López Gonzales, Javier Linkolk

Preferred name
López Gonzales, Javier Linkolk
Main Affiliation
Email
linkolklg@upeu.edu.pe
ORCID
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    ¿Qué tan válidas y confiables son las interpretaciones derivadas del Florida Patient Acceptance Survey en español?
    (2019-03-27)
    Renzo Felipe Carranza Esteban
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    Tomás Caycho‐Rodríguez
    que define la validez como el grado en que, tanto la evidencia como la teora, apoyan las interpretaciones
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    Mortality from all causes before and during the COVID-19 pandemic in Peru: The role of sociodemographic factors and accessibility
    (2022-10-19)
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    Lucía Vargas
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    Jorge Sánchez-Garcés
    Abstract Peru suffered the highest mortality rates worldwide during the COVID-19 pandemic. In this study, we assessed the Peruvian districts' all causes of mortality-associated sociodemographic factors before and during the COVID-19 pandemic using mixed-effects Poisson regression models. During the pre-pandemic and the first four COVID-19 waves, the Peruvian districts reported mean weekly mortality of 22.3 (standard deviation 40.4), 29.2 (38.7), 32.5 (47.2), 26.8 (38.9), and 24.4 (38.0), respectively. We observed that before the COVID-19 pandemic, the districts' weekly deaths were associated with the human development index ((HDI) adjusted incidence rate ratio (aIRR) 0.11 (95% confidence interval 0.11–0.12)), accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99 (0.99–0.99)). However, during each of the first four COVID-19 waves, the magnitude of association between the districts' weekly deaths and HDI decreased (first, aIRR 0.61 (0.58−0.64); second, aIRR 0.54 (0.52−0.57); third, aIRR 0.20 (0.19−0.22); fourth, aIRR 0.17 (0.15−0.19)), but the association with accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99(0.99–0.99)) remain constant. Before and during the COVID-19 pandemic, a solid association existed between all-cause mortality and the district's sociodemographics, increasing with lower HDI, accessibility, poverty, and anemia rates.
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    An exploratory analysis of PM$$_{2.5}$$/PM$$_{10}$$ ratio during spring 2016–2018 in Metropolitan Lima
    (2024-04-23)
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    Natalí Carbo‐Bustinza
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    Edison Alessandro Romero-Cabello
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    Jeremias Macias Ureta Tolentino
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    Elías A Torres Armas
    Abstract Aerosols (PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> and PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ) represent one of the most critical pollutants due to their negative effects on human health. This research analyzed the relationship of PM and its PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ratios with climatic variables in the austral spring (2016–2018) in Metropolitan Lima. Overall, there was an average PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ratio of 0.33 with fluctuations from 0.30 to 0.35. However, there have also been high point values that reached ratios greater than one. This situation indicates a moderate condition of contamination by particulate matter with a predominance of coarse aerosols in spring, with an increasing trend over the years. The locations Ate and Villa Maria del Triunfo , especially Ate , presented poor quality conditions. Thursdays showed outstanding pollution peaks by PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> , and a decrease is visible on Sundays. On the other hand, the PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> showed a similar pattern every day, including Sundays. The maximum peaks occurred in the morning and night hours. The increase in anthropogenic emissions associated with the formation of secondary aerosols has been evident, being the case of the location Campo de Marte , the one that had a significant increase in ratios PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> , which confirms a greater intensity of secondary formations of carbonaceous particles from industrial oil sources, vehicle exhaust, as well as aerosols from metal smelting and biomass burning. There were negative correlations of the ratios with PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> , temperature, wind speed, and direction, and positive correlations with PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> and relative humidity. Contour lines were successfully developed that demonstrated the interaction of climate with PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ratios. This will deepen the exploration of emission sources and modeling, which allows for optimizing air quality indices to control emissions and adequately manage air quality in Metropolitan Lima.
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    Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru
    (2024-05-30)
    César Raúl Castro Galarza
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    Omar Nolberto Díaz Sánchez
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    Jonatha Sousa Pimentel
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    Rodrigo de Souza Bulhões
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    Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.
      4
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    Sex differences in social network addiction and emotional eating among adolescents in a Peruvian region: a cross-sectional analysis
    (2024-10-25)
    Jacksaint Saintila
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    Miguel Basauri-Delgado
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    Sandra P. Carranza-Cubas
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    Maladaptive eating behaviors are becoming increasingly prevalent among adolescents, and their relationship to exposure to media messages has been extensively examined. However, more specifically, the influence of social networks on emotional eating (EmE) has received relatively less attention in the Peruvian context. To determine the association between social network addiction (SNA) and EmE among Peruvian adolescents, considering sex differences. A cross-sectional study was conducted. A total of 878 adolescents were considered. The Social Network Addiction Questionnaire and the Emotional Eating Questionnaire were administered. Similarly, sociodemographic data were collected. To explore the association between SNA and EmE, we performed Poisson regression analyses with robust variance. We considered a p-value of equal to or less than 0.05 as statistically significant. Adolescents with an EmE score greater than 5 had significantly higher SNA scores, averaging 24.7 (± 12.3), compared to 15.5 (± 10.3) in those with an EmE score between 0 and 5 (p < 0.001). In multiple regression analyses, men with moderate/severe SNA were 1.77 times more likely to have EmE compared to those without SNA (95% CI 1.45–2.15; p < 0.001), while in women, moderate/severe SNA was associated with a 1.98 times higher likelihood of EmE (95% CI 1.61–2.45; p < 0.001). These findings highlight the need for public health interventions to educate adolescents about the use of social networks to minimize the negative effects of prolonged exposure on eating habits. Many teenagers are developing unhealthy eating habits, and researchers have studied how media influences this. However, the effect of social media on emotional eating (eating in response to emotions) in Peru has received less attention. This study explored the link between social media addiction and emotional eating among Peruvian teenagers, focusing on differences between boys and girls. We studied 878 teenagers using two questionnaires: one about social media addiction and one about emotional eating. We also collected basic background information. A statistical analysis called Poisson regression was used to see how likely emotional eating is in teenagers with high social media use. Results showed that teenagers with higher emotional eating scores also had higher social media addiction scores. Boys with moderate or severe social media addiction were 1.77 times more likely to have emotional eating behaviors, while girls were 1.98 times more likely. These results highlight the need for programs to teach teenagers about the healthy use of social media to reduce its negative impact on eating habits.
      4
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    Analysis and forecasting of electricity prices using an improved time series ensemble approach: an application to the Peruvian electricity market
    (2024-01-01)
    Salvatore Mancha Gonzales
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    Hasnain Iftikhar
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    &lt;p&gt;In today's electricity markets, accurate electricity price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, the complex characteristics of electricity price time series hinder accessibility to accurate price forecasting. This study addressed this challenge by introducing a novel approach to predicting prices in the Peruvian electricity market. This approach involved preprocessing the monthly electricity price time series by addressing missing values, stabilizing variance, normalizing data, achieving stationarity, and addressing seasonality issues. After this, six standard base models were employed to model the time series, followed by applying three ensemble models to forecast the filtered electricity price time series. Comparisons were conducted between the predicted and observed electricity prices using mean error accuracy measures, graphical evaluation, and an equal forecasting accuracy statistical test. The results showed that the proposed novel ensemble forecasting approach was an efficient and accurate tool for forecasting monthly electricity prices in the Peruvian electricity market. Moreover, the ensemble models outperformed the results of earlier studies. Finally, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electricity prices for the Peruvian electricity market.&lt;/p&gt;
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    Optimal features selection in the high dimensional data based on robust technique: Application to different health database
    (2024-09-01)
    Ibrar Hussain
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    Moiz Qureshi
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    Muhammad Ismail
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    Hasnain Iftikhar
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    Justyna Żywiołek
    Bio-informatics and gene expression analysis face major hurdles when dealing with high-dimensional data, where the number of variables or genes much outweighs the number of samples. These difficulties are exacerbated, particularly in microarray data processing, by redundant genes that do not significantly contribute to the response variable. To address this issue, gene selection emerges as a feasible method for identifying the most important genes, hence reducing the generalization error of classification algorithms. This paper introduces a new hybrid approach for gene selection by combining the Signal-to-Noise Ratio (SNR) score with the robust Mood median test. The Mood median test is beneficial for reducing the impact of outliers in non-normal or skewed data since it may successfully identify genes with significant changes across groups. The SNR score measures the significance of a gene's classification by comparing the gap between class means and within-class variability. By integrating both of these approaches, the suggested approach aims to find genes that are significant for classification tasks. The major objective of this study is to evaluate the effectiveness of this combination approach in choosing the optimal genes. A significant P-value is consistently identified for each gene using the Mood median test and the SNR score. By dividing the SNR value of each gene by its significant P-value, the Md score is calculated. Genes with a high signal-to-noise ratio (SNR) have been considered favorable due to their minimal noise influence and significant classification importance. To verify the effectiveness of the selected genes, the study utilizes two dependable classification techniques: Random Forest and K-Nearest Neighbors (KNN). These algorithms were chosen due to their track record of successfully completing categorization-related tasks. The performance of the selected genes is evaluated using two metrics: error reduction and classification accuracy. These metrics offer an in-depth assessment of how well the selected genes improve classification accuracy and consistency. According to the findings, the hybrid approach put out here outperforms conventional gene selection methods in high-dimensional datasets and has lower classification error rates. There are considerable improvements in classification accuracy and error reduction when specific genes are exposed to the Random Forest and KNN classifiers. The outcomes demonstrate how this hybrid technique might be a helpful tool to improve gene selection processes in bioinformatics.
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    A hybrid AI approach for predicting academic performance in RBE students
    (2025-10-21)
    Willy Gonzales
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    Zindel Cordero
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    Edgar Tito Susanibar Ramírez
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    Hasnain Iftikhar
    Machine learning has advanced significantly in recent years and is being used in higher education to perform various types of data analysis. While the literature demonstrates the application of machine learning algorithms to predict performance in university education, no such applications are found in EBR, let alone in private institutions of a denominational nature, which presents an opportunity to study prediction in these institutions. To address this gap, this research aims to propose a predictive approach as a decision-support tool for regular basic education, using machine learning techniques. Among the techniques utilized, three machine learning models (Logistic Regression, Support Vector Machine, and Random Forest), along with deep learning models (AlexNet, Gated Recurrent Unit, and Bidirectional Gated Recurrent Unit), were analyzed, as well as ensemble models. Nonetheless, the Ensemble model, which combines deep learning and machine learning techniques, is preferred due to its superior accuracy, precision, and sensitivity performance metrics.
      4
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    Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context
    (2026-06-01)
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    Lloy Pinedo
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    Gorky Vizalote
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    Manuel Enrique Navas-Vásquez
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    Aquaculture is an essential productive activity in food security, economy, and the sustainability of water resources globally. The study analyzes the application of artificial intelligence techniques in aquaculture on the American continent through a systematic review of 31 articles published between 2020 and 2024 in the Scopus and SciELO databases. Five key areas of application were identified: monitoring and control, organism identification and counting, biomass and mortality rate prediction, behavioral analysis, and production optimization. The most commonly used techniques include machine learning, deep learning, artificial vision, and genetic algorithms, with models such as Convolutional Neural Networks, Random Forest, and YOLO standing out, demonstrating high accuracy in aquaculture processes. However, research has focused mainly on fish, while other organisms, such as shellfish and shrimp, have received less attention. In addition, adopting these technologies faces challenges related to infrastructure, data availability, and staff training. It is concluded that the integration of AI in aquaculture has a high potential to improve the efficiency and sustainability of the sector. However, it is necessary to expand the study to other species and strengthen technological accessibility for small and medium-sized producers.
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    A Systematic Review of the Application of Maturity Models in Universities
    (2020-10-01)
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    Sandro Paz Collado
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    A maturity model is a widely used tool in software engineering and has mostly been extended to domains such as education, health, energy, finance, government, and general use. It is valuable for evaluations and continuous improvement of business processes or certain aspects of organizations, as it represents a more organized and systematic way of doing business. In this paper, we only focus on college higher education. For this reason, we present a novel approach that allows detecting some gaps in the existing maturity models for universities, as they are not models that address the dimensions in their entirety. To identify these models and their validities, as well as a classification of models that were identified in universities, we carried out a systematic literature review on 27,289 articles retrieved with respect to maturity models and published in peer-reviewed journals between 2007 and 2020. We found 23 articles that find maturity models applied in universities, through exclusion and inclusion criteria. We then grouped these items into nine categories with specific purposes. We concluded that maturity models used in Universities move towards agility, which is supported by the semantic web.
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