Search Results (239)

Non-food geographical indications (GIs) are emerging as strategic policy instruments in the European Union after Regulation (EU) 2023/2411 extended protection to craft and industrial products. While the literature on agri-food GIs is extensive, empirical and comparative evidence on non-food GIs remains scarce and fragmented. This study addresses this gap by constructing a harmonised dataset, combining 132 registered and 380 potential non-food GIs identified by EUIPO (512 in total across the EU). Using secondary institutional data, descriptive and comparative statistics, and a hierarchical clustering (Ward, squared Euclidean distance) on normalised indicators total GIs, GIs per million inhabitants (GI/POP), and GIs per € billion of GDP (GI/GDP), the analysis identifies three country typologies differing by scale and intensity. Results reveal a strong geographical concentration in Southern Europe but also unexpectedly high intensity in smaller or mid-sized economies such as Portugal, Cyprus, and Slovenia. A forward-looking scenario analysis based on Cost of Non-Europe (CoNE) estimates suggests that the full implementation of the new Regulation could generate 284,000–338,000 new jobs and € 37–50 billion in additional intra-EU trade. The study contributes to EU policy debates by introducing comparative indicators (GI/POP, GI/GDP) as monitoring tools for evidence-based policymaking and by highlighting the role of non-food GIs as hybrid institutions connecting industrial competitiveness, cultural identity, and sustainability transitions. Full article

The passenger car market has experienced a radical shift: the rise of SUV, crossover vehicles, but also Battery Electric Vehicle (BEV) and Plug-In Hybrid Vehicle (PHEV), has blurred the borders between traditional vehicle segments as well as body types, resulting in reduced applicability of conventional taxonomies of vehicle types. This study aims to provide an overview of the vehicle market by proposing a new, machine-learning-based segmentation of the entire German vehicle fleet covering the past years. We merge over 40 million registered vehicles with a technical specifications database and apply data-mining techniques to derive an improved market segmentation. We demonstrate that unsupervised learning techniques, specifically Ward and k-means clustering, yield clusters with enhanced separation, clarity, and practical usability. Clustering was applied to both raw technical features and engineered features designed to capture aspects of economy, ecology, usability, and performance. The silhouette scores can reach 0.19, a significant increase over the +0.05/−0.05 scores of the existing vehicle segments or chassis types. Full article

Background/Objectives: Table tennis is commonly perceived as a recreational or competitive sport; however, growing evidence highlights its potential as a multidimensional tool for health promotion. This study investigates the perceived physical, cognitive, emotional, and social benefits of regular table tennis practice, emphasizing its contribution to health beyond the purely sporting dimension. Methods: A mixed-method design with a predominantly quantitative approach was employed. A structured questionnaire was administered to 329 table tennis players in Catalonia. Descriptive statistics, exploratory factor analysis (EFA), multiple linear regression, Pearson correlations, and hierarchical cluster analysis (Ward’s method) were conducted to examine perceived benefits and identify user profiles. Factor analysis revealed two dimensions: physical–cognitive and emotional–social benefits. Results: The EFA produced a robust two-factor structure, explaining 76.6% of the variance (KMO = 0.941; Bartlett’s test, p < 0.001). Both dimensions showed excellent internal consistency (Cronbach’s α > 0.91). Regression analysis demonstrated that both factors significantly predicted the overall perception of table tennis as a health-enhancing activity (R² = 0.199), with physical–cognitive benefits exerting the strongest effect (β = 0.375; p < 0.001). Cluster analysis identified three distinct profiles: Skeptical, Functional, and Integrative—with significant differences in perceived benefits (η² = 0.710 for the emotional–social factor). Conclusions: Table tennis emerges as an inclusive, low-impact activity with strong potential to foster physical, emotional, and social well-being. Its accessibility and adaptability make it appropriate for diverse populations. These findings support its inclusion in public health strategies and community programs promoting holistic wellness. Future research should further explore motivational drivers across profiles and extend analyses to underrepresented populations. Full article

Background: Pimenta dioica is a medicinal plant rich in various natural compounds, giving it significant potential for applications in the pharmaceutical, cosmetic, food, and agricultural industries. However, little is known about the metabolites present in the leaves of female and male trees, as well as their toxicity and genetic variability. Therefore, in this study, molecular characterization was conducted, the volatile compounds in the leaves of female and male trees were identified, and their cytotoxicity was assessed. Methods: For molecular characterization, a clustering analysis was performed using Ward’s minimum variance method; genetic distances were determined using Jaccard’s coefficient (similarity) and an analysis of molecular variance. Hexane extracts were obtained using the Soxhlet method and analyzed by gas chromatography coupled to mass spectrometry (GC–MS). The cytotoxicity of the extracts was evaluated by a bioassay with Artemia salina. Results: Forty-two metabolites were identified in leaf extracts from female and male trees, of which 17 are reported for the first time in this tissue. The female tree exhibited a distinct metabolite profile compared to the male tree and was slightly more toxic than the male tree. However, both were considered to be moderately toxic (282.00 and 222.87 μg/mL, respectively). Conclusions: Pimenta dioica has a high potential for various uses, primarily for anthropocentric purposes due to its composition of specific metabolites and moderate toxicity. The sampled trees showed a high molecular genetic variability among individuals. Full article

Introduction: Hepatitis C virus (HCV) infection prevalence in Italy varies according to geographical areas and clusters of infection. Moreover, epidemiological studies are old, and the actual prevalence of HCV active infections is also affected by the use of direct-acting antiviral therapies (DAAs) that achieve sustained virologic response (SVR) in >95% of treated patients. We aimed to evaluate the prevalence of HCV infections in in- or outpatients referred to a Sicilian hospital. Materials and methods: The study was conducted in the Buccheri La Ferla Hospital, in Palermo (Sicily), from 1 November 2019 to March 2022. We consecutively screened for HCV infections all inpatients who were evaluated on admission to the ward and all outpatients who referred to the central laboratory. All patients were screened using serological detection of HCV antibodies. Results: In the entire cohort, 469 out of 15,550 patients (3%) showed anti-HCV positivity, and this rate progressively increased according to classes of age (0.4% for <40 yrs, 3% for 40–60 yrs, 4% for >60–80 yrs, and 6.4% for >80 yrs). Among patients with anti-HCV positivity, 44.3% were HCV-RNA negative, 39.2% had HCV-RNA not available, and 16.4% were HCV-RNA positive. In total, 44.1% of patients with HCV-RNA positivity underwent DAA-based antiviral therapy. Conclusions: HCV screening programs can be useful in identifying infected patients at risk of liver disease progression and/or infection spreading. The implementation of laboratory strategies based on HCV reflex testing, the activation of dedicated linkage-to-care plans, and a focus on higher-risk groups could increase the effectiveness of screening programs. Full article

The characterization and mapping of urban landscape spatial form are critical for advancing sustainable planning and informed environmental management. From a morphometric perspective, this study introduces a novel, data-driven framework for typo-morphological analysis. First, morphological cells (MCs) are defined as objectively and universally applicable spatial units for morphometric investigation. Second, by integrating a multi-dimensional cognition of full-scale morphological and associated landscape elements, we construct a set of 48 spatial form indicators and attach them to morphological cells, enabling a precise description of each unit. Third, a Gaussian mixture model (GMM) is employed to cluster the metrical information within the spatially lagged context derived from the topological structure of the morphological cells, resulting in the delineation of distinct typo-morphological zones (TMZs). We then adopt Ward’s algorithm to establish a hierarchical relationship among identified urban landscape types. Using Wuxi City, China, as a case study, our results demonstrate the effectiveness of the proposed framework in capturing the heterogeneity and underlying connotation of urban landscape spatial characteristics. Building upon the unsupervised clustering results, we further apply the classification and regression tree (CART) to provide a supervised interpretation of the key spatial form conditions driving typological decisions. It facilitates the systematic identification of the components and formative mechanisms of spatial form. The findings contribute a scalable, reproducible, and interpretable typo-morphometric approach for analyzing urban landscape spatial characteristics, thereby providing a robust quantitative foundation for integrated decision-making in landscape planning, socio-ecological assessment, and urban design practices. More broadly, the study carries both applied and theoretical significance for advancing refined urban governance and fostering interdisciplinary research related to urban sustainable development. Full article

Mulberry (Morus sp.) trees, traditionally cultivated for their leaves used in sericulture, have recently gained recognition for their adaptability and valuable ecosystem services. The biochemical composition of mulberry leaves varies both qualitatively and quantitatively, depending on genotype, environmental conditions, and cultivation practices. This study aimed to (1) identify differences in old local white (M. alba L.) and black mulberry (M. nigra L.) leaves, (2) perform a chemotype analysis of monitored local varieties, and (3) evaluate the influence of selected bioclimatic factors and pruning practices on the biochemical composition of leaves of white mulberry trees across Slovenian mesoregions. Black mulberry exhibited a higher phenolic content, particularly caffeoylquinic acid derivatives (16.05 mg/g dry weight (DW)), while white mulberry contained more quercetin glycosides (6.04 mg/g DW). Ward’s clustering identified three chemotypes, two of which had elevated protein and hydroxycinnamic acid levels, making them particularly suitable for silkworm feeding. Considering pruning practices of white mulberries, we determined significantly increased protein contents in yearly pruned trees (187.24 mg/g DW). Principal component analysis revealed interactions between bioclimatic, morphological, and biochemical factors, distinctly separating mulberries from the Sub-Mediterranean and Sub-Pannonian macroregions. White mulberries from Sub-Pannonian regions accumulated more caffeoylquinic acids in leaves under lower precipitation and total insolation, while those from Sub-Mediterranean regions exhibited higher kaempferol derivatives due to photo-thermal stress. These findings highlight the influence of climate and pruning on mulberry biochemical diversity and adaptation. Full article

Bumblebees are among the most important wild pollinators; however, their populations are declining worldwide due to factors such as climate change, habitat loss, and pesticide use. For their conservation, it is important to understand the community structure at the local scale and the drivers responsible for their assemblages. However, little is known about bumblebee community assemblages and their drivers in Yunnan Province, China. In this study, we mapped bumblebee community assemblages across 125 counties in Yunnan Province using field-collected and published data. We also quantified the climatic and land use/land cover (LULC) drivers shaping these assemblages. The climatic habitat suitability for 21 bumblebee species was assessed at the county level across Yunnan using species distribution modeling. The biogeographic zones (groups of counties) were identified using Ward’s agglomerative cluster analysis, and the impacts of 12 bioclimatic and LULC drivers on the zonation pattern were assessed using Canonical Correspondence Analysis (CCA). Results indicated that more than 70% of bumblebee species showed their highest environmental suitability in the northern region of Yunnan. Among climatic factors, temperature-related bioclimatic variables were identified as dominant drivers influencing the spatial distribution of 15 out of 21 bumblebee species within the counties of Yunnan. In contrast, five species, B. grahami, B. impetuosus, B. lepidus, B. picipies, and B. securus, showed the highest contribution from precipitation-related factors. Six biogeographic zones (I, II, III, IV, V, and VI) were identified using Ward’s agglomerative cluster analysis. All 12 drivers were found to play critical roles in shaping the community assemblages of bumblebee species. This study provides essential insights for devising targeted conservation strategies at a local scale to maintain bumblebee populations in Yunnan. Full article

Coral reef ecosystems are home to diverse marine flora and fauna. However, these ecosystems are threatened by an array of environmental and anthropogenic factors. Here, we investigated coral reef diversity, structure, and health status, and identified their key environmental drivers. Coral reef data were collected from Koh Seh Island, located inside the Marine Fisheries Management Area in the Kep archipelago. We found that the reef cover largely comprised live corals (64%, mainly Porites and Tubinaria species), followed by Zoanthids (15%) and sand/rubble (15%). Based on Ward’s hierarchical cluster analysis, coral communities were grouped into three zones: East, South, and West zones. Coral diversity was slightly higher in the East zone, though not statistically significant. Zone East showed a positive association with sediment loads and water temperature. Elevated levels of salinity, dissolved oxygen, and pH were characteristic of the East and South zones, whereas the West zone was distinguished by deeper water conditions. We also found that Favites was the key indicator for coral communities in the East zone, which features shallow, high-DO, high-pH waters with more sediments, strong currents, and significant human activities like fishing and transportation. Goniastrea species were abundant in the South and East zones, making it the indicator taxon, while the West zone had no indicator, suggesting that coral species are sparse in this zone. Interestingly, only a few dead corals were found, and no signs of diseases were detected around the Koh Seh coral reefs. This may reflect the effectiveness of joint protection efforts by Marine Conservation Cambodia and the Marine Fisheries Department in Kep province. Overall, our study provides a valuable baseline for assessing future changes in benthic reefs and coral communities on Koh Seh island, throughout the Kep Archipelago and its surrounding areas. Full article

This study investigates the concentrations of essential and trace elements (Ni, Cu, Fe, Zn, Mn, and Al) in packaged ice cream samples collected from markets in Kırşehir province, located in Central Anatolia, Turkey, aiming to assess potential health risks associated with their consumption. Among the detected trace elements, Al (3.21–16.6 mg/kg) and Fe (2.03–24.0 mg/kg) had the highest concentrations, followed by Zn (0.56–3.00 mg/kg), Ni (0.84–4.84 mg/kg), Cu (1.15–3.46 mg/kg), and Mn (0.18–1.56 mg/kg). To explore the relationships between trace elements and identify possible contamination sources, chemometric approaches including principal component analysis, correlation matrices, and hierarchical cluster analysis (Ward’s method) were applied. Human health risk assessment was conducted by calculating Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), Hazard Index (HI), and Carcinogenic Risk (CR), with uncertainty evaluated through Monte Carlo Simulation (10,000 iterations). HI values above 1 in children and adults indicate that trace element exposure through ice cream consumption may pose a health risk. High Al-THQ and Ni-CR values in children may require stricter monitoring and regulatory measures in case of long-term and regular consumption. Full article

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

Background/Objectives: Fatigue is a highly prevalent and burdensome symptom among individuals with Marfan syndrome (MFS), yet its heterogeneity and underlying psychosocial and clinical correlates remain underexplored. This study aimed to identify and characterize distinct fatigue-related profiles in MFS patients using a data-driven cluster analysis approach. Methods: A cross-sectional study was conducted involving 127 patients with MFS from a specialized connective tissue disorder center in Italy. Participants completed self-reported measures of fatigue severity (Fatigue Severity Scale, FSS), depressive symptoms (Patient Health Questionnaire-9, PHQ-9), and insomnia (Insomnia Severity Index, ISI). The body mass index (BMI) and clinical data were also collected. A t-distributed stochastic neighbor embedding (t-SNE) analysis was performed to reduce dimensionality, followed by hierarchical clustering (Ward’s method), exploring solutions from k = 2 to k = 10. The optimal cluster solution was identified based on silhouette scores and clinical interpretability. Results: Three distinct clusters emerged: (1) a cluster characterized by low fatigue with minimal psychological and sleep-related symptoms (younger patients, lower PHQ-9 and ISI scores), (2) a cluster characterized by moderate fatigue with moderate psychological and sleep-related symptoms (intermediate age, moderate PHQ-9 and ISI scores), and (3) a cluster characterized by high fatigue with elevated psychological and sleep-related symptoms (older patients, higher PHQ-9, ISI, and FSS scores). Significant differences were observed across clusters in age, BMI, depressive symptoms, insomnia severity, and fatigue levels (all p < 0.05). Conclusions: Our findings highlight the heterogeneity of fatigue experiences in MFS and suggest the importance of profiling patients to guide personalized interventions. This approach may inform precision medicine strategies and enhance the quality of life for individuals with this rare disease. Full article

Background/Objectives: Enterococci, particularly Enterococcus faecalis and Enterococcus faecium, are Gram-positive cocci that can cause severe infections in hospitalized patients. The rise of vancomycin-resistant enterococci (VRE) and vancomycin-variable enterococci (VVE) poses significant challenges in healthcare settings due to their resistance to multiple antibiotics. Methods: We conducted a point prevalence survey (PPS) to assess the prevalence of VRE and VVE colonization in hospitalized patients. Rectal swabs were collected from 160 patients and analyzed using molecular assays (MAs) and culture. Whole-genome sequencing (WGS) and core-genome multilocus sequence typing (cgMLST) were performed to identify the genetic diversity. Results: Of the 160 rectal swabs collected, 54 (33.7%) tested positive for the vanA and/or vanB genes. Culture-based methods identified 47 positive samples (29.3%); of these, 44 isolates were identified as E. faecium and 3 as E. faecalis. Based on the resistance profiles, 35 isolates (74.5%) were classified as VRE, while 12 (25.5%) were classified as VVE. WGS and cgMLST analyses identified seven clusters of E. faecium, with sequence type (ST) 80 being the most prevalent. Various resistance genes and virulence factors were identified, and this study also highlighted intra- and inter-ward transmission of VRE strains. Conclusions: Our findings underscore the potential for virulence and resistance of both the VRE and VVE strains, and they highlight the importance of effective infection control measures to prevent their spread. VVE in particular should be carefully monitored as they often escape detection. Integrating molecular data with clinical information will hopefully enhance our ability to predict and prevent future VRE infections. Full article

This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative and divisive clustering techniques (e.g., Ward and DIANA) with different distance metrics (Euclidean, Manhattan, and Minkowski). Clustering quality was assessed using internal validation indices such as Silhouette, Dunn, Calinski–Harabasz, Davies–Bouldin, and Gap statistics. The results show that Ward.D and Ward.D2 methods using Euclidean distance generate well-balanced and clearly defined clusters. Two clusters offer the best overall quality, while four clusters allow for meaningful segmentation of game situations. The analysis revealed that teams that did not request timeouts often exhibited better scoring efficiency, particularly in the advanced game phases. These findings offer data-driven insights into timeout dynamics and contribute to strategic decision-making in professional basketball. 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