Facultad de Ingenierías
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Item type:Publication, Lightweight deep learning for sustainable adobe construction: a block-level baseline aligned with circular economy principles and sustainable development goalsIntroduction – The construction sector accounts for nearly 37% of global energy-related CO2 emissions, motivating renewed interest in low-carbon vernacular materials such as stabilized adobe. The integration of recycled inputs into adobe formulations advances circular-economy principles in the built environment, yet large-scale adoption is constrained by the absence of objective, scalable quality-control methods. Methods – We present a methodologically rigorous baseline for AI-driven visual quality control in stabilized adobe construction. A dataset of 330 images was constructed from 55 physical adobe blocks fabricated under controlled laboratory conditions with three circular-economy stabilization mixtures: recycled polyethylene terephthalate (1.5%), sugarcane bagasse fiber (8%), and Portland cement (10%). Each block was photographed from six standardized angles. To eliminate data leakage, we implemented strict block-level data splitting. A MobileNetV2 transfer learning pipeline with frozen ImageNet weights, aggressive data augmentation, L2 regularization, and balanced class weights was developed. Performance was characterized through accuracy with bootstrap 95% confidence intervals, per-class metrics, ROC AUC and Average Precision, five-fold block-level cross-validation, ablation study, and Grad-CAM interpretability. Results – The model achieved 80.95% test accuracy (95% CI: 69.0%–92.9%) on a held-out set of seven physical blocks (42 images). AUC = 0.858 and Average Precision = 0.944. Per-class F1-scores were 0.692 (sound) and 0.862 (defective). Block-level five-fold cross-validation confirmed stability at 70.0% (95% CI: 60.7%–79.2%). Ablation showed: simple CNN 71.43%, MobileNetV2 without augmentation 71.43%, complete pipeline 80.95% (+9.5 pp). Grad-CAM confirmed attention to physically meaningful surface features. The 9.24 MB model footprint enables offline deployment on commodity smartphones. Conclusion – This work establishes a methodologically rigorous baseline for AI-driven quality control in stabilized adobe construction. By demonstrating meaningful performance with modest datasets and edge-deployable architectures, this study supports broader adoption of circular-economy adobe construction and contributes to Sustainable Development Goals 9, 11, 12, and 13. Future work should expand the dataset, evaluate hierarchical classification, and assess field deployment. Copyright © 2026 Pinto Almeida and Jordán Buenaño. Conclusion This work establishes a methodologically rigorous baseline for AI-driven quality control in stabilized adobe construction. By demonstrating meaningful performance with modest datasets and edge-deployable architectures, this study supports broader adoption of circular-economy adobe construction and contributes to Sustainable Development Goals 9, 11, 12, and 13. Future work should expand the dataset, evaluate hierarchical classification, and assess field deployment. - 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, IoT Monitoring to Control a Bicycle Parking Lot(2022); 43 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Interfaces and Usability in Digital Flight Logs: Applied Design in the Aeronautical Context(2026)In Ecuador, the management of flight logs is still predominantly conducted in physical format, representing a significant barrier to operational efficiency and aviation safety. This situation highlights an important gap in the availability of digital solutions specifically designed for the local context and validated with real users. This study seeks to address that gap by designing and validating a user interface focused on user experience (UX) principles, aimed at optimizing the flight logging process and improving information quality. The research adopts a qualitative approach with elements of applied research, structured into four phases: requirements gathering through semi-structured interviews, iterative prototyping using Figma, usability testing with the “think aloud” technique, and heuristic evaluation by UX experts. The sample was intentionally selected and included 12 Ecuadorian civilian pilots (6 in training and 6 professionals), all with experience in using physical logbooks and basic familiarity with digital devices. The results show substantial improvements in process efficiency: a 40% reduction in average data entry time, an average of only 1.2 errors per session, and an average System Usability Scale (SUS) score of 89 out of 100, classified as “excellent usability”. 91% of participants expressed high satisfaction with the developed interface, highlighting its intuitive navigation, visual clarity, and offline usability—an essential factor in contexts with limited connectivity. These findings confirm that applying user-centered design principles can produce highly effective and well-accepted digital solutions, even in technical and regulated sectors such as aviation. The study lays the groundwork for future institutional implementations, emphasizing the importance of integrating co-creation processes with end users and planning progressive training and adoption strategies to ensure successful and sustainable technological change. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.4 - 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