Search Results (2,597)

This paper proposes a fuzzy copula-based optimization framework for modeling dependence structures and financial risk under parameter uncertainty. The parameters of selected copula families are represented as trapezoidal fuzzy numbers, and their α-cut intervals capture both the support and core ranges of plausible dependence values. This fuzzification transforms the estimation of copula parameters into a fuzzy optimization problem, enhancing robustness against sampling variability. The methodology is empirically applied to gold and oil futures (1 January 2015–1 January 2025), comparing symmetric copulas, i.e., Gaussian and Frank and asymmetric copulas, i.e., Clayton, Gumbel and Student-t. The results prove that the fuzzy copula framework provides richer insights than classical point estimation by explicitly expressing uncertainty in dependence measures (Kendall’s τ, Spearman’s ρ) and risk indicators (Value-at-Risk, Conditional Value-at-Risk). Rolling-window analyses reveal that fuzzy VaR and fuzzy CVaR effectively capture temporal dependence shifts and tail severity, with fuzzy CVaR consistently producing more conservative risk estimates. This study highlights the potential of fuzzy optimization and fuzzy dependence modeling as powerful tools for quantifying uncertainty and managing extreme co-movements in financial markets. Full article

University advising at matriculation must operate under strict information constraints, typically without any post-enrolment interaction history.We present a unified, leakage-free pipeline for predicting early dropout risk and generating cold-start programme recommendations from pre-enrolment signals alone, with an optional early-warning variant incorporating first-term academic aggregates. The approach instantiates lightweight multimodal architectures: tabular RNNs, DistilBERT encoders for compact profile sentences, and a cross-attention fusion module evaluated end-to-end on a public benchmark (UCI id 697; n = 3630 students across 17 programmes). For dropout, fusing text with numerics yields the strongest thresholded performance (Hybrid RNN–DistilBERT: f1-score ≈ 0.9161, MCC ≈ 0.7750, and simple ensembling modestly improves threshold-free discrimination (Area Under Receiver Operating Characteristic Curve (AUROC) up to ≈0.9488). A text-only branch markedly underperforms, indicating that numeric demographics and early curricular aggregates carry the dominant signal at this horizon. For programme recommendation, pre-enrolment demographics alone support actionable rankings (Demographic Multi-Layer Perceptron (MLP): Normalized Discounted Cumulative Gain @ 10 (NDCG@10) ≈ 0.5793, Top-10 ≈ 0.9380, exceeding a popularity prior by $25–27$ percentage points in NDCG@10); adding text offers marginal gains in hit rate but not in NDCG on this cohort. Methodologically, we enforce leakage guards, deterministic preprocessing, stratified splits, and comprehensive metrics, enabling reproducibility on non-proprietary data. Practically, the pipeline supports orientation-time triage (high-recall early-warning) and shortlist generation for programme selection. The results position matriculation-time advising as a joint prediction–recommendation problem solvable with carefully engineered pre-enrolment views and lightweight multimodal models, without reliance on historical interactions. Full article

Descriptive geometry plays a fundamental role in developing spatial reasoning and geometric problem-solving skills in engineering education. This study investigates the comparative effectiveness of two instructional methodologies—Monge’s traditional projection system and the CADOP method, which integrates computer-aided design tools with orthographic projection principles. A quasi-experimental design was implemented with 90 undergraduate engineering students randomly assigned to two groups. Both groups followed the same instructional sequence and were evaluated using baseline surveys, rubric-based performance assessments, and post-training reflections. Quantitative analysis included mean comparisons, t-tests, and effect sizes, while inter-rater reliability confirmed scoring consistency. The results showed that CADOP students significantly outperformed those taught with Monge’s method across all criteria—conceptual under-standing, graphical accuracy, procedural consistency, and spatial reasoning—with very large effect sizes. Qualitative data indicated that CADOP enhanced clarity, efficiency, and confidence, while Monge promoted conceptual rigor but higher cognitive effort. The findings confirm that CADOP effectively reduces procedural complexity and cognitive load, supporting deeper spatial comprehension. Integrating CADOP with selected manual practices offers a balanced pedagogical approach for modernizing descriptive geometry instruction in engineering education. Full article

Dimensionality reduction is fundamental for analyzing high-dimensional data, supporting visualization, denoising, and structure discovery. We present a systematic, large-scale benchmark of three widely used methods—Principal Component Analysis (PCA), Isometric Mapping (Isomap), and t-Distributed Stochastic Neighbor Embedding (t-SNE)—evaluated by average silhouette scores to quantify cluster preservation after embedding. Our full factorial simulation varies sample size $n\in \{100,200,300,400,500\}$, noise variance ${\sigma }^2\in \{0.25,0.5,0.75,1,1.5,2\}$, and feature count $p\in \{20,50,100,200,300,400\}$ under four generative regimes: (1) a linear Gaussian mixture, (2) a linear Student-t mixture with heavy tails, (3) a nonlinear Swiss-roll manifold, and (4) a nonlinear concentric-spheres manifold, each replicated 1000 times per condition. Beyond empirical comparisons, we provide mathematical results that explain the observed rankings: under standard separation and sampling assumptions, PCA maximizes silhouettes for linear, low-rank structure, whereas Isomap dominates on smooth curved manifolds; t-SNE prioritizes local neighborhoods, yielding strong local separation but less reliable global geometry. Empirically, PCA consistently achieves the highest silhouettes for linear structure (Isomap second, t-SNE third); on manifolds the ordering reverses (Isomap > t-SNE > PCA). Increasing ${\sigma }^2$ and adding uninformative dimensions (larger p) degrade all methods, while larger n improves levels and stability. To our knowledge, this is the first integrated study combining a comprehensive factorial simulation across linear and nonlinear regimes with distribution-based summaries (density and violin plots) and supporting theory that predicts method orderings. The results offer clear, practice-oriented guidance: prefer PCA when structure is approximately linear; favor manifold learning—especially Isomap—when curvature is present; and use t-SNE for the exploratory visualization of local neighborhoods. Complete tables and replication materials are provided to facilitate method selection and reproducibility. Full article

Introduction: The integration of Immersive Virtual Reality (IVR) into teacher education is a significant innovation that can enhance the learning and practical training of future teachers. IVR enables highly interactive, immersive experiences in simulated educational environments where student teachers confront realistic classroom challenges. The objective was to synthesize how IVR is implemented in the training of future teachers and its level of effectiveness, in order to develop recommendations for practice and identify potential barriers to implementation. Method: A systematic review was carried out following the PRISMA model. A total of 1677 articles published in the Web of Science, Scopus, and SciELO databases were reviewed between 2021 and 2025, with 13 articles selected for analysis. Results: The reviewed articles highlight Immersive Virtual Reality (IVR) as a virtual tool that facilitates the training of future teachers. Among its most common applications are the use of virtual and augmented reality for conflict resolution, classroom management, and teacher adaptation. However, its implementation is limited by access to equipment, scenario development, and integration into university institutions. Conclusions: There is converging evidence that supports the strengths of using IVR as an emerging technology in teacher training, offering facilitating elements for the development of pedagogical competencies through the simulation of practical situations in a safe environment. Thus, this review summarizes recommendations for practice and warnings about implementation barriers, identifying the most potential uses and proposing actionable steps for its phased adoption in initial teacher training. Full article

Background: The development of calcium silicate materials and new techniques have resulted in significant clinical benefits in endodontics and microapical surgery. The objective of this investigation was to analyze the percentage of dentinal tubule penetration of two retrograde obturation techniques in microapical surgery, namely the conventional technique and the lid technique. Methods: 60 single-root human teeth were selected, which were divided into two groups (n = 30). These teeth were subjected to an endodontic procedure using the single-cone technique. They were prepared with apicoectomy and 3 mm apical retrocavity and then obturated using two retrograde obturation techniques with bioceramic materials: TotalFill RRM fast set Putty^® (RRM) using the conventional technique and TotalFill BC Sealer HiFlow^® (HiFlow) and RRM using the lid technique. The teeth were selected and evaluated using 1 mm portions in the apical third. In each case, the images were obtained using a Leica TCS SP8 Confocal Microscope (CLSM). The extent of penetration into the dentinal tubule regions was measured using AutoCad^®. Results: Statistical analyses were performed using the Levene test (p ≤ 0.05) and Student’s t-test (p ≤ 0.05). Analysis of the penetration area of calcium silicate materials into the dentinal tubules revealed that the relative penetration percentages were higher when using the conventional technique with the RRM than the lid technique with RRM + HiFlow in the apical third evaluated. Conclusion: The conventional technique yields significantly better outcomes, showing statistically significant differences in the percentage of penetration into the intratubular area compared to the lid technique. Full article

Improving the quality of higher education (HE) is a global priority as universities strive to equip graduates with skills necessary for today’s dynamic world. Well-trained educators are key to fostering these skills and can best develop them by adopting active learning approaches that deepen student understanding. Educator training is thus vital. In 2022 Széchenyi István University (Hungary), launched a four-year Continuing Professional Development (CPD) programme to upskill its academic staff. Given the traditional teaching culture in Hungarian HE, the CPD helps teachers adopt active learning practices to better prepare students for today’s world. This study explores the impact CPD has had on teaching practices thus far. Using a mixed-methods design, data were collected through questionnaires completed by 97 teachers (13% of staff) in 2022–2023 and follow-up group interviews with 13 teachers in 2025. Findings indicate that the CPD initiative has fostered professional growth to a certain extent, with teachers selectively experimenting with new methods, enhanced teacher motivation and increased student engagement. However thus far, systemic pedagogical change is limited, constrained by cultural and institutional barriers. The study highlights the importance of institutional support to achieve widespread pedagogical change in Hungarian higher education. Full article

The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) datasets provide a rich source of climatological data. However, their Network Common Data Form (NetCDF) structure can be a barrier for researchers who are not experienced with specialized data tools or programming languages. To address this challenge, we developed ncPick, a lightweight, Windows-based application designed to make ERA5 data more accessible and easier to use. The software enables users to load NetCDF files, select points of interest manually or through shapefiles, and export the data directly to Comma-separated values (CSV) format for further processing in common tools such as Excel, R, or within ncPick itself. Additional modules allow for quick visualization, descriptive statistics, interpolation, and the generation of time-of-day heatmaps, as well as practical data handling functions such as merging and downsampling CSV files based on the time-axis. Validation tests confirmed that ncPick outputs are consistent with those from established tools (such as Panoply). The toolkit was found to be stable across different Windows systems and suitable for a range of datasets. While it has limitations with very large files and does not include automated data download for version 1 of the software, ncPick offers an accessible solution for researchers, students, and other professionals seeking a reliable and intuitive way to work with ERA5 NetCDF data. Full article

Earthquakes are sudden and destructive natural events caused by tectonic movements in the Earth’s crust. Although they cannot be predicted with certainty, rapid and reliable detection is essential to reduce loss of life and property. This study aims to automatically distinguish earthquake and noise signals from real seismic data by analyzing time-frequency features. Signals were scaled using z-score normalization, and extracted with Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT), and combined EMD+DWT methods. Feature selection methods such as Lasso, ReliefF, and Student’s t-test were applied to identify the most discriminative features. Classification was performed with Ensemble Bagged Trees, Decision Trees, Random Forest, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The highest performance was achieved using the RF classifier with the Lasso-based EMD+DWT feature set, reaching 100% accuracy, specificity, and sensitivity. Overall, DWT and EMD+DWT features yielded higher performance than EMD alone. While k-NN and SVM were less effective, tree-based methods achieved superior results. Moreover, Lasso and ReliefF outperformed Student’s t-test. These findings show that time-frequency-based features are crucial for separating earthquake signals from noise and provide a basis for improving real-time detection. The study contributes to the academic literature and holds significant potential for integration into early warning and earthquake monitoring systems. Full article

To increase information accuracy when using ultrawide-band (UWB) localization for robotic dogs, we introduce a switching method for a Student’s t-distributed extended Kalman filter (EKF) that achieves UWB localization under colored measurement noise (CMN). First, a distributed UWB localization framework under CMN is designed, which can reduce the impact of CMN caused by carrier jitter on positioning accuracy. Then, a Student’s t-distributed EKF under CMN with a switch factor is proposed, which effectively improves the adaptability of the algorithm through adaptive selection of colored factors. Finally, experimental validation demonstrates the efficacy and high performance of the proposed method for two practical scenarios. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

This article investigates the use of visual cues, such as colour coding, to enhance educational materials and optimise students’ learning. The aim of the study was to examine how colour coding (CC) of selected components of a task influenced students’ cognitive engagement (CE) when solving algorithmic problems. We present experimental results from studies using eye-tracking techniques, which provide fine-grained behavioural indicators serving as proxy insights into learners’ cognitive processes. The findings reveal that the distribution of visual attention—measured through fixation time percentage, fixation count in areas of interest (AOIs), and the sequence in which task components were viewed—differed significantly between colour-coded and black-and-white task formats. Furthermore, analysis of two key eye-tracking indicators—fixation duration total (FDT) and average fixation duration (FDA)—suggests an increased level of cognitive engagement in students who had difficulty understanding the presented concepts while solving the colour-coded tasks. These results indicate that colour coding may help sustain students’ attention and engagement, especially when they face challenges in interpreting educational materials or engaging in complex problem-solving tasks. Full article

This paper investigates the advantages and challenges associated with synchronous and asynchronous activities in intercultural virtual collaboration (IVC) projects, particularly in relation to student satisfaction and learning outcomes. This study draws parallels between two distinct IVC projects. The first facilitated real-time interaction among students, lecturers, and peers from partner universities in the Netherlands and Japan. In contrast, the second project involved separate live classes led by local instructors in the Netherlands and Spain and featured asynchronous interactions among peers. This latter arrangement required students to exercise a greater degree of autonomy in their collaborative efforts. In both IVC projects, students developed a business case study that explored the influence of cultural factors on international marketing strategies. They participated in discussions and reflective exercises concerning the issue of greenwashing within the selected company. Our research employs data derived from students’ final business case reports and satisfaction surveys. The surveys include both closed and open-ended questions to assess the effectiveness of the distinct IVC formats. Our research provides insights into the impact of the IVC formats on the student experience and learning. Findings indicate no substantial differences in the quality of work produced between the two formats; however, student satisfaction was notably higher in the synchronous model, highlighting that the way interactions are structured impacts the collaborative experience, even when final outputs are similar. This study offers important insights for educators navigating the challenges of virtual teaching and for policymakers looking to use digital technologies to foster a globally aware and responsible generation in an increasingly digital world. Full article

Higher education is often positioned as a pathway to upward social mobility, yet access to highly selective universities (HSUs) remains limited, with first-generation college (FGC) students from low-income and ethnoracially minoritized backgrounds disproportionately constrained by structural barriers. This study applies an asset-based lens to examine how a cross-generational team of six Latine FGC affiliates of an HSU (i.e., alumni, doctoral students, professor) resiliently persisted in their educational and professional journeys, leveraging cultural and social capital. Employing Chicana/Latina feminist methodology and dialogic inquiry, we engaged in pláticas to critically reflect on factors that shaped our life trajectories. Findings reveal that social mobility was negotiated collectively rather than individually, highlighting tensions between personal advancement and commitments to family and community. We also consider the role of structured happenstance in pivotal encounters (e.g., being recognized by mentors, recruited by scholarship programs) that appeared serendipitous but were situated within systems where opportunity is inequitably distributed. Structured happenstance exposes the precariousness of such pathways and systemic gaps in FGC student support, challenging the notion that access to elite, capital-rich institutions is the product of merit alone. Our narratives offer a nuanced portrait of how FGC students navigate social mobility across the life course. Full article

This paper presents the design process of a composite wheel rim for a Formula Student race car. First, the design requirements for composite rims are outlined, which are driven to ensure safe operation as well as compliance with race regulations. Next, a novel methodology for the iterative design of composite wheel rims is proposed, and its steps are individually presented. The load cases were determined using data from lap time simulations and from practical experience from the operation of previous race cars. Material cards for the simulations were created by measuring the characteristics of the prepreg composites. The geometry of the rims was created by prioritizing the optimum contact with the tires. After creating the rim geometry, the composite material cards, and the simulation’s pre-processing, the layup iteration process began. In this manual iterative process, FEM simulations were run and their results were evaluated. The desired component properties were reached after 11 layup iterations. The final result is a weight reduction of 35% compared to the aluminum rims and 15% compared to the previous multi-piece CFRP rims, without a compromise in operational safety. The main novelty of the paper is the description of the iterative layup selection logic and process in detail, as well as demonstrating this on a concrete use case. Full article