Search Results (1,519)

Objective: Village Health Volunteers (VHVs) are vital to Thailand’s primary healthcare, yet many face high risks for non-communicable diseases (NCDs). This preliminary study aimed to implement health empowerment theory-based personalized health promotion for individuals in the NCD-risk group. Methods: The preliminary mixed-methods study implemented a 6-month empowerment-based health promotion program for 21 VHV leaders (mean age 62.43 ± 7.28 years) at risk for NCDs. The intervention integrated laboratory data, behavioral and qualitative focus-group insights, and quantitative anthropometric data obtained via bioelectrical impedance analysis (BIA). Results: Participants’ exercise adequacy significantly improved after the intervention, increasing from 8.3% to 61.9% (p = 0.03). BIA revealed a physiological shift toward improved energy homeostasis, including decreased body weight, reduced visceral fat area, and increased muscle hydration. While biochemical markers did not reach statistical significance, clinically favorable downward trends were observed in median HbA1c (8.0% to 7.3%) and LDL cholesterol (141.8 to 119.0 mg/dL), alongside stable renal and liver function. Qualitative thematic analysis identified four primary domains of impact: sustainability and systemic advocacy, personal transformation, broad competence acquisition, and enhanced social capital. Participants reported a marked increase in self-efficacy, transitioning from inactive beneficiaries to active health advocates. This change was largely driven by mastery experiences, such as visible improvements in body composition and functional health literacy. Conclusions: The empowerment program significantly improved physical activity and body composition while fostering the social capital and health literacy necessary for community leadership, suggesting that personal health mastery is a critical precursor to effective systemic advocacy and long-term sustainability in community-led health programs. Full article

Emerging evidence indicates that non-coding RNAs (ncRNAs) play important regulatory roles in honeybee social behavior and development. However, the regulatory roles of ncRNAs in honeybees remain largely elusive. To systematically identify ncRNAs associated with queen-regulated ovary activation, we conducted whole-transcriptome sequencing on the heads of Apis mellifera workers from queenright and queenless colonies. Subsequent bioinformatics analyses were conducted to profile differentially expressed (DE) RNAs and construct potential regulatory networks. High-quality sequencing data provided a foundation for subsequent analyses. This transcriptome data yielded 3968 lncRNA transcripts, comprising 3146 known and 822 novel candidates, all of which exhibited typical structural features of lncRNAs. Comparative expression analyses revealed that 246 lncRNAs, 1439 mRNAs, and 10 miRNAs were differentially expressed. Comprehensive functional analyses indicated that the identified DElncRNAs potentially regulate sensory perception-related target mRNAs via cis-regulation, and coordinate metabolic and proteostatic reprogramming via trans-regulation to support the transition to reproductive activation in workers. Furthermore, a competing endogenous RNA network was constructed which integrated 74 DElncRNAs, 5 DEmiRNAs, and 36 DEmRNAs to predict their potential post-transcriptional interactions. Our findings highlight a comprehensive analysis of ncRNAs and mRNAs in worker heads, providing a foundation for functional validation of their roles in honeybee ovary development. Full article

This study examines the acceptance-related and usage patterns of smart office furniture among Chinese office users. Building on UTAUT2 and a usability-oriented perspective, we compare five alternative structural models that assign different roles to perceived usability in explaining intention and self-reported use of smart office furniture. Using partial least squares structural equation modeling on 239 valid responses, and comparing explanatory performance with out-of-sample prediction, we found that the model in which perceived usability and behavioral intention jointly predict use performed slightly better, although two specifications should be regarded as closely competing alternatives. Results show that facilitating conditions, effort expectancy, price value, and habit are positively associated with behavioral intention, whereas habit, perceived usability, and behavioral intention are positively associated with self-reported usage behavior. Robustness checks with sedentary-context controls indicate that discomfort is positively associated with adoption intention, whereas posture-management tendency is positively associated with self-reported usage behavior. Overall, the main pattern of associations remains broadly similar. The findings suggest a cross-sectional intention–behavior pattern among experienced Chinese users and offer actionable implications for usability-centered design, service delivery, and health-needs-based market segmentation. Full article

In social science degree programs, where Statistics is not a core subject, students often experience anxiety and negative attitudes that influence their engagement and may hinder academic performance. This study examines the role of positive emotions in the teaching of Probability Calculus and Inferential Statistics in Business Administration and Management studies, analyzing their relationship with students’ engagement in Problem-Based Learning (PBL). The research is framed as an exploratory single-campus case study conducted with a modestly sized sample of undergraduate students from a single Faculty. Moving beyond traditional approaches that view emotions merely as outcomes of learning, our model assumes that positive emotions, both prior to and following the PBL experience, shape students’ perceptions of its usefulness, their collaborative behaviors, and their communication with instructors. Using Structural Equation Modeling (SEM) and Cluster Analysis, the findings show that positive emotions are a key driver of students’ predisposition toward and engagement with PBL, indicating that cultivating a supportive emotional climate enhances participation and deepens the understanding of statistical concepts. These results suggest that fostering emotional engagement is essential not only for improving motivation and academic outcomes in Statistics but also for developing transversal and sustainability-related competencies such as critical thinking, collaboration, communication, and evidence-based decision-making. The study contributes to current discussions on sustainable and inclusive teaching practices by highlighting the importance of integrating socio-emotional dimensions into active learning methodologies in higher education. Full article

This study examines the role of artificial intelligence (AI) technologies in identifying and sustaining leadership talent within the educational sector in Oman, addressing the increasing demand for evidence-based and innovative approaches to leadership development. A qualitative phenomenological research design was employed to explore how AI experts and educational leaders perceive, evaluate, and conceptualize AI-driven tools for leadership talent identification and sustainability. In-depth semi-structured interviews were conducted with 25 participants from three major Omani educational institutions. Data were analyzed using thematic analysis, allowing systematic identification of recurring patterns, conceptual relationships, and shared professional insights. The findings indicate that AI applications—including big data analytics, behavioral assessment tools, competency identification platforms, and predictive analytics—provide effective mechanisms for early detection and assessment of leadership potential. Furthermore, integrating AI into personalized professional development programs and continuous performance evaluation contributes to the long-term sustainability and strategic utilization of leadership talent. This study underscores the potential of AI to enhance strategic leadership planning within educational institutions. The results expand our empirical understanding of AI-driven leadership development and offer practical insights for implementing AI-informed strategies in Oman and the broader Gulf region. Full article

Although ego threat is known to influence workers’ aggressive behavior, little is understood about how support and narcissism shape this relationship. Accordingly, the present study conceptualized narcissistic traits as distinct self-regulatory strategies for maintaining self-worth and examined whether the meaning of support under ego threat varies depending on these traits. An online survey was conducted with 1621 Japanese workers, and the participants were classified into three types—Self-Assertion, Need for Attention and Praise, and Sense of Superiority and Competence—based on the highest scores on the three factors of the Narcissistic Personality Inventory Short version. Hierarchical multiple regression analyses were then conducted separately for each type. The results showed that the behavioral consequences of ego threat varied substantially across narcissistic types and that support did not uniformly suppress power harassment. For the Self-Assertion type, perceived organizational support was positively associated with Invasion of Privacy. For the Need for Attention and Praise type, men and managers tended to choose Excessive Demands. For the Sense of Superiority and Competence type, supervisor support reduced harassment; however, under strong ego-threatening conditions, such support paradoxically amplified harassment. These findings suggest that support functions as a socially meaningful cue whose interpretation depends on narcissistic self-regulatory strategies. Full article

Background: This study examined the predictive effect of parental marital satisfaction on suicidal behaviors among preadolescents and adolescents in China, with positive youth development (PYD) attributes as a mediator. Methods: A total of 3665 matched pairs of students (aged 9–19, 51.3% boys) and their parents completed questionnaires, with parental marital satisfaction reported by parents and suicidal behaviors (ideation, plan, and attempt) and PYD attributes reported by students. Results: The prevalence of overall suicidal behavior was 15.5% in this sample, with a higher prevalence observed among those with lower parental education levels. Hierarchical regression and structural equation modeling analyses revealed that: (1) after controlling for socio-demographic variables, parental marital satisfaction negatively predicted suicidal behaviors; (2) PYD attributes negatively predicted suicidal behaviors, accounting for the largest proportion of variance (ΔR² range = 0.036–0.102); (3) parental marital satisfaction was positively correlated with PYD attributes; and (4) PYD attributes partially mediated the predictive relationship between parental marital satisfaction and suicidal behavior, with a significant indirect effect (β = −0.06) accounting for 56.6% of the total effect. Conclusions: This study illuminates protective pathways through which a positive family environment cultivates individual competencies, ultimately contributing to reduced suicidal behavior. Full article

Background: The resurgence of Mpox (formerly known as monkeypox) since the 2022 global outbreak has exposed weaknesses in surveillance, diagnosis, and public risk communication systems. Despite increased clinical understanding, limitations in knowledge, attitudes, and practices (KAP) among both healthcare workers (HCWs) and the general population continue to challenge prevention and control measures. Numerous systematic reviews have been published on KAP toward Mpox, yet their findings remain fragmented. This review aimed to consolidate the existing evidence from published systematic reviews to provide a unified understanding of global KAP levels related to Mpox. Methods: We followed the PRISMA guidelines for this systematic review of systematic reviews. The article search was conducted in PubMed, Embase, and the Cochrane Library for systematic reviews published between January 2010 and October 2025. Data was extracted on study design, population, and reported quantitative outcomes. Results: Five studies met the inclusion criteria: three focused on HCWs, while two focused on the general population. Among HCWs, knowledge ranged from 26.0% to 46.7%, and attitudes from 28.2% to 62.2%. In the general population, knowledge ranged from 33.0% to 46.6%, attitudes from 40.0% to 71.9%, and perceptions averaged around 40.0%. Across both groups, Mpox knowledge was limited, attitudes were moderately positive, and preventive behaviors remained consistently low, revealing a persistent gap between awareness and practice. Conclusions: This review highlights persistent gaps in knowledge, attitudes, and practices among HCWs and the general population. Although global attention increased substantially following the 2022 outbreak, important weaknesses remain in translating knowledge into consistent preventive behaviors. Addressing these gaps requires structured and context-specific interventions. Integrating Mpox-focused modules into mandatory Continuing Medical Education credits for HCWs could ensure sustained competency in diagnosis, infection prevention, and outbreak response beyond peak epidemic periods. For the general population, strategic risk communication campaigns should leverage trusted community leaders and social media influencers in high-risk regions to counter misinformation, reduce stigma, and promote evidence-based preventive behaviors. Embedding these targeted strategies within broader pandemic preparedness and global health security frameworks will be essential to strengthening early detection, public trust, and coordinated outbreak response in future Mpox or other emerging infectious disease events. Full article

The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article

Episodic future thinking (EFT) has been confirmed as a promising cognitive intervention for enhancing prospective memory (PM), yet emerging evidence suggests its effects may depend on the specificity of induction. The current study investigated this issue by dichotomizing EFT into two distinct methods: specific (researcher-guided detailed mental simulations) versus non-specific (participants’ self-guided imagination), implemented through differentially structured future thinking instructions. We also analyzed the distinct cognitive strategies mainly employed under each EFT condition based on the Dynamic Multiprocess Framework. The latent profile analysis (LPA) was further conducted to characterize individual variability in responsiveness to EFT manipulations. Behavioral results revealed comparable PM accuracy improvements across both EFT methods relative to the control group; moreover, specific EFT uniquely accelerated response times for both PM and ongoing task execution. The LPA further identified three distinct EFT response patterns—self-competent, proactive, and reactive—each exhibiting unique state-dependent cognitive characteristics. These findings provide a refined understanding of the EFT-PM relationship: (1) specific EFT facilitates more automatic retrieval of PM intentions, whereas non-specific EFT predominantly engages strategic monitoring; (2) individual differences in baseline mental images influence the effectiveness of EFT methods, suggesting the potential benefits of personalized intervention approaches for PM enhancement. Full article

Skeletal muscle differentiation is tightly regulated by membrane potential dynamics and voltage-dependent ion channel activity. Potassium (K⁺) and calcium (Ca²⁺) currents cooperate to orchestrate the transition of myoblasts into fusion-competent myotubes, and alterations in this process are associated with dystrophic phenotypes. Here, we investigated the electrophysiological remodeling accompanying C2C12 myogenesis and the modulatory effects of the polyphenol resveratrol (RES) on calcium voltage-gated channel subunit alpha 1 S (CACNA1S, Cav1.1, L-type) currents. Whole-cell patch-clamp recordings were performed in proliferating and differentiating C2C12 cells to characterize the temporal expression of K⁺ currents and voltage-dependent Ca²⁺ channels (VDCCs). During differentiation, three electrophysiological subpopulations were identified according to K⁺ current profiles: SK4+/EAG−/Kir−, SK4−/EAG+/Kir−, and SK4−/EAG+/Kir+. This sequence paralleled a progressive membrane hyperpolarization from −20 mV to −70 mV, consistent with the physiological maturation of myogenic cells. In C2C12 myocytes, nimodipine-sensitive L-type currents were the only Ca²⁺ conductance observed. Their activation threshold (~−30 mV) and half-activation voltage (V/2 ≈ −12 mV) indicated the co-expression of embryonic and adult Cav1.1 isoforms. Exposure to RES (30 µM, 48 h) produced a depolarizing shift in activation (ΔV/2 ≈ +9 mV) and a reduction in current amplitude across all voltages, consistent with a transition toward the adult splice variant of Cav1.1. These findings suggest that RES promotes electrophysiological maturation of skeletal muscle cells by modulating calcium channel expression and gating behavior. Given its known ability to correct splicing abnormalities in CACNA1S and related genes, resveratrol emerges as a promising pharmacological agent for restoring calcium homeostasis in neuromuscular disorders such as myotonic dystrophy type 1 (DM1). Full article

Microplastics (MPs) are synthetic polymer particles that are generally less than 5 mm in size and have attracted heightened scrutiny due to their pervasive presence in the environment, along with their toxicological significance. Several research investigations documented its presence in humans as a profound finding in biological tissues and fluids crossing barriers, leading to oxidative and inflammatory pathways alterations associated with blood, placenta, cardiovascular, pulmonary, nephrotic, other systems, and their disorders. Given the ubiquitous utilization of microplastics across diverse sectors, it is imperative to systematically investigate and elucidate their potential toxicological effects on biological systems through rigorous and mechanistically informed research. This review will also provide the synthesis of recent mechanistic data on the toxicity that can be caused by MPs and will determine key gaps that impede efficient human health risk evaluation. A structured literature search was conducted via PubMed, Web of Science, and Scopus databases, mostly from the studies published between 2010 and 2026. The studies of exposure characteristics and biological effects were analyzed in vitro, in vivo, and in human biomonitoring, and the primary focus of the interventions includes oxidative stress, inflammation, apoptosis, hepatotoxicity, and metabolic malfunction. MPs possess various physicochemical properties, such as a low particle size, various shapes, surface area, polymer composition, and the presence of sorbed or intrinsic additives. When MPs are taken up by cells, they can induce oxidative stress via increasing ROS, eventually leading to high lipid peroxidation, mitochondrial malfunction, DNA fragmentation, and eventually cell death. MPs also cause pro-inflammatory cytokine responses, including TNF-α, IL-1β, and IL-6, altering the immune system and cell profile, leading to systemic inflammation. In aquatic and terrestrial organisms, these microplastics have a harmful impact on growth, reproduction, and behavior in a time- and dose-dependent manner. Under conditions of controlled exposure, the organ-specific toxicities that have been reported include hepatic, renal, neurological, reproductive, and cardiovascular systems. Although the fields of mechanistic knowledge are growing, there is still a substantial amount of uncertainty; there is a lack of characterization of the long-term effects of low-dose chronic exposure, the kinetics of bioaccumulation, biodegradation potential, and transgenerational effects. In addition, there are no standardized procedures for the characterization of MPs, nor the reporting of the distribution of size or exposure measurements, which limits the comparability of cross-studies and makes it difficult to assess risks quantitatively. The dynamics of interactions of MPs between co-adsorbed contaminants like heavy metals, polycyclic aromatic hydrocarbons, and endocrine-disrupting chemicals are also yet to be explored. Although all evidence available to date does indicate biologically plausible mechanisms of MP-induced toxicity, integrated research employing standardized analytical protocols, an environmentally relevant exposure model, and human epidemiological data is required to ensure that laboratory results are translated into evidence-based public health and regulatory actions. This review offers an in-depth analysis of the existing molecular understanding of MP-induced toxicity, demonstrates organism-level impacts throughout species, and establishes vital fields for future studies. In order to develop competent guidelines to minimize MP exposure and its adverse health effects, it is crucial to cover these gaps via research that incorporates toxicology and environmental science. Full article

A new odd exponential-type (NOT-Exp) distribution provides a flexible and analytically tractable framework for modeling lifetime data exhibiting non-constant hazard behaviors, including increasing, decreasing, bathtub-shaped, and unimodal forms, which are commonly observed in real-world reliability and survival studies. In this work, a comprehensive inferential methodology is developed for the NOT-Exp model under a unified progressive Type-II hybrid censoring, allowing several traditional censoring designs to be treated as special cases within a single unified structure. The main advantages of the proposed model lie in its ability to capture complex risk dynamics while maintaining mathematical simplicity, making it particularly suitable for censored lifetime data. Classical inference is conducted via maximum likelihood estimation, along with two asymptotic confidence interval constructions based on normal and log-normal approximations for both model parameters and reliability characteristics. In addition, a Bayesian estimation framework is introduced using independent gamma priors and Markov chain Monte Carlo techniques to obtain posterior estimates, credible intervals, and highest posterior density regions. Extensive simulations demonstrate the accuracy, stability, and robustness of the proposed estimators under varying sample sizes, censoring intensities, and prior specifications. Applications to airborne toxicological variation data and bank customer waiting times highlight the practical importance of the methodology, where the NOT-Exp model consistently outperforms twelve competing lifetime distributions according to standard goodness-of-fit criteria. These results confirm that the suggested design gives a strong and versatile tool for analyzing complex censored lifetime data across environmental and service-system applications. Full article

Candidate interview assessment is primarily reliant on subjective human judgment, while existing AI-based methods rely on end-to-end predictions with no psychometric basis. In this paper, we propose an interpretable multi-modal framework that combines nonverbal behavior, LLM-based verbal analysis, and Big Five personality traits into three theory-based constructs: professional-cognitive competence, observed leadership behavior, and leadership disposition. The proposed method utilizes computer vision and larger language models to extract features from video interviews. Rather than targeting predictive accuracy, the proposed method prioritizes construct validity and transparent aggregation under severe label scarcity. The proposed method aggregates the constructs into a Top Potential Score that reflects the executive abilities of the candidate. Experiments on the method show its ability to significantly differentiate top candidates from others (Cliff’s delta = 0.91 for the composite Top Potential Score, permutation p = 0.0002). Leave-one-out analysis verifies robustness, while rank-based evaluation yields 100% recall of executive candidates in the top 20% of rated applications. The findings justify the use of the proposed multi-modal method as an interpretable decision-support tool for candidate interview assessment. Full article

Background: Artificial Intelligence (AI) and Generative AI (GenAI) are increasingly integrated into educational and professional settings, offering personalized learning, productivity gains, and enhanced engagement. However, excessive reliance may compromise critical thinking, autonomous problem-solving, and emotional regulation among youth (i.e., adolescents and young adults) and early-career professionals. Aim: This review examines the cognitive, educational, and behavioral impacts of AI and GenAI use in youth, highlighting implications for their responsible integration in learning and professional development. Methods: A narrative review was conducted, synthesizing empirical studies, psychometric instruments, and international policy frameworks addressing AI engagement. Emphasis was placed on cognitive, behavioral, educational, and ethical dimensions across youth and early-career professionals. Results: AI enhances learning efficiency, creativity, and professional decision-making but may also foster cognitive offloading, dependency, and addiction-like behaviors. Instruments such as the Conversational AI Dependence Scale (CAIDS) and the Problematic ChatGPT Use Scale (PCGUS) help identify maladaptive patterns. Effective strategies include structured pedagogy, human oversight, reflective practice, AI literacy, and ethical guidance. Paradoxically, higher AI competence and trust may increase reliance, underscoring the need for guided and balanced engagement. Conclusions: Responsible AI integration requires multidimensional approaches combining instructional scaffolding, metacognitive strategies, supervision, and governance to preserve autonomy, professional judgment, and cognitive development in youth. Full article