Search Results (106)

Maintaining good indoor air quality (IAQ) is essential for student health, cognitive performance, and overall well-being. Traditional ventilation strategies, particularly constant air volume systems and manual window operation, often fail to maintain optimal IAQ while simultaneously increasing building energy consumption. In response, smart ventilation systems have emerged as a promising alternative capable of dynamically modulating airflow based on occupancy patterns and real-time pollutant levels. This study presents a systematic review of fourteen carefully selected peer-reviewed studies (2015–2025) that represent the most recent and methodologically robust research on smart ventilation applications in school environments across diverse climatic conditions. The selected studies encompass experimental, simulation-based, and hybrid methodologies, and classify control strategies into demand-controlled, temperature-adaptive, occupancy-based, AI-enhanced, and building management system (BMS)-integrated approaches. Collectively, the findings demonstrate measurable improvements in IAQ indicators (e.g., carbon dioxide (CO₂), particulate matter (PM_2.5), ozone (O₃), and volatile organic compounds (VOCs)) and significant energy savings, in some cases exceeding 60%, while also identifying system vulnerabilities such as fault sensitivity, short monitoring durations, and limited long-term validation. Importantly, the review reveals critical geographic and climatic research gaps, particularly in hot–arid regions where ventilation-related cooling demand is substantial, as well as limited long-term assessments in cold climates. Furthermore, although smart ventilation systems perform effectively under controlled conditions, insufficient real-world verification, user interaction analysis, and climate-specific optimization constrain broader implementation. Addressing these gaps through climate-dependent performance evaluation and long-term operational studies is essential to unlocking the full potential of smart ventilation systems in delivering healthier, energy-efficient classrooms. Full article

Power electronics is a cornerstone of modern electrical engineering, underpinning technologies from renewable energy systems to electric vehicles. Traditional lecture-based methods often emphasise rote learning and procedural skills but provide limited opportunities for higher-order thinking or experiential practice. To meet the needs of Generation Z learners and align with industry expectations, new pedagogical frameworks are required that combine cognitive rigour with authentic, technology-enhanced learning. This study introduces a Higher-Order Thinking Skills with Real-Time Simulation pedagogical framework to enhance learning outcomes in diploma-level power electronics. A quasi-experimental mixed-methods design was applied with 40 students divided into control and experimental groups. The control group received lectures, while the experimental group engaged with the HOTS–RTS framework across four topics: rectifiers, converters, inverters, and applications. Pre- and post-tests, Likert-scale surveys, reflections, and instructor observations provided data for both quantitative (t-tests, effect sizes) and qualitative thematic analysis. The experimental group achieved higher post-test gains (20.1 vs 9.5 points), with a large effect size (d = 1.9). Surveys revealed that 65 per cent of respondents rated RTS as highly effective, and Likert scores improved by 1 or more points in HOTS-related skills. Reflections emphasised clarity, confidence, and collaboration. HOTS–RTS effectively integrates cognitive rigour with real-time practice, aligning with STREAMS principles and equipping learners with next-generation industry competencies. Full article

Artificial intelligence (AI) can help students accelerate assignment completion, but it may also foster cognitive outsourcing and learning detached from authentic contexts. This paper presents E3-HOT, a conceptual framework that leverages embodied intelligence to sustain learners’ cognitive agency and higher-order thinking for sustainable learning, aligned with SDG 4 (Sustainable Development Goal 4) and its emphasis on inclusive and equitable quality education and lifelong learning. Using an iterative conceptual synthesis, we distill three embodied pathways—situational embedding, embodied participation, and cognitive creation—and translate them into a practical system design with a three-module E3 core. It includes a virtual–real integrated learning environment for rich scenarios, embodied interaction for action and sensing, and an intelligent core that provides bounded and teacher-controlled support. To facilitate equitable adoption across resource-diverse settings, we specify multi-fidelity enactment options and an auditable set of evidence artifacts for subsequent expert review and future validation studies. We further provide an illustrative university human–AI design project that outlines a week-by-week workflow and corresponding evidence plan, presented as a worked example rather than a report of an implemented study. E3-HOT offers a traceable design-and-evidence blueprint without claiming measured learning gains. Full article

Background/Objectives: The perceived-time-based model posits that time perception is a critical factor in intertemporal decision-making; however, the mechanisms underlying this influence remain inadequately explored. Despite growing behavioral and neuroimaging findings, no study has directly compared the temporal neural dynamics of individuals who overestimate or underestimate time during intertemporal choices. Methods: This study screened participants with time overestimation or underestimation to examine differences in their electroencephalogram (EEG) activity during an intertemporal choice task. Results: Behavioral results revealed that the time overestimation group selected the smaller-sooner (SS) option at a higher rate than the time underestimation group, exhibiting a myopic decision-making tendency. EEG results revealed that, compared to the time overestimation group, the time underestimation group exhibited a more pronounced N2 amplitude, an enhanced P300 amplitude, and greater beta band oscillations. Within the time overestimation group, the larger-later (LL) option elicited a more negative N2 amplitude than the SS option. Conversely, in the time underestimation group, the LL option elicited a more positive P300 amplitude than the SS option. Conclusions: The results indicate that, during intertemporal decision-making, the time overestimation group experienced more conflict in the LL option, demonstrating lower cognitive control and fewer cognitive resources. This tendency may be driven by a hot system, resulting in more impulsive choices. Full article

Dementia is an increasing public health challenge, yet scalable methods for early risk detection using non-clinical data remain limited. This study develops and evaluates interpretable machine learning models to predict dementia risk among older adults using nationally representative longitudinal data. Data were sourced from the National Health and Aging Trends Study (NHATS, 2011–2022) and included 5984 community-dwelling U.S. adults aged 65 and older who were dementia-free at baseline. Dementia onset was identified using the validated NHATS classification algorithm based on cognitive assessments, proxy reports, and physician diagnoses. After data preprocessing and feature engineering, missing values in continuous variables were imputed with k-nearest neighbors, while categorical variables were handled via one-hot encoding and mode-based imputation. Five supervised machine learning algorithms were trained and evaluated through stratified cross-validation, using performance metrics that account for class imbalance. Among these models, XGBoost showed the strongest overall performance, achieving the highest classification accuracy (0.881 ± 0.004), the lowest Brier score (0.094 ± 0.002), and the highest ROC–AUC (0.823 ± 0.005), with RF showing comparable results. Explainable AI analyses with SHapley Additive exPlanations (SHAP) consistently identified digital technology use, outdoor activity frequency, and social network size as the most influential predictors across models. These findings indicate that interpretable machine learning based on non-clinical, modifiable behavioral and social factors can support scalable, prevention-focused dementia risk assessment and inform prevention-oriented strategies that promote digital inclusion and social engagement among older adults. Full article

Intelligent Clinical Decision Support Systems (ICDSS) are increasingly integrated into healthcare settings to enhance clinical decision-making, efficiency, and patient safety. Despite advances in artificial intelligence-enabled decision support, ICDSS adoption remains inconsistent, particularly in complex clinical environments where professional autonomy, workflow alignment, and accountability are critical. This study examines healthcare providers’ perspectives on ICDSS through a grounded theory approach informed by established Information Systems theories, including the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and the Human-Organization-Technology fit (HOT-fit) framework. Semi-structured interviews were conducted with 11 providers within a large, integrated healthcare organization, and data were analyzed using open, axial, and selective coding. The findings reveal three interrelated dimensions shaping ICDSS use: provider experience, clinical utility, and adaptation. While ICDSS were perceived as valuable for improving efficiency, supporting treatment decisions, and enhancing patient safety, their adoption was constrained by cognitive overload, workflow misalignment, data quality concerns, and perceived threats to professional autonomy. Trust, explainability, and workflow fit emerged as central mechanisms influencing selective use rather than full adoption. By grounding provider perspectives within a sociotechnical lens, this study extends existing IS theories to the context of AI-enabled clinical decision support and offers empirically grounded insights for designing ICDSS that better align with clinical practice. Full article

Children and adolescents in hot and humid climates face increasing health risks due to climate change. Although the concept of Health-Promoting Schools (HPSs) is widely recognized, a systematic framework that integrates climate adaptability, child-specific needs, and multidimensional environmental design is still lacking. To address this gap, this study conducted a systematic literature review of 89 publications with three objectives: (1) synthesize research from the past decade on the impact of school physical environments on the health and academic performance of children and adolescents; (2) develop an evidence-based synergistic design framework with a categorized indicator system; and (3) integrate qualitative and quantitative evidence on how these indicators influence different health dimensions. The resulting framework emphasizes multidimensional, synergistic optimization and provides climate-responsive design strategies tailored to educational settings in hot and humid regions. By offering a theory-to-practice pathway, the framework complements existing healthy building guidelines for K–12 schools and supports designers and policymakers in creating environments that enhance thermal resilience, cognitive performance, and holistic child development. Full article

Background/Objectives: Executive functions (EFs) and Theory of Mind (ToM) are often compromised in children with Specific Learning Disorders (SLD). Although evidence highlights the malleability of EF, studies have yet to investigate whether school-based interventions can enhance both cool and hot EF domains and support ToM development in this population. This study evaluated the effectiveness of a structured, classroom-based EF training program in improving cool EF, hot EF, and ToM in children with SLD. Methods: Forty students with SLD (aged 8–10 years) were allocated to an intervention group (n = 24) or a passive control group (n = 16). The program was delivered in small groups during regular school hours over 6–9 weeks (18 sessions). Pre- and post-intervention assessments measured cool EFs (working memory, planning, cognitive flexibility, inhibition), hot EFs (affective decision-making, delay of gratification), and ToM (false belief understanding, mental state/emotion recognition). Results: The intervention group showed significant within-group improvements in working memory, planning, and cognitive flexibility, whereas the control group showed no significant changes. Between-group comparisons revealed significant effects for working memory, planning, and ToM mental state/emotion recognition, with medium-to-large effect sizes. No significant group differences were found for hot EFs or ToM false belief understanding. Conclusions: These findings suggest that participation in a structured, school-based EF program is associated with selective improvements in specific cool EF components and one aspect of ToM in children with SLD, supporting the potential value of classroom-based interventions for cognitive and socio-cognitive development. Full article

In vitro and in vivo evidence demonstrates that EGT exerts neuroprotective effects through multiple mechanisms: scavenging reactive oxygen species, suppressing neuroinflammatory cytokines (TNF-α, IL-1β, IL-6), activating Nrf2 antioxidant pathways, and preserving mitochondrial integrity. Low blood EGT levels correlate with cognitive decline and dementia, supporting its role as a conditionally essential micronutrient for healthy aging. Mushroom by-products retain EGT concentrations comparable to commercial fruiting bodies, making them viable sources for dietary supplements and functional foods. Mushroom processing generates substantial residual biomass—including stems, culls, and spent substrate—that represents an underexploited dietary source of ergothioneine (EGT), a naturally occurring antioxidant with exceptional neuroprotective and anti-inflammatory properties. Since humans cannot synthesize EGT endogenously, dietary intake is essential for maintaining neuroprotection against neurodegenerative diseases. This review examines sustainable extraction strategies—including hot-water, ultrasound-assisted, and high-hydrostatic-pressure methods—enabling integration into circular biorefinery systems. Applications in nutraceuticals and pharmaceuticals targeting oxidative stress-related neurodegeneration are highlighted. Despite challenges in standardization and regulatory approval, valorizing mushroom residuals offers a sustainable pathway to increase dietary availability of this neuroprotective antioxidant, supporting both environmental sustainability and therapeutic innovation for neurodegenerative disease prevention. Full article

This study investigates how the traditional Chinese “philosophy of self-cultivation and awakening” (xiu-wu) can be systematically harnessed to foster Higher-Order Thinking Skills (HOTS) among undergraduates. Through historical–philosophical reconstruction and conceptual analysis, the study distills three recurring instructional principles—gradual cultivation (jian-xiu), gradual awakening (jian-wu), and sudden awakening (dun-wu), and their dialectical synthesis, and re-casts them as design parameters for thinking-centered instruction. These principles are then translated into a macro-level instructional metaphor, the Bridge-Building Model, which sequences curricular elements as bridge piers (the teaching process of “gradual cultivation”), bridge deck (student-constructed “an isolated fragments of knowing”), and final closure (holistic knowledge). The model integrates constructivist, behaviorist and intuitive dimensions: repetitive, scaffolded tasks foster behavioral automaticity; guided reflection precipitates incremental insight; and calibrated “epistemic shocks” elicit sudden reorganization of conceptual schemata. The framework clarifies the locus, timing and contingency of each phase while acknowledging the metaphysical indeterminacy of ultimate “holistic” mastery. By translating classical Chinese pedagogical insights into operational design heuristics, the paper offers higher-education instructors a culturally grounded, theoretically coherent blueprint for systematically nurturing HOTS without sacrificing the spontaneity essential to creative cognition. Full article

Purpose: The aim of this review is to provide an overview of the effects of commonly used dietary supplements on soccer performance and to bridge the gap between scientific evidence and their practical application by practitioners working with elite soccer players. Methods: Relevant literature involving dietary supplement use in soccer players was identified through searches of PubMed, EMBASE, Scopus, and Web of Science. Additionally, insights were gathered from a cross-sectional online questionnaire completed by practitioners (nutritionists, physicians, sport scientists, strength and conditioning coaches, and heads of performance) working with first-division men’s teams across five European leagues. Eligible respondents were over 18 years old with >2 years of experience in elite sport. The 20-question survey, designed on Qualtrics and pilot-tested for content validity, covered practitioner background, beliefs about supplementation, and real-world practices. The study was approved by the Ethical Independent Committee in Genoa, Italy (Ref. 2020/12). Results: Among performance-enhancing supplements, caffeine has been shown to improve endurance, sprint performance, power, and cognitive function, while creatine consistently enhances short-duration, high-intensity efforts. Beta-alanine and sodium bicarbonate help reduce the buildup of acidity in muscles during repeated high-intensity exercise, supporting repeated sprint performance. For hydration and endurance support, dietary nitrates improve blood flow and oxygen delivery to muscles, and glycerol enhances fluid retention in hot environments and during compressed match schedules, where players compete in multiple matches within a short recovery window. Regarding recovery aids, protein and tart cherry supplementation have been shown to accelerate recovery, reduce muscle damage, and support training adaptations. Field insights revealed that creatine and caffeine were widely adopted by practitioners (>90%), with protein powders also commonly recommended (>80%). In contrast, beta-alanine, tart cherry, and dietary nitrates were only partially integrated into daily practice (30%, 32%, and 48.5%, respectively), while sodium bicarbonate (24%) and glycerol (10.5%) were used by a minority. Conclusions: Although scientific evidence provides a strong foundation for the efficacy of dietary supplements, their translation into elite soccer practice is shaped by a range of practical factors, including cultural resistance, taste preferences, gastrointestinal side effects, established team routines, and individual player preferences. These findings highlight the importance of targeted education for players and staff, individualized supplementation plans, and close collaboration between nutritionists, coaches, and medical teams. However, our survey did not directly assess reasons for non-implementation. In addition to practical barriers reported by practitioners, unfamiliarity with current evidence likely contributes to this evidence–practice gap. Full article

Enhancing indoor environmental quality while reducing building energy consumption represents a critical challenge for sustainable building design, particularly in hot arid climates where cooling loads dominate energy use. Despite extensive research on green wall systems (GWSs), robust quantitative data on their combined impact on air quality and thermal performance in real-world office environments remains limited. This research quantified the synergistic effects of an active indoor green wall system on key indoor air quality indicators and cooling energy consumption in a contemporary office environment. A comparative field study was conducted over 12 months in two identical office rooms in Dhahran, Saudi Arabia, with one room serving as a control while the other was retrofitted with a modular hydroponic green wall system. High-resolution sensors continuously monitored indoor CO₂, volatile organic compounds via photoionization detection (VOC_PID; isobutylene-equivalent), and PM2.5 concentrations, alongside dedicated sub-metering of cooling energy consumption. The green wall system achieved statistically significant improvements across all parameters: 14.1% reduction in CO₂ concentrations during occupied hours, 28.1% reduction in volatile organic compounds, 20.9% reduction in PM2.5, and 13.5% reduction in cooling energy consumption (574.5 kWh annually). Economic analysis indicated financial viability (2.0-year payback; benefit–cost ratio 3.0; 15-year net present value SAR 31,865). Productivity-related benefits were valued from published relationships rather than measured in this study; base-case viability remained strictly positive in energy-only and conservative sensitivity scenarios. Strong correlations were established between evapotranspiration rates and cooling benefits (r = 0.734), with peak performance during summer months reaching 17.1% energy savings. Active indoor GWSs effectively function as multifunctional strategies, delivering simultaneous air quality improvements and measurable cooling energy reductions through evapotranspiration-mediated mechanisms, supporting their integration into sustainable building design practices. Full article

Menopause is a natural phase in a woman’s life marked by the cessation of menstruation, typically accompanied by hormonal fluctuations that have significant impacts on physical and mental health. While much attention has been given to the physical symptoms of menopause, such as hot flashes and osteoporosis, the neurocognitive consequences of hormonal fluctuations during the menopausal transition and the subsequent sustained estrogen loss after menopause have received less focus. Estrogen receptors (ERs), specifically ERα and ERβ, play a critical role in maintaining brain health, influencing mood, memory, and cognition. This review explores the connection between estrogen receptor signaling and mental health during menopause, focusing on mood disorders such as depression and anxiety, as well as cognitive decline and dementia. We discuss the molecular mechanisms by which ERs modulate brain function, including their effects on neuroplasticity, neurotransmitter systems, and gene expression. The review also examines current clinical approaches to managing menopausal cognitive and mental health issues, including hormone replacement therapy and selective ER modulators, while emphasizing the need for further research into alternative therapies and individualized treatments. The importance of estrogen receptors in the menopausal brain and their potential as therapeutic targets is critically evaluated, aiming to shed light on this often-overlooked aspect of menopause and aging. Full article

We investigate whether a belief in trend continuation, often associated with the so-called “hot-hand effect,” can be endogenously triggered by personal performance feedback in a controlled financial experiment. Participants allocated funds across assets with randomly generated prices, under conditions of known probabilities and varying levels of risk. In a two-stage setup, participants were first exposed to random price sequences to learn the task and potentially develop perceptions of personal success. They then faced additional price paths under incentivized conditions. Our findings show that participants initially increased purchases following gains—consistent with a feedback-driven belief in momentum—but this pattern faded over time. When facing sustained losses, loss aversion dominated decision-making, overriding early optimism. These results highlight how cognitive heuristics and emotional biases interact dynamically, suggesting that belief in trend continuation is context-sensitive and constrained by the reluctance to realize losses. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article