Search Results (4,270)

Introduction: Lifestyle is characterized by identifiable behavioral patterns that can affect individuals’ health, and is considered one of the predominant factors for maintaining both quality of life and people’s health. This triad (lifestyle, quality of life and health) is closely associated with well-being. Objective: The aim of this study is to evaluate the relationship between lifestyles, well-being, and self-esteem in students who have completed secondary education and are in the process of transitioning to higher education. Methods: Data were collected using a questionnaire of sociodemographic (e.g., age, gender) and physical (e.g., BMI) variables, a Self-Esteem Scale, a Well-Being scale, and the FANTASTICO Lifestyle questionnaire. Statistical analysis was performed using canonical correlation analysis and a Structural Equation Model. Results: The sample consisted of 235 students, with a mean age of 18.4 years. Canonical correlation analysis revealed that lifestyle explains 58.5% of the variance in mental health. The first (most important) canonical function (r = 0.86; p < 0.001) highlighted that the domains of introspection, sleep/stress management, and family/social support are the primary predictors of higher levels of self-esteem and psychological well-being. Structural Equation Modeling confirmed that lifestyle positively predicts psychological well-being through both direct and indirect pathways (β = 0.172; 95.0% BC CI [0.095, 0.253]). Self-esteem emerged as a significant partial mediator in this relationship, suggesting that healthy lifestyle habits reinforce the perception of personal competence, which, in turn, enhances emotional adjustment. Together, these findings validate the dynamic triad between behavior, self-perception, and well-being during the transition to higher education. Conclusions: This study shows that the transition to higher education is a pivotal period where lifestyle patterns significantly shape students’ psychological adjustment. The findings confirm that a healthy lifestyle, specifically centered on stress management, sleep, and social support, serves as a robust predictor of both self-esteem and psychological well-being. By identifying self-esteem as a key partial mediator, the results suggest that healthy habits do more than just improve physical health. Full article

Traffic accidents are among the leading causes of injury worldwide, highlighting the urgent need to better understand the factors that contribute to accident occurrence and severity in order to improve road safety and reduce injuries and fatalities. This study analyzes the U.S. Accidents dataset, comprising data collected from 2016 to 2023, to identify the key determinants of accident severity and to evaluate feature-selection techniques for predictive modeling. To this end, several feature-selection methods are examined, including L1-regularized logistic regression, minimum redundancy maximum relevance (mRMR), conditional mutual information maximization (CMIM), ReliefF, and tree-based importance measures; these are compared with the Weighted Conditional Mutual Information (WCFR). The selected feature subsets are then evaluated using three machine learning models: logistic regression, random forest, and XGBoost. Experimental results show that WCFR consistently outperforms the competing methods, achieving higher validation accuracy (up to approximately 0.84) and Macro-F1 scores (up to approximately 0.55), while using fewer features and maintaining model interpretability. These results indicate that WCFR is particularly effective for accident severity modeling and highlight its potential as a robust feature selection strategy for large-scale transportation safety analytics and future severity prediction studies. Full article

Structural symmetry appears as a natural feature in both optimal solution landscapes and hospital scheduling behaviors, representing an inherent balance that can be deliberately leveraged to improve how quickly algorithms converge and how reliably systems perform in intricate healthcare optimization contexts. Managing hospital resources is a multifaceted challenge that requires simultaneously addressing several competing goals, such as reducing costs, improving patient experiences, making the most of available resources, distributing staff workload fairly, and strengthening readiness for emergencies. Traditional optimization approaches frequently struggle to cope with the complexity and ever-changing nature of modern healthcare environments. To address this gap, this study introduces a novel Multi-Objective Dream Optimization Algorithm (MO-DOA) tailored for smart healthcare resource management, which adapts a biologically inspired optimization framework to meet the specific demands of healthcare settings. The MO-DOA is built around three core mechanisms: a foundational memory component that retains high-quality solutions, a forgetting-supplementation component that maintains a productive balance between exploration and exploitation, and a dream-sharing component that promotes diversity among candidate solutions. Rigorous testing across realistic hospital environments confirms MO-DOA’s outstanding effectiveness, with results showing a 21.86% gain in resource utilization, a 30.95% decrease in patient waiting times, a 19.06% boost in patient satisfaction, and a 29.56% improvement in how evenly staff workloads are distributed. The algorithm’s emergency response capabilities are especially noteworthy, achieving bed assignments within 4.23 min and an equipment deployment success rate of 94.56%. Computationally, the algorithm proves highly efficient, with an average response time of 18.87 s and strong scalability across different operational scales. Collectively, these findings position MO-DOA as a powerful and practical tool for optimizing hospital operations in real time. Full article

Nanoparticle-catalysed microwave-aided multicomponent reactions (MCRs) have been demonstrated to be competent and environmentally benign tools for the quick synthesis of a wide spectrum of fused heterocyclic systems. The distinctive physicochemical properties of nanoparticles, including a substantial surface area, readily modifiable surface functionality, and heightened catalytic activities, when coupled with microwave irradiation, have enabled a marked improvement in reaction rates, product yields, and selectivity compared to conventional heating methods. This review highlights recent advancements in microwave-assisted MCRs facilitated by diverse nanomaterials, such as magnetic nanocatalysts, metal and metal oxide nanoparticles, mesoporous silica systems, and nanohybrids. It emphasises catalyst design, catalytic efficacy, scope, recyclability, and alignment with green chemistry principles in both solvent-free and aqueous environments, as well as the utilisation of recyclable catalysts. In summary, microwave-assisted multi-component reactions catalysed by nanoparticles are ecofriendly and versatile methods for the sustainable synthesis of such fused heterocycles containing bioactive pyridine, pyrazole, phenazine, pyrimidine, pyran, imidazole, and relevant pyridine derivatives, possessing potential in medicinal and material chemistry. Full article

Human organoids and organ-on-chip/microphysiological systems (OoC/MPS) are increasingly used as new-approach methodologies for biotoxin assessment. They retain human-relevant tissue organization and enable interpretable analysis of exposure geometry, barrier transport, perfusion, and (when needed) multi-organ coupling. In this review, we synthesize primary evidence across major toxin classes, including bacterial enterotoxins (e.g., cholera toxin, heat-stable enterotoxins, Shiga toxins), mycotoxins (e.g., aflatoxin B1, ochratoxin A, deoxynivalenol), and algal/cyanobacterial toxins (e.g., saxitoxin, domoic acid, microcystins, biliatresone). We emphasize studies that clearly define toxin identity and exposure context and that demonstrate mechanism-critical model competencies under assay conditions. We highlight decision-informative functional endpoints that align with the dominant pathophysiology. These include cystic fibrosis transmembrane conductance regulator (CFTR)-dependent secretion in human enteroids/colonoids, transporter-linked proximal tubular injury in kidney MPS, gut–kidney axis injury from Shiga toxin-producing E. coli in microfluidic systems, and multi-electrode array (MEA) network readouts in human 3D neural tissues. We then summarize best practices that improve cross-study comparability. These include reporting delivered versus nominal exposure, assessing recovery/mass balance and device/material interactions, applying proportional biological qualification (polarity, transporter/enzymatic competence, functional stability), defining a minimal comparable endpoint core, and preserving QIVIVE readiness in reporting. Finally, we outline near-term priorities for the field, including chronic low-dose and mixture designs, harmonized reference panels and acceptance criteria, and fit-for-purpose escalation to coupled OoC/MPS only when perfusion or organ–organ coupling is expected to change the interpretation. Full article

The integration of immersive educational technologies into medical education has attracted growing attention owing to their potential to improve the learning of complex anatomical structures and specialized terminology. This study investigates the use of zSpace technology as an interactive, learner-centered instructional tool for teaching the human respiratory system to undergraduate students in Nursing, Midwifery, and Physician Assistant programs. A structured pedagogical framework combined prior theoretical instruction in anatomy and Latin medical terminology with a zSpace-based practical learning activity was used. After the workshop, the students completed a survey evaluating perceived learning effectiveness, student engagement, and the quality of three-dimensional (3D) visualization. Data from 34 participants were analyzed using descriptive statistics and reliability analysis. The results indicated high levels of student satisfaction regarding the clarity, anatomical detail, and educational value of the immersive 3D models, along with higher levels of engagement compared with traditional methods. Despite challenges related to technical infrastructure, lecturer readiness, and students’ digital competencies, the findings support the pedagogical relevance of immersive 3D technologies in medical education. Overall, the findings suggest that students perceive zSpace technology as supporting anatomical understanding and enhancing engagement within the studied context. Full article

Waste heat capture and reuse from battery storage systems for cogeneration of heat and power has the potential to both improve their energy efficiency and reduce the carbon footprint. This study performs a comparison of technologies capable of converting the waste heat extracted to a useful purpose. This analysis is accomplished using the literature data as a basis for an analytical hierarchy process (AHP) applying technological efficiency, cost effectiveness, footprint and integration, and safety and environmental concerns as the criteria. Of these, cost effectiveness was found to be dominant, with technological efficiency also showing high importance. Heat pumps were found to be the most effective based on the objective and criteria of this analysis. This study dictates a pathway that allows stakeholders and decision makers to determine a route by which site-specific comparisons may be made, aiding them to navigate the complex interplay of competing objectives. Full article

The transition period in dairy cows is marked by metabolic, oxidative, and immune challenges that increase susceptibility to periparturient diseases. Injectable mineral supplementation (IMS) has been proposed to support immunometabolic adaptation by enhancing antioxidant capacity and immune function, with consistent associations with improved health outcomes but variable effects on production. Therefore, this study evaluated the effects of repeated intramuscular multi-mineral supplementation during the transition period on health, metabolic stress, immune status, and productive performance in Holstein cows. Supplementation was associated with lower odds of subclinical hypocalcemia on day 4 postpartum in primiparous cows (p = 0.02) and overall for persistent subclinical hypocalcemia (p = 0.03). Multiparous cows (p = 0.04) and the overall population (p = 0.01) showed consistent effects on metritis following IMS. Supplemented cows had improved metabolic and uterine health indicators without affecting energy metabolism-related disorders. Although no differences were detected for major postpartum health disorders, its main benefits may involve immune competence, oxidative regulation, and physiological resilience rather than energy balance. Full article

Corporate governance reforms in emerging and frontier markets frequently assume that strengthening board oversight, audit committees, and ownership monitoring will improve audit quality and enhance firm value. Yet, in weak institutional environments, these mechanisms often function symbolically rather than substantively. This study rethinks the governance–audit–value nexus by integrating Agency Theory, Institutional Theory, and the concept of symbolic governance to explain why governance may appear structurally robust while failing to constrain managerial discretion. Using panel data from Ghanaian listed firms between 2015 and 2023, the analysis shows that audit committee independence and board independence are negatively associated with both audit quality and firm value, indicating that formal independence without expertise, authority, or enforcement capacity does not translate into meaningful oversight. By contrast, institutional and managerial ownership positively influence both outcomes, suggesting that incentive alignment and informed monitoring can substitute for weak formal governance. Foreign ownership improves firm value but does not consistently enhance audit quality, while macroeconomic conditions such as inflation and GDP growth further shape firm performance. The study advances the literature by reconceptualising governance effectiveness in weak institutional environments, demonstrating that governance mechanisms may exist in form without functioning in substance. The findings underscore the need for governance reforms that prioritise enforcement capacity, board expertise, and audit committee competence rather than structural compliance alone. Full article

Advances in Industry 4.0 manufacturing have accelerated the adoption of machine learning (ML) for automated classification. Polyester (PES), a widely used synthetic fiber, competes with natural fibers like cotton and other synthetics, highlighting the need for continuous research and improvement. In the textile sector, distinguishing recycled polyester (rPES) from virgin polyester (vPES) remains challenging due to overlapping chemical signatures and material variability. A combination of Fourier transform infrared (FTIR) spectroscopy and ML has not been explored for this purpose. In this study, we evaluated ML models to discriminate three PES fiber types (45 vPES, 65 rPES, and 55 mixed PES) using 165 FTIR spectra across four spectral regions, R1, R2, R3, and R4, as well as their combined representation. Six ML approaches were tested on data reduced with fast independent component analysis (FastICA) (1–30 components) using an 80/20 train–test dataset split. The Decision Tree classifier achieved the highest Accuracy in four of the five spectral evaluations, with classification accuracies ranging from 66.67% to 77.78% for region R4, which also had a balanced classification profile with an area-under-the-curve (AUC) value of 0.81. Notably, despite the moderate overall Accuracy, the model achieved 100% discrimination of rPES when distinguishing it from both mixed and vPES. Mixed fibers remained the most difficult to classify, highlighting the need for improved feature representation. Full article

Nanotechnology, based on nanoparticles, has become an emerging interdisciplinary tool in reproductive biotechnology, offering innovative opportunities to improve fertilization efficiency and reproductive performance in farm animals. The purpose of this review is to provide an updated synthesis of current research on nanoparticle-based approaches that enhance in vitro fertilization outcomes and other assisted reproductive technologies. The focus is on the biological mechanisms, potential benefits, and limitations of nanoparticle use in animal reproduction. Nanoparticles—including gold, silver, zinc oxide, selenium, and magnetic iron oxide—exhibit distinctive physicochemical properties that enable targeted interactions with gametes and reproductive cells. When used in semen extenders or culture media, nanoparticles improve sperm motility, acrosome and membrane integrity, and reduce oxidative stress and apoptosis. These effects contribute to enhanced fertilization rates and higher embryo developmental competence. In addition, nanoparticles can function as carriers for hormones, antioxidants, and growth factors, stabilizing reagents essential for oocyte maturation, sperm capacitation, and early embryo culture. The review also discusses nanopurification (selectively isolating and removing particles) and nanosorting (separating or organizing nanoscale objects) techniques that allow for non-invasive selection of viable gametes, and fluorescence- and magnet-assisted sorting systems that increase precision in sperm sexing. The mechanical aspects of nanoparticle–cell interactions are analyzed, emphasizing the influence of particle size, dose, and surface modification on both biological efficacy and cytotoxicity. Safety, toxicological concerns, and regulatory frameworks—including International Organization for Standardization (ISO) standards and European Commission recommendations—are critically reviewed to highlight the need for harmonized biocompatibility criteria. Although nanoparticle use in animal reproduction remains largely experimental, accumulated evidence demonstrates its potential to improve reproductive efficiency and reduce economic losses. Integrating nanoparticle-based systems with existing reproduction platforms may represent a transformative step toward sustainable and precision-driven livestock breeding. Full article

There is a need to enhance the behavioral health (BH) workforce. Paraprofessionals and peers are often on the “front lines” working with families affected by substance misuse. While they possess valuable lived experience, they often lack the requisite education to be most effective, resulting in high burnout and turnover. This study describes a novel training program for paraprofessionals working in family BH that included three online, 8-week courses (Level I) and a 12-month supervised apprenticeship (Level II). This study measured program satisfaction and effectiveness (knowledge, confidence, and perceived competence) and explored effects on career intention. A sample of paraprofessionals in the BH workforce provided data at baseline, after Level I, and after Level II. After Level II, 87% of participants rated their satisfaction with the program as high. Statistically significant improvements were found for knowledge, confidence, and competence across all domains. Almost all participants reported increased confidence after each level (93% and 94%, respectively). The majority (69%) reported increased interest in continuing their BH career and education. Overall, results suggest that the program was well-received by participants and was associated with improvements. Results provide preliminary support for apprenticeship-based training to enhance the BH workforce and address workforce challenges. Full article

The combination of Artificial Intelligence (AI) and Formative Assessment (FA) in Teacher Education explores how emerging technologies can enhance teaching practices and professional development. AI tools can provide personalized feedback, identify learning needs, and support reflective practice among educators. Integrating AI-driven formative assessment methods allows for continuous evaluation of teaching competencies, promoting adaptive learning, data-informed decision-making, and improved instructional quality in teacher education programs. The purpose of this study was to conduct a systematic review of the use of Formative Assessment (FA) and Artificial Intelligence (AI) in Teacher Education (TE) during the period 2020–2025 (inclusive). The review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, which ensures a rigorous, transparent, and reproducible process in the selection and analysis of studies. To this end, scientific articles published in the Scopus, Web of Science and Dialnet databases were reviewed, considering publications in English and Spanish. The objective was to identify trends, methodological approaches, results, and research gaps that show how AI is being integrated, or not, into FA processes in TE. The review also sought to analyze the impact of AI on student participation in assessment, feedback, decision-making, and the learning and assessment process itself, synthesizing the current evidence on the relationship between AI and FA in TE. Full article

Generating domain models from requirements is a vital and complex challenge in automated software engineering. Although large language models (LLMs) have exhibited significant competence in this area, their propensity for hallucination frequently results in models that are redundant, inconsistent, or structurally unsound. To enhance the quality of automatically generated models, this paper introduces MoRe, an LLM-based approach to domain model generation with self-refinement. Within our approach, an LLM is first tasked with producing an initial domain model draft. Subsequently, a hybrid refinement—combining LLMs with a rule-based scanner—is employed to identify and correct common issues in the model. An empirical study was conducted using 30 domain modeling problems and four open-source LLMs. The results indicate that MoRe significantly improves the quality of generated domain models. This paper advocates for incorporating a self-refinement phase as a standard component in any automated modeling workflow. Full article

Soft-tissue sarcomas (STSs) comprise a rare, heterogeneous group of mesenchymal malignancies in which histologic grade remains the strongest determinant of outcome, metastatic risk, and therapeutic strategy. Intermediate/high-grade STSs exhibit a pronounced propensity for early distant relapse, yet growing evidence indicates that metastatic behaviour is not uniform. Within this spectrum, an oligometastatic phenotype, characterised by a limited number of metastases, often confined to the lung, has emerged as a clinically and biologically distinct state associated with more indolent metastatic kinetics and improved survival when treated with aggressive local interventions. However, the criteria that define true oligometastatic STSs remain unsettled, and prospective evidence is lacking. Emerging molecular and immunological correlates provide a potential framework for biological triage. Low genomic complexity (low-risk CINSARC), a B-cell/TLS-rich tumour microenvironment, high immune-cytotoxic signatures, and persistently low or undetectable circulating tumour DNA (ctDNA) are each linked to reduced metastatic competence and may underpin oligometastatic trajectories. Conversely, high chromosomal instability, immunosuppressive microenvironments, and elevated ctDNA levels align with covertly polymetastatic biology despite limited radiographic disease. In this context, artificial intelligence and machinelearning approaches applied to computational genomics, immune profiling, imaging, and liquid-biopsy data offer a powerful strategy to integrate these multi-dimensional features and refine predictions of metastatic behaviour in STS. Oligometastatic STS therefore represents a biologically definable subset amenable to multimodal management integrating local ablative therapies, systemic agents, and immune-based strategies. Prospective, biomarker-stratified trials are needed to validate selection frameworks and optimise treatment sequencing in this evolving therapeutic space. Full article