Search Results (2,782)

In recent years, there has been growing recognition that reducing environmental pollution, particularly from building emissions, is essential for improving residents’ well-being. Buildings contribute substantially to worldwide greenhouse gas and pollutant emissions, making effective mitigation strategies a priority in achieving Sustainable Development Goals (SDGs). Using data from the 2021 China General Social Survey (CGSS), this study examines the relationship between perceived building environmental pollution and residents’ well-being, as well as the mechanism underlying this relationship, through an ordered probit model. The results indicate that higher levels of building environmental pollution significantly reduce residents’ well-being. To explore heterogeneity, the sample was further divided by urban–rural differences, local environmental protection expenditure level, and geographic region. The research found that residents with lower environmental protection expenditures, residents in rural areas and those in the central region are more likely to be negatively affected by building environmental pollution, with the correlation coefficients being −0.111, −0.104 and −0.101 respectively. Furthermore, the analysis indicates that annual income, the number of children, and type of work have moderating effects on this relationship, with correlation coefficients of 0.047, −0.054, and −0.095 respectively. Overall, this study provides empirical evidence for perceiving the social impact of building pollution in the context of building-related emissions and offers policy-related insights for strengthening environmental protection measures in the construction industry to enhance residents’ well-being. Full article

This study investigated how metal concentrations and microplastic abundance co-vary temporally and spatially in sediments in Durban Harbour, South Africa. The effects of sediment contamination on the amphipod Grandidierella lignorum was additionally investigated. Sediments from five sites in the harbour, namely Little Lagoon (LL), Yacht Bank (YB), Marina Bank (MB), Western Bank (WB), and Central Bank (CB), were analysed for metals using ICP-OES, and microplastic particles were counted. Sediment metal concentrations varied across sites and seasons, with Al and Fe dominating. Elevated levels of Cu, Zn, and Pb were observed, particularly in areas with high industrial activity, suggesting point-source contamination. Trace concentrations of As, Cd, and Ni were found and these metals were excluded from further analysis. Abundance ranged from 0.2 to 2.5 particles per gram dry weight, and differed significantly among sites (p < 0.01) with the highest concentrations in LL and YB. Amphipod survival rates following exposure to sediment did not significantly differ among sites but correlated moderately with microplastic abundance (p > 0.05, R² = 0.57). Tissue analysis revealed selective metal accumulation, following the trend Al > Fe > Zn > Cu > Cr, with Mn, As, and Pb undetected. These results highlight the spatial heterogeneity of sediment contamination in Durban Harbour and demonstrate the bioaccumulation potential and ability to regulate metals in G. lignorum, particularly for essential metals like Fe and Zn. Despite no clear evidence linking microplastics to metal concentrations, the findings highlight the complex interactions between contaminants and their potential ecological impact. Full article

The prediction of thin-bedded, favorable sand bodies within the Triassic Baikouquan Formation fan delta on the western slope of the Mahu Sag is challenging due to their strong spatial heterogeneity. To address this, we propose an integrated workflow that synergizes seismic sedimentology with geologically constrained seismic inversion. This study leverages well logging, core data, and 3D seismic surveys. Initially, seismic attribute analysis and stratal slicing were employed to delineate sedimentary microfacies, revealing that the fan delta front subfacies comprises subaqueous distributary channels, interdistributary bays, and distal bars. Subsequently, the planform distribution of these microfacies served as a critical constraint for the Seismic Waveform Indicative Inversion (SWII), effectively enhancing the resolution for thin sand body identification. The results demonstrate the following: (1). Two NW-SE trending subaqueous distributary channel systems, converging near the BAI65 well, form the primary reservoirs. (2). The SWII, optimized by our workflow, successfully predicts high-quality sand bodies with a cumulative area of 159.2 km², primarily located in the MAXI1, AIHU10, and AICAN1 well areas, as well as west of the MA18 well. This study highlights the value of integrating sedimentary facies boundaries as a geological constraint in seismic inversion, providing a more reliable method for predicting heterogeneous thin sand bodies and delineating future exploration targets in the Mahu Sag. Full article

Areas characterised by high ecological and cultural value are increasingly exposed to overtourism and intensifying land-use pressures, often exacerbated by mobility policies aimed at enhancing regional accessibility and promoting tourism. These dynamics create spatial tensions, particularly in environmentally sensitive areas such as those within the Natura 2000 network and Sites of Community Importance (SCIs), where intensified visitor flows, and infrastructure expansion can disrupt the balance between conservation and development. This study offers a geospatial analysis of the current state (2024) of such dynamics in the Lazio Region (Italy), evaluating the effects of mobility strategies on ecological vulnerability and tourism pressure. By applying isochrone-based accessibility modelling, GIS buffer analysis, and spatial overlays, the research maps the intersection of accessibility, heritage value, and environmental sensitivity. The methodology enables the identification of critical zones where accessibility improvements coincide with heightened ecological risk and tourism-related stress. The original contribution of this work lies in its integrated spatial framework, which combines accessibility metrics with indicators of ecological and heritage significance to visualise and assess emerging risk areas. The Lazio Region, distinguished by its heterogeneous landscapes and ambitious mobility planning initiatives, constitutes a significant case study for examining how policy-driven improvements in transport infrastructure may inadvertently exacerbate spatial disparities and intensify ecological vulnerabilities in peripheral and sensitive territorial contexts. The findings support the formulation of adaptive, place-based policy recommendations aimed at mitigating the unintended consequences of accessibility-led tourism strategies. These include prioritising soft mobility, enhancing regulatory protection in high-risk zones, and fostering coordinated governance across sectors. Ultimately, the study advances a replicable methodology to inform sustainable territorial governance and balance tourism development with environmental preservation. Full article

Background/Objectives: Schizophrenia is associated with cognitive deficits that may compromise everyday functioning, including driving. This review systematically examined recent original research (2015–2025) on driving performance in individuals with schizophrenia with a focus on neuropsychological factors, applying a narrative synthesis given the heterogeneity of designs and outcomes, while no quantitative meta-analysis was feasible. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a structured search of PubMed and Scopus was conducted on 4 May 2025. The inclusion criteria were original studies involving individuals diagnosed with schizophrenia, published between 2015 and 2025. Studies on animals, other psychiatric or neurological conditions, and healthy populations were also excluded. Critical appraisal was performed using the Joanna Briggs Institute (JBI) tools. Extracted data included sample demographics, cognitive deficits, neuropsychological assessments, brain imaging, and the main findings. A narrative synthesis was then performed. Results: Six high-quality studies met the inclusion criteria. Findings were grouped into three categories: (1) driving behavior: fitness to drive varied widely across individuals, (2) cognitive deficits and brain activity: poorer driving-related performance was consistently associated with specific impairments in cognition and brain structure, and (3) medication effects: individuals taking certain atypical antipsychotics demonstrated better driving performance compared to those on other types of medication, while extrapyramidal symptoms negatively influenced driving fitness. Conclusions: Driving in schizophrenia is shaped by cognitive, clinical, and pharmacological factors. These findings highlight the clinical relevance of individualized evaluations, integration into personalized care and targeted rehabilitation to promote driving autonomy and community inclusion. This area remains under-researched, as only six studies met the inclusion criteria, which restricts the robustness and generalizability of the conclusions. Funding: This review received no funding from any external sources. Registration: The review protocol was submitted to PROSPERO (International Prospective Register of Systematic Reviews) under registration number CRD420251060580. Full article

Small-diameter vascular grafts often fail due to thrombosis and compliance mismatch. Decellularized plant scaffolds are a biocompatible, sustainable alternative. Leatherleaf viburnum leaves provide natural architecture and mechanical integrity suitable for tissue-engineered vessels. However, the persistence of immunogenic plant biomolecules and limited degradability remain barriers to clinical use. This study tested whether mild heat treatment improves scaffold biocompatibility without compromising mechanical performance. Decellularized leatherleaf viburnum scaffolds were treated at 30–40 °C in 5% NaOH for 15–60 min and then evaluated via tensile testing, burst pressure analysis, scanning electron microscopy, histology, and in vitro assays with white blood cells and endothelial cells. Scaffold properties were compared to those of untreated controls. Heat treatment did not significantly affect scaffold thickness but decreased fiber area fraction and diameter across all anatomical layers. Scaffolds treated at 30–35 °C for ≤30 min retained >90% of tensile strength and achieved burst pressures ≥820 mmHg, exceeding physiological arterial pressures. Heat treatment reduced surface fractal dimension while increasing entropy and lacunarity, producing a smoother but more heterogeneous microarchitecture. White blood cell viability increased up to 2.5-fold and endothelial cell seeding efficiency improved with treatment duration, with 60 min producing near-confluent monolayers. Mild alkaline heat treatment therefore improved immune compatibility and endothelialization while preserving mechanical integrity, offering a simple, scalable modification to advance plant-derived scaffolds for grafting. Full article

Background/Objectives: Forensic dental determination plays a central role in human identification, age estimation, and trauma analysis in medico-legal contexts. Traditional approaches—including clinical examination, odontometric analysis, and radiographic comparison—remain essential but are constrained by examiner subjectivity, population variability, and reduced applicability in fragmented or degraded remains. Recent advances in cone-beam computed tomography (CBCT), three-dimensional surface scanning, intraoral imaging, and artificial intelligence (AI) offer promising opportunities to enhance accuracy, reproducibility, and integration with multidisciplinary forensic evidence. The aim of this review is to synthesize conventional and emerging approaches in forensic odontology, critically evaluate their strengths and limitations, and highlight areas requiring validation. Methods: A structured literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2025. Search terms combined forensic odontology, dental identification, CBCT, 3D scanning, intraoral imaging, and AI methodologies. From 108 records identified, 81 peer-reviewed articles met eligibility criteria and were included for analysis. Results: Digital methods such as CBCT, 3D scanning, and intraoral imaging demonstrated improved diagnostic consistency compared with conventional techniques. AI-driven tools—including automated age and sex estimation, bite mark analysis, and restorative pattern recognition—showed potential to enhance objectivity and efficiency, particularly in disaster victim identification. Persistent challenges include methodological heterogeneity, limited dataset diversity, ethical concerns, and issues of legal admissibility. Conclusions: Digital and AI-based approaches should complement, not replace, the expertise of forensic odontologists. Standardization, validation across diverse populations, ethical safeguards, and supportive legal frameworks are necessary to ensure global reliability and medico-legal applicability. Full article

The NewEcoSmart project addresses the need to foster inclusive green and digital transitions in rural habitat sectors by systematically assessing local socio-technical readiness and tailoring capacity-building interventions. We adapted the validated SCIROCCO Exchange Maturity Self-Assessment Tool—selecting eight dimensions relevant to environmental, technological and social innovation—and conducted a two-phase evaluation across three pilot sites in Italy, Portugal and Spain. Phase 1 mapped stakeholder evidence against predefined criteria; Phase 2 engaged local actors (45+ adults, SMEs and micro-firms) in a self-assessment to determine digital, green and entrepreneurial skill gaps. For each domain of the SCIROCCO Tool, local actors can assign a minimum of 0 to a maximum of 5. The final score of the SCIROCCO tool can be a minimum of 0 to a maximum of 40. Quantitative maturity scores revealed heterogeneous profiles (Pacentro and Majella Madre = 5; Yecla = 10; Adelo Area = 23), underscoring diverse ecosystem strengths and limitations. A qualitative analysis, framed by Smart Healthy Age-Friendly Environments (SHAFE) domains, identified emergent training needs that are clustered at three levels: MACRO (community-wide awareness and engagement), MESO (decision-maker capacity for strategic planning and governance) and MICRO (industry-specific practical skills). The adapted SCIROCCO tool effectively proposes the assessment of socio-technical maturity in rural contexts and guides the design of a modular, multi-layered training framework. These findings support the need for scalable deployment of interventions that are targeted to the maturity of the local ecosystems to accelerate innovations through equitable green and digital transformations in complex socio-cultural settings. Full article

This article reviews the progress made in applying machine learning to predict the osteogenic differentiation of mesenchymal stem cells. Bone defects pose a significant clinical challenge due to limitations of traditional therapies such as autologous bone graft donor shortages, allograft immune risks and so on. Mesenchymal stem cells offer a promising solution for bone regeneration due to their osteogenic differentiation potential, but their clinical utility is hindered by unpredictable differentiation efficiency and heterogeneity. Machine learning has emerged as a powerful tool to address these issues by enabling early, non-invasive prediction of osteogenic differentiation and high-throughput analysis of complex data like morphology and omics. This review systematically summarizes the application of ML in three key areas: early prediction using cellular morphology, omics data analysis for biomarker discovery, and drug/biomaterial screening for enhancing osteogenesis. We compare the performance of multiple ML models like ResNet-50, LASSO, and random forests and highlight their advantages and limitations. Additionally, we discuss challenges in data standardization and model interpretability, and propose future directions for translating ML into clinical practice. This review provides a comprehensive overview of how ML can revolutionize MSC-based bone regeneration by improving prediction accuracy and optimizing therapeutic strategies. Full article

Urban areas are increasingly challenged by the combined effects of climate change, rapid population growth, and high energy demand. The integration of renewable energy systems, such as photovoltaic (PV) panels, and nature-based solutions, such as green roofs, represents a key strategy for sustainable urban development. This study evaluates the spatial potential for PV and green roof implementation in Iași, Romania, using moderate to high-resolution geospatial datasets, including the ALOS AW3D30 Digital Surface Model (DSM) and the Copernicus Urban Atlas 2018, processed in ArcMap 10.8.1 and ArcGIS Pro 2.6.0. Solar radiation was computed using the Area Solar Radiation tool for the average year 2023, while roof typology (flat vs. pitched) was derived from slope analysis. Results show significant spatial heterogeneity. The Copou neighborhood has the highest PV-suitable roof share (73.6%) and also leads in green roof potential (46.6%). Integrating PV and green roofs can provide synergistic benefits, improving energy performance, mitigating urban heat islands, managing stormwater, and enhancing biodiversity. These findings provide actionable insights for urban planners and policymakers aiming to prioritize green infrastructure investments and accelerate the local energy transition. Full article

This study aims to explore whether the dual-scale management model, formed by integrating service-scale management with land-scale management, can further break through the benefit limits of single land-scale management and unlock additional profit potential in agricultural scale operations. This study used data from a 2024 questionnaire survey of 2166 farming households in Anhui Province and employed a coupling coordination degree model to measure the level of dual-scale management. Subsequently, we utilized OLS regression and mediation effect models to empirically examine the impact of dual-scale management on agricultural operational benefit and their underlying mechanisms. We find that dual-scale management significantly improves agricultural operational benefit. Our measurements show that dual-scale management not only breaks through the upper limit of the optimal operating area inherent in single land-scale management but also yields a greater improvement in agricultural operational benefit than single land-scale management. Heterogeneity analysis reveals that dual-scale management significantly enhances the agricultural operational benefit of farmers in plain areas and farmers with fully developed high-standard farmland. Mechanism analysis indicates that dual-scale management enhances agricultural operational benefit through an endogenous efficiency improvement mechanism and an exogenous risk-burden-sharing mechanism. These findings suggest that fostering a synergistic development system for land-scale management and service-scale management is conducive to improving the economic returns for land scale operators and unlocking new dividend spaces for agricultural scale operation in China’s post-land transfer era. Full article

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area. Full article

With accelerating urbanization and global climate warming, Urban Heat Islands (UHIs) pose serious threats to urban development. Existing UHI research mainly focuses on inland regions, lacking systematic understanding of coastal city heat island mechanisms. We selected eight Chinese coastal cities with different backgrounds, quantitatively assessed urban heat island intensity based on summer 2023 Landsat 8 remote sensing data, established block-LCZ spatial analysis units, and employed a combination of machine learning models and causal inference methods to systematically analyze the regional differentiation characteristics of Urban Heat Island Intensity (UHII) and the influence mechanisms of multi-dimensional driving factors within land–sea interaction contexts. The results revealed the following: (1) UHII in the study area presents obvious spatial differentiation, with the highest value occurring in Hong Kong (2.63 °C). Northern cities generally had higher values than southern ones. (2) Different Local Climate Zone (LCZ) types show significant differences in thermal contributions, with LCZ2 (compact midrise) blocks presenting the highest UHII values in most cities, while LCZ G (water) and LCZ A (dense trees) blocks exhibit stable cooling effects. Nighttime light (NTL) and distance to sea (DS) are dominant factors affecting UHII, with NTL marginal effect curves generally presenting hump-shaped characteristics, while DS shows different response patterns across cities. (3) Causal inference reveals true causal driving mechanisms beyond correlations, finding that causal effects of key factors exhibit significant spatial heterogeneity. The research findings provide a new cognitive framework for understanding the formation mechanisms of thermal environments in Chinese coastal cities and offer a quantitative basis for formulating regionalized UHI mitigation strategies. Full article

Using 31 provinces (cities and districts) on the Chinese mainland (2013–2023) as the research object, this study analyzes the development level of e-commerce through the entropy weight method and uses panel data to empirically test the driving effect of e-commerce development level on low-carbon development. According to this study, the overall development of e-commerce has a positive driving effect on low-carbon development. E-commerce development lowers the intensity of carbon emissions by optimizing regional industrial structures, innovating green technologies, and establishing resource sharing. Moreover, the analysis of the effects of regional heterogeneity reveals that, although low-level areas still have great development potential, high-level economic development areas have the greatest effect on low-carbon development. In conclusion, we clarify how e-commerce contributes to low-carbon development and provide resources for enhancing the quality and efficiency of e-commerce to conserve energy and reduce emissions. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article