Del-Valle-Soto, Carolina

Main Affiliation
Preferred name
Del-Valle-Soto, Carolina
Official Name
del Valle Soto, Carolina
ORCID
0000-0002-0272-3275
Researcher ID
T-5779-2017
Scopus Author ID
56257456700

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    Does the use of dedicated mobile devices in magnetism classes improve student learning?
    (Frontiers Media SA, 2026-04-21)
    Varela-Aldás, José
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    Collay, Washington
    ;
    Del-Valle-Soto, Carolina  
    ;
    Palacios-Navarro, Guillermo
      8
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    Stability-Aware Security–Performance Trade-Off Analysis in Resource-Constrained IoT Systems: A Time-Series and Bootstrap-Based Evaluation of TLS and Hybrid ECC–AES Mechanisms
    (MDPI AG, 2026-05-02)
    Del-Valle-Soto, Carolina  
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    Alvarez-Garcia, Maria Fernanda
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    Briseño Martínez, Ramón Alejandro  
    ;
    Visconti, Paolo
    ;
    Rodríguez, Jafet  
    The increasing deployment of resource-constrained Internet of Things (IoT) devices requires security mechanisms that preserve confidentiality without compromising energy efficiency or responsiveness. Although Transport Layer Security (TLS) provides standardized protection for MQTT-based communication, its computational overhead may significantly affect embedded architectures. This study presents a controlled experimental evaluation of three communication configurations implemented on ESP32-based nodes: unencrypted Message Queuing Telemetry Transport (MQTT), MQTT over TLS 1.2, and an application-layer hybrid scheme combining Elliptic Curve Diffie–Hellman key exchange with AES-128 encryption. Second-level measurements of instantaneous current, accumulated energy, end-to-end latency, and memory footprint were collected across repeated experimental runs. Time-series diagnostics were performed to assess autocorrelation and stationarity, and block bootstrap resampling was applied to ensure dependence-aware statistical inference. The results indicate that TLS introduces the highest cumulative energy growth and latency dispersion, while the hybrid ECC–AES configuration demonstrates intermediate behavior with reduced overhead relative to TLS. Pareto frontier analysis shows that TLS is dominated in the joint energy–latency space, whereas the hybrid scheme represents a non-dominated compromise between security and efficiency. These findings provide a stability-aware and statistically robust framework for evaluating security–performance trade-offs in embedded IoT systems.
      31
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    Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models
    (MDPI AG, 2025-04-04)
    Pedro Torres-Bermeo
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    Kevin López-Eugenio
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    Del-Valle-Soto, Carolina  
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    Guillermo Palacios-Navarro
    ;
    José Varela-Aldás
    The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.
      27
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    Cama semiautomática medidora para esterilización de animales
    (IMPI, 2022-12-13)
    Del-Valle-Soto, Carolina  
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    Valdivia, Leonardo  
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    Rosa Sierra, Luis Alberto
    La presente invención consiste en una cama semiautomática medidora para esterilización de animales y su proceso de utilización, la cual cuenta con una cama que puede medir el peso y la temperatura de forma automática mediante sensores, arrojando la cantidad exacta de anestesia a utilizar en el proceso de esterilización. Además, cuenta con sensores de oxigenación y ritmo cardiaco para monitorear los signos vitales del animal y reportarlos en una pantalla táctil que cuenta con leds y sonido que se activan como alerta en caso de que estos signos se encuentren cerca de los parámetros – altos o bajo – fijados según la epecie, raza y genero del animal a esterilizar. La cama esta provista de pasos a seguir para su correcta y eficiente utilización, además de estar diseñada como una unidad desplegable para trabajo en campo y ser trasladada fácilmente.
      1  30
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    Mechanical, Thermal, and Environmental Energy Harvesting Solutions in Fully Electric and Hybrid Vehicles: Innovative Approaches and Commercial Systems
    (MDPI AG, 2025-04-11)
    Giuseppe Rausa
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    Maurizio Calabrese
    ;
    Velázquez, Ramiro  
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    Del-Valle-Soto, Carolina  
    ;
    Roberto De Fazio
    Energy harvesting in the automotive sector is a rapidly growing field aimed at improving vehicle efficiency and sustainability by recovering wasted energy. Various technologies have been developed to convert mechanical, thermal, and environmental energy into electrical power, reducing dependency on traditional energy sources. This manuscript provides a comprehensive review of energy harvesting applications/methodologies, aiming to trace the research lines and future developments. This work identifies the main categories of harvesting solutions, namely mechanical, thermal, and hybrid/environmental solar–wind systems; each section includes a detailed review of the technical and scientific state of the art and a comparative analysis with detailed tables, allowing the state of the art to be mapped for identification of the strengths of each solution, as well as the challenges and future developments needed to enhance the technological level. These improvements focus on energy conversion efficiency, material innovation, vehicle integration, energy savings, and environmental sustainability. The mechanical harvesting section focuses on energy recovery from vehicle vibrations, with emphasis on regenerative suspensions and piezoelectric-based solutions. Specifically, solutions applied to suspensions with electric generators can achieve power outputs of around 1 kW, while piezoelectric-based suspension systems can generate up to tens of watts. The thermal harvesting section, instead, explores methods for converting waste heat from an internal combustion engine (ICE) into electrical power, including thermoelectric generators (TEGs) and organic Rankine cycle systems (ORC). Notably, ICEs with TEGs can recover above 1 kW of power, while ICE-based ORC systems can generate tens of watts. On the other hand, TEGs integrated into braking systems can harvest a few watts of power. Then, hybrid solutions are discussed, focusing on integrated mechanical and thermal energy recovery systems, as well as solar and wind energy harvesting. Hybrid solutions can achieve power outputs above 1 kW, with the main contribution from TEGs (≈1 kW), compared to piezoelectric systems (hundreds of W). Lastly, a section on commercial solutions highlights how current scientific research meets the automotive sector’s needs, providing significant insights for future development. For these reasons, the research results aim to be guidelines for a better understanding of where future studies should focus to improve the technological level and efficiency of energy harvesting solutions in the automotive sector.
      19
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    Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review
    (MDPI AG, 2026-03-25)
    Fierro-Silva, Carlos Julio
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    Del-Valle-Soto, Carolina  
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    Mostafa, Samih M.
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    Varela-Aldás, José
      9
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