Search Results (1,499)

Background/Objectives: Dutch legislation sets requirements for the safe reporting of and learning from incidents. It also specifies the required competence of nurses in using medical technology. However, not all certified homecare nurses are adequately trained in patient safety. Patient safety management is reflected at different levels within homecare organisations. This study aimed to report on initial consensus among homecare nurses on responsibilities in quality and safety management at organisational, team and individual levels. It also explored nurses’ educational needs related to the use of advanced medical technologies (AMTs) in homecare. Methods: An exploratory qualitative study using consensus-oriented member checking was conducted. Building on research into incidents and safety management practices of AMTs, two semi-structured group interviews were conducted online with 11 homecare nurses from across the Netherlands. In a second round, feedback and comments were solicited on the resulting conclusions and statements in writing. Results: Distinguishing between high-risk and low-risk incident reports enhances the efficiency and effectiveness of safety management for AMTs in homecare. Team-based discussions increase the likelihood of incident reporting. Nurses advocate for periodic, mandatory assessments for technical homecare teams, conducted by an external body. They also emphasise individual responsibility for maintaining up-to-date knowledge and skills and taking action accordingly. Conclusions: In this study, key statements on which Dutch technical homecare nurses reached consensus are presented. The results underscore the importance of a safe organisational and team culture for incident reporting, as well as the need for an effective and efficient incident management system at a team level. An effective learning organisation contributes to enhancing patient safety. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

The Fourth Industrial Revolution reshapes manufacturing and workforce demands, yet a persistent gap remains between industry needs and engineering education. While proficiency in industrial robotics, adaptive control, and automation becomes critical, traditional education struggles to bridge the theory–practice divide. This systematic review examines technology transfer from factory to classroom to develop authentic Industry 4.0 competencies. Following PRISMA 2020 guidelines, we synthesized 52 empirical studies (2019–2025) focusing on technology complexity, pedagogical approaches, and learning outcomes. Random-effects meta-analysis of 12 representative studies reveals large positive effects: Hedges’ g of 0.786 (95% CI: 0.726–0.846, p < 0.001) with homogeneous effects ($I^2$ = 0.00%, p = 0.464), indicating robust generalizability. However, critical gaps emerged: only 7.7% employ actual industrial manipulators versus educational kits, adaptive control pedagogy remains limited, and fault-tolerant systems teaching receives minimal attention. Technology complexity analysis reveals clear progression from educational kits through semi-industrial platforms to industrial systems, with significant differential effects on transferable skills (r = 0.68, p < 0.001). This study proposes the ARC Framework integrating technology taxonomy, competency progression, pedagogical strategies, and assessment rubrics. Cost–effectiveness analysis demonstrates remote labs optimize impact-per-investment ratios ($45 vs. $280 per student), providing an evidence-based framework for technology transfer in engineering education. Full article

Rice blast disease is one of the most contagious and destructive diseases affecting rice, posing a serious threat to global rice production and the agricultural economy. To enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA). The algorithm integrates five spectral feature selection methods—partial least squares, successive projections algorithm (SPA), principal component analysis loading (PCA-Loading), genetic algorithm (GA), and random forest (RF)—and employs the Borda count method for comprehensive feature ranking and selection. Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47. A total of 4893 hyperspectral samples were collected under natural field conditions. The results demonstrate that IFSA effectively identifies key spectral wavelengths for the early diagnosis of rice blast disease, achieving significantly higher detection accuracy than conventional single-method dimensionality reduction approaches. Based on the IFSA-selected wavelengths, an early detection model (Inception-CBAM) was further developed by integrating a multi-channel convolutional neural network with a convolutional block attention module, thereby enhancing the extraction and recognition of early disease-related features. Compared with six baseline models (InceptionV4, ResNet, BiGRU, RF, support vector machine, and extreme learning machine), Inception-CBAM achieved an overall accuracy of 95.44 ± 0.50% and a Kappa coefficient of 93.92 ± 0.67% for early rice blast disease detection, outperforming all competing methods. This study confirms the effectiveness of IFSA for hyperspectral feature selection and demonstrates that the proposed Inception-CBAM model provides strong capability for early disease detection. Nevertheless, the data were collected from a single cultivar and a single region; therefore, the model’s generalization performance across broader environments requires further improvement. Future work will extend the evaluation to multi-cultivar and multi-region scenarios to facilitate practical deployment for real-time field diagnosis. Full article

This study introduces a hybrid point cloud compression method that transfers from octree-nodes to voxel occupancy estimation to find its lower-bound bitrate by using a Binary Arithmetic Range Coder. In previous attempts, we demonstrated that our entropy compression model based on index convolution achieves promising performance while maintaining low complexity. However, our previous model lacks an autoregressive approach, which is apparently indispensable to compete with the current state-of-the-art of compression performance. Therefore, we adapt an autoregressive grouping method that iteratively populates, explores, and estimates the occupancy of 1-bit voxel candidates in a more discrete fashion. Furthermore, we refactored our backbone architecture by adding a distiller layer on each convolution, forcing every hidden feature to contribute to the final output. Our proposed model extracts local features using lightweight 1D convolution applied in varied ordering and analyzes causal relationships by optimizing the cross-entropy. This approach efficiently replaces the voxel convolution techniques and attention models used in previous works, providing significant improvements in both time and memory consumption. The effectiveness of our model is demonstrated on three datasets, where it outperforms recent deep learning-based compression models in this field. Full article

Background/Objectives: Primary care faces transformation due to workforce shortages and reform. Italy’s Decree 77/2022 promotes Community Centers and extended care, while postgraduate training in general practice involves early clinical responsibility. In South Tyrol, trainees assume significant patient care duties early in a three-year program. This review examines traditional apprenticeship-based training and explores system-based supervision and AI as strategies for improving quality and safety. Methods: A narrative review synthesized the literature and policy on postgraduate general practice education, supervised autonomy, and AI tools in primary care. Searches used the PubMed and Consensus platforms, focusing on Italian primary care reform and South Tyrol. Evidence was analyzed using SANRA guidance. Results: Evidence consistently indicates that training quality depends less on individual supervisors and more on structured, system-based supervision frameworks, clear entrustment criteria, and supportive organizational contexts. Early supervised clinical autonomy in community-based primary care settings can accelerate competency development without compromising the quality of care when robust supervision and team structures are in place. AI-supported educational tools have the potential to augment feedback, assessment, and learning analytics, especially in settings with limited supervisory capacity; however, current evidence supports their use only as adjuncts to human supervision. Conclusions: Evidence supports system-based, competency-oriented supervision models over traditional apprenticeships in settings characterized by workforce constraints and distributed training sites. Integrated general-practitioner-led primary care settings offer favorable learning environments for postgraduate training, while service-oriented community hubs need careful governance as training sites. Though AI may support supervision, professional oversight remains essential for quality and safety. Full article

Background/Objectives: Adolescence is pivotal for establishing dietary habits; however, school-based nutritional education remains focused on information dissemination, with minimal effects on behavior modification. Evidence from neuroscience, education, and food literacy indicates that attention, engagement, sensory experiences, and social contexts are integral to effective learning in nutrition education. This article conceptualizes a framework for adolescent food education beyond knowledge transmission, aiming to cultivate taste competence using the Mediterranean Diet as a pedagogical ecosystem. Methods: This study employed a conceptual methodology, utilizing interdisciplinary literature from food literacy, sensory education, developmental neuroscience, educational theory, and public health nutrition. It synthesizes empirical findings and theoretical models to develop the Teaching Autonomous Sensory Taste in the Mediterranean Diet (TASTE–MED) framework. Results: This study introduces taste competence as a multifaceted educational outcome, encompassing sensory, relational, cultural, and reflective dimensions. The TASTE–MED framework outlines how experiential, multisensory, and socially embedded learning processes can be implemented in schools, facilitated by the Mediterranean Diet, which provides a sensory-rich and culturally significant context. The educational implications are discussed in terms of curriculum design, teacher training, family involvement and digital tools. Conclusions: The TASTE–MED framework redefines food literacy as an embodied and socially situated competence rather than a cognitive construct. This framework provides a theoretical foundation for informing the design, evaluation, and research of future interventions, advocating for the transition from information-based nutrition education to competence-oriented food education during adolescence. Full article

Self-regulated learning (SRL) is a critical competency for learners in increasingly technology-enhanced educational environments, yet little is known about how SRL is fostered within video-based interventions in K-12 settings. While existing reviews and meta-analyses focus on the effectiveness of SRL interventions, this study aims to address current gaps by specifically examining the implementation processes, instructional tools, and the role of video. Addressing this, the present study conducted a systematic literature review of peer-reviewed K-12 intervention studies published since 2010, guided by PRISMA standards and other methodological frameworks in the field of SRL. 30 quantitative or mixed-methods studies focusing on K-12 SRL interventions were selected from Web of Science and ERIC, with the requirement that video served as an instructional component rather than a research tool. These studies were then systematically coded by eight researchers for SRL strategies, instructional methods, video roles, and pedagogical settings. Findings show that most video interventions targeted multiple SRL strategies across different phases of the SRL cycle, offering a comprehensive approach to fostering regulation. However, while cognitive and metacognitive strategies were frequently addressed, motivational and resource-management strategies were seldom included within explicit instruction, which focused primarily on cognitive and metacognitive training. Video played multiple pedagogical roles, including delivering disciplinary content, teaching SRL strategies, or combining both. A thematic analysis identified four pedagogical settings that characterized successful interventions: Teacher-guided, Active, Social, and Knowledge-based (TASK) learning. These settings appear to mitigate common challenges of video-based learning, such as cognitive load and learner passivity. The review contributes a novel synthesis of SRL-video integration and proposes TASK learning as a framework for designing SRL interventions. Full article

Background: Contrast-enhanced magnetic resonance imaging (MRI), including late gadolinium enhancement (LGE) and cerebral blood volume (CBV) maps, is essential for characterizing pathologies such as myocardial scars and brain tumors. However, acquiring these images requires gadolinium-based contrast agents (GBCAs), which are contraindicated in certain patient populations. Although deep learning enables cross-modality image translation, current methods frequently fail to preserve lesion details, limiting their clinical utility. Methods: We propose KGSynth, a knowledge-guided framework designed to synthesize contrast-enhanced MRI from non-contrast sequences. This approach incorporates a knowledge estimator to extract lesion and anatomical features, paired with a style mapping network to capture contrast-specific visual characteristics. By explicitly modeling these distinct components, the framework aims to improve pathological fidelity in the synthesized images. Results: Extensive validation on cardiac and brain MRI datasets indicates that KGSynth outperforms existing competing methods. In cardiac LGE synthesis, the model achieved an SSIM of 0.567 and PSNR of 19.48 dB. Similarly, for quantitative brain CBV map synthesis, it yielded an SSIM of 0.697 and PSNR of 24.49 dB. Notably, the method demonstrated improved accuracy in delineating myocardial infarctions and tumor regions compared to baseline models. Conclusions: Integrating explicit knowledge guidance into generative models effectively produces diagnostic-quality images without GBCAs. KGSynth preserves pathological accuracy, offering a viable solution for virtual contrast enhancement. This approach holds promise for clinical workflows, particularly for patients with contraindications to contrast agents. Full article

In the digital era, industries increasingly demand innovation and problem-solving capabilities, making cross-disciplinary integration and creative thinking essential competencies for information management professionals. Although previous studies have shown that Problem-Based Learning (PBL) enhances students’ problem-solving abilities and proactive learning behaviors, its effectiveness in supporting creative extension and conceptual deepening remains limited without structured thinking frameworks. To address this issue, this study integrated PBL with a Two-Tier Mandala Thinking approach based on a nine-grid structure. The proposed method combines first-tier divergent thinking with second-tier spiral convergence to guide students in establishing conceptual foundations, differentiating ideas, and refining design directions. A quasi-experimental study was conducted in a course in which students completed a game design task using either the Two-Tier Mandala Thinking Method or conventional brainstorming strategies. Quantitative results indicate that students in the Mandala Thinking group significantly outperformed those in the brainstorming group across three learning performance metrics. Qualitative findings further revealed that students using the proposed approach exhibited enhanced creative self-efficacy and greater confidence in their creative outcomes. Overall, integrating Two-Tier Mandala Thinking into PBL effectively supported the experimental group in structuring and developing in-depth creative thinking processes, providing empirical evidence for its application in innovation-oriented information education. Full article

Water allocation remains a critical global challenge due to increasing scarcity, competing sectoral demands, and environmental pressures, requiring approaches that balance efficiency, equity, and ecosystem sustainability while facing the inherent contextual uncertainty. Recent developments in operations research and statistical learning have paved the way for a new paradigm in nonlinear modeling under uncertainty, i.e., contextual optimization. This emerging framework seamlessly combines predictive analytics with robust optimization techniques to address sustainable decision-making problems in dynamic environments. In this study, we introduce a novel learning-enabled optimization method that extends the current domain of contextual stochastic optimization. Leveraging regression-based statistical learning techniques, our approach enhances predictive accuracy and reinforces decision robustness. Unlike traditional methods, which often struggle with parameter variability and unbounded solution spaces, our model establishes clear predictive bounds that reduce the uncertainty region, thereby minimizing deviations from optimality. We apply our methodology to water allocation in Tunisia’s coastal tourism sector (2010–2022), where resource availability is constrained and highly variable. While developed for this specific context, the framework is transferable to similar Mediterranean arid/semi-arid tourism regions subject to certain data and governance conditions. The proposed approach accurately predicts water demand and optimizes the allocation of diverse water sources, contributing to sustainable water resource management. This paper presents both theoretical foundations and practical applications of our method in complex, data-driven decision environments, demonstrating its relevance for achieving sustainable development goals. Full article

Data science has become central to contemporary social, civic, and professional life, yet its integration into initial teacher education remains fragmented and undertheorised. This paper addresses the need to support teacher educators in designing learning experiences that develop pre-service teachers, who are non-data science specialists, competence in data science. A systematic scoping review of the literature was conducted across major academic databases and complemented by an expert-informed literature identification strategy. The review examined how data science is described conceptually, how it is structured within school curricula and teacher education, and what knowledge and practices are emphasised for teachers. Findings indicate that while core processes and practices of data science, such as problem formulation, data preparation, exploratory analysis, modelling, visualisation, and ethical engagement, are widely recognised, their translation into teacher education is inconsistent and often lacks coherence. In response, the paper presents a conceptual framework designed to support pre-service teachers in engaging with the processes and practices of doing data science. The framework offers a flexible, practice-informed structure that is accessible to non-specialist teachers and aligned with pedagogical decision-making in educational settings. The paper concludes by discussing how the framework, alongside practical considerations for enactment, can support the preparation of data-literate teachers capable of fostering critical, ethical, and inquiry-based engagements with data in schools. Full article

Engineering education is increasingly expected to prepare graduates capable of addressing sustainability challenges, public safety concerns, and ethical responsibilities. However, in many civil and environmental engineering curricula, sustainability and ethics are still treated as supplementary topics rather than being systematically embedded in core technical courses. This study reports a sustainability-oriented curriculum reform implemented in a Building Water Supply and Drainage Engineering course, integrating Education for Sustainable Development (ESD) principles into CDIO-aligned project-based learning activities. A single-group pre–post quasi-experimental design was adopted with 100 undergraduate students. Quantitative data were collected using a competency-based questionnaire, and paired-sample t-tests, effect sizes, and 95% confidence intervals were applied to examine changes in students’ self-reported competencies. Qualitative data were obtained from reflective learning reports and analyzed through thematic analysis. The results indicate statistically significant improvements in sustainability awareness, ethical and professional responsibility, human-centered design, and systems thinking, with large effect sizes. These findings provide context-specific descriptive evidence supporting the feasibility of embedding sustainability and ethical responsibility within discipline-specific technical engineering courses. Nevertheless, the absence of a control group and the reliance on self-reported measures limit causal interpretation. Future research is recommended to adopt comparative or longitudinal designs and incorporate more objective performance-based assessments. Full article

The performance of deep learning approaches for Synthetic Aperture Radar (SAR) target detection is often limited by the scarcity of annotated data. While Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate data dependence, its potential in SAR target detection remains largely underexplored. In this study, we propose SARDet-MIM, a comprehensive framework based on Masked Image Modeling (MIM), to enhance SAR target detection. The approach consists of two stages. In the self-supervised pre-training stage, we propose an innovative Structural and Scattering Masked Autoencoder (SSMAE) method for SAR imagery. Unlike conventional MIM methods, which typically reconstruct raw pixels, SSMAE employs a physics-aware reconstruction target comprising multi-scale gradient and SAR-Harris features. This strategy explicitly guides the network to capture discriminative structural contexts and intrinsic scattering features that benefit SAR target detection. For downstream detection, we construct a Maximally Pre-trained Detector (MPD), which integrally transfers the pre-trained ViT encoder–decoder architecture to the detection network to fully exploit pre-trained representations. Extensive experiments on three SAR target detection datasets demonstrate that SARDet-MIM consistently outperforms competing methods. Full article

This study examines the effectiveness of a purposefully constructed Foreign Language Learning Environment (FLLE) in developing foreign language communicative competence within Kazakhstani higher education. Focusing on four interrelated components—pedagogical resources, physical learning space, motivational strategies, and ICT integration—the study addresses the limited opportunities for authentic English communication characteristic of EFL contexts. A quasi-experimental design involving 69 undergraduate students was employed, with participants divided into experimental and control groups. Statistical analysis using the Mann–Whitney U test revealed significantly higher post-test results in the experimental group, particularly in speaking performance. The findings demonstrate that communicative competence development can be significantly enhanced when (1) instructional materials prioritize authentic, task-based communication, (2) classroom spaces are organized to facilitate face-to-face interaction, (3) motivational support is provided through speaking activities and extracurricular activities, and (4) ICT tools, including conversational AI, are used to extend communicative interaction beyond classroom time. Full article