Search Results (1,921)

Most educational buildings in the north of Italy, whether of dated or recent construction, were designed to comply with the thermal comfort and energy performance requirements set for the heating season due to limited use in the summer months. In the latest years, however, with greater frequency, school buildings have been used to host indoor summer activities, and, due to the warm temperature conditions and heat waves, indoor thermal discomfort is often experienced, with negative impacts on occupants’ task performance. Consequently, the need to guarantee adequate indoor thermal comfort in schools in the warm season is becoming a growing concern for local public authorities. In this context, this work examines a set of strategies for the enhancement of the energy performance and indoor thermal comfort of public school buildings in the cooling season. Thus, two case study public school buildings of dated and recent construction located in Bolzano, Italy, were analyzed and compared. This study shows the potential of passive and semi-passive measures in improving indoor thermal comfort in the spring–summer months and the limit beyond which mechanical cooling and ventilation systems are required to ensure adequate levels of indoor environmental quality and task performance in the warmest months. Full article

Background: Breakfast habits in Mediterranean countries often include processed products with hidden sugars, which may compromise children’s oral and general health. Objectives: This study assessed the sugar content of breakfast foods commonly consumed by children using °Brix refractometry and examined its implications for dental caries and obesity. Methods: Forty-nine breakfast food samples (processed products, homemade alternatives, and fresh fruits) were analysed using a digital °Brix refractometer to quantify soluble sugar concentrations. Comparative statistical analyses were performed to evaluate differences among food categories. Results: Processed foods consistently exhibited significantly higher °Brix values (mean ± SD: 14.1 ± 4.9), reflecting greater levels of extrinsic sugars, compared with homemade preparations (10.9 ± 1.1) and fresh fruits (10.7 ± 5.2) (p < 0.01). Processed items contained on average 25% more sugar than the other categories. Fresh fruits and homemade options demonstrated moderate °Brix levels, with no added sugars, whereas processed products—despite some being marketed as “no added sugars”—frequently contained substantial sugar content. Conclusions: The findings highlight the urgent need for educational strategies and clearer labelling to reduce sugar intake during childhood breakfasts. Promoting natural and homemade alternatives could be a key preventive approach to lowering the risk of dental caries, obesity, and other diet-related conditions. Full article

Student behavior analysis plays a critical role in enhancing educational quality and enabling personalized learning. While previous studies have utilized machine learning models to analyze campus card consumption data, few have integrated multi-source behavioral data with large language models (LLMs) to provide deeper insights. This study proposes an intelligent educational system that examines the relationship between student consumption behavior and academic performance. The system is built upon a dataset collected from students of three majors at Xinjiang Normal University, containing exam scores and campus card transaction records. We designed an artificial intelligence (AI) agent that incorporates LLMs, SageGNN-based graph embeddings, and time-series regularity analysis to generate individualized behavior reports. Experimental evaluations demonstrate that the system effectively captures both temporal consumption patterns and academic fluctuations, offering interpretable and accurate outputs. Compared to baseline LLMs, our model achieves lower perplexity while maintaining high report consistency. The system supports early identification of potential learning risks and enables data-driven decision-making for educational interventions. Furthermore, the constructed multi-source dataset serves as a valuable resource for advancing research in educational data mining, behavioral analytics, and intelligent tutoring systems. Full article

Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions. Full article

Background: Cardiometabolic risk in adolescents represents a growing public health concern that is closely linked to modifiable factors such as physical fitness. Traditional statistical approaches often fail to capture complex, nonlinear relationships among anthropometric and fitness-related variables. Objective: To develop and evaluate supervised machine learning algorithms, including artificial neural networks and ensemble methods, for classifying cardiometabolic risk levels among Chilean adolescents based on standardized physical fitness assessments. Methods: A cross-sectional analysis was conducted using a large representative sample of school-aged adolescents. Field-based physical fitness tests, such as cardiorespiratory fitness (in terms of estimated maximal oxygen consumption [VO_2max]), muscular strength (push-ups), and explosive power (horizontal jump) testing, were used as input variables. A cardiometabolic risk index was derived using international criteria. Various supervised machine learning models were trained and compared regarding accuracy, F1 score, recall, and area under the receiver operating characteristic curve (AUC-ROC). Results: Among all the models tested, the gradient boosting classifier achieved the best overall performance, with an accuracy of 77.0%, an F1 score of 67.3%, and the highest AUC-ROC (0.601). These results indicate a strong balance between sensitivity and specificity in classifying adolescents at cardiometabolic risk. Horizontal jumps and push-ups emerged as the most influential predictive variables. Conclusions: Gradient boosting proved to be the most effective model for predicting cardiometabolic risk based on physical fitness data. This approach offers a practical, data-driven tool for early risk detection in adolescent populations and may support scalable screening efforts in educational and clinical settings. Full article

The understanding of hidden emotions—situations in which individuals deliberately express an emotion different from what they genuinely feel—is a key skill in theory of mind (ToM) development. This ability allows children to reason about discrepancies between internal emotional states and external expressions and is closely tied to linguistic development, particularly vocabulary related to mental states, which supports complex emotional reasoning. Children who are deaf or hard of hearing (DHH), especially those born to hearing non-signing families and raised in oral language environments, may face challenges in early language exposure. This can impact the development of social and emotional skills, including the ability to understand hidden emotions. This study compares the understanding of hidden emotions in hearing children (n = 59) and DHH children (n = 44) aged 7–12 years. All children were educated in spoken language environments; none of the DHH participants had native exposure to sign language. Participants completed a hidden emotions task involving illustrated stories where a character showed a certain emotion in front of two observers, only one of whom was aware of the character’s true emotional state. The task assessed children’s understanding of the character’s emotional state as well as their ability to reason about the impact of hiding emotions on the beliefs of the observers. The results showed that the hearing children outperformed their DHH peers in understanding hidden emotions. This difference was not attributed to hearing status per se but to language use. Specifically, children’s spontaneous use of cognitive verbs (e.g., think or know) in their explanations predicted task performance across the groups, emphasizing the role of mental state language in emotional reasoning. These findings underscore the importance of early and accessible language exposure in supporting the emotional and social cognitive development of DHH children. Full article

Sign language is a three-dimensional (3D) visual language that conveys meaning through hand positions, shapes, and movements. Traditional sign language education methods, such as textbooks and videos, often fail to capture the spatial characteristics of sign language, leading to limitations in learning accuracy and comprehension. To address this, we propose a 3D Korean Sign Language Learning System that leverages pseudo-hologram technology and hand gesture recognition using Leap Motion sensors. The proposed system provides learners with an immersive 3D learning experience by visualizing sign language gestures through pseudo-holographic displays. A Recurrent Neural Network (RNN) model, combined with Diffusion Convolutional Recurrent Neural Networks (DCRNNs) and ProbSparse Attention mechanisms, is used to recognize hand gestures from both hands in real-time. The system is implemented using a server–client architecture to ensure scalability and flexibility, allowing efficient updates to the gesture recognition model without modifying the client application. Experimental results show that the system enhances learners’ ability to accurately perform and comprehend sign language gestures. Additionally, a usability study demonstrated that 3D visualization significantly improves learning motivation and user engagement compared to traditional 2D learning methods. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article

This study aims to explore the values of character education in the Chinese animated film Three Monks. This film serves as a teaching tool, not only imparting animation principles to university students majoring in animation but also showcasing Chinese cultural philosophy and educational values in implicit, exaggerated, and humorous action design. We employed a descriptive qualitative method. A total of 73 college students majoring in animation watched the film without any prior explanation of animation principles and moral education and then listened to detailed explanations of the character education and animation principles integrated into the film. Through repeated viewing, analysis, and summarization of the storyline, character behaviors, and action design in Three Monks, the values of character education, such as religion, kindness, diligence, independence, responsibility, tolerance, self-reflection, unity and cooperation, and courage to innovate, were embodied. These values are manifested through the film’s storyline, conflicts, character actions, animated performances, and background music. We compared the students’ pre- and post-viewing attitudes based on their discussions, reflections, and course evaluations. The results revealed that conveying moral values through animated films internalized and transmitted character education among university students, shaping cultural identity and social norms. This approach enhanced students’ learning engagement and improved their learning efficiency. Full article

Integrated teaching encourages students to think across disciplines and view key human issues from various perspectives. Although mathematics and geography are taught as separate subjects in schools, they frequently intersect in real-world issues, with scientific problems often analyzed using mathematical methods. The purpose of this article is to systematically review the understanding of study characteristics, teaching content, and forms of integration between geography and mathematics. A systematic review of 26 studies was conducted in accordance with PRISMA guidelines, involving searches of four databases from 2000 to 2023. Screening and selection were performed independently by two researchers. Data were analyzed via structured qualitative content analysis. This systematic review demonstrates that integrated teaching can improve knowledge and skills of students compared to segregated teaching. The findings reveal that contents such as Education for Sustainable Development, cartography, and astronomy and space travel are the main topics covered in subject-integrated mathematics and geography lessons. The study also highlights gaps, especially in long-term effects and teacher involvement in quantitative research. Full article

Background/Objectives: Preeclampsia (PE) is a major cause of maternal and perinatal morbidity and mortality, particularly in low- and middle-income countries. Early-onset PE (EOP) and late-onset PE (LOP) are distinct clinical entities with differing pathophysiological mechanisms and prognoses. However, few studies have explored differential risk factors for EOP and LOP in Latin American populations. This study aimed to identify and assess clinical risk factors for predicting EOP and LOP in a cohort of pregnant women from Bogotá, Colombia, using traditional statistics and machine learning (ML). Methods: A cross-sectional observational study was conducted on 190 pregnant women diagnosed with PE (EOP = 80, LOP = 110) at a tertiary hospital in Bogotá between 2017 and 2018. Risk factors and perinatal outcomes were collected via structured interviews and clinical records. Traditional statistical analyses were performed to compare the study groups and identify associations between risk factors and outcomes. Eleven ML techniques were used to train and externally validate predictive models for PE subtype and secondary outcomes, incorporating permutation-based feature importance to enhance interpretability. Results: EOP was significantly associated with higher maternal education and history of hypertension, while LOP was linked to a higher prevalence of allergic history. The best-performing ML model for predicting PE subtype was linear discriminant analysis (recall = 0.71), with top predictors including education level, family history of perinatal death, number of sexual partners, primipaternity, and family history of hypertension. Conclusions: EOP and LOP exhibit distinct clinical profiles in this cohort. The combination of traditional statistics with ML may improve early risk stratification and support context-specific prenatal care strategies in similar settings. Full article

Multisensory processing has long been recognized to enhance perception, cognition, and actions in adults. However, there is currently limited understanding of how multisensory stimuli, in comparison to unisensory stimuli, contribute to the development of both motor and verbally assessed working memory (WM) in children. Thus, the current study aimed to systematically review and meta-analyze the associations between the multisensory processing of auditory and visual stimuli, and performance on simple and more complex WM tasks, in children from birth to 15 years old. We also aimed to determine whether there are differences in WM capacity for audiovisual compared to unisensory auditory or visual stimuli alone after receptive and spoken language develop. Following PRISMA guidelines, a systematic search of PsycINFO, MEDLINE, Embase, PubMed, CINAHL and Web of Science databases identified that 21 out of 3968 articles met the inclusion criteria for Bayesian meta-analysis and the AXIS risk of bias criteria. The results showed at least extreme/decisive evidence for associations between verbal and motor reaction times on multisensory tasks and a variety of visual and auditory WM tasks, with verbal multisensory stimuli contributing more to verbally assessed WM capacity than unisensory auditory or visual stimuli alone. Furthermore, a meta-regression confirmed that age significantly moderates the observed association between multisensory processing and both visual and auditory WM tasks, indicating that verbal- and motor-assessed multisensory processing contribute differentially to WM performance, and to different age-determined extents. These findings have important implications for school-based learning methods and other educational activities where the implementation of multisensory stimuli is likely to enhance outcomes. Full article

The aim of this study is to explore the role of virtual reality in engineering education. Specifically, it analyzed 342 studies that were published during 2010–2025 following a systematic approach. It examined how virtual reality is used in engineering education, explored the document main characteristics, and identified emerging topics. The study also revealed existing limitations and suggested future research directions. According to the outcomes, the following six topics emerged: (i) Immersive technologies in engineering education, (ii) Virtual laboratories, (iii) Immersive and realistic simulations, (iv) Hands-on activities and practical skills development, (v) Engineering drawing, design, and visualization, and (vi) Social and collaborative learning. Virtual reality was proven to be an effective educational tool which supports engineering education and complements existing learning practices. Using virtual reality, students can apply their theoretical knowledge and practice their skills within low-risk, safe, and secure learning environments characterized by high immersion and interactivity. Virtual reality through the creation of virtual laboratories can also effectively support social, collaborative, and experiential learning and improve students’ academic performance, engagement, interaction, and motivation. Learning using virtual reality can also enhance students’ knowledge acquisition, retention, and understanding. Improvements on students’ design, planning, and implementation skills and decision making, problem-solving skills, and visual analytic skills were also observed. Finally, when compared to physical laboratories, virtual reality learning environments offered lower costs, reduced infrastructure requirements, less maintenance, and greater flexibility and scalability. Full article

Predicting undergraduate student success is critical for informing timely interventions and improving outcomes in higher education. This study leverages over a decade of historical data from Louisiana State University (LSU) to forecast graduation outcomes using advanced machine learning techniques, with a focus on convolutional autoencoders (CAEs). We detail the data processing and transformation steps, including feature selection and imputation, to construct a robust dataset. The CAE effectively extracts meaningful latent features, validated through low-dimensional t-SNE visualizations that reveal clear clusters based on class labels, differentiating students likely to graduate from those at risk. A two-year gap strategy is introduced to ensure rigorous evaluation and simulate real-world conditions by predicting outcomes on unseen future data. Our results demonstrate the promise of CAE-derived embeddings for dimensionality reduction and computational efficiency, with competitive performance in downstream classification tasks. While models trained on embeddings showed slightly reduced performance compared to raw input data, with accuracies of 83% and 85%, respectively, their compactness and computational efficiency highlight their potential for large-scale analyses. The study emphasizes the importance of rigorous preprocessing, feature engineering, and evaluation protocols. By combining these approaches, we provide actionable insights and adaptive modeling strategies to support robust and generalizable predictive systems, enabling educators and administrators to enhance student success initiatives in dynamic educational environments. Full article

(1) Background: Educational systems across the world experienced significant changes during 2020–2022, with potential implications for progress toward Sustainable Development Goal 4 (SDG 4: Quality Education), which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all across European Union member states. Understanding how educational sustainability performance evolved during the pre-pandemic period (2017–2019) and the pandemic-affected period (2020–2022) is essential for developing effective educational policies. (2) Objective: This quantitative comparative study aimed to (i) assess and rank sustainable education developments across EU-27 countries in two periods, Period 1—the pre-pandemic period (2017–2019)—and Period 2—the pandemic-affected period (2020–2022); (ii) identify performance changes between these periods; and (iii) classify countries into performance groups to guide targeted interventions. (3) Methods: Using data from the Eurostat database, we evaluated six key SDG 4 indicators: low-achieving students in reading, mathematics, and science; participation in early childhood education; early school leavers; tertiary educational attainment; adult participation in learning; and adults with basic digital skills. The Multiobjective Optimization based on Ratio Analysis (MOORA) method was used to rank countries and assess sustainable education development. (4) Results: Sweden maintained the highest educational sustainability performance across both periods, while Romania and Bulgaria consistently ranked lowest. Nine countries improved their rankings during the pandemic-affected period, while others maintained stable positions or experienced declines in their rankings. Adult participation in learning showed the greatest variation among the indicators, with top performers, such as Sweden, scoring 0.445 compared to Romania’s 0.051 in Period 2. The proportion of early school leavers decreased from an EU average of 9.0% in Period 1 to 8.3% in Period 2, indicating a positive trend across the study periods. While differences were observed across countries and periods, these should not be interpreted as causally linked to the pandemic alone (5). Conclusions: The performance of educational sustainability varied across EU member states between the two periods, with some countries demonstrating remarkable resilience or improvement, while others declined. These findings underscore the need for targeted educational policies that address specific sustainability weaknesses in individual countries, particularly those in the warning and danger categories. Sweden’s consistent performance offers valuable lessons for educational sustainability, especially during and after major disruptions. Full article