Search Results (2,427)

Background/Objectives: Simulation-based education is increasingly used in health sciences to promote active learning and the development of clinical and non-technical skills. However, its implementation in undergraduate dental education remains limited. This study aimed to explore dental students’ perceptions of the Self-Learning Methodology in Simulated Environments (MAES©) applied to high-fidelity simulation. Methods: A mixed-methods, cross-sectional pilot study was conducted with 80 fourth-year dental students enrolled in a Pediatric Dentistry course at a Spanish university. Quantitative data were collected using a validated satisfaction questionnaire (Cronbach’s alpha = 0.905), and descriptive statistics were performed. Qualitative data were obtained through open-ended questions and analyzed using inductive content analysis. Results: Students reported high levels of satisfaction, motivation, and perceived learning, with mean scores above 8.5 out of 10 across all evaluated dimensions. The facilitator’s role received the highest ratings. Qualitative analysis identified four main themes: perceived advantages of the methodology, increased engagement and participation, the value of structured debriefing, and areas for improvement related to group dynamics and performance-related stress. Conclusions: The MAES© methodology was well received and perceived as a feasible approach in dental simulation-based education. It may support student-centered learning, collaboration, and reflective practice, providing practical guidance for educators interested in implementing active learning strategies. As an exploratory pilot study conducted in a single institution, these findings should be interpreted cautiously and warrant further research. Full article

This essay offers a relational reinterpretation of the doctrine of the Trinity through perichoresis, understood as the mutual indwelling communion of Father, Son and Holy Spirit in which distinction is preserved without separation and unity without domination. More than a technical term of patristic theology, perichoresis names the dynamic communion that constitutes the life of the triune God. Drawing on biblical intuition and patristic formulation and engaging modern Trinitarian theologians in sustained dialogue, this essay develops a historical, contextual and practical approach that incorporates Korean cultural metaphors and Clinical Pastoral Education (CPE). It argues that perichoresis functions not merely as a doctrinal safeguard but as a theological grammar that reorients ontology toward relationality and frames Christian life as participatory communion. Rather than remaining a conceptual proposal, this essay ultimately envisions a perichoretic paradigm in which life itself is understood as participation in the living communion of the Trinity. Full article

Support professionals in organizational domains—encompassing information technology, administrative services, human resources, and records and archives management (RAM)—confront enduring obstacles, including peripheral status, interdisciplinary coordination imperatives, and standards proliferation. This conceptual synthesis investigates how congruence with Management System Standards (MSSs) can alleviate these predicaments by advancing system-level integration across support areas. Rooted in General Systems Theory, the inquiry scrutinizes ISO standards from pivotal technical committees and 2024 ISO Survey adoption metrics. It accentuates the voluminous standards burdening support functions and the attendant systemic complexity. The communal Plan–Do–Check–Act (PDCA) cycle and High-Level Structure (HLS) of MSSs are framed as unifying instruments that diminish fragmentation and augment coherence. Employing RAM as the principal exemplar, the examination discloses constrained alignment with overarching MSSs despite vigorous global embrace of standards like ISO 9001, ISO 14001, and ISO/IEC 27001. A succinct conceptual model is advanced to depict how PDCA and HLS can interlink support subsystems with organizational objectives. The study underscores strategic harmonization to amplify the prominence of underappreciated support roles, with ramifications for information technology (IT), human resources (HR), and administrative services. Recommendations are proffered for standards developers, practitioners, and professional associations, as well as educators, complemented by avenues for future empirical scholarship. Full article

This study presents pedagogical innovations in the undergraduate course Postharvest Technology and Quality Management at Sultan Qaboos University (SQU), where project-based learning (PBL) is used to integrate academic quality assurance and sustainability education, aligning with the United Nations Sustainable Development Goals (SDGs). This study adopts a descriptive multiple-case approach to analyze five representative student projects and their alignment with the SDGs. The projects address real-world postharvest challenges, including quality preservation, renewable energy use, and food loss reduction. A qualitative cross-case analysis based on SDGs mapping criteria was used to evaluate project alignment and societal outcomes. Representative student projects demonstrate how inquiry-driven learning enhances technical competence and research skills. Quantitative outcomes include a reduction in weight loss from 27.1% to 18.8% in coated tomatoes, increased weight loss up to 46.37% under severe mechanical damage in zucchini, and significant firmness reduction in bruised apples (53.23 N to 21.64 N). Hybrid infrared–hot air drying improved drying efficiency by reducing drying time and enhancing moisture removal, while banana coating experiments showed reduced moisture loss and delayed ripening. The analysis shows that all five projects align with at least two SDGs, with SDG 12 addressed in 100% of the cases. The curriculum is explicitly aligned with SDG 2 (Zero Hunger), 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), 12 (Responsible Consumption and Production), and 13 (Climate Action). The study highlights the societal relevance of course-based projects through their contribution to SDG-related challenges and emphasizes the role of mentorship, teamwork, and experiential learning infrastructure in sustaining effective PBL implementation. Cross-case comparison highlights common sustainability contributions, including a reduction in postharvest losses, adoption of natural preservation methods, and improvements in energy-efficient processing. The findings highlight the potential of course-based PBL as a context-specific approach for integrating sustainability into undergraduate education. Full article

As organizations become increasingly digital, the environmental impact of digital infrastructures is gaining growing attention within ESG agendas. However, many organizations still struggle to translate digital infrastructure data into clear, measurable, and reliable ESG reporting outcomes. This study develops and empirically tests a socio-technical model explaining how organizations achieve ESG measurement and reporting readiness through sustainable computing practices. Drawing on a quantitative cross-sectional survey of 312 respondents from government, private, and educational organizations in Saudi Arabia and the GCC region, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis (MGA). The findings reveal that organizational drivers are the strongest predictors of sustainable computing practices, while organizational barriers exert significant negative effects on adoption. Sustainable computing practices play a critical mediating role by enabling organizations to transform fragmented digital data into structured and credible ESG reporting systems. Sectoral differences further highlight the influence of institutional contexts on adoption pathways. The study contributes by positioning sustainable computing as a foundational organizational capability that bridges digital transformation and ESG reporting, offering both theoretical insights and practical implications for enhancing ESG measurement and reporting readiness. Full article

The quality of electrical power on distribution networks depends heavily on the performance of the harmonic filtering method implemented according to the operating conditions of the system under study. This article proposes a new experimental approach that allows us to determine the dynamic behavior of the harmonic signature of the nonlinear load connected to the electrical network. The ultimate goal is to propose a new law for extracting the reference currents required during the overall online harmonic filtering process using a hybrid filter. This offers advantages in robustness and accuracy during variations in the electrical network compared to the classical methods used in the current literature. The methodological approach consists of selecting several nonlinear loads according to specific profiles and technical characteristics, then experimentally analyzing their harmonic signatures over time to obtain new models for extracting reference currents that will ultimately be faster to implement. In a decentralized global harmonic filtering strategy using a TLC adaptive hybrid filter compliant with the IEEE-519-2022 standard, the results obtained offer THD (total harmonic distortion) values of 1.88%, 3.29%, and 2.78% in three-phase currents, with a reduced DC voltage consumption of 105 V for the inverter. This contrasts with similar models in the current literature, which require input DC voltages exceeding 850 V for identical performance. This work therefore represents a major contribution to new models for extracting experimentally obtained reference currents, enabling the optimization of power quality on electrical networks through the use of a hybrid filter. Full article

This paper assesses the challenges and policy responses for the adoption of electric vehicles (EVs) in Africa. We applied a decision support system framework comprising a new integration of the RANking COMparison Method (RANCOM) and Root Assessment Method (RAM) for the first time in the literature to address the multi-criteria decision analysis (MCDA) problems based on expert opinions. Six experts evaluated five criteria along with ten policy responses. While the weights of criteria are computed via the RANCOM method, the RAM approach ranks the policy responses. Moreover, the Compromise Fuzzy Ranking (CFR) method defines the consensus rankings via both positional ranks and preference scores. Furthermore, a three-stage comparative analysis is carried out for criteria weighting, policy responses ranking, and alternative consensus ranking. A sensitivity analysis is carried out including the consideration of experts’ significance according to their experience and their omission. The findings indicated the most critical challenges were the scarcity in charging infrastructure and the affordability and accessibility issues. The resilient charging infrastructure is the most appropriate policy response. The findings direct planners and EVs policymakers across the continent toward a policy that will ensure a clean and sustainable transportation system. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping teaching, learning, and assessment, particularly in programming education. While AI coding assistants can enhance feedback, scaffolding, and student engagement, their educational value depends on pedagogical alignment, institutional readiness, and faculty practice, not merely technical capability. Existing adoption frameworks, however, inadequately address these pedagogical and institutional dimensions in domain-specific contexts. This study proposes the AI Coding Assistant Adoption Framework (AICAAF), a theoretically grounded model integrating the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Determination Theory (SDT). The framework was developed iteratively from prior literature and refined through faculty perspectives. It conceptualises adoption across four interrelated dimensions: usability, pedagogical adequacy, institutional readiness, and faculty engagement. Using PyChatAI as an instrumental case study, this qualitative research draws on semi-structured interviews with 15 faculty members teaching programming courses at Jouf University, a public institution in Saudi Arabia operating in a low- to mid-resource context. Data were analysed using reflexive thematic analysis. Findings indicate that PyChatAI is intuitive and beneficial for novice learners, particularly through instant feedback and automated error correction. However, its pedagogical value is limited in advanced and industry-aligned contexts. Institutional barriers, such as inadequate infrastructure, limited technical support, and the absence of policy frameworks, significantly constrain effective integration. Despite this, faculty expressed strong commitment to adopting AI tools, proposing strategies including curriculum redesign, professional development, and gamified instruction. The study reconceptualises AI adoption as a pedagogical and institutional transformation rather than a purely technological shift. The AICAAF provides a robust foundation to guide curriculum design, teaching practice, and policy development for responsible AI integration in programming education. Full article

This study investigates the design, development, and practical exploitation of an educational ontology for classical guitar instruction, within a semantically driven and application-oriented framework. The proposed approach aims to bridge the gap between formal knowledge representation and its functional use in real educational contexts. The ontology is developed using OWL in the Protégé environment and systematically models core pedagogical elements, including learning objectives, technical skills, instructional practices, and assessment processes, in alignment with the official curriculum. The semantic model is stored and managed as an RDF graph within a GraphDB repository, where it supports consistency checking and semantic querying through SPARQL. For application development, the ontological model is subsequently translated into a structured tabular schema suitable for the AppSheet low-code environment. Thus, GraphDB functions as a semantic validation and knowledge management layer, whereas the educational application operates on an application-oriented representation derived from the ontology rather than on a live RDF backend. The proposed three-tier architecture (Ontology–GraphDB–Application) demonstrates how Semantic Web technologies can support the transformation of abstract knowledge models into functional educational systems. The results highlight the capacity of ontology-driven approaches to enhance the organization, reusability, and pedagogical coherence of instructional knowledge, while enabling scalable and accessible application development through low-code technologies. The study contributes to the field of educational technology by providing a practical framework for integrating semantic knowledge representation into music education and laying a semantic foundation for future extensions toward adaptive and intelligent learning environments. Full article

This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R² = 0.64) and acceptable predictive capability (R² = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution. Full article

This study provides a mapping of the current state of electromobility education in Mexico, aiming to identify gaps and opportunities in developing specialized talent for the energy transition and sustainable mobility. A scoping review was conducted using a structured methodological framework, involving searches across official platforms, academic institutions, and government sources, with specific criteria to select programs related to electric vehicle technology and sustainable transport. The results reveal a concentration of programs in the northern and central industrial regions, while southern and southeastern areas remain underrepresented, creating territorial disparities. Additionally, specialized programs were identified, alongside gaps in emerging areas such as battery recycling, renewable energy integration, and policy and regulatory training. The review highlights that, although Mexico has a substantial workforce, there are significant deficiencies in advanced, innovation-oriented training, limiting the country’s participation in more specialized segments of global EV value chains. It is concluded that strengthening academia–industry collaboration, expanding curricula in strategic domains, and investing in postgraduate education and research are essential steps to support a more inclusive and sustainable electromobility transition. Full article

Agricultural soils are a major source of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄), highlighting the need for cost-effective and field-applicable monitoring solutions. This study developed and evaluated a low-cost real-time monitoring system for soil CO₂ and CH₄ emissions by integrating surface emission chambers, low-cost gas sensors, a solar-powered energy supply, and IoT-based wireless communication. Three acrylic chambers with different heights (40, 60, and 80 cm) were fabricated to investigate the influence of chamber geometry on measurement performance. System performance was assessed through simultaneous measurements against a Biogas 5000 analyzer under simulated conditions and during field deployment in a sugarcane cultivation area in Khon Kaen Province, Thailand. Relative agreement was used to compare the developed system with the reference instrument. The results showed that relative agreement varied with chamber height for both gases. Under simulated conditions, the 80 cm chamber achieved the highest overall relative agreement for CO₂ and CH₄, underscoring the importance of sufficient headspace volume in chamber-based measurements. Field experiments confirmed the system’s capability for continuous CO₂ monitoring in an agricultural environment. However, CH₄ emissions were not detected during the study period, likely due to drought-induced, well-aerated soil conditions. The developed system demonstrated stable autonomous operation, low energy consumption, and ease of installation, making it suitable for long-term field applications. Overall, the proposed platform provides a practical and scalable approach for real-time soil GHG monitoring and offers strong potential for integration into precision agriculture and climate-smart farming systems to support GHG mitigation strategies. Full article

The development of sustainable tourism in rural areas brings new challenges in terms of innovation and social inclusion. In this context, this study examines the adoption of social innovation by managers of rural guesthouses in Morocco. The objective is to identify the factors that influence their intention to adopt such practices, which may strengthen local cohesion, enhance cultural resources, and meet visitors’ expectations regarding sustainability. The analysis draws on the conceptual framework of the diffusion of innovation, which allows for the exploration of perceptions related to the relative advantage, compatibility, ease of use, visibility, and trialability of innovative practices. The research is based on a questionnaire survey conducted with a sample of 174 managers. The data collected underwent confirmatory factor analysis to validate the theoretical dimensions of the model, and were then analyzed using an ordered Logit model to account for the ordinal nature of the dependent variable measuring the intention to adopt. The empirical results indicate that several perceived factors—namely the superiority of the innovation, its economic or symbolic benefits, its cultural compatibility, its simplicity of understanding and use, and the visibility of its effects—have a significant influence. Other dimensions, such as technical compatibility or risk perception, do not show a notable effect. The study also highlights the role of education level and gender in the propensity to adopt social innovation. Full article

Background/Objectives: Aim: Dental practice involves continuous exposure to saliva and blood, creating persistent opportunities for cross-infection if contaminated instruments are not processed correctly. This study aimed to evaluate dental students’ knowledge regarding blood-borne infections and infection prevention measures, and to compare knowledge levels according to academic year and sex. Materials and Methods: A structured questionnaire consisting of 21 single-best-answer questions was administered to 93 undergraduate dental students (Years I–VI) from the Faculty of Dental Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, Iași, Romania. The questionnaire evaluated knowledge related to instrument classification, cleaning and disinfection procedures, sterilization parameters, autoclave monitoring tests, and storage conditions. Demographic data were also collected. Statistical analysis was performed using IBM SPSS Statistics version 31, and associations between responses and demographic variables were assessed using chi-square tests. Associations between responses and demographic variables (academic year and sex) were evaluated using chi-square tests (p < 0.05). Results: Most participants correctly identified several key steps in the instrument processing circuit, including the use of high-level disinfectant–detergent solutions (88.2%) and the need for disinfection followed by sterilization (76.3%). However, important knowledge gaps were identified regarding autoclave pre-use checks, correct sterilization temperatures and exposure times, recommended sterile storage periods, and the interpretation of sterilization monitoring tools such as type 5 chemical integrators, Bowie–Dick tests, and Helix tests. Knowledge levels differed significantly according to academic year (p < 0.05). Conclusions: Although overall awareness of instrument processing procedures among dental students was generally satisfactory, several inconsistencies were observed in critical technical aspects of sterilization and monitoring. These findings highlight the need for strengthened infection control education and repeated practical training to reduce the risk of cross-infection in dental practice. Full article

Debates over teacher evaluation and teacher effectiveness have largely focused on methodological refinement, particularly the technical adequacy of value-added models (VAMs). Far less attention has been paid to the validity of the evidentiary foundations on which these systems rest. In this theoretical synthesis of validity theory related to use, interpretation, and consequences, we argue that validity must be treated not as a technical property of measures alone, but as an interpretive, political, and governance-laden construct that precedes legitimate policy use. We examine achievement tests as the primary evidentiary base for teacher effectiveness claims, highlighting persistent misalignments between test purposes, interpretations, and high-stakes uses. We then show how VAMs extend rather than resolve these foundational validity problems by amplifying weak inferential links between student test scores and judgments of teacher quality. Drawing on contemporary validity theory, we emphasize the centrality of interpretation–use arguments and consequential validity in accountability contexts. We conclude by outlining what a validity-first approach to teacher evaluation would require and why unresolved validity questions represent ethical and political failures in education policy. Full article