Paradise Valley Hospital

Abstract This paper introduces QRL-AFOFA, a Q-learning-enhanced adaptive fractional-order firefly algorithm developed to address the challenges of large-scale and dynamic multiobjective optimization problems. While fractional-order metaheuristics provide memory-driven search dynamics and reinforcement learning (RL) offers adaptive policy control, existing hybrid methods often face critical limitations such as parameter sensitivity, premature convergence, and poor diversity preservation. To overcome these challenges, QRL-AFOFA integrates five synergistic innovations: real-time adaptive tuning of fractional-order parameters, entropy-regularized Q-value updates, stagnation-aware restart strategies, reflection-based boundary handling, and dual-phase learning rate scheduling. Extensive experiments on the 2021 IEEE Congress on Evolutionary Computation (CEC2021) benchmark functions demonstrate that QRL-AFOFA consistently outperforms other state-of-the-art algorithms across diverse problem categories. Accordingly, the proposed QRL-AFOFA demonstrated superior performance in 97.5% of test cases and outperformed the state-of-the-art algorithms in 34-40 out of 40 benchmark problems, with particularly impressive gains in dynamic and large-scale scenarios. Statistical validation using the Wilcoxon signed-rank and Friedman tests confirms the significance of the improvements. Notably, QRL-AFOFA achieves exceptional performance in high-dimensional (up to 10,000 variables) and dynamic optimization settings. Its self-adaptive design eliminates manual parameter tuning, making it a robust, scalable, and intelligent optimization framework for complex real-world applications.

The purpose of this study was to investigate the prevalence of burnout and job satisfaction in Cabanglasan, Bukidnon, during the 2024-2025 school year. This study used a descriptive-correlational research method to investigate the importance of the association between burnout prevalence and its effect on job satisfaction among teachers. A survey questionnaire is used so that responses from respondents can be easily obtained and collated. The study found that burnout was common among public secondary teachers in terms of emotional weariness, as well as depersonalization and personal success. Furthermore, the study found that teachers' burnout had a rare effect on job satisfaction in terms of supervision and a common effect on working conditions. While the impact of teacher burnout on job satisfaction in terms of responsibility and progress is constant. Furthermore, the study found that all three markers of burnout (emotional exhaustion, depersonalization, and personal success) have weak negative relationships with job satisfaction in terms of supervision. However, none of these correlations were statistically significant at the 0.05 level. The study found that three burnout indicators (emotional exhaustion, depersonalization, and personal achievement) have weak correlations with job satisfaction regarding working conditions. However, there is a significant relationship between two burnout indicators (depersonalization and personal achievement) and job satisfaction concerning responsibilities. Emotional weariness does not have a substantial association. These findings imply that in order to improve job satisfaction among public secondary teachers and effectively treat burnout, school administrators should implement targeted interventions aimed at minimizing depersonalization and enhancing perceptions of personal achievement.

This study aimed to investigate the "learning loss" resulting from recent academic disruptions. This study used a classroom action research, utilizing peer tutoring as the primary intervention amidst various psychological factors. The pretest identifies ten least-mastered mathematics skills, including simplifying numerical expressions, operations on integers, converting fractions to decimals, ordering rational numbers, and solving word problems. Challenges are also noted in approximating volume and area, understanding angles, and interpreting graphical data. Focus group discussions highlight the positive feedback for the intervention and factors contributing to learning loss, such as limited study time, connectivity issues, social media distractions, poor study habits, and math difficulties. Implementing tutorial intervention before the post-test effectively addresses learning loss and significantly reduces learning gaps.

SummaryMuscle stem cells (MuSCs) contribute to muscle regeneration following injury. In many muscle disorders, the repeated cycles of damage and repair lead to stem cell dysfunction. While telomere attrition may contribute to aberrant stem cell functions, methods to accurately measure telomere length in stem cells from skeletal muscles have not been demonstrated. Here, we have optimized and validated such a method, named MuQ-FISH, for analyzing telomere length in MuSCs from either mice or humans. Our analysis showed no differences in telomere length between young and aged MuSCs from uninjured wild-type mice, but MuSCs isolated from young dystrophic mice exhibited significantly shortened telomeres. In corroboration, we demonstrated that telomere attrition is present in human dystrophic MuSCs, which underscores its importance in diseased regenerative failure. The robust technique described herein provides analysis at a single-cell resolution and may be utilized for other cell types, especially rare populations of cells.

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