Background: The compromised well-being of educators engenders a range of educational, social, and economic issues that cannot be adequately addressed through the mere awareness of risk factors. The present research explores the mediating roles of self-compassion and emotion regulation strategies (cognitive reappraisal and expressive suppression) on the relationship between academic teachers’ psychological resilience and mental health. Methods: A total of 405 academic teachers from four colleges in Tabuk City, Saudi Arabia, participated in the study between September and November 2023. The standardized questionnaires measuring psychological resilience (The Brief Resilience Scale- BRS), self-compassion (The Self-Compassion Scale-SCS), emotion regulation (The Emotion Regulation Scale-ERS), and mental health (General Health Questionnaire-12) were used to collect data. The bivariate correlation, regression, and path analysis were employed to investigate their associations. Results: The findings demonstrated a significant positive impact of psychological resilience on mental health (β=0.39, p< 0.001). Self-compassion emerged as a significant positive mediator, strengthening the association between resilience and mental health (β=0.18, p< 0.01). Cognitive reappraisal was found to have a positive mediating effect on this relationship (β=0.16, p< 0.01), highlighting its functional significance in regulating emotions. Conversely, expressive suppression was found to negatively mediate the relationship, with an indirect effect of β=− 0.12 (p< 0.05), indicating a maladaptive influence on mental health. Conclusion: The research emphasizes the significance of fostering self-compassion and adaptive regulatory strategies, like cognitive reappraisal, while mitigating maladaptive approaches, including expressive suppression, in order to enhance the mental health of university educators. These findings underscore the necessity for culturally sensitive mental health interventions within educational institutions in Saudi Arabia.

Background. Measuring the potential socioeconomic factors of cardiac mortality is fundamental to identifying treatments, setting priorities, and effectively allocating resources to minimize disease burden. The study sought to present a methodology that explores the connections between urbanization, population growth, human development index (HDI), access to energy, unemployment, and cardiovascular disease (CVD) mortality within the South Asian Association for Regional Cooperation (SAARC) nations to mitigate the cardiac disease burden. Methods. This investigation uses multiple-criteria decision-making methodologies to analyze data between 2001 and 2017 commencing with a mathematical grey incidence analysis (GIA) methodology to estimate weights and rank nations based on CVD mortality. Then, utilizing the conservative min-max model approach, we sought to determine which country contributes the most to CVD mortality among all South Asian nations. The grey preference by similarity to ideal solution (G-TOPSIS) method is adopted for further optimization by prioritizing the selected factors that have the greatest influence on CVD mortality. Results. The estimated statistic highlights that, among SAARC nations, Pakistan has a significant proportion of the disease burden attributable to cardiac events. In addition, HDI showed a significant contribution in the reduction of CVD mortality, whereas unemployment showed a significant contribution in the rise of CVD mortality among all selected variables. Conclusions. This investigation may facilitate researchers with a multiple-criteria decision-making roadmap to help them enhance the quality of their studies and their understanding of how to use multiple-criteria decision-making techniques to evaluate and prioritize the influencing factors of disease mortality in healthcare research. Further, the study outcomes provide additional practical knowledge for appropriate policy solutions.

Impulsivity, cognitive arousal, and sleep quality are critical factors affecting college students' well-being and academic performance. While impulsivity negatively influences sleep quality, the mediating role of cognitive arousal remains underexplored. The present study assesses how impulsivity affects sleep quality through cognitive arousal. Specifically, it explores whether impulsive behavior heightens cognitive arousal, leading to impaired sleep quality, and whether these relationships hold over time. A longitudinal design was employed, collecting data from 521 college students across three waves over an academic year. Standardized questionnaires were administered to measure impulsivity, cognitive arousal, and sleep quality at each wave. Structural equation modeling and random-intercept cross-lagged panel models (RI-CLPMs) were utilized to analyze the associations, assessing both the direct effects and the mediating role of cognitive arousal over time. Impulsivity significantly predicted increased cognitive arousal over time, which in turn negatively affected sleep quality. Cognitive arousal was confirmed as a significant mediator in the relationship between impulsivity and sleep quality. These relationships remained consistent across the three-time points, with significant direct effects of impulsivity on cognitive arousal and cognitive arousal on sleep quality. Interventions to reduce cognitive arousal and impulsivity-related behaviors could improve sleep quality among college students. Future research should explore additional environmental and psychological factors influencing these relationships.

Cancer treatment is a challenging endeavor because of the intricacy, heterogeneity, and diversity of cancer causes. Comprehensive therapeutic approaches are crucial for cancer treatment. Anticancer peptides (ACPs) present a potentially effective therapeutic option. However, the extensive identification and synthesis of these peptides present a persistent difficulty that calls for the creation of effective prediction techniques. Existing techniques either suffer from low accuracy or employ high-dimensional feature sets, frequently producing sparse features and leading to ineffective model designs. This work presents a novel set of features and a long-short-term-memory (LSTM)-based classification strategy to create an efficient model. The suggested feature set includes three new and two modern feature extraction methods. The binary profile feature and k -mer sparse matrix of the reduced amino acid alphabet are part of the modern feature set. The combination of the composition of the K-spaced side chain pairs (CKSSCP), the composition of the K-spaced electrically charged side chain pairs (CKSECSCP), and the combination of [pk(CO2H)] + [pk(NH2)] + [pk(R)] + [isoelectric point] is used to derive the novel features. The suggested LSTM model is trained using the combined feature set. The trials are carried out with a k -fold cross-validation method on benchmark datasets. The results indicate that the proposed model outperforms alternative ACP classification techniques in terms of Mathew’s correlation coefficient (MCC) and accuracy. The ACP740 dataset with 5-folds yields an MCC score of 75%, which is 12%, 11%, 3%, and 8% greater than those of the ACP-DL, ACP-DA, ACP-MHCNN, and ACP-KSRC approaches. For the ACP344 dataset with 10-folds, the proposed method achieves an MCC score of 85.14%, which is 23% and 2% higher than the MCC scores of ACP-DL and SAP methods, respectively. Better classification performance offered by the proposed approach could help identify new ACPs and better understand their structural and chemical characteristics. The source code and the datasets are available on the author’s GitHub page (https://github.com/Shujaat123/ACP-LSTM-NFR).

The increasing integration of digital technologies in sport training environments has raised concerns regarding their potential psychological implications. This study examined the associations among technostress, athlete burnout, self-efficacy, and cognitive weariness among university students enrolled in sports-related academic disciplines and organized university athletic training programs. A cross-sectional survey was conducted among 1,612 university students majoring in sports-related disciplines and/or formally engaged in organized university-level athletic training programs across universities in Xi’an, China, between November and December 2025. Data were collected using validated self-report measures of technostress, self-efficacy, cognitive weariness, and athlete burnout. Structural equation modeling and regression analyses were performed using IBM SPSS and AMOS. Indirect associations were examined using bootstrapping with 5,000 resamples. Sensitivity analysis was conducted to assess robustness to potential unmeasured confounding. Technostress was directly associated with athlete burnout (β = 0.22, p < 0.001) and indirectly associated with burnout through cognitive weariness and self-efficacy. The path from technostress to cognitive weariness (β = 0.42, p < 0.001) and, subsequently, from cognitive weariness to athlete burnout (β = 0.43, p < 0.001) represented the stronger indirect pathway. In contrast, the pathways from technostress to self-efficacy (β = − 0.34, p < 0.001) and from self-efficacy to athlete burnout (β = − 0.24, p < 0.001) indicated concurrent but comparatively weaker indirect associations. The model accounted for 56% of the variance in athlete burnout (R² = 0.56). Sensitivity analysis indicated that the indirect associations remained stable unless an unmeasured confounder explained approximately 30% of the residual variance (|ρ| ≈ 0.55). These findings underscore the importance of adopting a balanced perspective on technology integration in sport training environments, where performance enhancement and athlete well-being are considered simultaneously. In student-athlete populations, attention to cognitive demands and individual coping resources may be particularly relevant for understanding technology-related strain. Future research using longitudinal and multi-source designs may further clarify these relationships in evolving sport contexts.