Search Results (3,961)

Accurate and up-to-date knowledge of land use and land cover represents one of the central challenges in spatial planning and landscape sciences. In this context, the present work introduces GANCIU (Geospatial Analysis with Neural Classification and Image Understanding), an original hybrid pipeline for the automatic extraction of man-made infrastructure from high-resolution satellite imagery. The primary methodological contribution lies in the sequential integration of four technologically heterogeneous components: a per-pixel Random Forest classifier, a guided image modulation step, edge detection via the Mumford–Shah variational functional solved through the Ambrosio–Tortorelli approximation, and final object delineation via the Segment Anything Model (SAM). Each component does not operate independently but conditions and informs the next: The RF probability map guides the modulation, which in turn directs the sensitivity of the variational step exclusively towards regions of interest; the AT edges provide spatial prompts to SAM, for which its masks are finally filtered by the RF probability in an adaptive manner through a Gaussian Mixture Model. This progressive conditioning scheme constitutes the architectural core of GANCIU and distinguishes it from approaches that combine classification and segmentation in parallel or in purely sequential fashion with each stage conditioning the next but without any reverse correction between them. The Random Forest classifier was trained on 44 manually annotated scenes, geographically disjoint from the twelve independent scenes used for quantitative validation. This validation, based on an instance matching protocol (precision, recall, F1 score, and IoU), confirms the contribution of the full pipeline over a Random-Forest-only baseline: Pooled false positives fall by close to two orders of magnitude (from 8320 to 209), while true positives rise nearly twentyfold (from 5 to 95), with a mean IoU of 0.742 ± 0.060 on correctly matched objects. Notably, the entire pipeline—including SAM-based segmentation—runs end-to-end on a modest, GPU-free consumer laptop (four logical CPU cores, under 16 GB RAM), demonstrating that competitive infrastructure-extraction performance does not require specialised computing hardware. Full article

European Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) cheeses represent a diverse sector in which geographical origin, production rules, species, territorial context, and market orientation shape product value. This study aimed to examine product, territorial, and market factors associated with variation in estimated retail price among European protected cheeses. A cross-sectional product-level dataset was constructed using 253 PDO and PGI cheeses registered in the EU geographical indication system. The dataset combined information on country, production region, species, production volume, territorial characteristics, registration status, market orientation, and estimated retail price. Retail price, expressed in €/kg, was analysed using a generalised linear model with a normal distribution and an identity link. The mean estimated retail price was 17.85 €/kg, with a range of 7.00 to 30.00 €/kg. Cow-milk cheeses represented 51.8% of the dataset, followed by mixed/other cheeses, sheep cheeses, and goat cheeses. The model was statistically significant overall (p < 0.001), indicating that the selected explanatory variables were jointly associated with estimated retail price. Higher estimated retail prices were associated with silage prohibition, mountain location, goat milk, and older registration age, whereas export dependency was negatively associated with price. Cow cheeses were significantly cheaper than mixed/other cheeses, while goat cheeses were significantly more expensive. These findings suggest that protected cheeses are economically heterogeneous and that estimated retail price is associated with more than protected status alone. Production constraints, territorial embeddedness, species identity, and market orientation were all associated with price differentiation. This study provides an exploratory quantitative basis for understanding value formation in the European protected cheese sector. Full article

Background: Job stress is a pervasive issue in modern workplaces and is associated with numerous adverse outcomes, including employees’ turnover intention when stress levels are high. Objective: This study aimed to examine the association between job stress and turnover intention among employees working in healthcare institutions through meta-analysis. Methods: A systematic literature review and meta-analysis were performed following PRISMA guidelines. The study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; ID: CRD42024581906). Considering variations in sample sizes, publication years, and measurement scales, a random-effects model was employed. Effect sizes were illustrated with a forest plot, and publication bias analyses were performed. Subgroup analyses were conducted by occupational group, COVID-19 period, and geographic region, while meta-regression tested the moderating effects of publication year. Study quality was assessed using the AXIS tool. Results: Based on predefined inclusion criteria, 38 independent studies were included. Findings revealed a moderate-to-high, positive, and statistically significant relationship (r = 0.441; 95% CI [0.389, 0.490]) between job stress and turnover intention among healthcare workers. Moreover, although between-study heterogeneity was substantial (I² = 95.5%), the direction and statistical significance of the association remained consistent across different countries and healthcare systems. Conclusions: This meta-analysis demonstrated a statistically significant positive association between job stress and turnover intention among healthcare workers (r = 0.441). Although substantial heterogeneity was observed across studies, the relationship remained consistent across occupational groups, regions, and study contexts. Addressing job stress may therefore play an important role in supporting workforce retention in healthcare organizations. Full article

Within the framework of the dual carbon strategy, investigating the spatiotemporal characteristics and driving factors of carbon sequestration in metropolitan areas through land use analysis is important for mitigating climate change and promoting regional ecological protection and sustainable development. On the basis of land use time points for five phases from the Jinan metropolitan area (JMA) covering the period from 2000 to 2024, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was coupled with the extreme gradient boosting (XGBoost)–Shapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) models to explore the spatiotemporal variations in carbon storage and its driving factors. In the last 24 years, cropland has been the predominant land use category in the JMA, representing almost 62% of the overall area. Throughout the five periods, the transition from cropland to construction land predominated, resulting in an 11.78% reduction in farmland and a 49.73% expansion in construction land. Between 2000 and 2024, carbon storage in the JMA decreased overall, with a total reduction of 3.70 Tg. The occupation of farmland for construction purposes was the primary cause of the decrease in carbon storage. The spatial pattern of carbon storage was similar to that of land use in the JMA, characterized by a distribution pattern with elevated values in the southeast and reduced values in the northwest. The SHAP analysis results demonstrated that the contributions of driving factors such as elevation, vegetation coverage, human footprint, and population density were generally high, making them the main drivers affecting carbon storage, with a significantly greater contribution of natural factors than human activity factors. The MGWR model results revealed that the digital elevation model and fractional vegetation cover positively influenced carbon storage in the JMA, whereas the population density imposed a negative effect. These results could guide the judicious allocation and utilisation of resources in urban regions, the establishment of ecological conservation areas, and the advancement of regional sustainability. Full article

To support groundwater protection and sustainable utilization in southern Sichuan, this study aims to clarify the multi-year variation characteristics of groundwater levels (GWLs) and identify their main driving factors in the Yibin region. In this paper, 2019–2024 GWL monitoring records, hydrometeorological data, and multi-source geospatial datasets were integrated. Trend analysis, centroid migration modeling, continuous wavelet transform, Geodetector, and Fast Fourier Transform-based cross-correlation analysis were used to examine GWL dynamics and their controlling factors. The results show that GWL depth exhibits a distinct “shallow-northwest to deep-southeast” pattern, which is closely associated with regional aquifer lithology and hydrogeological conditions, with the most pronounced fluctuations occurring in the northwest. From 2019 to 2024, GWLs showed multi-scale periodic oscillations, with dominant periods of 50–64 months. GWLs in the red-bed region showed a continuous and slow decline, whereas those in the carbonate rock region remained relatively stable with a slight decreasing trend. Among the 13 hydrometeorological, geographic, and human activity factors, cropland area and precipitation had the strongest individual explanatory power. Their interactions with other factors produced nonlinear or bi-factor enhancement effects. The sustained expansion of cropland, together with declining precipitation, suggests that the observed phased and gradual decline in GWLs during 2019–2024 may be associated with a combined climate–human activity forcing mechanism. Annual GWL peaks were weakly and positively correlated with rainfall and temperature, while the lag between rainfall infiltration and GWL response varied with lithology. Full article

Lethal foodborne illness outbreaks represent a critical intersection of public health surveillance, environmental health, and forensic toxicology. When acute gastrointestinal syndromes lead to sudden death, traditional postmortem investigation techniques are often hindered by tissue autolysis and the overgrowth of putrefactive microflora, which complicate conventional microbiological assays. This review establishes a comprehensive framework for chemical forensics by evaluating the utility of stable isotope analysis (SIA) as a supportive, probabilistic chemical proxy to complement traditional epidemiological investigations of postmortem food contamination sources. Following JBI scoping review guidelines and the PRISMA-ScR reporting framework, data from 42 peer-reviewed articles (2000–2026) were charted and synthesized to map natural isotopic variations (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H and δ³⁴S) across both forensic decedents and environmental reservoirs. The findings outline a structured, multi-tissue diagnostic cascade governed by biological metabolic turnover rates: unabsorbed gastric chyme provides a direct chemical match to contaminated source food items within a hyper-acute 0–6 h window; high-turnover visceral matrices (liver, blood plasma) shift to reflect acute exposure profiles within 1–7 days; and continuously fixed keratinized matrices (hair, nails) archive multi-month dietary and transcontinental transit histories. Furthermore, compound-specific isotope analysis (CSIA) of individual amino acids offers unprecedented structural resolution, utilizing the carbon discrimination metric (Δ¹³C_glu-phe) to differentiate pristine agricultural signatures from endogenous metabolic distortions while biochemically verifying pre-mortem physiological stress and hyper-catabolic muscle wasting. Taphonomic thresholds were explicitly defined, establishing that bulk visceral soft tissues remain isotopically stable (±0.3‰) for up to 48 h at room temperature (~21 °C) before microbially induced nitrogen enrichment (δ¹⁵N > +2.8‰) alters native profiles, whereas hair and nail keratin maintain absolute isotopic stability for over 180 days postmortem. When pristine multi-isotope signatures are coupled with mandatory chloroform–methanol lipid extraction and processed through spatial Bayesian assignment models, geographic provenance tracking via environmental isoscapes achieves a predictive accuracy of 97%. This review introduces a standardized environmental health protocol designed to harmonize field environmental sampling with medical autopsies. This protocol provides a legally robust strategy for investigating unresolved lethal foodborne illness case-outbreaks, particularly those involving pediatric mortalities linked to the consumption of counterfeit or fraudulent food products in low- and middle-income countries. Furthermore, it aims to strengthen national and municipal legal frameworks and international biosecurity enforcement. Full article

ZIKV is an emerging mosquito-borne pathogen with the unique capability to cause severe congenital abnormalities. Mosquito-borne viruses are reliant on an optimal temperature within the mosquito host to spread to humans. Few studies explore how day/night temperature variations impact virus replication within the mosquito. Most laboratory studies are conducted at constant temperatures, or with very minimal temperature oscillations. In consequence, ZIKV transmission models and risk maps utilize biological data derived from constant temperature studies. We sought to determine if the use of viral replication data obtained in more biologically relevant, fluctuating temperatures alters the geographic range for ZIKV transmission risk. We used a growing degree day (GDD)-based model to determine ZIKV transmission potential across the US. Replication curves were conducted at eight different average temperatures between 18 °C and 32 °C with daily fluctuations of either +/−2.5 °C or +/−5 °C from the baseline. We found that at lower temperatures (18–22 °C), fluctuation was beneficial for virus replication, where it had less of an impact at more optimal temperatures (24–30 °C). Our transmission potential map exhibited the highest proportion of infected mosquitoes in southeastern United States, with discrete pockets of elevated potential across interior regions, including the southern Great Plains and lower Mississippi Valley. Full article

Maintaining durable road infrastructure is crucial for reducing resource consumption, minimizing repair costs, and supporting sustainable urban mobility. However, accurately detecting small and morphologically diverse pavement cracks remains challenging due to variations in lighting, road textures, and crack shapes across different geographic regions. YOLO (You Only Look Once) is one of the most widely adopted deep learning (DL) frameworks for object detection. Traditional inspection methods are labor-intensive and often inconsistent, while existing DL models can be computationally heavy or limited to single crack types, restricting real-time deployment and scalability. To address these challenges, this study presents YOLO26s, a lightweight DL model for multi-class pavement crack detection across diverse environmental and geographic conditions. Using a curated subset of 6972 annotated images from the Road Damage Dataset 2022, YOLO26s identifies four crack types: longitudinal, transverse, pothole, and alligator cracks. Compared to baseline models (YOLOv8s, YOLOv8n, YOLO26n), YOLO26s achieves higher detection accuracy (mAP@0.5 = 89.0%) while reducing computational complexity by 14.3% in parameters and 7.7% in FLOPs, enabling real-time deployment on edge devices. By facilitating early and accurate crack detection, the proposed approach supports proactive maintenance, extends pavement lifespan, and reduces material and energy usage, contributing to more sustainable road network management. These findings highlight the potential of efficient AI-driven inspection systems to enhance environmental and economic sustainability in civil infrastructure. Full article

Many law enforcement agencies operate crisis service models that utilize the expertise of mental health professionals in assisting law enforcement officers (LEOs) when interacting with people experiencing a mental health crisis. This study examines three agencies with two different types of established crisis service models in the same geographical area to answer three research questions: (1) Does the type of crisis service model affect facilitators of success for the model? (2) Does the type of crisis service model affect how practitioners personally measure the success of the model they use? (3) What patterns exist in involuntary psychiatric examination rates before and after the implementation of the crisis service models? Sixteen practitioners representing two crisis service models responded to a survey investigating the first two questions. Participants were recruited through email. The analysis used inferential and descriptive statistics to examine their responses. A before-and-after design using descriptive statistics of publicly available data about involuntary psychiatric examinations was employed to investigate the third research question. Results showed variation across the model participants’ choices of facilitators important for model success and metrics, but only differences between the responses from members of two models in choosing the facilitator of clear policies and procedures (e.g., clearly written protocols) and the metric of use of force were at statistically significant levels. The practitioners of the models are the experts. Understanding how they judge the success of their work and what facilitates that success is vital for allocating resources effectively and adjusting policies as needed. The study determined that involuntary psychiatric examinations decreased concurrently with the use of crisis service models, but it is not possible to establish causation. More investigation is needed. Full article

Understanding morphological variation across geographic space is central to evolutionary and functional studies of marine vertebrates. Here, we examine variation in dorsal fin shape in the Commerson’s dolphin (Cephalorhynchus commersonii), a small coastal cetacean with a disjunct distribution across South America and the Kerguelen Islands. Dorsal fin shape was quantified using geometric morphometrics based on photographs, and comparisons were conducted across subspecies and populations. Principal Component Analysis revealed partial overlap in morphospace, with higher shape variability in the Kerguelen subspecies. Canonical Variate Analysis showed separation among geographic groups, although patterns of overlap remained. Differences in mean fin shape were observed among populations, particularly in the relative development of the free span and base of the fin. Kerguelen dolphins exhibited a relatively broader fin base and reduced free span, whereas South American populations generally showed a wider free span, with additional variation among regional populations. Morphological patterns did not fully correspond to population genetic structure, suggesting that multiple factors may contribute to phenotypic variation in this species. Similarities between geographically distant populations highlight the potential influence of shared environmental conditions. These results emphasize the value of dorsal fin morphology for investigating spatial variation in small cetaceans and provide a basis for future integrative studies combining ecological, physiological, and genetic data. Full article

Vegetation carbon sinks are an important component of the terrestrial carbon cycle, and net ecosystem productivity (NEP) is widely used to indicate ecosystem carbon-sink strength. However, the long-term dynamics and environmental associations of vegetation carbon sinks in the middle and lower Yellow River Basin remain insufficiently understood. Based on remote-sensing net primary productivity (NPP) and an empirical heterotrophic-respiration model, annual NEP was estimated for 2001–2024. Theil–Sen trend analysis, the Mann–Kendall test, coefficient of variation, optimal-parameter geographical detector (OPGD), and regression residual analysis were applied. Basin-mean annual NEP increased from approximately 214 to 425 g C m⁻² yr⁻¹, with significantly increasing areas accounting for 89.93% of the study area. Areas with NEP above 300 g C m⁻² yr⁻¹ expanded from 18.51% to 78.86%. Precipitation and solar radiation showed the highest explanatory power for NEP spatial differentiation, with mean q values of 0.538 and 0.490, and their interaction reached 0.722. Comparison with a published NEP product, parameter-sensitivity analysis, and an NPP-based robustness test supported the main temporal patterns and factor rankings. The residual-derived non-climatic component exceeded 40% of the combined component-trend magnitude across approximately 94% of the study area, but should not be interpreted as a direct measure of human activities. These findings support regional carbon-sink monitoring, water-constrained ecological restoration, and land-use management. Full article

Mimusops zeyheri Sond. is an underutilized indigenous fruit tree with significant nutritional, ecological, and socio-economic value in southern Africa. However, its domestication potential remains constrained by a limited understanding of intraspecific variation across geographically distinct populations. This study integrated morphological, sensory, and chloroplast genetic analyses to assess the diversity and population structuring of M. zeyheri in two regions of South Africa (Limpopo and Mpumalanga). A total of 40 trees (20 per region) were evaluated for fruit, nut, and leaf traits, while sensory attributes were assessed by 100 participants using a 9-point hedonic scale. Genetic diversity was examined using the chloroplast markers matK and trnH–psbA. Significant regional differences were observed in reproductive morphology, with Mpumalanga populations producing larger fruits (29.41 ± 0.61 mm) and nuts (2.11 ± 0.36 cm) than those from Limpopo. In contrast, sensory evaluation revealed consistently higher preference scores for Limpopo fruits across all attributes, particularly taste, aroma, and overall acceptability (mean scores 7.7–8.0 vs. 5.0–5.4). Genetic analyses identified two major chloroplast lineages corresponding broadly to geographic origin, alongside rare haplotypes indicating localized divergence. Leaf morphology remained largely conserved across regions. The combined evidence demonstrates strong regional structuring driven by both environmental gradients and historical genetic divergence. Importantly, the decoupling of fruit size and sensory preference highlights the need to prioritize quality traits in domestication strategies. These findings point to a promising domestication direction, through crossbreeding superior-tasting Limpopo genotypes with larger-fruited Mpumalanga genotypes to combine consumer-preferred sensory quality with an improved fruit size. These findings provide a foundation for conservation planning and the selection of superior genotypes for the development of M. zeyheri as a high-value indigenous fruit crop. Full article

Phenotypic variation does not always correspond closely to genetic structure within species, particularly for morphological traits that are also influenced by environmental conditions. We examined whether leaf morphology in Daphne blagayana reflects previously identified phylogeographic structures and whether size- and shape-related traits differ in the hierarchical distribution of variation. Leaf morphometric analyses were performed on 3439 leaves collected from 596 shrubs in 21 populations covering the entire distribution range of the species. Ten leaf morphological traits were analysed using nested mixed-effects models, principal component analysis (PCA), MANOVA, Mantel tests and additional conservative mixed-effects models accounting for population-level structure. Leaf size-related traits showed stronger population-level differentiation and clearer geographic structuring, whereas shape-related traits exhibited higher within-shrub variation and weaker correspondence with phylogeographic structure. PCA and MANOVA revealed significant morphological differentiation among the three previously defined evolutionarily significant units (ESUs), although overlap among groups remained considerable. When population-level structure was taken into account, ESU effects were reduced but remained detectable for leaf length and selected shape-related traits. The northwestern ESU was characterized by larger and broader leaves, while differences between the central–southeastern and central ESUs were associated mainly with leaf shape traits. Mantel analysis revealed a weak but significant correlation between geographic and morphological distances among populations, indicating that geographic distance contributes to, but does not fully explain, morphological differentiation. The results show that leaf morphology in D. blagayana is associated with the previously identified phylogeographic structure of the species and that size- and shape-related traits differ markedly in their degree of geographic differentiation and hierarchical variation. However, because phylogeographic and geographic structure are spatially confounded in the present dataset, their individual effects on leaf morphology cannot be statistically separated. The study also highlights the importance of hierarchical sampling, appropriate statistical analyses, and cautious interpretation of genotype–phenotype correspondence in observational studies of intraspecific morphological variation. Full article

Great Wall villages are living cultural landscapes within a linear heritage corridor, yet they face growing pressures from population decline, rural hollowing, uneven development, and tourism-related transformation. This study evaluates the cultural landscape resilience of 20 Great Wall villages in Chicheng County, China. A Cultural Landscape Resilience Index (CLRI) was constructed from three dimensions: resistance, recovery, and learning capacity. The AHP-CRITIC method was used to determine indicator weights, while spatial autocorrelation analysis, principal component analysis (PCA), and multiscale geographically weighted regression (MGWR) were applied to identify the spatial pattern of CLRI and the multiscale effects of its influencing factors. The results show that CLRI was generally low and spatially uneven. Villages with relatively high resilience were mainly concentrated near the county seat and major transportation corridors, whereas many villages along the Great Wall corridor showed lower resilience levels. MGWR results revealed clear spatial heterogeneity in the influencing factors. Heritage revitalization and social development features, heritage preservation and protection, tourism development and utilization, and village patterns and collaborative heritage protection were positively associated with CLRI. In contrast, the diversity of rural village landscapes and the strength of the human-land relationship showed negative associations with CLRI. Heritage revitalization and social development features and rural village landscape diversity operated at relatively broad spatial scales, whereas heritage preservation and protection and tourism development and utilization exhibited stronger local variation. The findings demonstrate that improving the resilience of Great Wall village cultural landscapes requires spatially differentiated conservation and development strategies, including strengthened heritage protection, coordinated conservation of village patterns and heritage elements, context-sensitive tourism development, and improved public services and development opportunities in less resilient villages. Full article

Cryptosporidium spp. are important gastrointestinal disease-causing parasites found in humans and animals. Cattle are major hosts for Cryptosporidium species such as C. parvum, C. bovis, C. ryanae, and C. andersoni, with C. parvum being the most pathogenic and a key zoonotic agent. However, systematic epidemiological data on Cryptosporidium in dairy cattle from Anhui Province remain scarce. In this study, we aim to investigate its prevalence and genetic diversity. Cryptosporidium species and subtypes were identified by targeting the small subunit ribosomal RNA (SSU rRNA) and 60 kDa glycoprotein (gp60) genes. PCR testing revealed an overall Cryptosporidium infection rate of 35.7% (338/948) across four dairy farms in Anhui Province, with significant regional variation (p < 0.01). Bengbu exhibited the highest infection rate (61.5%), whereas the lowest was found in Fuyang (8.6%). Age-specific distribution indicated that calves aged 2–6 months exhibited the highest infection rate (50.2%). Four Cryptosporidium spp. were identified, C. parvum, C. andersoni, C. ryanae, and C. bovis, with C. parvum and C. bovis being predominant. All C. parvum subtyped isolates belonged to the IId subtype family. Seven and five gp60 subtype families were detected in C. ryanae and C. bovis, respectively. Our findings revealed distinct geographical and age-associated variations in the distribution of Cryptosporidium spp. in dairy cattle in Anhui Province, highlighting the need for targeted control strategies in high-risk regions and young calves. Full article