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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      8
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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
    ;
    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  
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    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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    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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    Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
    (MDPI AG, 2026-01-18)
    Del-Valle-Soto, Carolina  
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    Corona Cabrera, Alba Violeta  
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    Gomez Romero Borquez, Jesus Alberto  
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    David Contreras-Tiscareno
    ;
    Diego Sebastian Montoya-Rodriguez
    Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.
      34Scopus© Citations 1
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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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    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
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    Velázquez, Ramiro  
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    Del-Valle-Soto, Carolina  
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    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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    Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management
    (2020)
    Paolo Visconti
    ;
    Roberto de Fazio
    ;
    Velázquez, Ramiro  
    ;
    Del-Valle-Soto, Carolina  
    ;
    Nicola Ivan Giannoccaro
    <jats:p>The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers’ usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products’ traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system.</jats:p>
    Scopus© Citations 65  2  10