Buele, Jorge
Preferred name
Buele, Jorge
Main Affiliation
Ambato
Email
jorgebuele@uti.edu.ec
ORCID
0000-0002-7556-0286
Scopus Author ID
57200380202
104 results
Now showing 1 - 10 of 104
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, The illusion of flexibility and the limits of teacher autonomy in the Ecuadorian curriculumIntroduction Curriculum reforms in Latin America emphasize flexibility, contextualization, and teacher autonomy. In Ecuador, the 2016 curriculum operationalizes these through Skills with Performance Criteria (PC). Methods An analytical–interpretive approach was applied, examining the national curriculum alongside literature on educational governance and critical pedagogy. Results The PC-based structure, with its criteria and indicators, organizes planning, evaluation, and documentation around predefined outcomes. While this promotes coherence, it fragments practice into discrete units. Contextualization occurs in teaching, but validation relies on standardized references, creating tension between adaptation and assessment. Teacher work is structured by planning and verification processes. Discussion Interpreted as controlled freedom, autonomy is recognized but bounded, and flexibility emerges as a practice shaped by technical constraints. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of replacing mixing water with milk on the compressive strength of concrete at different curing ages(Frontiers Media SA, 2026-07-17); ; Zurita, MarcoIntroduction – The chemical composition of mixing water directly affects cement hydration and the strength development of concrete. Although alternative mixing liquids—such as treated wastewater, leachates, and wash water—have shown moderate strength reductions compatible with structural use, the behavior of concrete exposed to complex organic liquids remains largely uncharacterized. Methods – This study investigates how replacing mixing water with whole bovine milk, a medium rich in lactose, casein, and fat, affects cement hydration and compressive strength in conventional Portland concrete. Three mixtures with increasing replacement levels were produced: 0% (M0, control), 50% (M50), and 100% (M100). Twenty-seven cylindrical specimens (3 mixtures × 3 ages × 3 replicates) were tested under axial compression following ASTM C39/C39M at 15, 28, and 45 days. Peak strength, stress–strain curves, within-condition dispersion, and failure modes were recorded. Results – The control developed strengths of 14.39 ± 0.64, 16.47 ± 0.73, and 20.91 ± 0.81 MPa at 15, 28, and 45 days, whereas M50 and M100 remained below 1 MPa at all ages (M50: 0.61 ± 0.03, 0.67 ± 0.03, 0.90 ± 0.06 MPa; M100: 0.38 ± 0.02, 0.55 ± 0.02, 0.60 ± 0.03 MPa), a strength loss greater than 95% relative to the control. Coefficients of variation ranged from 3.9% to 6.1%, below the 10% threshold of ACI 214R. The modified mixtures also lost the ascending branch of the stress–strain curves and exhibited diffuse failure modes, without the longitudinal cracking typical of sound concrete. Conclusion – The magnitude of the strength loss (>95%) places milk in a category distinct from previously reported alternative mixing liquids. Rather than producing a degraded variant of conventional concrete, it generates a system in which the cementitious reaction is practically inhibited. This behavior, attributable to the combined action of lactose, casein, and fats on C3S hydration, redefines the operational limits of chemical compatibility between organic fluids and cementitious systems and provides a reference calibration point for predictive models of organic inhibition. Copyright © 2026 Pinto, Zambrano, Apraez, Gómez, Zurita and Buele. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Rethinking criminal profiling through cognitive artificial intelligenceIntroduction Traditional criminal investigation often struggles to integrate dispersed and heterogeneous information, delaying the identification of serial or escalating patterns. Advances in artificial intelligence (AI) and cognitive computing offer data-driven approaches for cross-source correlation and temporal anomaly detection. Methods A focused narrative review of peer-reviewed literature on AI applications in forensic analysis, pattern detection, and investigative support was conducted using major multidisciplinary databases. Selected studies were synthesized into two analytical dimensions: evidence correlation and anomaly detection, and further examined through a retrospective case-based illustration. Results AI-based approaches support the linkage of low-level traces with higher-level events, enabling structured reconstruction and large-scale pattern identification. Machine learning models integrate heterogeneous data into operational representations, achieving high predictive performance under controlled conditions, often reported above 90% accuracy. Statistical and learning-based methods also detect temporal compression, behavioral drift, and cross-source anomalies, revealing patterns that may remain fragmented under manual analysis. Retrospective examination of historically fragmented cases highlights longitudinal regularities, including shrinking inter-event intervals, increasing severity, and the accumulation of weak signals that become meaningful when analyzed jointly. Discussion AI contributes primarily at a methodological level by enabling continuous integration and re-evaluation of investigative signals, supporting more data-informed and longitudinally grounded profiling practices. However, a gap persists between high performance in controlled settings and limited validation in real-world contexts. Future research should prioritize empirical benchmarking using operational datasets, the development of explainable and auditable systems, and governance frameworks ensuring transparent, accountable, and human-supervised deployment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Validation of an Emotional Model in Programming Learning in the University Context(Springer Nature Switzerland, 2026); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Robotics in higher education and its impact on digital learningIn recent years, robotics has transformed various industrial processes but has also influenced teaching methodologies. Although there are literature reviews on its application in professional training, many are outdated or lack a current focus on its impact in higher education. Addressing this gap, the present mini review examines the impact, challenges, and opportunities of this technology in the university setting. To this end, a search was conducted in the PubMed, Scopus, IEEE Xplore, APA PsycNet, and Web of Science databases, selecting 11 studies that addressed diverse applications of robotics, including educational robotics, robotic telepresence, human-robot interaction, and artificial intelligence applications. Their effects on teaching, the factors influencing their adoption, and the strategies used to optimize their implementation were analyzed. The findings show that educational robotics enhances student motivation and engagement, with prediction models reaching an accuracy of 98.78% in assessing academic engagement. Additionally, robotic telepresence emerges as an effective alternative for hybrid education, and social robots and AI-based tutors demonstrated their potential to personalize learning. However, methodological and structural challenges persist, such as the need to develop more accurate evaluation metrics and ensure accessibility and educational equity. Future research should focus on improving these aspects, enabling more efficient integration to enhance teaching processes. This study was registered in the Open Science Framework under the code: 10.17605/OSF.IO/KHDTU.57 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ethical Use of Generative Artificial Intelligence Among Ecuadorian University Students(2025); 28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Energy Transition in Industry as a Viable Path to Sustainable Decarbonization(2025)15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Virtual environment application that complements the treatment of dyslexia (VEATD) in children(2020); ; 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting Academic Performance in Mathematics Using Machine Learning AlgorithmsSeveral factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Ergonomic Risks Based on Physical and Postural Characteristics in the Food Industry(2026)Musculoskeletal disorders are one of the leading causes of occupational disability worldwide, affecting workers across various industries, particularly in the food industry. These disorders are caused by factors such as improper postures, repetitive movements, and manual handling of loads. Despite efforts to mitigate these risks, ergonomic assessment in many work environments remains insufficient, as it is often limited to posture observation and does not include precise anthropometric measurements. This study aims to integrate anthropometric measurements with the Rapid Entire Body Assessment method for a more accurate and personalized evaluation of ergonomic risks in the food industry. Through a quantitative approach, anthropometric measurements of workers in a food production plant were analyzed and the Rapid Entire Body Assessment method was applied to assess working postures. The data obtained were statistically analyzed to identify correlations between the physical characteristics of employees and musculoskeletal discomfort. The results showed a significant correlation between anthropometric dimensions, such as elbow height and functional reach, with discomfort in areas such as the elbow, knees, and lower back. These findings emphasize the need to adapt workstations to the physical characteristics of employees to prevent injuries. The integration of anthropometric measurements and the Rapid Entire Body Assessment method provides a more accurate tool for assessing ergonomic risks and designing personalized interventions that improve occupational health and productivity in the food industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.6