Search Results (1,491)

Collaborative design is recognized as a promising approach to strengthening preservice teachers’ digital competence, yet its potential when design tasks approximate the complexities of classroom practice remains underexplored. This mixed-methods study investigated a Synthesis of Qualitative Evidence (SQD)-aligned collaborative design course in which 23 final-year preservice secondary mathematics teachers, organized in six teams, spent ten weeks designing technology-enhanced lesson materials for authentic design challenges posed by in-service teachers. Using questionnaires and interviews, this study explored preservice teachers’ perceived digital competence development and their perceptions of the SQD-aligned course supports. Regarding competence development, participants indicated increases in self-assessed cognitive (technological knowledge, TPACK) and motivational (technology-integration self-efficacy, perceived ease of use) digital competence dimensions. Qualitative findings linked these perceptions to heightened technological awareness and confidence but noted limited tool mastery due to reliance on familiar technologies and efficiency-driven task division. Concerning course supports, authentic challenges enhanced motivation and context-sensitive reasoning, while layering scaffolds (guidelines, coaching, and microteaching feedback) supported navigating the open-endedness of the design task. Yet calls for earlier feedback and technology-related modeling underscore the need for further scaffolding to adequately support autonomy in technology selection and integration. Findings inform teacher education course design for fostering preservice teachers’ digital competencies. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Efficient simplification of 3D models is essential for mobile and other resource-constrained application scenarios. Industrial 3D assemblies, typically composed of numerous components and dense triangular meshes, often pose significant challenges in rendering and transmission due to their large scale and high complexity. The Quadric Error Metrics (QEM) algorithm offers a practical balance between simplification accuracy and computational efficiency. However, its application to large-scale industrial models remain limited by performance bottlenecks, especially when combined with curvature-based optimization techniques that improve fidelity at the cost of increased computation. Therefore, this paper presents a parallel implementation of the QEM algorithm and its curvature-optimized variant using the OpenMP framework. By identifying key bottlenecks in the serial workflow, this research parallelizes critical processes such as curvature estimation, error metric computation, and data structure manipulation. Experiments on large industrial assembly models at a simplification ratio of 0.3, 0.5, and 0.7 demonstrate that the proposed parallel algorithms achieve significant speedups, with a maximum observed speedup of 5.5×, while maintaining geometric quality and topological consistency. The proposed approach significantly improves model processing efficiency, particularly for medium- to large-scale industrial models, and provides a scalable and practical solution for real-time loading and interaction in engineering applications. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

The evolution of the generation mix towards deep decarbonization poses pressing questions about the role of hydropower and its possible share in the future mix. Most technical–economic analyses of deeply decarbonized systems either rule out hydropower growth due to lack of additional hydro resources or take it into account in terms of additional reservoir capacity. This paper analyzes a generation mix made of photovoltaic, wind, open-cycle gas turbines, electrochemical storage and hydroelectricity, focusing on the optimal generation mix’s reaction to different methane gas prices, hydroelectricity availabilities, pumped hydro reservoir capacities, and mean filling durations for hydro reservoirs. The key feature of the developed model is the sizing of both optimal peak power and reservoir energy content for hydropower. The results of the study point out two main insights. The first one, rather widely accepted, is that cost-effective decarbonization requires the greatest possible amount of hydro reservoirs. The second one is that, even in the case of totally exploited reservoirs, there is a strong case for increasing hydro peak power. Application of the model to the Italian generation mix (with 9500 MWp and 7250 MWp of non-pumped and pumped hydro fleets, respectively) suggests that it is possible to achieve methane shares of less than 10% if the operating costs of open-cycle gas turbines exceed 160 EUR/MWh and with non-pumped and pumped hydro fleets of at least 9200 MWp and 28,400 MWp, respectively. Full article

Background/Objectives: The integration of artificial intelligence (AI) into obstetric care poses significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods in maternal-foetal medicine often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. By integrating computational innovation with mechanistic biology and rigorous clinical validation, AI can finally fulfil the promise of precision obstetrics by transforming pregnancy complications into a preventable, personalised continuum of care. This study aims to map the current landscape of AI applications across the continuous spectrum of maternal–foetal health, identify the types of models used, and compare clinical targets and performance, potential pitfalls, and strategies to translate innovation into clinical impact. Methods: A literature search of peer-reviewed studies that employ AI for prediction, diagnosis, or decision support in Obstetrics was conducted. AI algorithms were categorised by application area: foetal monitoring, prediction of preterm birth, prediction of pregnancy complications, and/or labour and delivery. Results: AI-driven models consistently demonstrate superior performance to traditional approaches. Nevertheless, their widespread clinical adoption is hindered by limited dataset diversity, “black-box” algorithms, and inconsistent reporting standards. Conclusions: AI holds transformative potential to improve maternal and neonatal outcomes through earlier diagnosis, personalised risk assessment, and automated monitoring. To fulfil this promise, the field must prioritize the creation of large, diverse, open-access datasets, mandate transparent, explainable model architectures, and establish robust ethical and regulatory frameworks. By addressing these challenges, AI can become an integral, equitable, and trustworthy component of Obstetric care worldwide. Full article

Background/Objectives: In view of demographic change and the increase in chronic illnesses, home care poses a considerable challenge. Telemedical technologies offer considerable potential for improving the quality of care and relieving the burden on family caregivers. With this study, we aim to develop appropriate training strategies for the use of telemedical applications in home care, focusing on the specific requirements of patients with dementia, heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and stroke. Methods: A comprehensive survey was conducted among 31 family caregivers to record their experience with digital technologies and to analyze caregiver acceptance of these technologies and barriers to their use. The survey comprised 29 questions, including a mix of multiple-choice, Likert scale, and open-ended questions. The internal consistency of the questionnaire was high (Cronbach’s alpha = 0.8876). Results: The results show that although 32% of respondents already use digital technologies, there is a significant need for training and support. Key barriers identified include a lack of technical skills (cited by 45% of respondents), limited access to suitable devices (38%), and privacy concerns (35%). In addition, 90% of respondents expressed a willingness to participate in training programs. Conclusions: Based on the survey results, evidence-based recommendations are provided for the design of training programs tailored to the individual needs of family caregivers. Through a targeted combination of e-learning modules, webinars, and practical exercises, family caregivers can be empowered to take full advantage of telemedical technologies and thus significantly improve the quality of care at home. The results underscore the importance of overcoming technical barriers and providing comprehensive training to ensure the effective use of telemedicine in home care. Full article

Posture recognition is critical in modern educational and office environments for preventing musculoskeletal disorders and maintaining cognitive performance. Existing methods based on human keypoint detection typically rely on convolutional neural networks (CNNs) and single-scale features, which limit representation capacity and suffer from overfitting under small-sample conditions. To address these issues, we propose MSBN-SPose, a Multi-Scale Bayesian Neuro-Symbolic Posture Recognition framework that integrates geometric features at multiple levels—including global body structure, local regions, facial landmarks, distances, and angles—extracted from OpenPose keypoints. These features are processed by a multi-branch Bayesian neural architecture that models epistemic uncertainty, enabling improved generalization and robustness. Furthermore, a lightweight neuro-symbolic reasoning module incorporates human-understandable rules into the inference process, enhancing transparency and interpretability. To support real-world evaluation, we construct the USSP dataset, a diverse, classroom-representative collection of student postures under varying conditions. Experimental results show that MSBN-SPose achieves 96.01% accuracy on USSP, outperforming baseline and traditional methods under data-limited scenarios. Full article

This study investigates the environmental stability of ferronickel slag (FNS) and primary lead slags (GCS and FCS) from historical metallurgical complexes in Greece, in rainwater and seawater media. Leaching experiments revealed that nickel is the most mobile element from FNS (43.5 μg·g⁻¹ in seawater after 90 days). Chromium release, on the other hand, is very limited, not exceeding 0.04 μg·g⁻¹. In lead slags, zinc and lead exhibit significant leaching (up to 650 and 230 μg·g⁻¹, respectively), while arsenic release reaches 22.6 μg·g⁻¹. GCS contains pores primarily in the range of 50–90 Å. The majority of pore volume in FCS is centered around 30 Å. The porosity appears to have a significant effect on the element’s leachability. Pb, Zn, As, Sb, and Cd are released in significantly higher amounts from the finely porous FCS compared to GCS. Thermodynamic modeling was used to identify the pollutant speciation in water media in relation to the oxygen concentration. The release of toxic elements such as Cr from FNS and As from lead slags is enhanced under oxic (open-air) conditions. Therefore, their land disposal poses a greater environmental threat compared to sea disposal, where anoxic conditions prevail. Full article

Antimicrobial resistance (AMR) poses a significant global threat to human and animal health, necessitating robust surveillance and stewardship tools. While existing systems address aspects of veterinary AMR, a comprehensive, user-centric dashboard for U.S. veterinarians remains a critical unmet need. This study aimed to identify U.S. veterinarians’ preferences and perceived needs for such a dashboard, to help guide its design and development. A cross-sectional survey was conducted between January and March 2024, targeting U.S. veterinarians through professional channels. The survey instrument captured demographics, experiences with existing tools, preferences for data types and visualizations, desired technical specifications, and open-ended feedback. Of the 677 respondents, a near-unanimous consensus (over 75%) emerged on the importance of functionalities like antimicrobial stewardship education, off-label use guidance, surveillance data, and empirical treatment support. Over 70% expressed comfort sharing aggregated geographic and de-identified animal data. A strong preference was observed for making the dashboard accessible by veterinary colleges (78.87%), diagnostic laboratories (72.61%), and federal agencies (USDA: 71.47%, CDC: 66.67%, FDA: 62.11%), indicating a desire for a collaborative, authoritative system. The findings provide a robust foundation for developing a U.S. veterinary AMR dashboard. Future phases should adopt an iterative, user-centered design, incorporating qualitative research with diverse stakeholders and piloting a prototype with preferred institutional partners. This approach will ensure a trusted, sustainable tool that effectively translates surveillance data into actionable insights for improved animal and public health. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

This article presents a deep reinforcement learning (DRL) approach for adaptive robotic grasping in dynamic environments. We developed UR5GraspingEnv, a PyBullet-based simulation environment integrated with OpenAI Gym, to train a UR5 robotic arm with a Robotiq 2F-85 gripper. Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) were implemented to learn robust grasping policies for randomly positioned objects. A tailored reward function, combining distance penalties, grasp, and pose rewards, optimizes grasping and post-grasping tasks, enhanced by domain randomization. SAC achieves an 87% grasp success rate and 75% post-grasp success, outperforming PPO 82% and 68%, with stable convergence over 100,000 timesteps. The system addresses post-grasping manipulation and sim-to-real transfer challenges, advancing industrial and assistive applications. Results demonstrate the feasibility of learning stable and goal-driven policies for single-arm robotic manipulation using minimal supervision. Both PPO and SAC yield competitive performance, with SAC exhibiting superior adaptability in cluttered or edge cases. These findings suggest that DRL, when carefully designed and monitored, can support scalable learning in manipulation tasks. Full article

Vaccines are among the most effective public health interventions, significantly reducing morbidity and mortality from infectious diseases. Despite their proven efficacy, vaccine hesitancy has emerged as a pressing global challenge. This review examines the drivers, barriers, and interventions associated with vaccine hesitancy and uptake, focusing on childhood vaccinations and the role of parents as primary decision-makers. Misinformation, safety concerns, and political decisions have contributed to declining vaccination rates, posing threats to public health. The article proposes targeted programs and policies to rebuild vaccine confidence, emphasizing the role of trusted messengers, health literacy, and structural reforms to reduce barriers. Recommendations highlight the importance of accurate information, open communication, and advocacy for school vaccine mandates. The conclusion stresses the urgent need to implement robust policies and community-based initiatives to ensure widespread immunization and safeguard population health. Full article

Basil (Ocimum basilicum L.), a key herb in Mediterranean cuisine, holds substantial economic and cultural value due to its aromatic and medicinal properties. Cultivated globally, particularly in Italy’s Liguria region, basil is consumed both fresh and processed, with pesto sauce as its most notable derivative. Despite its commercial success, basil production is significantly constrained by a broad spectrum of fungal pathogens, with Peronospora belbahrii, the causal agent of downy mildew, posing the most severe threat. This study aims to provide a comprehensive overview of basil’s disease susceptibility and control. Special emphasis is placed on the biology, epidemiology, global spread, and diagnosis of P. belbahrii, which has become a critical challenge for both conventional and organic farming systems. Disease management strategies, including cultural practices, genetic resistance, fungicide applications, resistance inducers, and biocontrol agents, are reviewed in detail. The development of downy mildew-resistant cultivars—although limited for PDO-designated Genovese basil—has emerged as the most sustainable control measure; however, the increasing genetic variability in P. belbahrii underscores the ongoing need for integrated pest management and resistant cultivar development. Seed health and quality remain the starting points of any fully integrated approach, although the suggested management measures for basil production should be combined with appropriate cultivation techniques aimed at reducing the relative humidity of the environment, while taking into account whether basil production takes place in open fields or under protection. Full article

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis and is associated with high morbidity and mortality, especially in resource-limited settings. In Mozambique, where both tuberculosis and HIV are highly prevalent, TBM poses significant diagnostic and therapeutic challenges. This study aimed to describe the clinical characteristics and to identify predictors of TBM mortality among persons living with HIV (PLWH) in a rural hospital in Mozambique. Methods: We conducted a retrospective cohort study at Carmelo Hospital of Chokwe (CHC) between 2015 and 2020. We included 372 PLWH diagnosed with TBM (PTBM); data on demographics, clinical presentation, and laboratory findings were extracted from patient records. TBM diagnosis was considered for confirmed cases based on a hospital-adapted algorithm incorporating clinical features, cerebrospinal fluid (CSF) analysis, TB-LAM, and Xpert MTB/RIF testing. Cox proportional hazard models were used to identify independent predictors of mortality, and Kaplan–Meier survival curves with log-rank tests were used to assess survival differences across clinical subgroups. Significance was considered at a p value ≤ 0.05 with an adjusted hazard ratio (AHR) 95% CI in the multivariate analysis. Results: Overall, 372 PTBM contributed to a total of 3720 person-months (PM) of treatment follow-up, corresponding to a mortality incidence of 3.76 deaths per 100 person-months. Factors independently associated with increased mortality included male sex (adjusted hazard ratio [aHR]: 1.80; 95% CI: 1.21–2.68; p = 0.004), BMI < 18.5 kg/m² (aHR: 2.84; 95% CI: 1.46–5.55; p = 0.002), Immunovirological failure to ART (aHR: 2.86; 95% CI: 1.56–5.23; p = 0.001), CSF opening pressure >40 cmH₂O (aHR: 2.67; 95% CI: 1.46–4.86; p = 0.001), and TBM severity grading III (aHR: 4.59; 95% CI: 1.79–11.76; p = 0.001). TBM involving other organs also significantly worsened survival (aHR: 2.03; 95% CI: 1.27–3.25; p = 0.003). Conclusions: TBM mortality in PLWH was driven by ART failure, high CSF pressure, and malnutrition. Male sex and severe neurology also increased risk. Urgent interventions are proposed: optimize ART, manage intracranial pressure, provide nutritional support, and use corticosteroids. An integrated care approach is essential to improving survival in resource-limited settings. Full article