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Peruvian elementary teachers evaluation of bullying behaviors: perceptions of severity, empathy, and intent to intervene
No full textThis study investigated Peruvian elementary school teachers’ perceptions of bullying severity, empathy toward victims, and intentions to intervene, with attention to teacher gender, student gender, and bullying type. Participants were 794 public school teachers from Northern Lima. Using case-based vignettes, results showed relational bullying was perceived as most severe, especially when victims were female. Teachers expressed greater empathy in physical bullying cases and were more likely to intervene in physical and relational bullying than in verbal bullying. Female teachers reported higher empathy and intervention toward female students. Findings underscore the need for gender-sensitive teacher training
Measuring recurrent victimization: evaluating operationalization strategies and predictors using the crime survey for England and Wales
Full text linkVictimization is concentrated among a small group of individuals, commonly referred to as recurrent victims. However, there is no consensus on the operationalization of recurrent victimization. This study investigates optimal measurement strategies and identifies predictors of recurrent victimization through a meta-analytic synthesis of multiple analytic approaches estimated on the 2019/20 Crime Survey for England and Wales. The results suggest that defining recurrent victimization using a Top 10% binary categorization and estimating logistic regression models can lead to biased conclusions. In contrast, operationalizations based on experiencing two or more victimization types or incidents performed substantially better when paired with bivariate probit models. Count-based operationalizations, particularly total victimization counts across crime types, also performed well when analysed using negative binomial or zero-inflated negative binomial models. Taken together, the findings indicate that researchers wishing to categorise recurrent victims should employ theoretically informed category- or incident-based measures analysed with bivariate probit models, whereas those seeking to identify individuals who experience higher volumes of victimization should use count-based measures estimated with negative binomial frameworks. Across all approaches, mental health conditions consistently emerged as the strongest correlate of recurrent victimization
Deep technologies and safer gambling: a systematic review
Full text linkDeep technologies combine engineering innovation and scientific findings to solve complex problems and are becoming particularly relevant to the gambling industry. With the global rise of gambling practices and the subsequent increase of gambling-related problems and disorders, deep technologies have emerged as a way to create safer online gambling environments. However, there is still limited knowledge regarding their applicability and consequences. The present study systematically reviewed the existing literature on deep technologies in gambling environments, such as online casinos and betting platforms, and explored their potential benefits, risks, and effectiveness in promoting safer gambling experiences. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Searches were conducted in Web of Science, PubMed, Scopus, EBSCO, and IEEE databases, and manually. A total of sixty-eight studies were included in the review. In general, four primary applications of deep technologies in online settings were found: (i) behavioural monitoring and feedback; (ii) predictive risk modelling; (iii) decision support and AI classifiers; and (iv) limit-setting/self-exclusion tools. They were primarily used to identify and classify problematic gambling, prompt individual action, regulate gambling behaviours, raise awareness of risk levels, promote responsible gambling practices, support research, interventions, and evaluate player protection initiatives. Together, the findings suggest that deep technologies offer ample opportunities to enhance gambler safety and reduce potential risks, although challenges may arise from their implementation, such as privacy and ethical concerns, malicious data use, misclassification of risk levels, and difficulties in large-scale application. Limitations and directions for future studies are discussed
Post-legislative scrutiny in the Scottish Parliament - committee recommendations
No full textThis dataset contains the outputs from the quantitative content analysis of 253 committee recommendations from across ten post-legislative scrutiny inquiries during Session 5 of the Scottish Parliament
Multilayer deep neural network modeling of fatigue crack growth in proton exchange membrane
No full textThis work proposes a novel hybrid multi‑layer deep neural network that integrates Convolutional Neural Network (CNN), Bi-directional Long Short-Term Memory (BiLSTM), and Self‑Attention to capture the cross‑scale interactions between macroscopic crack growth and mesoscopic plastic‑zone evolution, enabling accurate and interpretable prediction of frequency‑dependent fatigue crack growth in Proton exchange membrane (PEM). PEM is one of the most crucial polymer materials for electrochemical devices. However, fatigue-induced mechanical degradation significantly compromises its safety and durability. Unfortunately, the intelligent damage assessment methodologies associated with multi-scale fatigue crack growth behavior of PEM are not yet fully understood. To address this, based on the in-situ DIC fatigue testing with four loading frequencies, a hybrid deep learning framework that integrates physical insights and time-series modeling is proposed to predict the fatigue crack growth. Results show clear time-dependent fatigue crack growth behavior. With increasing frequency, the macroscale crack growth rate decreases, while the mesoscopic cyclic plastic zone size increases. The proposed approach comprises six components: (1) Data collection and preprocessing, (2) Hybrid neural network modeling, (3) Prediction performance evaluation, (4) Small sample optimization, (5) Generalization verification, and (6) Shapley Additive Explanations (SHAP) analysis. Comparative model evaluations confirm the framework's predictive accuracy. SHAP analysis identifies loading frequency and plastic zone size as critical factors influencing crack evolution, corresponding to a "small plastic zone—slow growth" regime at high frequencies and a "large plastic zone—rapid growth" regime at low frequencies
Family and school environment as mediators in mental health outcomes among gender-diverse youth: insights from the Adolescent Brain Cognitive Development Study
No full textInferences from TPHs removal kinetics during phyto- and myco-remediation of a soil heavily contaminated with crude oil
Full text linkDespite existing knowledge gaps such as the specific concentrations to which soils heavily contaminated with total petroleum hydrocarbons (TPHs) should be diluted for effective application of phyto and –myco-remediation agents and how enhancing agents influence TPHs removal rates at specific time intervals; kinetic modelling of TPHs removal during phyto- and myco-remediation can provide insightful inferences with practical implications. Therefore, in this study, soils heavily contaminated with crude oil were collected, and phyto- and myco-remediation carried out, both with and without the addition of Tween 80, over a period of 90 days, accompanied by concurrent soil analysis. The results revealed up to 445 grams of total petroleum hydrocarbons per kilogram of dry soil (g/kg dry soil), representing approximately 50 % TPHs contamination in the soil; and remediation efficiencies of 19, 68, 74, 87, and 88 %, for natural attenuation, sunflower, ferns, fermented palm wine, and the white rot fungus (Pleurotus ostreatus), respectively. Optimisation with Tween 80 increased the respective efficiencies to 31, 96, 93, 98 and 95 %. Kinetic modelling of the data revealed that natural attenuation of the highly contaminated soil proceeded predominantly by zero-order kinetics, which explains why natural attenuation is often ineffective for such soils. The phyto- and myco-remediation treatments shifted the removal kinetics from zero- order towards pseudo-first-order (PFO), and pseudo-second-order (PSO). The kinetic modelling, combined with tolerance limits, has been used to project the ideal initial (starting) concentrations for each agent. Thus, over the 90 days, the optimal initial concentrations are as follows: natural attenuation < 5 % of TPHs in soil; sunflower (Helianthus annuus) and ferns (Dryopteris affinis) < 9 %; fermented palm wine < 18 %; and Pleurotus ostreatus < 23 %. Finally, while appropriate dilution is necessary for optimal progression of natural attenuation and most phyto- and myco-remediation treatments, a thorough understanding of tolerance limits and removal kinetics will facilitate better decision-making during phyto- and myco-remediation