Search Results (12,574)

Background: COVID-19 increased psychological distress among healthcare workers. Italian studies have suggested lower distress levels among mental health professionals compared to hospital-based colleagues. Methods: In May 2025, we conducted a cross-sectional observational study in community mental health centers and non-psychiatric hospital outpatient departments in Sardinia, Italy, involving 101 professionals from three community mental health centers and 97 staff members from four non-psychiatric hospital outpatient departments. The SF-12 was used to assess health-related quality of life (HRQoL) and the PHQ-9 was used to assess depressive symptoms. Results were also compared with pre-pandemic community samples. Results: Mental health professionals reported fewer depressive symptoms and better quality of life than hospital-based colleagues: PHQ-9 ≥ 10: 10.9% vs. 35.0% (11/101 vs. 34/97) (OR = 0.23; 95%CI 0.11–0.47; p < 0.001); PHQ-9 ≥ 9: 14.8% vs. 38.1% (OR = 0.31; 95%CI 0.16–0.60; p < 0.001). Mean PHQ-9 score: 4.45 ± 3.51 vs. 8.35 ± 3.95 (p < 0.001). Low HRQoL (SF-12 ≤ 36): 34.6% vs. 62.9% (35/101 vs. 61/97) (OR = 0.31; 95%CI 0.19–0.52; p < 0.001). No significant within-group differences were found by sex, age, or professional role. Compared with pre-pandemic community data, the well-being of mental health professionals remained stable. Limitations: Cross-sectional design and convenience sampling limit causal inference and generalizability. Conclusions: The organizational structure, operational flexibility, and peer collaboration typical of community-based services may contribute to the sustained psychological well-being of mental health professionals. The community-based model deserves consideration for future reforms aimed at improving staff well-being. Full article

Food delivery logistics sustainability includes environmental impact, economic efficiency, and service quality. Traditional logistics models mainly rely on fixed ''pickup buffer'' policies (such as a set 10 min wait). These systems do not account for the changing nature of restaurant operations and delivery conditions, leading to higher operating costs, driver idle time, and poorer food quality. To move delivery systems from reactive decision-making to proactive, dynamically forecasted operations, an adaptive control mechanism is needed. In on-demand food delivery, this offers a clear path to sustainability through better dispatch accuracy, order prep, and pickup coordination. To resolve these bottlenecks, this study examines how a smart logistics framework based on a dynamic Gradient Boosting Regressor (GBR) and policy-sensitive GBR can provide more accurate estimates of drivers' waiting times in light of contextual factors such as rush hour, time of day, and operational constraints. In last-mile food delivery, the proposed method aims to reduce operational costs, improve scheduling effectiveness, and maximize resource utilization by moving beyond static, predefined waiting periods to adaptive, context-aware decisions. The developed framework analyzes a proprietary dataset of 368,250 instant orders from a major Saudi Arabian logistics provider to evaluate the efficacy of static thresholds versus a proposed predictive, dynamic machine-learning-based approach. After rigorous data cleaning and temporal-logic adjustments, a ''High-Fidelity Ground-Truth'' subset of 1842 verified orders is used to simulate policy performance. This 99.5% reduction is necessitated by the widespread absence of the ''Order Ready'' timestamp in operational logs, which is the critical target variable for supervised learning; comparative analysis confirms the subset remains representative of the parent population’s spatiotemporal dynamics. The baseline analysis reveals severe inefficiencies in the static model, with a 61.67% violation rate for driver wait times, particularly in Riyadh (p < 0.001) and during late-night operations. The simulation results demonstrate that the dynamic policy reduces the ''Buffer Miss Rate'' (premature driver arrivals) from 59.08% to 7.32%, resulting in a 68.5% reduction in total operational waste costs. Full article

Arid region ecosystems are among the most fragile ecological types worldwide. They depend heavily on limited water resources and are strongly influenced by intensive human activities, leading their ecosystem services to exhibit nonlinear and threshold responses to driving factors. Identifying the thresholds of ecosystem services under the combined influence of natural and socio-economic interactive drivers is of great significance for regional ecological risk warning and differentiated management. Taking the Tarim River Basin as a case study, this research establishes an integrated analytical framework that combines causal inference, interaction term construction, interpretable machine learning (XGBoost-SHAP), and piecewise linear regression. The framework is used to evaluate the variations in four types of ecosystem services in 2000, 2010, and 2023, to analyze the interactive effects of driving factors, and to identify their thresholds influencing ecosystem service functions. The results indicate that (1) different types of ecosystem service functions exhibited distinct trends from 2000 to 2023, with habitat quality and water yield showing declining tendencies, while soil conservation and Windbreak and sand fixation demonstrated gradual increases; (2) Causal Screening and interaction modeling revealed that the interaction between precipitation and population density (Pre × Pop) served as the key synergistic driver of changes in the four ecosystem service functions. Both the ecosystem services and the coupled natural–social driving processes exhibited pronounced nonlinear characteristics, with evident trend shifts occurring within specific threshold intervals. (3) The precise coupling thresholds of different ecosystem services under natural–social drivers were identified, intuitively revealing the coupling threshold characteristics of various ecosystem services; (4) The integration of causal inference with interpretable machine learning enhances the reliability of threshold identification, revealing the heterogeneous response mechanisms of different services and providing a quantitative basis for the zoning regulation and differentiated management of regional ecosystems. The findings offer a transferable methodological framework to support ecological governance in arid regions. Full article

Airborne particulate matter (PM) poses a major health risk, yet species selection for vertical greening systems (VGS) is poorly quantified. We evaluated PM retention by seven commercially available shade-tolerant species grown in a modular living wall system (LWS) on a north-facing façade at Sapienza University of Rome. After 3 months of in situ exposure, leaves were analyzed via SEM (1000×), collecting 210 images, 30 per species. An automated FIJI/ImageJ pipeline segmented particles, computed equivalent circular diameters, and classified them into (PM < 0.5, PM [0.5, 1), PM [1, 2.5), PM [2.5, 10), and PM ≥ 10 µm). Across species, ultrafine and fine fractions dominated deposits, with the <0.5 µm class typically comprising 60–70% of counts. Vinca minor cv. albomarginata exhibited the highest densities in ultrafine and fine classes, closely followed by Fatsia japonica; Hedera helix captured more coarse particles (2.5–10 µm and >10 µm). Heuchera sanguinea consistently displayed the lowest densities across all size classes. Performance patterns aligned with leaf surface traits: wax-coated, moderately rough or gently structured cuticles favored adhesion, whereas highly irregular microrelief did not consistently enhance retention. Methodological considerations include thresholding sensitivity, use of equivalent circular diameter for irregular particles, and an upper area filter that may undercount large aggregates. The findings identify Vinca minor cv. albomarginata and Fatsia japonica as priority species for PM mitigation in shaded VGS, with Hedera helix complementing coarse PM capture. The results provide trait-based, design-oriented guidance for living wall species selection in Mediterranean urban and indoor contexts. Full article

This study proposes a three-layer gateway control framework for a behind-the-meter virtual power plant (VPP) comprising vehicle-to-grid (V2G)-capable electric vehicle (EV) chargers, battery energy storage systems (BESS), and rooftop photovoltaic (PV) generation in multi-dwelling residential developments, creating a sustainable future through maximising distributed energy resource (DER) utilisation. In particular, the first layer performs day-ahead scheduling to determine the hourly grid import baseline and frequency regulation ancillary service capacity for the following day. In the second layer, real-time regulation dispatch is performed by following the dynamic regulation signal from the grid operator, wherein V2G-capable EVs are coordinated alongside BESS as active demand-side participants in frequency regulation ancillary services, enabling the aggregated behind-the-meter fleet to respond to regulation signals in real time. The third layer performs per-minute three-phase load balancing to maintain network power quality compliance across the multi-dwelling site. The overall goal is to coordinate distributed energy resources behind a single network connection point to simultaneously reduce peak demand, maximise renewable self-consumption, and provide demand-side frequency regulation as a dispatchable VPP asset. Full article

Quench–polish–quench (QPQ) of 40Cr steel was performed to improve its tribological properties and corrosion resistance, thereby enhancing the service performance of components such as gears and bearings. The 40Cr steel was treated by QPQ at 580 °C and 620 °C for 90 or 120 min. Optical microscopy (OM, Sunny Group, Ningbo, China), scanning electron microscopy (SEM, Hitachi, Tokyo, Japan), and X-ray diffraction (XRD Rigaku Corporation, Tokyo, Japan) were used to characterise the microstructure and phase constitution. Ball-on-disk tribometry, electrochemical tests, and salt spray tests in 3.5 wt.% NaCl evaluated surface performance. At 580 °C, a composite structure of Fe₃O₄ and ε-Fe₂₋₃N formed on the surface. When the temperature rose to 620 °C, ε-Fe_2–3N gradually transformed into γ′-Fe₄N. Within the scope of this study, the diffusion layer depth exhibits an approximately linear relationship with increasing processing temperature and holding time, and the surface hardness is 67–112% higher than that of the untreated sample. After QPQ treatment, the wear mechanism changed from adhesive wear to abrasive wear. However, under the treatment conditions of 620 °C × 120 min, brittle surface spalling increased roughness, thereby increasing the coefficient of friction. As treatment time increases, nitrogen atoms continue to diffuse outward as Fe₂N transforms to the γ′ phase. This increases the composite layer’s porosity and decreases its corrosion resistance. The best corrosion resistance was observed at 580 °C for 120 min, with a corrosion potential of −0.4325 V, corrosion current density of 1.80 × 10⁻⁶ A·cm⁻², and polarisation resistance of 24,500 Ω. Corrosion performance depends on overall surface integrity. Porosity morphology strongly influences this property. For 40Cr steel, the results show that surface properties are primarily determined by the quality of the compound layer’s microstructure. Specifically, density, phase-composition stability, and defect control are more important than the commonly held view of layer thickness. Full article

While telemedicine has proliferated globally, its sustainable implementation in resource-constrained settings remains understudied. This study evaluates the efficacy, determinants of patient satisfaction, and systemic resilience of a “Home Ward” model at a rural Thai community hospital. Employing a convergent mixed-methods design, we surveyed 51 participants and conducted in-depth interviews with service users (n = 5) and a multidisciplinary team (n = 7). Multiple linear regression revealed high patient satisfaction ( = 3.70), explaining 67.3% of the variance (R² = 0.673). Notably, Perceived Usefulness (β = 0.589, p < 0.001) and the Effectiveness of Symptom Monitoring (β = 0.317, p < 0.05) significantly predicted satisfaction. Conversely, Overall System Quality was not a significant predictor (β = 0.142, p > 0.05), highlighting a ‘Low-Tech, High-Touch’ paradox. Qualitative analysis elucidated this through the “Human-in-the-Loop” mechanism, where Village Health Volunteers (VHVs) and healthcare providers bridge the digital divide. However, the study identifies an “invisible workload”—non-formalized discretionary effort—that sustains this resilience. Findings suggest that rural digital health governance should prioritize human intermediaries and pragmatic utility over purely technical upgrades. The study concludes that long-term sustainability requires institutionalizing human support networks while mitigating the exploitation of healthcare personnel’s goodwill. Full article

Mountainous Mediterranean rivers provide essential ecosystem services but are increasingly affected by land-use change, hydraulic works, and inadequate wastewater management. This study investigates the links between geomorphological transformation and river water quality in the Central Eurytania drainage basin (Greece) over the past two decades, within the institutional framework of European and Greek environmental legislation, with emphasis on the protection and restoration of aquatic ecosystems. Georeferenced satellite imagery from 2003/2010 and 2023, Google Earth Engine (GEE, Python Earth Engine API: 1.7.20)-based spatial analysis, high-resolution UAV orthomosaics, and seasonal spectrophotometric analyses were integrated to assess spatial and temporal dynamics. Results indicate that land-use changes, including the construction of solar parks, expansion of tourism infrastructure, and partial agricultural abandonment, reflect ongoing socio-economic shifts influencing fluvial processes. Water-quality analyses further showed that channel alteration and wastewater inputs jointly degrade ecological conditions. The findings highlight the need for integrated watershed management focused on riparian buffer restoration, improved wastewater control, and systematic monitoring of hydromorphological change. The proposed interdisciplinary framework contributes to the assessment of environmental resilience in Mediterranean mountainous watersheds, which are increasingly vulnerable to climatic and socio-economic pressures. Full article

Coral reefs are among the most biodiverse marine ecosystems, playing irreplaceable roles in maintaining marine ecological balance and coastal services. Under dual pressures of global climate change and human activities, coral bleaching and degradation have become increasingly frequent, creating an urgent need for large-scale, long-term, and highly automated monitoring technologies. In recent years, advances in underwater imaging and deep learning have made visual recognition a core approach for coral classification and health assessment. However, most studies only focus on isolated model accuracy optimization, lacking systematic full-chain analysis integrating datasets, model evolution, cross-domain generalization, engineering constraints, and ecological adaptation, which severely hinders large-scale cross-regional and long-term application. This paper systematically reviews coral visual recognition technologies. It summarizes underwater image acquisition, public dataset characteristics, and annotation system evolution, then compares traditional feature engineering and deep learning in key tasks, highlighting their differences in feature representation and generalization. Four core challenges are identified: class imbalance, poor underwater image quality, weak cross-device/region generalization, and mismatched algorithm metrics with ecological needs. Finally, feasible solutions based on self-supervised pre-training, domain adaptation, and multimodal fusion are discussed to enhance model robustness and ecological interpretability, providing methodological support for intelligent coral reef monitoring systems. Full article

Voluntary turnover represents a critical challenge in essential public services, where workforce attrition affects both employee well-being and service quality. The primary objective of this study was to identify the psychosocial predictors of well-being profiles and turnover intention among public transport workers, using the Job Demands–Resources model as a theoretical framework. A cross-sectional study design was employed, with 131 employees of an Italian public transport company completing a questionnaire assessing turnover intention and key psychosocial factors (job satisfaction, perceived work-related stress, work engagement, meaning of work, and perceived workplace safety). The analytical strategy integrated Latent Profile Analysis (LPA), logistic regression, and path analysis. LPA identified two distinct well-being profiles: a “low well-being profile,” with high perceived stress and low engagement and meaning of work; and a “high well-being profile,” with low stress and high engagement and work meaning. Logistic regression analyses showed that satisfaction with pay and the intrinsic nature of work tasks predicted membership in the high well-being profile. Path analysis indicated that profile membership significantly predicted turnover intention, with employees in the high well-being profile reporting lower turnover intention. Additionally, satisfaction with supervision, perceived workplace safety, and age showed direct effects on turnover intention. These findings highlight the organizational and psychological resources that can increase employee well-being and retention in the public transport sector, offering insights for preventive interventions and for promoting safer and more sustainable public transport systems. Full article

Against the backdrop of high-quality urbanization in cities, the rapid expansion of metro networks has led to severe spatial mismatches in land use around station areas, which seriously restricts the full exertion of the comprehensive benefits of the transit-oriented development (TOD) model. Taking 139 operational metro stations in Xi’an in 2024 as the research sample, this study constructs a multi-objective land use optimization model with the richness of public services, transportation accessibility and population distribution balance as the three core maximization objectives. A hierarchically adaptive improved NSGA-III algorithm is proposed, with the following four key technical optimizations implemented: multi-dimensional adaptive reference point adjustment, design of real-integer hybrid coding genetic operators, construction of an enhanced multi-criteria environmental selection mechanism, and dynamic regulation of algorithm iteration. Experimental results show that the performance of the improved algorithm is significantly superior to that of the traditional NSGA-III algorithm: the values of the three core objectives are increased by 59.58%, 12.94% and 7.35% respectively compared with the original data; the algorithm achieves stable convergence after 25 iterations, with the convergence efficiency improved by 30%. The obtained Pareto optimal front features good uniformity (U = 0.92) and coverage (C = 0.95), and all the 80 non-dominated solutions meet all constraint conditions, with the solution set highly coupled with the urban functional zoning and spatial planning of Xi’an. This study proposes a zoned, prioritized and phased hierarchical land use optimization strategy for the areas around metro stations in Xi’an. The research findings provide a replicable research framework and methodological reference for the TOD practice and land use optimization of metro station areas in other rapidly urbanizing central cities in China and developing countries worldwide with the characteristic of rapid rail transit expansion. Full article

Ecological restoration—whether active or passive—includes forest development, forest rehabilitation, and a range of other activities that contribute to ecosystem services. To provide a formal framework, we hypothesized how does reforestation (through different forestry practices) affect the conservation of soil functionality? That is, how does reforestation/afforestation/forest restoration improve soil quality? And, specifically, how do they improve physical properties (such as structural stability, infiltration) and chemical properties (such as acidity, electrical conductivity)? For this purpose, we conducted a bibliometric analysis review of the peer-reviewed scientific literature and research reports of numerous articles in order to compile a large database of forest restoration studies, with an emphasis on the Mediterranean region. The final focus was to obtain conclusions about how it affects soil quality. Overall, our examination confirms that deforestation drives a decline in soil carbon and nitrogen, subsequently impairing microbial activity. Consequently, forest removal frequently leads to accelerated erosion, nutrient depletion, and compaction. In contrast, reforestation acts as a critical intervention, stabilizing soil structure, reestablishing fertility, and enhancing soil quality overall. Additionally, three case studies are synthetically presented concerning the short-, medium-, and long-term results of forest restoration projects carried out mainly in central and northern Spain. These cases corroborate the significant role of forest restoration in the control and enhancement of ecosystem services, particularly in relation to soil improvement, the enhancement of hydrological regulation processes within watersheds (runoff, infiltration, erosion), landscape amelioration, and the socio-economic aspects of rural environments. Ultimately, forest restoration is established as a necessary and essential practice in ecological restoration efforts to counteract the impacts of anthropogenic activities. Full article

Background/Objectives: Excessive sodium (primarily from sodium chloride, NaCl) intake remains one of the leading dietary risk factors associated with hypertension, cardiovascular disease, and other chronic health conditions worldwide. Commercial diet catering services providing ready-to-eat daily meal plans have become increasingly popular and are often perceived as nutritionally balanced; however, analytical evidence regarding their actual salt content remains limited. The aim of this study was to evaluate the NaCl content of daily food rations (DFRs) offered by commercial diet catering services in Poland. Methods: A total of 120 DFRs representing three dietary patterns (Hashimoto diet, DASH diet, and low-carbohydrate diet) were collected from 40 catering providers. Sodium chloride content was determined using the Mohr titration method. Sodium intake values were estimated by conversion from NaCl equivalents to allow comparison with dietary recommendations. Results: The median NaCl content across all analyzed diets was 14.19 g/day (Q1: 10.62 g; Q3: 17.49 g), corresponding to approximately 284% of the World Health Organization recommended maximum intake of 5 g/day of salt. Nearly half of the analyzed DFRs (45.83%) exceeded the recommended intake by more than threefold. Overall, 99.2% of the analyzed DFRs exceeded recommended NaCl intake levels, while 91.9% did not comply with the values declared by manufacturers. DFRs consisting of five meals contained higher NaCl levels than three-meal plans (p < 0.0196); however, this difference may be related to variation in total food mass rather than meal frequency, as the number of meals was confounded with diet type. Conclusions: These findings suggest that commercially prepared diet catering meals may represent a substantial source of dietary NaCl when used as a primary daily food source. Improved nutritional monitoring, clearer nutrient reporting, and quality control of commercially prepared dietary plans may support public health strategies aimed at reducing NaCl intake. Full article

As a core area for soil and water conservation on the Loess Plateau and a national primary shale oil production zone, Qingyang City faces an increasingly acute contradiction between its inherently fragile ecological base and energy development activities. From the dual perspectives of ecological regulating services and production-supporting services, this study selected six key ecosystem services—habitat quality (HQ), soil retention (SR), carbon storage (CS), water yield (WY), food supply (FS), and grassland forage supply (GS)—to comprehensively assess their spatiotemporal evolution, trade-off/synergy relationships, and driving mechanisms from 2000 to 2020. The results indicate: (1) Significant changes occurred in the total amounts and spatial patterns of all ecosystem services during 2000–2020. HQ showed a fluctuating upward trend, while SR, FS, and GS increased overall; by contrast, CS and WY generally declined. (2) Ecosystem services exhibited a differentiated pattern characterized by “intra-category synergy and inter-category trade-off.” Regulating and supporting services were generally dominated by synergistic relationships, although clear differences remained among specific service pairs; provisioning services generally showed trade-offs with regulating services, among which the trade-offs between FS–HQ and between FS–GS were the most pronounced, whereas FS–CS showed a certain degree of synergy. (3) Driving force analysis revealed a continuous decline in the influence of natural factors and a sharp intensification of human activity factors. Groundwater level and land-use intensity became core drivers of pattern shifts, with their explanatory power increasing significantly. The study reveals that ecosystem services in Qingyang have rapidly transitioned from being dominated by natural hydrothermal conditions to being profoundly reshaped by energy development activities, exposing the region to the ecological risk of a “resource curse.” These findings provide a scientific basis and management insights for achieving coordinated development between resource exploitation and ecological conservation in ecologically fragile areas of the Loess Plateau. Full article