Facultad de Ingenierías
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Item type:Publication, Geospatial modeling of urban expansion scenarios and their influence on local development in the Quevedo canton (Ecuador) with a vision towards the year 2030(Malque Publishing, 2026-08-12) - Some of the metrics are blocked by yourconsent settings
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, Rain-Triggered Mass Movement Hazard Assessment of the 2025 Tejar Chimbo Landslide in Andean Ecuador(Highlights of Science, S.L., 2026-06-16) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ARLOS: Adaptive Forecasting and Risk-Calibrated Load Operation in Distribution Substations Under Distribution ShiftForecast accuracy alone is an incomplete proxy for operational value when load distributions change. This paper presents ARLOS, an auditable forecast–uncertainty–decision framework that combines static and adaptive XGBoost forecasts, rolling performance monitoring, residual-bootstrap uncertainty, and explicit fixed-margin, quantile, and risk-target operating rules. The evaluation uses 2021 for ex ante training and capacity-proxy definition and 2022–2023 for strictly sequential testing on two Ecuadorian distribution substations under a measured baseline, smooth growth, and intraday structural shift. Under the four shifted station–scenario cases, adaptive fixed-margin operation reduced the weighted operational objective by 24.8–46.0% relative to the static fixed-margin baseline; baseline-regime changes ranged from (Formula presented.) to 11.2%, showing that adaptation is valuable primarily when mismatch is present rather than universally. A (Formula presented.) ablation further shows that adaptation and uncertainty are distinct, non-additive sources of operational value: aggregate normalized cost changes from 0.987 for static/fixed operation to 0.650 for adaptive/fixed, 0.515 for static/quantile, and 0.546 for adaptive/quantile. Realized one-sided exceedance, computed from observed load rather than from the bootstrap sample itself, remains within 0.0039–0.0111 of the target across the controlled cases, while nominal 10–90% interval coverage ranges from 78.0% to 78.6%. A nine-substation deployment check corroborates the calibration and identifies a measured drift episode in which adaptation limits, but does not eliminate, forecast degradation. The results support ARLOS as a transparent framework for studying how adaptation and uncertainty propagate into operational consequences under distribution shift.These contributions align with Sustainable Development Goal 7 (Affordable and Clean Energy) and Sustainable Development Goal 9 (Industry, Innovation and Infrastructure) by supporting more reliable, efficient, and intelligent operation of electricity distribution infrastructure. © 2026 by the authors. - 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, 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, U-Net Models for Breast Cancer Detection: Improving Diagnostic Accuracy and Specificity(2024); Elena Blanco-RomeroBreast cancer remains a critical global health issue, necessitating continuous research and innovative approaches for diagnosis, treatment, and prevention. This study evaluates the effectiveness of U -Net models in enhancing diagnostic precision and efficiency using real hospital samples. We aim to improve key diagnostic metrics such as accuracy, sensitivity, and specificity through the application of U-Net models. Our image classification model, tailored for 256 × 256 × 3 input images, excels in detecting and categorizing tumor cells. The architecture begins with initial convolutional layers featuring 64 filters, progresses to layers with 128 filters, and includes a Dropout layer to prevent overfitting. The deep network for object detection utilizes both region proposal and regression/classification approaches, achieving 92.27% confidence and 100% accuracy. Additionally, our deep learning algorithms accurately segment nuclei in histopathological images, employing a clustering strategy that delivers 88.81% confidence and 100% accuracy. Visual results demonstrate precise tumor cell localization and prediction confidence. Performance metrics from ten experimental runs indicate average confidence levels between 74.19% and 92.31%, with 90.0% accuracy and specificity in benign analysis. The model's ability to classify non-carcinomas versus carcinomas achieved an AUC of 0.78, illustrating its effective differentiation between classes.10 - 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