Search Results (1,233)

The transition toward sustainable production requires engineering and science education to adopt active, interdisciplinary, and practice-oriented teaching strategies. This article presents a comparative analysis of two educational initiatives implemented in Panama aimed at fostering sustainability competencies at the university and secondary school levels. The first initiative, developed at the Technological University of Panama, integrates project-based learning and circular economy principles into an extracurricular module focused on production planning, sustainable design, and quality management. Students created prototypes using recycled HDPE and additive manufacturing technologies within a simulated startup environment. The second initiative, carried out in two public secondary schools, applied project- and challenge-based learning through the Design Thinking framework, supporting teachers and students in addressing real-world sustainability challenges. Both programs emphasize hands-on learning, creativity, and iterative development, embedding environmental awareness and innovation in both formal and informal educational settings. The article identifies key opportunities and challenges in implementing active methodologies for sustainability education. Challenges such as limited infrastructure and rigid schedules were identified, along with lessons learned for future implementation. Students connected local issues to global goals like the SDGs and saw themselves as agents of change. These initiatives offer practical models for advancing sustainability education through innovation and interdisciplinary collaboration. Full article

Magnetohydrodynamic (MHD) flow and heat transfer in porous media are central to many engineering applications, including heat exchangers, MHD generators, and polymer processing. This study examines the boundary layer flow and thermal behavior of an electrically conducting viscous fluid over a porous stretching tube. The model accounts for nonlinear thermal radiation, internal heat generation/absorption, and Darcy–Forchheimer drag to capture porous medium resistance. Similarity transformations reduce the governing equations to a system of coupled nonlinear ordinary differential equations, which are solved numerically using the BVP4C technique with Response Surface Methodology (RSM) and sensitivity analysis. The effects of dimensionless parameters magnetic field strength (M), Reynolds number (Re), Darcy–Forchheimer parameter (Df), Brinkman number (Br), Prandtl number (Pr), nonlinear radiation parameter (Rd), wall-to-ambient temperature ratio (rw), and heat source/sink parameter (Q) are investigated. Results show that increasing M, Df, and Q suppresses velocity and enhances temperature due to Lorentz and porous drag effects. Higher Re raises pressure but reduces near-wall velocity, while rw, Rd, and internal heating intensify thermal layers. The entropy generation analysis highlights the competing roles of viscous, magnetic, and thermal irreversibility, while the Bejan number trends distinctly indicate which mechanism dominates under different parameter conditions. The RSM findings highlight that rw and Rd consistently reduce the Nusselt number (Nu), lowering thermal efficiency. These results provide practical guidance for optimizing energy efficiency and thermal management in MHD and porous media-based systems.: Full article

Background: Mixed martial arts (MMA) is becoming increasingly popular and is developing dynamically in terms of training methods and number of participants involved, while weightlifting, powerlifting, and other kinds of strength disciplines are well established. In this study, the aim was to compare the body composition, as an anthropometric effect of training in MMA fighters and strength athletes, and then analyze and find reasoning for observed differences. Methods: Thirty-four young healthy male participants (body weight 84.9 ± 10.2 kg, body height 182.0 ± 6.8 cm, BMI 25.8 ± 2.51 kg/m², tier 2/3 in McKay’s sports level classification) represented two groups: MMA (n = 17) and powerlifting athletes (STR, n = 17). The measured anthropometric characteristics were skeletal muscle mass (SMM), percentage of body fat (PBF), body fat mass (FM) and visceral fat mass (VFM). Phase angle (º) was measured as an indicator of tissue quality and we performed detailed investigations of soft fat-free tissue mass (SLM) and of fat mass in body parts separately in each lower and upper limb and trunk. Results: The groups did not differ in terms of body weight, height, BMI, SMM, PBF, FM, VFM, SLM in upper limbs and trunk, FM in the body parts, or the phase angle (all p > 0.05). The statistically significant differences were only observed in the SLM of both lower limbs (greater in STR, p < 0.05) but, after statistical correction with the Holm’s method, these parameters also did not show statistically significant differences despite high effect sizes. Conclusions: The MMA athletes do not differ significantly from strength training athletes in measured anthropometric parameters despite distinct differences in training methodology. The reasons for these observations need future research, combining anthropometric measurements with training and competing load monitoring. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article

The integration of emerging technologies is reshaping the teaching skills required in the 21st century, yet little evidence exists on how contextualized training supports rural teachers in adopting active methodologies and critically incorporating AI into entrepreneurship education. This study evaluated the impact of a 40-h professional development program implemented in Educational District 15D01 in the Ecuadorian Amazon. Thirty-nine secondary school teachers participated (mean age = 43.1 years); 36% lacked prior entrepreneurship training, and 44% had not recently mentored student projects. A sequential explanatory mixed-methods design was employed. The quantitative phase employed a 22-item questionnaire that addressed four dimensions: entrepreneurial knowledge, competencies, methodological strategies, and AI integration. Significant pre–post improvements were found (p < 0.001), with large effects for knowledge (d = 1.43), methodologies (d = 1.39), and AI integration (d = 1.30), and a moderate effect for competences (d = 0.66). The qualitative phase analyzed 312 open-ended responses, highlighting greater openness to innovation, enhanced teacher agency, and favorable perceptions of AI as a resource for ideation, prototyping, and evaluation. Overall, the findings suggest that situated, contextually aligned training can strengthen digital equity policies, foster pedagogical innovation, and empower educators in underserved rural communities, contributing to sustainable pathways for teacher professional development. Full article

School textbooks are central to the teaching, studying, and learning processes because they mediate the interaction between the prescribed curriculum and the educational experience in the classroom. Evaluating their didactic structure critically allows us to determine the degree to which they align with current curriculum guidelines and promote meaningful learning. This study aimed to analyze the extent to which Ecuadorian natural science textbooks reflect constructivist learning principles and promote the development of key competencies established in the National Priority Curriculum. This curriculum guides the achievement of essential results and strengthens fundamental competencies for students’ comprehensive development. Content analysis was adopted as the methodological approach given its relevance in examining the didactic and curricular dimensions of educational materials. The analysis covered twelve eighth-grade General Basic Education textbooks and their supplementary materials. The analysis was based on two instruments: specialized summary analysis sheets (RAE) and a purpose-built checklist. The ATLAS.ti 25 and IRaMuTeQ programs supported the systematization and visualization of the data. The results showed limited integration of constructivist strategies, such as teaching for comprehension, inquiry-based learning, and problem solving, in most of the analyzed texts. These findings underscore the need to expand and strengthen the incorporation of contextualized, critical, and meaningful learning experiences to improve the didactic design of school textbooks. Such improvements would promote coherent articulation between objectives, content, methods, resources, and assessment in line with constructivist principles of the Ecuadorian curriculum. Furthermore, given these approaches’ affinity with curricular frameworks in other regional countries, the results could offer relevant guidance and starting points for reflection on developing and using textbooks in Latin American contexts with comparable educational characteristics. Full article

Scientific awareness is rising regarding fish and sea invertebrates’ sensitivity to the sound field’s particle motion component. The AquaVib, a distinctive laboratory setup, provides a practical methodology for controlled sound exposure experiments on small aquatic organisms, enabling a separate assessment of their acoustic pressure- and particle motion-elicited responses across a range of realistic scenarios. The chosen facility design permits the reproduction of realistic sound exposures at different kinetic-to-potential energy ratios, with characteristics similar to underwater-radiated noise from human activities such as shipping or offshore installations (<1 kHz). It provides a cost-efficient multimodal approach to investigate potential physiological, pathological, and ultrastructural effects on small aquatic organisms at any stage of maturity. This study details the vibroacoustic characterization of the AquaVib system, identifies key challenges, and outlines planned improvements. The ultimate goal of the presented approach is to contribute to the scientific community and competent authorities in covering the main gaps in current knowledge on the sensitivity of aquatic organisms to the particle motion component and to identify and quantify potential acute and long-term detrimental effects arising from human activities. Full article

Dyadic co-construction, the mutual adaptation that occurs between dogs and their owners, is often discussed in terms of cooperation and participation, yet it remains poorly defined and under-conceptualized in the literature. This review proposed that self-determination theory (SDT), with its three core psychological needs—autonomy, competence, and relatedness (attachment)—offers a valuable framework for understanding this phenomenon within a dyadic context. The objectives of this review were twofold: (1) to conceptualize co-construction in owner–dog interactions through the lens of SDT, and (2) to propose methodological approaches for studying this process, while acknowledging their current limitations. Dyadic co-construction emerges as a dynamic, evolving process of mutual influence, shaped by biopsychosocial factors, individual and shared experiences, and the physical and social environments of both human and dog, as well as the dyad as a unit. Depending on the nature of the interaction, co-construction can be beneficial or detrimental. Positive training practices and secure attachment patterns in both humans and dogs tend to foster more harmonious co-construction, whereas aversive methods and insecure attachment may hinder it. Although existing methodologies offer promising insights into this process, they often lack standardization, statistical robustness, and true bidirectionality. This review underscores the need for more integrative, longitudinal, and empirically grounded approaches to fully capture the complexity and clinical relevance of owner–dog dyadic co-construction. Full article

Learning Natural Sciences represents a key opportunity to spark scientific interest and foster fundamental skills across different educational stages. This study aimed to analyze the influence of motivation on academic performance in the learning of Natural Sciences at various educational levels. To this end, a systematic review method was employed following PRISMA guidelines, consulting the Web of Science and Scopus databases, identifying four relevant studies. The results showed that high levels of motivation were associated with a more positive classroom attitude and better conceptual understanding, which enhanced academic performance. The use of innovative methodological strategies, such as implementing immersive virtual reality in the classroom, PhET simulations (Physics Educational Technology), and the use of hypertext, significantly increased both student motivation and academic performance. The meta-analysis revealed a favorable effect in experimental groups, showing moderate heterogeneity (I² = 49) and significance of p = 0.0001. The concurrence analysis reported that current pedagogical practices should focus on strengthening student autonomy and active engagement, integrating critical reflection, the use of innovative methodological strategies, and technological resources that enhance meaningful learning in scientific literacy. Among the instruments used to measure motivation, the Motivation to Learn Science Questionnaire was identified, and for academic performance, the Motivated Strategies for Learning Questionnaire. In conclusion, the importance of implementing the identified methodological strategies across different educational stages is emphasized, in order to promote competency-based learning through meaningful and innovative acquisition of content in Natural Sciences. Full article

This paper investigates the subjective value perceived by teachers, defined as their overall appreciation of EU-funded educational projects in terms of usefulness, relevance, and impact on education, regarding projects implemented in Romanian schools during the period 2014–2022. The main factors influencing the perceived value were identified through a quantitative approach using a questionnaire-based survey, administered to a sample of 1050 teachers from various regions of the country. The results reveal that improvements achieved in various aspects of the educational environment quality have a positive influence on the analyzed indicator. These improvements can be grouped into two categories of factors that act at the level of school, on the one hand, and at the level of students, on the other hand, both having a significant impact on increasing the perceived value of EU-funded educational projects. The differences between schools that benefited from such educational projects and other schools were also addressed, as well as the influence of the dominant socio-economic status of children studying in different schools on the improvement of the quality of the educational environment. The conclusions highlight the strategic role of European funding in reducing educational disparities and the need to target support to vulnerable schools. The practical and managerial implications include strengthening infrastructure, adapting methodologies, and developing staff competencies, alongside interventions aimed at improving student progress. Full article

Farmland shelterbelts are vital ecological infrastructure for sustaining agriculture in arid regions, where high winds, soil erosion, and water scarcity severely constrain productivity. While their protective functions—reducing wind speed, controlling erosion, moderating microclimates, and enhancing yields—are well documented, previous studies have largely examined individual structural elements in isolation, leaving their interactive effects and trade-offs poorly understood. This review synthesizes current research on the structural optimization of shelterbelts, emphasizing the critical relationship between their physical and biological attributes and their protective functions. Key structural parameters—such as optical porosity, height, width, orientation, and species composition—are examined for their individual and interactive impacts on shelterbelt performance. Empirical and modeling studies indicate that moderate porosity maximizes wind reduction efficiency and extends the leeward protection zone, while multi-row, multi-species configurations effectively suppress soil erosion and improve microclimate conditions. Sheltered areas experience reduced evapotranspiration, increased humidity, and moderated temperatures, collectively enhancing crop water use efficiency and yielding significant improvements in crop production. Advanced methodologies, including field monitoring, wind tunnel testing, computational fluid dynamics, and remote sensing, are employed to quantify benefits and refine designs. A multi-objective optimization framework is essential to balance competing goals: maximizing wind reduction, minimizing water consumption, enhancing biodiversity, and ensuring economic viability. Future challenges involve adapting designs to climate change, integrating water-efficient and native species, leveraging artificial intelligence for predictive modeling, and addressing socio-economic barriers to implementation. Building on this evidence, we propose a multi-objective optimization framework to balance competing goals: maximizing wind protection, minimizing water use, enhancing biodiversity, and ensuring economic viability. We identify key research gaps including unresolved porosity thresholds, the climate resilience of alternative species compositions, and the limited application of optimization algorithms and outline future priorities such as region-specific design guidelines, AI-driven predictive models, and policy incentives. This review offers a novel, trade-off–aware synthesis to guide next-generation shelterbelt design in arid agriculture. Full article

Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low. Full article

The global demographic transition toward an ageing population has necessitated substantive reforms in dental education, particularly within the field of geriatric prosthodontics. Conventional curricula have frequently prioritized technical competencies while insufficiently addressing the integration of biological, psychosocial, and ethical complexities inherent in the care of older adults. This scoping review critically examined these curricular deficiencies by synthesizing evidence from 34 peer-reviewed studies, employing Bloom’s Taxonomy as a conceptual framework to inform a systematic and pedagogically grounded curriculum redesign. The primary aim was to identify existing gaps in undergraduate and postgraduate education, evaluate the efficacy of active and simulation-based learning modalities, assess the utility of reflective practices and standardised assessment tools, and formulate strategic, taxonomy-aligned pedagogical guidelines. Following the PRISMA-ScR methodology, the included studies were thematically analysed and categorized across the six cognitive levels of Bloom’s Taxonomy. Findings highlighted the effectiveness of integrated educational strategies, including Case-Based Learning, interprofessional education, virtual simulations, and structured assessments such as Objective Structured Clinical Examinations (OSCE). Furthermore, reflective models such as “What? So What? Now What?” fostered higher-order cognitive processes, ethical reasoning, and self-directed learning. By aligning cognitive levels—from foundational knowledge recall to innovative creation—ten evidence-based educational guidelines were developed. These guidelines are pedagogically sound, empirically supported, and adaptable to diverse curricular contexts. The proposed framework ensures a deliberate, progressive trajectory from theoretical comprehension to clinical expertise and ethical leadership. Future research should explore longitudinal outcomes and develop scalable, culturally responsive models to support the broader implementation of curricular reform in geriatric dental education. Full article

Meaning conflation deficiency (MCD) presents a continual obstacle in natural language processing (NLP), especially for low-resourced and morphologically complex languages, where polysemy and contextual ambiguity diminish model precision in word sense disambiguation (WSD) tasks. This paper examines the optimisation of contextual embedding models, namely XLNet, ELMo, BART, and their improved variations, to tackle MCD in linguistic settings. Utilising Sesotho sa Leboa as a case study, researchers devised an enhanced XLNet architecture with specific hyperparameter optimisation, dynamic padding, early termination, and class-balanced training. Comparative assessments reveal that the optimised XLNet attains an accuracy of 91% and exhibits balanced precision–recall metrics of 92% and 91%, respectively, surpassing both its baseline counterpart and competing models. Optimised ELMo attained the greatest overall metrics (accuracy: 92%, F1-score: 96%), whilst optimised BART demonstrated significant accuracy improvements (96%) despite a reduced recall. The results demonstrate that fine-tuning contextual embeddings using MCD-specific methodologies significantly improves semantic disambiguation for under-represented languages. This study offers a scalable and flexible optimisation approach suitable for additional low-resource language contexts. Full article

In recent years, the remarkable versatility of large language models (LLMs) has spurred considerable interest in leveraging their capabilities for recommendation systems. Critically, we argue that the intrinsic aptitude of LLMs for modeling sequential patterns and temporal dynamics renders them uniquely suited for sequential recommendation tasks—a foundational premise explored in depth later in this work. This potential, however, is tempered by significant hurdles: a discernible gap exists between the general competencies of conventional LLMs and the specialized needs of recommendation tasks, and their capacity to uncover complex, latent data interrelationships often proves inadequate, potentially undermining recommendation efficacy. To bridge this gap, our approach centers on adapting LLMs through fine-tuning on dedicated recommendation datasets, enhancing task-specific alignment. Further, we present the temporal Integration Enhanced Fine-Tuning of Large Language Models for Sequential Recommendation (TisLLM) framework. TisLLM specifically targets the deeper excavation of implicit associations within recommendation data streams. Its core mechanism involves partitioning sequential user interaction data using temporally defined sliding windows. These chronologically segmented slices are then aggregated to form enriched contextual representations, which subsequently drive the LLM fine-tuning process. This methodology explicitly strengthens the model’s compatibility with the inherently sequential nature of recommendation scenarios. Rigorous evaluation on benchmark datasets provides robust empirical validation, confirming the effectiveness of the TisLLM framework. Full article