Search Results (353)

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially pertinent to inquiries around the role of spirituality in mental health, with special attention to chronic pain conditions. The latter have become an open channel for novel avenues to explore the field of spirituality-based interventions within the arena of psychological inquiry. To address this, the authors developed and implemented the Church and Academia Model, a prototype for an innovative collaborative research project, with the aim of exploring the role of devotional practices, and their potential to be used as therapeutic co-adjuvants or tools to enhance the coping skills of patients with chronic pain. Keeping in mind that the church presents a rich landscape for clinical inquiry with broad relevance for clinicians and society at large, we created a unique hybrid research model. This is a new paradigm that focuses on distinct and well-defined studies where the funding, protocol writing, study design, and implementation are shared by experts from both the pastoral and clinical spaces. A team of theologians, researchers, and healthcare providers, including clinical pain psychologists, built a coalition leveraging their respective skill sets. Each expert is housed in their own environs, creating a functional network that has proven academically productive and pastorally effective. Key outputs include the creation and validation of a new psychometric measure, the Pain-related PRAYER Scale (PPRAYERS), an associated bedside prayer tool and a full-scale dissemination strategy through journal publications and specialty society conferences. This collaborative prototype is also an ideal fit for integrated knowledge translation platforms, and it is a promising paradigm for future collaborative projects focused on spirituality and mental health. Full article

Stress is a major factor that threatens the body’s homeostasis or well-being. Excessive stress causes psychological anxiety and tension, which disrupts the balance of the autonomic nervous system that maintains the body’s balance, resulting in hormonal imbalance and brain changes. In this study, we investigated the effects of Withania somnifera (Ashwagandha) extract on depression, neurobehavior, and hippocampal changes in model mice exposed to stress. Using an excessive restraint stress-induced depression model, we measured the behavioral changes and the levels of brain-derived neurotrophic factor (BDNF) and antioxidant genes in five groups: control, stress, low-dose W. somniferous extract (20 mg/kg/day), high-dose W. somniferous extract (40 mg/kg/day), and L-theanine (50 mg/kg/day, positive control). Stressed mice showed poorer performance in the open field and elevated plus maze tests compared with the control group. The impaired performance was restored following W. somniferous extract administration. In addition, W. somniferous extract restored the decreased expression of BDNF in the hippocampus caused by restraint stress, improved the balance of stress hormones (i.e., cortisol, dopamine, and norepinephrine), and also regulated BDNF, inflammatory genes, and antioxidant genes in brain tissue. Therefore, W. somniferous extract can induce antidepressant and anti-stress effects by maintaining brain BDNF expression and preventing hippocampal tissue alterations caused by restraint stress. Full article

This study presents a systematic review regarding the use of augmented reality and virtual reality in exergaming by analyzing studies published during 2010–2025. This study focuses on providing an overview of the field and on examining and synthesizing the findings of related studies to identify the contexts, applications, and domains in which extended reality exergames are being used and the related implications, benefits, and challenges. Based on the results, augmented reality and virtual reality exergames offer immersive, enjoyable, engaging, and personalized experiences that support physical, cognitive, and emotional well-being, while enhancing physical performance, cognitive functioning, psychological outcomes, and mental health. They promote motivation, active lifestyles, and sustainable health behaviors across diverse populations, including older adults, individuals with disabilities, and neurological groups, as well as the general adult and youth populations. Although emphasis is placed on their use in physical and cognitive rehabilitation and treatment, they also show great potential to be effectively used in different domains, including education. Among the technologies examined, the significant majority of studies focused on virtual reality exergames, a limited number of studies involved augmented reality, and only a few studies examined mixed reality, extended reality, and the metaverse. Finally, nine main topics were identified through topic modeling, providing a clear representation of the core themes within the literature. Full article

This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching. Full article

Background: Gender dysphoria refers to the psychological distress arising from a mismatch between an individual’s physical embodiment and their internal sense of gender. Gender-affirming mastectomies can be a pivotal component of gender affirmation for transgender, non-binary, and gender expansive individuals assigned female at birth. The impact of obesity on the outcomes of gender-affirming mastectomies has yet to be fully defined. Methods: A retrospective review of 205 gender-affirming mastectomies performed by the senior author was conducted. Patients were categorized into obese (BMI ≥ 30) and non-obese groups. Baseline characteristics, intraoperative variables, and complication rates were compared. Univariate and multivariate models were performed to evaluate the association between obesity and postoperative complications. Results: Obese patients had higher mean resection weights and liposuction volumes (p < 0.001). Significant differences were observed in the prevalence of fibromyalgia, prior chest surgeries, and hormone therapy usage (p = 0.002, 0.002, and 0.03, respectively). However, no statistically significant differences were found in overall complication rates between obese and non-obese groups in the univariate or multivariate analyses. Conclusions: Our study suggests that obesity is not a significant risk factor for complications in gender-affirming mastectomies patients. The varying impact of high BMI and obesity on surgical outcomes in different surgical fields highlights the importance of patient-centered care and a holistic and individual approach for each patient. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

Background/Objectives: Academic stress is a major factor affecting university students’ psychological well-being and overall functioning. This study examined whether three active coping strategies—positive reappraisal, social support seeking, and strategic planning—mediate the relationship between academic stressors and self-reported stress responses. Methods: A quantitative, cross-sectional, non-experimental design was employed. The sample comprised 1014 students from the University of Extremadura (M_age = 20.56, SD = 3.50). Three subscales of the Academic Stress Questionnaire (CEA) were administered: Academic Stressors (E-CEA), Stress Responses (R-CEA), and Coping Strategies (A-CEA). Descriptive statistics, correlation analyses, and a multiple mediation model using structural equation modeling (SEM) tested direct and indirect effects, controlling for gender, study year, and academic field. Results: (1) Academic stressors were inversely related to positive reappraisal (β = −0.34, p < 0.001), planning (β = −0.12, p < 0.001), and social support seeking (β = −0.09, p < 0.01). (2) All three coping strategies were significantly associated with fewer stress symptoms, with positive reappraisal showing the strongest effect (β = −0.13, p < 0.001), followed by social support seeking (β = −0.06, p < 0.05) and planning (β = −0.03, p < 0.05). (3) Stressors had a strong positive direct effect on stress responses (β = 0.54, p < 0.001). (4) Coping strategies partially mediated the stressor–symptom link (total indirect effect: β = 0.12, p < 0.001, 95% CI [0.08, 0.16]). Conclusions: Active coping partially buffers the negative effects of academic stressors on perceived distress. Findings underscore the importance of enhancing students’ coping skills and implementing institutional policies that reduce structural stress and support psychological well-being. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

This study examines how employees perceive technology-based demands during the digital transformation process and how these perceptions affect job performance. The research utilized data obtained from 388 experts in the accounting and financial reporting profession, a knowledge-intensive field that heavily employs new technologies (e.g., ERP systems, digital audit tools). The data collected through a convenience sampling method was analyzed using SPSS 27 and SmartPLS 4 software. The findings reveal that the direct effect of technostress on job performance is not significant; however, this stress indirectly contributes to performance through techno-eustress. In this study, techno-eustress refers to the cognitive appraisal of technology-related demands as development-enhancing challenges rather than threats. This concept is theoretically grounded in the broader eustress framework, which views stressors as potentially motivating and growth-promoting when positively interpreted. The model is based on Cognitive Evaluation Theory, the Job Demands–Resources Model, and Self-Determination Theory. This study demonstrates that digital transformation can promote not only operational improvements but also organizational resilience by enhancing employees’ psychological resources and adaptive capacities. By highlighting the mediating role of techno-eustress, this research offers a nuanced perspective on how human-centered cognitive mechanisms can strategically support performance and sustainability in the face of technological disruption—an increasingly relevant area for organizations striving to thrive amid uncertainty. Full article

The development of a Spanish musical profile questionnaire, designed to assess both objective aspects of musical engagement and psychological dimensions, represents a significant advancement in the fields of music psychology and music education. Grounded in prior research and created by experts in music education, this instrument offers a comprehensive portrait of musicians by capturing their training, experience, and psychological characteristics. A total of 261 musicians, ranging in age from 16 to 61 years, were recruited through incidental sampling to complete the questionnaire. The final version encompasses both objective indicators of musical dedication and a psychological profile scale. The psychometric evaluation of the scale supported a robust three-factor model comprising “Musical Self-Perception”, “Personal Cost of Music”, and “Existential Contribution of Music”. The 10-item scale demonstrated acceptable internal consistency, along with strong criterion and convergent validity, affirming the instrument’s reliability. The discussion regarding the inclusion or exclusion of items related to theoretical content reflects an evidence-based approach aimed at continuous refinement of the tool. Furthermore, the identification of future directions, including exploration of additional psychological dimensions and potential adaptations for diverse cultural or educational contexts, underscores a forward-looking commitment to the ongoing development and applicability of the questionnaire. Full article

Organizational behavior examines the interactions of individuals and groups within businesses, while human resource management (HRM) focuses on enhancing workforce efficiency through recruitment, training, and employee relations. The success of an organization depends on the relationship between employee engagement and performance, as engaged individuals enhance productivity and innovation. This study aims to conduct a comprehensive bibliometric analysis of the academic research on the relationship between artificial intelligence (AI), employee engagement, and performance. This study highlights trends, countries, sources, and keywords in this field. The authors analyzed 11,291 articles in the first phase, 42,358 articles were analyzed in the second phase, and 606 articles were analyzed in the third phase. This study highlights the growth of the research in this area and identifies the most productive years and regional contributions. The citation analysis is used to identify the relevant research and renowned authors. This study also addresses ethical concerns related to the implementation of artificial intelligence (AI) in the workplace. This study indicates theme variations among national contributions, highlighting differing socio-cultural and theoretical perspectives on AI adoption in HRM, from behavioral leadership models to efficiency-oriented frameworks. In summary, this bibliometric study provides valuable insights into the evolution of the research topics related to AI’s impact on employee engagement and productivity, spanning multiple disciplines, such as psychology, organizational behavior, and computer science. It is relevant for the researchers, practitioners, and businesses interested in understanding and utilizing AI in the workplace. Full article

With the continual expansion of global urbanization and population growth, urban energy demands have intensified, and anthropogenic activities have precipitated profound shifts in the global climate. These climatic changes directly alter urban environmental conditions, which in turn exert indirect effects on human physiological function. Consequently, the comfort of outdoor community environments has emerged as a critical metric for assessing the quality of human habitation. Although existing studies have focused on improving singular environmental factors—such as wind or thermal comfort—they often lack an integrated, multi-factor coupling mechanism, and adaptive strategy systems tailored to hot-summer, cold-winter regions remain underdeveloped. This study examines the Minhe Community in Qian’an City to develop a performance evaluation framework for outdoor spaces grounded in subjective comfort and to close the loop from theoretical formulation to empirical validation via an interdisciplinary approach. We first synthesized 25 environmental factors across eight categories—including wind, thermal, and lighting parameters—and applied the Analytic Hierarchy Process (AHP) to establish factor weights, thereby constructing a comprehensive model that encompasses both physiological and psychological requirements. Field surveys, meteorological data collection, and ENVI-met (V5.1.1) microclimate simulations revealed pronounced issues in the community’s wind distribution, thermal comfort, and acoustic environment. In response, we proposed adaptive interventions—such as stratified vegetation design and permeable pavement installations—and validated their efficacy through further simulation. Post-optimization, the community’s overall comfort score increased from 4.64 to 5.62, corresponding to an efficiency improvement of 21.3%. The innovative contributions of this research are threefold: (1) transcending the limitations of single-factor analyses by establishing a multi-dimensional, coupled evaluation framework; (2) integrating AHP with ENVI-met simulation to realize a fully quantified “evaluation–simulation–optimization” workflow; and (3) proposing adaptive strategies with broad applicability for the retrofit of communities in hot-summer, cold-winter climates, thereby offering a practical technical pathway for urban microclimate enhancement. Full article

Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique promisingly used to treat neurological and psychological disorders. Nevertheless, the inter-subject heterogeneity in its after-effects frequently limits its efficacy. This can be attributed to fixed-dose methods, which do not consider inter-subject anatomical variations. This work attempts to overcome this constraint by examining the effects of age and anatomical features, including the volume of cerebrospinal fluid (CSF), the thickness of the skull, and the composition of brain tissue, on electric field distribution and cortical excitability. A computational approach was used to map the electric field distribution over the brain tissues of realistic head models reconstructed from MRI images of twenty-three subjects, including adults and children of both genders. Significant negative correlations (p < 0.05) were found in the data between the maximum electric field strength and anatomical variable parameters. Furthermore, this study showed that the percentage of brain tissue exposed to an electric field amplitude above a pre-defined threshold (i.e., 0.227 V/m) was the main factor influencing the responsiveness to tDCS. In the end, the research suggests multiple regression models as useful tool to predict subjects’ responsiveness and to support a personalized approach that tailors the injected current to the morphology of the patient. Full article