Search Results (15,566)

Rapid urbanization-induced extreme rainstorms severely disrupt social functions. Previous research often focused on “de-densification” strategies, which are difficult to adapt to high-density Sponge City Stormwater Management Units (SMUs) that carry core development functions. This study uses Shenzhen as a case study, utilizing Keep movement big data as a “social sensor” for system function perception and introducing the Socio-Ecological-Technological Systems (SETS) theory to construct a “recovery (RCN)–resistance (MI)” binary assessment framework. Through systematic clustering and hierarchical regression models, the driving mechanisms of blue landscape patterns, topography, road networks, and the built environment on social behavioral resilience are systematically parsed. The results show: (1) Road network morphology dominates resistance, while multi-dimensional elements collaborate for recovery. Resistance (MI) is primarily dominated by macro road network detour resistance (TPD2000, β = 0.956), while recovery depends on the synergistic support of blue space interspersion (Blue_IJI), topography, and micro-circulation road networks. (2) Green infrastructure fails in the model due to efficiency bottlenecks, empirical evidence of weakened regulation caused by green space fragmentation in ultra-high-density environments. (3) Low-density, eco-centric built environments provide dual synergistic gains for resilience. Based on this, a “Bidirectional Socio-Ecological Resilience Needs Pyramid” model is constructed, identifying four governance types such as the “Synergistic Balanced Type”. This study provides a quantitative basis for the transition from administrative control to precise morphological governance in high-density cities. Full article

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

While environmental awareness is crucial for ecological governance, its sociocultural foundations across different macro-institutional contexts remain underexplored. This study compares the sociocultural correlates of environmental awareness in China and Singapore—two developmental states with state-centric governance but distinct institutional configurations. Integrating Sociocultural Theory and the Theory of Planned Behavior, this exploratory study analyzes World Values Survey data Wave 7 using ordered logistic and probit models. We identify three key patterns. First, both nations exhibit a pervasive “attitude-behavior gap,” with cognitive environmentalism significantly outpacing actual civic action. Second, universally, social trust is correlated with environmental attitudes, while political action and religiosity are positively linked to actual behavior. Third, distinct institutional mechanisms emerge: China reflects a “state-dependent environmentalism” where attitudes are associated with post-materialist values and institutional deterrence, and behavioral participation is strongly related to government trust. Conversely, Singapore displays an “institutionalized civic environmentalism,” where routine political action shows a strong positive association with environmental attitudes—an association neutralized in China. These findings demonstrate that pathways to ecological sustainability in developmental states are structurally divergent, necessitating context-specific governance interventions. Full article

The watershed boundaries in arid and semi-arid regions are critical zones where ecological vulnerability and socio-economic development are in severe conflict. The upper and middle reaches of the Yellow River basin are a typical example of this dilemma. Intensive land use and human developmental interventions in this region have severely disrupted the integrity and balance of the ecosystem. While spatially designated, networked conservation areas can effectively promote the integrity and balance of regional ecosystems, these areas may fail to capture dynamic changes in vulnerability. This study develops a “functional diagnosis-structural diagnosis-integrated optimization” framework. It integrates various scenarios to diagnose vulnerability under uncertainty and identifies bottlenecks in ecological networks. For functional diagnosis, the coupling of the sensitivity–resilience–pressure (SRP) model and the Ordered Weighted Averaging (OWA) algorithm accurately locates vulnerable areas within the regional ecosystem. In terms of structural diagnosis, the Morphological Spatial Pattern Analysis (MSPA), Minimum Cumulative Resistance model (MCR), and Circuit Theory are integrated to identify structural bottlenecks. The main findings of this study are as follows: (1) Functional Diagnosis: The coupling of SRP and OWA reveals the non-linear vulnerability responses to policy preferences and identifies areas that consistently exhibit functional vulnerability across different scenarios. (2) Structural Diagnosis: The circuit theory combined with MSPA and MCR analysis identifies 72 ecological pinch points. These bottlenecks represent the weakest structural nodes crucial for maintaining regional ecological robustness. (3) Coupled Delineation and Differentiated Restoration Strategies: High vulnerability areas identified by SRP and consistently vulnerable areas identified by OWA are combined to delineate four distinct ecological restoration units: Alpine Fragile Matrix Unit, Loess Hilly Soil Conservation Unit, Anthropogenic Pressure Pinch Point Unit, Key Structural Stepping Stone Unit. Differentiated ecological restoration strategies are proposed based on the varying sensitivity, resilience, and pressure characteristics of these units. The “functional-structural” coupled ecological vulnerability evaluation framework can precisely identify vulnerable areas. The delineated restoration units and their corresponding restoration strategies provide reference and supplementation for the protected areas system, offering transferable tools for enhancing regional ecological efficiency. Full article

Industrial volatile organic compound (VOC) emissions from large-scale petroleum storage represent a persistent environmental challenge, particularly in agricultural perimeters where atmospheric “breathing” cycles drive localized soil loading. This study investigates the thermodynamic and spatial relationship between gasoline storage emissions and chemical contamination in the Constanta South terminal area using a multi-layered analytical approach. By integrating gas chromatography (GC-MS) headspace analysis with an artificial intelligence (AI) framework utilizing high-order polynomial regression, we quantified the source–path–receptor dynamics across a thermal gradient (12 °C to 70 °C). The results reveal a non-linear surge in VOC emissions at temperatures exceeding 37 °C, characterized by a shift toward medium-weight hydrocarbons (C4–C6) that act as carriers for heavier aromatics. The AI risk model identified a significant spatial gradient, identifying a 500 m “critical zone” where the Hazard Quotient (HQ) is elevated, necessitating technological upgrades like Vapor Recovery Units (VRUs) to mitigate ecological risks. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Background and Objectives: Despite critically low levels of chronic kidney disease (CKD) awareness worldwide, there is no psychometrically validated instrument to comprehensively assess CKD awareness across socioecological levels. This study aimed to develop, psychometrically evaluate and validate a multidimensional awareness scale grounded in socioecological theory for patients with stage 3–4 CKD. Materials and Methods: This methodological study enrolled 908 stage 3–4 CKD patients. Scale development proceeded through systematic stages: comprehensive literature review, qualitative interviews (n = 15), expert panel evaluation (n = 25), and pilot testing. The initial 72-item pool was refined to 41 items (Content Validity Index = 0.912). The sample was randomly split for exploratory factor analysis (EFA; n = 454) and confirmatory factor analysis (CFA; n = 454). Psychometric evaluation encompassed internal consistency (Cronbach’s α, McDonald’s ω), test–retest reliability (n = 30; 4-week interval), convergent validity (average variance extracted [AVE], composite reliability [CR]), discriminant validity (Fornell–Larcker criterion), and criterion validity (correlation with Turkish Health Literacy Scale-32 [TSOY-32]). Results: EFA revealed a seven-factor structure with an acceptable explained variance of 43.8%. Following iterative item elimination based on communalities (h² < 0.20) and factor loadings (λ < 0.30), CFA confirmed the final 34-item model with good fit (CFI = 0.972; RMSEA = 0.070 [90% CI: 0.067–0.074]). The factor structure captured awareness across core socioecological levels (Individual, Interpersonal/Institutional, Community, and Systemic), complemented by Treatment Adherence and Social Impact dimensions. Internal consistency coefficients were α = 0.884 and ω = 0.889 for the total scale. Test–retest reliability yielded an ICC of 0.954 (95% CI: 0.907–0.978). Convergent and discriminant validity were confirmed via composite reliability (CR: 0.740–0.953) and the Fornell–Larcker criterion. Criterion validity analysis revealed a significant correlation with TSOY-32 (r = 0.810, p < 0.001). Conclusions: The CKD Awareness Scale (CKD-AS-34) represents a novel, psychometrically validated, multidimensional awareness instrument for CKD. This scale enables clinicians to identify awareness deficits spanning individual to systemic levels, facilitating personalised patient education and targeted public health interventions. Full article

Differential equations with fractional order play an important role in modeling some natural phenomena. This paper investigates the dynamics of the fractional-order commensal symbiosis model with the Allee effect. This model describes the relationship between prey and predator populations. The piecewise-constant approximation technique is applied to discretize this model. Equilibrium points are established, and local stability conditions are calculated using fractional-order linearization and eigenvalue-based arguments. Moreover, the bifurcation theory is successfully invoked to discuss the period-doubling bifurcation. In particular, sufficient conditions are effectively determined for the emergence of the period-doubling bifurcation. We utilize the hybrid control approach to control the behavior of the considered system. Then, some numerical examples are presented to demonstrate the accuracy and validity of the theoretical results. The findings indicate that fractional order and Allee effects improve system dynamics and substantially improve stability limits and bifurcation structures, providing new insights into how to handle the dynamics of ecological systems. Full article

Since 2007, green tides have recurrently occurred in the Yellow Sea during spring and summer, with a massive outbreak recorded in 2021. Given the critical significance of green tide monitoring and prediction for marine ecological security and sustainable development, this study developed a satellite remote sensing-validated coupled simulation system for green tide drift and growth, by integrating multi-source satellite remote sensing data and oceanographic reanalysis datasets. Leveraging this system, we systematically analyzed the spatiotemporal evolution characteristics and underlying driving mechanisms of both routine green tide processes in 2014–2015 and the extreme 2021 event. Satellite images with low cloud cover and extensive green tide distribution were screened to confirm the accuracy of green tide drift trajectories and distribution ranges for validating the model’s reliability, and the results demonstrated the spatial consistency between simulation results and satellite observations. The validated model was used to track the drift and growth–decline processes of green tides and investigate the underlying cause of high-biomass appearance in 2021. Combined with environmental parameters, our analyses revealed that variations in attachment substrates alter wind resistance coefficients, thereby potentially accelerating the northward drift velocity of green tides. Furthermore, substrate properties may exert a significant regulatory effect on the attachment, germination, and biomass accumulation of Ulva prolifera spores, which could be a leading factor driving the massive green tide outbreak. Full article

Canopy fractional vegetation cover (FVCc) is a critical indicator for evaluating the effectiveness of ecological restoration, and its accurate estimation provides valuable data for regional ecological management. In this study, Sentinel-2 and MODIS data were integrated to develop an angular relationship model for MODIS reflectance, which was then used to estimate Sentinel-2 reflectance at a 45° viewing angle. Background reflectance at a 10 m spatial resolution was derived using a four-scale model, and total and shrub-herb fractional vegetation cover were estimated using a pixel dichotomy model. Finally, an empirical model tailored to the characteristics of the study area was developed to retrieve FVCc. Cross-validation results demonstrated that the multi-angle retrieval method proposed in this study achieved higher accuracy than the single-angle approach. The spatial distribution of FVCc in Changting County is characterized by higher values in peripheral areas and lower values in the central region. Temporal transitions among fractional vegetation cover classes were predominantly upward, indicating an overall trend of continuous improvement. These findings provide important technical support and a scientific basis for estimating and monitoring dynamic changes in forest canopy fractional vegetation cover. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

The recognition of a clean, healthy, and sustainable environment as a human right has reshaped global human rights discourse, yet its operationalisation remains uneven. This article examines how the African human rights system which is uniquely grounded in collective rights, has reframed environmental protection as a constitutive element of development, sovereignty, and justice. Through doctrinal and case-law analysis, it traces the evolution from the African Commission’s foundational jurisprudence in SERAC, which extended state duties to the regulation of private and transnational corporate actors, to the African Court’s landmark judgment in LIDHO v. Côte d’Ivoire. The study demonstrates how the Court transforms the aspirational ‘greening’ of human rights into binding obligations by articulating a robust duty of vigilance and linking environmental harm to violations of the rights to life, health, and development. It further shows that LIDHO inaugurates a post-sovereign model of shared and polycentric responsibility, in which state accountability encompasses corporate conduct within their jurisdiction and, potentially, beyond it. The article concludes that the African Charter’s collective framework offers an implicit regional model of ecological justice, one capable of addressing extractive asymmetries and informing emerging climate-related obligations across the continent. Full article

Peatlands cover approximately 10% (640,000 ha) of Latvia’s territory, of which about 51,000 ha is officially classified as degraded due to peat extraction and related activities. This study assesses the current status of peat extraction site recultivation in Latvia and evaluates future after-use requirements under contrasting policy pathways using a review of scientific literature, project reports, national statistics, and updated peat extraction licence records. A simple allocation model was applied to estimate recultivation trajectories for the nationally defined degraded peatland area under two scenarios: (i) a licence-expiry baseline scenario and (ii) an accelerated immediate-stop-peat-mining scenario. The results show that full recultivation would require average annual efforts of approximately 1500 ha yr⁻¹ under the baseline scenario and around 2000 ha yr⁻¹ under the accelerated scenario. Although European Union-funded projects and corporate initiatives have demonstrated the potential of rewetting, paludiculture, and renewable energy integration, only a limited number of sites have been officially recognised as fully recultivated or restored. Because ecological recovery of peatland functions may take decades, administrative closure alone does not guarantee climate or biodiversity benefits. A phased recultivation strategy linked to licence expiry and prioritising degraded and self-regenerating sites emerges as the most pragmatic pathway for Latvia, balancing European Union climate objectives, institutional capacity, and socio-economic constraints. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

As a novel approach to address the lack of systematic studies on spatial Gross Ecosystem Product (GEP) accounting and Ecological Security Pattern construction, this study integrates GEP thresholds with Morphological Spatial Pattern Analysis (MSPA) to identify ecological sources. A resistance surface is constructed using five representative influencing factors, and the Minimum Cumulative Resistance (MCR) model is applied to extract ecological corridors, thereby establishing the Ecological Security Pattern for the Yellow River-Fronting Region of Henan in 2020. The results indicate the following: (1) GEP in the study area exhibits a spatial distribution of “high in the northwest, low in the southeast,” with regulating services accounting for more than 90% of the GEP. (2) A total of 11 ecological sources, 13 ecological corridors, and 7 ecological nodes were identified, primarily distributed in mountainous regions. (3) The Ecological Security Pattern exhibits spatial imbalance, with dense corridors in the western mountains and sparse distribution in the eastern plains. These findings provide scientific support for formulating ecological conservation measures and optimizing ecosystem management in the Yellow River Basin. Full article