Search Results (14,162)

Accurate genealogical records are essential in livestock breeding for maintaining genetic diversity, preventing inbreeding, and mapping of economically important traits in beef production. This study aimed to assess parent–offspring relationships within South African Bonsmara and Nguni cattle populations using both traditional pedigree records and genomic data. Hair samples from 119 Nguni and 311 Bonsmara cattle were genotyped using the BovineSNP50 array, and these were imputed to Illumina BovineHD BeadChip using updated SNP coordinates from the assembly genome (ARC—UCSD 1.2). Quality control and data filtering were performed using PLINK v1.9, while relationship inference was conducted using KING v2.2.8 and PLINK v1.9 software for principal component analysis, IBD metrics and Mendelian error-based exclusion. Categories of relatedness through network relationship analysis revealed a predominance of half-sibling relationships in both breeds, with 2317 such relationships identified in Nguni and 1221 in Bonsmara. Inference of parent–offspring pairs showed discrepancies with the recorded pedigrees, with 49 inferred pairs compared to 47 recorded pairs in Nguni, and 62 inferred pairs compared to 75 pairs recorded in Bonsmara. Relationships based on IBD using PLINK with a ‘PI-HAT’ threshold greater than 0.45 revealed unique parent–offspring inferences that differed from those obtained using KING v2.2.8. Phylogenetic network analysis assigned each individual’s genomic origin independent of the pedigree records, supporting the efficiency of SNP data for genetic assignment. These results demonstrated that SNP-based pedigree verification can accurately identify parent–offspring and half-sibling relationships, providing a reliable foundation for recombination analysis and supporting precise trait mapping and informed selection in breeding programs. Full article

Unsaturated polyester resin (UPR)–quartz composites have become increasingly important in structural, sanitary, and architectural applications. However, their manufacturing processes still rely heavily on empirical knowledge. This review compiles recent developments in materials science, curing kinetics, and digital manufacturing, outlining a pathway toward data-driven, adaptive production of quartz-filled thermosets. The chemical and physical fundamentals of UPR polymerization are summarized, including the influence of initiator systems, filler characteristics, and thermal management on network formation. Challenges associated with highly filled formulations—such as viscosity control, dispersion, shrinkage, and exothermic peak prediction—are discussed in detail. Recent advances in digital twins (DTs) and artificial intelligence (AI) are reviewed, demonstrating how physics-based simulations, machine learning models, and hybrid mechanistic–data-driven approaches improve the prediction of rheology, curing behavior, and quality outcomes in thermoset polymer processes. A practical application example demonstrates the prediction of peak time in quartz–UPR composites using Random Forest and Gradient Boosting ensemble models. Two prediction scenarios are evaluated: Scenario A with gel time by Leave-One-Out cross-validation, and Scenario B without gel time, representing post-mixing and pre-process prediction contexts, respectively. Stratified bootstrap augmentation improves Gradient Boosting in both scenarios. Principal component analysis confirms that the curing process is governed by three independent physical dimensions: curing reactivity, thermal environment and resin thermal state. Full article

Understanding how climate and land use interact to shape freshwater quality remains challenging across heterogeneous river basins. This study analysed monthly citizen-science measurements of nitrate (NO₃), phosphate (PO₄), and turbidity, collected between 2016 and 2024, across seven Italian river basins representing contrasting climatic and land-use contexts. A non-parametric analytical framework combining Kruskal–Wallis tests, aligned rank transform analyses, principal component analysis (PCA), and basin-specific Somers’ D statistics was applied to ordinal concentration data. Significant differences among basins revealed persistent spatial structuring of water-quality regimes. PCA identified two largely independent gradients: a dominant nutrient axis defined by NO₃ and PO₄, and a secondary turbidity axis. Urban and industrial land use aligned with higher nutrient categories, while vegetated landscapes were associated with lower concentrations. Climatic effects were basin specific. Precipitation showed opposing relationships with NO₃, suggesting both mobilisation and dilution processes, whereas temperature was positively associated with PO₄ in several basins and negatively related to NO₃. Turbidity displayed variable links with precipitation and temperature, reflecting hydrological and seasonal controls. Overall, results indicate that land use represents the primary structural driver of nutrient variability, while climatic factors modulate basin-specific responses. The integration of citizen science observations with robust non-parametric approaches provides a scalable framework for detecting environmental drivers and supporting the targeted management of Mediterranean river systems. Full article

Blueberries (Vaccinium corymbosum L.) are widely appreciated for their flavor, bioactive compounds, and health promoting properties, yet cultivar-dependent differences in metabolic composition and postharvest stability remain incompletely understood. This study evaluated five commercial blueberry cultivars (‘Aurora’, ‘Chandler’, ‘Elliot’, ‘Legacy’, and ‘Liberty’) at harvest and after 15 days of cold storage (postharvest stage) (4 °C), assessing fruit color, size, firmness, primary metabolites, volatile organic compounds (VOCs), anthocyanins, phenolics, and antioxidant capacity. Cultivar-specific differences were observed in fruit morphology, sugar/acid balance, and biochemical composition: ‘Liberty’ and ‘Elliot’ accumulated higher monosaccharides and disaccharides, whereas ‘Aurora’ and ‘Chandler’ showed higher organic acids and amino acids. Volatile profiling at harvest revealed that ‘Liberty’ exhibited the richest aromatic profile, with elevated aldehydes, ketones, acids, phenols, alcohols, and esters. Postharvest storage caused minor changes in primary metabolites but altered anthocyanin content in a cultivar-dependent manner. Principal component analysis indicated that volatile compounds were the primary factors differentiating cultivars, while primary metabolites largely influenced sweetness–acidity balance. Overall, the results demonstrate that blueberry fruit quality is strongly cultivar-dependent, with cultivar-specific metabolic and volatile signatures shaping sensory and nutritional attributes, and provide valuable information for breeding, postharvest management, and cultivar selection to optimize flavor, bioactive content, and shelf-life. Full article

Morphological characteristics of pollen grains, including shape, size, pore number, and exine thickness, vary significantly among species and enable the reliable use of palynological data in taxonomic studies. In this context, the present study investigates the pollen morphology of seven Betulaceae taxa (Alnus glutinosa, Betula pendula, Carpinus betulus, Carpinus orientalis, Corylus avellana, Corylus colurna, and Ostrya carpinifolia). Detailed morphometric measurements were carried out using Light Microscopy (LM), and high-resolution images were obtained using Scanning Electron Microscopy (SEM). For each taxon, thirty measurements were taken for the main pollen characters, including polar axis length (P), equatorial diameter (E), pore length (plg), pore width (plt), and exine thickness (Ex). Interspecific differences were evaluated using one-way ANOVA, Tukey’s HSD test, and Principal Component Analysis (PCA), and a diagnostic pollen identification key was developed for the investigated species. The results demonstrate statistically significant interspecific variation in pollen size, pore characteristics, and exine thickness. In the PCA ordination, the first principal component (PC1) was mainly associated with pollen size (P and E), clearly separating Carpinus betulus from the remaining taxa. The second principal component (PC2) was primarily related to pore length (plg) and contributed to the separation of Alnus glutinosa from the other small-pollen species. These results show that quantitative pollen morphological characters provide reliable criteria for distinguishing closely related Betulaceae taxa. Full article

This study investigates how financial technology (FinTech) contributes to economic growth, focusing on whether it acts primarily as a mediator or a moderator within the finance–growth nexus. A composite FinTech index is constructed using Principal Component Analysis based on cross-country data for 2021, and the analysis distinguishes between High-Income and Non-High-Income economies following the World Bank classification. The results show that in developing and emerging economies, FinTech mainly serves as a mediator, helping to close structural gaps in financial intermediation and expanding access to financial services. In High-Income countries, by contrast, FinTech acts as a moderator, enhancing innovation and efficiency in mature financial systems. When financial inclusion is disaggregated by gender, the findings reveal additional nuances. FinTech fosters growth through inclusion for both men and women, but its effects are stronger for male account ownership in developing economies and more balanced in High-Income contexts. In general, the study contributes to the literature by developing a multidimensional FinTech index, clarifying its dual mediating and moderating functions, and introducing a gender-sensitive perspective that highlights the uneven distribution of FinTech’s growth benefits between income levels and genders. Full article

Strawberry aroma and flavor are key determinants of consumer acceptance and market value, yet their relationship with physico-chemical and functional traits remains complex and cultivar-dependent. This study aimed to characterize the volatile profile, quality parameters, antioxidant capacity, microbial load, and sensory attributes of three strawberry cultivars (‘Rossetta’, ‘Melissa’, and ‘Gioelita’) grown in soilless culture systems and harvested at the commercial ripening stage. ‘Melissa’ showed significantly higher total soluble solids (8.65 °Brix) than ‘Rossetta’ (7.78 °Brix) and ‘Gioelita’ (7.47 °Brix), while titratable acidity was highest in ‘Gioelita’ (4.97 mg CA/L). Regarding phytochemical traits, ‘Melissa’ exhibited the greatest total polyphenol, flavonoid, and antioxidant capacity values, followed by ‘Rossetta’ and ‘Gioelita’. Sixty-four volatile organic compounds (VOCs) were identified, semi-quantified, and combined with physico-chemical and sensory data related to odor and taste perception. Principal component analysis was applied to evaluate cultivar discrimination and identify the key discriminatory volatiles. The results revealed clear separation among cultivars based on their compositional and sensory profiles. ‘Rossetta’ was characterized by a higher abundance of esters, lactones, and mesifuran and received the highest sensory scores for sweetness and overall flavor, consistent with its elevated anthocyanin content. ‘Gioelita’ was associated with key esters contributing to strawberry flavor and with higher titratable acidity and perceived acidity. ‘Melissa’ showed a balanced volatile composition, higher antioxidant capacity, and greater phenolic content but also had higher microbial counts. Overall, the integration of chemical and sensory analyses provided useful insights into cultivar-specific quality traits relevant for breeding and production strategies. Full article

The rapid adoption of artificial intelligence (AI) across public services and critical infrastructure is reshaping digital governance. While AI promises efficiency and innovation, its reliance on large, high-dimensional datasets introduces privacy, bias, transparency and accountability risks that existing frameworks struggle to address. This study evaluates the maturity of current AI governance frameworks and develops an integrated risk-tiering model that connects ethical principles to auditable technical controls, aligning with Sustainable Development Goal 9 on industry, innovation and infrastructure. A systematic literature review of 450 records from major databases was conducted using PRISMA 2020 guidelines; 95 high-quality studies were analyzed using principal component analysis and k-means clustering. The analysis produced a heat map of governance frameworks, a co-occurrence network of themes, a cluster analysis of framework coverage and an integrated governance risk framework supported by a risk-tiering matrix. Findings reveal a fragmented landscape dominated by ethics/privacy-centric and compliance/risk-focused approaches, with few integrated frameworks and evident tension between privacy and security. This synthesis bridges the gap between values and practice, offering a policy-ready model for secure and sustainable AI governance. Full article

Immortelle (Helichrysum italicum (Roth) G. Don, family Asteraceae) essential oils (HiEOs) and hydrosols (HiHYs) are widely used in cosmetic, pharmaceutical, and agricultural formulations. However, their composition and quality vary depending on geographical origin and production practices, while standardized reference values—particularly for hydrosols—are still lacking. The aim of this study was to investigate and compare the physicochemical properties and chemical composition of commercial HiEOs and HiHYs from the Adriatic and continental regions of Croatia. Samples were analysed using standard pharmacopoeial methods and gas chromatography–mass spectrometry (GC–MS). Physicochemical analyses (relative density, acid value, refractive index, pH, turbidity, and essential oil content) showed that all samples were within generally accepted quality ranges, with no significant differences observed between regions using the Mann–Whitney U test. HiEOs were dominated by sesquiterpene hydrocarbons (53.15–55.60%), whereas HiHYs contained predominantly oxygenated monoterpenes (43.54–69.86%). The main compounds identified in both fractions were α-pinene, neryl acetate, γ-curcumene, and β-selinene, which formed a consistent chemical signature and served as practical biomarkers for the quality of H. italicum EO and hydrosol. Principal Component Analysis (PCA) distinguished sample groupings based on physicochemical properties and chemical composition, indicating regional variability without exceeding accepted quality limits. This study presents the first comparative dataset of Croatian commercial HiEOs and HiHYs, and defines practical parameter ranges to support standardized specifications, ensure consistent quality, and enhance the industrial applicability of immortelle-based products. Full article

In offshore wind farms, modular multilevel converters (MMCs) may operate under a deloading condition to accommodate wind-speed volatility and dispatch constraints. Here, deloading is defined as transmitted power < 0.2 pu (scenario S2, low-power non-reversal). Under this condition, submodule capacitor-voltage fault signatures are weak and exhibit strong operating-point-dependent drift, which degrades conventional threshold-based or offline-trained methods. We propose a lightweight switch-level IGBT open-circuit fault localization framework for deloaded MMCs. Wavelet packet decomposition is used to extract time–frequency energy features, and principal component analysis reduces feature dimensionality for lightweight deployment. An enhanced XGBoost model further integrates severity-index weighting to alleviate class imbalance and incremental learning to adapt to condition drift induced by wind-power fluctuations. MATLAB2024b/Simulink results show 99.6% accuracy in S2 with less than 2 ms inference latency, and robust performance in extended scenarios including partial-power operation and power reversal. Full article

The application of ultra-supercritical power plant boilers is becoming increasingly widespread. P92 steel, as a typical material used for boiler main steam pipes, plays a critical role in unit safety, making the detection of its creep damage highly significant. However, existing conventional non-destructive testing methods are difficult to effectively detect creep damage. To address this issue, a magnetoacoustic emission (MAE)–magnetic Barkhausen noise (MBN) composite measurement system is developed, which is adapted to 20 Hz and 0.3 A sine wave excitation to trigger the synchronous pickup of MBN and MAE signals of P92 steel. After collecting signals with different creep life ratios (0%~100%) under working conditions of 650 °C and 100 MPa, time-domain (absolute mean, peak value, etc.) and frequency-domain (bandwidth) features are extracted. In response to the non-monotonicity between the magnetoacoustic features and the creep damage grade, principal component analysis (PCA) is introduced to reduce dimensionality. Different creep levels of samples in the two-dimensional principal component space are presented as clear gradient clustering, achieving the accurate differentiation of creep stages. Research has shown that the MAE-MBN composite system combined with PCA can effectively characterize the creep damage of P92 steel, providing a novel non-destructive detection path for the in-service life assessment of power plant components. Full article

Multimodal epileptic seizure detection using physiological biosignals remains challenging due to signal noise, inter-subject variability, weak cross-modal alignment, and the limited interpretability of many machine learning models. To address these challenges, this study proposes a parameterized multimodal feature-fusion framework that unifies normalization, modality weighting, and nonlinear cross-modal interaction within a single mathematical representation. Four fusion parameters, the fusion exponent $\rho$, interaction weight $\left(\delta \right)$, normalization factor $\left(\lambda \right),$ and the cross-modal interaction term $\left(\eta \right)$, are introduced at the feature-fusion level, while all classifiers retain their original learning mechanisms. The framework is evaluated using synchronized EEG, ECG, EMG, and accelerometer signals from 120 subjects, segmented into 2 s windows at 512 Hz and analyzed using twelve classical and deep learning classifiers. Principal Component Analysis (PCA) applied to the fused feature space reveals improved class separability compared to unimodal representations, with EEG exhibiting the strongest intrinsic discrimination and peripheral modalities contributing complementary structure when fused. SHapley Additive exPlanations (SHAP) further identify entropy as the most influential feature across all modalities, followed by RMS and energy, yielding physiologically coherent attributions. Quantitative performance evaluation and ablation analysis confirm that the observed improvements arise from the proposed representation design rather than classifier-specific modifications. Unlike existing architecture-dependent fusion strategies, the proposed method introduces a mathematically parameterized feature-space formulation that enhances separability and interpretability without modifying classifier architectures, thereby establishing a representation-driven paradigm for explainable multimodal seizure detection. These results demonstrate that mathematically principled feature-space modeling can simultaneously enhance predictive performance and interpretability, providing a transparent and robust foundation for explainable multimodal seizure detection. Full article

Background/Objectives: Morning erections provide an intercourse-independent indicator of nocturnal erectile physiology. We aimed to examine whether body mass index (BMI) and muscle strength are associated with morning-erection frequency in apparently healthy Japanese male university students. Methods: This cross-sectional study analyzed 125 men with complete data (170 assessed; 45 excluded). Handgrip and back muscle strength were measured using dynamometry; BMI was calculated from height and weight. Morning-erection frequency was assessed using a single 6-category item and was dichotomized as low vs. high. Univariable and multivariable logistic regression models were fitted. Exploratory principal component analysis (PCA) and k-means clustering (k = 2, silhouette-supported) were performed. Results: Seventy-four participants (59.2%) were classified as low frequency. Back muscle strength was associated with high frequency (univariable odds ratio [OR] 1.61; 95% confidence interval [CI] 1.07–2.42; and p = 0.021) and remained significant after adjustment for BMI and handgrip strength (OR 1.88; 95% CI 1.02–3.47; and p = 0.045), whereas BMI and handgrip strength were not significant. Clustering identified two clusters (n = 41 and n = 84); Cluster 2 (higher BMI/strength) had a higher proportion of high morning-erection frequency (48% vs. 27%). Conclusions: In apparently healthy young men, greater back muscle strength was independently associated with higher self-reported morning-erection frequency. In this cohort, 59.2% reported infrequent morning erections, suggesting potential relevance even in early adulthood. Given the exploratory clustering, the single-item outcome, and likely residual confounding, these findings are hypothesis-generating and warrant longitudinal validation. Full article

Background/Objective: To investigate short-term multidimensional recovery trajectories after physiotherapy with or without adjunctive pain education in individuals with Chronic low back pain (CLBP). Methods: This was a secondary analysis of a randomized controlled trial (RCT) of 92 participants (46 participants per group) comparing physiotherapy alone with physiotherapy plus pain education. Changes from baseline values over six weeks were calculated for pain intensity, disability, psychological well-being, and self-efficacy to define short-term recovery trajectories across domains, and were standardized prior to analysis. Descriptive characterization of recovery dimensions by principal component analysis and identification of different recovery trajectory clusters by k-means clustering were performed. Sensitivity analyses with multinomial logistic regression were performed to determine robustness after adjustment for baseline characteristics. Results: Three recovery trajectories were found: minimal recovery (n = 40), psychosocial-dominant recovery (n = 26), and global recovery (n = 26). In the physiotherapy-only group, participants were classified as minimal recovery (61%) or psychosocial-dominant recovery (39%), with no cases of global recovery. In contradistinction, 57% of participants receiving physiotherapy with pain education were classified as within the global recovery trajectory, with fewer classified as minimal recovery (26%) or psychosocial-dominant recovery (17%). Psychosocial-dominant recovery occurred in both groups, and was characterized by large improvements in psychological well-being and self-efficacy with more modest changes in pain and disability. The distribution of recovery trajectories between treatment groups was large (χ²(2)= 36.25, p < 0.001; Cramer’s V = 0.63). Conclusions: Distinct short-term recovery trajectories were found after physiotherapy with or without pain education in individuals with CLBP, reflecting heterogeneity in multidimensional recovery that is not reflected in mean-based outcome analyses. Full article

Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) hyperspectral imaging (HSI; 449.54–2399.17 nm; 563 bands) was used to classify 32 grain–legume accessions (n = 3200 seeds; 100 seeds per accession), comprising 30 common bean (Phaseolus vulgaris L.) landraces plus two outgroup legumes (Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession (n = 2240) reserved for fully independent training and 30 seeds per accession (n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R² of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54–456.30 nm and 577.02–597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification. Full article