Search Results (1,010)

This study developed a virtual reality (VR) simulator for training the inferior alveolar nerve block (IANB) procedure using Leap Motion-based hand tracking and the Unity engine, and evaluated its interaction performance, task-level outcomes within the simulator, and usability. Built on a 3D anatomical model, the system provides a pre-clinical practice environment for realistic syringe manipulation and visually guided needle insertion, enabling repeated rehearsal of the procedural workflow. Interaction stability was assessed using participant-level gesture recognition rates and input latency. Usability was evaluated via a questionnaire addressing ease of use, cognitive load, and perceived educational usefulness. The results indicated participant-level mean gesture recognition rates of 88.8–90.5% and mean response latencies of approximately 64–66 ms. In usability testing (n = 40), the item related to perceived procedural skill improvement received the highest score (4.25/5.0). Because this study did not include controlled comparisons with conventional training or objective measures of clinical competency transfer, the findings should be interpreted as preliminary evidence of technical feasibility and learner-perceived usefulness within a simulated setting. Controlled comparative studies using objective learning outcomes are warranted. Full article

Background: Evidence-based practice (EBP) has become a foundational component of modern healthcare globally. In Saudi Arabia, the understanding and application of EBP by respiratory therapists (RTs) remains underexplored. This study aimed to assess RTs’ behaviors, attitudes, awareness, knowledge, and barriers related to EBP. Methods: A previously validated online questionnaire was distributed to RTs across Saudi Arabia between February and July 2025. The survey collected sociodemographic data and included 14 items assessing behaviors, attitudes, awareness, knowledge, prior formal EBP training, and perceived barriers to EBP implementation. Results: A total of 301 RTs participated, with 290 completing the survey. Most participants (75.2%) held a bachelor’s degree. Overall, respondents demonstrated positive attitudes toward EBP, with more than 60% agreeing that understanding research methods is essential to respiratory therapy practice. The most frequently used resources for clinical decision-making were personal experience (67.3%), expert opinion (65.5%), and national or international guidelines (65.5%). Awareness of core EBP concepts was moderate; approximately 30% of participants reported a good understanding of terms such as “systematic review,” “quality of evidence,” and “risk of bias.” Several barriers to EBP implementation were identified, most commonly limited access to resources (25.2%), insufficient research knowledge and skills (23.8%), and lack of interest (21.0%). Conclusions: RTs in Saudi Arabia generally support EBP principles and use evidence-based resources in clinical decision-making. However, gaps in training, access to resources, and research competency limit full EBP implementation. Targeted strategies, including integrating mandatory EBP education, expanding professional development, and enhancing access to research resources, are recommended. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

Background: Hygiene and food-safety training is a critical public health strategy for preventing contamination and promoting safe food-handling practices in community settings. This study evaluated the long-term effectiveness of In-person, Online, and Hybrid instructional modes in enhancing hygiene and food-safety competencies among trainees in Ilocos Norte, Philippines. Methods: Using a longitudinal quasi-experimental design, performance was measured at 12, 24, and 36 months across four domains: Personal Health & Hygiene, Food Hazards, Cleaning and Sanitation, and Good Manufacturing Practices. A total of 384 students met all inclusion criteria and completed the full series of evaluations. Descriptive and inferential statistical analyses were employed. Results: Competency scores increased significantly over time in all instructional modes (p < 0.001). Hybrid learners demonstrated the highest early longitudinal gains at 12 months (mean score, M = 20.88), compared with In-person (M = 10.28) and Online (M = 10.57). At 36 months, Online learners achieved the highest performance (M = 19.50), indicating stronger long-term retention. Effect size analysis using eta squared (η²) showed large effects for Cleaning and Sanitation (η² = 0.196), Good Manufacturing Practices (η² = 0.115), and overall performance (η² = 0.138). Standardized Mean Change (SMC) indicated substantial improvement across modes, with Hybrid showing the greatest early change (SMC = 41.76 at 12 months) and Online exhibiting the strongest long-term improvement (SMC = 38.80 at 36 months). Training Efficiency Index (TEI) identified In-person instruction as most efficient (TEI = 30.55), followed by Online (29.49) and Hybrid (19.56). Linear Mixed-Effects Regression confirmed significant main effects of Time (β = 4.82, p < 0.001) and Mode (β = 3.97, p < 0.001), as well as a significant Time × Mode interaction (β = −1.42, p < 0.01). Conclusions: The findings indicate that Hybrid instruction supports rapid early competency gains, while Online instruction yields superior long-term mastery of hygiene and food-safety competencies. These results provide evidence-based guidance for optimizing hygiene training programs in community and public health contexts. Full article

Teacher education represents a global strategic priority for improving educational systems and fostering inclusive, high-quality processes. Recent studies highlight the need for systematic and replicable education models capable of addressing the challenges of contemporary complexity and bridging the gap between theory and practice. Teaching professionalism is increasingly recognized as a key driver of change, requiring a balance of pedagogical, relational, and technological competences, along with strong reflective capacity. Within this framework, practicum programs play a crucial role for the development of professional identity and authentic teaching skills. Methods: This contribution adopts a theoretical–argumentative approach grounded in a critical analysis of the international scientific literature on teacher education, with specific focus on the role of practicums. The aim is to present the model implemented by Pegaso University in the context of practicum activities within initial teacher education programs to outline an interpretative framework and provide pedagogical reflections in light of the results arising from critical reflection and systematic monitoring (not covered in this specific contribution) of the effectiveness of the model implemented in the first two training cycles (academic years 23–24 and 24–25), with the involvement of 5 regions and a total of 2834 teachers in the first cycle and 10 regions and a total of 5551 teachers in the second cycle. Convenience sampling based on a non-probabilistic method was adopted, using the entire sample of teachers admitted to the training program who met the requirements of Article 7 of the Decree of the President of the Council of Ministers (DPCM). Results: This paper outlines the theoretical and methodological trajectories of the model, offering interpretative frameworks and pedagogical reflections in light of the outcomes achieved during the initial implementation phase. Conclusions: In accordance with recent national and European regulatory frameworks, the Pegaso teaching model is presented as an example of good practice for initial teacher education. It aims to foster a reflective, situated, and responsible teaching professionalism, moving beyond traditional approaches toward a continuous and transformative learning process. Full article

This article introduces a framework for scientific and professional language training tailored for STEM (Science, Technology, Engineering and Mathematics) specialists, emphasising the integration of digital technologies and artificial intelligence (AI) in language education. The framework aims to develop students’ research communication skills and digital competencies, which are essential for effective participation in both national and international scientific discourse. The article discusses contemporary trends in STEM education, emphasising the importance of interdisciplinary approaches, project-based learning, and the utilisation of digital tools to boost language skills and scientific literacy. The article outlines the development and deployment of a digital platform aimed at supporting personalised and adaptive learning experiences, integrating various educational technologies and approaches. Empirical research conducted through a pedagogical experiment demonstrates the effectiveness of the framework, showing significant improvements in students’ academic and linguistic competencies across multiple modules. The findings highlight the importance of combining language training with STEM education to equip future engineers for the challenges of a globalised and digitalised professional world. This work reports on the “Enhancing Scientific and Professional Language Learning for Engineering Students in Kazakhstan through Digital Technologies” project conducted at Saginov Technical University (STU) in Kazakhstan and funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19678460). The research contributes to the ongoing discussion on improving language teaching in STEM fields, offering a framework that aligns with current educational demands and technological progress. Full article

This study diagnoses the level and profile of university lecturers’ digital competence at a Chilean higher education institution, drawing on the DigCompEdu and OpenEdu frameworks. A non-experimental correlational design was used, based on a self-perception questionnaire adapted from the DigCompEdu Check-In tool and administered to 569 lecturers through the Qualtrics platform. The instrument underwent external expert validation and demonstrated excellent internal consistency (Cronbach’s α = 0.96). Results indicate that 44% of lecturers position themselves at the “Integrator” level, 22% at the “Explorer” level, and 19% at the “Expert” level, with three clearly differentiated competence profiles. These findings informed the development of a structured training programme centred on three components: the pedagogical use of digital technologies, the incorporation of open educational practices aligned with OpenEdu, and the strengthening of students’ digital competence. The programme includes modular workshops, mentoring led by high-competence lecturers, and the creation of open educational resources. Overall, the study provides empirical evidence to guide institutional policies and to foster a reflective, ethical, and pedagogically grounded integration of digital technologies in university teaching. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges characterized by resource constraints, heterogeneous network architectures, and severe class imbalance in attack detection datasets. This paper presents a comprehensive benchmark evaluation of five Generative Adversarial Network (GAN) architectures for energy-aware intrusion detection: Standard GAN, Progressive GAN (PGAN), Conditional GAN (cGAN), Graph-based GAN (GraphGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP). Our evaluation framework introduces novel energy-normalized performance metrics, including Accuracy-per-Joule (APJ) and F1-per-Joule (F1PJ), that enable principled architecture selection for energy-constrained deployments. We propose an optimized WGAN-GP architecture incorporating diversity loss, feature matching, and noise injection mechanisms specifically designed for classification-oriented data augmentation. Experimental results on a stratified subset of the BoT-IoT dataset (approximately 1.83 million records) demonstrate that our optimized WGAN-GP achieves state-of-the-art performance, with 99.99% classification accuracy, a 0.99 macro-F1 score, and superior generation quality (MSE 0.01). While traditional classifiers augmented with SMOTE (i.e., Logistic Regression and CNN1D-TCN) also achieve 99.99% accuracy, they suffer from poor minority class detection (77.78–80.00%); our WGAN-GP improves minority class detection to 100.00% on the reported test split (45 of 45 attack instances correctly identified). Furthermore, WGAN-GP provides substantial efficiency advantages under our energy-normalized metrics, achieving superior accuracy-per-joule performance compared to Standard GAN. Also, a cross-dataset validation across five benchmarks (BoT-IoT, CICIoT2023, ToN-IoT, UNSW-NB15, CIC-IDS2017) was implemented using 250 pooled test attacks to confirm generalizability, with WGAN-GP achieving 98.40% minority class accuracy (246/250 attacks detected) compared to 76.80% for Classical + SMOTE methods, a statistically significant 21.60 percentage point improvement ($p<0.0001$). Finally, our analysis reveals that incorporating diversity-promoting mechanisms in GAN training simultaneously achieves best generation quality AND best classification performance, demonstrating that these objectives are complementary rather than competing. Full article

Multi-branch deep neural networks like AASIST3 achieve state-of-the-art comparable performance in audio anti-spoofing, yet their internal decision dynamics remain opaque compared to traditional input-level saliency methods. While existing interpretability efforts largely focus on visualizing input artifacts, the way individual architectural branches cooperate or compete under different spoofing attacks is not well characterized. This paper develops a framework for interpreting AASIST3 at the component level. Intermediate activations from fourteen branches and global attention modules are modeled with covariance operators whose leading eigenvalues form low-dimensional spectral signatures. These signatures train a CatBoost meta-classifier to generate TreeSHAP-based branch attributions, which we convert into normalized contribution shares and confidence scores ($C_b$) to quantify the model’s operational strategy. By analyzing 13 spoofing attacks from the ASVspoof 2019 benchmark, we identify four operational archetypes—ranging from “Effective Specialization” (e.g., A09, Equal Error Rate (EER) 0.04%, $C=1.56$) to “Ineffective Consensus” (e.g., A08, EER 3.14%, $C=0.33$). Crucially, our analysis exposes a “Flawed Specialization” mode where the model places high confidence in an incorrect branch, leading to severe performance degradation for attacks A17 and A18 (EER 14.26% and 28.63%, respectively). These quantitative findings link internal architectural strategy directly to empirical reliability, highlighting specific structural dependencies that standard performance metrics overlook. Full article

Background/Objectives: In light of the concerning research data on students’ mental health, it is essential to provide high-quality programs that support children and young people in strengthening their psychological well-being. To address this need, the three-year Erasmus+ KA3 international project PROMEHS: Promoting Mental Health at Schools was developed. The project involved universities and education policy representatives from seven European countries, Italy (project leader), Greece, Croatia, Latvia, Malta, Portugal, and Romania. Its core activities included the development of the PROMEHS curriculum, grounded in three key components: social and emotional learning, resilience, and the prevention of behavioral problems, alongside a rigorous evaluation of its implementation. The main research aim was to test the effect of PROMEHS on students’ and teachers’ mental health. Methods: In Croatia, the curriculum was introduced following the training of teachers (N = 76). It was implemented in kindergartens, and primary and secondary schools (N = 32), involving a total of 790 children. Using a quasi-experimental design, data were collected at two measurement points in both experimental and control groups by teachers, parents, and students. Results: The findings revealed significant improvements in children’s social and emotional competencies and resilience, accompanied by reductions in behavioural difficulties. These effects were most evident in teachers’ assessments, compared to parents’ ratings and student self-reports. Furthermore, teachers reported a significantly higher level of psychological well-being following implementation. Conclusions: Bearing in mind some study limitations, it can be concluded that this study provides evidence of the positive effects of PROMEHS in Croatian educational settings. Building on these outcomes and PROMEHS as an evidence-based program, a micro-qualification education was created to ensure the sustainability and systematic integration of the PROMEHS curriculum into Croatian kindergartens and schools. Full article

Immersive virtual reality is increasingly used for safety training, yet many initiatives remain technology-led pilots that enhance scenario realism and engagement without explaining how training becomes embedded in everyday work (e.g., alignment with SOPs, assessment routines, scheduling, and accountable debrief practices) or how skills reliably transfer back to duty. This paper addresses that gap by introducing Work-Integrated Safety Training (WIST) as a socio-technical training approach that couples IVR-based immersion with work-integrated routines to develop competence in safety-critical, passenger-facing work. Using Action Design Research (ADR) with Sweden’s national rail operator (SJ), we iteratively designed and evaluated a gamified immersive prototype for onboard conflict management, drawing on interviews, incident reports, co-design workshops, and in situ evaluations. We formalize four transferable design principles—specified with mechanisms and boundary conditions that guide how immersive training can (i) scaffold composure before intervention, (ii) make dynamic risk legible through interpretable cues, (iii) support SOP-aligned adaptive communication with replay-based reflection, and (iv) strengthen team coordination through role-specific checkpoints and psychologically safe debriefs. The paper contributes design knowledge for moving from isolated IVR demonstrations to work-integrated training systems that are implementable in organizations and testable in further longitudinal evaluation. Full article

Basketball is an intermittent sport with high neuromuscular and metabolic demands. To optimize specificity, training tasks should replicate competitive loads, but little is known about how drills compare to official matches. This study compared the physiological and biomechanical load of training tasks with official competition in elite U18 basketball players. Twelve male players (16.9 ± 0.8 years) were monitored across two seasons (179 training sessions, 21 matches). A total of 3136 individual records were collected using Catapult Vector S7 LPS units. Training drills were classified by specificity (0–5). Physiological (distance and intensity zones) and biomechanical variables (accelerations, decelerations, jumps, explosive efforts, PlayerLoad™) were analyzed using cluster analysis and linear mixed models. Competition imposed the highest physiological and biomechanical loads. Non-opposition drills (1v0–5v0) showed limited transfer, though 1v0–2v0 accumulated higher jump density. Among opposition formats, 3v3 full-court was the best at replicating match demands. Continuous opposition tasks (3v3v3, 4v4v4, 5v5v5) elicited lower physiological but comparable biomechanical load. Small-sided formats, particularly 3v3 and 4v4, are the most effective training tools for reproducing competition demands, while non-opposition drills are better suited for technical or rehabilitation purposes. Full article

The maritime sector is undergoing a profound transformation driven by disruptive technologies and global decarbonization objectives, placing new demands on Maritime Education and Training (MET) systems. Equipping maritime professionals with competencies for low-carbon shipping is now as critical as technological advancement itself. This study examines how disruptive technologies can be effectively integrated into MET frameworks to support environmental sustainability, using Panama as a representative case study of a major flag and maritime service state. A mixed-methods approach was adopted, combining a structured literature review, expert surveys, and a multi-criteria decision-making analysis based on the Analytic Hierarchy Process (AHP). The findings reveal a significant misalignment between existing MET curricula and the competencies required for decarbonized maritime operations. Key gaps include limited training in alternative fuels, emissions measurement and reporting, energy-efficient technologies, digital analytics, and regulatory compliance. Stakeholders also reported fragmented training provision, uneven access to emerging technologies, and weak coordination between academia, industry, and regulators, particularly in developing contexts. The results highlight the urgent need for curriculum reform and stronger cross-sector collaboration to align MET with evolving technological and regulatory demands. The study provides an applied, evidence-based framework for MET reform, with insights transferable to other systems facing similar decarbonization challenges. Full article

Effective communication in English is a critical behavioral competency for seafarers in a multilingual maritime environment, directly impacting operational safety. However, a gap exists between current Maritime English (ME) training in China and the actual communication demands of global seafaring. This study aims to identify the specific ME skills including linguistic, behavioral, and sociolinguistic dimensions that are most important for on-board performance and safety management from the perspective of pre-service maritime students at Shanghai Maritime University. A mixed-methods approach was used, combining structured questionnaires (n = 313) with in-depth follow-up interviews (n = 10). The results identified 24 highly needed ME skills, particularly focused on areas governing safety-critical behaviors, such as wireless communication, security protocols, and emergency procedures. In addition, based on learner profiling, the study depicts two different learner characteristics: exam-focused and work-focused students, each with different views on the importance of skills. Work-focused students place greater emphasis on the practicality of their skills. The interview data confirms and enriches these quantitative research results. The research findings emphasize that ME courses must be more closely aligned with real-world communicative scenarios and behaviors, prioritize scenario based teaching and practical operations, and tailor differentiated teaching based on learner psychology and behavioral preference. This study offers references for maritime education institutions with similar learner profiles to optimize ME curricula, prioritize secure communication skills, and strengthen industry-education collaboration, thereby enhancing pre-service maritime students’ safety behavior and professional competitiveness in China. Full article

Serious games are increasingly recognized as powerful pedagogical tools, often offering engaging, interactive, and practical learning experiences. This paper presents the design, implementation, and evaluation of a 3D virtual serious game specifically tailored for cybersecurity governance and policy education. In particular, the nature of the game is an escape room, drawing on military training principles: players must solve a problem to escape one room before advancing to the next. Set within a virtual company environment, the game features three interactive zones that guide students through analyzing cyber risks, aligning security frameworks, and drafting appropriate policies. This structure cultivates critical thinking and decision-making skills and strengthens practical cybersecurity competencies. The primary contribution lies in the integration of game-based learning and 3D virtual technology to create robust, hands-on educational materials. The design incorporates structural features that create barriers to generative AI delegation to address challenges related to generative AI misuse, ensuring that the activities cannot be easily replicated and thereby supporting academic integrity. A post-activity perception survey (n = 20) suggests that students found this approach both engaging and effective, with participants self-reporting enhanced understanding and enthusiasm toward cybersecurity governance and policy concepts. These findings highlight the potential of gamified environments to bridge theory and practice in cybersecurity education, equipping learners with industry-relevant skills while fostering deeper engagement and active learning. Full article