Search Results (296)

Introduction: Although gait alterations associated with excess body weight have been widely studied, most available evidence comes from laboratory-based analyses, which limit ecological validity and the translation of findings into clinical practice. This study addresses this gap by examining gait biomechanics across BMI categories using portable sensor-based insoles that allow gait assessment in real-world conditions. Methods: A cross-sectional study including 96 adults categorized as normal weight (NW), overweight (OW), or obese (OB) was conducted. Gait biomechanics were recorded using PODOSmart^® intelligent insoles, which capture spatiotemporal and angular parameters during natural walking. Foot health, quality of life and comorbildities were evaluated throught valeted questionnarires. Differences between groups were analyzed using ANOVA and chi-square tests. Age and sex, known to influence gait, were comparable across BMI groups and were considered in the interpretation of the results. Results: Overall, the participants in the OB group exhibited reduced stride length, gait speed, and swing time, increased double-support time, and greater pronation–supination and progression angles than OW and NW participants. Partial eta-squared values (η²p) were predominantly medium to large, reinforcing the robustness of these between-group differences (e.g., double-support time, p > 0.001; η²p = 0.19). Individuals with obesity reported poorer general and foot health and more difficulty finding suitable footwear. BMI was also significantly associated with hypertension, dyslipidemia, arthritis, and depression (all p <0.05), whereas diabetes, cardiopathies, knee pain, and fatigue andwalking or social activity limitations showed no significant differences. Conclusions: By using portable gait analysis technology in ecological conditions, this study provides novel evidence of clinically meaningful gait impairments across BMI groups. Higher BMI is associated with clinically relevant gait impairments, poorer perceptions of foot and general health, and a higher prevalence of several comorbidities. Full article

Tool wear remains a critical limiting factor in machining performance, particularly in dry cutting conditions where friction and tribological interactions dominate. This study investigates the influence of a 5–8 μm PVD-deposited TiN coating on the wear behavior of high-speed steel (HSS) end mills during milling of three representative wood species (oak, beech, and fir). A spatially resolved wear evaluation methodology was employed, based on ten measurement points distributed along a 20 mm active cutting edge, enabling simultaneous assessment of mean wear and maximum localized wear (Umax). A factorial experimental design combining material type and feed rate (1500–2500 mm/min) was analyzed using two-way ANOVA with effect size quantification (η²). The results reveal a statistically significant reduction in mean wear for TiN-coated tools (F = 7.46, p = 0.0195, η² = 0.34), corresponding to an average decrease of approximately 46% compared to uncoated tools. Maximum wear was influenced by both coating (F = 14.73, p = 0.0028, η² = 0.399) and material (F = 4.37, p = 0.040, η² = 0.237). The experimental findings are interpreted through a tribological framework, indicating a transition from abrasion- and micro-chipping-dominated degradation in uncoated tools to a controlled wear regime in TiN-coated tools, characterized by reduced asperity penetration, delayed crack initiation, and limited tribochemical interactions. These results demonstrate that coating effects dominate global wear evolution, while material properties influence localized degradation. The proposed combined experimental–statistical–mechanistic approach provides a robust framework for understanding and optimizing tool performance in dry machining environments. Full article

Background/Objectives: Cholinergic dysfunction plays a central role in memory impairment, yet trihexyphenidyl (THP)-based paradigms remain less explored than scopolamine-based models. This study aimed to characterize a THP-induced cholinergic challenge in a two-trial Y-maze with a 24 h interval and to compare the effects of donepezil and tacrine on recall-phase exploratory allocation. Methods: Male Wistar rats (n = 9/group) were studied in a validation phase including saline, THP 5 mg/kg, and THP 10 mg/kg groups, followed by an intervention phase including control, THP 10 mg/kg, donepezil 1 and 3 mg/kg + THP, and tacrine 3 and 5 mg/kg + THP groups. All treatments were administered intraperitoneally (i.p.). Acquisition- and recall-phase behavior was analyzed. Recall outcomes included arm times, arm entries, the novel-to-familiar arm time ratio (U/K time ratio), the novel-to-familiar arm entry ratio (U/K entry ratio), discrimination indices and time-per-entry indices. Data were analyzed by one-way ANOVA; Tukey’s post hoc test was used in the validation experiment, whereas Dunnett’s test was used in the intervention experiment for comparisons against THP 10. Results: THP at 10 mg/kg produced a robust recall-phase phenotype, with increased familiar-arm exploration, reduced novel-arm exploration and lower normalized indices. Under THP challenge, donepezil was associated with clearer effects at 3 mg/kg, whereas tacrine displayed a broader dose-dependent profile, with the strongest shift in recall-phase exploratory allocation toward the novel arm observed at 5 mg/kg. Conclusions: THP 10 mg/kg produced a robust recall-phase exploratory phenotype in a 24 h two-trial Y-maze paradigm. Under THP challenge, donepezil and tacrine were associated with shifts in recall-phase exploratory allocation. These findings support the potential utility of THP-based paradigms for studying cholinergic disruption in Y-maze settings, while direct comparison with scopolamine-based models remains to be established. Full article

The fading affect bias (FAB) is the faster fading of unpleasant than pleasant affect, and this effect is positively and negatively related to healthy/adaptive and unhealthy/non-adaptive outcomes, respectively. Research has argued that the FAB makes people happy and it prompts them to seek out pleasant experiences. Although Pillersdorf and Scoboria found a negative relation between the FAB and marijuana consumption, they only examined non-marijuana events. This investigation was limited because the relation between the FAB and marijuana consumption may be absent or positive for marijuana events. The current study examined the relation of the FAB to marijuana consumption measures across marijuana and non-marijuana events. The study was conducted both in person (Experiment 1; n = 328) and online (Experiment 2; n = 232). Analyses included ANOVAs to examine fading affect across initial event affect and event type, and Process Model 1 was used to evaluate the fading affect bias across initial event affect in 2-way interactions with continuous variables. Process Model 3 was used to investigate fading affect across initial event affect and event type in 3-way interactions with continuous variables. Both experiments showed a robust FAB that was positively related to adaptive variables and negatively related to non-adaptive variables, and it was positively related to marijuana consumption/reactivity. In addition, the positive relations between FAB and marijuana consumption (hours) and reactivity (highness) measures in Experiment 1 and a marijuana reactivity measure (highness) in Experiment 2 were only found for marijuana events. Implications, limitations, and future research directions are discussed. Full article

Emotional recognition relies on the integration of multiple affective cues. In everyday contexts, however, facial expressions, vocal prosody, and semantic content may convey incongruent emotional information, generating emotional conflict and increasing cognitive demands. The present study examined how multimodal emotional conflict affects emotion recognition during video viewing, focusing on short videos in which a single actor simultaneously conveyed incongruent emotional cues across facial, vocal, and semantic channels. Forty-seven undergraduate students completed a gaze-based response task in which, after each short video, they provided a single judgment of the overall emotion conveyed by the stimulus. The videos depicted either congruent or incongruent combinations of semantic content, facial expressions, and vocal prosody across six basic emotions and a neutral condition. Data were analyzed using repeated-measures ANOVAs and generalized linear mixed-effects models. Accuracy was consistently higher for congruent than incongruent stimuli across all domains, indicating a robust emotional interference effect. Critically, the magnitude of this effect differed by domain. Semantic content showed the largest performance reduction under incongruence, followed by facial expression and vocal prosody. Mixed-effects models confirmed these effects while accounting for participant- and item-level variability and revealed a significant Congruency × Domain interaction. In a gaze-based response task requiring a single overall emotion judgment, emotional conflict disrupted recognition in a domain-specific manner, with semantic information being particularly vulnerable to multimodal interference. Full article

Petroleum hydrocarbons frequently contaminate arid oilfield soils, but remediation is challenging because these soils typically contain little organic matter, retain little moisture, and are exposed to high temperatures, that hinder natural attenuation. This study evaluated indigenous bioaugmentation of an aged crude oil-contaminated sandy soil from the Burgan oilfield in Kuwait, in contrast to exogenous commercial microbial products and to natural attenuation. In a 140-day bench-scale tray study, aged crude oil–contaminated soil from the Burgan oilfield (initial TPH 2.49–4.78%, dry wt.) was treated with an enriched indigenous consortium, a commercial consortium, or no inoculum under controlled moisture, nutrient, and aeration conditions. TPH was quantified as hexane-extractable material, and degradation kinetics were evaluated using a first-order model. A statistical comparison of replicate-derived decay constants (k) was conducted using one-way ANOVA and subsequent post hoc testing. Among the replicated treatments, the indigenous consortium showed the strongest performance. In the low-TPH indigenous group, TPH removal reached 63.8 ± 3.1% and fell below 1% by day 140; at higher starting TPH, removal remained substantial but slower. Commercial inoculation was less effective and more variable, while uninoculated controls showed minimal decline. The decay constant for the indigenous (0.0053–0.0075 day⁻¹) was much higher (p < 0.001) than those in commercial (0.0025 day⁻¹) and natural attenuation (0.0005 day⁻¹). Furthermore, the model fit was robust for indigenous treatments (R² = 0.89–0.91) but weaker for commercial and uninoculated controls. The study findings demonstrate that bioaugmentation utilizing well-adapted indigenous consortia offers a statistically validated and kinetically predictable strategy for TPH remediation in desert soils. Full article

The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of $804.7\pm 1.7$ bytes and near-constant verification time of $0.662\pm 0.032$ s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling ($\alpha =0.256$, $R^2=0.608$), with statistically significant differences between circuit categories confirmed by one-way ANOVA ($F=355.0$, $p<{10}^{-79}$, ${\eta }^2=0.94$). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks. Full article

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches, including the baseline methodology proposed in previous studies, typically optimize global predictive accuracy and therefore fail to capture business-critical outcomes, especially the identification of high-risk clients. This study extends the existing approach by evaluating two complementary business-aware classification strategies: (i) a balanced bagging ensemble specifically designed to handle class imbalance and maximize expected profit under explicit customer-omission constraints, and (ii) a lightweight Transformer-based architecture capable of learning richer feature representations. Both approaches incorporate the asymmetric financial cost structure of insurance and operate under operational selection limits. The empirical analysis is conducted on a proprietary large-scale auto insurance dataset comprising 51,618 customers and is complemented by validation on nine synthetic datasets to assess robustness. Model performance is evaluated using statistical tests (ANOVA, Friedman, and pair-wise comparisons) together with business-oriented metrics. The results show that both proposed approaches consistently outperform the baseline methodology (p < 0.001) in terms of profit, with the ensemble offering a better balance of performance and efficiency, while the Transformer shows stronger robustness and generalization under data perturbations. The balanced ensemble provides the most favourable trade-off between predictive performance, robustness, interpretability, and computational efficiency, making it suitable for deployment in regulated insurance environments, while the Transformer achieves competitive results and exhibits stronger generalization under data perturbations. The proposed approach aligns machine learning with actuarial portfolio optimization by explicitly integrating profit-driven objectives and operational constraints, offering two practical and scalable solutions for risk-based decision-making in real-world insurance settings. Full article

Light exposure is a primary zeitgeber for the human circadian system and plays a key role in shaping sleep–wake patterns during adolescence, a period marked by biological sensitivity and social constraints. How the temporal organization and spectral composition of daily light exposure differ between weekdays and weekends remains poorly understood. Eighteen adolescents (15–17 years) were monitored for seven days using wrist actigraphy with integrated light sensors. Sleep parameters, nonparametric circadian rhythm indices, and time-resolved profiles of ambient and spectral (blue, green, and red) light exposure were analyzed. Repeated-measures ANOVA tested the effects of time of day and day type. Total sleep time and time in bed were longer on weekdays than on weekends (p < 0.05), while sleep latency and WASO did not differ. Circadian indices indicated preserved rhythmic organization. Light exposure showed a robust diurnal profile, with higher spectral irradiance on weekends (p < 0.001), especially in the morning and early afternoon. Significant time × day-type interactions were observed across all spectral bands (p < 0.001), indicating systematic reshaping of daily light profiles. Adolescents exhibit weekday–weekend differences in the temporal and spectral organization of light exposure, affecting the amplitude and shape of overall daily profiles. Full article

The comprehensive quality assessment of raw milk from small-scale producers remains essential for improving dairy sector competitiveness. This study employed a multivariate approach to correlate the physicochemical, colorimetric, and sensory profiles of raw milk from eleven producers in the town of Supe, Barranca, Lima, Peru. Milk samples were analyzed using a Lactoscan MCC ultrasonic analyzer, CIEL*a*b* colorimetry, and the Flash Profile sensory method. Data integration and interpretation were performed using Analysis of Variance (ANOVA), Generalized Procrustes Analysis (GPA) and Hierarchical Multiple Factor Analysis (HMFA). The results revealed significant heterogeneity, identifying two distinct producer groups. A high-quality group (DF7, DF10, DF11) presented adequate physicochemical parameters: high fat content (>3.77%), total solids (>12.06%), normal freezing point (≈−0.53 °C), creamy color (high L* and b*), and positive sensory attributes (“fatty”, “creamy”). In contrast, a low-quality group (DF4, DF5, DF8, DF9) showed evidence of water adulteration (12–16%), reflected in an elevated freezing point (up to −0.44 °C), low solids-not-fat, and defective sensory profiles (“tasteless”, “salty”). The HMFA demonstrated a strong concordance between instrumental and sensory data sets, identifying water adulteration and fat content as the primary drivers of quality variation. This integrated methodology provides a robust diagnostic tool for quality-based payment systems and targeted technical assistance, offering a replicable model for enhancing quality control and valorizing raw milk in smallholder dairy systems. Full article

Industrial symbiosis (IS) research has documented many successful ecosystems but still lacks an empirically grounded typology linking resource flow configurations to environmental outcomes across diverse contexts. This study develops such a typology and tests whether distinct configurations achieve comparable environmental performance through different pathways—the configurational principle of equifinality. Drawing on 68 documented IS ecosystems across 48 countries, we apply k-means clustering to five flow-intensity dimensions—material, energy, water, logistics, and knowledge—and characterise the resulting partition using one-way ANOVA, Tukey HSD post hoc tests, multinomial logistic regression, and a Cox proportional-hazards model. Four configurations emerge: a dominant low-flow group (n = 34) and three coordinated configurations—energy–knowledge (n = 11), material-dominant (n = 16), and water-oriented (n = 7). The three coordinated configurations all significantly outperform the low-flow group on environmental performance (F(3, 57) = 11.60, p < 0.001), with effect sizes very similar and no significant differences among them, providing direct empirical evidence for equifinality. Economic performance does not differ significantly across configurations, and the multinomial model of contextual predictors is jointly insignificant—a pattern we read as consistent with equifinal contextual pathways rather than as a methodological flaw. Robustness checks across alternative clustering algorithms, operationalisations, and sub-samples support the typology’s stability. This study contributes an empirically grounded framework for circular economy practice that moves beyond one-size-fits-all prescriptions and offers a configurational lens for the design of sustainable industrial ecosystems. Full article

Against the backdrop of uneven educational development and structural constraints in rural Mainland China, teacher agency is critical for driving professional growth and instructional improvement. Rural educators face distinct challenges—limited resources, isolated work contexts, and systemic pressures—that shape their capacity to enact change. While scholarship has documented the roles of contextual resources and individual beliefs in shaping teacher agency, less is known about the mediating mechanisms linking job resources and self-efficacy to agency within China’s rural educational landscape. This study examines how perceived job resources (teaching resources, administrative support, colleague support, parental support) and teaching self-efficacy collectively shape rural teachers’ agency, to inform policy and practice for strengthening their professional capacity. Drawing on a quantitative survey of 625 rural teachers, we employ a two-stage analytical approach: first, descriptive statistics, t-tests, ANOVA, and Pearson correlations to map baseline variable relationships; second, Hayes’ PROCESS macro (Model 4) with bootstrapping to test the mediating role of teaching self-efficacy between job resources and teacher agency. Findings reveal the following: (1) Rural teachers report moderate agency (M = 3.53/5), indicating room for growth; (2) All four job resource dimensions significantly and positively predict agency (β = 0.099–0.163); (3) Teaching self-efficacy is a robust predictor of agency (β = 0.785–0.822, p < 0.001) after controlling for resources; (4) Self-efficacy partially mediates the links between each job resource and agency, with indirect effects ranging from 0.269 (teaching resources) to 0.451 (colleague support), highlighting its central role in translating contextual resources into agentic action. We conclude that fostering rural teacher agency requires a holistic approach addressing both external job resources and internal self-efficacy. Policymakers and administrators should prioritize investments in teaching resources, collaborative support structures, and professional development to build educators’ confidence and competence. Limitations include self-report bias, cross-sectional design constraints on causal inference, and limited generalizability. Future research should use longitudinal designs and broader samples to deepen understandings of agency in structurally constrained educational settings. Full article

This study investigates the feasibility of acoustic signal analysis for the assessment of weld bead quality in the shielded metal arc welding (SMAW) process. The work focuses on comparing time-domain acoustic signals and time–frequency spectrogram representations for the classification of welds as accepted or rejected according to standard welding inspection criteria. Two key acoustic descriptors, the fundamental frequency (F₀) and the harmonics-to-noise ratio (HNR), were extracted and analyzed to evaluate statistical differences between the two weld quality classes. Statistical tests, including Anderson–Darling, Levene, ANOVA, and Kruskal–Wallis (α = 0.05), revealed significant differences between accepted and rejected welds. Accepted welds exhibited a bimodal HNR distribution associated with transient arc instability at the beginning and end of the bead, whereas rejected welds showed more uniform acoustic behavior throughout the process. Subsequently, the acoustic data were represented using both audio signals and spectrograms and used as inputs for ten supervised machine learning models, including Support Vector Classifier (SVC), Logistic Regression (LR), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), and Naïve Bayes (NB). The results demonstrate that spectrogram-based representations significantly outperform time-domain signals, achieving accuracies of 0.95–0.96, ROC-AUC values above 0.95, and false positive and false negative rates below 6%. These findings indicate that, while scalar acoustic descriptors provide statistically significant insight into weld quality, time–frequency representations combined with machine learning enable a more robust and reliable framework for automated non-destructive evaluation, particularly in manual SMAW processes under realistic operating conditions. Full article

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

Previous studies on velvet antler processing have mainly evaluated single techniques, and systematic comparisons of processing combinations are limited. This study investigated the effects of different processing combinations on the nutritional composition and physicochemical properties of velvet antler from red deer and sika deer. A 2 × 2 factorial design was applied: Blood-Retained vs. Blood-Removed and Boiled/Fried (zhuzha; no deep-frying) vs. Vacuum Freeze-Dried. In this study, Boiled/Fried was treated as a single processing method. The four processing combinations were analyzed as independent groups using one-way ANOVA. Additionally, two-way ANOVA was conducted to evaluate the main effects of pretreatment, dehydration method, and their interaction on the measured indices. To account for species background, a three-way ANOVA (species × pretreatment × dehydration) was further conducted for key indices. Moisture, crude protein, ash, and crude fat contents were determined. All composition-related indices were evaluated on both wet-weight and dry-weight bases to distinguish moisture-driven concentration or dilution effects from processing-related retention changes. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted for multivariate evaluation. Spearman’s rank correlation was used for association analysis, and Pearson’s correlation with linear regression was applied to quantify linear relationships (reported as r). Freeze-drying significantly reduced moisture content (p < 0.01) and increased crude protein content (p < 0.05). PCA and OPLS-DA demonstrated clear compositional separation among the four processing combinations, with moisture and crude protein as the main contributors (cumulative explained variance > 83%). The effects of Blood-Retained and Blood-Removed treatments differed between species. Three-way ANOVA indicated significant species-dependent effects (e.g., species × pretreatment and or species × dehydration interactions), while the pretreatment × dehydration interaction was significant for TAAs. In the Boiled/Fried groups, total amino acid content (TAA) decreased with increasing moisture. In the Freeze-Dried groups, moisture was significantly negatively correlated with TAAs in the Blood-Retained treatment (Pearson r = −0.886, p < 0.05), whereas no significant correlation was observed in the Blood-Removed treatment (r = 0.429, p > 0.05). Wet- versus dry-basis comparisons indicated that some between-treatment differences were attributable to moisture-related concentration or dilution effects, whereas differences persisting on a dry basis more directly reflected processing-related nutrient retention. Processing combinations produced species-dependent effects in velvet antler. The three-way ANOVA supported species-dependent pretreatment effects and confirmed that the influence of blood retention or removal on amino acid outcomes was contingent on the dehydration regime (pretreatment × dehydration for TAAs). From an application standpoint, no single processing route is universally optimal across all quality attributes; freeze-drying provides a robust baseline, whereas the choice of blood retention or removal should be made in a target-oriented manner (e.g., physicochemical stability versus protein and amino acid retention) while accounting for species background and interaction effects. Therefore, these findings provide a scientific basis for improving product quality, processing efficiency, and standardization in China’s velvet antler industry. Full article