Facultad de Ingenierías

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Universidad Indoamérica

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Advances in end-user water conservation technologies: a scoping review
(Frontiers Media SA, 2026-04-15)
Fernández-Vizcarra, Luis
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Chambilla-Zuñiga, Josué
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Gutiérrez-Aguilar, Olger
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Buele, Jorge
Introduction Water scarcity, intensified by climate change, urbanization, and increasing demand, requires sustainable strategies aimed at reducing consumption among end users. In this context, this article develops a scoping review to examine what types of sustainable technologies aimed at water conservation among end users have been investigated, how they are evaluated, and what types of evidence are reported regarding their effectiveness. Methods Studies published between 2020 and 2025 were retrieved from the Scopus, Web of Science, IEEE Xplore, Wiley Online Library, and EBSCO Engineering Source databases following PRISMA-ScR guidelines. The review integrates a bibliometric analysis of the field and a synthesis of 14 selected empirical studies addressing domestic efficiency technologies, adoption dynamics, digital monitoring systems, and resource management approaches. Results The evidence shows high methodological diversity and limited standardization of effective metrics, as only a small proportion of studies explicitly report quantified or directly measured water savings. Nevertheless, consistent patterns emerge depending on the type of intervention. Circular water management and decentralized reuse strategies report the highest potential impacts, with potable water savings ≥30% through greywater treatment and reductions of up to 80% in integrated urban scenarios. Rainwater harvesting and green infrastructure report runoff reductions between 58.3 and 94.1%, generating indirect benefits for urban water management. In contrast, informational and behavioral interventions tend to produce moderate but consistent reductions in consumption. Digital technologies based on IoT, smart metering, and advanced analytics demonstrate strong technical performance in monitoring and leak detection, although they rarely quantify post-implementation water savings. Conclusion The findings suggest that effective water conservation among end users depends on approaches that combine technological solutions, user engagement, and standardized evaluation frameworks capable of linking technological performance with verifiable reductions in water consumption.
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Exhaustive Differential Verification of Rule Execution in Building Compliance Software: A Method and Its Application to Seismic Provisions for Earthen Buildings
(MDPI AG, 2026-09-22)
Pinto Almeida, Carlos
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Jordan, Nancy
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Lucio, Diego Ricardo
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Ambato-Jordán, Christopher
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Mendoza, Alvaro Javier
Digital compliance tools increasingly stand between building regulations and the people who apply them, yet research on automated compliance checking has concentrated on whether a rule can be extracted from regulatory text rather than on whether the extracted rule is then executed correctly, so tools issuing compliance verdicts circulate without evidence of computational fidelity. This study proposes a verification method for the large class of provisions stating their requirements as algebraic inequalities over measurable building dimensions. The method combines division-free formalisation, cross-validation against an exact rational oracle, exhaustive enumeration of the discretised design space, differential comparison using asymmetric error classes, and a boundary coverage test suite. It was applied to the wall geometry provisions governing earthen buildings in Peru and Ecuador and to a mobile tool in free circulation. Exhaustive enumeration at the full input resolution of the tool, over 292,364,966,481 configurations, indicates that 7.93 percent of the baseline domain is admissible and that 72.1 percent of inadmissible configurations violate two or more requirements at once. The audit identified three execution defects; the rule sets disagree over 8.26 percent of the domain, and 55.9 percent of the verdicts issued lack normative support. A corrected version reproduced the reference verdict in all 32 device-executed test cases against 24 of 32 for the original, and agrees with the reference on every one of the 292,364,966,481 enumerated configurations. Applying the method to a second provision, the slenderness limit of EN 1996-1-1, reproduces the same defect signature in an unrelated code. Verification of rule execution is therefore inexpensive and necessary before compliance tools can be relied upon on site. The conditions under which the method applies are stated explicitly and are met by provisions in several codes, although transferability beyond the two cases examined remains to be demonstrated empirically. © 2026 by the authors.
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Rethinking criminal profiling through cognitive artificial intelligence
(Frontiers Media SA, 2026-05-08)
Buele, Jorge
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Miranda-Toapanta, Davis
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Rojas-Cañizares, David
Introduction 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.
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Configuration-Sensitive Decomposition of the Response Modification Factor in Reinforced Concrete Moment Frames
(MDPI AG, 2026-07-10)
Suquillo, Betzabeth
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Villavicencio, Stefanía
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Medina Robalino, Christian David
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Cagua, Brian
The response modification factor R is a fundamental parameter in seismic design, linking the elastic demand expected under strong ground motion to the reduced forces used in practice. It reflects the capacity of well-detailed structures to dissipate energy through stable inelastic behavior while maintaining sufficient strength, stiffness, and deformation capacity to prevent collapse. Accordingly, R directly influences design base shear, member forces, reinforcement demands and expected seismic performance. It is prescribed by seismic codes as a single typology dependent value, although analytical evidence indicates that its magnitude varies systematically with structural configuration. Therefore, this study decomposes R for twelve reinforced concrete moment-resisting frame archetypes that combine three heights (4, 8, and 14 stories) with four span configurations (1–4 spans) over a constant 12 m plan length. All frames are designed per ACI 318-19 and ASCE/SEI 7-22 for the Pedernales, Ecuador, subduction-zone seismic hazard. The response modification factor R is evaluated through a component-based decomposition that separates the effects of ductility, overstrength, and redundancy—namely the capacity ductility (Formula presented.), the demand ductility (Formula presented.), the overstrength (Formula presented.), and a geometric redundancy index (Formula presented.), using bilinearized pushover analyses. Dynamic verification, used here as a consistency check, is explicitly restricted to the low-rise class (four-story frames) through nonlinear response-history analysis under eleven spectrum-matched ground-motion records; results for the 8- and 14-story frames are therefore pushover-based only. To bracket the inelastic reduction capacity, a demand-based companion factor (Formula presented.) is reported and defined as the demand-based counterpart of R, providing a capacity-oriented estimate R and a demand-oriented companion estimate (Formula presented.). R ranges from 3.80 to 14.56, whereas (Formula presented.) ranges from 1.82 to 7.63. The component ranges are the capacity ductility (Formula presented.) – (Formula presented.), the demand ductility (Formula presented.) – (Formula presented.), the overstrength (Formula presented.) – (Formula presented.), and the geometric redundancy index (Formula presented.) – (Formula presented.). Capacity ductility saturates in taller frames (about (Formula presented.) variation). In addition, (Formula presented.) and (Formula presented.) exhibit a mechanical trade-off that challenges the independence assumption implicit in the multiplicative decomposition. Dynamic results corroborate the pushover-implied demand only for the low-rise class; no extrapolation to taller frames is claimed. Overall, the findings motivate configuration-sensitive analytical calibration as a prerequisite for any future normative discussion on R. © 2026 by the authors.
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Artificial intelligence for resilient transportation infrastructure: the first Ecuadorian pavement damage dataset and an assessment of domain transfer across international road networks
(Frontiers Media SA, 2026-08-26)
Zambrano-Salazar, Leonardo
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López, Enma Gamboa
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Pinto Almeida, Carlos
Background Pavement-condition monitoring is a cornerstone of resilient and sustainable transportation infrastructure, yet existing public datasets for automatic damage detection are dominated by data from high-income countries. Deep-learning models trained on those data often transfer poorly to Latin American road networks, where deterioration patterns, materials, and maintenance regimes differ. No publicly available pavement damage dataset for Ecuador has been reported to date, to the best of our knowledge. Methods We present the Ecuadorian Pavement Distress Dataset (EcuPDD), 1,295 images annotated under ASTM D6433 into six damage classes (4,559 annotated instances), collected across seven cities in the three geographic regions of Ecuador (Sierra, Coast, and Amazon). The dataset is released under CC BY 4.0 on Zenodo (DOI: 10.5281/zenodo.20454801 ). On this base we trained five YOLOv8n variants progressively fused with three open international datasets—RDD2022 Japan, RDD2022 China_MotorBike, and McMaster Pavement Crack Detection—for a total corpus of up to 15,941 images (of which roughly 80% were used for training and 20% for validation, following the splits described in Section 3). Each version was trained five times with independent random seeds (25 runs in total), and results are reported as mean ± standard deviation. We evaluated each version on its mixed validation set and, separately, on a fixed 259-image Ecuadorian held-out subset. We complemented the standard mAP50/ mAP50-95 metrics with Wilson 95% confidence intervals on per-class detection rates, a Gini coefficient for class imbalance, and a Domain Gap Index (DGI) that we define as 1 − (mAP50Ecuador/ mAP50general), summarising the divergence between the general and local performance of each model. Results Adding international data lifted the mixed-validation mAP50 from 14.65% (V1, Ecuador only) to 57.66% (V5, Ecuador + McMaster + China_MotorBike), an absolute gain of 43.01 percentage points. However, when evaluated only on the 259 Ecuadorian held-out images, mAP50 stayed between 14.65% and 15.90% across all five versions—a swing of 1.25 pp despite the inclusion of up to 14,646 additional images. The Wilson 95% confidence intervals on those Ecuadorian-only mAP50 values overlap heavily for all versions, indicating substantial overlap and no clear evidence of separation among versions at this sample size. The Domain Gap Index reaches 0.733 for V5 and 0.713 for V2, while remaining ∼0 for V1 and V3. Patching, despite contributing only 2.3% of the instances, reached a Wilson-CI-supported detection rate of 0.78 [0.73, 0.82] thanks to its strong visual distinctiveness when paired with the McMaster dataset. Conclusion International datasets improve a YOLOv8n detector globally but do not, by themselves, close the domain gap to Ecuadorian roads. The bottleneck is the volume and representativeness of local annotated data. EcuPDD provides the first openly licensed Ecuadorian benchmark for this task and an empirical calibration point that researchers in Latin America and other under-represented regions can use to plan dataset-construction and model-development efforts aligned with the resilience and sustainability goals of the AI-for-infrastructure agenda.
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El Clima Organizacional en Instituciones de Educación Superior: Comunicación, Liderazgo y Gestión Emocional
(CEIT S.A., 2026-04-10)
Proaño-Vinueza, Carolina
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Vayas Ortega, Germania
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Sánchez, Patricio
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Romero Riaño, Paola
El clima organizacional en instituciones de educación superior constituye un factor clave para el bienestar laboral y el desempeño institucional, influido por la comunicación interna, el liderazgo y la gestión emocional. No obstante, estas dimensiones han sido estudiadas de forma fragmentada, lo que limita la comprensión de su interacción. El estudio tuvo como objetivo determinar la influencia de estas variables en el clima organizacional. Se adoptó un enfoque cuantitativo con diseño no experimental de corte transversal y alcance descriptivo-correlacional. La investigación incluyó personal académico y administrativo de instituciones de educación superior en Ambato, Ecuador. Se aplicó un cuestionario estructurado validado y los datos se analizaron mediante estadística descriptiva, correlacional y regresión lineal múltiple. Los resultados evidenciaron relaciones positivas y estadísticamente significativas entre las variables analizadas y el clima organizacional. Asimismo, el modelo de regresión mostró que estas variables predijeron de manera conjunta el clima organizacional, destacándose la gestión emocional como la dimensión con mayor valoración. Se concluye que la comunicación interna, el liderazgo transformacional y la gestión emocional son determinantes del clima organizacional en educación superior, favoreciendo el bienestar laboral y la calidad del entorno institucional.
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The illusion of flexibility and the limits of teacher autonomy in the Ecuadorian curriculum
(Frontiers Media SA, 2026-05-21)
Núñez Torres, María Giovanna
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Buele, Jorge
Introduction 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.
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Impact of mixed reality on the care of the elderly: a scoping review
(Springer Science and Business Media LLC, 2026-07-06)
Avilés-Castillo, Fátima
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Buele, Jorge
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Palacios-Navarro, Guillermo
Population aging requires innovative technological strategies that promote autonomy, functionality, and well-being in old age. In this context, mixed reality (MR), a technology that integrates physical and digital elements in a shared and interactive real-time environment, emerges as a promising alternative. To analyze the current state of evidence on the use of MR in the care of older adults, including its theoretical and practical implications. Scientific articles were reviewed in Scopus, Web of Science, PubMed, IEEE Xplore, and EBSCO. Data from 15 studies were organized into an analytical matrix to identify methodological and impact patterns. MR interventions have shown promising preliminary results in physical rehabilitation, cognitive stimulation, emotional well-being, and social interaction by combining sensory feedback, functional tasks, and meaningful experiences. The included studies represent heterogeneous MR configurations along the virtual reality continuum, including transparent or pass-through optical viewers, VR-HMD-based systems with real-world interaction, and sensor- or projection-based platforms. Rather than demonstrating superiority over virtual or augmented reality, the evidence suggests specific advantages related to contextual awareness, physical interaction, and cognitive-motor-affective integration. These features were associated with potential improvements in mobility, executive functions, motivation, positive emotions, and social presence, supporting an interpretive cognitive-motor-emotional framework and the design of safe, meaningful, and context-sensitive interventions. MR shows promising potential for the care of older adults by improving cognitive, motor, emotional, and social outcomes. However, current evidence remains preliminary due to small sample sizes, methodological heterogeneity, a scarcity of comparative studies, and a lack of longitudinal evidence. © The Author(s) 2026.
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Impact of the Integration of Digital Channels on Customer Service in Apparel Stores
(Springer Nature Switzerland, 2026)
Álvarez Sánchez, Ana
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Suárez del Villar, Alexis
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Hidalgo-Potosí, Daniela Alejandra
This study aims to evaluate the impact of integrating digital channels and social networks on customer service in the clothing retail sector. A descriptive and correlational design was used with a random sample of 300 consumers who had interacted with the stores through digital channels in the last six months. Data was collected using a structured questionnaire that assessed customer profile, interaction on digital channels, and customer satisfaction. Descriptive and correlational analyses were performed using statistical software, with the primary evaluation criterion being customer satisfaction, measured using a 5-point Likert scale. The results show that the integration of digital channels increases the satisfaction of very satisfied customers by 26.7%, and 40% of the surveyed consumers expressed a greater willingness to repeat purchases. The correlational analysis revealed a moderate positive relationship (r = 0.56, p < 0.01) between the integration of digital channels and customer satisfaction. Additionally, a reduction in the variability of response times was observed after integration, indicating greater consistency and efficiency in customer service. The questionnaire demonstrated high internal consistency, with a Cronbach's alpha value of 0.84, which ensures the reliability of the measurements. In conclusion, this study demonstrates that the digitalization of customer service in the clothing retail sector significantly improves consumer satisfaction and loyalty, supporting the need for companies to continue innovating in their customer service strategies to maintain a competitive advantage in an increasingly demanding market. These findings have important implications for future research, which could delve deeper into the analysis of the long-term impact of automation on customer relationships and the development of artificial intelligence algorithms that further optimize digital service. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Lightweight deep learning for sustainable adobe construction: a block-level baseline aligned with circular economy principles and sustainable development goals
(Frontiers Media SA, 2026-07-08)
Pinto Almeida, Carlos
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Jordan, Nancy
Introduction – 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.

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