Search Results (23,227)

The intensification of armed conflicts has forced thousands of women to seek refuge in other countries, exposing them to conditions of heightened vulnerability, characterized by inequality, violence, and mental health challenges. Despite these adversities, refugee women demonstrate resilience through diverse coping strategies. However, qualitative research centring women’s experiences within gender-sensitive and intersectional frameworks remains limited. This study aims to characterize the experiences of forced migration among refugee women in Portugal and to explore the coping strategies they have developed. Semi-structured interviews were conducted with six refugee women residing in Portugal. Based on thematic analysis, the data revealed a trajectory marked by material and symbolic losses, disruption of social bonds, and symptoms consistent with post-traumatic stress. Motherhood emerged both as motivation and as an obstacle to social and professional integration. Participants reported various coping strategies, including social support, self-efficacy, optimism, emotional regulation, religious coping, and contemplative practices. These findings indicate that coping strategies among refugee women are shaped not only by trauma exposure, but also by gendered structural conditions in the host context. These results highlight the need for gender-sensitive and trauma-informed public policies, as well as community-based interventions to promote social integration and well-being. Full article

Depressive symptoms among university students are a growing public health concern, motivating unobtrusive monitoring with smartphone-based digital phenotyping. This study examined whether passively collected behavioral features can identify elevated depressive symptom risk. We collected smartphone sensing data from 36 university students over two weeks, yielding 551 participant-days. We extracted 21 digital phenotyping features, including 14 additional behavioral features and 7 baseline features. Multiple machine learning models were evaluated using repeated bootstrap validation, and participant-independent generalization was further assessed with Leave-One-Subject-Out (LOSO) validation. We also examined PHQ-9 thresholds, class-imbalance mitigation, and feature importance using SHAP. Tree-based ensemble models achieved the best performance under bootstrap validation, and the proposed feature set consistently outperformed the baseline set. However, performance dropped substantially under LOSO validation, indicating that participant-independent generalization remains challenging. Class weighting and SMOTE did not meaningfully improve performance. SHAP analysis identified home-stay behavior and time-of-day smartphone use as the most influential predictors. These findings highlight that participant-independent prediction remains challenging and underscore the importance of rigorous participant-level validation when developing smartphone-based digital phenotyping models. Full article

Infrared thermography enables non-contact monitoring of induction motor thermal behavior, but absolute temperature alone may not distinguish faults with similar surface heating. This paper presents a proof-of-concept case study on the explainable thermographic diagnosis of three-phase induction motors using transient thermal signatures. Two faults were imposed on the same Siemens 1LA2080-4AA10 squirrel-cage motor: loss of forced ventilation (hereafter, cooling failure) and a resistive-bank-induced phase unbalance condition denoted in the test bench as 50% phase unbalance. The approach combines motor-specific regions of interest, transient thermal descriptors, hot area expansion, first-order thermal modeling, healthy baseline residuals, and two physically motivated indices: the Cooling Failure Index (CFI) and Phase Unbalance Thermal Index (PUTI). Cooling failure was analyzed from radiometric CSV data, whereas phase unbalance was evaluated from color-mapped thermal video through scale-based temperature reconstruction and is therefore interpreted as an estimated thermal signature. For the baseline self-reference consistency check, the residual-based fault flag remained false. Cooling failure increased the maximum radiometric temperature from 77.2 °C to 91.6 °C, with 43,399 pixels above 80 °C. Phase unbalance showed a localized stator-dominated rise without hot area expansion above 80 °C in the reconstructed sequence. The rule-based layer assigned high CFI to cooling failure and high PUTI to phase unbalance, supporting explainable case-study-based discrimination while avoiding claims of general classifier validation. Full article

This study examines the effectiveness of an integrated teaching model that combines project-based learning (PBL), interactive simulations, and ChatGPT in the teaching of physics. This study was conducted in the context of teaching Rutherford’s atomic model and Bohr’s postulates, and it aimed to assess the impact of the proposed model on students’ academic achievement, the development of research skills, motivation to learn, engagement, and digital competence. A quasi-experimental pre-test–post-test design was employed involving 39 Year 11 students from a specialised secondary school with a focus on physics and mathematics in Kazakhstan. Participants were assigned to one control group (CG, n = 16) and two experimental groups (EG1, n = 12; EG2, n = 11). Academic achievement was assessed using a physics comprehension test, while students’ perceptions of the intervention were examined through a 16-item Likert-scale questionnaire administered to students in the experimental groups (n = 23). Analysis of covariance (ANCOVA) revealed a statistically significant effect of group membership on final test results after controlling for pre-test scores. Post hoc comparisons revealed significantly higher adjusted final test scores in EG2 than in the control group, whereas no significant differences were observed between CG and EG1 or between the two experimental groups. Normalised measures of knowledge gain were higher in both experimental groups than in the control group. Also, Hake’s gain showed improvement in learning outcomes. The results of questionnaires showed an overall positive perception among students. The findings are of significant importance for the training of future physics teachers. The model developed demonstrates the potential for the effective integration of project-based learning, digital simulations, and artificial intelligence tools into school science education. Full article

Pancreatic lipase activity depends strongly on interfacial conditions created by bile salts, motivating the search for plant-derived surfactants that may modulate lipid digestion. In our previous work, ginseng root and horse chestnut seed extracts were identified as potent stimulators of pancreatic lipase. Here, we expanded this investigation to additional Panax and Aesculus species and to saponin-rich extracts from Acer pseudoplatanus, Herniaria glabra, and Polypodium vulgare. Extracts were evaluated for effects on pancreatin lipolysis in relation to equilibrium surface tension, surface rheology, and olive-oil emulsion stability. Antiradical and antioxidant activities, total phenolic content, and flavonoid content were also determined. White ginseng root extract (Panax ginseng) produced the strongest stimulation, followed by horse chestnut seed extract (Aesculus hippocastanum), whereas Aesculus marylandica seed peel extract inhibited lipase activity. Several extracts showed concentration-dependent switching from weak inhibition to stimulation. Antioxidant and antiradical activities correlated strongly with total phenolic content, while emulsion stabilization did not correlate with lipase stimulation. Molecular modeling showed that cholate, escinescin Ia, osladin, and ginsenoside Rg1 did not persistently occlude the catalytic pocket, whereas several other saponins showed active-site blocking. Together, the results support a mechanism in which saponin-rich extracts regulate lipolysis through combined interfacial effects and compound-specific enzyme interactions. Full article

Plant vacuolar H⁺-ATPases (V-type H⁺-ATPases, VHA) and vacuolar H⁺-pyrophosphatases (V-PPase, VHP) are key proton pumps. VHA and VHP regulate cellular pH homeostasis and ion transport via the proton motive force across the vacuolar membrane, functioning in plant responses to environmental stress. However, they remain poorly characterized in melon, a high-value horticultural crop limited by environmental stresses. Here, a total of 24 V-type proton pump genes were identified from the whole melon genome. Collinearity analysis indicates that the proton pump genes are highly conserved in melons, although species-specific expansions and gene duplication events have been observed in the a and c subunits. Predictions of cis-acting elements in the promoters indicate that these genes are rich in regulatory elements associated with responding to plant hormones and abiotic stress. It was found that most genes were stimulated in response to salt treatment and exhibited consistent expression trends in both Elizabeth and Baishami. However, in Elizabeth, the responses of CmVHA-c″1 and CmVHA-a3 were slower, while the induction of CmVHP2;1 was almost undetectable in response to salt stress. This study provides a foundation for further functional analysis of proton pump genes and new insights into melon stress responses. Full article

Modeling bivariate count data with complex dispersion and dependence structures remains a significant challenge in statistical data analysis. This article introduces two new bivariate count distributions derived from the generalized shifted inverse trinomial distribution. The proposed models, denoted by BGIT-I and BGIT-II, are constructed using convolution and trivariate reduction methods. They provide flexible joint frameworks for modeling correlated count data while accommodating different marginal dispersion patterns. BGIT-I allows negative, near-zero, and positive dependence, whereas BGIT-II induces non-negative dependence through a common component. The proposed models have simple, tractable probability generating functions, which facilitate the derivation of probabilistic properties and motivate a probability-generating-function-based estimation approach alongside maximum-likelihood estimation. The finite-sample performance of the estimators is further examined through a Monte Carlo simulation study. The practical utility of the proposed models is illustrated using two real bivariate count data sets involving shunter accidents and patient counts in critical care and emergency room settings. The results show that the proposed BGIT models provide competitive alternatives for modeling bivariate count data with different dispersion and dependence characteristics. Full article

This study investigated the structural relationships among language learning strategies (LLSs), motivational beliefs (MBs), and English language achievement (ELA) among first-year Thai university students learning English as a foreign language (EFL). Grounded in self-regulated learning, social cognitive, and expectancy-value theories, the study examined how LLSs and MBs jointly contribute to ELA. A quantitative cross-sectional design was used, with 913 participants from a public university in Thailand. Data were collected via a structured questionnaire measuring learners’ cognitive, metacognitive, and social strategies; intrinsic motivation, extrinsic motivation, task value, and self-efficacy; and overall assessment of their perceived English learning achievement and self-rated English proficiency across the four language skills. Descriptive statistics, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modelling (SEM) were conducted. Results indicated moderate use of LLSs, with cognitive and metacognitive strategies used more frequently than social strategies. Students reported moderate-to-high motivational beliefs, with task value emerging as the strongest dimension. EFA and CFA supported the reliability and construct validity of the measurement model. SEM analysis demonstrated excellent model fit and revealed that intrinsic motivation, task value, and self-efficacy significantly predicted strategy use and ELA. LLSs showed the strongest direct effect on achievement, whereas extrinsic motivation had no significant direct effect. In addition, LLSs partially mediated the effects of intrinsic motivation, task value, and self-efficacy on achievement. The SEM results indicated that ELA is shaped by the combined influence of adaptive motivation and effective strategic behaviour. Therefore, instructional interventions should synchronously enhance strategic language learning and increase learners’ motivational beliefs to promote English learning achievement. Full article

Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links the German Federal Government energy transition pathway (2025–2045) with state estimation performance requirements, evaluated at five milestone years from 2025 to 2045 on two SimBench reference networks across three equipment size levels (large, medium, small) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer- and feeder-level instrumentation. Within this work’s analysis, congestion is caused exclusively by transformer overloading and voltage-band violations. No individual line exceeds its thermal rating (maximum: 98.6%). Equipment size governs congestion onset for a given deployment trajectory: under large equipment, congestion remains absent through 2045, under medium equipment it emerges from 2035 (4 of 10 scenarios), and under small equipment from 2025 (9 of 10). Without transformer instrumentation, median voltage estimation errors reach 6–42% regardless of smart meter penetration. Adding a single transformer measurement reduces errors by an order of magnitude, achieving median errors of 0.5–1.4%. In urban networks, transformer-level instrumentation meets the VDE FNN voltage accuracy target (99th percentile voltage error below 2%) in all configurations. In rural networks under small equipment, the target is approached but not met. These findings motivate prioritizing transformer instrumentation as an effective first step for grid observability and supplementing the current consumption-driven metering rollout with risk-based deployment criteria linked to local congestion exposure. Full article

This study examines the moderating role of liquidity in the relationship between extreme capital structure and firm performance among listed firms in emerging markets. It is motivated by the need to better understand how financing constraints and liquidity management influence firm performance in environments characterised by high financial frictions and limited access to external capital. Extreme capital structure is defined as firms maintaining very low levels of debt, measured using thresholds of 1% (ultra-low debt) and 5% for both long-term debt and total debt. The analysis is based on a panel dataset of non-financial listed firms over the period 2006–2024 and employs a dynamic panel System Generalised Method of Moments (System GMM) complemented by a Random Effects model for robustness. Empirical results indicate that liquidity has a meaningful and predominantly positive moderating effect. This is observed when firms maintain extremely low long-term debt (1% threshold) and low long-term debt (5% threshold). Liquidity enhances firm performance. This effect is strongest for return on assets (ROA) and return on equity (ROE). The effect on Tobin’s Q is weaker but remains generally positive. These findings highlight the strategic importance of liquidity in improving profitability and financial resilience under conservative financing structures. However, the findings are limited to listed non-financial firms in emerging markets and may not be generalizable to SMEs or unlisted firms. Future research could explore the threshold at which liquidity ceases to generate benefits or begins to produce diminishing returns in ultra-low leverage contexts. Full article

Smart agriculture technologies are poised to transform farming, yet their adoption remains uneven even in well-resourced peri-urban settings. This study examines the factors shaping both adoption intention and actual use behavior among 206 suburban farmers across nine Shanghai districts via an extended UTAUT framework that incorporates hedonic motivation, trust, and perceived risk alongside the original constructs. To address a common but underexamined limitation in adoption research, both OLS path analysis and full Maximum Likelihood Structural Equation Modeling with latent variables were estimated and compared through systematic specification-sensitivity analysis. Three categories of findings emerged. Performance expectancy and perceived risk were robust predictors of behavioral intention across all model specifications, while facilitating conditions consistently dominated in terms of actual use behavior. By contrast, social influence and hedonic motivation proved unstable: their effects traded off across specifications because of high shared variance, indicating that these constructs may represent overlapping facets of a broader social–hedonic motivational factor rather than independent predictors. Most notably, the intention-to-behavior path weakened to non-significance in the latent variable model, revealing a substantial gap between farmers’ willingness and their actual adoption. This gap was bridged almost entirely by facilitating conditions, i.e., the availability of infrastructure, technical support, and implementation resources. Geographic analysis further revealed significant inter-district variation in trust, suggesting that local institutional context shapes technological confidence. Alternative mediation analysis showed that effort expectancy, trust, and hedonic motivation influenced intention indirectly through performance expectancy rather than acting as independent drivers. These findings suggest that promoting smart agriculture requires shifting policy emphasis from attitude change to developing the infrastructural and service conditions that enable willing farmers to adopt smart agricultural systems. Full article

Background: Cultured meat (CM) has emerged as a promising, albeit controversial, alternative to conventional livestock production, offering potential benefits in terms of sustainability, animal welfare, and resource efficiency. Despite these potential advantages, consumer acceptance remains uncertain, particularly in countries where CM is not yet commercially available. Objectives: this study investigates the determinants of willingness to try (WTT) cultured meat among Italian university students (n = 335), with particular attention to the role of product-related perceptions and personal values and motivations. Methods: data were collected through an online survey and analyzed using binary logistic regression to identify the main drivers of respondents’ willingness to try CM. Results: the findings suggest that acceptance of CM among university students is driven primarily by familiarity, perceived safety, and ethical considerations, particularly those related to animal welfare, rather than by demographic characteristics or resistance to novel food technologies. Conclusions: these findings offer practical implications for policymakers and industry stakeholders, highlighting the importance of transparent communication strategies emphasizing product safety and animal welfare benefits as a means of increasing familiarity with and acceptance of CM among younger consumers. Full article

Cadmium sulfide has been widely used as a conventional n-type window/buffer layer or heterojunction partner in several p-type thin-film solar cells, including CdTe/CdSeTe-, chalcopyrite-, kesterite-, antimony chalcogenide-, tin sulfide- and iron pyrite-based devices. Its success is related to its ability to form suitable heterojunctions, partially passivate absorber surfaces and provide favorable electronic selectivity. However, the parasitic absorption associated with the relatively narrow band gap of CdS, the toxicity and waste-management issues related to cadmium-containing auxiliary layers and the need for improved band alignment have motivated extensive research on CdS-free window and buffer layers. This review summarizes the main efforts devoted to replacing CdS in thin-film solar cells based on absorbers such as CdTe/CdSeTe, CIS, CIGS, CZTS, CZTSe, CZTSSe, Sb₂S₃, Sb₂Se₃, Sb₂(S,Se)₃, SnS and FeS₂. The most investigated alternative materials, including Zn(O,S), ZnS, In₂S₃, ZnMgO, ZnSnO, TiO₂, SnO₂ and SnS₂, are discussed with emphasis on their optical properties, band alignment, interface quality, deposition methods and impact on device performance. The analysis highlights that CdS replacement cannot be treated as a universal material substitution problem. Instead, each absorber and device architecture requires a specific front-interface design, where chemical compatibility, conduction band offset, defect passivation, optical transparency and process-induced interfacial modifications play a decisive role. CdS-free approaches are relatively mature for CdTe/CdSeTe- and CIGS-based solar cells, whereas kesterite absorbers, antimony chalcogenides and SnS still require further interface engineering. In FeS₂, by contrast, buffer-layer substitution remains secondary to the control of intrinsic surface and bulk electronic defects. This review provides a concise comparison of the most relevant CdS-free front/window materials and identifies key challenges for the future design of sustainable thin-film solar cells. Full article

Landslides are a recurrent hazard in the Andean region, where steep terrain and intensifying rainfall amplify population exposure. This study analyzed the relationship between landslide risk perception and protective behavior among residents of Loja Province, southern Ecuador—a territory where landslides accounted for 58.12–78.26% of all adverse events recorded between 2024 and 2026—using Protection Motivation Theory (PMT) as the conceptual framework. A cross-sectional survey was administered to 345 adult residents, capturing eight PMT Likert-scale items, prior landslide experience, and eight protective behavior indicators. Data were analyzed in R using reliability analysis, non-parametric group comparisons, Spearman correlations, and multiple linear regression. Results indicated that institutional efficacy (trust in emergency authorities and early warning systems) was the strongest predictor of protective behavior (β = 0.426, p < 0.001), followed by hazard knowledge (β = 0.222, p = 0.019). Perceived severity, community susceptibility, and response efficacy showed significant bivariate associations but negligible unique contributions in the multivariate model. A marked perception–behavior gap was documented: stated behavioral intentions exceeded implemented measures by 40–60 percentage points. Risk perception was higher among men and respondents with prior landslide experience, but prior experience did not independently predict protective behavior. These findings indicate that risk communication in high-exposure Andean settings should prioritize strengthening institutional trust and accessible hazard knowledge over threat-focused messaging alone. Full article

Cannabis use is prevalent among LGBTQ+ youth and is often framed within deficit-oriented models emphasizing risk and mental health vulnerability. This study moves beyond these perspectives by examining how LGBTQ+ youth understand and mobilize cannabis use in relation to mental health, gender, and sexuality. Guided by queer theory and an ethics of care, and grounded in a community-based participatory approach, this photovoice study was conducted in collaboration with three LGBTQ+ youth peer researchers who contributed to data collection, analysis, and interpretation. A total of 46 LGBTQ+ youth (aged 15–24) in Quebec, Canada, generated photographs and narratives exploring their motivations and experiences of cannabis use, which were analyzed using reflexive thematic analysis. Findings highlight cannabis use as a situated and relational practice embedded in three intersecting processes: reconfiguring thought, enacting identity, and practicing care. Cannabis was mobilized to facilitate introspection, support identity exploration, and affirmation, and foster relational and collective forms of healing, while also producing ambivalent effects, including disorientation and emotional numbing. By centering youth perspectives, these findings challenge harm-focused framings and underscore the need for more nuanced, youth-informed and strengths-based approaches to LGBTQ+ youth well-being. Full article