Search Results (9,866)

Religious psychopathology as a field lies at the intersection of psychiatry, theology, and culture. It addresses scientific discoveries and questions relating to the manifestation of mental health disorders that are expressed through religious content, ideation, and/or behavior. Religious psychopathology, being a multifaceted phenomenon, challenges clinicians, researchers, and religious leaders because it is non-trivial to distinguish between culturally normative religious experiences and pathological symptoms. The present integrative narrative review examines historical perspectives, diagnostic challenges, clinical manifestations, cultural considerations, therapeutic interventions, neurobiological models, ethical issues, and future directions in the field of religious psychopathology. It focuses primarily on literature from 2013 to 2025, while also incorporating selected foundational historical, theoretical, and clinical sources necessary for conceptual clarification. A special emphasis is placed on culturally informed and interdisciplinary approaches. Particular focus is given to approaches that respect spiritual frameworks while concurrently promoting evidence-based mental health care. Full article

To effectively reduce fuel consumption and emissions in personal transport, it is essential to understand how energy transfer inefficiencies arise under real-world driving conditions. This study investigates the behavioral and spatial determinants of energy transfer inefficiency in personal diesel vehicles using high-resolution vehicle telematics data. The research proposes a composite Energy Inefficiency Index (EII) derived from real-world indicators of driving behavior, including acceleration, braking, idling, speed variability, and trip structure. These indicators are normalized and weighted using principal component analysis to quantify inefficiency at trip and spatial levels. Geospatial analysis, including Global Moran’s I and heatmap visualization, is employed to identify spatial clustering of energy inefficiency across urban and extra-urban environments. The results reveal a moderate average level of energy inefficiency across the analyzed vehicle fleet, with braking frequency, acceleration frequency, trip duration, and idling time emerging as the primary behavioral drivers of inefficient energy transfer. A statistically significant positive spatial autocorrelation indicates pronounced clustering of inefficiency in dense urban areas characterized by congestion and stop–start traffic dynamics. Furthermore, this study evaluates potential fuel, cost, and CO₂ emission reductions achievable through improved driving behavior and compares these gains with those associated with vehicle electrification. The findings demonstrate that targeted behavioral interventions—such as eco-driving and idling reduction—can yield substantial efficiency improvements and emission reductions, complementing the benefits of electrification. Overall, this research provides a data-driven framework for managing energy transfer inefficiency in personal diesel vehicles by integrating behavioral analysis, spatial assessment, and telematics-based monitoring, offering practical insights for policymakers, transport planners, and vehicle technology developers. Full article

Background: Falls and fall-related injuries are common in older adults and are frequently accompanied by fear of falling (FoF), which may lead to activity avoidance and functional decline. Because many interventions target falls or FoF in isolation, we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to identify, describe, and evaluate interventions reporting both falls and FoF outcomes in older adults. Methods: This systematic review and meta-analysis were registered in PROSPERO (CRD420251113137) and conducted in accordance with PRISMA guidelines. PubMed, Embase, and Web of Science were searched from inception to 4 November 2025. Eligible studies were English-language RCTs that included adults aged ≥60 years, evaluated nonpharmacological interventions, and reported both FoF and falls. Methodological quality was assessed using the PEDro scale. Random-effects meta-analyses were performed for FoF (Hedges g), and Bayesian random-effects binomial meta-analyses were conducted for falls. Results: Ten RCTs published between 1998 and 2018 (sample sizes per trial: n = 27–540) were included. Interventions included cognitive–behavioral therapy-based programs, Tai Chi, physiotherapist-led strength and balance training, computerized visual feedback, and video-guided home exercise. PEDro scores ranged from 6 to 9 (mean, 7.7). Pooled analyses showed no significant effect on FoF at the end of intervention (g = −0.20, 95% CI −1.45 to 1.05; p = 0.68; high heterogeneity) or at follow-up (g = −0.14, 95% CI −0.60 to 0.33; p = 0.50). For falls, postintervention evidence favored the null (BF₁₀ = 0.16; pooled estimate −0.01, 95% credible interval [CrI] −0.30 to 0.14). Follow-up results were inconclusive (BF₁₀ = 2.07; pooled CrI −0.56 to 0.00), with substantial uncertainty. Conclusions: Across RCTs that measured both outcomes, interventions did not consistently improve both FoF and falls outcomes. These findings may suggest a partial dissociation between psychological and physical fall-related outcomes, highlighting the need for integrated, adequately powered trials that utilize standardized measures and longer follow-up periods. Full article

This study presents an adaptive learning framework that integrates fuzzy logic and learning analytics to support personalized education and multi-factor student assessment. The proposed system combines cognitive and behavioral indicators to provide an interpretable representation of the learner’s state within a dynamic digital learning environment. The architecture is based on adaptive learner modeling and classroom-level monitoring mechanisms, enabling personalized guidance, adaptive content sequencing, and continuous performance monitoring at both individual and classroom levels. A core contribution of the approach is a fuzzy logic-based evaluation mechanism that aggregates multiple signals, including quiz performance, time spent on theory, help-seeking behavior, and system interaction patterns. These inputs are transformed into fuzzy sets and combined through inference rules to produce interpretable learning level estimates aligned with Bloom’s taxonomy. The approach is grounded in Vygotsky’s Zone of Proximal Development, supporting adaptive scaffolding and targeted instructional interventions. The evaluation results demonstrate a strong correlation between the model outputs and conventional exam performance (r ≈ 0.91), while exhibiting reduced variability (SD ≈ 0.15 compared to SD ≈ 0.20), indicating a more stable representation of learner performance. Furthermore, statistical analysis confirms that the differences between traditional and model-based scores are significant (p < 0.01), suggesting that the proposed approach captures additional dimensions of learner behavior beyond conventional grading metrics. Overall, the findings indicate that integrating fuzzy reasoning with behavioral analytics enables a more interpretable, stable, and pedagogically grounded approach to learner assessment, supporting adaptive and interpretable personalized learning. Full article

Background/Objectives: Assessing inhibitory function and error detection is crucial for the early detection of age-related cognitive decline. This study aimed to investigate the neural network dynamics underlying these functions in younger and older adults to better understand age-related changes in cognitive control. Methods: We recorded electroencephalograms (EEGs) during an inhibitory control task in 17 older and 15 younger healthy adults. Behavioral performance was assessed, and directional functional connectivity was analyzed using Low-Resolution Electromagnetic Tomography (LORETA), isolated effective coherence (iCoh), and Full Vector Field analysis across the theta, alpha, and beta frequency bands. Results: Older adults showed significantly fewer correct responses than younger adults. During incorrect responses, older adults exhibited strong beta-band directionality from the ventral anterior cingulate cortex (ACC) to the left frontal polar cortex (FPC), alongside strong intra-ACC connectivity. During correct responses, they demonstrated alpha- and beta-band directionality from the left dorsolateral prefrontal cortex (DLPFC) to the right FPC. Conversely, compared with older adults, younger adults demonstrated significantly stronger mutual directionality within the ACC and widespread robust connectivity among the ACC, bilateral DLPFC, and FPC during correct responses. Conclusions: Efficient inhibitory control in older adults appears to rely on higher-order error-monitoring and error detection networks. The altered network dynamics in older adults suggest an age-related decline in immediate cognitive control. Evaluating these neural networks via EEGs provides a potential non-invasive biomarker for early cognitive decline and highlights higher-order executive control as a promising target for preventive interventions. Full article

The main objective of this study is to investigate smoking dynamics, identify the most influential factors governing smoking behavior, and develop effective intervention strategies through the integration of fractional-order modeling, sensitivity analysis, optimal control theory, and artificial neural networks (ANNs). A nonlinear fractional-order compartmental model is formulated by dividing the population into potential smokers, light smokers, heavy smokers, and quit smokers. The smoking reproduction number is derived to characterize the transmission and persistence of smoking behavior within the population. To determine the impact of model parameters on smoking dynamics, both normalized forward sensitivity analysis and global sensitivity analysis based on Latin Hypercube Sampling (LHS) with Partial Rank Correlation Coefficient (PRCC) are performed. The obtained results identify the most sensitive transmission and progression parameters and demonstrate their important role in shaping smoking prevalence within the community. Furthermore, the classical integer-order model is compared with the fractional-order formulation, where the fractional model provides a more realistic description due to its ability to incorporate memory and hereditary effects associated with smoking behavior. An optimal control framework involving awareness and treatment strategies is further introduced to investigate effective smoking reduction policies. The numerical results demonstrate that awareness campaigns reduce smoking initiation, while treatment interventions increase smoking cessation, and the combined implementation of both strategies produces the most significant reduction in smoking prevalence. The consistency between the sensitivity analysis and optimal control results further supports the reliability of the proposed framework. Numerical simulations are carried out to analyze the qualitative and quantitative behavior of the system under different epidemiological scenarios. In addition, an ANN-based computational framework is employed as an efficient numerical tool to accurately approximate the complex dynamics of the proposed fractional-order smoking model with very low prediction error. Overall, the present study provides a comprehensive mathematical and computational framework for understanding, analyzing, and controlling smoking behavior within a population. Full article

Background and objectives: Social media use has become pervasive among the general population, with growing concern regarding its potential effects on mental health and sleep. While existing studies report associations between social media engagement and psychological outcomes, limited attention has been given to users’ self-perceived impact. To assess the self-perceived impact of social media use on mental health and sleep-related outcomes among healthy adolescents and adults aged 16–50 years old, and to identify associated demographic and behavioral factors. Methods: A cross-sectional survey was conducted among residents of Jeddah, Saudi Arabia, aged 16–50 years without a history of psychiatric or chronic sleep disorders, using a structured online questionnaire. Perceived mental health impact was assessed using a six-item study-specific questionnaire evaluating participants’ subjective perceptions regarding emotional and psychological responses to social media exposure. Higher perceived impact was defined as a composite score of 12–24 points on the study-specific scale. Data included sociodemographic characteristics, patterns of social media use, perceived mental health impact assessed through a 6-item Likert scale, and sleep-related outcomes. Associations were evaluated using chi-square tests and logistic regression analysis. Results: Most participants reported daily social media use exceeding 3 h, with 44.9% engaging in late-night use and 87.6% using devices within 30 min before sleep. Overall, 18.6% exhibited higher perceived mental health impact. Higher odds were observed among younger participants, students, and single individuals. Snapchat and YouTube use, and late-night engagement were independently associated with increased perceived impact. Approximately one-third reported insomnia after social media use, and 44.3% perceived improved sleep with reduced usage. Conclusions: Social media use is widely prevalent and commonly perceived to negatively affect mental well-being and sleep, particularly with intensive and late-night use. Self-awareness of these effects may represent a valuable leverage point for prevention, supporting the need for targeted digital wellness strategies and public health interventions. Full article

Opioid misuse continues to be a major public health issue in the United States. Older adults (≥65) are at particular risk of harm from opioids due to changes in opioid pharmacokinetics with age; however, healthcare professionals lack training and confidence in addressing opioid harm reduction strategies in this population. Therefore, the purpose of this study was to improve healthcare professional knowledge and beliefs regarding opioid harm reduction strategies amongst older adults. An 8 h interprofessional conference was conducted 1 May 2025 to educate healthcare providers about opioid misuse prevention strategies for older adults. This study utilized a quasi-experimental one-group pretest–posttest design to assess changes in healthcare professional knowledge and beliefs before and after the conference. Healthcare professionals in the U.S. were recruited to participate in the conference via email listservs with national reach, predominantly concentrated in Alabama. Data were collected at pre- and post-conference via an anonymous online survey informed by the Theory of Planned Behavior and Health Belief Model. Primary outcome measures included: (1) knowledge of opioid use and misuse in older adults (5 items); (2) prescribing and dispensing attitudes surrounding opioids and medications for opioid use disorder (MOUD) (5 items); (3) perceived susceptibility to harm from opioids (4 items); and (4) perceived barriers to opioid harm reduction in older adults (17-items). Constructs were measured using multiple-choice questions (knowledge) and Likert-type scales (1 = strongly disagree, 5 = strongly agree). Secondarily, intention to join a Microsoft Teams working group for ongoing collaboration was assessed through a single categorical (Yes/No/Unsure) multiple-choice question at post-conference. Data were analyzed using descriptive statistics, and differences in mean knowledge, attitudes, susceptibility, and barriers scale scores from pre- to post-conference were analyzed using Wilcoxon signed-rank tests (alpha = 0.05). Of N = 75 survey respondents, the majority were White (86.7%), female (74.7%), 50 years of age on average, and employed as pharmacists (68%). Overall, mean (SD) knowledge (83.73% [19.92] versus 90.67% [12.45]; p = 0.011) and perceived susceptibility (3.82 [0.63] versus 4.03 [0.63]; p = 0.002) increased from pre- to post-conference, while perceived barriers decreased (2.71 [0.54] versus 2.54 [0.58]; p = 0.001). Despite an upward trend, there was no statistically significant change in the mean prescribing and dispensing attitudes from baseline to post-conference. Additionally, 34.7% intended to join the Microsoft Teams working group at post-conference. Findings support the utility of interprofessional educational interventions to increase healthcare provider knowledge and beliefs regarding opioid harm reduction strategies amongst older adults. Full article

Applied Behavior Analysis (ABA) is recognized as one of the most evidence-based interventions for students with autism spectrum disorder (ASD), although its effectiveness relies on consistent classroom implementation by teachers. This study investigated the extent to which teachers of students with ASD in Qatar implement ABA techniques and whether implementation levels vary by gender, educational level, school type, years of experience, and teaching stage. A descriptive–analytical design was utilized on a sample of 155 teachers from government and private schools. Data were collected using the Applied Behavior Analysis Implementation Scale for Teachers of Students with ASD in Qatar (ABAIS-Qatar), a 26-item instrument developed and validated for this study across five dimensions. Teachers reported a high overall level of ABA implementation (M = 4.10, SD = 0.48). The Behavior Identification and Goal Setting and Strategy Application and Intervention dimensions received the highest ratings, while the Motivation and Corrective Procedures dimensions were rated at a moderate level. A five-way MANOVA revealed significant multivariate differences across years of experience and teaching stage. Post hoc analyses indicated that teachers with more than 15 years of experience reported significantly higher implementation of motivational and corrective procedures than those with 6–10 years of experience and that primary-stage teachers demonstrated superior classroom behavior management compared to intermediate-stage teachers. The findings have implications for teacher professional development and ABA training in both inclusive and specialist educational settings in Qatar. Full article

This scoping review examined the current literature on routines and habits in individuals with attention deficit hyperactivity disorder (ADHD). To our knowledge, research in this area remains limited. Therefore, this review mapped which areas of daily routines are most affected in children and adults with ADHD and explored related assessments and interventions. A comprehensive search was conducted across four databases: PubMed, Scopus, CINAHL, and PsycINFO, using keywords including “attention deficit hyperactivity disorder,” “ADHD,” “routine,” “habit,” and “lifestyle.” The findings identified four main domains in which individuals with ADHD experience difficulties: sleep hygiene, feeding, physical activity, and sedentary behaviors, with sleep hygiene addressed in more than half of the included studies. Study habits were addressed in only one included study. Among the 31 included studies, six involved interventions. The review also found that no validated assessment was specifically designed to measure routines or habits in individuals with ADHD, and that broader measures of routines, habits, or lifestyle were often non-validated or developed for a single project. Overall, the existing studies were concentrated primarily in pediatric populations, with limited research involving adults. These findings highlight important gaps in the literature and underscore the need for more research on routines and habits in adults with ADHD. They also support the development of assessments and interventions that specifically address these areas. Full article

This study addresses the critical challenge of food waste in the hospitality sector, directly contributing to Sustainable Development Goal (SDG) 12.3. We conducted an intervention at a community-based culinary innovation center involving 18 participants. The research integrated the Collaborative System Model (CSM)—a framework that facilitates multi-stakeholder co-creation through knowledge interaction and feedback loops—into a Participatory Design (PD) process. Results demonstrated that the intervention reduced fruit waste mass by 72% per session and increased byproduct reuse rates from 15% to 68%. Sensory evaluations confirmed that these waste-reduction practices did not compromise product quality (p > 0.05). This approach provides a behavior-anchored unit process for pre-consumer waste reporting. Full article

From the perspective of interactions between energy firm behavior and government intervention strategies, this study develops a contagion control model for energy firm debt default risk utilizing cellular automata and complex network theory. This research investigates the spatio-temporal evolution of risk transmission and evaluates the efficacy of various mitigation protocols through computational simulation. The research results indicate that: (1) An escalation in both the transmission likelihood and the rate of immunity decay significantly amplifies the propagation strength of debt default risks. Conversely, the stability of the energy firm network is bolstered as the probabilities of immunity and recovery increase. (2) The contagion intensity for debt default risk is positively correlated with market noise, the risk appetite of energy firms, and their corporate influence. It is negatively correlated with risk awareness, creditworthiness, regulatory intensity, and policy subsidies. Furthermore, it exhibits an inverted U-shaped relationship with investor sentiment. (3) Within the interconnected network of energy firms, risk contagion can be effectively mitigated not only by enhancing risk perception and credit standing but also by guiding risk preference and managing firm influence. Furthermore, the integration and adjustment of government intervention strategies, such as regulatory intensity and policy subsidies, can more efficiently accelerate the eradication of debt default risk among energy firms. Full article

Hepatocellular carcinoma (HCC) is sustained by coordinated interactions among malignant hepatocytes, immune cells, and stromal populations that collectively drive tumor growth, immune evasion, and vascular remodeling. Using integrative single-cell transcriptomics on 93,032 cells from tumor and healthy human liver, we characterized cell-type-specific transcriptional programs underlying immunometabolic reprogramming and reconstructed the intercellular communication circuits that maintain the tumor microenvironment. Malignant hepatocytes displayed upregulation of genes encoding both glycolytic and oxidative phosphorylation (OXPHOS) metabolic enzymes, consistent with metabolic plasticity, while concurrently suppressing genes involved in antigen presentation—a transcriptional pattern indicative of coordinated metabolic and immune-evasive reprogramming. Tumor-associated macrophages acquired TREM2-enriched, lipid-handling phenotypes consistent with immunosuppressive polarization, and tumor endothelial cells upregulated angiocrine and extracellular matrix programs while silencing innate immune outputs. Ligand–receptor inference revealed a qualitative rewiring of intercellular communication: the antigen-presentation-centered network of the healthy liver was replaced by a tumor-driven architecture dominated by pro-angiogenic, ECM–integrin, inflammatory chemokine, and lipid-associated signaling circuits, with malignant hepatocytes, TAMs, and TECs collectively assuming the dominant signaling burden. These findings establish that HCC progression is an emergent property of a stabilized multicellular network, rather than the autonomous behavior of malignant cells, and define cooperative immunometabolic modules that constitute tractable targets for combinatorial therapeutic intervention. Full article

An administrator-assisted CLP-SEIRS-T${}^{+}$ framework is developed to model malware propagation and urban cyber-resilience in clustered temporal communication networks. The model extends CLP-SEIRS-T by integrating community structure, predicted links, asynchronous node activation, and an endogenous defense layer in which administrator nodes remain infectable, recover faster than ordinary nodes, and trigger local patch diffusion when community-level prevalence exceeds a risk threshold. Unlike formulations that treat defense as an external or perfectly reliable safeguard, the proposed framework embeds administrator intervention directly within the epidemic state space and couples propagation dynamics with resilience-oriented performance measures, including safe functionality, absorptive capacity, spillover attenuation, recovery time, and service continuity. To keep experimental evidence scale-explicit, the validation is organized as a tiered protocol: a 48-node isolated virtual-machine cyber-range verifies safe mechanism realization; emulation-calibrated logical traces and pilot repeated comparisons examine trajectory behavior, pathway composition, and defense-component effects; and expanded numerical sweeps assess scalability, threshold sensitivity, alternative link-prediction scores, and adaptive-stress assumptions. The results show that direct links dominate local amplification, whereas predicted links contribute disproportionately to cross-community spillover. In the pilot comparison, the full CLP-SEIRS-T${}^{+}$ configuration achieves the best observed balance, reducing mean peak burden by 56.9%, shortening mean recovery time by 86.7%, increasing absorptive capacity by 37.1%, and improving service continuity by 12.0% relative to the no-intervention baseline. Larger-network sweeps over $N=48,100,150,200,$ and 500 logical hosts preserve the same qualitative mechanism ordering while keeping functionality error below 0.02. Threshold analysis indicates that intermediate trigger values provide a better burden–cost balance than either overly aggressive or delayed patching. Link-score comparisons show that local-neighborhood predictors yield consistent spillover interpretations, whereas degree-driven prediction can increase bridge exposure. Parameterized adaptive-stress tests further indicate that the mechanism remains beneficial under moderate stress but degrades under severe patch suppression, false telemetry, or intensified bridge seeking. These findings suggest that urban cyber-resilience depends jointly on network modularity, temporal availability, structurally likely bridge formation, state-dependent local defense, and the integrity of administrative response. Full article

Background/objective: Cybersex addiction and hypersexual behavior represent escalating challenges in global mental health. This study analyzed the sociodemographic and clinical profiles of a cohort of males undergoing treatment, examining the concurrent associations between therapeutic support duration and symptomatic severity. Method: An observational, descriptive, and cross-sectional study was conducted with a predominantly male clinical cohort (n = 27; 26 males and 1 female) enrolled in therapeutic support programs. Assessment instruments included the Internet Sex Screening Test (ISST), the Hypersexual Behavior Inventory (HBI), and the Rosenberg Self-Esteem Scale (RSES). Results: Findings revealed a pornography consumption pattern characterized by high intensity and early onset of behavioral addiction. A significant prevalence of low self-esteem was detected (48.1%). Statistical analysis demonstrated that neither age of onset nor self-esteem levels significantly correlated with current disorder severity. Conclusions: A descriptive pattern was identified regarding the duration of therapeutic follow-up, where patients with longer program tenure exhibited lower scores in impulse control difficulties. While early onset and self-esteem function as concurrent clinical characteristics, they do not linearly correlate with current clinical severity. Therapeutic support duration is associated with specific indicators of clinical stability, suggesting the potential utility of long-term nursing-led interventions. Full article