Search Results (4,161)

The current mainstream methods for online detection of transformers all have shortcomings such as low sensitivity and susceptibility to interference from the testing environment. Aiming at the shortcomings of the existing online detection methods for transformer winding deformation in terms of feature sensitivity and diagnostic accuracy, this paper proposes a fault intelligent diagnosis method based on high sensitivity multimodal feature fusion. First, the winding deformation experiment is designed for typical fault data, which is obtained to extract multiple frequency and time domain response features and construct a multidimensional feature library. Subsequently, principal component analysis is used to evaluate the sensitivity of each feature to different faults and establish a highly sensitive multimodal feature system. On this basis, a TCN-BiGRU-PHA diagnostic model combining time convolutional network, bidirectional gated loop unit and attention mechanism is constructed to realize accurate identification of winding deformation faults. The experimental results show that the method has higher recognition accuracy under multiple types of faults, which provides feasible ideas and methodological support for realizing online intelligent monitoring of transformer winding deformation. Full article

Sugarcane production in China is severely constrained by frequent seasonal droughts, especially in the major planting region of Guangxi. Identifying drought-resistant varieties is crucial for ensuring yield stability. This study aimed to comprehensively evaluate the drought resistance of 15 sugarcane varieties and screen key identification indicators. A pot experiment was conducted with both well-watered (control) and drought-stress treatments. Fifteen agronomic and physiological traits were measured, and drought resistance was assessed using the comprehensive drought resistance evaluation value (D value), the comprehensive drought resistance coefficient (CDC), and the weighted drought resistance coefficient (WDC). Results showed significant variations in trait responses to drought: green leaf number (NGL) decreased the most (66.06%), while proline (Pro) increased the most (88.09%). PCA reduced 15 traits to 5 principal components, with a cumulative variance contribution rate of 82.26%. Comprehensive evaluation using D values, comprehensive drought resistance coefficients (CDCs), and weighted drought resistance coefficients (WDCs) showed consistent overall drought resistance rankings, with slight differences in individual varieties. Cluster analysis based on D values classified the 15 varieties into three groups: 10 drought-resistant (66.67%, e.g., YZ08-1609, LT5), 3 moderately drought-resistant (e.g., GT08-56), and 2 drought-sensitive (GT10-612, ZT13-012). Grey relational analysis identified single stalk weight (SSW), number of leaves (NL), and number of green leaves (NGL) as key indicators closely associated with drought resistance. This study provides a scientific basis for establishing a drought-resistant sugarcane variety evaluation system and lays the foundation for breeding drought-resistant varieties. Full article

The Lagunera Region, located in northern Mexico, is home to the country’s most important dairy basin, situated in a semi-arid environment. In this region, forage corn (Zea mays L.) is the main input in dairy cattle feed. In this context, optimizing water use and nitrogen nutrition is a priority to ensure the sustainability of this activity. The main objective of this study was to evaluate the productivity and water use efficiency of forage corn under different humidity, nitrogen, and substrate type levels. A randomized block design with sub-subdivided plots was used. The larger plot contained two usable moisture levels (80 and 50%); the subplots were assigned according to three nitrogen levels: 13.6 (N1), 6.8 (N2), and control 0.35 (N3) NO₃ mmol·L⁻¹; the sub-subplots were assigned based on two substrates: sand and a mixture (MI) of sand, perlite, and peat moss. The results showed significant triple interactions (p < 0.05) in the root volume traits, where nitrogen played a determining role, as well as double interactions (Nutrition*Substrate) for all vegetative and radicle production variables and water use efficiency. Principal components analysis explained 91.4% of the total observed variation, where basal diameter had the vector with the highest load value. Cluster analysis identified that the main discriminant factor was nutrition. It is concluded that usable moisture levels up to 50% with 6.8 mmol·L⁻¹ of NO₃ show acceptable levels of vegetative production and root volume in forage corn. These results suggest the possibility of reducing water and nitrogen fertilizer consumption without compromising yield, with significant economic and environmental benefits for agriculture in arid and semi-arid regions. Full article

Pear (Pyrus spp.) is a globally significant fruit crop with high economic and nutritional value. This study was conducted in 2024 to determine the pomological and chemical characteristics of 16 wild pear genotypes growing in Gürağaç village, located in the Güce district of Giresun province. The fruit weight of the investigated genotypes ranged from 60.30 to 98.50 g, fruit diameter from 40.92 to 59.14 mm, and fruit length from 58.32 to 97.42 mm. Fruit flesh firmness was found to be between 4.39 and 7.79 kg/cm², soluble solids content between 8.95 and 13.05%, pH value between 3.69 and 4.90, and titratable acidity between 0.74 and 1.85%. Correlation analyses revealed strong positive relationships between fruit weight, diameter, length, soluble solids content, and color parameters. Hierarchical cluster analysis divided the genotypes into two main clusters based on their phenotypic traits, with some genotypes standing out in terms of fruit quality and size. Principal component analysis (PCA) showed that a large portion of the phenotypic diversity among the genotypes could be explained by two components. The findings provide important information for the evaluation of wild pear genotypes in the region as genetic resources. Specifically, genotypes G-10 through G-16 were identified as superior candidates for future breeding programs aimed at improving fruit quality and size. Full article

Ptelea trifoliata L. is a perennial plant of the Rutaceae family and contains secondary metabolites with potential biological relevance. Due to limited information on its activity, the objective of this study was to evaluate the biological properties of its flower extracts and to determine their phytochemical composition. Flowers were dried and subjected to ultrasound-assisted extraction using methanol, 60% methanol and water. LC–MS/MS was used for qualitative profiling, HPLC for quantitative determination, and spectrophotometry for measuring total phenolic and flavonoid content. The antioxidative capacity of the extracts was determined using DPPH, CUPRAC, FRAP, and iron chelation assays. Enzymatic inhibition analyses were performed for hyaluronidase, indicative of anti-inflammatory properties, and tyrosinase, associated with pigmentation mechanisms. The wound-healing capacity was evaluated in vitro using a scratch assay. Our research revealed the highest levels of polyphenols in the 60% methanol extract and of flavonoids in the methanol extract. The occurrence of chlorogenic acid, rutin, hyperoside, and astragalin was also demonstrated. Both methanol and 60% methanol extracts demonstrated antioxidant effects. The methanol extract showed the greatest hyaluronidase inhibition, while the 60% methanol extract was the most effective in suppressing tyrosinase activity and promoting wound closure. Principal component analysis showed that the contents of polyphenols or flavonoids were associated with enzyme-inhibitory or antioxidant activities. Moreover, the 60% methanol and water extracts exhibited notable wound healing properties. These results highlight the antioxidant, enzyme-modulating and regenerative potential of P. trifoliata flower extracts, suggesting their possible use in biomedical and dermatological applications. Full article

Chemical contaminants in drinking water represent a widespread threat to human health, making water quality monitoring an essential mitigation measure. This study aimed to assess the quality of drinking water by conducting comprehensive multi-year seasonal monitoring at seven distribution points in central Romania, determining the spatial and temporal trends of relevant physical parameters (pH and electrical conductivity) and chemical contaminants (NO₂, NO₃, NH₄, Cl, and SO₄). The pollution degree was evaluated using the pollution index and the overall pollution assessment index. The principal component analysis attributed over 60% of water quality variance to NO₂, NO₃, and NH₄ pollution, linked to incomplete nitrification or external loading, such as agricultural practices. Additionally, a human health risk assessment was performed according to U.S. EPA guidelines, calculating the chronic daily intake, hazard quotient, and hazard index for nitrogen compounds via oral and dermal exposure pathways for both adults and children. The results showed significant seasonal fluctuations in nitrogen compounds and electrical conductivity. The pollution indices classified the water bodies across a spectrum from “light” to “significant” pollution degrees. The health risk assessment revealed that NO₃ was the primary risk driver, with hazard index values exceeding the threshold of one in specific locations and seasons, indicating potential adverse health effects, particularly for children. Full article

This study evaluated 362 chickpea accessions by analyzing the phenotypic variation of 17 major traits. The main agronomic traits and quality traits were comprehensively evaluated using principal component analysis (PCA) and cluster analysis. The results revealed a Shannon diversity index (H’) for the five qualitative traits ranging from 0.76 to 1.20, while for the twelve quantitative traits, it ranged from 1.45 to 2.07. The coefficient of variation (CV) ranged from 7.63% to 41.69%, demonstrating substantial variation and significant differences among the 362 germplasm resources. Correlation analysis revealed that traits such as growth period, plant height, seed weight per plant, and hundred-seed weight were closely correlated with yield. PCA extracted five principal components, collectively explaining 76.06% of the total variance, representing most of the agronomic traits and quality traits. Cluster analysis categorized the accessions into five distinct groups, which can be used as germplasm alternative materials with high yield, mechanization potential, large grain size, early maturity, stress resistance, and high protein content. Using a membership function, a comprehensive evaluation score (F-value) was calculated, leading to the identification of ten accessions with superior overall traits. These could be used as materials for breeding and germplasm creation of new chickpea varieties. This research provides a scientific basis for future parental selection in chickpea breeding programs and for the screening of specific chickpea germplasm resources. Full article

Modern Shape Analysis integrates mathematical and statistical methods to study morphology using geometric data from 2D and 3D digitization. Beyond visualization, local deformation analysis offers deeper insight into longitudinal changes like ontogenetic growth. This study explores strain tensors as analytical tools for shape transformations, evaluating Thin Plate Spline, Quadratic Trend, and Cubic Trend interpolants in estimating deformation tensors at landmarks. Simulated 2D data and 3D hominid skulls confirm that tensor-based multivariate analyses, such as Principal Component Analysis, yield results comparable to Parallel Transport methods. Thin Plate Spline more accurately recovers assigned tensors than least square interpolants, making it preferable for local deformation analysis. This method integrates traditional shape comparisons, proving particularly useful for studying ontogenetic trajectories and biomechanical deformations. Full article

Vaccine hesitancy represents a threat to immunization programs and herd immunity. Our objective was to validate a Spanish-language questionnaire to assess knowledge, attitudes, and practices (KAP) of students in the healthcare field regarding vaccination and the immunization schedule. Methods: An online questionnaire was developed and distributed via RedCap v.13.7.1 to healthcare students undertaking clinical placements at the University Hospital of Santiago de Compostela during the 2024–2025 academic year. The questionnaire assessed nine dimensions through thirty-four items. Validation was carried out in two phases: (1) translation and expert content validation, and (2) reliability testing using Cronbach’s alpha and validity assessment through principal component analysis (PCA). Results: A total of 398 students completed the questionnaire. The mean age was 23.78 ± 3.77 years. Of these, 19.60% were men (n = 80) and 77.50% were women (n = 316). Validation of the questionnaire was carried out with a random sample of 294 students. The final 30-item questionnaire demonstrated high internal consistency (Cronbach’s alpha = 0.83) and construct validity, confirmed by PCA, supporting the presence of nine dimensions that explained 60.93% of the total variance. Overall, 74.70% of students reported that scientific evidence was the main influence on their opinion about vaccines. Regarding practices, 76.10% believed that certain vaccines should be mandatory for healthcare personnel. Conclusions: The questionnaire demonstrated reliability and validity for evaluating KAP on vaccination among future healthcare professionals. Having this instrument available will help guide future educational interventions and strengthen their role as trusted agents in immunization. Full article

Coffee quality arises from the interaction among genotype, environment, and postharvest management, yet few large-scale studies jointly integrate agronomic, phytochemical, and processing traits. We characterized 150 Coffea arabica L. accessions from six Peruvian regions, evaluated in the INIA coffee germplasm collection, quantifying agro-morphological traits, colorimetric parameters in cherries and beans, fermentation indicators, bioactive compounds, and antioxidant activity. Correlation analyses showed that total phenolics (TPCs) and total flavonoids (TFCs) were strongly associated with antioxidant activity, whereas caffeine content (CAF) varied, largely independently. Several chromatic parameters in parchment and green coffee (a*, b*, C*) showed positive correlations with phenolic content and antioxidant activity (ABTS, DPPH, FRAP), while final fermentation pH (FPH) was negatively associated with these compounds, supporting both color metrics and pH as operational indicators of chemical quality. Principal component analysis disentangled a morphometric gradient from a functional (phenolic–antioxidant) gradient, indicating that bean size and antioxidant potential can be improved in a semi-independent manner. Hierarchical clustering identified complementary ideotypes, and a multi-trait selection index highlighted promising accessions—PER1002197 (Cajamarca), PER1002222 (Cajamarca), PER1002288 (Pasco), and PER1002184 (Cajamarca)—that combine high phenolic/antioxidant levels, favorable chlorogenic acid (CGA)/trigonelline (TGN) profiles, contrasting (high/low) caffeine, and competitive yield (YPP)/bean size. These accessions represent promising candidates for breeding climate-smart and nutraceutical-oriented coffee. Full article

Background: Myelodysplastic syndromes (MDSs) and acute myeloid leukemia (AML) are clonal hematological disorders that share molecular origins but present with distinct clinical features. MicroRNAs (miRNAs) are key post-transcriptional regulators, and their altered expression may reflect biological shifts contributing to disease progression. Methods: Expression levels of miR-155, miR-181, miR-221, miR-222, and miR-223 were analyzed by RT-qPCR in bone marrow samples from 37 MDS patients, 20 AML patients, and 7 controls. Group comparisons were performed using ANOVA (with Benjamini–Hochberg correction) and Tukey post hoc testing. Diagnostic performance and network behavior were evaluated using ROC analysis, Pearson correlation matrices, and principal component analysis (PCA). Results: miR-155, miR-181, and miR-223 were upregulated in AML, whereas miR-221 and miR-222 were downregulated. miR-222 showed the highest diagnostic accuracy (AUC ~0.87 for both AML vs. control and MDS vs. control). Its expression was significantly higher in high IPSS-R MDS cases (p = 0.046), with a similar upward tendency for miR-221 (p = 0.054). Progressive loss of coordinated miRNA expression was observed from controls to MDS and AML. PCA supported these findings by showing separation mainly driven by miR-222 and miR-155. Conclusions: Combined miRNA profiling highlights miR-222 and, to a lesser extent miR-155, as consistent indicators of myeloid disease transformation. While further validation in larger and genetically stratified cohorts is warranted, these findings support the potential contribution of miRNA signatures to diagnostic evaluation and risk stratification in MDS and AML, in line with precision hematology approaches. Full article

Accurate quantification of ocean tide loading (OTL) is essential for sustainable coastal geodetic monitoring, infrastructure stability assessment, and the interpretation of GNSS vertical displacement time series. This study analyzes long-term vertical displacements observed at the Palmido GNSS station, located in Korea’s largest tidal-range environment, to resolve dominant semi-diurnal and diurnal tidal constituents. Coherent-gain–corrected Fast Fourier Transform (FFT) and continuous wavelet analysis were applied to decompose the GNSS time series, with particular emphasis on the principal lunar (M2) and principal elliptical lunar (N2) constituents. The extracted tidal amplitudes and phases were benchmarked against the NAO99 ocean tide loading model after applying load Love number (LLN) and site-scale corrections. Quantitative evaluation demonstrates that the corrected NAO99 predictions reduce the root mean square difference (RMSD) of the M2 constituent from approximately 14.5 mm to 13.3 mm (≈8% improvement) and that of the N2 constituent from about 2.1 mm to 1.2 mm (≈40% improvement), compared to uncorrected model outputs. Linear regression analyses further show that amplitude scaling improves toward unity for M2 after correction, while maintaining strong phase coherence. Continuous wavelet scalograms reveal persistent semi-diurnal energy with a clear fortnightly modulation, whereas diurnal components appear intermittently and are more sensitive to local environmental conditions. These results demonstrate that combining coherent-gain–corrected FFT, time–frequency wavelet diagnostics, and physics-based NAO99 benchmarking significantly enhances the reliability and interpretability of GNSS-derived tidal loading estimates. The proposed workflow provides a transferable and reproducible framework for high-precision coastal deformation monitoring and long-term sustainability assessments in macrotidal environments. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Background/Objectives: Employee engagement is fundamental for the quality and sustainability of the Slovak healthcare sector. While the concept is critical, its operational challenges lie in the differentiated perception of its drivers across the highly heterogeneous workforce. This study aimed to empirically identify and structure the key antecedent factors of engagement and examine their perception based on structural and sociodemographic characteristics among healthcare workers in Slovakia. Methods: This research employed a quantitative, cross-sectional design, utilizing a self-administered questionnaire distributed widely among healthcare providers throughout Slovakia. To achieve the study’s objectives, several advanced mathematical and statistical methods were applied: the Kaiser-Meyer-Olkin (KMO) Measure and Bartlett’s Test for sample adequacy, Principal Component Analysis (PCA) for empirical factor structuring and Analysis of Variance (ANOVA). Results: Three common antecedent factors for healthcare workers’ engagement and well-being were identified: Factor 1—Organizational Commitment and Identity; Factor 2—Meaningful Involvement and Job Satisfaction; and Factor 3—Organizational Citizenship and Retention Intent. Factor 1 was evaluated positively in public (state-owned) and mixed organizations but negatively in private healthcare providers, confirming a statistically significant difference. Factor 2 also exhibited significant differences based on work position: it was negatively rated by management, physicians, and nurses, but positively by other staff categories. Conclusions: The contribution of this study lies in the empirical confirmation that a universal managerial approach to increasing employee engagement in Slovak healthcare is ineffective. A differentiated managerial approach based on organizational type and work position directly supports the transition from blanket, expensive, and ineffective HR policies to strategic and targeted engagement management, which is essential for the long-term sustainability and improvement of care quality in Slovak healthcare. Full article

Urban lakes are essential for ecological balance and urban development. This study developed a comprehensive framework to evaluate the ecosystem health of urban lakes in China. Nineteen representative lakes from four lake zones were examined using three decades of remote-sensing data combined with hydrological, water-quality, and aquatic–biological investigations. An extended DPSIR model guided the selection of 52 indicators, and a hierarchical weighting scheme was used: the analytic hierarchy process determined criterion-level weights, while principal component analysis with Softmax normalization was used for indicator-level weights. The established index system was applied to Xuanwu Lake and Erhai Lake, and an obstacle-degree model was used to identify key ecological constraints from 2010 to 2020. Results showed that urban lakes in the Yunnan–Guizhou Plateau and Eastern Plain zones were mainly constrained by eutrophication and intensive urbanization, with state- and impact-related indicators contributing most to the health index. The framework captured the decline of Xuanwu Lake, driven by poor water exchange and external nutrient loading, and its subsequent improvement following governance interventions, as well as the post-2014 degradation of Erhai Lake driven by climate-induced hydrological stress and non-point source pollution, providing a practical tool for diagnosing constraints and supporting adaptive, region-specific lake management. Full article