Search Results (380)

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire risk and accessibility. Kernel density estimation quantified POI distributions across four risk categories, and the Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE) model combined these with NPP/VIIRS data to generate a composite fire risk map. Accessibility was evaluated using the enhanced two-step floating catchment area (E2SFCA) method with road network travel times; 80.13% of demand units were covered within the five-minute threshold, while 53.25% of all units exhibited low accessibility. Spatial autocorrelation analysis (Moran’s I) revealed clustered high risk in central basins and service gaps on surrounding hills, reflecting the dominant influence of terrain alongside protected forests and farmlands. The results indicate that targeted road upgrades and station relocations can improve fire service coverage. The approach is scalable and supports more equitable emergency response in mountainous settings. Full article

This study investigates the economic role of wine tourism in Nemea, Greece, a prominent Protected Designation of Origin (PDO) wine-producing region. Employing a mixed-methods approach, the research combines interviews with local stakeholders and a structured post-wine-tasting visitor survey to assess wine tourism’s contribution to local development. A two-step multivariate analysis, incorporating Multiple Correspondence Analysis and Hierarchical Cluster Analysis, reveals five distinct visitor profiles differing in spending behaviour, familiarity with the destination, and engagement patterns. While high-spending visitors support winery revenues, their limited local integration reduces their broader developmental impact. Conversely, younger and repeat domestic visitors offer more dispersed economic benefits through overnight stays, gastronomy, and cultural participation. In addition, local stakeholders highlight the region’s viticultural identity and growing tourism interest as strengths but also note persistent weaknesses such as inadequate infrastructure, limited coordination, and underdeveloped visitor services. The study concludes that visitor segmentation offers actionable insights for enhancing wine tourism’s developmental role. Targeted strategies tailored to specific visitor types are essential for improving integration with the local economy. These findings contribute to ongoing discussions on how wine tourism can act as a lever for inclusive, sustainable rural development in traditional wine regions. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

The National Recovery and Resilience Plan (NRRP) represents a transformative opportunity to reduce territorial, gender and generational disparities in Italy. It plays an even more important role for inner areas, which make up about three-fifths of the entire national territory and require structural investment to improve infrastructure, social services and access to healthcare services. This study aims to analyse the distribution of funds by project type, and to develop a geostatistical analysis-based methodology to critically evaluate two key aspects: the ability of small municipalities to access resources, and the effectiveness of the funding programme in meeting the specific needs of inner areas. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. This methodology is then applied to a case study of the Abruzzo region (Italy), which is considered particularly interesting due to its physical, historical and socio-economic characteristics that make it particularly vulnerable to natural disasters. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. The results of the spatial autocorrelation and cluster analyses were then overlapped and compared with the internal areas defined by the National Strategy for Inner Areas (NSIA). The outcomes reveal how investments interact with existing spatial planning instruments and development strategies, underscoring the critical role of accessibility, infrastructure, and public services in fostering equitable and sustainable regional development. The analysis offers insights into addressing structural disparities and enhancing territorial cohesion, with implications for policy alignment across multiple levels of governance. Full article

Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. Full article

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article

This study examines the genetic variation and structure of the spectacled caiman (Caiman crocodilus) on Gorgona Island, Colombia, compared to continental populations. We analyzed 178 partial Cytochrome b gene sequences, most of which were obtained from GenBank, and identified 23 haplogroups, with five of these specifically found on the Island. Phylogenetic analysis using maximum likelihood placed C. crocodilus, including the Gorgona Island population, in a distinct monophyletic group. Genetic structure analysis identified two main clusters, with Gorgona Island caimans primarily assigned to the Trans-Andean cluster. The haplogroup network illustrates the two major groups, with a maximum of 12 mutational steps between them. Additionally, Tajima’s D statistic suggests an excess of rare alleles in the spectacled caiman. Genetic differentiation across regions suggests historical isolation, likely shaped by geographical barriers and limited gene flow. The distinct genetic patterns of island populations highlight their disparity in terms of evolutionary dynamics and conservation importance. Further genomic analysis is recommended to explore demographic history. Conservation strategies should prioritize the maintenance of genetic diversity to mitigate the effects of isolation, while also incorporating insights from the species’ biogeographic history. Our findings highlight the unique contribution of the small population in Gorgona Island to the species’ spatial genetic structure. Full article

This study aimed to investigate cattle farmer livelihoods that relate to cattle welfare in the region with the newly emerging Lumpy Skin Disease (LSD) in Indonesia. A semi-structured interview survey was conducted with randomly selected cattle farmers (n = 102), in Riau. Cattle were bled for analysis of LSD-post-vaccinal seroconversion. The Sustainable Livelihood Framework (SLF) was used; data on livelihood assets, activities, and outcomes were analysed using Multiple Correspondence Analysis (MCA), two-step clustering, and the radar chart of asset possessions. The survey showed that vaccination and veterinary services covered 82.4% and 90.2% of farms. Seroconversion was detectable in vaccinated (15.0%, n = 173) and in non-vaccinated animals (23.1%, n = 13). Farmers mostly fed only grass to cattle (92.2%), with neither pastoral management nor ad libitum water provision. The MCA and cluster analyses indicated that cattle shelter roofing and flooring and manure disposal were the most important markers of the community’s livelihood. Poverty among cattle farmers was 23.5%. The cluster with lower income per capita had lower quality of shelter roofing and flooring, a lack of regular manure disposal, jobless second children, and the lowest possession of natural and physical assets. Helping to possess natural and physical assets might improve cattle farmers’ well-being and cattle welfare. Full article

Introduction. Chronic pain (CP) is a multidimensional condition that exerts a considerable impact on individuals’ quality of life and presents a wide range of clinical and psychological expressions. This study sought, firstly, to identify distinct clinical profiles among individuals with CP based on clinical indicators, and secondly, to examine the differences in psychological vulnerability and pain-related coping strategies according to the clinical profiles. Methods. A total of 251 adults diagnosed with CP and residing in Spain participated in the study. Participants completed the Purpose in Life Test, the Reflective Functioning Questionnaire, the Interpersonal Needs Questionnaire, the Beck Hopelessness Scale, the Difficulties in Emotion Regulation Scale, and the Pain Coping Questionnaire. A two-step cluster analysis was performed to identify subgroups within the sample, followed by independent samples t-tests to assess psychological differences between clusters. Results. This study identified two clinical profiles among individuals with CP, distinguished by diagnostic delay, disease progression, and functional impact. Cluster 1 exhibited greater functional impairment, lower quality of life, and higher emotional distress (uncertainty, perceived burdensomeness, emotional dysregulation, and hopelessness). In contrast, Cluster 2 showed lower functional impairment, better quality of life, greater use of distraction strategies, and a higher meaning in life. Discussion. These findings suggest that both medical and psychological aspects appear to be associated with each other and may influence the perception, evolution and adaptation to CP. Full article

Background: The aim of the present study was to apply machine learning (ML) techniques to define clusters relating patient demographics, compliance, and outcome variables in colorectal enhanced recovery after surgery (ERAS) patients and improve data-driven, predictive decision-making. Methods: To uncover inherent patient subgroups from the data without pre-defined labels, the unsupervised K-means clustering algorithm was utilized. This technique was selected for its effectiveness in partitioning patients into distinct groups by iteratively assigning them to the nearest cluster mean, thereby minimizing within-cluster variance across key variables. The top five recovery goals and the top 10 clinical outcome variables were defined based on clinical considerations (incidence and importance). In a second step, the cluster transition was traced by monitoring the transitions between clusters from demographic through compliance to outcome variables. Results: A total of 1381 patients were available for final analysis, revealing three clusters (low risk, n = 490, 36%; intermediate risk, n = 157, 11%; and high risk, n = 734, 53%) for demographic, two clusters (high compliance, n = 1011, 73%, and low compliance n = 370, 27%) for perioperative, and two clusters (good and poor outcomes) for the top five recovery goals and the top 10 clinical outcomes, respectively. The cluster transition for the top five recovery goals and the top 10 clinical outcomes revealed that most patients (488/490, 99.6%) of the low-risk demographic cluster had high perioperative compliance, and over 90% of them had favorable functional and clinical outcomes. Of the 2/3 of intermediate risk patients who had poor perioperative compliance, over 40% had a poor functional recovery, whereas 83% had good clinical outcomes. Of the high-risk demographic group, 100% (734/734) had low perioperative compliance, and over 40% of them had poor functional recovery. Conclusions: This ML-based analysis of demographic, compliance, and recovery clusters and associated cluster transition allowed us to identify patient clusters as a first step to tailored ERAS protocols aiming to improve compliance and outcomes. Full article

A necessary step towards the development of genetic diversity is the protection of the valuable genetic resources of farm animals that are at risk of extinction. We analyzed 375 individuals of nine local sheep breeds bred in Central Europe (Carpathia area) from Czech Republic, Slovakia, Poland, Ukraine, and Romania using a panel of 13 microsatellite markers to investigate genetic differences and evaluate the genetic structure among and within breeds, thereby improving future breeding and conservation strategies. The mean number of alleles was 8.84, the mean number of effective alleles was 4.76, and the polymorphism information content (PIC) was 0.79. Diversity was measured using principal coordinate analysis (PCoA) as well as genetic structure, which revealed two main clusters. The first cluster was the Czech Wallachian sheep (CVA) and the Świniarka (SWI). The second cluster consisted the Improved Wallachian sheep (IVA), the Šumava sheep (SUM), the Slovak Wallachian sheep (SVA), the Polish Mountain sheep (POG), the Uhruska sheep (UHR), the Ukrainian sheep (UKR) and the Tsurcana sheep (TUR). The values of genetic distance and the fixation coefficient indicate sufficient differences between the analyzed breeds (Gst = 0.052 and Fst = 0.063). Negative values of the inbreeding coefficient also confirmed the predominance of outbreeding (Fis = −0.015). The results obtained may be helpful in breeding programs and conservation plans for local sheep breeds, as their genetic resources must be preserved to maintain an adequate level of biodiversity in animal husbandry. Full article

Rugby union involves intermittent high- and low-intensity activities, making it essential for strength and conditioning practitioners to understand specific physical demands. While GPS technology has enhanced this understanding, limited research focuses on Tier 2 national teams. This study aimed to describe the speed-related physical demands of a Tier 2 national rugby union team. This retrospective observational study analyzed 230 GPS files from 55 professional male players of an international Tier 2 national rugby union team, collected across 17 international matches. Speed-related performance variables were analyzed. Players who played ≥55 min were included. A Kruskal–Wallis test with post hoc comparisons was used to examine positional differences. Principal Component Analysis (PCA) identified four main components explaining 84.65% of the variance, while a two-step cluster analysis grouped players into Low-, Mid-, and High-Demand profiles based on these components. Backs showed greater high-intensity running demands compared to forwards. This study’s results provide novel insights into the physical demands of Tier 2 international rugby union, highlighting differences among player positions and clustering players based on their specific speed demands. These findings can help strength and conditioning practitioners design position-specific training loads, implement tailored recovery strategies, and reduce injury risk in Tier 2 international rugby union. Full article

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. Due to the limited availability of in situ SSM data, the performance of three global SSM datasets—SMAP, MERRA-2, and CFSv2—from 2015 to 2023 was evaluated using agrometeorological stations. SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. The RF model yielded R² values of 0.89, 0.83, 0.70, and 0.75 for winter, spring, summer, and autumn, respectively, with corresponding RMSE values of 0.076, 0.081, 0.098, and 0.061 m³/m³. SHAP analysis revealed that climatic factors primarily drive SSM in winter and autumn, while vegetation and soil characteristics are more influential in spring and summer. The clustering results showed that Iran’s catchments can be grouped into five categories based on the SHAP method coefficients, highlighting regional differences in SSM controls. Full article