article

Against a backdrop of growing rural differentiation and ongoing rural revitalization efforts, understanding how farmers develop intentions to relinquish their rural homesteads is essential for improving context-sensitive policy design. Drawing on survey data from 407 farmers in the pilot areas of homestead system reform in Shenyang City, this study proposes a “household capital endowment-value cognition/perceived risk-coping capacity-homestead withdrawal intention” framework. A structural equation model (SEM) is used to estimate both the direct effects of multidimensional household capital endowment and its indirect effects through the two mediating variables. Multi-group analyses are further conducted across generations, farm household pluriactivity, and village types. The results indicate that human and economic capital are positively associated with homestead withdrawal intention, whereas natural and social capital are negatively associated with it. Value cognition and perceived risk-coping capacity mediate several pathways: the former weakens withdrawal intention, whereas the latter strengthens it. The multi-group analyses further suggest variation in within-group significance patterns and coefficient estimates across generations, farm household pluriactivity, and village types. These findings highlight the need for differentiated homestead withdrawal policies that are responsive to farmers’ capital endowments and value–risk perceptions, thereby promoting the productive use of rural land resources while safeguarding farmers’ livelihood security. Full article

Synergistic pollution–carbon mitigation is critical for China’s dual carbon targets. Taking coal-resource Shanxi Province as the case, this study developed an integrated Long-range Energy Alternatives Planning (LEAP)–Community Multiscale Air Quality (CMAQ) coupled framework combined with a three-dimensional vector model to simulate energy consumption, CO₂, and major air pollutant emissions (CO₂, CO, SO₂, NO₂, PM_2.5, and PM₁₀) under Baseline and Policy scenarios (2026–2050). The core novelty of this study lies in methodological innovation: the multi-model linkage realizes full-chain energy-emission-atmosphere simulation, remedying the isolation flaw of single models in prior research. The results indicated that low-carbon levels would rise steadily in both scenarios from 2026 to 2050. The Policy scenario achieved superior long-term low-carbon performance compared with the Baseline scenario and narrowed gaps in underdeveloped social subsystems, despite short-term transition costs. This scenario optimized the overall energy structure yet failed to fully reduce emission loads from residential and transport sectors. It drastically cut carbon and pollutant emissions, optimized spatial emission patterns, and decoupled most air pollutants from carbon emissions. However, this scenario still had prominent limitations: phased delays in emission abatement, strong coupling of CO, NO₂ and carbon emissions, and rising residential carbon emissions. Further pollution–carbon synergy assessment revealed worsening multi-dimensional imbalances under the Baseline scenario. While the Policy scenario experienced temporary systemic imbalance, its long-term coordination level improved steadily. This finding verified that systematic, long-term low-carbon governance constituted the core driver of Shanxi’s green transition. Targeted phased, classified collaborative governance strategies were proposed to resolve structural transformation risks for resource-based regions. Full article

Public Fire Emergency Literacy (PFEL) is a critical determinant that fundamentally shapes individual survivability and the efficacy of societal safety governance—particularly amid intensifying fire risks characterized by growing complexity and destructive potential. Traditional single-perspective or linear analytical frameworks fail to capture PFEL’s multi-causal and configurational nature. To address this gap, this study integrates the Emergency Management Life Cycle Theory with the WSR system approach to measure PFEL across 36 major Chinese cities using 3872 survey responses and explores its multidimensional attributes and generative mechanisms via multiple methods (Delphi technique, entropy weighting, GIS spatial analysis, multiple regression, fsQCA). Key findings: (1) PFEL exhibits a pronounced cognition precedes capability gap with marked demographic heterogeneity; (2) PFEL displays a distinct “Central > Northeast > East > West” hierarchical gradient and notable spatial disequilibrium—core cities in the Central region (e.g., Wuhan and Zhengzhou) outperform traditional first-tier metropolises in the East; (3) physical infrastructure, organizational management, and individual cognition jointly shape PFEL with significant regional heterogeneity—participation in emergency training emerges as the most potent driver; (4) configurational path analysis indicates that PFEL is determined by a complex conjunctive causal mechanism formed by the combined effects of physical facilities, organizational management and individual initiative. Policy implications cover strengthened public emergency response capacity, differentiated policies, and multi-factor collaborative governance. The findings offer theoretical references and practical guidance for improving public resilience systems and emergency resource allocation. Full article