Zapata, Mireya

Preferred name

Zapata, Mireya

Main Affiliation

Centro de investigación en Mecatrónica y Sistemas Interactivos

Quito

mireyazapata@uti.edu.ec

ORCID

0000-0003-3382-2724

Scopus Author ID

56818703100

Now showing 1 - 10 of 41

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The Biometric Imperative: Low-Cost Facial Recognition to Counter Critical Access Vulnerabilities in High-Insecurity Contexts
(Springer Nature Switzerland, 2026)
Zapata, Mireya
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Vargas-Bustamante, Miguel
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Cevallos-Sornoza, Angie
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Álvarez-Tello, Jorge
Rising insecurity in Ecuador, especially on urban areas of Guayaquil, has exposed the high vulnerability of public and educational institutions to unauthorized access. Conventional security control measurements base on physical credentials, or passwords have proved to be insufficient, driving the need for technological solutions for secure access control. In this context, facial recognition has emerged as a feasible biometric solution, even though its implementation gets limited when applied to resource-constrained environments. This study presents and validates an access control system developed with accessible and budget-friendly hardware, open-source tools, aimed at strengthening security in vulnerable spaces. The research adopted a quantitative, experimental, and applied approach, evaluating prototype performance through indicators of operational reliability, correct authentication rate, and rejection of unauthorized attempts under diverse environmental conditions. Results show that the system achieved an identification success rate of 97% of legitimate users and blocked 95% of non-authorized attempts, with an average response time of under two seconds and high resistance to basic spoofing attempts. These findings confirm that low-cost biometric solutions can achieve satisfactory levels of security and usability, contributing to the democratization of technology and offering a replicable and ethical alternative for educational institutions in high-insecurity contexts
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Using Predictive Modeling to Assess Social Media's Impact on Parental Well-Being: A Pilot Study
(Springer Nature Switzerland, 2026)
Tutillo, Jimena
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Martinez, Danilo
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Zapata, Mireya
Social media is part of daily life, facilitating communication, entertainment, and immediate access to information. However, it can negatively affect personal well-being and contribute to the development of addictive behavior. The objective of this paper is to examine how social media users in the economically productive age group of 36 to 56 years are affected by these platforms, and how machine learning (ML) and app development can be used to mitigate the impacts of these dopamine-stimulated behaviors. For this purpose, an empirical study using the Social Media Disorder Test showed that 37.5% of the sample had prolonged usage. To aid in the solution, an app was developed that uses machine learning (ML) to classify addiction levels into 8 categories across 3 social media apps. The app monitors usage in real time and issues alerts for excessive consumption patterns to reduce digital dependence. The results show real patterns of moderate addiction among users. It also seeks to raise awareness of social media use so they can set limits and reduce the impact of their use
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Engineering Analysis of Half-Hourly Hydropower Plant Records: Operating-Regime Reconstruction, Energy Estimation, and State-Aware Data-Quality Assessment
(MDPI AG, 2026-09-22)
Vasquez-Cevallos, Leonel
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Zapata, Mireya
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Sarango-Gualan, Marco
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Maliza Cruz, Wellington Isaac
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Parrales-Bravo, Franklin
Half-hourly hydropower plant records support the engineering analysis of operating states, energy, and measurement consistency; however, legacy spreadsheet structures complicate their integration within a traceable workflow. We analyzed authorized records from an anonymized plant using source-preserving data processing. The workbook comprised 31 sheets and included 1519 rows. Of these, 1488 complete intervals constituted the analytical dataset, with no imputation applied. A common unit-state model was used to reconstruct the operating regimes, allocate the interval energy, and condition the data quality diagnostics. The estimated monthly energy was 3689.75 MWh (119.02 MWh/day). The both-online configuration represented 291.0 allocated hours and accounted for 54.04% of the estimated energy; single-unit configurations represented 435.5 allocated hours; and the both-offline configuration represented 17.5 allocated hours. Daily energy was strongly associated with the mean turbine flow (Pearson’s r = 0.967; Spearman’s rho = 0.950; n = 31). Unit power-factor formula errors coincided with inactive unit states, all 35 negative line-power records occurred while both units were offline, and one interval had a 0.584 MW aggregation discrepancy. By using the reconstructed unit state as a shared analytical layer, the framework links energy allocation and measurement diagnostics while preserving record-level provenance. Energy and time allocations follow an explicit representative-interval convention. © 2026 by the authors.
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Low-Cost Interfaces and Technological Accessibility: A Human–Computer Interaction Approach for the Inclusion of Individuals with Motor Disabilities
(Springer Nature Switzerland, 2026)
Arias Flores, Hugo Patricio
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Piedra-Ansatuña, Mario
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Zapata, Mireya
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RUBIO PROAÑO, Andrès
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Palacio-Fierro, Andrés
Cerebral palsy (CP) is a neurological disorder of prenatal or perinatal origin that affects motor control, posture, and frequently speech, thereby limiting autonomy and social participation among individuals who experience it. During the transition to adulthood, these limitations tend to intensify, highlighting the need for assistive technologies (AT) that effectively address real-world contexts of use. However, the high cost and limited adaptability of many existing solutions restrict their adoption, particularly in low- and middle-income countries. This study is situated within the field of Human–Computer Interaction (HCI) and aims to evaluate low-cost input devices based on principles of accessibility, ergonomics, and usability, to improve the work performance of individuals with motor disabilities. A qualitative methodology was employed, based on the think-aloud technique, applied to a case study involving an adult with cerebral palsy who interacted with three devices: a conventional trackpad, a MAKEY MAKEY-based interface, and a wireless keyboard with an integrated touchpad. The results indicate that, although the trackpad allows for a degree of progressive adaptation, it presents limitations in tasks requiring bimanual coordination. The MAKEY MAKEY interface provides greater discrete control but demands high levels of coordination and generates fatigue due to its physical configuration. In contrast, the wireless keyboard with an integrated touchpad emerges as the most efficient and ergonomic alternative, as it distributes functions between both hands and reduces physical effort. It is concluded that the adaptation of mass-market consumer technologies, under a user-centered design approach, can offer accessible, functional, and sustainable solutions for the digital inclusion of individuals with motor disabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Real-Time Industrial Risk Mitigation through an IIoT-Based Acoustic Alert System with Embedded Edge Processing and Remote Monitoring
(Springer Nature Switzerland, 2026)
Vargas-Bustamante, Miguel
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Álvarez-Tello, Jorge
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Cevallos-Sornoza, Angie
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Zapata, Mireya
Environmental monitoring schemes have been transformed by the incorporation of IoT technologies in industrial environments, enabling a shift toward continuous monitoring models and immediate response to risk situations. However, many available solutions rely on highly complex and costly SCADA infrastructures, which limits their adoption in facilities with budgetary constraints. In this context, the present research posed as its central question whether it is possible to design and implement a low-cost IoT system, based on open-source technologies, capable of providing real-time environmental monitoring with immediate acoustic alerts and reliable remote notifications. To address this question, an experimental architecture was developed based on Arduino and ESP32 platforms, integrating temperature and gas sensors, a DFPlayer Mini module for customized acoustic feedback, and a wireless transmission scheme to a remote platform. The system was validated through controlled tests involving the exceedance of critical thresholds, evaluating detection accuracy, alert activation latency, and communication stability during continuous operation. The results showed a correct detection rate above 95%, an average alert activation time below 1.5 s, and remote transmission stability greater than 97%. In addition, an approximate 40% reduction in operator response time was observed compared to traditional manual schemes. Consequently, the study demonstrates that an open and scalable IoT architecture can achieve adequate performance levels for industrial safety, contributing a robust human–computer interaction approach and establishing a solid foundation for future extensions toward predictive and interoperable capabilities.
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nanoHEENS: Biomimetic Near-Memory-Computing 16-Core SIMD Processor Node for Evolutive Spiking Neural Networks
(IEEE, 2026-05-24)
Larre-Alos, Arnau
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Vallejo-Mancero, Bernardo
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Torres, Victor
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Fernandez, Daniel
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Zapata, Mireya
The design of a digital spiking neural processor (NP) ASIC for biomimetic evolutive applications is reported. The architecture is briefly described, highlighting its fundamental characteristics, namely: Near-Memory Computing (NMC), efficient Single-Instruction Multiple-Data (SIMD) computation, multi-model support, 16-bit precision, axonal delay support and on-the-fly reconfiguration. A sub-mm2, 16-core NP testchip has been designed in 28 nm CMOS and sent to manufacture. A post-implementation simulation, including a controller and two NPs, is shown to demonstrate the system's correct operation and capabilities
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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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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
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Evidence-Driven Reproducibility Audit for Asset-Level PV Assessment in Distribution Networks
(MDPI AG, 2026-09-19)
Vasquez-Cevallos, Leonel
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Zapata, Mireya
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Garces-Quinaloa, Edith
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Maliza-Cruz, Wellington
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Parrales-Bravo, Franklin
Archived distribution studies are difficult to reuse when source calculations, public asset records, processed outputs, and proprietary model files support different levels of inference. This study develops a claim-specific audit for incomplete distribution network archives. Three evidence gates distinguish documentary consistency, asset attribution, and decision-specific adequacy; four archive stages separate preservation and integrity documentation from executable import and numerical reproduction. The method was applied to three retained operating points of a 13.8 kV feeder in Ambato, Ecuador. Eight roofs associated in the worksheet with T54 (code 21076) yielded 205.364 kWp from 1506 m2 under the retained sizing assumptions. A public EEASA GIS record corroborated the code, 125 kVA rating, and 13.8/0.22 kV nominal voltages, while the physical-to-model correspondence remained unverified. In the maximum-PV state labeled 1.5825 MWp, the archived feeder-head export was 0.670 MW, and the archived T54 loading metric was 156.915%, triggering a nameplate-based review without establishing thermal overload. Only T54 satisfied the four-element documentary attribution rule; model execution and numerical reproduction were not tested. This contribution is a domain-specific synthesis of established provenance and reproducibility principles that makes claim boundaries explicit. It remains a single-case demonstration, without empirical evidence of improved analyst agreement or decision quality. © 2026 by the authors.
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A Cyclical Action Research Approach to Active Learning in Data Structures
(IEEE, 2026-07)
Aguas-Bucheli, Luis
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Zapata, Mireya
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Acosta-Vargas, Patricia
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Ponce-Villacis, Paulina
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Amoroso-Garcés, Sylvia
The teaching of Data Structures in the Software Engineering program poses a fundamental challenge in the development of core algorithmic thinking and problem-solving skills. This article reports on an educational innovation experience implemented during the fourth semester of the program, which integrates Active Learning strategies with iterative Action Research cycles tailored to the specific demands of the subject. The pedagogical intervention is grounded in participatory and gamified methodologies, with a strong emphasis on professional practice and real-world application. By fostering student engagement through interactive problemsolving activities, code-based challenges, and collaborative learning environments, the experience aims to enhance both academic performance and the meaningful understanding of data structures. The results of the implementation revealed significant improvements in students' motivation, practical competencies, and their ability to apply data structures contextually to solve complex programming problem © 2026 IEEE.

Zapata, Mireya

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