Search Results (19,434)

Background/Objectives: Stress during pregnancy critically impacts maternal and fetal health. While prior research has linked sociodemographic and biological factors to stress levels, the role of specific biomarkers, such as vitamin D (VD), remains unexplored. This study examined the relationships among sociodemographic factors, VD status (as measured by serum 25-hydroxyvitamin D [25(OH)D] concentration), and perceived stress in pregnant women. We hypothesized that 25(OH)D concentration would be associated with perceived stress levels during pregnancy. Methods: A post hoc analysis of the Kellogg Pregnancy VD study was conducted on a cohort of 232 pregnant women with Perceived Stress Scale (PSS-10) scores at months 2, 5, and 7 with corresponding 25(OH)D concentrations. PSS-10 scores were classified into two groups: patients with scores of 0–13 were considered to have low stress, while those with scores of 14–40 were considered to have moderate-to-high stress. Logistic regression models identified factors associated with moderate-to-high stress. Results: At month 2, univariate analyses showed that being married (p = 0.002), having a college education (p = 0.0013), and lower BMI (p = 0.018) were associated with lower perceived stress, whereas Black race was associated with higher perceived stress (p = 0.027). By month 7, higher serum 25(OH)D concentration was the only significant predictor of perceived stress in univariate analysis (p = 0.002). In multivariate models at month 7, 25(OH)D approached significance (p = 0.053). Conclusions: Early in pregnancy, race, marital status, college education, and BMI were significantly associated with PSS-10 score. By month 7, 25(OH)D concentration over time emerged as a factor that was significantly associated in univariate analysis and showed a trend toward significance in multivariate models. VD status, as measured by 25(OH)D concentration, may act as a biomarker of stress during pregnancy. Results warrant further study in prospective intervention trials. Full article

Urban fragmentation has emerged as a central issue in the study of socio-spatial dynamics in contemporary cities, reflecting processes of inequality, segregation, and spatial discontinuities. This article introduces a new methodological approach to measure fragmentation by focusing on discontinuities at limits rather than on the content of statistical units alone. The method applies robust standardization of selected socioeconomic variables—higher education, foreign-born population, and low-income population—at the census tract scale in the city of Lleida, Spain. Rupture intensity is measured through a Rupture Intensity Index, which integrates standardized differences across 217 limits. Principal component analysis identifies the most influential variables, while cluster analysis characterizes the multidimensional nature of limits. Results show that fragmentation in Lleida does not follow a simple center–periphery model but a tessellated pattern of fracture lines and gradient zones. Intense fractures emerge at borders between advantaged and disadvantaged neighborhoods, whereas gradients mark gradual transitions. The study demonstrates that limits are critical sites for observing and quantifying urban fragmentation and proposes a transferable methodology for comparative research and urban policy design in diverse urban contexts. This approach provides a replicable tool for urban analysis and the design of cohesion-oriented policies. Full article

The widespread adoption of virtual classrooms during the COVID-19 pandemic accelerated the integration of Learning Management Systems (LMSs) into Higher Education, positioning them as essential tools in blended learning environments. LMSs provide teachers with a wide range of tools and functionalities, generating heterogeneous teaching strategies and providing many useful indicators for analysis. However, the complexity of log data combined with the intricacies of hybrid environments presents a significant challenge. This paper presents a quantitative approach to analysing LMS log data in Higher Education, with a specific focus on identifying and characterising teaching strategies implemented in the post-pandemic context. It seeks to examine the extent to which virtual classrooms have been effectively integrated into teaching practices and to assess how different techno-pedagogical approaches influence students’ academic performance. Moreover, we try to develop and define a comprehensive methodology for data treatment, including selection of analytical variables, the identification and clustering of usage profiles based on LMS interactions, and a comparative interpretative analysis of the findings. Our results suggest that the techno-pedagogical strategies are not uniformly effective across all areas of knowledge. This highlights the need for a more nuanced understanding of how these strategies interact with disciplinary traditions, pedagogical practices, and student profiles. Full article

This research examines how perceived social support shapes the legal motivation of Chinese university students by unraveling the underlying psychological pathways. Integrating the relational legal socialization framework with self-determination theory, we test a moderated mediation model in which subjective social support, comprising emotional and informational resources from families, peers, and institutions, exerts both a direct effect on legal motivation and an indirect effect through core self-evaluation, which is characterized by stable, positive self-beliefs. Additionally, we investigate the role of objective social support, defined as concrete resources and formal assistance, in moderating the link between subjective support and core self-evaluation. Utilizing survey responses from 365 undergraduates across mainland China, mediation and moderated mediation analyses with bootstrapped confidence intervals demonstrate that subjective support significantly enhances legal motivation, partially via improvements in core self-evaluation. Crucially, the mediating influence of core self-evaluation is stronger when objective support is high, confirming the moderation hypothesis. These findings enrich legal socialization theory by bridging individual psychological processes with behavioral outcomes in a Chinese context and underscore the importance of simultaneously cultivating perceived support experiences and strengthening formal support structures to foster legal motivation and compliance among college students. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

Cyber-sexual crimes have become a growing concern in the digital age, as rapid technological progress continues to create new forms of violence and victimization. These offenses affect society unevenly, striking more intensely among minors, women, and other vulnerable groups. Their prevalence is shaped by structural inequalities, educational, economic, and technological, that condition both exposure to digital risks and the capacity for protection. Although international research has connected these disparities with digital victimization, evidence from Spain remains limited. The aim was to analyze the regional distribution of cyber-sexual crimes in Spain between 2011 and 2022 and to explore how education, income, and digital access relate to their incidence. To this end, official data from the Spanish Statistical Crime Portal (PEC) were combined with structural indicators provided by the Spanish National Institute of Statistics. The analysis encompassed reported cases of sexual abuse, sexual harassment, corruption of minors, online grooming, exhibitionism, pornography, and sexual provocation, using standardized incidence rates per 100,000 inhabitants. Statistical methods included ANOVA with post hoc comparisons, correlation analyses, and K-means clustering to identify territorial patterns. Results revealed a sustained national increase in cyber-sexual crimes, with grooming and sexual harassment showing the most pronounced growth. The Balearic Islands (mean 4.9), Canary Islands (4.0), and Andalusia (3.9) registered the highest incidence rates, well above the national average (3.0). Educational disadvantages and low income were linked to sexual abuse and corruption of minors, whereas greater digital connectivity, expressed through higher mobile phone use, broadband access, and computer ownership, was strongly associated with grooming and other technology-facilitated offenses. Cluster analysis identified three distinct territorial profiles: high-incidence regions (Balearic and Canary Islands, Andalusia), intermediate (Murcia, Madrid, Navarre, Valencian Community), and low-incidence (Galicia, Catalonia, Castile and León, among others). In conclusion, the findings demonstrate that cyber-sexual crimes in Spain are unevenly distributed and closely linked to persistent structural vulnerabilities that shape digital exposure. These results underscore the need for territorially sensitive prevention strategies that reduce educational and economic inequalities, foster sexual and digital literacy, and promote safer online environments. Without addressing these underlying structural dimensions, public policies risk overlooking the conditions that sustain regional disparities and limit adequate protection against technology-driven sexual crimes. Full article

Background: Eating disorders (EDs) adversely affect health and athletic performance, yet their prevalence in adolescent female team athletes is understudied. Objectives: This exploratory pilot study assessed ED presence and associated factors in female basketball players aged 10–18 years from a high-level club in Granada, Spain. Methods: This cross-sectional pilot study examined 36 participants (mean age 13.3 ± 2.2 years) who completed the Spanish Eating Disorder Examination Questionnaire (S-EDE-Q). Body composition (Tanita TBF-300), sociodemographic characteristics, and sport-related factors were recorded. Associations between these covariates and S-EDE-Q global scores (GS) were examined via linear regression. Results: A higher Global Score (GS), indicating increased ED risk, correlated significantly with several factors: Lower paternal education (primary vs. university: p < 0.005) and lower socioeconomic level. Older age (AME = 0.30; p < 0.005) and greater muscle mass (p < 0.001). National competition level (p < 0.001), being a player in the pivot position (mean GS = 1.62), and presence of sport-related medical history (mean GS = 1.07). No associations were found with sport experience or weekly training hours. Conclusions: Although the overall risk of EDs is low, the association with specific factors (sociodemographic, anthropometric, and sports-related) allows vulnerable players to be identified. Longitudinal and larger-scale research is needed to confirm these initial findings and design targeted and early prevention strategies. Full article

Background: The application of large language model (LLM) in surgical decision-making is rapidly expanding, yet its potential in hand and peripheral nerve surgery remains largely unexplored. This study assessed the diagnostic and therapeutic performance of a large language model (ChatGPT-4o) in scenarios characterized by multiple valid management strategies and absent expert consensus. Methods: Three representative cases—thumb carpometacarpal (CMC I) arthritis, scaphoid nonunion, and carpal tunnel syndrome (CTS)—were developed to reflect frequent conditions in hand surgery with competing but accepted treatment options. Each case was submitted to ChatGPT-4o using a standardized prompt. LLM-generated responses were evaluated by 52 participants (34 board-certified hand surgeons and 18 residents) across diagnostic accuracy, clinical relevance, and completeness. Readability indices, including Flesch–Kincaid Grade Level, were analyzed to assess appropriateness for a medical audience. Results: ChatGPT-4o demonstrated coherent but limited diagnostic accuracy (mean 2.9 ± 1.2 SD), moderate clinical relevance (3.5 ± 1.0 SD), and slightly higher completeness (3.4 ± 1.1 SD). Performance was strongest in the standardized scenario (carpal tunnel syndrome, CTS) and weakest in individualized reasoning (CMC I arthritis). No significant differences were observed between experts and residents (p > 0.05). In higher-level reasoning, ChatGPT-4o performed best in CTS and weakest in CMC I arthritis. Readability confirmed professional-level language (mean Flesch–Kincaid Grade Level: 16.4). Conclusions: ChatGPT-4o shows promise as a supportive tool for diagnostic reasoning and surgical education, particularly where standardized frameworks exist. Its limitations in ambiguous scenarios highlight the ongoing need for expert oversight. Future large language model development should emphasize specialty-specific training and context-aware reasoning to enhance their role in surgical decision support. Full article

Theory of planned behavior (TPB) constructs and shopping routines are strong predictors of food waste behavior, while socio-demographic factors show mixed and weaker associations. We analyzed survey data from a nationally representative sample of 1066 U.S. households, measuring self-reported food waste frequency across meals, food types, and disposal methods. We applied k-medoid clustering on 19 TPB constructs and 25 shopping characteristics to identify three distinct consumer segments. “Structured Planners” (Cluster 1) showed the most deliberate shopping habits and strongest engagement in food waste reduction. “Flexible Planners” (Cluster 2) shared similar waste outcomes but approached shopping with greater spontaneity, while “Younger Wasters” (Cluster 3) were younger, lower-income, and less educated, with casual shopping habits, lower ratings of TPB constructs, and the highest food waste frequency overall. These distinct behavioral profiles enable policymakers to directly identify and target specific demographic segments for tailored food waste interventions. Particularly, “Younger Wasters” reported a significantly higher food waste frequency at 6.7 times per week, while “Structured Planners” and “Flexible Planners” were statistically similar at approximately 4.6 and 4.4 times per week. Dinner is the meal resulting in the most food waste across all groups, and “Younger Wasters” reported the highest frequency of waste in protein, oil, and grain. Post-clustering ANOVA analysis tested the predictive power of TPB, shopping characteristics, and cluster membership on food waste frequency. Results show that “Younger Wasters”, along with variables like attitude, store shopping frequency, and shopping behavior, are significantly positively associated with food waste frequency. This study demonstrates the potential of clustering analysis in exploring food waste determinants and suggests using clustered indices as proxies for respondents’ overall traits. Full article

This study focuses on the adaptation and psychometric validation of the Spanish versions of the Agentic Engagement Scale (AES) and the Enlarged Version of the Agentic Engagement Scale (EVAES) in Spanish undergraduate students. Agentic engagement, denoting the educational insights provided by students during classroom instruction, has garnered attention for its potential impact on teaching and learning. Reeve initially developed the AES in 2013, which Mameli and Passini extended from 5 to 10 items in 2019 to create the EVAES. In a sample of 278 undergraduate students, this study thoroughly examined various psychometric properties, including item response distribution, factor structure, internal consistency, test–retest reliability, and validity evidence. The results consistently demonstrated high item-total correlations, a one-factor structure, good internal consistency, and satisfactory test–retest reliability for both scales. Additionally, the study established validity evidence through positive and significant correlations between agentic engagement and academic engagement, as well as with personality traits such as extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience. In conclusion, the Spanish versions of the AES and EVAES emerge as promising tools for assessing agentic engagement among Spanish-speaking undergraduate students, offering valuable insights into their active participation and contributions to the instructional process. Full article

This paper addresses the challenge of producing lightweight, low-cost, and highly functional devices and components for the electronics industry. To tackle this issue, functionally graded materials consisting of a polymer base and a metallic conductive layer were developed. Technology for producing functionally graded materials was created and optimized. To evaluate the influence of key process parameters on the functional and mechanical properties of the composites, three-dimensional models were constructed and mathematical equations were formulated. The continuity and thickness of the surface layer were examined, the dielectric properties of the polymer material were measured, the resistance of the conductive surface layer was assessed, and adhesion tests of the surface layer were performed. Full article

Tomato (Solanum lycopersicum) is the most important vegetable crop globally; however, its production is often hindered by soil-borne biotic and abiotic stresses. The use of rootstocks provides an effective strategy to mitigate these soil-related challenges. Hence, the development of new rootstock cultivars remains crucial to meet the demands of rapidly changing environmental conditions. Wild tomato species represent valuable genetic resources for rootstock improvement and are increasingly utilized in rootstock breeding programs. Nevertheless, the genetic mechanisms, particularly quantitative trait loci (QTL), underlying rootstock–scion interaction, remain poorly understood. In this study, 38 introgression lines (ILs) derived from S. lycopersicoides were used as rootstock and grafted with the commercial cultivar ‘Torry F₁’ to evaluate their effects on morphological and fruit quality traits under greenhouse conditions. The evaluations included assessments of morphological and fruit quality traits for QTL analysis. A total of 19 QTLs were identified, associated with 11 traits such as yield, antioxidant capacity, flavonoid content, and fruit color parameters (L*, a*, b*, C*, h°), with the phenotypic variance explained ranging from 12% to 61%. Of these QTLs, seven favorable alleles originated from S. lycopersicoides, notably including a major yield-associated locus (Fy5.1). In addition, the identification of a QTL for scion stem thickness (Tsc3.1) highlights the genetic contribution of the rootstock to scion development. This study represents the first evaluation of the rootstock potential of S. lycopersicoides ILs and provides novel insights into the genetic basis of rootstock–scion interaction in tomato. The identified QTLs offer valuable information for future breeding efforts aimed at developing improved rootstock cultivars for sustainable tomato production. Full article

To tackle the poor emulsibility of hydrophilic cellulose nanocrystals (CNCs), this study prepared octenyl succinic anhydride (OSA)-modified bamboo shoot CNC (OSNC) via acid hydrolysis and esterification, using microcrystalline cellulose (MCC) as a control. The degree of substitution (DS), chemical structure, crystalline structure, morphological characteristics, zeta potential, wettability, and thermal stability of OSNC and OSA-modified MCC (OSA-MCC) were characterized using multiple techniques. Results showed that at the optimal cellulose-to-OSA ratio (1:0.225), OSNC had a higher DS (0.029 ± 0.01) than OSA-MCC (0.024 ± 0.02). FTIR confirmed successful OSA grafting; XRD showed a preserved cellulose I crystal form with slightly reduced crystallinity; OSNC had improved dispersion stability (zeta potential: −44.0 mV), balanced amphiphilicity (contact angle: 61.8°), and enhanced thermal stability. This work enables high-value utilization of bamboo shoot by-products and supports developing green food-grade cellulose-based nanomaterials for food emulsions. Full article

Undergraduate life science education faces high attrition rates, especially among students from underrepresented groups. These disparities are often linked to differences in prior knowledge, self-efficacy, and interest, which are rarely addressed in traditional lecture-based instruction. This work explores the use of machine learning-based Intelligent Tutoring Systems (ITSs) to support personalized instruction in biology education by examining stochasticity in molecular systems. Accordingly, we developed and validated a Random Forest classification model and used it to assign instructional materials based on students’ prior knowledge and interests. We then applied the model in an introductory biology classroom and individually estimated the most promising instructional format. Results show that the most effective instruction can be reliably predicted from student performance and interest profiles, and model-based assignments may help reduce pre-existing opportunity gaps. Thus, machine-learning-driven instruction holds promise for enhancing equity in life science education by aligning materials with students’ needs, potentially reducing differences in achievement, self-efficacy, and cognitive load, which might be relevant to promoting underrepresented students. To facilitate a straightforward implementation for educators facing similar challenges associated with teaching molecular stochasticity, we developed an open-access ITS tool and provided a scalable approach for developing similar personalized learning tools. Full article

This paper applies a hybrid multi-criteria decision-making (MCDM) model that integrates the Analytic Hierarchy Process (AHP) for structured weighting of evaluation criteria with the Net Worth Analysis (NWA) method for value-based aggregation of scores. The proposed framework was employed to evaluate Learning Management Systems (LMS) in higher education, involving two independent expert panels representing management and IT perspectives. Results of the AHP analysis show that cost (28%), security (22%), and usability (17%) are the most influential criteria in the decision-making process, reflecting institutional priorities for financial efficiency, safety and ease of use. Based on the combined AHP-NWA model, Moodle 4.3 emerged as the most sustainable choice (0.586), followed by Atutor 2.2.1 (0.541) and Blackboard (SaaS edition) (0.490). The inclusion of sensitivity and scenario analyses confirmed the robustness of the model, demonstrating that the ranking of alternatives remains stable under variations in weighting factors and different strategic priorities. By framing LMS evaluation within the context of sustainable digital transformation, the study emphasizes how transparent and systematic decision-making supports long-term institutional resilience and aligns with the principles of Education for Sustainable Development (ESD). In addition, the framework contributes to the achievement of Sustainable Development Goal 4 (Quality Education), by guiding higher education institutions toward inclusive, resilient and cost-effective digital solutions. Full article