Search Results (371)

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

While researchers of children in early years education promote the development of computational thinking (CT) abilities, many teachers are unaware of, or resistant to, the idea of teaching CT to such young children. This study explored the possibility of utilising everyday items and topics to develop CT abilities in a class of 24 four-to-five-year-old children. Over six weekly sessions, the children took part in innovative guided play activities integrated with class topics: Celebrations, Forest School and Christmas. Each session consisted of two activities: Task A consisted of deconstructing, evaluating and choosing equipment or items, and Task B consisted of sequencing and debugging the order of the activity, e.g., wrapping a birthday present. Two methods of assessment were utilised: quantitative where children were asked to do simple pencil and paper tasks and the sequencing or placement of pictures to record their accuracy; and qualitative where children were individually asked to explain their results. The findings indicate progress was made in task performance and the development of children’s logical reasoning and thinking abilities. Full article

Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and manual discourse analysis are often limited by subjectivity, high labor costs, and poor scalability. These challenges underscore the need for automated, objective tools to support pedagogical assessment. This study explores and compares the use of Transformer-based language models for the automatic classification of teaching behaviors from real classroom transcriptions. A dataset of over 1300 utterances was compiled and annotated according to the teaching styles proposed in the circumplex approach (Autonomy Support, Structure, Control, and Chaos), along with an additional category for messages in which no style could be identified (Unidentified Style). To address class imbalance and enhance linguistic variability, data augmentation techniques were applied. Eight pretrained BERT-based Transformer architectures were evaluated, including several pretraining strategies and architectural structures. BETO achieved the highest performance, with an accuracy of 0.78, a macro-averaged F1-score of 0.72, and a weighted F1-score of 0.77. It showed strength in identifying challenging utterances labeled as Chaos and Autonomy Support. Furthermore, other BERT-based models purely trained with a Spanish text corpus like DistilBERT also present competitive performance, achieving accuracy metrics over 0.73 and and F1-score of 0.68. These results demonstrate the potential of leveraging Transformer-based models for objective and scalable teacher behavior classification. The findings support the feasibility of leveraging pretrained language models to develop scalable, AI-driven systems for classroom behavior classification and pedagogical feedback. Full article

The reactivation of BK polyomavirus (BKPyV) during severe immunosuppression plays a crucial role in two significant syndromes observed in transplant recipients: BK polyomavirus-associated nephropathy (BKPyVAN) in kidney transplant patients and BK polyomavirus-associated hemorrhagic cystitis (BKPyV-HC) in hematopoietic cell transplant (HCT) recipients. This review aims to summarize the current understanding and lingering ambiguity by looking at three primary questions: (1) In cases with BKPyV-related illnesses in transplant patients, which diagnostic methods have the best track record of accuracy and success? (2) Which therapy approaches have the best track records of safety and efficacy in real-world clinical settings? (3) What can immunological research teach us about the development of future tailored treatments? Diagnosis involves the patient’s appearance, ruling out other potential causes, and employing quantitative PCR to identify active viral replication in urine or plasma. BKPyV-HC can vary from self-limited hematuria to potentially fatal bleeding, while BKPyVAN may lead to loss and dysfunction of the allograft. Reducing immunosuppression remains the key aspect of treatment. However, the effectiveness of antivirals (such cidofovir and leflunomide) is not always the same, and supporting measures depend on the syndrome. Researchers are looking into new immunotherapies, such as virus-specific cytotoxic T cells. Due to the intricate viro-immunopathology and lack of defined treatment regimens, future initiatives should focus on prospective studies to establish validated thresholds, enhance management algorithms, and integrate immune surveillance into individualized therapy. Full article

Sustainable construction has evolved into a global priority to mitigate the impacts of climate change, as the construction industry significantly contributes to environmental degradation and the overexploitation of resources. This study considers the effects on sustainability, particularly the inadequate management of resources, the ecological impact, and the anticipated degradation of the structures, all of which are due to the omission of the structural analysis during the design phase of the reinforced concrete (RC) structure. A methodical survey was conducted in three major cities among 258 professionals in the construction sector in Burundi, a developing country that has suffered socio-political and infrastructural challenges. The study examines the impact of these challenges on construction results. Quantitative analysis was carried out using SPSS v.30 and Amos 26 Software. For this research, reliability analysis, Kaiser-Meyer-Olkin test (KMO), Bartlett test, Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and the Relative Importance Index (RII) were used to ensure the reliability and accuracy of the data. The results indicate that many projects are taking place in the absence of proper structural analysis due to financial constraints, poor quality materials, lack of qualified personnel, poor enforcement of regulations, and insufficient monitoring. These parameters have led to structural deficiencies compromising sustainability. The study recommends that government agencies, professional construction workers, and building owners improve regulation, teaching effectiveness, and professional responsibility to ensure that fundamental practices, such as structural analysis and the use of right sustainable materials, are logically applied to improve public safety and environmental resilience. Full article

Identifying knowledge gaps and predictors of performance are proven ways to implement changes to a curriculum. This cross-sectional study investigates the subjective and objective competency of 52 medical students at McGill University in musculoskeletal (MSK) medicine, with a focus on orthopaedic surgery. We surveyed medical students to assess their confidence levels in orthopaedic surgery and their perceptions of its teaching. The students then completed a 25-question orthopaedics-focused exam as an objective assessment of their knowledge. Descriptive statistics were calculated, exam performance was compared across academic years, predictors of exam scores were analyzed, and student self-assessment accuracy was evaluated. Students reported lower confidence in orthopaedic surgery than in many other specialties, exam scores varied significantly across academic years (p = 0.007), and predicted exam performance was the only significant predictor of test score in multiple linear regression (R² = 0.313, p = 0.025). Calibration analysis revealed a substantial miscalibration, where students with higher predicted scores tended to overestimate their performance, while those with lower predictions tended to underestimate themselves (intercept = 27.2, slope = 0.54). A Bland–Altman plot demonstrated wide limits of agreement between predicted and actual scores (mean bias −1.2%, 95% LoA −35.0% to +32.6%). These findings highlight meaningful orthopaedic knowledge gaps and miscalibrated self-assessment, emphasizing the need for targeted, structured educational interventions in the MSK curriculum. Full article

The rapid advancement of AI technologies and the emergence of large language models (LLMs) such as ChatGPT have facilitated the integration of intelligent question-answering systems into education. However, students often hesitate to ask questions, which negatively affects learning outcomes. To address this issue, this study proposes a closed, locally deployed generative AI teaching assistant system that enables instructors to upload course PDFs to generate customized Q&A platforms. The system is based on a Retrieval-Augmented Generation (RAG) architecture and was developed through a comparative evaluation of components, including open-source large language models, embedding models, and vector databases to determine the optimal setup. The implementation integrates RAG with responsive web technologies and is evaluated using a standardized test question bank. Experimental results demonstrate that the system achieves an average answer accuracy of up to 86%, indicating a strong performance in an educational context. These findings suggest the feasibility of the system as an effective, privacy-preserving AI teaching aid, offering a scalable technical solution to improve digital learning in on-premise environments. Full article

Mental calculation is key to the development of number sense and flexibility in thinking, but in practice, it is often neglected in favour of written algorithms. The aim of this study was to examine the relationship between the success in mental calculation and the number of strategies used, as well as to explore differences between age groups and genders. The study included 233 participants from various age groups, and data were collected through a mental calculation test and individual interviews regarding the strategies employed. The study follows quantitative, cross-sectional, correlational-comparative design, and the data was analyzed using key statistical techniques including the Kolmogorov–Smirnov test, Pearson’s correlation analysis, linear regression, one-way ANOVA with Bonferroni post hoc correction, and two-way ANOVA to examine main effects and interactions. The results showed a statistically significant positive correlation between the number of strategies and success in mental calculation. Differences between age groups were marginally significant; it was found that upper primary and secondary school students used a greater number of strategies. Additionally, boys, on average, applied more strategies than girls. In conclusion, the variety of mental calculation strategies positively correlates with accuracy in mental calculation, and teaching a greater number of strategies may contribute to the development of flexibility and confidence in mathematical thinking. It is recommended that greater emphasis is placed on the development of mental strategies within formal education. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

Narrative methods are increasingly recognized in science teaching for their potential to deepen conceptual understanding and foster meaningful connections to scientific content. This review explores their educational significance by examining three main formats—historical narratives, realistic fiction, and science fiction or fantasy—highlighting how each can render complex scientific principles more accessible and memorable. Special attention is given to chemistry education, a field where abstract, multilevel concepts often pose significant challenges for students. Furthermore, the review explores the integration of narratives into gamified environments, examining how storytelling functions as both a motivational engine and a cognitive scaffold to support deeper learning in science. Finally, the review proposes directions for future research, underscoring the need for empirically grounded narrative resources that balance imaginative appeal with scientific accuracy across diverse educational settings. Full article

Teachers often raise their vocal volume to improve intelligibility or capture students’ attention. While this practice is common in second language (L2) teaching, its effects on tone perception remain understudied. To fill this gap, this study explores the effects of loud speech on Mandarin tone perception for L2 learners. Twenty-two native Mandarin speakers and twenty-two Thai L2 learners were tested on their perceptual accuracy and reaction time in identifying Mandarin tones in loud and normal modes. Results revealed a significant between-group difference: native speakers consistently demonstrated a ceiling effect across all tones, while L2 learners exhibited lower accuracy, particularly for Tone 3, the falling-rising tone. The loud speech had different impacts on the two groups. For native speakers, tone perception accuracy remained stable across different speech modes. In contrast, for L2 learners, loud speech significantly reduced the accuracy of Tone 3 identification and increased confusion between Tones 2 and 3. Reaction times in milliseconds were prolonged for all tones in loud speech for both groups. When subtracting the length of the tones, the delay of RT was evident only for Tones 3 and 4. Therefore, raising the speaking volume negatively affects the Mandarin tone perception of L2 learners, especially in distinguishing Tone 2 and Tone 3. Our findings have implications for both theories of L2 tone perception and pedagogical practices. Full article

Artificial intelligence (AI)-generated content (AIGC) is an innovative technology that utilizes machine learning, AI models, reward modeling, and natural language processing (NLP) to create diverse digital content such as videos, images, and text. It has the potential to support various human activities with significant implications in teaching and learning, facilitating heuristic teaching for educators. By using AIGC, teachers can create extensive knowledge content and effectively design instructional strategies to guide students, aligning with heuristic teaching. However, incorporating AIGC into heuristic teaching has controversies and concerns, which potentially mislead outcomes. Nevertheless, leveraging AIGC greatly benefits teachers in enhancing heuristic teaching. When integrating AIGC to support heuristic teaching, challenges and risks must be acknowledged and addressed. These challenges include the need for users to possess sufficient knowledge reserves to identify incorrect information and content generated by AIGC, the importance of avoiding excessive reliance on AIGC, ensuring users maintain control over their actions rather than being driven by AIGC, and the necessity of scrutinizing and verifying the accuracy of information and knowledge generated by AIGC to preserve its effectiveness. Full article

This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching. Full article

Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to the absence of universal guidelines and trust-related concerns. This study examines how trust, defined across three key dimensions (accuracy and relevance, privacy protection, and nonmaliciousness), influences the adoption and use of GenAI tools in academic environments. Using survey data from 823 participants across different academic roles, this study employs multiple regression analysis to explore the relationship between trust, user characteristics, and behavioral intention. The results reveal that trust is primarily experience-driven. Frequency of use, duration of use, and self-assessed proficiency significantly predict trust, whereas demographic factors, such as gender and academic role, have no significant influence. Furthermore, trust emerges as a strong predictor of behavioral intention to adopt GenAI tools. These findings reinforce trust calibration theory and extend the UTAUT2 framework to the context of GenAI in education. This study highlights that fostering appropriate trust through transparent policies, privacy safeguards, and practical training is critical for enabling responsible, ethical, and effective integration of GenAI into higher education. Full article

A novel and cost-effective methodology is introduced for the precise prediction of the melting time of metals and alloys in a 700 W domestic microwave oven, using a hybrid SiC–graphite susceptor to ensure efficient heating without direct interaction with microwaves. The study includes experimental trials with multiple alloys (Sn–Bi, Zn, Zamak, and Al–Si, among others) and variable masses, whose results made it possible to construct a dimensionless model, trained with XGBoost on easily measurable thermophysical properties (specific heat, density, thermal conductivity, mass, and melting temperature). The model achieves high accuracy, with a relative error below 5%, and metrics of MAE = 4.8 s, RMSE = 6.1 s, and R² = 0.9996. The generalization of the model to different microwave powers (600–1100 W) is also validated through analytical adjustment, without the need for additional experiments. The proposal is implemented as a Python application with a graphical interface, suitable for any academic or teaching laboratory, and its performance is compared with classical models. This approach effectively contributes to the democratization of thermal testing of metals in educational and research settings with limited resources, providing thermodynamic rigor and advanced artificial intelligence tools. Full article