Search Results (1,760)

For hydrological parameterization in high-Andean catchments, it is necessary to understand whether near-surface hydro-structural soil properties can provide a surrogate signal of particle-size composition when direct texture information is sparse. This study evaluated the extent to which sand, silt, and clay fractions can be approximated from organic matter ($\mathrm{OM}$), bulk density (${\rho }_b$), and saturated hydraulic conductivity ($K_{\mathrm{sat}}$) in the Zamora Huayco (ZH) and Irquis catchments, southern Ecuador. A harmonized dataset ($n=44$) was analyzed through exploratory statistics, compositional assessment, correlation analysis, PCA, fraction-wise regression, $\mathrm{ILR}$-based modeling, AIC/BIC term reduction, sensitivity analysis excluding $\mathrm{OM}$, nested $\mathrm{LOOCV}$, and bootstrap-based uncertainty intervals. Among LULC classes, samples classified as paramo occupied a distinct high-Andean hydro-edaphic domain, characterized by a differentiated relationship between soil physical properties and hydrological behavior. PCA showed that the dominant covariance structure involved $\mathrm{OM}$, ${\rho }_b$, $K_{\mathrm{sat}}$, and the redistribution between sand and silt. The BIC-reduced $\mathrm{ILR}$ model provided the most balanced formulation, with positive nested $\mathrm{LOOCV}$ performance for sand, silt, and clay ($R_{\mathrm{LOOCV}}^2=0.147$, $0.704$, and $0.124$, respectively) and exact $100\%$ compositional closure after inverse transformation. Silt was the most stable predicted fraction, whereas sand and clay retained larger residual uncertainty, stronger tail departures, and partial compression of the observed variability. The proposed equations provide local hydro-pedotransfer support, although their predictive signal remains dependent on further refinement, uncertainty assessment, and external validation before regional application. Full article

Understanding the drivers and barriers of wine consumption is of substantial importance for both market development and sensory science research, and this is particularly salient in rapidly changing non-Western markets. Young, highly educated Chinese consumers represent one of the fastest-growing segments in the global wine market, yet large-scale studies of their consumption preferences and rejection patterns remain limited. This study aimed to characterize the conditional dependence structure of wine-consumption behavior in this population and to examine the associations between common consumption barriers and sociodemographic variables. A nationwide cross-sectional online survey collected 4823 valid responses. Non-parametric tests were used to compare sociodemographic groups, and a regularized Gaussian graphical model (GGM) was estimated to characterize the conditional associations among 15 consumption-behavior variables. The sample was dominated by young respondents (18–24 years) and individuals with higher education. The three most frequently endorsed barriers were taste aversion (51.1%), price sensitivity (38.7%), and lack of knowledge (19.6%). Age and education were the most central sociodemographic variables in the network. The knowledge barrier showed a moderate negative conditional association with education (partial r ≈ −0.171), whereas taste aversion—although the most frequently endorsed barrier—did not show clear conditional associations with sociodemographic variables in the network. Gender was not conditionally associated with any other variable in the network. These observations suggest that the three consumption barriers may operate through different network pathways and may therefore have different implications for intervention design, a possibility that warrants further confirmatory and longitudinal research. Full article

The aim of this study was to evaluate the possibility of using rapeseed protein–fiber concentrate (RPFC) as a functional ingredient for wheat pasta fortification, with emphasis on dough rheology, gel-like network formation, microstructure, and cooking quality. For this purpose, five formulations of rigatoni pasta were produced by partially substituting wheat flour with 0, 5, 10, 15, and 20% RPFC. Dough rheological behavior was assessed by frequency sweep and creep–recovery tests, while mixing and pasting behavior was evaluated using the Mixolab device. Microstructure was analyzed by scanning electron microscopy (SEM), and pasta technological and chemical parameters were determined using standard methods. All dough systems exhibited viscoelastic, gel-like behavior characterized by the dominance of the storage modulus (G’) over the loss modulus (G”), confirming the formation of a structured gluten-based network. Moderate RPFC incorporation (5–15%) enhanced G′, indicating reinforcement of the continuous protein–starch gel matrix and improved structural integrity and deformation resistance. Mixolab results showed a significant increase in water absorption and dough stability with RPFC addition, reflecting improved hydration and strengthening of the gel-forming protein network. SEM observations confirmed the development of a more compact and continuous starch–protein gel system, associated with reduced stickiness and improved structural cohesion. However, higher RPFC levels (15–20%) disrupted the continuity of the gel network, leading to increased cooking losses (8.8–10.4%), higher fracturability, and reduced firmness of cooked pasta. According to the data obtained, RPFC represents a promising functional protein ingredient for gel-like food systems such as cereal-based products, particularly pasta. These findings offer feasible formulation strategies and support its use as a sustainable, high-quality plant protein ingredient in pasta production. Full article

Fluctuations in ovarian hormones across the menstrual cycle (MC) have been suggested to influence neuromuscular performance in female athletes. However, phase-specific variations in lower-limb muscle strength remain underexplored, particularly within the soccer population. This study investigated phase-related differences in lower-limb muscle strength across MC phases in female soccer players. A repeated-measures design was employed involving 50 competitive female soccer players. Bilateral lower-limb muscle strength was assessed using a handheld dynamometer (VALD DynaMo Plus) during the three MC phases: menstruation, late follicular, and luteal phase. Estimated menstrual cycle phases were identified using calendar-estimated tracking or an MC monitoring application (FitrWoman). Phase-related differences were analysed using repeated-measures analysis of variance with Bonferroni-adjusted post hoc comparisons, and effect sizes were reported as partial eta squared (ηp²). Significant differences in lower-limb muscle strength were observed across estimated MC phases (p < 0.05, ηp² = 0.12–0.31). Both the non-dominant and dominant limbs demonstrated higher strength values during the late follicular phase, with hip abductors emerging as the strongest muscle group bilaterally (≈149 ± 37 kg). Most muscle groups exhibited lower strength values during the menstruation phase. Lower-limb muscle strength appears to vary across calendar-estimated MC phases in female soccer players, with higher strength values observed during the late follicular phase and lower values during menstruation. These findings should be interpreted with caution due to the method of phase identification but may have implications for the scheduling of strength assessments and training load management in female athletes. Full article

Reliable diagnosis of incipient transformer faults is essential for preventing catastrophic failures and enabling predictive asset management in power systems. Although dissolved gas analysis (DGA) is the most established diagnostic tool for assessing transformer internal condition, fault discrimination remains difficult when gas features are highly correlated, redundant, and only partially separable across fault classes. This study presents a PCA-enhanced artificial neural network (ANN) framework for multiclass transformer fault diagnosis using DGA data. The method is developed on 595 samples classified into six IEC 60599 fault categories and uses a 15-feature representation comprising raw gas concentrations, total hydrocarbon content, and engineered gas-ratio descriptors. To identify an evidence-based diagnostic representation, principal component analysis (PCA) was evaluated across all dimensionalities from k = 1 to 15 before ANN training. The proposed model was benchmarked against alternative feature sets and conventional classifiers, including Gaussian Naïve Bayes, k-nearest neighbours, support vector machines, and ANN without PCA. The best-performing configuration was obtained at k = 13, yielding a test accuracy of 68.1%, compared with 63.9% for ANN without PCA, 56.3% for raw-gas-only ANN, and 33.6% for the IEC three-ratio feature configuration. In addition to improving diagnostic performance, the PCA stage revealed interpretable component structures associated with dominant gas and ratio patterns underlying fault separation. The results indicate that PCA-based feature extraction improves ANN generalization by reducing redundancy and multicollinearity in DGA-derived variables, and provides a practical, lightweight, and interpretable framework for transformer fault diagnosis. Full article

This study investigates the decay-like degradation mechanisms of the matrix material in composite insulators, focusing on the pronounced influence of humid environments on partial discharge (PD) characteristics and degradation pathways. A sealed chamber discharge platform was established, integrating PD signal monitoring, surface characterization, and gas chromatography-mass spectrometry (GC-MS) with molecular network analysis to examine the synergistic effects of thermal influences from PD and active atmospheric particles at humidity levels of 0% RH, 50% RH, and 100% RH. Results show that dry conditions favor high-energy, low-repetition-rate discharges, promoting cleavage and recombination of high-bond-energy bonds (e.g., benzene rings and (α)C–O), yielding primarily long-chain carboxylic acids (C9 and above). In contrast, humid conditions shift to low-energy, high-repetition-rate discharges, with water vapor decomposition generating highly oxidizing hydroxyl radicals (·OH). These facilitate selective scission of lower-bond-energy (β)C–O bonds and deep oxidation, significantly increasing short-chain dicarboxylic acids—especially oxalic acid—whose acidity and water solubility are nearly an order of magnitude higher than in dry environments, becoming the dominant acidic products. The work demonstrates that many PD-generated organic acids act as intrinsic corrosive agents in insulating systems, independent of ambient nitric acid. This elucidates, at the reaction pathway level, how high humidity modulates PD to enhance corrosive acid production, providing a microchemical basis for understanding regional decay-like failure patterns in composite insulators. Full article

This study (Part B) examines the potential utilization of municipal solid waste (MSW) ash, produced in a semi-industrial incinerator in Israel, as a partial substitute for cement and natural sand in industrial concrete mixtures. The ash was produced at the temperature range 600–850 °C, and the ash was characterized using XRD and SEM to determine its mineralogical composition and morphology. The results indicate that ash composition is dominated by calcium-rich phases, with hatrurite (Ca₃SiO₅) representing approximately 51–66 wt.% of the identified crystalline phases, along with calcite, MgO, and silica phases. The ash consists of irregular, porous particles with a broad distribution. Concrete performance was evaluated in both fresh and hardened states. In terms of fresh concrete properties, it is observed that concrete containing ash showed improved workability, better workability retention, and better concrete density compared to concrete without ash. In terms of hardened concrete properties, the use of MSW ash as a partial sand replacement preserved the mechanical performance of the concrete, with compressive strength remaining within approximately 2% of the reference mixture. These findings suggest that semi-industrially produced MSW ash is more suitable as a fine aggregate replacement than as a supplementary cementitious material and represents a promising route for reducing landfill disposal and promoting circular economy practices in the construction industry. Full article

Femtosecond laser surface texturing offers a promising route to tailor the functionality of bio-based wood coatings, yet the interplay between coating composition and laser processing remains poorly understood. In this study, bio-based epoxy coatings with eventual micronized wax additives were textured using a femtosecond laser to investigate the effects of laser processing parameters on pattern formation and resulting hydrophobicity. The epoxy coatings containing PE, PE/PTFE, HDPE, and rice bran waxes at 1, 5, and 7 wt.-% were characterized in terms of morphology, roughness, wettability, and chemical stability, followed by systematic variation of pulse repetition rate and laser power. The results reveal that the ablation threshold strongly depends on intrinsic coating properties. Ablation resistance increases with surface roughness and wax melting enthalpy, reflecting the role of phase transition energy in laser–matter interaction. The wax-filled coatings exhibit a transition from melting-dominated behavior at low energy input to ablation-dominated behavior at a higher energy. Laser texturing enhances hydrophobicity in parallel with theoretical values calculated from the Cassie–Baxter wetting model, with the highest hydrophobicity achieved for coatings combining intrinsic hydrophobicity and stable pattern formation. Chemical analysis confirms limited degradation of the epoxy matrix without significant carbonization, while wax additives provide partial thermal shielding. Overall, this work demonstrates clear options for tailoring surface morphology and wettability of hydrophobic polymer coatings through controlled femtosecond laser processing. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

This study analyzes the determinants of self-reported behaviours and perceptions associated with pesticide toxicity risk in children using the Social Practice Theory framework, linking individual factors and agricultural practices to understand vulnerability and prevention opportunities. This research was conducted in Pattapang Village, Tinggimoncong District, Gowa Regency, South Sulawesi Province, Indonesia. To examine the relationship between pesticide use patterns, social norms, competence, material, and individual aspects and the risk of sensitive toxicity in children, data were analyzed using structural equation modeling-partial least squares (SEM-PLS) with bootstrapping resampling. Pesticide use patterns had a significant negative effect on toxicity risk. Competence was the strongest predictor of pesticide use patterns, followed by materials and short-term goals. Personal values dominate personal norms and long-term goals, while social norms only influence personal norms. Self-efficacy, personal norms, and long-term goals showed no significant effects. The novelty of this research lies in the integration of a socio-ecological approach with individual psychological factors in a comprehensive structural model that explains the complex mechanisms of children’s protective behavior formation from pesticide toxicity, identifying that personal values—not personal norms or self-efficacy—are the most effective leverage points for farmer behavior change interventions. Full article

Flexible polyurethane (PUR) foams and latex rubber foams are widely used in furniture and mattress cushioning, yet conventional standardized mechanical tests only partially capture comfort-relevant behavior, particularly in layered constructions where material interactions and sequencing can alter elastic response. This study aimed to compare the mechanical (elastic) properties of selected three-layer composites of approximately 60 mm thickness (composed of conventional PUR, high-resilience PUR, low-resilience PUR, and latex foam) and to preliminarily assess whether combining foam types improves support of such setup and whether changing layer order modifies elasticity and support. Indentation hardness testing of multilayer cushions was conducted by ISO 2439:2008 Method E. Six three-layer systems (Alpha–Zeta) were assembled in two groups. Group X showed nearly identical support factors (2.6–2.7), high recovery (64.3–66.2%), low hysteresis loss (24.3–24.5%), and overlapping force–indentation (IFD) curves, indicating minimal effect of layer order and dominance of the PUR layers. Group Y exhibited higher but more sequence-dependent support (3.1–3.7), markedly reduced, wider range recovery (30.0–45.9%), increased hysteresis (33.0–34.7%), and more dispersed IFD curves. Placing high-resilience foam at the top partially improve recovery, whereas locating low-resilience foam at the surface increase energy loss. The research contributes in part to the body of knowledge about the behavior of the tested materials according to standardized rules. These preliminary results can be compared with other research findings and used in the preparation of testing models for multilayer foam composites, thereby generating new knowledge to improve the design of future experiments, which will result in increased sitting and lying comfort. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Marine invertebrates, such as sponges, corals, mollusks and sea squirts, are appropriate climate-change descriptors on sublittoral rocks. The present study assesses the diversity, relative abundance and health condition of epibenthic invertebrates inhabiting sublittoral rocky habitats within the Natura 2000 network (Chalkidiki, north Aegean), after the 2021 marine heatwaves. Samplings were made with non-destructive techniques in autumn 2021 by diving along vertical belt transects (up to 30 m). Fourteen stations were surveyed, revealing 56 macroscopic invertebrates, 16 algae and 15 reef-associated fishes. Richness showed increased values at the deepest and steepest cliffs. Reefs were the dominant habitat type, hosting different facies of infralittoral algae and coralligenous biocenoses. Three algal (Halimeda tuna, Peyssonelia squamaria, Lithophyllum strictiforme) and 12 invertebrate (Aplysina aerophoba, Chondrilla nucula, Chondrosia reniformis, Ircinia variabilis, I. oros, Sarcotragus foetidus, Spongia officinalis, Balanophyllia europaea, Cladocora caespitosa, Pinna nobilis, Spondylus gaederopus, Microcosmus sabatieri) species were found in partial or full necrosis. According to relevant data collected about 20 years ago, the biota had higher diversity without signs of necrosis. Sarcotragus foetidus, I. variabilis, B. europaea, C. caespitosa and S. gaederopus were the most affected by necrosis species over the surveyed area. They represent appropriate climate change descriptors to assess the resilience of Mediterranean MPAs, being priority species in marine conservation. Full article

Autoimmune myocarditis frequently progresses to inflammatory cardiomyopathy through dysregulated immune–stromal interactions. This study employs single-nuclei RNA-sequencing (snRNA-seq) to profile 46,233 cardiac nuclei from the experimental autoimmune myocarditis (EAM) mouse model at four timepoints: day 0 (healthy), day 14 (inflammation), day 21 (acute inflammation), and day 40 (late cardiac remodelling). Single-nuclei RNA profiling identified 18 transcriptionally distinct cell populations. Global cell–cell communication analysis revealed a dramatic peak of intercellular signalling at day 14 (5907 interactions), with fibroblast subpopulations and macrophages as dominant hubs, followed by partial resolution at day 21 (2264 interactions) and renewed remodelling at day 40 (4862 interactions). Subclustering of the macrophage compartment identified five subpopulations: Mac-TLF, Mac-MHCII, Mac-rMHCII, Mac-ResL, and Classical Monocytes. Tissue-resident macrophages (Mac-TLF, CCR2-) dominated at healthy state (~55%) but were rapidly depleted at day 14, coinciding with a dramatic influx of recruited CCR2⁺ macrophages (Mac-rMHCII), which expanded to over 70% of the compartment and maintained dominance through day 40. At inflammation (day 14), the expanded Mac-rMHCII subpopulation displayed a strongly pro-inflammatory signature (Il1b, Stat2, Parp14, Apoe), and the overall macrophage compartment was enriched for cytokine response, Fc-gamma receptor, and Notch signalling pathways, while downregulating homeostatic and mitochondrial metabolic programmes, potentially contributing to impaired efferocytosis and cardiomyocyte dysfunction. Macrophage-centred communication networks expanded markedly at day 14 (1047 interactions), with resting fibroblasts (FB-R) as the primary signalling partner, driving pro-inflammatory stromal activation marked by upregulation of Ccl2, Ccl7, and Csf2. Intra-macrophage subcluster communication also intensified at this timepoint (447 interactions). These findings delineate the temporal and functional heterogeneity of cardiac macrophages during EAM progression and identify key immune–stromal interactions driving pathological cardiac remodelling. The coexistence of pro-inflammatory and transitional reparative macrophage subsets highlights the limitations of broad immunosuppression and supports precision strategies targeting CCR2-mediated recruitment, the SPP1 signalling axis, and macrophage–fibroblast crosstalk as therapeutic avenues in myocarditis and its progression. Full article

Nonparametric inference for residual-life functionals is a fundamental problem in mathematical statistics, reliability theory, and survival analysis, particularly in studies with limited sample sizes where empirical plug-in estimators may exhibit substantial sampling variability. In comparative lifetime analysis, additional qualitative information is often available regarding the relative behavior of two populations; however, such information is frequently too weak to justify classical stochastic dominance assumptions. Stochastic precedence provides a natural and interpretable framework for representing this partial ordering through a pairwise probabilistic constraint. This paper develops a constraint-adjusted nonparametric inference framework for estimating the mean residual life (MRL) and quantile residual life (QRL) functions under stochastic precedence information. The proposed approach replaces the ordinary empirical distribution function in standard residual-life plug-in estimators with a constraint-adjusted empirical distribution function that enforces the stochastic precedence relation at the sample level. The adjustment is governed by a data-driven scaling factor and is asymptotically negligible, thereby preserving the large-sample behavior of the ordinary empirical estimators while incorporating meaningful structural information in finite samples. Strong consistency of the proposed MRL and QRL estimators was established under mild regularity conditions. A Monte Carlo study based on Weibull and gamma lifetime models demonstrates that in the simulation settings considered, the proposed estimators provide improved finite-sample stability and generally achieve smaller mean squared errors than their ordinary empirical counterparts, especially for small and moderate sample sizes. The methodology is further illustrated using survival data from patients with squamous cell carcinoma of the oropharynx, highlighting its practical relevance in biomedical survival analysis. The proposed method offers a flexible, interpretable, and computationally simple framework for nonparametric inference with structured lifetime data under weak stochastic ordering information. Full article