Search Results (207)

We present WM-Classroom v1.0, a pedagogical multi-species agent-based model (ABM) designed for educational purposes in predator–prey–harvest systems. The model embeds a predator, two prey breeds, and human harvesters on a homogeneous 50 × 50 grid with weekly time steps, implementing random movement, abstract energetics, prey consumption, reproduction, legal harvest with species-specific cut-offs and seasons, optional predator control, and a poaching switch. After basic technical checks (energetic calibration, prey composition, herbivore viability), we explore the consistency of the model under illustrative scenarios including no hunting, single-prey harvest, hunter-density and season-length gradients, predator removal, and poaching. In the no-hunting baseline (n = 100), mean end-of-run abundances were 22 deer, 159 boar, and 45 wolves, with limited extinction events. Deer-only harvest often drove deer to very low end-of-run counts (mean 1–16) with extinctions in 2–7/10 replicates across cut-offs, whereas boar-only harvest showed higher persistence (mean 11–74) and boar extinctions occurred only at the lowest cut-off (3/10). Increasing hunter numbers or season length depressed prey and could indirectly reduce wolves via prey depletion. Legal predator control reduced predators as designed, while poaching had little effect under the implemented rules. Because interaction and prey-choice rules are simplified for transparency, outcomes should be interpreted as conditional on model assumptions. WM-Classroom v1.0 provides a didactic sandbox for courses, professional training, and outreach, with extensions (habitat heterogeneity, age/sex structure, probabilistic diet/kill success, and calibration/validation) outlined for future versions. Full article

Population ageing has led to a rapid increase in the number of individuals aged 80 and above, yet empirical evidence on the determinants of quality of life among the oldest-old remains limited. This study investigates the socioeconomic, lifestyle, and care-related factors associated with functional independence at very advanced ages using harmonized cross-national data from the Survey of Health, Ageing and Retirement in Europe (SHARE). Focusing on individuals aged 80 and above, we estimate logistic regression models to examine the probability of experiencing limitations in activities of daily living (ADL). The results reveal a persistent socioeconomic gradient in functional health: educational attainment is significantly associated with lower odds of ADL limitations, even after controlling for age, gender, physical functioning, living arrangements, and country fixed effects. Preserved physical functioning, proxied by the absence of walking difficulties, emerges as a strong protective factor against functional dependency. By contrast, institutional residence does not exhibit an independent association with ADL limitations once individual characteristics are taken into account. These findings demonstrate that functional independence among the oldest-old reflects long-term life-course resources and lifestyle-related capacities rather than late-life care settings alone. Policies aimed at promoting successful ageing should therefore adopt a life-course perspective, emphasizing education, health literacy, and the maintenance of physical functioning to enhance quality of life at very advanced ages. Full article

As a primary economic engine and strategic region in China, the development of the green building industry in the Yangtze River Economic Belt (YREB) holds demonstrative significance for the low-carbon transition of the country’s construction sector. Utilizing panel data from 11 provinces and municipalities within the YREB during 2012–2022, this study constructs a comprehensive evaluation index system to measure the coupling coordination degree (CCD) between the green building industry and the development environment. The spatio-temporal evolution of the CCD is analyzed using methods including kernel density estimation, the Dagum Gini coefficient, spatial autocorrelation, and standard deviational ellipse. A fixed-effects model is further employed to identify its influencing factors. The results show that (1) both the green building industry and its development environment in the YREB exhibited upward trends, with the gap between them gradually narrowing. (2) The CCD across provinces and municipalities showed an overall upward trend, characterized by simultaneous “overall improvement” and “internal gradient differentiation” in spatio-temporal distribution, and displayed a spatial pattern of “higher values in the east and lower in the west.” (3) Urbanization level, government regulation, technological innovation, and consumption capacity exerted significant positive effects on the CCD, whereas the influence of education level and public environmental awareness remained insignificant. This study provides insights for formulating differentiated regional policies and optimizing the development environment for the green building industry. Full article

Green space equity is increasingly recognized as a critical environmental condition for healthy aging, yet existing research often overlooks how different green space attributes—accessibility and diversity—are associated with distinct dimensions of older adults’ health. Limited attention has been paid to their nonlinear threshold effects or to the social pathways through which green spaces influence health outcomes. Using the United States county-level panel data from 2020 to 2023, this study integrates fixed-effects models, Extreme Gradient Boosting (XGBoost), and mediation analysis to examine the associations between green accessibility measured by the Two-Step Floating Catchment Area (2SFCA) method, and green diversity measured by the Shannon Index, on the general, physical, and mental health of older adults. Findings indicate that (1) higher green accessibility is associated with better general health, whereas green diversity shows a stronger association with physical health, reflecting its link to more heterogeneous ecosystem service environments. (2) Green accessibility demonstrates the threshold effect, in which the strength of association with health becomes steeper once accessibility approaches higher levels. (3) Green space equity is linked to health partly through social structures. Education clustering and marital stability mediate the associations with general health, while mental health appears to depend more on the social interaction opportunities embedded within green environments than on their physical attributes alone. The study proposes an integrated “physical environment–social structure–health outcome” framework and a threshold-oriented spatial intervention strategy, highlighting the need to prioritize improvements in green accessibility in underserved areas and prioritizing green diversity and age-friendly social functions where accessibility is already high. These findings offer evidence for designing inclusive, health-oriented urban environments for aging populations. Full article

Background/Objectives: Family caregivers of individuals with dementia frequently experience substantial psychological distress, yet their emotional responses are heterogeneous. Depression, anxiety and psychological well-being may co-occur in distinct patterns, and socio-economic resources such as education and income are often hypothesized to buffer caregiver distress. This study aimed to identify latent psychological profiles among dementia caregivers and to examine whether education and income moderate the association between affective symptoms and well-being. Methods: A cross-sectional study was conducted with 73 family caregivers of dementia patients attending the Neurology–Psychiatry Department of C.F.2 Clinical Hospital, Bucharest (November 2023–April 2024). Participants completed the PHQ-9 (depression), the COVI Anxiety Scale and Ryff’s Psychological Well-Being Scales. Care recipients’ cognitive status was extracted from medical records using the MMSE. Gaussian Mixture Modeling was used for latent profile analysis (LPA). Between-profile differences were examined using one-way ANOVAs and Tukey post-hoc tests and Pearson correlations were used to assess associations between affective symptoms and psychological well-being, and examined whether education and income were associated with profile membership and psychological well-being. Results: LPA supported a three-profile solution: (1) lower depressive symptoms with moderate anxiety (33%), (2) severe combined depression and anxiety (18%) and (3) moderately severe depression with severe anxiety (49%). Profiles differed significantly in depressive symptom severity, whereas anxiety severity did not differ significantly across profiles. Caregivers in Profile 3 (moderately severe depression–severe anxiety) reported significantly higher overall psychological well-being than those in Profile 1 (moderate depression–moderate anxiety). In contrast, caregivers in Profile 2 (severe depression–severe anxiety), who exhibited the highest affective symptom burden, showed intermediate levels of overall well-being, with comparatively lower scores on specific dimensions such as purpose in life. Depressive symptoms were weakly but significantly associated with autonomy and self-acceptance, whereas anxiety symptoms showed no significant associations with psychological well-being. Education level and household income were not significantly associated with profile membership or psychological well-being. Conclusions: Family caregivers of individuals with dementia can be meaningfully described as forming three exploratory psychological profiles characterized by different configurations of depressive and anxiety symptoms. These findings indicate that caregiver distress does not follow a simple severity gradient and that psychological well-being is not solely determined by symptom burden. Socio-economic characteristics did not account for differences in caregiver adjustment, underscoring the importance of individualized psychological assessment and tailored interventions to support caregiver mental health. Full article

The parameter extraction of proton exchange membrane fuel cells (PEMFCs) has been an active area of study over the past few years, relying on metaheuristic optimizers and experimental datasets to achieve accurate current/voltage (I/V) curves. This work develops a mirage search optimizer (MSO) to precisely estimate the PEMFC model parameters. The MSO employs two search techniques based on the physical phenomena of light bending caused by atmospheric refractive index gradients: a superior mirage for global exploration and an inferior mirage for local exploitation. The MSO employs optical physics to direct search behavior, in contrast to conventional optimization approaches, allowing for a dynamic balance between exploration and exploitation. Convergence efficiency is increased by its iteration-dependent control and fitness-based influence. Using two common PEMFC modules, a comparison study with previously published methodologies and new, recently developed optimizers—the Educational Competition Optimizer (ECO), basketball team optimization (BTO), the fungal growth optimizer (FGO), and the naked mole rat optimizer (NMRO)—was conducted to evaluate the proposed MSO for parameter identification. Furthermore, the two models were tested under various temperatures and pressures. For the three examples studied, the MSO achieved the best sum of squared errors (SSE) values with an intriguing overall standard deviation (STD). It is undeniable that the STD and cropped SSE values, among other difficult techniques, are quite competitive and display the fastest convergence. According to the MSO, the BCS 500W, Ballard Mark V, and Modular SR-12 each have MSO values of 0.011697781, 0.852056, and 1.42098181379214 × 10⁻⁴, respectively. Additionally, the comparison results demonstrate that the proposed MSO can be successfully used to quickly and accurately define the PEMFC model. Full article

This study investigates the drivers of green production practices among forest-cultivated ginseng growers in Jilin Province, China, by integrating the Theory of Planned Behavior (TPB) and the Technology–Organization–Environment (TOE) framework. Based on survey data from 369 households in the major production regions of Tonghua, Baishan, and Yanbian areas, an Ordered Probit model and a Light Gradient Boosting Machine (LGBM) algorithm are employed for cross-validation. The results indicate that growers’ cognitive traits (awareness of green production standards and ecological/quality safety) and willingness (acceptance of price premiums for green products) are the most stable and critical drivers. Policy incentives (e.g., certification subsidies and outreach) not only directly promote green practices but also exhibit synergistic effects through interactions with resource endowments and psychological cognition. Regional heterogeneity is evident: Tonghua shows policy–market co-drive, Baishan is dominated by ecological constraints and safeguard policies, while Yanbian relies more on education and individual resources. Accordingly, this study proposes a differentiated policy system based on diagnosis–intervention–evaluation to support the high-quality development of forest-cultivated ginseng industry and ecological-economic synergies. Full article

Student dropout prediction remains critical in higher education, where timely identification enables effective interventions. Learning Management Systems (LMSs) capture rich temporal data reflecting student behavioral evolution, yet existing approaches underutilize this sequential information. Traditional machine learning methods aggregate behavioral data into static features, discarding dynamic patterns that distinguish successful from at-risk students. While Long Short-Term Memory (LSTM) networks model sequences, they assume discrete time steps and struggle with irregular LMS observation intervals. To address these limitations, we introduce Completion-aware Risk Neural Ordinary Differential Equations (CR-NODE), integrating continuous-time dynamics with completion-focused features for early dropout prediction. CR-NODE employs Neural ODEs to model student behavioral evolution through continuous differential equations, naturally accommodating irregular observation patterns. Additionally, we engineer three completion-focused features: completion rate, early warning score, and engagement variability, derived from root cause analysis. Evaluated on Canvas LMS data from 100,878 enrollments across 89,734 temporal sequences, CR-NODE achieves Macro F1 of 0.8747, significantly outperforming LSTM (0.8123), Extreme Gradient Boosting (XGBoost) (0.8300), and basic Neural ODE (0.8682). McNemar’s test confirms statistical significance ($p<0.0001$). Cross-dataset validation on the Open University Learning Analytics Dataset (OULAD) demonstrates generalizability, achieving 84.44% accuracy versus state-of-the-art LSTM (83.41%). To support transparent decision-making, SHapley Additive exPlanations (SHAP) analysis reveals completion patterns as the primary prediction drivers. Full article

Background: Traditional regression explains only part of the variation in forced expiratory volume in one second (FEV₁). Machine learning (ML) methods may capture nonlinear patterns beyond linear assumptions. Methods: We analyzed 23,943 non-smoking Taiwanese men aged 45–55 years from the MJ Health Screening Cohort. Random Forest (RF), Stochastic Gradient Boosting (SGB), and XGBoost were compared with multiple linear regression (MLR) using repeated train–test splits. Model performance was evaluated with RMSE, RAE, RRSE, and SMAPE. Shapley additive explanations (SHAP) were used to interpret variable effects. Results: ML models achieved slightly lower prediction errors than MLR. The most influential predictors across models were lactate dehydrogenase (LDH), body weight (BW), education level, leukocyte count, total bilirubin, and sport area. SHAP indicated negative effects of LDH and leukocyte count and positive associations for BW, bilirubin, education, and physical activity. Conclusions: ML approaches provided modest accuracy gains and clearer interpretability compared with MLR. Biochemical and lifestyle factors—including LDH, BW, education, inflammation markers, and physical activity—contribute meaningfully to FEV₁ among healthy middle-aged men. Full article

Background/Objectives: Depression has increased substantially in Korea following the COVID-19 pandemic, with prevalence reaching 7.3% in 2023, the highest level in a decade, raising urgent concerns about widening mental health disparities. Although physical activity (PA) is associated with reduced depressive symptoms, nationally representative post-pandemic evidence from Korean adults remains limited. This study descriptively examined patterns of PA participation and depressive symptoms across key sociodemographic groups using 2023 Korea Community Health Survey (KCHS) data. Methods: We analyzed cross-sectional data from 228,249 adults aged ≥19 years in the 2023 KCHS. Depressive symptoms were measured using nine PHQ-9 items (1–4 on Likert scale). PA was assessed as the number of days per week (0–7) of moderate (MPA) and vigorous (VPA) physical activity according to KCHS operational definitions. All analyses incorporated complex survey features (strata, clusters, weights). Group differences were examined using design-corrected t-tests and ANOVA. Results: Women, adults aged 60 years or older, bereaved individuals, and those with lower educational attainment reported higher depressive symptom levels (p < 0.001). PA participation was higher among men, younger adults, married individuals, and those with higher education. Depressive symptom scores decreased with increasing PA frequency, with the lowest levels observed among adults active 5–7 days per week. Although mean differences were modest (0.02–0.12 points on the four-point scale; η² < 0.06), these steady population-level gradients provide meaningful baseline information for understanding post-pandemic mental health patterns in Korea. Conclusions: Although individual-level differences were small (η² < 0.06), the population-level gradients are important for monitoring mental health disparities in post-pandemic Korea. Women, older adults, bereaved individuals, and lower-education groups represent key high-burden populations. Future studies should employ longitudinal designs, objective PA measures, and confounder-adjusted models to clarify mechanisms and directionality, and evaluate the effectiveness of community-based PA interventions. Full article

Background: Household contacts of smear-positive tuberculosis (TB) patients face an elevated risk of infection and disease progression, particularly young children and individuals living in overcrowded households. Despite WHO recommendations for systematic contact screening and provision of TB preventive therapy (TPT), implementation remains suboptimal in high-burden rural areas. This study aimed to develop a practical framework for identifying and prioritizing high-risk families by examining demographic predictors, household clustering, and machine learning-based risk models. Methods: A total of 437 household contacts linked to smear-positive index cases were assessed and classified as high or low risk. Statistical analyses included descriptive measures, χ² tests, Z-tests for age-group differences, and multivariable logistic regression. Household-level vulnerability patterns were explored using network visualizations, clustered heatmaps, and risk-ranking charts. Three machine learning models, logistic regression, random forest, and gradient boosting, were trained using demographic and household variables with 5-fold cross-validation and an 80/20 hold-out test split. Model performance was evaluated using the AUROC, AUPRC, accuracy, F1-score, calibration curves, and decision curve analysis. Results: Of the 437 contacts, 290 (66.4%) were classified as high risk. A younger age was strongly associated with high-risk status (χ² = 16.61, p = 0.005), with children aged 0–4 years being significantly more likely to be in a high-risk category (Z = 2.706). Gender showed no significant association (p = 0.523). Logistic regression identified younger age (aOR = 2.41, 95% CI: 1.48–3.94) and larger household size (aOR = 1.12 per additional member, 95% CI: 1.01–1.25) as independent predictors of the outcome. Visual analytics revealed apparent clustering of high-risk individuals within “hotspot families,” enabling prioritization through composite risk scores. Gradient boosting achieved the strongest performance (AUROC = 0.65; AUPRC = 0.76), with acceptable calibration (Brier score = 0.21) and a positive net clinical benefit in the decision curve analysis. Conclusions: TB risk is highly clustered at the household level, with large families and young children carrying disproportionate vulnerability. Combining demographic risk assessment, household-level visualization, and predictive modeling provides a practical, data-driven approach to prioritizing households during contact investigation. These findings support the WHO’s family-centered strategy and underscore the need to strengthen clinical governance and community-engaged education to optimize TB prevention in resource-limited rural settings. Full article

Background: Academic burnout significantly impacts students’ cognitive and psychological well-being and may result in adverse behavioral changes. An effective and timely detection of burnout in the student population is crucial as it enables educational institutions to mobilize necessary support systems and implement intervention strategies. However, current survey-based detection methods are susceptible to response biases and administrative overhead. This study investigated the feasibility of detecting academic burnout symptoms using machine learning trained exclusively on university records, eliminating reliance on psychological surveys. Methods: We developed models to detect three burnout dimensions—exhaustion, cynicism, and low professional efficacy. Five machine learning algorithms (i.e., logistic regression, support vector machine, naive Bayes, decision tree, and extreme gradient boosting) were trained using features engineered from administrative data. Results: Results demonstrated considerable variability across burnout dimensions. Models achieved the highest performance for exhaustion detection, with logistic regression obtaining an F1 score of 68.4%. Cynicism detection showed moderate performance, while professional efficacy detection has the lowest performance. Conclusions: Our findings showed that automated detection using passively collected university records is feasible for identifying signs of exhaustion and cynicism. The modest performance highlights the challenges of capturing psychological constructs through administrative data alone, providing a foundation for future research in unobtrusive student burnout detection. Full article

The brain and the immune system communicate in many ways and interact directly at neuroimmune interfaces at brain borders, such as hippocampus, choroid plexus, and gateway reflexes. The first part of this review described intercellular communication (synapses, extracellular vesicles, and tunneling nanotubes) during homeostasis and neuroimmunomodulation upon dysfunction. This second part compares spatial orientation, learning, and memory function in both systems. The hippocampus, deep in the medial temporal lobes of the brain, is reported to play a central role in all three functions. Its medial entorhinal cortex contains neuronal spatial cells (place cells, head direction cells, boundary vector cells, and grid cells) that facilitate spatial navigation and allow the construction of cognitive maps. Sensory input (about 100 megabytes per second) via engram neurons and top down and bottom up information processing between the temporal lobes and other lobes of the brain are described to facilitate learning and memory function. Output impulses leave the brain via approximately 1.5 million fibers, which connect to effector organs such as muscles and glands. Spatial orientation in the immune system is described to involve gradients of chemokines, chemokine receptors, and cell adhesion molecules. These facilitate immune cell interactions with other cells and the extracellular matrix, recirculation via lymphatic organs (lymph nodes, thymus, spleen, and bone marrow), and via lymphatic fluid, blood, cerebrospinal fluid, and tissues. Learning in the immune system is summarized to include recognition of exogenous antigens from the outside world as well as endogenous blood-borne antigens, including tumor antigens. This learning process involves cognate interactions through immune synapses and the distinction between self and non-self antigens. Immune education via vaccination helps the process of development of protective immunity. Examples are presented concerning the therapeutic potential of memory T cells, in particular those derived from bone marrow. Like in the brain, memory function in the immune system is described to be facilitated by priming (imprinting), training, clonal cooperation, and an integrated perception of objects. The discussion part highlights evolutionary aspects. Full article

This study examines factors driving reliance on mobile phones as a primary information source. Using Information-Seeking Complementarity Theory (ISCT), which posits that frequent use of diverse media channels builds digital habits that reinforce mobile reliance, we analyze World Values Survey (WVS) Time Series 1981–2022 (v4.0), validated with WVS v5.0 and Integrated Values Survey (IVS). A multi-stage pipeline integrates AdaBoost (R 4.3.1), LASSO/BMA (Stata v17), Histogram Gradient Boosting (Python 3.12.7), and mixed-effects logistic regression. Missing data (DK/NA) were excluded or median-imputed. The final model (AUC-ROC > 0.85) identifies five robust predictors: age (negative), and positive associations with digital mail, online social networks, peer interaction, and radio listening—all stable across methods, datasets, and reverse causality checks. Subgroup analysis reveals stronger effects among males, unmarried individuals, urban residents, and higher education/employment groups. Nomograms enable probabilistic forecasting and policy simulation. By identifying technology-agnostic behavioral drivers validated across three decades of global survey data (1981–2022), with mobile reliance measured from 2010 onward, this work provides a transparent, replicable predictive framework with implications for emerging AI and wearable contexts. Full article

Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence. Full article