Search Results (3,089)

Cacao swollen shoot disease (CSSD) remains a major viral threat to cacao production in West Africa and is associated with a genetically diverse complex of badnaviruses. We combined alignment-free whole-genome distances with circular sliding-window mosaic barcodes to characterize global divergence and local compositional modularity across 48 full-length CSSD-associated badnavirus genomes, hereafter termed the cacao swollen shoot virus (CSSV) complex. Whole-genome tetranucleotide cosine distance clusters matched published species assignments exactly (adjusted Rand index = 1.00; normalized mutual information = 1.00; silhouette = 0.703) and were strongly concordant with an alignment-based distance derived from open reading frame 3 (ORF3) proteins (Spearman ρ ≈ 0.951; permutation p ≈ 0.0002). A three-component mosaic-complexity score summarized low dominant-label purity, barcode entropy normalized by log₂(K), and circular switch rate; ORF–mosaic agreement was retained as a separate diagnostic and did not contribute to the ranking. Threshold sensitivity showed that barcode switchpoints were enriched within ±100 and ±200 bp of predicted ORF boundaries, but not within ±400 bp. The ten highest-scoring genomes showed slightly higher mean local nucleotide entropy but lower entropy variance and dispersion than the remaining genomes, indicating more uniformly distributed compositional complexity rather than isolated local spikes. These analyses provide a transparent framework for post-sequencing classification, comparison, and prioritization of complete viral genomes. The barcode and score outputs are exploratory summaries and are not direct field diagnostic assays or nucleotide-resolution recombination tests. Full article

Atmospheric deposition in industrial basins of Central Asia is strongly influenced by local emissions and wintertime dispersion conditions. This study characterized snowpack at 63 sampling stations across Ust-Kamenogorsk, Kazakhstan, including operational background station 1, on 24–26 February 2025 after a 116-day accumulation period. Major ions were determined in a spatially distributed exploratory subset of 16 samples, and trace elements were measured in samples from all 63 stations by means of inductively coupled plasma mass spectrometry and optical emission spectrometry. Mean meltwater pH and total dissolved solids were 6.55 ± 0.34 and 37.3 ± 18.0 mg L⁻¹, respectively. Charge-balance errors for the 16 hydrochemical samples ranged from −0.3% to +0.7%. Using the contamination index based on exceedances of the current Kazakhstan water-quality thresholds, 48 stations had CI < 1, seven had CI = 1–3, and eight had CI > 3; the highest value (60.21) occurred at station 26. Principal component analysis showed that the first three components explained 53.6% of the variance and separated a broad mineral/industrial aerosol association from a Pb–Cd–Zn association consistent with non-ferrous metallurgy and mixed urban sources. Cadmium was therefore interpreted as the principal contributor to the MPC-normalized index at the most affected stations, rather than as the dominant component by absolute concentration. The dissolved fraction can be mobilized during spring melt, indicating a potential pathway to soils and receiving waters, although direct ecological or human-health risk was not quantified. Station-level point mapping and projection along the NW–SE axis showed localized multi-element maxima rather than a monotonic citywide gradient. Full article

This study presents an integrated statistical framework for identifying representative large-scale climate teleconnection indices associated with total burned area and for supporting the selection of climate predictors in wildfire-related statistical models. A large set of seasonally resolved climate indices, including the North Atlantic Oscillation (NAO—two versions), the Arctic Oscillation (AO), the Atlantic Multidecadal Oscillation (AMO), the Mediterranean Oscillation (MO—two versions), the East Atlantic–West Russia pattern (EAWR), the Tropical North Atlantic (TNA), and the Atlantic Meridional Mode (AMM), was examined. Because many of these indices describe related atmospheric and oceanic processes, dimensionality reduction and predictor selection were required to limit multicollinearity. Principal component analysis (PCA) was first used to identify groups of interrelated climate indices, followed by partial correlation analysis to distinguish redundant predictors from those retaining independent information with respect to total burned area. Finally, LASSO regression was applied to evaluate the relative explanatory contribution of candidate indices and to perform automatic variable selection. The PCA solution identified ten rotated components explaining 82.31% of the total variance. The results indicate that several seasonal NAO and MO indices contain highly overlapping information, whereas selected indices, particularly MOI2 spring and MOI2 summer, retain comparatively stronger independent associations with total burned area. The integrated PCA–partial correlation–LASSO framework provides a systematic approach for reducing redundant climate predictors and identifying large-scale climate signals that may be informative for understanding variability in total burned area and for supporting statistical analyses of wildfire–climate relationships. Full article

Wool is often regarded as a low-value by-product of Alpine sheep farming, despite its potential to convey the ecosystem services (ESs) generated by these systems. This study examines how awareness and perception of these services are associated with consumer attitudes towards locally produced wool in the Lanzo Valleys in Northwestern Italy. A convenience sample of 400 adults familiar with the study area completed an online questionnaire. Principal Component Analysis identified two orientations towards wool products—Sustainability Orientation and Product-Value Orientation—which together explained 69.33% of the variance. A TwoStep Cluster Analysis based on the component scores identified four profiles within the study sample: Sustainability-sensitive (22%), Disinterested (14.25%), Eco-aesthetic (36%), and Aesthetically aware (27.75%). Sustainability-oriented respondents attributed greater importance to ES related to biodiversity, native-breed conservation and animal welfare, whereas aesthetic and experiential attributes represented an additional, distinct dimension of product evaluation. Residence status was not significantly associated with cluster membership. Given the non-probabilistic sampling design, these profiles should be interpreted as exploratory attitudinal patterns. The findings may inform differentiated communication and wool-valorisation strategies linking environmental, cultural, aesthetic and functional values. Full article

Landfill leachate circuits that operate without discharge, by recirculating aerated leachate onto the waste mass, are widespread in South-East Europe, yet their long-term behaviour is rarely documented with sample-level data. This study reports a six-year (2020–2025) seasonal monitoring campaign at a sanitary landfill in northern Serbia (alluvial aquifer of the Sava River, transboundary Danube basin) and re-examines it with a transparent multivariate protocol. Seventy-two leachate samples (collection well, aeration lagoon, sedimentation lagoon; n = 24 each, 30 parameters), 28 realised surface-water campaigns, and six years of groundwater summaries were evaluated by principal component analysis/factor analysis (PCA/FA, Varimax normalized), hierarchical cluster analysis, PERMANOVA, non-parametric paired tests and, for benchmarking, supervised machine learning. The pooled leachate model (n = 72; 21 variables; KMO = 0.700; Bartlett χ² = 956, p < 0.001) retained four factors by parallel analysis, explaining 61.6% of total variance; after rotation the factors accounted for 27.7%, 14.3%, 10.4%, and 9.3%. Factor 1 grouped organic load with particle-reactive metals (COD, BOD₅, Fe, Ni, Cr, As, Zn), Factor 2 a reduced sulfur–fluoride–BTEX signature, Factor 3 temperature-driven nitritation, and Factor 4 a nitrate–manganese redox contrast. Crucially, paired campaign-by-campaign comparison showed no removal of the dominant pollutants along the circuit. Median COD, BOD₅ and NH₄-N were not lower in the sedimentation lagoon than in the collection well, while pH rose from 8.08 to 8.75 (p < 0.001); only Cu, Pb, NO₃-N, and NO₂-N decreased significantly. The circuit therefore homogenises and concentrates dissolved load rather than removing it. Downstream surface water was significantly enriched in electrical conductivity (+110 µS/cm), total dissolved solids, NH₄-N, and NO₂-N relative to upstream (Wilcoxon, p < 0.05), and groundwater showed episodic conductivity up to 12,760 µS/cm and NH₄-N up to 102 mg/L. Cytotoxicity (MTT) confirmed biological relevance, with MRC-5 viability falling to 37% after 24 h exposure to 50 vol.% groundwater (Pw3) versus 60% in A549 cells. A random-forest classifier separated circuit units far better than PCA-based discrimination (76.4% versus 54.2% cross-validated accuracy) and distinguished the 2020–2021 pandemic period from 2022–2025 with 94.2% accuracy, a period effect also confirmed by PERMANOVA (R² = 7.2%, p < 0.001). The results indicate that closed-loop recirculation without an engineered discharge barrier transfers, rather than eliminates, contaminant load, and that after-care of such systems requires mass-balance monitoring and polishing treatment. Full article

Chemical disinfectants used in livestock production commonly combine citric acid (CA) and benzalkonium chloride (BZK), yet the respective contributions of these active ingredients to virucidal efficacy remain poorly understood. This study quantified the relative contributions of CA and BZK against the non-enveloped foot-and-mouth disease virus (FMDV) and the enveloped avian influenza virus (AIV). Virucidal efficacy was evaluated using a full-factorial design comprising six CA concentrations and six BZK concentrations at contact times of 3 and 30 min. Log reduction values (LRVs) were analyzed by two-way ANOVA and multiple linear regression, and the relative importance of each predictor was estimated using the Lindeman–Merenda–Gold (LMG) method. FMDV inactivation was primarily determined by CA, which accounted for 99.99% and 99.87% of the explained variance at 3 and 30 min, respectively, whereas BZK and the interaction term contributed minimally. In contrast, BZK was the dominant determinant of AIV inactivation, explaining 69.79% and 78.02% of the variance at 3 and 30 min, respectively, while CA made a smaller contribution. These findings demonstrate that the dominant active ingredient in CA–BZK combination disinfectants differed markedly between the FMDV and AIV models examined. Quantifying the relative contribution of individual components provides a basis for the rational formulation and optimization of veterinary disinfectants for different target viruses. Full article

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article

The Old City of Sana’a, a UNESCO World Heritage Site, remains morphologically unclassified at the urban block scale, with understanding still grounded in descriptive narrative rather than quantitative data—leaving conservation decisions without a measurable, reproducible spatial evidence base. This study introduces a numerical taxonomy of 115 historic urban blocks built from four standardized morphometric indicators—area, compactness, elongation, and rectangularity—together with axially encoded orientation, reduced through Principal Component Analysis (74.7% variance, three components) and partitioned by K-means into five morphologically distinct block types ($n=36,29,19,16,15$). Kruskal–Wallis tests confirmed significant differentiation across all types for every rankable indicator (all $p<0.001$; Dunn’s post-hoc: 28 of 50 pairwise contrasts significant). Qibla deviation—a culturally specific measure of angular proximity to Mecca, deliberately withheld from clustering—emerged as the strongest discriminator between types (${\eta }_H^2=0.714$), showing that shape and orientation alone recover a coherent pattern in which one type’s mean orientation axis falls within $4.5°$ of the Qibla axis. However, this statistical discriminator reflects the distinctiveness of a single type rather than a universal cultural organizing principle across the fabric. Types are distributed across all three historic zones rather than clustering spatially, and shape regularity shows no association with distance from the Great Mosque. Building on these signatures, a percentile-based screening instrument offers a reproducible, block-level morphometric baseline to help prioritize this UNESCO-listed site’s conservation under active conflict-related threat. Full article

The growing demand for gluten-free products has increased the need for a better understanding of the rheological behavior of alternative flours and their suitability for bakery applications. This study aimed to evaluate the effect of different hydration levels on the rheological properties of gluten-free flours and compare their behavior with that of wheat flour. Rice flour, corn flour, chickpea flour, buckwheat flour, and wheat flour were analyzed using Mixolab 2 at hydration levels of 55%, 58%, and 60%. The resulting torque curves were examined to assess dough development, stability, and behavior during mixing and heating. Differences among flour types were observed throughout dough development and protein weakening. One-way ANOVA identified significant flour-type effects for all 19 Mixolab variables at 55% and 60% hydration and for 17 of 19 variables at 58%, where T(C4) and γ-slope were not significant. Across the three common hydration levels, two-way ANOVA showed significant main effects of flour type and hydration for every variable and significant flour × hydration interactions for all variables (p ≤ 0.035), confirming flour-specific hydration responses. Among the gluten-free flours, buckwheat maintained the most stable and comparatively robust torque profile, rice was particularly sensitive at 60% hydration, and corn and chickpea showed pronounced structural weakening during heating, most notably chickpea. A focused principal component analysis (PCA) of the five directly measured torque points (C1–C5), using the hydration levels common to all flour types, identified a dominant first component that explained 76.9% of the total variance. The first two axes together accounted for 94.3% and provided a concise two-dimensional representation of flour-specific and hydration-dependent differences. These findings highlight the importance of hydration management in gluten-free formulations and provide useful information for optimizing bakery processes involving alternative flours. Full article

Fused filament fabrication (FFF) has become an important additive manufacturing technique for producing functional polymer-composite components. The tensile performance of carbon fiber-reinforced polyethylene terephthalate glycol (PETG/CF) fabricated by FFF depends on multiple printing parameters. This study presents an integrated experimental and predictive framework for investigating the effects of extrusion temperature, printing speed, layer height, infill pattern, and infill density on the tensile strength of PETG/CF containing 15 wt.% carbon fiber. A mixed-level Taguchi L36 orthogonal array was employed, comprising 36 experimental runs with three independently printed specimens per run, resulting in 108 ASTM D638 Type V specimens. Analysis of variance showed that the printing speed had the largest contribution to tensile strength (20.51%), followed by layer height (18.29%). The highest tensile strength of 33.225 MPa was obtained using grid infill, 60% infill density, 270 °C extrusion temperature with 40 mm/s printing speed, and 0.3 mm layer height. An artificial neural network (ANN) was developed for the tensile-strength prediction, achieving R = 0.9801, R² = 0.9569, and MAPE = 1.52% for the overall dataset. Scanning electron microscopy qualitatively revealed bead-interface defects, fiber pullout, and localized void-like features. The proposed framework provides a systematic approach for evaluating process-parameter effects and predicting tensile strength within the investigated PETG/CF parameter domain. Full article

Nurses’ psychological preparedness is an important component of effective disaster response; however, its relationship with preparedness perceptions across disaster management phases remains insufficiently understood. This cross-sectional correlational study examined the relationship between disaster preparedness perceptions and psychological preparedness among 237 nurses working in three hospitals in Balıkesir, Türkiye. Participants were selected using purposive sampling. Data were collected using the Disaster Preparedness Perception Scale and the Psychological Preparedness for Disaster Threat Scale. Descriptive statistics, Pearson correlation, and hierarchical multiple regression analyses were performed. The mean psychological preparedness score was 63.16 ± 11.88. Psychological preparedness was positively correlated with overall disaster preparedness perception (r = 0.697, p < 0.001), response-phase preparedness (r = 0.735, p < 0.001), and post-disaster preparedness (r = 0.650, p < 0.001), but not preparedness-phase perception (r = 0.080, p = 0.220). In the final regression model, response-phase (β = 0.480, p < 0.001) and post-disaster preparedness perceptions (β = 0.321, p < 0.001) were positively associated with psychological preparedness. Nurses without disaster training had lower psychological preparedness (β = −0.107, p = 0.017), as did nurses with postgraduate education compared with those with high school education (β = −0.167, p = 0.037). The final model explained 60.2% of the variance (adjusted R² = 0.602). These findings highlight the relevance of disaster preparedness perceptions and training to nurses’ psychological preparedness and may inform institutional training and support strategies. Full article

This research was conducted at Bursa Uludag University, Faculty of Agriculture, during the 2022–2023 and 2023–2024 growing seasons to determine the effect of nitrogen applied at different times and doses on grain yield and quality in a bread wheat variety. The experiment was carried out using a split-plot design in randomized blocks with three replications. Fertilizer treatment times were assigned to the main plots (T1; T2; T3; T4; T5; T6; T7), and fertilizer doses were assigned to the subplots (N1: control, N2: 80 kg N, N3: 160 kg N, N4: 240 kg N ha⁻¹). The characteristics examined included basic physiological indicators (SPAD meter values and flag leaf area), growth and vegetative characteristics (plant height and spike length), yield components (number of spikelets per spike, number of grains per spike, grain weight per spike), quality criteria (thousand-grain weight, hectoliter weight, and protein ratio), and grain yield characteristics. Analysis of variance (ANOVA) revealed statistically significant differences (p < 0.01) among treatment times, nitrogen doses, and their interactions for most of the characteristics examined in both seasons. The high nitrogen treatment (240 kg N ha⁻¹) significantly increased physiological and agronomic parameters, including chlorophyll content (SPAD), flag leaf area, plant height, spike length, and the number of spikelets per spike. Average grain yield ranged from 3539 to 8039 kg ha⁻¹ in the first year and 1167–4329 kg ha⁻¹ in the second year, with the highest values recorded at the 240 kg N ha⁻¹ treatment. Similarly, the protein ratio varied between 9.4 and 13.6% in 2022–2023 and 8.5–12.7% in 2023–2024, reaching its highest level at the 240 kg N ha⁻¹ dose. In both growing seasons, the highest grain yield (8039 kg ha⁻¹ and 4329 kg ha⁻¹) and high protein ratio (13.6% and 12.4%) were obtained in plots where the 240 kg N ha⁻¹ nitrogen dose was applied in three equal splits (1/3 at sowing, 1/3 at tillering, and 1/3 at heading; T7). Principal component analysis (PCA) successfully explained 73.6% of the total variation in the first season (PC1: 61.4%, PC2: 12.2%) and 77.1% in the second season (PC1: 67.2%, PC2: 9.9%), confirming that splitting nitrogen into three parts simultaneously optimizes both yield components and grain quality. Consequently, to maximize bread wheat yield and quality, a total treatment of 240 kg N ha⁻¹ split equally into sowing, tillering, and heading stages is recommended instead of traditional two-split treatments. Full article

Background/Objectives: Empathy is a fundamental component of patient-centered care and plays a critical role in the physician–patient relationship. This study aimed to evaluate empathy levels among undergraduate medical students at a medical university in Romania, examined from a Medical Humanities perspective, and to analyze how these scores are distributed according to gender, academic year, clinical stage, and direct experience with patient contact. Methods: A descriptive, cross-sectional study was conducted among 209 undergraduate medical students across all six academic years. Empathy levels were assessed using the Romanian-language version of the Jefferson Scale of Empathy—Student Version (JSE-S). Differences in empathy scores were analyzed using independent samples t-tests and a one-way analysis of variance (ANOVA) with Tukey Honestly Significant Difference (HSD) post hoc comparisons. Statistical significance was set at an alpha level of less than 0.05. Results: Internal consistency of the JSE-S total scale was good (Cronbach’s α = 0.829). The overall mean total JSE-S score was 107.34 ± 14.34. Female students demonstrated significantly higher empathy scores compared to male students (109.92 ± 13.10 versus 99.37 ± 15.20; t = 4.450; p < 0.001; Cohen’s d = 0.77). No statistically significant differences were found between preclinical and clinical stage students (106.84 ± 14.75 versus 108.98 ± 12.90; p = 0.363; Cohen’s d = −0.15), between students with and without direct patient contact (108.20 ± 14.14 versus 104.48 ± 14.78; p = 0.115; Cohen’s d = 0.26), or across the six academic years (F = 1.614; p = 0.158; partial η² = 0.038). Conclusions: Medical students demonstrated moderate-to-high empathy levels, with gender representing the only statistically significant demographic factor. The absence of major differences across academic-year cohorts and clinical exposure suggests that empathy levels were comparably represented across the different stages of training within this cross-sectional sample. These findings contribute descriptive data on empathy levels among Romanian medical students and highlight gender as the main factor associated with variation in empathy scores in this sample. Full article

Variation in maize performance under different nitrogen fertilization regimes depends on genotype response, environmental conditions, and their interaction. This study evaluated three commercial maize hybrids contrasting maturity groups (G1–P9537, G2–P9911 and G3–P0412, representing early, medium, and late FAO maturity groups, respectively) across two growing seasons and five nitrogen treatments (0, 50, 100, 150, and 200 kg N ha⁻¹) using the Additive main effects and multiplicative interaction (AMMI) model. Analysis of variance revealed significant genotype and environment effects on grain yield, whereas genotype by environment interaction (GEI) was not significant. However, decomposition of the GEI using the AMMI model indicated that the first interaction principal component (IPCA1) was statistically significant and accounted for 90.6% of the GEI sum of squares, indicating that the interaction component was predominantly described by a single multiplicative axis. Genotype G3 achieved the highest mean grain yield and low IPCA1 score, demonstrating relatively stable performance across the evaluated nitrogen environments in this study. In contrast, G2 exhibited narrower adaptation and was more closely associated with control and low-nitrogen environments, while G1 had the lowest mean yield but exhibited a low interaction IPCA score. These findings provide useful information for hybrid evaluation under various nitrogen environments and may contribute to improved nitrogen management and hybrid selection under conditions similar to those investigated in this study. Full article