Search Results (6,064)

Microplastic pollution is an escalating environmental and public health issue. Defined as plastic particles less than 5 mm in size, microplastics have been found in oceans, rivers, food, drinking water, air, and even human tissues. While scientific research on microplastics has expanded significantly, public understanding and behavioral change remain limited. This literature scan synthesizes global findings on public awareness, perceptions, and responses to microplastics, drawing from surveys, focus groups, and online behavioral data collected by existing studies. It explores the following: (1) general knowledge and perceived environmental and health risks; (2) trust in scientific and governmental sources; (3) willingness to adopt behavioral changes; (4) attitudes toward policy and corporate responsibility. Public concern is high, especially regarding marine life and food safety, but varies across populations based on education, socioeconomic status, and media exposure. Despite growing concern, psychological distance and persistent knowledge gaps hinder meaningful action. Communication strategies such as school programs, media campaigns, and eco-labels show mixed success, while regulatory interventions like plastic bags or microbead bans are more effective when supported by clear public messaging. This literature scan highlights the need for interdisciplinary collaboration to close the knowledge–behavior–policy gap and strengthen public engagement, particularly in urban settings where consumption and waste generation are concentrated. Full article

Background: Unintended adolescent pregnancy and sexually transmitted infections (STIs) remain pressing public health concerns in Northeastern Thailand. Although school-based sexuality education is widespread, risk behaviors persist, underscoring the need for innovative approaches. This study evaluated the SESS (System–Empowerment–Support–Social Network) model, a multi-component framework designed to strengthen adolescent sexual health. Methods: A quasi-experimental, two-school study was conducted among 240 students aged 15–19 years in Nakhon Ratchasima Province. One school (n = 120) implemented a 16-week SESS program, while a comparison school (n = 120) continued with the standard curriculum. The SESS model combined system coordination, empowerment workshops, peer and institutional support, and digital platforms (Facebook, LINE). Data were collected with validated questionnaires and analyzed using ANCOVA, adjusting for baseline values. Exploratory analyses reported mean differences with 95% confidence intervals (CIs). Results: Groups were comparable at baseline. Post-intervention, the intervention school showed higher perception scores (mean difference = +13.0; 95% CI: 10.5–17.0) and preventive practice scores (mean difference = +14.0; 95% CI: 10.1–17.9). Attitudes showed minimal change. No pregnancies or self-reported STI cases were documented among intervention participants during the follow-up period. Conclusions: In this two-school quasi-experimental evaluation, the SESS model was associated with improvements in perceptions and practices, though attitudinal changes were limited. Findings suggest the feasibility of integrating empowerment, social support, and digital engagement into school-based programs while highlighting the need for multi-school trials to establish effectiveness. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

Conventional plant disease management primarily depends on chemical pesticides. However, with the rising concerns related to human health, environmental sustainability, and the emergence of resistant pathogens, biocontrol agents (BCAs) have gained more attention as eco-friendly alternatives. Among the potential biocontrol agents, yeasts stand out due to their safety, adaptability, and diverse antagonistic mechanisms, ranging from competition and enzyme secretion to volatile compound production and immunity induction. Despite their potential, yeast-based BCAs face limitations in field efficacy, regulation, and an incomplete understanding of their molecular interactions. Most current studies focus on simple, pairwise interactions, overlooking the complexity of agroecosystems, where plants, pathogens, and BCAs interact within broader microbial communities. This review addresses the importance of understanding tripartite interactions among plants, pathogens, and yeasts, supported by integrated transcriptomic and comparative genomic approaches, as well as meticulous observations of phenotypic expressions to uncover strain-specific defense mechanisms and mode of action. By referring to well-studied models like Blumeria graminis f.sp. hordei–Hordeum vulgare–Pseudozyma flocculosa and Trichoderma tripartite systems, we highlight the underexplored potential of yeasts to modulate plant immunity and influence pathogen behavior through complex molecular crosstalk. Bridging these knowledge gaps through integrating proteomic, metabolomic, and transcriptomic analyses, we can better harness yeasts in sustainable and targeted biocontrol strategies. Full article

Amid ongoing educational reforms and the rapid advancement of the knowledge economy, innovative teaching behaviors are not only closely related to teachers’ professional growth and students’ academic achievement but are also regarded as the key driving force for the evolution of the educational system. Consequently, identifying effective ways to foster teachers’ innovative teaching behaviors has become a central concern in educational psychology and management. Grounded in the Job Demands–Resources framework, this study developed and tested a chained mediation model using survey data from 1163 Chinese elementary and secondary school teachers. The model examines how teachers’ emotional competence fosters innovative teaching behaviors and elucidates the underlying mechanisms. The results revealed that (1) emotional competence significantly and positively predicted innovative teaching behaviors, and (2) teaching efficacy and work vitality served not only as independent mediators but also as sequential mediators in this relationship. These findings extend the understanding of the antecedents of teachers’ innovative behaviors from an emotional perspective, demonstrating that emotional competence, as a critical psychological resource, can be transformed into innovative teaching behaviors through dual “cognitive–motivational” and “energy–motivational” pathways. This study offers both theoretical insights and practical implications for advancing teaching innovation by strengthening teachers’ emotional competence, teaching efficacy, and work vitality. Full article

Coccidiosis represents a major constraint to sheep productivity worldwide, with increasing concerns regarding anticoccidial resistance and growing interest in reducing dependency on conventional synthetic anticoccidials. This investigation evaluated the anticoccidial properties of faveira pods (Parkia platycephala pod—PpP) and their influence on productive performance in naturally infected lambs. Eighteen uncastrated Dorper × Santa Inês crossbred males (20.0 ± 2.5 kg, 5 months) were randomly allocated to three groups: G1 (0% PpP; n = 6), G2 (100% PpP replacing roughage, 30.0% of total diet; n = 6), and the control group (0% PpP plus 20 mg/kg toltrazuril; n = 5). Parasitological assessments, productive performance, and behavioral parameters were monitored over 45 days using oocyst counts, morphometric analysis, digestibility trials, and biometric measurements. Nine Eimeria species were identified, with E. crandallis, E. parva, and E. bakuensis representing 53.5% of total oocyst shedding. Group G2 demonstrated a numerical 8.5% reduction in parasite load compared to G1 (p = 0.42), while toltrazuril achieved 36.6% efficacy (p < 0.05). Species-specific effects were significant for E. crandallis, E. parva, and E. ovinoidalis (p < 0.01). A robust correlation emerged between parasite load and water consumption (r = 0.652, p = 0.0045), establishing a novel behavioral biomarker for coccidiosis monitoring. Environmental oocyst elimination decreased by 43.4% in the P. platycephala group. These findings demonstrate that PpPs possess moderate anticoccidial properties, offering a sustainable complementary strategy for integrated coccidiosis management while contributing to environmental sustainability. Full article

Seawater has been widely used in copper–molybdenum flotation plants due to the shortage of fresh water and the high cost of seawater desalination, especially in arid regions. There have been many studies concerning the molybdenite flotation in seawater. Due to the complication of seawater flotation, it is difficult to identify the key factors affecting molybdenite recoveries. It is known that the unique structure of molybdenite plays an important role in molybdenite flotation. The anisotropic property of molybdenite leads to the different surface properties of basal and edge plane surfaces. Electrochemical properties of sulfides have a significant effect on the surface properties which affect the flotation performance. Therefore, it is important to understand the surface electrochemical properties such as surface chemistry, redox processes, and reaction kinetics of molybdenite’s two different surfaces in seawater, and to determine what affects the molybdenite flotation behaviors in seawater. In this study, the surface properties of molybdenite basal and edge plane surfaces in both fresh water and seawater were investigated through various electrochemical techniques. Open circuit potential (OCP) measurement indicated that edge plane surfaces were easier to be oxidized than basal plane surfaces. Cyclic voltammetry (CV) studies showed that the basal plane surfaces were stable with a low electrochemical reactivity, while the edge plane surfaces had relatively high electrochemical reactivity. In addition, the redox property of the molybdenite surface was enhanced in seawater, which is a key to the improvement of fine molybdenite flotation in seawater. Electrochemical impedance spectroscopy (EIS) measurements further confirmed the stability of basal plane surfaces and indicated a greater charge transfer ability of edge plane surfaces in seawater. Different molybdenite particle sizes with different basal and edge ratios were applied in the flotation in both fresh water and seawater; the results illustrated that molybdenite flotation was enhanced in seawater especially to fine particles. The flotation and electrochemical studies reveal that the electrochemical reactivity of edge plane surface plays an important role in molybdenite seawater flotation. Full article

As the global economy undergoes rapid digital transformation, cryptocurrencies have emerged as a prominent alternative class of financial assets. Their decentralized nature, pseudonymity, and lack of centralized oversight have attracted considerable interest among investors while simultaneously raising significant concerns among regulators and compliance professionals. While cryptocurrencies offer benefits such as enhanced accessibility and transactional privacy, they also pose notable risks, particularly their potential misuse in financial crimes, including money laundering. This study explores the perceived risks associated with cryptocurrencies in the context of money laundering, drawing on insights from a survey conducted among 50 financial sector professionals. A quantitative research design was employed, using a structured online questionnaire to assess participants’ awareness, investment behavior, and perceptions of the role of cryptocurrencies in illicit finance and financial system security. The results reveal a complex perspective: while 70% of respondents acknowledged the potential for cryptocurrencies to facilitate money laundering, 60% expressed support for their wider adoption. Notably, statistically significant correlations emerged between active investment in cryptocurrencies and the belief that they could enhance financial market security and reduce laundering risks. However, self-reported knowledge levels and general awareness did not show a significant relationship with perceived risk. The findings underscore the importance of a balanced approach to regulation, one that fosters innovation while mitigating illicit finance risks. The study recommends increased investment in user education, the development of blockchain analytics, the adoption of global regulatory standards and enhanced international cooperation to ensure the responsible evolution of the cryptocurrency ecosystem. Full article

The increasing concern for road safety has driven the development of advanced driver behavior analysis systems. This study presents a comprehensive review of various techniques to detect unsafe driving behaviors, with a particular emphasis on using smartphone sensors. By leveraging data from accelerometers, gyroscopes, and GPS, these methods allow for the detection of aggressive driving patterns, which may result from factors such as driver distraction or drowsiness. Modern sensor technology plays a crucial role in real-time monitoring and has significant potential to enhance vehicle safety systems. A Long Short-Term Memory (LSTM) network combined with a Conv1D layer was trained to analyze driving patterns using a sliding window technique. As technology continues evolving, its application in driver behavior analysis holds great promise for reducing traffic accidents and improving driving habits. Furthermore, the ability to gather and analyze large amounts of data from drivers in various conditions opens new opportunities for more personalized and adaptive safety solutions. This research offers insights into the future direction of driver monitoring systems and the growing impact of mobile and sensor-based solutions in transportation safety. Full article

Green banking is a significant financial strategy for balancing environmental sustainability with economic progress. Banks can help address Indonesia’s environmental concerns by promoting sustainable behavior, financing green projects, and implementing environmentally friendly regulations. This study investigates how green banking practices affect perceived environmental performance and financial sustainability, with a particular emphasis on the involvement of young Indonesian bankers. A structured questionnaire was issued to 314 young bankers from various parts of Indonesia, using Likert-scale measures of three domains: banks’ perceived environmental performance, green banking activities, and sources of green finance. The findings show high perceived links between green banking operations and banks’ environmental performance, with green financing serving as a crucial mediator. Specific methods, such as paper reduction, internet banking, and supporting sustainable initiatives, were thought to improve bank performance. The findings underline the importance of younger generations in supporting and carrying out green activities, emphasizing their role in encouraging long-term change. Using Structural Equation Modelling (SEM), the study demonstrates that green finance improves perceived environmental performance and promotes sustainable banking practices. These findings emphasize the importance of incorporating green principles into banking strategy in order to achieve both financial and environmental sustainability in developing countries. Full article

Background: Type 2 diabetes mellitus (T2DM) is a global health concern driven by aging, lifestyle, and socio-economic disparities. Early detection is key, with tools like FINDRISC, QDScore, and CANRISK providing non-invasive screening. Yet, the combined effects of sociodemographic factors, healthy habits, and perceived quality of life on diabetes risk remain insufficiently studied in working populations. Objectives: To evaluate the association between sociodemographic variables, lifestyle habits (smoking, physical activity, adherence to the Mediterranean diet), and health-related quality of life (HRQoL) with the risk of developing type 2 diabetes, using three validated screening tools in a large cohort of Spanish workers. Methods: A cross-sectional study was conducted among 100,014 Spanish workers aged 18 to 69 years who underwent standardized medical evaluations between January 2021 and December 2023. Diabetes risk was assessed using the FINDRISC, QDScore, and CANRISK tools. Lifestyle variables and HRQoL (measured via the SF-12 questionnaire) were evaluated through validated instruments. Multivariate logistic regression models were used to examine the association of independent variables with moderate-to-high diabetes risk according to each score. Results: Among the strongest predictors, poor adherence to a Mediterranean diet (OR = 1.45, 95% CI: 1.32–1.58) and low physical activity (OR = 1.39, 95% CI: 1.27–1.52) were independently associated with higher diabetes risk. Poor HRQoL was also significant (OR = 1.33, 95% CI: 1.22–1.47). Conclusions: Sociodemographic factors, lifestyle behaviors, and perceived health status are independently associated with increased type 2 diabetes risk in Spanish workers. The integration of HRQoL assessments into occupational health surveillance may enhance early identification of at-risk individuals and guide tailored prevention strategies. Full article

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation method struggles with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user-detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed with scalability and inductive capabilities in mind, avoiding the need for full-graph training. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science. Full article

Climate risk, as a significant factor affecting human sustainable development, has emerged as a focal topic of concern for governments and all sectors of society. Using a dataset from China’s Shanghai and Shenzhen A-share markets spanning 2007 to 2019, this study empirically examines how climate risk influences corporate financialization. The empirical results show that heightened climate risk significantly reduces the level of corporate financialization, a finding that remains robust across multiple tests. Further heterogeneity analyses indicate that the suppressive effect of climate risk is particularly evident among state-owned enterprises, firms operating in intensely competitive industries, and those located in regions subject to more stringent environmental policies. Mechanism analysis suggests that climate risk inhibits corporate financialization primarily by intensifying firms’ financing constraints while simultaneously stimulating their innovation capacity. These findings imply that corporate financialization in China is largely driven by profit-maximizing behaviors rooted in “investment substitution” and “real-sector intermediation” motives. Collectively, this research enhances understanding of the channels through which climate risk impacts corporate financial behavior and offers valuable empirical insights for policymakers aiming to optimize climate regulations and redirect financial resources toward productive real-sector activities. Full article

The mechanically activated transition (MAT) from linear viscoelasticity to yielding is considered an essential part of the operational behavior of ductile materials. The MAT region is restricted by proportional limit at σ₀ and ε₀ and the yield point at σ_y and ε_y, or, in terms of this paper, $E_0={\sigma }_0/{\epsilon }_0$ and ε₀ and $E_y={\sigma }_y/{\epsilon }_y$ and ε_y, respectively. This stage precedes yielding and controls the parameters of the yield point. For bulk plastic (co)polymers and cellular polymeric foams, the quantitative correlations between $E_0$, ε₀, $E_y,$ and ε_y were determined. The ratios ${\displaystyle \raisebox{1ex}{$E_0$}\!\left/ \!\raisebox{-1ex}{$E_y$}\right.}=1.55\pm 0.15$ and ${\displaystyle \raisebox{1ex}{${\epsilon }_y$}\!\left/ \!\raisebox{-1ex}{${\epsilon }_0$}\right.}=2.1\pm 0.2$ were specified as yielding criteria. For all the samples studied, their mechanical response within the MAT region was unified in terms of master curve constructed via re-calculation of the experimental “stress–strain” diagrams in the reduced coordinates ${\displaystyle \frac{\mathit{lg} E-\mathit{lg} E_0}{\mathit{lg} E_0-\mathit{lg} E_y}}=f\left({\displaystyle \frac{\mathit{lg} \epsilon -\mathit{lg} {\epsilon }_0}{\mathit{lg} {\epsilon }_y-\mathit{lg} {\epsilon }_0}}\right),$ where $E=\sigma /\epsilon$ and ε are the current modulus and strain, respectively. To generalize these regularities found for bulk plastics and foams, our earlier experimental results concerning the rheology of soil-based pastes and data from the literature concerning the computer simulation of plastic deformation were invoked. Master curves for (1) dispersed pastes, (2) bulk plastics, (3) polymeric foams, and (4) various virtual models were shown to be in satisfactory coincidence. For the materials analyzed, this result was considered as the unification of their mechanical response within the MAT region. An algorithm for the express analysis of the mechanical response of plastic systems within the MAT region is proposed. The limitations and advances of the proposed methodological approach based on correlation studies followed by construction of master curves are outlined. Full article

Smartphone addiction has emerged as a significant public health concern, particularly among adolescents and young adults. This narrative review, conducted in line with the ANDJ checklist, synthesizes evidence from 25 systematic reviews and meta-analyses, complemented by randomized controlled trials and clinical studies, to provide a structured overview of the field. The study selection flow and publication trends reveal a rapidly expanding research landscape, with most evidence produced in the last decade, reflecting both the ubiquity of smartphones and increasing awareness of their health impacts. The synthesis highlights converging findings across reviews: excessive smartphone use is consistently associated with psychosocial, behavioral, and academic challenges, alongside sleep disturbances and mental health symptoms. Common messages include the recognition of smartphone addiction as a multidimensional phenomenon, while emerging themes point to heterogeneity in definitions, tools, and methodological approaches. Comparative analysis of reviews underscores both shared risk factors—such as emotional dysregulation and social isolation—and differences in study designs and target populations. Importantly, this review identifies critical gaps, including the lack of standardized definitions, limited longitudinal evidence, and scarce cross-cultural validation. At the same time, promising opportunities are noted, from lifestyle-based interventions (e.g., physical activity) to educational and policy-level strategies fostering digital literacy and self-regulation. The post-pandemic context further emphasizes the need for sustained monitoring and adaptive responses. Overall, this review calls for youth-centered, multi-sector interventions aligned with WHO recommendations, supporting coordinated, evidence-based action across health, education, and policy domains. Full article