RODRÍGUEZ FLORES, JESÚS ALBERTO

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    Random Forest modeling of bipolar affective disorder in Ecuador
    (AG Editor (Argentina), 2025-07-31)
    Cristhian Ismae Gómez Gaona
    ;
    Andrea del Rocío Mejía Rubio
    ;
    José Rubén León Pérez
    ;
    RODRÍGUEZ FLORES, JESÚS ALBERTO  
    ;
    Zilma Diago Alfes
    Bipolar affective disorder is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity of the diagnosis of bipolar affective disorder due to the overlapping of its symptoms with other mood disorders led researchers and doctors to search for new and advanced techniques for the precise detection of bipolar affection disorder. One of these methods is the use of advanced machine learning algorithms under a statistical methodology for building logistical regression models, Random Forest. Support vector machines, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, with 146 data collected from the psychiatric services affiliated with the mental health system of Ecuador. At the inferential level, the results suggest that the implementation of automatic algorithms based on the different methodologies for building models enables the successful prediction or classification of individuals with bipolar affective disorders in Ecuador compared to controlled patients who do not profile under this pathological picture. It is the best Random Forest statistical model (89.35 %) that dictates the best performance metrics compared to the Gradient Boosting model. The evolution of the overall prevalence of bipolar affective disorders in Ecuador over the past 22 years has increased by a small differential. However, from 2020 to 2022, there has been a considerable increase in the percentage prevalence of cases of bipolar affective disorders in Ecuador.
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    SARIMA models for power evolution in photovoltaic systems
    (AG Editor (Argentina), 2025-08-01)
    Juan Espinoza
    ;
    Christian Reyes
    ;
    Diana Campaña
    ;
    Elsa Basantes
    ;
    RODRÍGUEZ FLORES, JESÚS ALBERTO  
    Introduction.- The increasing use of renewable energy in power generation systems has highlighted the need for efficient schemes to predict model parameters. In particular, photovoltaic systems require accurate tools to model and forecast solar energy generation behavior. Objective.-To formulate SARIMA models with high accuracy in fitting, explanation, and prediction of energy yields in solar photovoltaic systems, specifically focused on the plant located at Plaza del Duque de Béjar, Spain. Method.- A fitting strategy based on genetic algorithms was adopted to accelerate the estimation of the SARIMA model using hourly solar photovoltaic generation data. The auto.arima package in RStudio was employed as a methodological tool, enabling automatic selection and optimization of the best model parameters. Results.- The selected model was SARIMA (5,0,0)(2,1,0)242424, characterized by a stationary stochastic process with a clear seasonal component. The model showed remarkable estimation accuracy, with low standard errors in the autoregressive coefficients. Additionally, the model residuals were well-adjusted, displaying independence and absence of serial autocorrelation. Conclusions.- The proposed model demonstrated excellent predictive performance, supported by training error metrics (ME (Mean Error)= -1.344268 and MASE (Mean Absolute Scaled Error)= 0.7048786). Its sound mathematical structure and strong fit make it a reliable tool for forecasting photovoltaic solar energy in systems with similar characteristics.
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    Stochastic State-Space Modeling for Sludge Concentration Height at the Ucubamba Guangarcucho Wastewater Treatment Plant
    (MDPI AG, 2025-03-10)
    Cristian Luis Inca Balseca
    ;
    Cristian Salazar
    ;
    RODRÍGUEZ FLORES, JESÚS ALBERTO  
    ;
    María Barrera
    ;
    Anna Igorevna Kurbatova
    Wastewater treatment plants consist of many biological reactors and a settler, representing an example of large-scale, nonlinear systems. The wastewater treatment plant in this study operates using an activated sludge system, which relies on biological processes to treat wastewater effectively. It is for this reason that iterative process modeling was used through the implementation of an Extended Kalman Filter (EKF) to predict the height of the sludge layer in secondary clarifiers, where the accumulation of activated sludge occurs during the sedimentation process. This technique consists of maximum likelihood estimation that works more consistently in various noise scenarios. As a result of the evaluation of the model estimated by the Extended Kalman Filter (EKF), the suitability of the process tends to be concluded on. In this sense, the prediction of the height in the sludge layer in sewage systems represents a complicated and heteroscedastic process, which can be understood as a phenomenon that can be influenced by a variety of factors. Therefore, this study does not identify problems in estimates through a thorough examination of residuals. It is concluded that the implementation of state-space modeling increases the adaptability and adjustability of the process to achieve structural optimization in a treatment plant. This approach is a viable and effective solution for the efficient management of polluting sludge levels and minimizing the possible environmental impact in out-of-control situations in wastewater treatment plants.
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    Condición de Legendre–Clebsch bajo hipótesis de rango débil en problemas de control óptimo con restricciones mixtas
    (2025)
    RODRÍGUEZ FLORES, JESÚS ALBERTO  
    Este trabajo aborda problemas de control con restricciones mixtas, en los que las condiciones clásicas basadas en la condición de independencia lineal de restricciones (LICQ) y en la condición de Legendre–Clebsch sobre todo el espacio de controles pueden resultar excesivamente conservadoras. Se propone un marco de segundo orden basado en una hipótesis de rango débil que permite gradientes activos linealmente dependientes, con el objetivo de vincular la formulación de la segunda variación en programación no lineal con una condición de Legendre–Clebsch formulada en el espacio de controles. A partir de la segunda variación se introduce un cono crítico de direcciones factibles y un subespacio de direcciones críticas del control, y se demuestra que la no negatividad de la forma cuadrática en dicho cono implica que la hessiana del hamiltoniano respecto del control es semidefinida positiva; en el caso coercivo se obtiene una cota de crecimiento cuadrático sobre esas direcciones. El enfoque se ilustra en un problema lineal–cuadrático con control en un cono poliédrico determinado por tres desigualdades, donde la matriz de gradientes tiene rango dos y la hipótesis de rango estándar falla mientras que la condición de Legendre–Clebsch generalizada sigue siendo válida, proporcionando un criterio más fino en problemas degenerados.