Centro de investigación en Mecatrónica y Sistemas Interactivos

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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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Fiber–matrix interaction governs compressive strength in agave-bagasse-reinforced adobe: a factorial experiment with two-way ANOVA and competing mechanism analysis
(Frontiers Media SA, 2026-07-31)
De-Obaldia-Escalante, Marcela
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Del-Valle-Soto, Carolina
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Acevedo-Parra, H. R.
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Montoya-Márquez, Orlando
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Varela Aldas, José
Natural-fiber reinforcement is widely cited as a pathway to improve the mechanical performance of adobe, but reported effects on compressive strength are inconsistent across studies: some find improvement, others find degradation, and the choice of experimental conditions rarely disentangles the role of the fiber from that of the matrix. This study quantifies the coupling through a balanced factorial experiment. Forty-nine adobe specimens of (Formula presented) cm were manufactured with three granular compositions (sand-dominated, jal-dominated, and balanced, where jal is a regional non-plastic silt of Jalisco, Mexico) and four mass fractions of agave-bagasse fiber (0%, 0.5%, 1%, and 2%), and were tested under Mexican standard NMX-C-036-ONNCCE by an accredited external laboratory. Three complementary analytical tools are applied to the resulting dataset: (i) a two-way analysis of variance (ANOVA), (ii) a reinforcement efficiency index (Formula presented) with bootstrap confidence intervals, and (iii) a competing mechanism phenomenological descriptor (Formula presented) that separates a saturating reinforcement term from a linear disruption term. The two-way ANOVA reveals a highly significant mixture–fiber interaction ((Formula presented), (Formula presented), and partial (Formula presented)), which is stronger than either main effect and statistically demonstrates that the sign of the fiber effect is not an intrinsic property of the fiber but rather a property of the fiber–matrix pair. For sand-containing mixtures, the reinforcement efficiency index is (Formula presented) [M1, 95% bootstrap CI (0.96, 1.32)] and (Formula presented) [M3, (0.92, 1.59)] at the optimum (Formula presented); a non-parametric bootstrap over 5, 000 resamples places the optimum at (Formula presented) with posterior probability (Formula presented) (M1) and (Formula presented) (M3). For the jal-dominated mixture, fiber inclusion is net destructive [(Formula presented), (0.68, 0.95) at (Formula presented)], with Welch (Formula presented)-tests rejecting equivalence with the control at (Formula presented) (0.5%) and (Formula presented) (2%) and Cohen’s effect sizes (Formula presented). The best-performing conditions yield mean compressive strengths of 3.22 MPa, which exceeds the 2.0 MPa minimum required by NMX-C-441-ONNCCE-2011 for non-structural masonry by 60%. An immersion test shows that unstabilized specimens disintegrate within 2–3 min, bounding applications to non-exposed or externally protected uses and defining the primary direction for future work.
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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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Channel-Specific Stimulus Sensitivity, Reliability, and Complementarity of Multimodal Wearable Sensing During Short-Form Influencer Video Exposure
(MDPI AG, 2026-10-08)
Del-Valle-Soto, Carolina
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Corona, Violeta
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Gómez Padilla, Regina
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Walls Fernández, Fátima Sofía
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López-Pimentel, Juan-Carlos
Multimodal wearable sensing is increasingly used to quantify audience engagement with short-form video, yet it remains unclear which sensing channels actually carry stimulus-driven information at the exposure durations typical of this format. We recorded electrodermal activity, photoplethysmographic pulse, triaxial acceleration, video-based eye tracking, and automated facial expression analysis from 50 young adults while they viewed 20 short-form influencer clips (5.6–19.3 s) presented in individually randomized order, yielding 1000 participant × clip trials and 12 primary indices spanning four modalities. Using crossed random-effects variance partitioning, split-half reliability with Spearman–Brown projection, permutation-based multivariate testing, participant-wise decoding, and inter-subject correlation, we show that stimulus-driven variance is strongly channel-specific: it reaches 13.7% for fixation rate and 10.2% for facial engagement but only 0.11% for pulse rate and 0.23% for RMSSD. Clip-level estimates were highly reliable for facial and oculomotor indices ((Formula presented.) – (Formula presented.)) and poor for autonomic indices ((Formula presented.) – (Formula presented.)), which would require 134–370 participants to reach (Formula presented.). Between-modality dependence was almost six times weaker than within-modality dependence, and five components were retained by parallel analysis. Oculomotor and facial features jointly identified individual clips at 16.8% balanced accuracy against a 5% chance level, whereas electrodermal and pulse features, taken separately, did not exceed chance. Electrodermal responses were nonetheless stimulus-locked in 18 of 20 clips. A per-second re-analysis of nine channels showed that the ordering partly reverses for within-clip dynamics: tonic electrodermal and pupil time courses were reliable across viewers ((Formula presented.) and (Formula presented.)), facial time courses were not ((Formula presented.)), and pulse rate was unreliable under both treatments. We derive explicit design guidance for sensor selection and sample size in short-exposure multimodal engagement studies. © 2026 by the authors.
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Invasion Status, Distribution, and Environmental Preferences of Non-Native Ornamental Thunbergia Species (Acanthaceae) in Ecuador: An Emerging Threat to Tropical Montane Forests
(2026)
Ana Reyes-Hernández
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Ileana Herrera
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Anahí Vargas
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Oleas Gallo, Nora Helena
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Josue Alvarez
Species of the genus Thunbergia, native to Africa, Asia, and Australia, are widely cultivated as ornamental plants; however, their ability to escape cultivation and establish themselves in novel environments poses a growing threat to tropical forests. Here, we provide the first nationwide assessment of Thunbergia species occurring in Ecuador, integrating data from citizen science platforms, herbarium collections, and field surveys. We analyzed spatiotemporal patterns of occurrence, evaluated invasion status based on wild persistence and spread, and assessed environmental preferences using climatic niche analyses. Species distributions were further examined across land-cover types, conservation areas, and forest–non-forest interfaces. We confirmed the presence of five Thunbergia species in Ecuador, two of which also occur in the Galapagos Islands. All species were recorded both in cultivation and in the wild, indicating ornamental horticulture as the main introduction pathway for the genus, and occurrences were documented within 24 conservation areas. Thunbergia alata, T. fragrans, and T. grandiflora were categorized as invasive in Ecuador. Among them, T. fragrans exhibited broad environmental tolerance across bioregions. Wild occurrences were predominantly associated with human-modified landscapes but frequently occurred near forest edges, indicating ongoing encroachment into natural forests. These findings highlight the urgent need for preventive and targeted management strategies, particularly against T. alata, which represents an emerging threat to Andean forest ecosystems.
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From Simulation to Reality: A Digital Twin Implementation of a 4-DOF Robotic Manipulator
(2025)
Bryan S. Guevara
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Varela Aldas, José
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Viviana Moya
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Angéelica Veróonica Quito Carrióon
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Marcelo Ortiz
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Experimental and data-driven evaluation of transport-layer and application-layer security for MQTT-based IoT networks
(Elsevier BV, 2026-12)
Del-Valle-Soto, Carolina
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Alvarez-Garcia, Maria Fernanda
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Valdivia, Leonardo J.
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Del-Puerto-Flores, José A.
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Varela Aldas, José
The rapid expansion of Internet of Things (IoT) deployments has intensified the need for secure and efficient communication mechanisms tailored to resource-constrained devices. Message Queuing Telemetry Transport (MQTT) is widely adopted due to its lightweight design; however, it lacks native security support, requiring external protection mechanisms that may significantly affect system performance. This paper presents a comprehensive experimental and data-driven evaluation of two security paradigms for MQTT-based IoT networks implemented on ESP32 microcontrollers: transport-layer security using TLS and application-layer encryption based on elliptic curve cryptography (ECC) for key exchange combined with AES symmetric encryption. The analysis jointly evaluates memory utilization, end-to-end latency, energy consumption, and resistance to passive traffic interception under identical experimental conditions. In addition to conventional metric-based comparisons, multivariate statistical analysis and unsupervised learning techniques are employed as exploratory tools to characterize the system-level behavior induced by each security scheme. Results show that Transport Layer Security (TLS) offers stronger confidentiality guarantees at the cost of higher memory overhead, while the ECC–(Advanced Encryption Standard) AES approach significantly reduces memory footprint with moderate latency penalties and comparable energy consumption. Multivariate analysis further reveals that each security mechanism induces a distinct performance regime, providing a compact joint characterization of the security–performance trade-offs across the evaluated configurations.
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Low-Cost IoT System with Containerized AI and Telegram Bot for Real-Time Air Quality Risk Communication and Preventive Behavior Change
(Springer Nature Switzerland, 2026)
Zapata, Mireya
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Álvarez-Tello, Jorge
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Rugel-Sanchez, Keyla
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Vargas-Bustamante, Miguel
Air pollution constitutes one of the main environmental risk factors for public health, particularly in urban environments with limited real time monitoring infrastructure. Although low-cost IoT architectures have emerged as scalable alternatives to extend the spatial coverage of measurements, many implementations lack statistically validated risk classification models capable of translating the environmental data into information to service the public. This study presents the development and statistically validates in real time a risk index for air quality, implemented though a low-cost IoT architecture which integrates supervised artificial intelligence (AI) models deployed at the edge. The system was implemented for eight weeks the Universidad de Guayaquil campus, taking records of PM2.5, PM10 concentrations, temperature and humidity using calibrated sensors. The classification model based on Classification and Regression Trees reached a global accuracy greater which surpassed 90%, with 91.7% concordance to data retrieved from official stations for moderate conditions of PM2.5. K-fold method was used during the validation process and direct comparison with certified infrastructure. Additionally, user evaluation (n = 85) showed 82% adoption of preventive behavior after the implementation of proactive communication with a conversational bot. The results show that the integration of the presented low-cost IoT with validated risk models and user focused communication can generate reliable environmental intelligence and supports preventive decision-making in urban contexts with infrastructure constraints.
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Does the use of dedicated mobile devices in magnetism classes improve student learning?
(Frontiers Media SA, 2026-04-21)
Varela Aldas, José
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Collay, Washington
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Del-Valle-Soto, Carolina
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Palacios-Navarro, Guillermo
Mobile devices have been evaluated from multiple perspectives as tools to support classroom learning; however, devices specifically dedicated to learning activities still require further research to elucidate their influence in the classroom. This study aimed to evaluate whether the use of dedicated mobile devices for teaching basic magnetism content improves learning among secondary school students. A quantitative, comparative quasi-experimental study with non-equivalent groups was conducted ((Formula presented)) with secondary school students in Ecuador. The experimental group used an educational application running on a dedicated mobile device (M5Stack Core2) and interacted with an electromagnet as a didactic peripheral, whereas the control group received a traditional teacher-centered lesson supported by slides. Learning outcomes were measured using a teacher-designed theoretical test administered at the end of a 90-min session. Mean scores were similar between groups (experimental: 7.32; control: 7.58). After verifying assumptions of normality and homogeneity of variance, an independent-samples t-test showed no statistically significant differences ((Formula presented)), and the effect size was small (Cohen’s (Formula presented)). Under the conditions evaluated, the dedicated mobile device did not produce measurable gains in knowledge performance compared to the traditional lesson. Longer interventions, larger samples, and longitudinal study designs are needed to clarify when and how this technology may provide educational benefits.
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Addressing the Accessibility Gap in Biometric Security: A Low-Cost, Open-Source Approach for High-Risk Environments
(Springer Nature Switzerland, 2026)
Vargas-Bustamante, Miguel
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Zapata, Mireya
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García-Muñoz, Gabriel
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Álvarez-Tello, Jorge
Facial recognition has become established as a key technology for access control in high-insecurity contexts; however, its adoption in resource-constrained environments remains limited. This study addresses the existing gap in the empirical validation of low-cost biometric systems deployed in real-world scenarios. A prototype access control system based on facial recognition was developed using low-cost embedded hardware and open-source software. The research followed a quantitative, experimental, and applied approach, evaluating system performance using standardized biometric metrics (accuracy, FAR, and FRR) under different environmental conditions. The system achieved 97% accuracy in authorized access attempts and 96% accuracy in unauthorized attempts, with an average latency of 1.8 s and complete resistance to basic spoofing attacks. The results demonstrate that low-cost biometric solutions can achieve acceptable levels of reliability and usability for access control in high-insecurity contexts, contributing to the democratization of technological security

Centro de investigación en Mecatrónica y Sistemas Interactivos

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