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School textbooks are central to the teaching, studying, and learning processes because they mediate the interaction between the prescribed curriculum and the educational experience in the classroom. Evaluating their didactic structure critically allows us to determine the degree to which they align with current curriculum guidelines and promote meaningful learning. This study aimed to analyze the extent to which Ecuadorian natural science textbooks reflect constructivist learning principles and promote the development of key competencies established in the National Priority Curriculum. This curriculum guides the achievement of essential results and strengthens fundamental competencies for students’ comprehensive development. Content analysis was adopted as the methodological approach given its relevance in examining the didactic and curricular dimensions of educational materials. The analysis covered twelve eighth-grade General Basic Education textbooks and their supplementary materials. The analysis was based on two instruments: specialized summary analysis sheets (RAE) and a purpose-built checklist. The ATLAS.ti 25 and IRaMuTeQ programs supported the systematization and visualization of the data. The results showed limited integration of constructivist strategies, such as teaching for comprehension, inquiry-based learning, and problem solving, in most of the analyzed texts. These findings underscore the need to expand and strengthen the incorporation of contextualized, critical, and meaningful learning experiences to improve the didactic design of school textbooks. Such improvements would promote coherent articulation between objectives, content, methods, resources, and assessment in line with constructivist principles of the Ecuadorian curriculum. Furthermore, given these approaches’ affinity with curricular frameworks in other regional countries, the results could offer relevant guidance and starting points for reflection on developing and using textbooks in Latin American contexts with comparable educational characteristics. Full article

The increasing prevalence of Arabic text in digital media offers significant potential for sentiment analysis. However, challenges such as linguistic complexity and limited resources make Arabic sentiment analysis (ASA) particularly difficult. In addition, explainable artificial intelligence (XAI) has become crucial for improving the transparency and trustworthiness of artificial intelligence (AI) models. This paper addresses the integration of XAI techniques in ASA through a scoping review of developments. This study critically identifies trends in model usage, examines explainability methods, and explores how these techniques enhance the explainability of model decisions. This review is crucial for consolidating fragmented efforts, identifying key methodological trends, and guiding future research in this emerging area. Online databases (IEEE Xplore, ACM Digital Library, Scopus, Web of Science, ScienceDirect, and Google Scholar) were searched to identify papers published between 1 January 2016 and 31 March 2025. The last search across all databases was conducted on 1 April 2025. From these, 19 peer-reviewed journal articles and conference papers focusing on ASA with explicit use of XAI techniques were selected for inclusion. This time frame was chosen to capture the most recent decade of research, reflecting advances in deep learning and the transformer-based and explainable AI methods. The findings indicate that transformer-based models and deep learning approaches dominate in ASA, achieving high accuracy, and that local interpretable model-agnostic explanations (LIME) is the most widely used explainability tool. However, challenges such as dialectal variation, small or imbalanced datasets, and the black box nature of advanced models persist. To address these challenges future research directions should include the creation of richer Arabic sentiment datasets, the development of hybrid explainability models, and the enhancement of adversarial robustness. Full article

The rapid integration of Generative Artificial Intelligence in higher education challenges social work as student adoption outpaces pedagogical guidance. This paper argues that the unguided use of AI fosters cognitive debt: a cumulative deficit in critical thinking, ethical reasoning, and professional judgment that arises from offloading cognitive tasks. To counter this risk, a pedagogical model is proposed, synthesizing experiential learning, andragogy, and critical pedagogies. The framework reframes AI from a passive information tool into an active object of critical inquiry. Through structured assignments across micro, mezzo, and macro practice, the model guides students through cycles of concrete experience with AI, reflective observation of its biases, abstract conceptualization of ethical principles, and active experimentation with responsible professional use. Aligned with professional ethical standards, the model aims to prepare future social workers to scrutinize and shape AI as a tool for social justice. The paper concludes with implications for faculty development, institutional policy, accreditation, and a forward-looking research agenda. Full article

Promoting children’s rights in early childhood education is internationally recognised as a priority, yet its practical implementation remains challenging. This qualitative study explored the perceptions of three early childhood educators in northern Portugal regarding children’s rights and how these are reflected in their practices. Guided by an interpretive paradigm, the study sought to understand participants’ beliefs through semi-structured interviews, conducted with full ethical compliance, including informed consent, withdrawal rights, and anonymity. Data were analysed using MAXQDA, through an inductively generated coding system. Findings indicate that educators acknowledge their vital role in upholding children’s rights and in fostering respectful learning environments. However, significant gaps were found in the realisation of the right to participation, with tensions between educators’ stated values and their described practices—particularly regarding children’s involvement in decision-making. A prevailing emphasis on protection often limited children’s autonomy and agency. The study highlights the complexities of translating policy frameworks, such as Portuguese legislation and the UNCRC, into consistent pedagogical action. Despite its small sample size, the study offers valuable insights into the barriers to implementing a rights-based pedagogy and underscores the need for enhanced educator training, active listening practices, and the recognition of play as a fundamental right. Full article

This study presents a vertically integrated Project-Based Learning (PBL) and Community-Based Learning (CBL) framework that connects postgraduate and undergraduate programs in Applied Chemistry and Agricultural Engineering. Postgraduate students synthesized zeolite-based materials for nutrient recovery, which were subsequently applied by undergraduate students in field trials conducted in collaboration with rural farming communities. The project was evaluated using rubrics, surveys, focus groups, and reflective journals. Results demonstrated substantial development of technical, communication, and critical thinking skills, with students highlighting the value of linking theory to practice. Community feedback confirmed the perceived benefits of the material for soil improvement and fertilizer efficiency, while also underscoring the need for sustained engagement. Despite challenges such as curricular coordination and resource constraints, the model effectively fostered interdisciplinary learning and social impact. These findings highlight the contribution of this sequentially instructional design to STEM education by connecting research, teaching, and outreach within a constructivist, sustainability-oriented approach. Full article

Optimally positioning renewable-based distributed generation (DG) units is vital for mitigating technical challenges in active distribution networks (ADNs). With the goal of achieving technical goals such as reduced losses and mitigated unstable voltage, two available optimization methods have been combined for positioning wind-energy DGs: grey wolf optimization (GWO) and opposition-based learning (OBL), which tries out opposite possibilities for each assessed population, thus addressing GWO’s susceptibility to becoming stuck in local optima. This new fusion technique enhances the algorithm’s scrutiny of each area under consideration and reduces the likelihood of premature convergence. Results show that, compared with standard GWO, the proposed OBL-GWO reduced active power losses by up to 95.16%, improved total voltage deviation (TVD) by 99.7%, and increased the minimum bus voltage from 0.907 p.u. to 0.994 p.u. In addition, the voltage stability index (VSI) was also enhanced by nearly 30%. The proposed methodology outperformed both standard GWO on the IEEE 33-bus test system and comparable techniques reported in the literature consistently. By accounting for the uncertainty in wind generation, load demand, and future growth, this framework offers a more reliable and practical planning approach that better reflects real operating conditions. Full article

In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

This study evaluates the impact of using group concept maps in the teaching of Applied Thermodynamics in the Bachelor’s Degree in Industrial Electronics and Automation Engineering. The methodology consisted of selecting topics with a high conceptual load, collaboratively creating concept maps, and subsequently evaluating them by both students and teaching staff. Students achieved average scores above 7/10 in the concept map activity, with teacher and student evaluations averaging 7.8 and 7.3, respectively. Knowledge assessment via pre- and post-tests revealed a 20% increase in concept comprehension. For example, in the topic of Principles of Thermodynamics, the percentage of correct answers on the most complex question increased from 13% in the Pre-Test to 40% in the post-test. In the topic of Refrigeration Cycles, some questions showed an improvement from 18% to 25%. The students’ perception of the activity was positive, with an average satisfaction rating of 6.9 out of 10. Furthermore, most students acknowledged that the activity helped them stay engaged with the subject matter and identify errors in their own learning. The high participation in the activity, despite its low impact on the final grade, demonstrates the students’ strong motivation for this study approach. Therefore, the implementation of concept maps not only facilitated the understanding of key concepts but also promoted critical reflection and collaborative learning, establishing itself as an effective strategy in the teaching of Applied Thermodynamics. Full article

Microplastics pose a growing environmental concern, necessitating accurate and scalable methods for their detection and classification. This study presents a novel deep learning framework that integrates a transformer-based architecture with domain adaptation techniques to classify microplastics using reflectance micro-FTIR spectroscopy. A key challenge addressed in this work is the domain shift between laboratory-prepared reference spectra and environmentally sourced spectra, which can significantly degrade model performance. To overcome this, three domain-adaptation strategies—Domain Adversarial Neural Networks (DANN), Deep Subdomain-Adaptation Networks (DSAN), and Deep CORAL—were evaluated for their ability to enhance cross-domain generalization. Experimental results show that while DANN was unstable, DSAN and Deep CORAL improved target domain accuracy. Deep CORAL achieved 99% accuracy on the source and 94% on the target, offering balanced performance. DSAN reached 95% on the target but reduced source accuracy. Overall, statistical alignment methods outperformed adversarial approaches in transformer-based spectral adaptation. The proposed model was integrated into a reflectance micro-FTIR workflow, accurately identifying PE and PP microplastics from unlabelled spectra. Predictions closely matched expert-validated results, demonstrating practical applicability. This first use of a domain-adaptive transformer in microplastics spectroscopy sets a benchmark for high-throughput, cross-domain analysis. Future work will extend to more polymers and enhance model efficiency for field use. Full article

The accuracy of numerical model outputs strongly depends on the quality of the initial wind field, yet ground observation data are typically sparse and provide incomplete spatial coverage. More importantly, many current mainstream correction models rely on reanalysis grid datasets like ERA5 as the true value, which relies on interpolation calculation, which directly affects the accuracy of the correction results. To address these issues, we propose a new deep learning model, PPWNet. The model directly uses sparse and discretely distributed observation data as the true value, which integrates observation point positions and a physical consistency term to achieve a high-precision corrected wind field. The model design is inspired by biological intelligence. First, observation point positions are encoded as input and observation values are included in the loss function. Second, a parallel dual-branch DenseInception network is employed to extract multi-scale grid features, simulating the hierarchical processing of the biological visual system. Meanwhile, PPWNet references the PointNet architecture and introduces an attention mechanism to efficiently extract features from sparse and irregular observation positions. This mechanism reflects the selective focus of cognitive functions. Furthermore, this paper incorporates physical knowledge into the model optimization process by adding a learned physical consistency term to the loss function, ensuring that the corrected results not only approximate the observations but also adhere to physical laws. Finally, hyperparameters are automatically tuned using the Bayesian TPE algorithm. Experiments demonstrate that PPWNet outperforms both traditional and existing deep learning methods. It reduces the MAE by 38.65% and the RMSE by 28.93%. The corrected wind field shows better agreement with observations in both wind speed and direction, confirming the effectiveness of incorporating position information and a physics-informed approach into deep learning-based wind field correction. Full article

Learning Natural Sciences represents a key opportunity to spark scientific interest and foster fundamental skills across different educational stages. This study aimed to analyze the influence of motivation on academic performance in the learning of Natural Sciences at various educational levels. To this end, a systematic review method was employed following PRISMA guidelines, consulting the Web of Science and Scopus databases, identifying four relevant studies. The results showed that high levels of motivation were associated with a more positive classroom attitude and better conceptual understanding, which enhanced academic performance. The use of innovative methodological strategies, such as implementing immersive virtual reality in the classroom, PhET simulations (Physics Educational Technology), and the use of hypertext, significantly increased both student motivation and academic performance. The meta-analysis revealed a favorable effect in experimental groups, showing moderate heterogeneity (I² = 49) and significance of p = 0.0001. The concurrence analysis reported that current pedagogical practices should focus on strengthening student autonomy and active engagement, integrating critical reflection, the use of innovative methodological strategies, and technological resources that enhance meaningful learning in scientific literacy. Among the instruments used to measure motivation, the Motivation to Learn Science Questionnaire was identified, and for academic performance, the Motivated Strategies for Learning Questionnaire. In conclusion, the importance of implementing the identified methodological strategies across different educational stages is emphasized, in order to promote competency-based learning through meaningful and innovative acquisition of content in Natural Sciences. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

Gallium nitride (GaN) power devices have attracted extensive attention due to their superior performance in high-frequency and high-power applications. However, the reliability and lifetime prediction of these devices under various operating conditions remain critical challenges. In this study, a hybrid approach combining finite element simulation and deep learning is proposed to predict the lifetime of GaN power devices. COMSOL Multiphysics (V6.3) is employed to simulate the thermal and mechanical stress behavior of GaN devices under different power and frequency conditions, while capturing key degradation indicators such as temperature cycles and stress concentrations. The variation in temperature over time can reflect the degradation of the device and also reveal the fatigue damage caused by the long-term accumulation of thermal stress on the chip. LSTM performs exceptionally well in extracting features from time series data, effectively capturing the long-term and short-term dependencies within the time series. By using simulation data to establish a connection between the chip temperature and its service life, the temperature data and the lifespan data are combined into a dataset, and the LSTM neural network is used to explore the impact of temperature changes over time on the lifespan. The method mentioned in this paper can make preliminary predictions of the results when sufficient experimental data cannot be obtained in a short period of time. The prediction results have a certain degree of reliability. Full article