Search Results (2,562)

Cardiac auscultation is a fundamental clinical skill for veterinary graduates; however, acquiring this competence remains challenging because opportunities for repeated exposure to a wide range of cardiac abnormalities may often be limited during undergraduate training. This study evaluated whether supplementing written self-directed learning with digital equine phonocardiographic recordings improves veterinary students’ ability to recognize and interpret equine cardiac auscultation findings. Forty-one fourth-year veterinary students from the Universities of Milan and Pisa were voluntarily enrolled and randomly assigned to receive either written learning materials alone (Group 1, n = 21) or the same materials supplemented with digital phonocardiographic recordings (Group 2, n = 20). Participants completed a baseline test (T0). After a four-week self-directed learning period, 30 students (Group 1 = 15; Group 2 = 15) completed the post-training test (T1) and were included in the final analysis. The statistical analysis provided no significant differences for the total score (p = 0.064) or Q1 score (p = 0.349), whereas a significant difference was observed for Q2 score (p = 0.039), showing greater improvement in Group 2. These findings suggest a potential educational benefit of supplementing written learning materials with digital phonocardiographic recordings, particularly for the ability to distinguish among different cardiac abnormalities. Full article

The use of technology is a central component of initial teacher education; yet how preservice teachers use technological resources during teaching internship remains poorly understood, particularly when assessed through actual deployment frequencies rather than self-perceived competence. This study aimed to analyze material technology use among student teachers (N = 705) at a Spanish university during teaching internship, to examine differences by personal, academic and contextual variables and to identify technology use profiles. A quantitative, non-experimental, cross-sectional design was employed. Exploratory factor analysis, cross-validated through confirmatory factor analysis in an independent subsample, revealed four dimensions of technology use: mobile, emerging and innovative technology; printing, digitization and storage equipment; audiovisual equipment and connectivity; and interactive presentation technologies. Multivariate analyses of variance, multiple linear regression and cluster analysis were conducted. Results showed differentiated patterns of technology use, with greater reliance on conventional tools and more limited adoption of mobile and emerging technologies. Among academic variables, year of study showed the most consistent associations with technology use dimensions, while educational stage was significantly related to the use of specific resources. Cluster analysis identified differentiated levels of technology use (low, moderate and high). The high-use profile was concentrated among fourth-year Primary Education students completing teaching internship in upper primary grades, a pattern that should be interpreted alongside the longer duration and greater autonomy of the final-year practicum. This study offers a more granular account of technology infusion than other competence-based approaches due to combined variable and person-centered analyses of technology use during real school-based internships. These findings indicate that technology infusion during teaching internship is shaped by both training and contextual factors, underscoring the need to strengthen technology-related learning opportunities in teaching internship and to foster closer university–educational centers coordination to support effective technology integration in teaching and learning. Full article

As artificial intelligence systems increasingly serve as a gateway for access to information, economic opportunity, and civic life, higher education programs training AI developers must evolve to prepare practitioners who are not only technically proficient in building AI solutions but also socially accountable in their work. This paper proposes a conceptual framework for integrating explainable AI (XAI) and social accountability into data science, computer science, and information science curricula. Based on accountability theory, information science, and recent XAI research, the proposed framework is organized around four interrelated pillars: answerability, responsibility, enforcement, and reflexivity. These pillars are further situated within technical, social, organizational, and political dimensions of XAI implementation, with particular focus on how XAI techniques such as LIME, SHAP, model cards, and counterfactual explanations can be operationalized as instruments of meaningful accountability. This paper then proposes a multi-level governance framework that links interpretability methods to institutional oversight, regulatory literacy, and participatory design, illustrated through a concrete scenario grounded in graduate data science education. Together, these elements represent a new pedagogical approach that can equip future AI developers to design and deploy AI systems that are accurate as well as transparent, justifiable, and responsive to the communities they serve. Full article

Visual working memory (vWM) undergoes age-related decline, affecting storage capacity, representational precision, feature binding, and inhibitory control, with consequences for everyday functioning. Nevertheless, accumulating evidence indicates that the aging brain retains substantial plasticity, providing an opportunity of intervention. This review synthesizes current evidence on approaches to improving vWM in healthy older adults, including classic and game-based cognitive training, non-invasive brain stimulation (NIBS), and multimodal protocols that combine cognitive training with physical exercise or brain stimulation. Across the literature, process-based cognitive training produces improvements on trained and closely related tasks, whereas evidence for durable far-transfer remains limited. NIBS demonstrates promising neurophysiological effects, particularly when targeting frontoparietal networks, although behavioral outcomes remain heterogeneous and appear to depend on stimulation parameters and individual characteristics. Emerging findings further suggest that combining interventions may enhance selected aspects of vWM, highlighting the importance of personalized approaches. Despite these advances, intervention effects are often modest, task-specific, and resource-intensive. As a complementary approach, the review proposes placebo and nocebo effects as a theoretically grounded hypothesis that warrants empirical investigation for potentially complementing existing interventions and promoting healthy cognitive aging rather than confounds. Full article

Education for Sustainable Development (ESD) is widely regarded as a key task for schools, yet its implementation depends strongly on teachers’ opportunities to develop ESD-related professional expertise. This study examines how ESD is represented in state-accredited in-service teacher training (TT) in Germany, focusing on the degree of ESD relevance and selected characteristics of ESD-related TTs, including duration, school-type specificity, participation numbers, and cancellation. The analysis is based on 1186 ESD-related TT course descriptions from six German federal states, collected for the school year 2022/23 and the first half of 2023/24. TTs were coded using a qualitative content analysis approach and categorized into three degrees of ESD relevance: less ESD-relevant, ESD-relevant, and highly ESD-relevant. Results show that most TTs were classified as ESD-relevant or highly ESD-relevant (n = 828/n = 1186). ESD relevance was not significantly associated with participant numbers, but highly ESD-relevant TTs showed higher cancellation rates than ESD-relevant TTs. Course duration was not significantly related to cancellation. Within school-type-specific offers, ESD relevance differed significantly by school type. The findings highlight the need to consider ESD-related TT offers from a differentiated perspective and to complement the present document-based analyses with additional data to better capture contextual needs and conditions. Full article

Ensuring that artificial intelligence contributes to sustainable, equitable education requires more than aggregate accuracy—it requires verifying that predictive systems serve all learners fairly, including across geographic regions. We audit a dropout-prediction pipeline built on the Open University Learning Analytics Dataset (OULAD) for disparities across gender, disability, and geographic region, using a student-level train/test partition to prevent the same student’s records from contaminating both sets. Logistic regression and random forest classifiers attain approximately 0.85 accuracy and 0.91–0.92 AUC overall, yet region-stratified recall (true-positive rate) ranges from 0.55 in Wales to 0.80 in the West Midlands Region, an equal-opportunity gap of 0.25 that is corroborated by region-specific AUC, by a random forest classifier, and, for actual withdrawals, by a likelihood-ratio test showing region predicts being missed by the classifier beyond what the Index of Multiple Deprivation (IMD) explains. A region-isolation test shows that excluding region as a model predictor is associated with a significantly narrower gap in both model families (0.13–0.19 without region versus 0.25–0.28 with region), an association not explained by IMD band alone; because the bootstrap 95% confidence interval on this difference ($[-0.003,0.173]$) narrowly includes zero, we treat the attribution to region specifically as suggestive rather than conclusively established. A naive region-specific decision-threshold mitigation, evaluated correctly on a held-out validation set, does not improve the gap; a shrinkage-regularized version recovers a modest, observed reduction (0.24 to 0.17) on the held-out test set, without a formal uncertainty interval for this difference, at the cost of a near-doubling of regional false-positive rates. Because our analysis is retrospective and several predictors are computed over the full module presentation, these findings support methodological lessons for the design and auditing of future systems rather than direct claims about the performance or fairness of an operational, real-time early-warning intervention; we report them, including the mitigation failure, as evidence that dropout-prediction systems audited only for aggregate accuracy, without a properly validated regional fairness assessment, risk under-serving or unevenly burdening students in specific regions, working against rather than for the aims of SDG 4. Full article

RNA activation (RNAa), mediated by small activating RNAs (saRNAs), upregulates endogenous gene expression through recruitment of the Argonaute proteins to the promoter, natural antisense transcripts, or other noncoding RNAs. This mechanistically distinct modality addresses an unmet need, as few RNA therapeutics currently function to activate endogenous gene expression. Three saRNA therapeutics are now in clinical trials; however, broader application remains constrained by largely empirical candidate screening and limited design guidelines, particularly in terms of saRNA targets. Unlike siRNAs, for which extensive sequence–activity relationships and computational design strategies exist, saRNA activity remains comparatively difficult to predict. Here, we assembled an updated and annotated database of publicly available saRNA sequences and paired it with an overview of the RNAa principles, the clinical and preclinical landscape of saRNA utility and current saRNA design guidelines. Finally, we outline opportunities for artificial intelligence-assisted rational design, highlighting how machine learning trained on curated saRNA datasets could accelerate discovery of novel saRNA candidates. Consolidation of saRNA records into a unified resource will hopefully enable systematic analysis of sequence features associated with activity and lay a foundation for moving saRNA design from largely empirical screening toward more rational design. Full article

Background/Objectives: Although artificial intelligence (AI) has the potential to strengthen disease surveillance, predictive analytics, preventive interventions, and population-level decision-making, its successful integration requires health systems to be adequately prepared across technical, workforce, governance, and organizational dimensions. Saudi Arabia’s rapid digital health transformation provides a unique national context for exploring these readiness conditions. This study explored stakeholder perceptions of conditions relevant to health system readiness for integrating AI into public health in Saudi Arabia, including perceived challenges, implementation requirements, and strategic opportunities. Methods: A qualitative descriptive study was conducted using semi-structured interviews with 32 participants, including healthcare professionals, health informatics experts, and policymakers or healthcare planning stakeholders in Saudi Arabia. Participants were purposively selected based on their relevant professional experience. Data were analyzed using Braun and Clarke’s six-phase reflexive thematic analysis. Results: Four major themes were identified: (1) infrastructure readiness, reflecting progress in digital health platforms alongside persistent interoperability and data-integration challenges; (2) workforce capacity, highlighting limited AI literacy and the need for practical, role-specific training; (3) data governance and ethics, emphasizing privacy, accountability, algorithmic bias, and the need for greater regulatory clarity; and (4) perceived strategic opportunities, including preventive care, population health monitoring, disease surveillance, and decision support. The first three themes reflected conditions perceived as relevant to health system readiness, whereas the fourth captured anticipated applications and potential benefits rather than a dimension of current readiness. Overall, participants perceived substantial national strategic commitment and digital health development while identifying important implementation challenges related to interoperability, workforce capability, governance, and regulation. Conclusions: Participants’ accounts suggest that responsible AI integration into public health requires coordinated attention to interoperable infrastructure, workforce competencies, governance mechanisms, regulatory oversight, and human-centered implementation. A phased implementation approach, supported by role-specific AI training and clear governance and regulatory frameworks, may help translate national AI ambitions into safe and effective public health practice. These findings may inform policy development, workforce planning, and implementation strategies for responsible AI integration in Saudi Arabia and other regions whose health systems are undergoing rapid digital transformation. Full article

The role of hospital pharmacists is broadening from primarily operational responsibilities towards greater involvement in clinical and research activities. Poland’s growing position in the clinical-trials sector and recent legislative changes create new opportunities for pharmacists to manage investigational medicinal products (IMPs) and contribute to multidisciplinary research teams. This narrative review and commentary, based on 27 sources, examines pharmacists’ roles and contributions in clinical trials. The reviewed literature describes pharmacists as contributors to medication safety, pharmacovigilance, drug-interaction assessment, IMP management, and maintenance of blinding. Their involvement may also support protocol adherence, patient education and participant care; however, the available evidence does not yet permit firm conclusions regarding its impact on medication-error rates, treatment adherence, recruitment, dropout rates, or clinical outcomes. In oncology and advanced therapy medicinal product (ATMP) trials, pharmacists may provide specialised expertise in complex medication-management and logistical processes. Nevertheless, a gap appears to persist in Poland between pharmacists’ professional readiness and their practical involvement in clinical research. Key barriers include minimum staffing requirements, insufficient protected research time and organisational constraints. Hospital pharmacists therefore represent a valuable but underused resource in clinical research. Appropriate training, institutional support, dedicated research time, and regulatory development could facilitate their broader participation in clinical-trial conduct and enable future evaluation of their effects on trial quality and participant safety. Full article

The 2021 eruption of Cumbre Vieja on La Palma, Canary Islands, produced extensive lava flows and major landscape transformation, providing an opportunity to evaluate remote sensing approaches for mapping the extent of an emplaced lava flow. This study assessed direct spectral, classic Rao’s quadratic entropy (RaoQ), and multidimensional RaoQ approaches using satellite observations acquired before and after the eruption. Optical, radar, thermal infrared, and night-time radiance datasets were evaluated within a common change-detection framework implemented in Google Earth Engine. Difference maps were converted into binary change maps using a histogram-based thresholding procedure calibrated on the reference delineation and evaluated against the Copernicus Emergency Management Service (CEMS) lava-flow reference and no-change validation areas derived from ESA WorldCover using multiple accuracy metrics. Because the change reference is the final CEMS lava-flow delineation and the no-change samples lie outside a 100 m buffer around it, the accuracy figures reported here quantify the mapping of lava-flow extent and not of other eruption-related effects such as ash deposition or vegetation damage beyond the flow margins. Among the direct spectral approaches, the NHI_SWIR index achieved the highest overall classification performance. Among the individual Sentinel-2 bands, B12 achieved the highest overall accuracy, whereas B8A achieved the highest true skill statistic; both exceeded the multidimensional RaoQ configurations in mean prevalence-independent discrimination. Within the classic RaoQ approach, MIRBI produced the strongest single-variable heterogeneity-based results. The best multidimensional configurations combined Sentinel-2 B8A and B12 with Sentinel-1 VV, demonstrating that radar backscatter provided complementary information to optical observations. Although multidimensional RaoQ did not surpass the best direct spectral variables, it produced competitive and spatially coherent representations of lava-flow disturbance. The evaluated thermal infrared and night-time radiance products did not provide competitive discrimination under the selected spatial and temporal conditions for different reasons: a thresholding limitation in the case of the Landsat thermal product, and an unfavourable ratio of pixel size to flow width in the case of the night-time radiance products, while the MODIS product returned no valid validation points and could not be evaluated. These product-specific explanations rest on a small number of comparisons and are provisional. These results show that carefully selected Sentinel-2 SWIR variables remain the strongest benchmark for detailed mapping of fresh lava-flow disturbance, while multidimensional RaoQ provides a framework for optical–radar integration that requires no training data or prior classification. Because the evaluation covers a single eruption in a single landscape, transfer of the framework to other events and settings remains to be demonstrated. Full article

Enterprise Resource Planning (ERP) systems generate transactional data, but their value for process improvement depends on converting these records into reliable event logs and useful predictions. This study evaluated a framework combining event-log readiness assessment, process mining, predictive process monitoring, transparent machine learning, and domain validation in an Ecuadorian steel manufacturer. The analysis used 3740 production orders and 31,791 ERP-recorded events, split chronologically into training, validation, and out-of-time test samples. Process discovery identified execution heterogeneity; rework, accumulated waiting, route deviations, and resource congestion were positively associated with deadline violation. At production start, the process-aware ridge logistic model achieved a PR-AUC of 0.839, compared with 0.419 for the static representation and 0.683 for the remaining-slack benchmark, with an ROC-AUC of 0.893, a Brier score of 0.123, and a sensitivity of 0.804. The first material transaction was the earliest operationally useful checkpoint, preserving a median intervention window of 98.7 h before the committed completion date among predicted-positive cases. Additive model decomposition highlighted material-related waiting, queue waiting, partial material issues, and accumulated deviations as contributors to fitted risk. A nine-expert panel translated the evidence into six operational and tactical improvement opportunities. Findings support ERP-based early-warning process intelligence when readiness, temporal validation, calibration, transparency, and actionability are addressed jointly. Full article

Over the past several decades, numerous methods have been developed to improve the signal-to-noise ratio, perceptual quality, and intelligibility of speech. In practice, no single method is universally optimal, as each category exhibits distinct strengths and limitations under specific acoustic conditions. The proposed taxonomy classifies speech enhancement methods according to their dominant signal modeling and enhancement mechanism, ranging from the classical signal processing approaches to modern machine learning techniques and advanced hybrid frameworks. The mathematical formulation illustrates the theoretical foundations of the different approaches, providing a clear understanding of their underlying principles and the evolution of performance across successive generations of speech enhancement methods. The comparative analysis demonstrates that statistical and subspace-based approaches offer low operational complexity but show limitations under highly nonstationary acoustic conditions. Adaptive filtering, transform-domain, and speech model-based methods exploit temporal, spectral, and speech production characteristics to achieve improved noise suppression, while perceptual methods improve subjective listening quality by incorporating psychoacoustic principles, thereby providing a more natural and intelligible listening experience. Machine learning-based techniques achieve excellent performance; however, they incur higher computational complexity and substantial training requirements. More recently, hybrid approaches have integrated complementary techniques from multiple paradigms, achieving robust performance under adverse acoustic environments. Overall, this review provides a unified perspective on speech enhancement techniques, identifies their strengths, and highlights emerging research opportunities for the development of robust, efficient, and intelligent speech enhancement systems. Full article

Introduction: Black men experience persistent disparities across the prostate cancer continuum, including inequities in access to survivorship support. This study examined the perceived value, acceptability, and experiences of accessing a multicomponent digital survivorship program among Black men with prostate cancer. Methods: This exploratory mixed-methods study was embedded within the ongoing international Phase 4 implementation trial of the Prostate Cancer Patient Empowerment Program (PC-PEP), a six-month digital intervention integrating exercise, pelvic floor muscle training, nutrition, stress management, psychosocial support, and peer connection. Fourteen self-identified Black participants contributed six-month program evaluation and qualitative data collected through open-ended responses and conference-based focus group discussions. Nine participants (64%) had undergone surgery with or without radiation and/or hormone therapy, four (29%) had received radiation with or without hormone therapy, and one (7%) was on active surveillance or had received no treatment. Quantitative data were summarized descriptively, and qualitative data were analyzed using inductive thematic analysis. Results: PC-PEP was highly valued, with median ratings of 10 (IQR 8–10) for likelihood of recommending the program and 9 (IQR 8–10) for overall usefulness. Among participants with available item-level data, 11/13 (85%) reported lifestyle improvement and 12/13 (92%) endorsed offering PC-PEP as standard care. Qualitative findings identified the value of holistic survivorship support, peer connection, normalization of vulnerability, and support for physical and psychological self-management. Participants also described limited awareness of PC-PEP at diagnosis and reliance on individual clinicians or incidental opportunities to learn about the program. Participants emphasized the need for earlier referral, greater representation, and culturally relevant community outreach. Conclusions: Black men who accessed PC-PEP reported high perceived value and identified benefits across multiple dimensions of survivorship. Their experiences, however, highlighted an important distinction between program availability and meaningful access: participants’ experiences suggest that availability alone may not ensure timely connection to survivorship support. Earlier referral, culturally responsive outreach, and integration of survivorship support into routine prostate cancer care may help close this gap. Full article

Federated learning enables model training in edge and distributed service environments without directly sharing raw data. In long-running edge federated services, fixed collaboration structures may become inefficient under workload drift, whereas frequent regrouping can incur migration overhead, group churn, and additional privacy-budget consumption. This paper studies budget-prioritized dynamic regrouping under workload drift, privacy-budget constraints, and migration or reconfiguration cost. We formulate a dynamic regrouping problem that jointly captures workload pressure, remaining privacy budget, service utility, and regrouping cost. We propose Budget-Prioritized Dynamic Regrouping (BP-DR), a triggered local method that evaluates single-node candidate operations and commits at most one regrouping operation per time slot. A candidate is accepted only when it is privacy-budget feasible and its utility improvement exceeds a threshold combining an anti-oscillation margin, migration or reconfiguration cost, and privacy-budget opportunity cost. We derive this trigger from a one-step local comparison and establish its monotonicity with respect to migration or reconfiguration cost and remaining privacy budget. Trace-driven experiments based on Alibaba Cluster Trace 2018 show that BP-DR maintains competitive migration-adjusted utility while controlling regrouping activity across dynamic and stress-test settings. FLamby Fed-Heart-Disease validation further shows similar learning performance across the compared methods while demonstrating the integration of BP-DR with group-aware federated training. Full article

Optimizing resource use in smallholder coffee production promotes environmental sustainability. Coffee production in the Sierra Sur of Oaxaca, Mexico, is dominated by small-holder systems characterized by high production variability and limited access to resources. Although organic and conventional systems coexist under similar agroecological conditions, limited evidence exists on how agronomic practices influence efficiency. This study estimated technical efficiency and its determinants using a one-stage stochastic frontier model with data from 290 coffee growers. Mean efficiency was 0.762 for conventional growers and 0.566 for organic growers. In the conventional system, planting density, plantation age, weed control, and shade management significantly reduced inefficiency, whereas off-farm employment increased it. In the organic system, training was the only factor significantly associated with higher efficiency. Despite tending to achieve higher average production and yield, organic growers exhibited larger production gaps and greater heterogeneity in resource use, indicating substantial opportunities to increase output through more efficient input utilization rather than expanding cultivated areas. These findings demonstrate that higher production does not necessarily imply greater technical efficiency, providing evidence to support public policies aimed at strengthening extension services, promoting training, and encouraging agronomic management to improve efficiency and enhance the sustainability of smallholder coffee production systems. Full article