Search Results (1,634)

Physical literacy (PL) has become a central concept in contemporary physical education (PE), promoting holistic movement experiences and lifelong engagement in physical activity. However, it remains unclear how PL principles are reflected in PE curricula and perceived by teachers. This study aimed to preliminarily evaluate the psychometric properties of the Croatian version of the PL-compatible pedagogy questionnaire, examine classroom teachers’ perceptions of Croatian PE curriculum alignment with PL principles, and investigate associations between teachers’ perceived PL and PL-compatible pedagogy. Thirty-three female classroom teachers from Croatia (49.80 ± 10.21 years) completed the Perceived Physical Literacy Questionnaire for Adults (PPLQ-SEE) and the PL-compatible pedagogy questionnaire. The questionnaire demonstrated acceptable internal consistency for exploratory purposes (Cronbach’s α = 0.65). The strongest agreement was observed for acknowledgement of students’ unique movement journeys (ID = 0.56), inclusion (ID = 0.55), meaningful engagement in physical activity (ID = 0.55), and embodiment (ID = 0.53), while lower agreement was found for student movement choice (ID = 0.38) and balanced holistic development (ID = 0.38). Significant associations between teachers’ perceived PL and specific PL-compatible pedagogy items provided initial evidence of construct validity, although these findings should be interpreted as exploratory. These preliminary findings suggest that future PL-based approaches may benefit from placing greater emphasis on student autonomy, movement choice, and holistic learning experiences within PE. However, confirmation in larger and more diverse samples is required. Full article

Critical thinking is a stated priority of higher education reform in China, yet its enactment in everyday teaching remains uneven. This study investigated how lecturers of ideological and political theory courses (IPTCs) understand and value critical thinking, and how they report teaching it. To our knowledge, few studies have modelled lecturers’ conceptions, attitudes, reported practices and contextual constraints jointly within these courses. An explanatory sequential mixed-methods design was employed. A validated questionnaire was completed by 362 IPTC lecturers across 20 universities in Anhui Province, and semi-structured interviews were subsequently conducted with 34 lecturers. The survey data were analysed using descriptive and inferential statistics, including multiple and moderated regression, whereas the interview data were examined thematically. Although lecturers endorsed critical thinking strongly as a general educational aim, they rated its place within IPTCs considerably lower, and they conceptualised it predominantly in terms of logic and reasoning. Reported classroom enactment fell below the scale midpoint. Conceptions did not predict practice; attitudes predicted it only weakly (standardised β = 0.13), and contextual constraints did not moderate the attitude–practice relationship. The regression models accounted for almost none of the variance in reported practice. These findings indicate a broad belief–practice gap that is shaped by structural conditions rather than by individual beliefs. The single-province, self-report design constrains wider generalisation. The findings direct reform attention towards assessment design and sustained, practice-based professional learning, since belief change alone appears unlikely to alter classroom practice. Full article

Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design. Full article

In schools located in the Colombian Amazon, there is still limited empirical evidence on how students connect pro-environmental values with emotions and environmental peace culture. In this article, environmental peace culture is understood as a way of learning to care for nature, live with it, and assume shared responsibility for its protection. The study investigated “Sembrando Paz Ambiental”, a place-based pedagogical proposal implemented among secondary school students through experiential, playful, and participatory activities. A convergent mixed-methods design was employed, integrating a quasi-experimental pretest–posttest component with a qualitative analysis of students’ perceptions and pedagogical productions. The quantitative component included 66 paired cases assessed through a 24-item Likert scale, with data analyzed using the Wilcoxon signed-rank test. The qualitative component included open-ended responses, drawings, and written productions analyzed through thematic coding and co-occurrence analysis in ATLAS.ti. The findings revealed statistically significant increases across all pro-environmental values, with the hedonic value showing the largest effect size. Qualitative findings associated environmental peace culture with emotional well-being, cooperation, care for nature, and territorial belonging. The study contributes to the literature by showing that the mobilization of pro-environmental values depends not only on normative conservation discourses, but also on pedagogical experiences that connect emotion, territory, and environmental care. Full article

Customer complaints are considered one of the key indicators of customer discontentment with a service. In organizations, such as telecommunications companies, not all customers raise their complaints, which raises concerns about their potential churn or retention. As firms usually rely on the complaints raised to customer services, there exists an important portion of customers who claim their complaints through other platforms, such as social media, even though another portion does not complain at all. This places the company’s image in jeopardy and might affect its productivity and profits. To address this challenge, it is important to address possible customer problems before they turn into effective complaints. To do so, the current study aims to predict the complaints of customers in a telecommunication company and their potential churn through the usage of supervised machine learning models to test the correlation between churn and complaints. Through a thorough data analysis, it becomes evident that a good portion of clients who encounter service issues decide not to present any complaints to the company. In addition, among complainers, some do not complain directly to the company, while others who contact the company have their problems postponed. Among those, there is a proportion, considered as having unresolved concerns, turned into churn. Using a dataset of 1000 clients, recruited over a period of six months, the results showed that a considerable portion of customers using the services during the day were non-churners and continued using it over the overall period of 6 months. Whereas, day churn and evening churn both showed much lower frequencies compared to non-churn customers, with fewer calls across all durations. Additionally, the findings showed that there exists a correlation between customer complaints and customer churn, where churn events frequently coincide with complaints, indicating that customers without churn are generally content with their service, while those having complaints are more likely to quit. This study presents important insights into telecommunications companies to improve their service offerings, enhance customer satisfaction, and reduce churn rates, leading to a more stable and profitable customer base. Full article

Background/Objectives: Evacuation policies for childbirth in Nunavut, Canada, continue to profoundly shape the lives of Inuit women, their families, and communities. The evacuation policy is known to disrupt kinship networks and contribute to the erosion of Inuit birthing knowledge. Addressing a gap in Inuit-led understandings of sexual and reproductive health priorities, this paper presents findings from research conducted in 2024 with Kivallirmiut women (‘People of’ in Inuktitut is miut; Kivallirmiut means people of the Kivalliq Region. Nunavummiut means people of Nunavut, and Arviarmiut means people of Arviat, Nunavut, Canada). The study took place in two communities in the Kivalliq Region of Nunavut. Methods: Employing a highly participatory, community-based Indigenous methodology, this study was underpinned by Inuit Qaujimajatuqangit, Indigenous feminist and postcolonial and decolonial theories, and principles of health equity. Data from a survey were generated to identify needs and priorities related to perinatal health and care. Results: Findings identify that Inuit women in these communities hold an interest in accessing perinatal information and support, with a strong desire for learning from Inuit Elders, as well as support via peer networks. Participants also emphasized a desire for more information concerning travelling for birth (medical evacuation), breastfeeding, and healing after birth. Different models for delivery of this information and perinatal support were also identified. Conclusions: Collectively, the priorities of Inuit women in this survey offer insights concerning a structural misalignment between existing perinatal care supports and services that rely on evacuation-based perinatal care and the needs and priorities within Kivalliq communities. This disconnection reveals enduring colonial assumptions embedded within current healthcare systems. The findings suggest a need for perinatal supports that centre culture, kinship, and relational forms of support. Full article

Background/Objectives: Obesity and related metabolic diseases arise from an interplay of lipid overload, insulin resistance and oxidative stress. Acetyl-CoA carboxylase 2 (ACC2) controls malonyl-CoA production and thereby gates mitochondrial fatty-acid oxidation, placing it at the intersection of lipid handling and redox-sensitive metabolic dysfunction. Dietary antioxidants such as polyphenols, flavonoids and terpenoids are increasingly studied as modulators of these pathways, yet systematic prioritization of food-derived antioxidant compounds against defined metabolic targets remains challenging. We developed an integrated deep learning and structure-based workflow to prioritize FooDB compounds with predicted ACC2-binding potential. Methods: A curated set of 3983 ACC2 bioactivity records from ChEMBL 36 was used to train scaffold-split models, including graph neural-network and graph–Morgan fingerprint-fusion architectures. The calibrated ensemble screened 139,988 FooDB compounds; 200 candidates with predicted activity probability above 0.70 were docked against the ACC2 carboxyltransferase domain (PDB ID: 3FF6), and six prioritized complexes underwent 500 ns molecular dynamics and MM/GBSA analysis. Results: Redocking of the co-crystallized ligand reproduced the experimental pose (RMSD 1.2 Å). Although the highest-ranked screening hits were antioxidant terpenoids and alkaloids, docking-based prioritization from the top candidates selected six larger, more polar food-derived compounds, including glycosides and two nucleotide/cofactor-like conjugates, which showed docking scores from −7.47 to −6.65 kcal/mol versus −6.21 kcal/mol for the reference ligand. Glu539 emerged as a recurrent interaction hotspot. All candidates gave more favourable MM/GBSA binding free energies than the reference (ΔG = −22.52 kcal/mol), led by FDB029596 (−35.65), FDB021568 (−34.14) and FDB017807 (−33.87 kcal/mol). Conclusions: This workflow provides a reproducible framework for prioritizing food-derived compounds as candidate ACC2 binders relevant to obesity and metabolic disease, generating structurally supported hypotheses for biochemical and nutritional validation. The prioritized compounds are computational candidates only and require biochemical and cellular (experimental) validation before any ACC2-related biological relevance can be established. Full article

The floods triggered by the 29 October 2024 cut-off low in Valencia (Spain) expose a persistent challenge in disaster risk reduction: extensive meteorological and territorial data do not automatically become timely, trusted or actionable public guidance. This conceptual synthesis uses the Valencia event as a diagnostic case and reconstructs selected evidence on rainfall, hydrological escalation and alert timing to develop a data-driven framework spanning observation, modelling, impact assessment, communication, decision-making and post-event learning. Here, “data-driven” denotes an end-to-end governance and translation process, not the development of a new forecasting model. The framework integrates four dimensions: data governance, user-centred visualization, uncertainty communication and school-based education. It also introduces territorial translation as the link between impact forecasts and place-specific infrastructures, routines, vulnerabilities and responsibilities. Its novelty lies in connecting the Early Warnings for All pillars and impact-based, people-centred warning approaches with an explicit educational and territorial learning loop. Its practical contribution is a responsibility matrix, a minimum governance package and an implementation roadmap with indicators for latency, reach, comprehension and protective action. The framework is intended for adaptation, rather than statistical generalization, across Mediterranean and other fast-onset flood contexts. Improved forecasts remain necessary but insufficient: loss reduction requires interoperable records, accessible impact-based messages and inclusive educational programmes that convert scientific information into situated collective competence. Full article

Rapid urbanization presents a fundamental challenge to global sustainable development, as urban expansion increasingly conflicts with resource constraints, ecological carrying capacity, and carbon emission mandates. Serving as a highly spatially heterogeneous laboratory, China offers a critical context for embedding green development into urban efficiency assessments. This study reconceptualizes new urbanization efficiency (NUE) through a multidimensional framework encompassing resource inputs, coordinated development processes, and sustainable outcomes. Using a panel of 281 prefecture-level cities, we evaluated NUE via a remote-sensing-constrained Super-SBM model, utilizing LISA time paths and an interpretable spatial machine learning framework (GWR-XGBoost-SHAP) to unpack its spatiotemporal dynamics. Key findings indicate: (1) China’s NUE exhibited a fluctuating upward trajectory, transitioning from an east-high/west-low to a south-high/north-low spatial pattern. (2) Spatiotemporal analysis revealed strong spatial inertia and profound path dependency in North China, characterizing it as a persistent low-value basin, whereas southeastern coastal cities demonstrated dynamic, path-breaking trajectories. (3) While green development intensity, industrial upgrading, and technological innovation emerged as primary drivers, they operate through complex nonlinear mechanisms. Specifically, green development and innovation exhibit threshold-triggered synergies, population agglomeration acts as a nonlinear amplifier, and external openness presents context-dependent negative interactions. These findings refine NUE measurement methodologies and provide a transferable analytical framework to inform differentiated, place-based urbanization policies for regions navigating the friction between urban growth and ecological limits. Full article

Traditional methods for wine bottle packaging leakage detection often suffer from low efficiency, high false-positive rates, or an inability to detect micro-leakages. This paper proposes a near-infrared laser leakage detection system based on tunable diode laser absorption spectroscopy at 1392 nm, combined with a LightGBM machine learning model. The system detects gaseous ethanol vapor escaping from leaking bottles, addressing the spectral interference caused by ambient water vapor. A total of 1410 samples were collected, and each raw 2000-point spectral contour was compressed into a 200-dimensional feature vector through baseline correction, Z-score normalization, and uniform down-sampling. A two-stage hyperparameter optimization strategy yielded the optimal LightGBM configuration with a 5-fold cross-validation. For the binary classification task, the model achieved an AUC of 0.9949 and an inference speed of 0.0058 ms per sample on a CPU, outperforming Random Forest, PLS, and four deep learning models. For the regression task, the model achieved an R² of 0.5854 ± 0.0919. An anti-interference experiment on 422 samples under varying flow rates, temperatures, and commercial wine types confirmed the model’s robustness, achieving an overall accuracy of 0.94 and an alcohol recall of 0.99. To further validate the system under realistic conditions, a simulated micro-leakage test was conducted using a negative-pressure extraction method: 320 samples were collected from artificially damaged commercial wine bottles placed in a custom-built acrylic vacuum chamber that replicates the production line enclosure. The model achieved an accuracy of 0.95 with zero false negatives. The complete detection cycle takes no more than 5 s per bottle, enabling non-destructive, rapid, and online packaging integrity assessment. The results demonstrate that the proposed system provides a low-cost and reliable solution for wine bottle leakage detection suitable for industrial deployment. Full article

Artificial intelligence (AI) has emerged as a transformative tool for plant health monitoring, offering new opportunities for scalable, timely, and data-driven pest and disease management in agriculture. This review provides a comprehensive synthesis of AI-based methods for pest and plant disease detection, systematically organizing existing literature across sensing modalities, learning paradigms, and deployment scales. We distinguish between population-level pest monitoring, plant-centric visual inspection, and field-scale surveillance, as well as between post-symptomatic disease recognition and pre-symptomatic detection enabled by spectral imaging technologies. Beyond summarizing recent advances, this work places strong emphasis on critical analysis, discussing fundamental limitations related to data scarcity, domain shift, generalization under field conditions, and the challenge of disentangling biotic from abiotic stress factors. The review further examines the distinction between correlation-driven AI predictions and causal disease understanding, positioning AI as a complementary decision-support tool alongside established diagnostic methods. Building on these insights, we outline key future research directions, including multimodal sensor fusion, explainable and trustworthy AI, edge-based deployment for real-time monitoring, and the development of foundation models for unified agricultural intelligence. This review aims to serve as both an accessible entry point and a critical reference for advancing AI-driven plant health management. Full article

Background/Objectives: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, with polyp miss rates of up to 26% reported during colonoscopy and classification accuracy remaining highly operator-dependent. Accurate multi-class polyp subtype classification is clinically critical, as it directly determines treatment decisions: adenomatous polyps require resection, whereas hyperplastic lesions may warrant only surveillance. This study aims to systematically compare five deep learning architectures for five-class gastrointestinal polyp classification and to provide clinically interpretable diagnostic insights through Grad-CAM visualisation. Methods: ResNet50, VGG16, EfficientNet-B3, DenseNet121, and Vision Transformer (ViT-B/16) were evaluated on the Kvasir Dataset V2 (5000 images, five classes) under a unified training and evaluation protocol on common GPU hardware. All models employed ImageNet transfer learning with a redesigned multi-layer classification head. Two optimisation strategies were applied: SGD with cosine annealing for CNN architectures, and AdamW with linear warmup for ViT-B/16. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to generate spatial attention heatmaps for qualitative clinical interpretation. Results: Under a single 80/10/10 split, ViT-B/16 attained the highest accuracy (97.2%); however, because a single split is sensitive to sampling, the evaluation was strengthened with stratified five-fold cross-validation (mean ± SD). Under cross-validation, EfficientNet-B3 achieved the highest accuracy at 95.90 ± 0.35%, followed closely by ViT-B/16 (95.12 ± 0.72%), then ResNet50 (91.74 ± 0.74%), DenseNet121 (90.32 ± 0.70%), and VGG16 (88.50 ± 1.72%); the small standard deviations indicate that all models, including ViT-B/16, were stable across folds. Pairwise McNemar tests with Holm correction found that every difference was statistically significant (p < 0.05), including the EfficientNet-B3 advantage over ViT-B/16 (p = 0.010). ViT-B/16 thus remained a strong, stable performer that significantly outperformed the three remaining CNNs, while the cross-validated ranking placed the most compact model, EfficientNet-B3, first: a Vision Transformer was highly competitive with, but not superior to, the strongest CNN. A consistent, architecture-agnostic misclassification pattern was identified between dyed-lifted polyps and dyed-resection margins across all five models, consistent with a task-level visual ambiguity that may also reflect overlapping class definitions and annotation factors, with direct clinical implications. Grad-CAM analysis, quantified by attention-entropy and concentration metrics, showed that model attention remained focused on relevant stained tissue regardless of whether predictions were correct, indicating that the dyed-class confusions reflect genuine visual ambiguity rather than a localisation failure. Conclusions: Under cross-validation, EfficientNet-B3 achieved the highest accuracy on the Kvasir V2 five-class task, significantly outperforming all other architectures, with ViT-B/16 being a close and competitive second. The identified confusion between post-procedural chromoendoscopic classes is unlikely to be fully resolved by architectural changes alone and may require higher-resolution imaging or domain expert re-annotation. These findings contribute to the evidence base for explainable deep learning in gastrointestinal endoscopy; external, multi-centre validation remains necessary before clinical adoption. Full article

Background/Objectives: In emergency settings, it is often infeasible to place patients in an anatomical position for CT scanning. Consequently, emergency brain CT scans of patients with traumatic brain injury frequently demonstrate considerable anatomical misalignment. These inconsistencies compromise the performance of deep learning-based 3D hematoma segmentation. This study aims to enhance segmentation robustness by proposing a registration-based data augmentation strategy utilizing anatomical landmarks. Methods: We propose a framework termed cross-subject registration-based augmentation (CSRA), which uses anatomical landmarks to rigidly register patient CT volumes to selected reference CT volumes and generate anatomically aligned CT-label pairs for training. Results: A total of 339 patients who underwent brain CT imaging were enrolled from a Level 1 trauma center. CSRA-3s achieved the highest mean Dice and IoU and the lowest mean HD95 among the evaluated augmentation strategies. Patient-level paired analysis showed that the most robust statistically supported benefit was a significant reduction in HD95 compared with a conventional geometric augmentation baseline, indicating improved boundary agreement. Conclusions: The proposed augmentation strategy mitigated the effect of anatomic position discrepancies on segmentation performance, particularly in terms of boundary agreement, without modifying existing model architectures. CSRA may serve as a model-agnostic training-time augmentation strategy for improving segmentation robustness in anatomically inconsistent emergency CT imaging, although multicenter external validation is required before clinical deployment. Full article

Language tourism combines language learning with cultural exploration and offers new opportunities for engaging visitors with local linguistic heritage. Recent advances in Augmented Reality (AR) and Artificial Intelligence (AI) have enabled the development of immersive tourism experiences; however, their application to low-resource dialects remains limited. This paper proposes TalkCorfiota, a conceptual framework that integrates AR, conversational AI, geolocation services, and cultural storytelling to support language tourism experiences centered on the Corfiot dialect. Drawing upon situated learning, experiential learning, and digital heritage perspectives, the framework conceptualizes dialect learning as a form of heritage interpretation through which visitors engage with local identity, collective memory, and place-based cultural narratives. The proposed architecture combines location-aware cultural exploration, AR-enhanced contextual interaction, and AI-supported conversational engagement within a unified tourism ecosystem. The framework is supported by a structured linguistic resource developed during the course of this study from publicly available repositories of the Corfiot dialect. The primary contribution of this research is the conceptual design of the TalkCorfiota architectural framework, which is the foundation for future development and evaluation on AR- and AI-supported language tourism applications for low-resource dialects. Full article

Understanding long-term hydroclimatic variability in the central Alps is essential when placing recent changes in precipitation and streamflow within a broader temporal context. This study reconstructs warm-season hydroclimatic variability in the central Alps using tree-ring-based hydroclimatic proxies from the Old World Drought Atlas (OWDA). Seasonal April–May–June–July–August (AMJJA) precipitation at Innsbruck, Austria, and seasonal May–June–July–August (MJJA) streamflow at the St. Jodok gauge were reconstructed using OWDA self-calibrating Palmer Drought Severity Index (scPDSI) predictors and moving-window Stepwise Linear Regression (SLR) models. Calibration windows of 30, 40, and 50 years were developed to account for temporal variability in predictor–climate relationships, and reconstruction uncertainty was quantified using multi-model ensemble bounds. An independent Deep Learning reconstruction was also developed for precipitation to provide an assessment of reconstruction skill and long-term climate trends. Specifically, the results demonstrate a robust reconstruction skill, with mean calibration R² values of 0.65 for streamflow and 0.59 for precipitation. The streamflow reconstruction indicates that recent sustained increases represent the strongest positive anomaly in approximately 650 years, while reconstructed precipitation suggests recent decades are among the wettest sustained intervals of the last ~2000 years. Both records reveal a pronounced transition from severe late 19th-century drought conditions to persistent modern pluvial conditions. Agreement between regression and Deep Learning reconstructions supports the robustness of the identified long-term wetting trend and highlights the exceptional nature of recent hydroclimatic conditions in the central Alps. Full article