Search Results (2,371)

Laboratories play a critical role in engineering education by bridging the gap between theoretical knowledge and practical applications. Dedicated laboratory stands serve as essential platforms for experiential learning, enabling students to interact directly with real or simulated systems. These stands enable a deeper understanding of complex engineering concepts and, at the same time, support the creation of essential skills such as problem-solving, critical thinking, and system integration. While physical laboratory stands provide a rich learning experience, their large-scale implementation can be both technically and financially challenging. In this context, virtual laboratory environments based on virtual prototypes offer a valuable complementary approach by improving access to practical learning while providing greater flexibility and reducing implementation costs. This paper presents the development of a virtual laboratory stand implemented using Simscape and designed for use in the teaching process in mechatronics engineering. The developed virtual prototype materializes a Segway-type self-balancing mobile robot and is used as a teaching tool in the lab activity of the control systems course. The educational effectiveness of the proposed virtual laboratory stand was evaluated by comparing students’ perceptions with those of a conventional analytical-model-based laboratory activity. The collected questionnaire data were analyzed using descriptive statistics and the Mann–Whitney U test. Students using the virtual laboratory reported consistently higher ratings than those using the analytical model, with statistically significant differences observed in three questionnaire items. These findings suggest that the proposed virtual laboratory stand enhances students’ learning experience and provides a valuable complementary approach for teaching control system design in mechatronics. Full article

Generalization under deployment shifts remains a major challenge for visuomotor robot manipulation, especially when changes in camera placement and control behavior are unknown to the policy. This paper introduces the System-Context-Conditioned Diffusion Policy (SCE-DP), a history-conditioned manipulation policy designed to adapt its latent context to simulated changes in camera extrinsics, action scale, and bounded control delay without explicit recalibration or test-time model-weight updates. SCE-DP encodes a short history of issued commands and their subsequent visual and proprioceptive responses into a latent system context, which conditions the diffusion-based action denoising process. Auxiliary system-parameter regression and one-step response prediction further encourage the context to capture deployment-relevant system properties and their behavioral consequences. We evaluate SCE-DP on five independently trained ManiSkill manipulation tasks across held-out in-range configurations, a withheld camera-delay composition, and mild extrapolation settings. SCE-DP improves the five-task macro-average over domain-randomized Diffusion Policy by 14.5 percentage points on held-out configurations and 15.7 points on the withheld camera-delay composition. Across four shifted evaluation suites, it achieves an average success rate of 64.3%, compared with 48.9% for the domain-randomized baseline, while preserving nominal performance. Command-only, response-only, previous-action, recovery-source, and matched explicit-parameter controls indicate that the gain is not explained solely by command statistics, redundant command input, or recovery-data composition. These results show in simulation that command–response history is an effective source of online latent system context under coupled visual and control shifts; real-robot generalization remains to be established. Full article

Polygonal patterned ground (PPG) is an important remote-sensing indicator of permafrost dynamics, yet its high-resolution distribution in alpine permafrost regions remains unknown. In this study, we developed an ensemble modeling framework to map PPG in the Three-River Source Region (TRSR), northeastern Qinghai–Tibet Plateau. A total of 4200 PPG occurrence samples and multi-source environmental variables were used to train four meta models: Random Forest (RF), Support Vector Machine (SVM), Maximum Entropy (MaxEnt), and the BIOCLIM package. Model performance was evaluated using the area under the curve (AUC) and true skill statistics (TSS). An AUC-weighted ensemble model was constructed from the best-performing models. RF showed the highest accuracy, with an AUC of 0.97 and TSS of 0.85, followed by SVM and MaxEnt. The final ensemble map achieved an overall accuracy of 93.7% and a kappa coefficient of 0.91 based on validation with high-resolution satellite imagery. The mapped PPG area was 59,082 km², accounting for 25.76% of the permafrost area and 16.01% of the TRSR. PPG was mainly distributed in the Yangtze River Source Region, with limited distribution in the Yellow River Source Region and no occurrence in the Lancang River Source Region. Compared with a previous Northern Hemisphere-scale PPG distribution product, our map reduced the estimated PPG area by 21.79% and improved local spatial detail. Variable importance analysis indicated that solar radiation, elevation, freeze–thaw indices, active-layer thickness, topography, soil moisture, and ground ice jointly controlled PPG distribution. PPG mainly occurred at elevations of 4400–5000 m, on gentle slopes of 0–3°, and in areas with 30–40% ground ice content. We also found that PPG can serve as a reliable geomorphic proxy for ice-rich and thermally sensitive permafrost in alpine regions. These findings highlight the utility of PPG for permafrost dynamic assessment and associated eco-hydrological impacts in alpine permafrost regions. Full article

Traditional frameworks evaluating the university academic experience often give limited attention to students’ reported use of Artificial Intelligence. This study psychometrically validates a novel measurement model, testing whether AI-related academic practices emerge as a distinct component of students’ perceived academic experience. Using a cross-validation design, 2403 undergraduate students from a private university in Bolivia were surveyed. The dataset was randomly split for Exploratory Factor Analysis (EFA, n = 1200) to uncover latent variables and Confirmatory Factor Analysis (CFA, n = 1203) to validate the structural stability of a refined 31-item scale. The EFA revealed a cohesive five-factor structure: (1) Academic Engagement and Student Life, (2) Student-Reported AI Use in Academic Learning, (3) Communication and Critical Thinking Skills, (4) Teaching Quality, and (5) Academic Dedication. The CFA confirmed an excellent model fit (CFI = 0.954, RMSEA = 0.046), supporting strong construct validity. Furthermore, inter-factor correlations showed that student-reported AI use was positively associated with academic engagement and with the broader Communication, Ethical, and Critical Thinking Skills domain. These empirical results suggest that, within this institutional context, students perceive AI as a relevant element of their academic routines. By validating the University Academic Experience and AI Integration Survey, this study provides a context-specific framework for assessing students’ self-reported use and perceived integration of AI in academic learning as a distinct dimension of their academic experience. The validated instrument is not intended for directly measuring learning outcomes, educational effectiveness, teaching effectiveness, or institutional quality. Full article

The practice of contemporary dentistry requires not only technical skills but also the development of soft skills, such as communication, empathy, and ethical and professional values. Although the importance of these skills is increasingly recognized, the perceptions of dental students in Romania regarding their relevance to their future professional practice remain insufficiently explored. Objectives: The study aimed to assess the perceptions of final-year students at the Faculty of Dentistry in Iași regarding the importance of soft skills, in relation to their demographic characteristics, professional motivations, and preferences regarding their future practice settings, as well as to identify distinct profiles through cluster analysis. Material and Methods: The study included 205 students in their fourth through sixth years at the Faculty of Dentistry in Iași, who anonymously completed a 26-item questionnaire rated on a Likert scale from 1 to 5. The statistical analysis included the chi-square test, Ward’s method, and k-means clustering. Results: Ethical and professional values, artistic skills, and empathy were most frequently rated as “very important.” Female students assigned significantly higher scores than male students to empathy, ethical and professional values, personal characteristics, and communication (p < 0.05–0.001). Professional independence, financial stability, and professional prestige were the main motivations for career choice. Cluster analysis identified four distinct profiles, with significant differences based on gender, professional motivations, and preferences regarding future practice. Conclusions: Students place a high value on soft skills, with differences based on gender and professional profile. The results support the integration of these skills into university education, to complement specialized clinical training. Full article

Poor water quality monitoring and delayed responses to pollution remain major challenges in low-resource areas. Traditional methods of monitoring water and wastewater resources are ineffective because they take a long time to report contamination. Therefore, this critical review aims to examine how a combination of artificial intelligence (AI) and edge computing can comprehend decentralised, real-time water quality monitoring, even in areas with limited infrastructure and internet, constrained maintenance capacity, and shortages of skilled personnel. To our knowledge, this study is a first-of-its-kind integrated framework that showcases edge AI architectures and refers to specific operational, societal, and infrastructural limitations in the water, sanitation, and hygiene (WASH) sector. The synthesis clearly shows that the implementation of edge AI techniques has the capability to improve global water quality through immediate pollution detection, disaster forecasting, and automatic filter or alarm response without the need for cloud infrastructure. The examples given from developing countries support this statement by demonstrating that the technologies are low-cost and implementable in the long term. The problem of sensor calibration, data quality, and energy efficiency was identified as the most important implementation challenge. There is enough evidence of pilot-scale tests, but long-term validation of the technology in field trials is needed. The authors also present future research directions, such as the integration of AI, edge computing, machine learning, and IoT, and open-source edge frameworks. Edge AI systems provide promising avenues for decentralised water safety surveillance in low-resource communities in real time and can support the sustainable development goals of the United Nations through the realisation of clean water and sanitation for all. Full article

Background: Robotic-assisted surgery has been increasingly adopted across surgical specialties, but objective and efficient assessment of robotic surgical skills remains challenging. The Global Evaluative Assessment of Robotic Skills (GEARS) is commonly used for expert-based evaluation; however, it requires manual review and may not fully capture detailed operative processes. This study aimed to investigate the associations between video-derived digital operative metrics and expert-derived GEARS scores and to conduct an exploratory multivariable analysis of metrics associated with GEARS scores. Methods: Data were collected from 42 surgeons who completed a standardized robotic surgery training program. On the final training day, anonymized animal-operation videos were evaluated by blinded experts using the GEARS scale. Surgical videos were processed to extract 43 digital operative metrics related to instrument visibility, spatial positioning, camera control, clutch use, switching use, and motion characteristics. GEARS scores and digital operative metrics were compared according to annual laparoscopic surgical volume, including dichotomized volume groups, specialty-based subgroups, and volume tertiles. Stepwise multiple linear regression was used as an exploratory variable-selection approach to identify video-derived metrics associated with expert-derived GEARS scores. Results: After false discovery rate correction, no significant differences in GEARS scores or digital operative metrics were observed across annual laparoscopic surgical volume groups, specialty subgroups, or volume tertiles. The final regression model retained three predictors: mean camera-use count per minute, minimum distance from the left-side center point, and maximum distance from the right-side center point. The model had an apparent in-sample R² of 0.509 (F = 9.588; p < 0.001). This value describes model fit within the present sample and has not been corrected for optimism. Conclusions: Video-derived digital operative metrics, particularly those reflecting camera control and instrument spatial distribution, were associated with expert-derived GEARS scores and may provide complementary quantitative information for expert-based robotic surgical skill assessment. Full article

Large ensembles of atmospheric dispersion simulations are necessary for uncertainty quantification, source attribution, and emergency response, however, physics-based models like HYSPLIT might be too computationally expensive for operational use. In addition, they have not fully benefited from recent advances in artificial-intelligence-based surrogate modelling, which offer the potential to reduce this computational burden. This study introduces the Dual-Head Mass-Consistent Plume Surrogate (DM-CPS), a machine-learning surrogate to promote the prediction of PM_2.5 transport at a much higher speed without compromising the geometry and integrated pollutant mass of the plume. The proposed framework utilizes two types of complementary histogram gradient-boosting models: one trained in the logarithmic concentration domain to reconstruct the plume morphology, and one trained in the original concentration domain to estimate the total pollutant burden. The outputs of the two branches are then combined through a field-level mass-consistency scaling process that preserves the spatial structure of the plume while enforcing a physically consistent integrated pollutant mass. The model is trained on a GDAS-driven ensemble of 96 HYSPLIT simulations over Almaty, Kazakhstan, which includes 1.88 million grid-cell records and physics-informed wind-aligned features. DM-CPS achieves the highest operational dispersion skill (FAC2 = 0.57) with a significant reduction in geometric bias (MG: 2.75 → 1.68) and an improvement in the median integrated-mass ratio (0.13 → 0.23) while maintaining plume structure (SSIM ≈ 0.83) compared to random forest, multilayer perceptron, and conventional gradient-boosted baselines. The surrogate predicts an entire concentration field in about 35 ms, a factor up to five times faster than running HYSPLIT. The cross-regime evaluation also shows a significant gap of generalization in the presence of unseen, out-of-training meteorological data, again emphasizing the need for increased diversity of meteorological training data for operational use. The results show that physically informed surrogate modeling can significantly speed up the prediction of atmospheric dispersion, which is fundamental to run large ensembles. Full article

Cities create their own local climate, and numerical weather models can reproduce it if given an accurate picture of the urban surface. The local climate zone (LCZ) framework classifies neighborhoods by building height, density, and materials and provides this information to fine-scale weather models. This approach has mostly been evaluated in warm seasons, leaving open how it performs in the cold, air-stagnant winters of high-latitude cities like Ulaanbaatar, Mongolia. We ran nine model versions over one cold week (22–29 February 2024) across three nested domains (12.5, 2.5, and 0.5 km) with LCZ data resolved to 100 m. Run 8 achieved the highest aggregate validation skill, whereas Run 9 was retained as the configuration most suitable for the LCZ-based analysis rather than as the best model overall. Run 9 reproduced near-surface temperature at the urban Bayanzurkh station (R = 0.89) and gave the smallest wind-speed error (1.5 m s⁻¹), while uniquely resolving the inter-class morphological contrasts required here. The dense urban core proved warmer than the non-urban area by 4.1 °C on average and up to 9.3 °C at night in the compact high-rise zone. Yet this warming barely relieves cold stress: the Universal Thermal Climate Index (UTCI) averaged −15.5 °C across the LCZ classes, within the strong-cold-stress range, with only the compact high-rise zone reaching a milder category. In the low-rise “ger districts”, wind speed rather than air temperature governs perceived cold, and compact high-rise form offers the strongest wind shelter. These findings provide a baseline for future scenario testing of winter thermal exposure in Ulaanbaatar. Full article

Aim: To evaluate the effect of a student-generated podcast intervention focused on maternal–child health topics on nursing students’ self-perceived knowledge, educational satisfaction, and gameful engagement. Introduction: Health education is essential for promoting healthy lifestyles and preventing disease, making the acquisition of effective communication skills vital for nursing undergraduates. While digital audio media and podcasts are increasingly popular pedagogical tools, traditional applications rely on passive listening. Student-generated podcast production represents an active learning approach that may enhance engagement, communication skills, and content synthesis. Design: Quasi-experimental study without randomization. Methods: The intervention was conducted across a 4-week framework comprising 8 contact hours of faculty guidance alongside independent teamwork. First-year nursing students organized into small collaborative groups (3–4 peers) received initial training on digital audio pedagogy, metadata, and production. Teams developed 5-to-7-min podcast capsules addressing eight evidence-based maternal–child health themes. Faculty provided structured formative checkpoints for script clinical validation and technical audio editing guidance. All completed podcasts were evaluated using a 100-point rubric assessing scientific accuracy (35%), communication clarity (25%), narrative engagement (20%), and technical quality (20%); thirty top-scoring episodes were selected for public radio broadcasting. Pre-and post-intervention self-administered questionnaires measured self-perceived knowledge across all eight topics, educational satisfaction, and gameful experience via the validated 27-item Gameful Experience Scale (GAMEX). Results: A total of 130 valid questionnaires were analysed at each assessment point. Self-perceived knowledge was higher at the post-intervention assessment across all eight topics (all Holm-adjusted p < 0.001). After Holm correction, general podcast satisfaction (adjusted p = 0.036), fulfilment of expectations (adjusted p = 0.036), recommendation to friends or family (adjusted p < 0.001), perceived importance of podcasting for maternal health (adjusted p = 0.036), and perceived ease of setting up a podcast (adjusted p = 0.005) remained statistically significant. Creative Thinking, Mastery, and the overall GAMEX score were nominally higher before multiplicity adjustment, but none remained statistically significant after Holm correction (adjusted p = 0.126, 0.234, and 0.158, respectively). Conclusions: Student-generated podcasting was associated with higher self-perceived knowledge and favourable appraisal on several educational outcomes. GAMEX differences were exploratory after Holm correction. Full article

Oil palm plantations play an important role in agricultural production and carbon storage in tropical regions. The accurate estimation of aboveground carbon (AGC) is essential for sustainable plantation management, climate change mitigation, and carbon monitoring. Although field measurements provide reliable estimates, they are often time-consuming, labor-intensive, and costly, particularly over large plantation areas. Recent advances in remote sensing and machine learning offer efficient alternatives for AGC estimation using satellite imagery. In this study, we developed a machine learning framework for AGC estimation in oil palm plantations using Sentinel-2 multispectral imagery and Sentinel-1 synthetic aperture radar (SAR) data. Field measurements were integrated with spectral variables, vegetation indices, and SAR-derived parameters extracted from satellite data. A hybrid feature selection approach combining Pearson correlation, mutual information and mRMR was used to identify the most relevant variables. Six machine learning algorithms were evaluated, including Linear Regression, Random Forest, XGBoost, Gradient Boosting, LightGBM, and Extra Trees. Because the 160 observations comprise sixteen 10 m × 10 m grid cells nested within ten 40 m × 40 m field plots, model performance was assessed with leave-one-plot-out cross-validation: all sixteen cells of a plot were held out together, and the hybrid feature selection was repeated inside every fold using only that fold’s training plots. Performance was measured on pooled out-of-fold predictions using R², root mean squared error (RMSE), and average absolute relative error (AARE%). Under this spatially independent design the combined Sentinel-1 + Sentinel-2 dataset gave the highest accuracy (R² = 0.7950, RMSE = 4.14 t C ha⁻¹, AARE = 33.53%), followed by Sentinel-1 alone (R² = 0.7631, RMSE = 4.46 t C ha⁻¹) and Sentinel-2 alone (R² = 0.6108, RMSE = 5.71 t C ha⁻¹). Linear Regression and Extra Trees were the most robust models, whereas the boosted ensembles did not generalize to unseen plots. Repeating the evaluation with an ungrouped random split of the same data inflated R² by up to 0.70, showing that a large part of the accuracy obtainable under that design reflects within-plot spatial autocorrelation rather than predictive skill. These findings indicate that optical-SAR imagery combined with machine learning can provide useful AGC estimates in oil palm plantations, and that spatially independent validation is essential for reporting them honestly. The proposed framework can be used to support plantation-scale carbon mapping, monitoring, and carbon stock assessment, subject to further calibration and independent validation across additional plantations. Full article

School-based prevention programs increasingly incorporate mental health-related components to prevent substance use among adolescents. This study aimed to evaluate the effectiveness of integrated school-based mental health and substance use prevention programs for adolescents through a systematic review and meta-analysis. A systematic literature search was conducted across 12 electronic databases for studies published between January 2016 and April 2026. Randomized controlled trials (RCTs) and cluster randomized controlled trials evaluating school-based integrated prevention programs for adolescents were included. Random-effects meta-analyses were performed for substance-use outcomes, including alcohol-related outcomes and binge drinking, using pooled odds ratios (ORs) and 95% confidence intervals (CIs). Mental health and psychosocial outcomes were synthesized narratively because heterogeneity in outcome definitions, measurement instruments, and assessment periods precluded meaningful quantitative pooling. A total of 16 reports representing 10 independent trials were included in the systematic review, and five independent trial comparisons were included in the primary meta-analysis. Integrated prevention programs significantly reduced alcohol-related behaviors among adolescents compared to control groups (OR = 0.56, 95% CI = 0.37–0.86, p = 0.008). Significant preventive effects were also observed for binge drinking outcomes (OR = 0.32, 95% CI = 0.16–0.64, p = 0.001). Most interventions incorporated emotional regulation, resilience enhancement, coping skills, stress management, and help-seeking promotion. Digital and online approaches were frequently used. Narrative findings suggested potential benefits for selected psychosocial and mental health-related outcomes; however, these outcomes were not quantitatively pooled and were not consistently sustained across follow-up periods. Integrated school-based prevention programs incorporating mental health-related components demonstrated beneficial effects on alcohol-related behaviors and binge drinking among adolescents. Evidence regarding mental health and psychosocial outcomes was based on narrative synthesis and remains less conclusive. These findings support the importance of integrated prevention approaches that simultaneously address substance use and mental health in school settings. However, additional large-scale RCTs with standardized outcome measures and long-term follow-up are needed to strengthen the current evidence base. Full article

Background: Resilience is an important resource in nursing education, supporting students in adapting to demanding academic and clinical environments. However, evidence regarding the factors associated with resilience among nursing students remains inconsistent. This study aimed to examine the factors associated with resilience among undergraduate nursing students, with particular emphasis on gender, age, academic progression, institutional context, and selected sociodemographic characteristics. Methods: A cross-sectional quantitative study was conducted with 263 undergraduate nursing students from two higher education institutions in Portugal. Data were collected using the Takviriyanun Resilience Factors Scale, adapted and validated for the Portuguese population. Descriptive and inferential statistical analyses were performed to examine the two dimensions of the scale—Personal and Social Support Skills and Problem-Solving Skills—and their associations with sociodemographic and academic variables. Results: Students reported generally favourable levels of resilience-related factors, with higher scores observed in items reflecting personal responsibility and perceived social support. Comparatively lower scores were observed for items related to calmness and patience, confidence, optimism, and perceived social acceptance. A statistically significant gender difference was found in Problem-Solving Skills, with male students showing higher mean ranks than female students (p = 0.038); however, the effect size was small (r = 0.13). A moderate positive correlation was observed between Problem-Solving Skills and Personal and Social Support Skills (ρ = 0.394, p < 0.001). No statistically significant associations were found between the resilience dimensions and age, year of study, higher education institution, displacement status, place of residence, or religious affiliation. Conclusions: Undergraduate nursing students demonstrated generally favourable levels of resilience-related factors. Personal and Social Support Skills and Problem-Solving Skills were positively associated, suggesting a meaningful relationship between interpersonal resources and problem-solving competencies. Although a small gender difference was observed in Problem-Solving Skills, the findings do not indicate substantial gender-related differences in resilience-related factors. Educational strategies aimed at strengthening problem-solving, adaptive coping, emotional self-regulation, and social support may be relevant throughout undergraduate nursing education. Longitudinal and multicentre studies are warranted to further investigate the factors associated with resilience and to evaluate the effectiveness of resilience-focused educational interventions. Full article

Labour markets across Europe increasingly describe workers through skills rather than job titles, and a growing number of large language model (LLM)-based tools now claim to extract these skills automatically from unstructured text at scale. Among these, ESCOX has gained particular traction for its open-source, taxonomy-aligned design, yet like any LLM-based system it remains susceptible to hallucination, prompt sensitivity, and non-deterministic output, risks that are rarely quantified before such tools are deployed in practice. The European Skills, Competences, Qualifications, and Occupations (ESCO) classification provides the standardised reference against which this risk can be measured, but no study has yet benchmarked an ESCO-aligned LLM extractor against an independent, expert-labelled dataset at scale. This study addresses that gap. Candidate skills generated by ESCOX are compared against reference skills already assigned to job vacancies on the EURES portal by national labour-market experts, using job-by-skill matrices to quantify agreement and skill co-occurrence networks to characterise how the two sets diverge structurally. Results reveal the extent to which ESCOX’s automatic output aligns with expert judgement and where systematic divergences occur. These findings offer HR practitioners, policymakers, and labour-market researchers an evidence-based basis for deciding when ESCOX’s output can be trusted directly and when expert oversight remains necessary. Full article

We examined the relationships between self-directed learning skills, university belonging, and student engagement in open online and distance learning environments within the framework of Self-Determination Theory. The research was conducted using a correlational survey model. Data were collected from 696 university students enrolled at a large-scale open, online and distance learning institution in Türkiye. We used three instruments measuring self-directed learning skills, university belonging, and student engagement. The data were analyzed using partial least squares structural equation modeling. The findings indicate that university belonging significantly and positively predicts all dimensions of student engagement. While self-control skills, learning skills, and sustaining the desire to learn significantly explain university belonging, metacognitive awareness and ability to identify sources were found to have no significant effect. Furthermore, it was found that university belonging acts as a significant mechanism in the relationships between self-control skills, learning skills, and sustaining the desire to learn, and student engagement. In this open and distance learning context, student engagement was not explained by individual learning skills alone. University belonging may therefore be one mechanism that partly accounts for the association between self-directed learning and academic participation. The study contributes by testing self-directed learning at the level of its dimensions, by identifying university belonging as a mechanism that partly accounts for its association with engagement, and by situating this model in a large-scale open and distance learning setting. Full article