Search Results (673)

Digital financial services have transformed consumer financial behavior, yet their effects on sustainable development outcomes remain poorly understood. This study examines how mobile financial services (MFS) usage influences financial behaviors across temporal dimensions and investigates the moderating role of financial literacy from a systemic sustainability perspective. Drawing on Construal Level Theory, Dual Process Theory, and Social Cognitive Theory, we analyze data from 21,757 U.S. adults from the 2021 National Financial Capability Study to explore relationships between MFS usage, financial literacy dimensions—objective knowledge (OK), subjective knowledge (SK), and perceived ability (PA)—and both short-term and long-term financial behaviors. The results reveal a dual temporal pattern: MFS usage negatively affects short-term behaviors, including spending control and emergency preparedness, while positively influencing long-term behaviors such as retirement planning and investment participation. Financial literacy dimensions demonstrate differential moderating effects, with OK providing protective benefits against short-term risks, while PA can paradoxically exacerbate these adverse short-term effects. These findings highlight complex implications for sustainable development, demonstrating how individual behaviors aggregate to influence systemic financial resilience and progress toward Sustainable Development Goals related to poverty reduction, economic growth, and inequality reduction. Policymakers should adopt behaviorally informed regulatory approaches that address temporal trade-offs. Educators should design digital-specific literacy programs emphasizing realistic risk assessment alongside confidence-building, thereby promoting sustainable financial behaviors in increasingly digital environments. Full article

Air pollution from fine particulate matter (PM2.5) is a recurring crisis in Northern Thailand, largely driven by seasonal biomass burning. This study investigates how socioeconomic and individual characteristics shape labor allocation during high-exposure periods. Using survey data from 400 individuals across eight provinces in April–May 2024, we applied a logit model to estimate the probability of reducing work hours. Results show heterogeneous and non-linear patterns of avoidance. The probability of work reduction rose across higher income strata but peaked in the third stratum before declining in the fourth, reflecting the trade-off between avoidance and the opportunity cost of foregone earnings. Education exhibited a strong awareness effect, with each additional year increasing avoidance behavior. Outdoor workers and individuals with respiratory conditions were most likely to reduce work, indicating rational prioritization under greater exposure risks. Together, these findings demonstrate environmental inequality: lower-income and less-educated groups remain disproportionately exposed due to limited coping capacity. The regional context of Northern Thailand further amplifies these vulnerabilities. Policy interventions should prioritize protective measures for vulnerable groups while promoting long-term alternatives to biomass burning. By highlighting nuanced behavioral responses, this study extends evidence on environmental inequality in developing-country contexts. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Background: To reduce work-related illnesses among teachers, various types of programs were implemented. Objectives: The aim of this study was to evaluate the effects of a 12-week multicomponent program on mental disorders, biochemical parameters, and immunological markers in female teachers with overweight. Methods: A total of 33 women who were basic education teachers with a body mass index (BMI) ≥ 25 kg/m² participated in this study. Participants were randomly assigned to either a control group (n = 16), which did not participate in the program, or an intervention group (n = 17), which underwent the multicomponent intervention. The program included physical exercise (three sessions per week), cognitive–behavioral therapy delivered monthly across three modules, and nutritional education consisting of both general and specific guidance. Assessments were conducted at baseline and after 12 weeks and included measurements of symptoms of depression, anxiety, and stress; fasting glucose; total cholesterol; LDL cholesterol; HDL cholesterol; VLDL cholesterol; triglycerides; and concentrations of immunoglobulins IgA, IgG, and IgM. Results: After 12 weeks, the intervention group showed a significant reduction in symptoms of depression, anxiety, and stress (p < 0.05), as well as in fasting glucose, triglycerides, and VLDL cholesterol levels compared to the control group (p < 0.05). No significant changes were observed in the levels of immunoglobulins IgA, IgG, or IgM (p > 0.05). Conclusions: The multicomponent program improved mental health and reduced the risk of developing metabolic and cardiovascular diseases in female teachers with overweight. Full article

Losing a loved one is a painful process that usually diminishes over time. Despite that, one out of ten bereaved adults are at risk of developing Prolonged Grief Disorder (PGD) Internet-delivered cognitive-behavioral therapies (iCBTs) can reach individuals in need of therapy and are both cost-effective and clinically effective. This study aimed to investigate the feasibility of an iCBT for Spanish-speaking individuals with PGD (GROw) compared to the same intervention delivered in a face-to-face videoconferencing format. As a secondary objective, the potential efficacy of GROw was explored. A total of 31 participants were randomized to the experimental group (N = 16) (GROw) and the active control group (N = 15) (videoconferencing treatment). There were four assessment points: baseline, after intervention, and 3- and 12-month follow-ups. Both GROw and videoconferencing treatments were well accepted in terms of preferences, expectations, satisfaction and evaluation about the usefulness of the intervention, and showed significant symptomatology reduction with large effect sizes in most of the outcomes. The dropout rate was 50% in the GROw and 33.33% in the videoconferencing group. GROw is a feasible, well-accepted iCBT for the treatment of PGD with promising results related to its potential efficacy. Full article

Background: Problematic internet use among adolescents is linked to poorer mental health, academic performance, and social functioning, yet evidence from Eastern Europe remains limited. Methods: We conducted a school-based cross-sectional study at a Romanian high school (Arad County) including 308 students aged 15–18 years (154 males, 154 females). Students completed a demographic/behavioral questionnaire and the 20-item Internet Addiction Test (IAT), a widely used measure of problematic internet use. The prespecified primary analysis was a multivariable linear regression of IAT score on sex, age group, residence, daily screen time, prior attempts to reduce use, and main internet purpose; supporting analyses included t-tests, ANOVA, and Pearson correlation (α = 0.05). Results: In bivariable comparisons, males, older adolescents (17–18 years), and urban residents reported higher IAT scores; screen time correlated with IAT (r = 0.460, p < 0.001), and prior reduction attempts were associated with higher scores (Cohen’s d = 0.80). In the adjusted model, male sex (β = 4.97), older age (β = 5.36), greater daily screen time (β = 1.67 per hour), prior attempts to reduce use (β = 4.13), and primarily using the internet for gaming (β = 5.71) remained significant predictors (all p ≤ 0.045); urban residence was not retained (p = 0.218). The model explained 43% of IAT variance (R² = 0.43). Conclusions: Demographic and behavioral factors independently predict adolescent problematic internet use, highlighting high-risk profiles (older males, heavy screen time, gaming focus, prior reduction attempts). These findings support school-based screening and targeted digital-health interventions in underrepresented contexts. Full article

Precise trajectory prediction in the airspace of a high-density terminal area (TMA) is crucial for Trajectory Based Operations (TBO), but frequent aircraft interactions and maneuvering behaviors can introduce significant uncertainties. Most existing approaches use deterministic deep learning models that lack uncertainty quantification and explicit spatial awareness. To address this gap, we propose the BST-Transformer, a Bayesian spatio-temporal deep learning framework that produces probabilistic multi-step trajectory forecasts and supports probabilistic conflict alerting. The framework first extracts temporal and spatial interaction features via spatio-temporal attention encoders and then uses a Bayesian decoder with variational inference to yield trajectory distributions. Potential conflicts are evaluated by Monte Carlo sampling of the predictive distributions to produce conflict probabilities and alarm decisions. Experiments based on real SSR data from the Guangzhou TMA show that this model performs exceptionally well in improving prediction accuracy by reducing MADE 60.3% relative to a deterministic ST-Transformer with analogous reductions in horizontal and vertical errors (MADHE and MADVE), quantifying uncertainty and significantly enhancing the system’s ability to identify safety risks, and providing strong support for intelligent air traffic management with uncertainty perception capabilities. Full article

Type 2 diabetes mellitus (T2D) and cardiovascular disease (CVD) are not merely coexisting epidemics but co-evolving manifestations of a shared cardiometabolic continuum. Despite advances in glycemic, lipid, and blood pressure control, residual cardiovascular risk remains high, underscoring the limitations of siloed approaches. In this perspective, we argue for reframing T2D and CVD as interconnected conditions driven by inflammation, adipose tissue dysfunction, and organ crosstalk. Beyond metformin, which remains foundational, several glucose-lowering drug classes are now evaluated not only for glycemic control but also for their cardiovascular and renal impact. Landmark trials and recent meta-analyses confirm that sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists improve cardiorenal outcomes. More recently, tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 receptor agonist, has shown unprecedented efficacy in weight and glucose management, with potential to further transform cardiometabolic risk reduction. Yet enthusiasm for these therapies must be tempered by heterogeneity of response, treatment costs, and inequitable access. Integrated care models, supported by multidisciplinary teams, digital health tools, and value-based reimbursement, are essential to close the gap between trial efficacy and real-world outcomes. Attention to sex, age, ethnicity, and comorbidity profiles is critical to ensure equity, as is the adaptation of strategies to low- and middle-income countries where the burden of cardiometabolic disease is rapidly rising. Ultimately, advancing cardiometabolic medicine requires not only novel therapies but also a unifying framework that integrates biology, behavior, economics, and health systems to deliver the right treatment to the right patient at the right time. Full article

This study investigates the combustion characteristics and ash behavior of coal–biomass co-combustion using Zhujixi coal and corn straw in a fixed-bed system. The research analyzes combustion stage division, gas release patterns, and mineral evolution of ash under varying blending ratios. Results indicate that biomass addition modifies the dynamic features of the combustion process by advancing the CO₂ release peak; extending the release of CO, CH₄, and H₂; and enhancing the completeness of char oxidation. At moderate blending levels (20–60%), oxygen utilization is significantly improved and combustion stability is strengthened. Ash fusion temperatures exhibit a consistent decline with increasing biomass proportion due to the formation of low-melting eutectic phases such as KAlSiO₄ and K, Ca-based phosphates. Mineralogical analysis further reveals that coal ash components promote the immobilization of alkali metals, thereby suppressing potassium volatilization. A blending ratio of 40% demonstrates the most favorable balance between burnout performance, oxygen efficiency, and alkali fixation, surpassing both pure coal and high-ratio biomass conditions. This optimized ratio not only improves energy conversion efficiency but also reduces slagging and corrosion risks, offering practical guidance for cleaner coal power transformation, stable boiler operation, and long-term reduction of carbon and pollutant emissions. Full article

In line with circular economy principles and the reduction of primary material exploitation, waste-to-energy (WtE) by-products such as bottom ash (BA) are increasingly being used as raw materials in cement and ceramics manufacturing. However, it is critical to verify that the final product presents not only adequate technical properties but also that it does not pose negative impacts to the environment and human health during its use. This study investigates the environmental compatibility of the use of ceramic porcelain stoneware tiles manufactured with BA as partial replacement of traditional raw materials, with a particular focus on the leaching behavior of the tiles during their use, and also after crushing to simulate their characteristics at their end of life. To evaluate the latter aspect, compliance leaching tests were performed on crushed samples and compared with Italian End-of-Waste (EoW) thresholds for the use of construction and demolition waste as recycled aggregates. Whereas, to assess the environmental compatibility of the tiles during the utilization phase, a methodology based on the application of monolithic leaching tests to intact tiles, and the evaluation of the results through multi-scenario human health risk assessment and the analysis of the main mechanisms governing leaching at different stages, was employed. The results of the study indicate that the analyzed BA-based tiles showed no significant increase in the release of potential contaminants compared to traditional formulations and fully complied with End-of-Waste criteria. The results of the monolith tests used as input for site-specific risk assessment, simulating worst-case scenarios involving the potential contamination of the groundwater, indicated negligible risks to human health for both types of tiles, even considering very conservative assumptions. As for differences in the release mechanisms, tiles containing BA exhibited a shift toward depletion-controlled leaching and some differences in early element release compared to the ones with a traditional formulation. Full article

Childhood obesity represents a growing global public health crisis, strongly driven by the widespread consumption of sugar-sweetened beverages (SSBs) and, increasingly, artificially sweetened beverages (ASBs). SSB intake drives excessive calorie consumption, reduces satiety, and disrupts hormones, leading to metabolic dysfunction such as insulin resistance and type 2 diabetes. Despite some regional declines, global consumption of SSBs remains high, with persistent socioeconomic disparities. Concurrently, ASBs, marketed as healthier alternatives, pose emerging metabolic and behavioral risks, such as gut microbiota disruption and altered appetite regulation, raising concerns about their long-term safety. Both beverage types displace nutritionally balanced food options in children’s diets and foster enduring preferences for sweetness, exacerbating poor dietary quality. Public health interventions targeting SSB reduction have demonstrated modest success; however, rising ASB use complicates prevention strategies. Effective mitigation of childhood obesity requires comprehensive approaches that emphasize reducing all sweetened beverage consumption, promoting water and whole-food hydration, and addressing the behavioral and environmental factors underlying unhealthy beverage choices to improve lifelong health outcomes. Full article

The prevalence of childhood obesity has risen significantly, with numerous associated health risks. Emerging research suggests a potential role for genetic factors; particularly, copy number variations (CNVs) of the amylase 1 gene (AMY1) may influence obesity through dietary behavior and metabolic regulation. This study aimed to examine the relationship between BMI z-score, dietary intake, and salivary AMY1 gene copy number (CN) in children with overweight and obesity, and to assess the relationship between AMY1 CN and the impact of lifestyle intervention on these parameters. The study included 90 children aged 2–6 years with overweight or obesity. Participants were randomized into either a parent support intervention group or a standard care control group. Anthropometric measurements, dietary intake, and salivary AMY1 gene copy number were assessed at baseline and after a 9-month intervention. Positive correlations were found between AMY1 gene copy number, BMI z-score, and carbohydrate intake, suggesting a potential role of this gene in dietary behavior-related obesity. The parent support intervention led to significant reductions in children’s BMI, BMI z-score, and energy and macronutrient intake compared to standard care. Although there was no direct association between AMY1 copy number and changes in BMI z-score, higher AMY1 copy numbers were associated with greater reductions in polyunsaturated fatty acid intake. These findings highlight an interaction between salivary AMY1 gene copy number, dietary intake, and obesity in children. These results support the relevance of genetic factors in obesity-related dietary patterns and emphasize the effectiveness of targeted family-based lifestyle interventions. Full article

Background: Stigmatizing attitudes toward individuals with mental health conditions are common among healthcare professionals in primary healthcare (PHC) settings, posing a major barrier to early diagnosis, appropriate treatment, and recovery. Methods: This systematic review and meta-analysis evaluated the effectiveness of interventions aimed at reducing mental health-related stigma among PHC professionals (general practitioners, nurses, community health workers, and allied providers). Eligibility was restricted to interventional studies targeting PHC staff; non-clinical populations and students without clinical practice were excluded. Comparators included usual training, waitlist control, or pre–post evaluation. A systematic search of PubMed, Scopus, and Web of Science was conducted in accordance with PRISMA guidelines, and the protocol was registered in PROSPERO (CRD420251074412). Results: Twenty-five studies met the inclusion criteria, of which three contributed to the quantitative synthesis. Interventions included educational, contact-based, and multicomponent approaches. Risk of bias was assessed using tools appropriate to study design. Interventions generally improved knowledge and attitudes and, to a lesser extent, behavioral intentions. Meta-analysis of pre–post changes using the Opening Minds Scale for Health Care Providers (OMS-HC) demonstrated a significant reduction in stigma (MD = −0.27, 95% CI −0.40 to −0.14; p < 0.001; I² = 91%). A difference-in-differences analysis of studies with intervention and control groups confirmed this effect with moderate heterogeneity (MD = −0.18, 95% CI −0.25 to −0.11; p < 0.0001; I² = 50%). Conclusions: Contact-based and multicomponent interventions were associated with stronger and more sustained effects. The main limitations of the evidence were short follow-up periods, reliance on self-reported outcomes, methodological heterogeneity, and the possibility of publication bias. Our findings suggest that reducing stigma among PHC professionals can enhance patient engagement, timely diagnosis, and quality of care in routine clinical practice. Full article

Ensuring traffic safety within urban public transport systems is essential for achieving sustainable urban development, particularly in densely populated metropolitan areas. This study investigates the integration of artificial intelligence (AI) technologies to enhance safety performance in closed public transport environments, with a focus on the city of Belgrade as a representative case. The research aims to evaluate how AI-enabled systems can contribute to the early detection and reduction of traffic incidents, thereby supporting broader goals of sustainable mobility, infrastructure resilience, and urban livability. A hybrid methodological framework was developed, combining computer vision, supervised machine learning, and time series analytics to construct a real-time risk detection platform. The system leverages multi-source data—including video surveillance, onboard vehicle sensors, and historical accident logs—to identify and predict high-risk behaviors such as harsh braking, speeding, and route adherences across various public transport modes (buses, trams, trolleybuses). The AI models were empirically assessed in partnership with the Public Transport Company of Belgrade (JKP GSP Beograd), revealing that the most accurate models improved incident detection speed by over 20% and offered enhanced spatial identification of network-level safety vulnerabilities. Additionally, routes with optimized AI-driven driving behavior demonstrated fuel savings of up to 12% and a potential reduction in emissions by approximately 8%, suggesting promising environmental co-benefits. The study’s findings align with multiple United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure). Moreover, the research addresses ethical, legal, and governance implications surrounding the use of AI in public infrastructure, emphasizing the importance of privacy, transparency, and inclusivity. The paper concludes with strategic policy recommendations for cities seeking to deploy intelligent safety solutions as part of their digital and green transitions in urban mobility planning. Full article

Background: Falls are a leading cause of morbidity and loss of independence among older adults, and occupational therapy (OT) offers a unique, multidimensional approach to fall prevention. This systematic review evaluates the effectiveness of OT-based interventions for improving balance, mobility, functional performance, and psychological outcomes related to fall risk in older adults. Methods: This review followed PRISMA (2020) guidelines. A comprehensive search of PubMed, Scopus, Dialnet, and OTseeker was conducted from March to May 2025. The inclusion criteria targeted studies involving non-pharmacological, OT-led interventions in adults aged ≥65. Seventeen studies were selected, including randomized controlled trials, pilot studies, and quasi-experimental designs. The data extraction and quality appraisal were performed independently by two reviewers. Results: The included interventions varied among exercise-based programs (e.g., Tai Chi, Pilates), virtual reality training, home safety modifications, cognitive–behavioral therapy, and wearable technologies. Most of the studies reported significant improvements in postural balance, fear of falling, and functional independence. Environmental adaptations and educational strategies also yielded positive outcomes. However, a real-world fall incidence reduction was inconsistently reported, and the methodological heterogeneity limited the meta-analytic synthesis. Conclusions: Occupational therapy contributes significantly to fall prevention through multimodal, person-centered strategies that integrate physical, cognitive, and environmental components. Future research should aim to standardize the outcome measures, include high-risk populations, and assess the long-term efficacy and cost-effectiveness of OT-led programs. Full article