López Gonzales, Javier Linkolk

Preferred name

Main Affiliation

linkolklg@upeu.edu.pe

ORCID

0000-0003-0847-0552

Scopus Author ID

57216856857

Mostrando 1 - 10 de 67

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Diseño de una boya multiparamétrica autónoma con energía fotovoltaica y comunicación remota basada en IoT para entornos de acuicultura
(2025-01-20)
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Matias Lévano-Casildo
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Se diseñó un prototipo de boya multiparamétrica autónoma para abordar las limitaciones tecnológicas en el monitoreo de la calidad del agua en ambientes de acuicultura. El objetivo fue desarrollar un sistema modular y sustentable que integre energía fotovoltaica y comunicación inalámbrica para monitorear en tiempo real parámetros críticos: pH, temperatura, oxígeno disuelto y conductividad eléctrica. El sistema consta de un módulo emisor, un módulo receptor y una plataforma de transmisión de datos a la nube. Los materiales incluyeron PLA reforzado y PETG, y los componentes electrónicos fueron alimentados por un panel solar de 20 W conectado a una batería de 12 V 7 Ah. Durante las pruebas, el prototipo demostró una autonomía energética de 48 horas y una transmisión LoRa confiable con un alcance de 500 m en la línea de visión directa. El diseño modular facilita la integración de sensores y la adaptación del sistema a diversas condiciones, beneficiando a los pequeños productores. Sin embargo, persisten desafíos como la resiliencia de los componentes en entornos hostiles y la optimización de la autonomía energética en condiciones adversas, lo que presenta oportunidades para futuras mejoras en robustez y escalabilidad.
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Influence measures in nonparametric regression model with symmetric random errors
(2022-06-25)
Germán Ibacache‐Pulgar
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Cristian Villegas
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Magaly Moraga
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Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models
(2023-08-16)
Hasnain Iftikhar
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Aimel Zafar
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Paulo Canas Rodrigues
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Crude oil price forecasting is an important research area in the international bulk commodity market. However, as risk factors diversify, price movements exhibit more complex nonlinear behavior. Hence, this study provides a comprehensive analysis of forecasting Brent crude oil prices by comparing various hybrid combinations of linear and nonlinear time series models. To this end, first, the logarithmic transformation is used to stabilize the variance of the crude oil prices time series; second, the original time series of log crude oil prices is decomposed into two new subseries, such as a long-run trend series and a stochastic series, using the Hodrick–Prescott filter; and third, two linear and two nonlinear time series models are considered to forecast the decomposed subseries. Finally, the forecast results for each subseries are combined to obtain the final day-ahead forecast result. The proposed modeling framework is applied to daily Brent spot prices from 1 January 2013 to 27 December 2022. Six different accuracy metrics, pictorial analysis, and a statistical test are performed to verify the proposed methodology’s performance. The experimental results (accuracy measures, pictorial analysis, and statistical test) show the efficiency and accuracy of the proposed hybrid forecasting methodology. Additionally, our forecasting results are comparatively better than the benchmark models. Finally, we believe that the proposed forecasting method can be used for other complex financial time data to obtain highly efficient and accurate forecasts.
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A Self-Organizing Topological Multilayer Perceptron for Improving the Prediction of Extreme Values of the PM2.5
(2022-05-12)
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Ana María Gómez Lamus
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Romina Torres
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Rodrigo Salas
Abstract The prediction of air pollutant levels plays an essential role in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter (PM). Even when elevated pollution episodes may be rare, they recirculate within an area where more people may present significant adverse health consequences. Thus, even when they are difficult to predict, pollution peaks prediction of air pollution particulate matter (PM2.5) is a crucial problem to address. Several machine learning (ML) approaches have been used to predict a set of air pollutants using different combinations of predictor parameters. Unfortunately, they are still not enough to generate accurate predictions of extreme values. This paper proposes a new hybrid method that combines the unsupervised learning Self-Organizing Maps with the supervised multilayer perceptron. The proposed method is applied for the prediction of extreme values of PM2.5, using five-year pollution data obtained from nine weather stations located in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves the performance metrics when predicting extreme values of PM2.5.
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Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University
(2022-11-06)
David Orrego Granados
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Jonathan Ugalde
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Rodrigo Salas
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Romina Torres
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The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university.
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Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru
(2024-01-01)
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Eddy Salcedo
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Hasnain Iftikhar
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Aimel Zafar
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Murad Khan
<abstract><p>The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O$ _{3} $ data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O$ _{3} $ forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner.</p></abstract>
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Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique
(2024-08-14)
Wilfredo Meza Cuba
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Juan Carlos Huaman Alfaro
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Hasnain Iftikhar
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The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future.
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Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant
(2025-12-30)
Ronny Ivan Gonzales Medina
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Juan Adriel Carlos Mendoza
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Eduardo José Zuñiga Goyzueta
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Rosa María Morán-Silva
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Optimizing coagulant dosages in Drinking Water Treatment Plants (DWTPs) is critical for reducing operational costs, minimizing chemical waste, mitigating environmental impacts, and ensuring consistent water quality, particularly in resource-constrained settings where conventional jar tests are labor-intensive and poorly suited to real-time demands. This study develops and validates a Random Forest (RF) machine learning model to predict optimal dosages of aluminum sulfate, polyaluminum chloride, and a polymer flocculant at the Miguel de la Cuba Ibarra DWTP in Peru, addressing the need for an efficient, real-time decision support system. Using a historical dataset of 2556 jar tests, a univariate RF model was developed to predict settled water turbidity, tailored to the plant’s typical operational range. The model demonstrated robust predictive performance, achieving a coefficient of determination (R2) of 0.92 during training and 0.76 during validation with unseen data, alongside a Root Mean Square Error (RMSE) of 0.11 NTU and a Mean Absolute Percentage Error (MAPE) of 0.11 in the training phase. Integrated into a digital platform, the model generates real-time NTU ppm dosing curves, providing a practical and responsive tool to enhance operational efficiency for DWTP operators. This work offers a scalable, data-driven solution to improve water treatment processes in resource-limited contexts.
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A new class of median estimators using auxiliary information under PPS sampling: theoretical properties and empirical evaluation
(2026-03-19)
Salman Shah
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Eisa Mahmoudi
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M. Umar Qureshi
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Hasnain Iftikhar
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Paulo Canas Rodrigues
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Analysis of University Mobility and Internationalization in Institutional Accreditation Processes in Higher Education in Peru
(2025-01-01)
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This study examines the interconnections between university mobility, internationalization, and institutional accreditation processes in Peruvian higher education institutions. Using a quantitative, non-experimental cross-sectional design, data were collected from 68 licensed public and private universities through structured surveys administered to international relations and academic quality officials. Secondary data from Peru's National System for the Evaluation, Accreditation, and Certification of Educational Quality (SINEACE) were incorporated to verify accreditation records. Statistical analyses included Shapiro-Wilk normality tests, ANOVA, and Spearman rank correlations using R Studio. Results revealed consistently positive perceptions of internationalization policies across institutions, with no significant differences between public and private universities in academic mobility (F(1,38) = 0.13, p = 0.721). However, weak correlations were found between mobility, policies, resources, and accreditation outcomes ( 0.20), indicating fragmented processes. Accreditation efforts were heavily concentrated at the undergraduate level (92.6%), with minimal representation in master's (5.9%) and doctoral programs (1.5%). Disciplinary segmentation emerged, with public universities leading in engineering and health program accreditation, while private institutions focused on social sciences. The findings suggest that despite progress in establishing internationalization frameworks, operational gaps and lack of synergy persist. This research contributes to understanding higher education quality assurance in Latin America and calls for more integrated approaches that leverage accreditation as a strategic tool for promoting transformative, context-sensitive internationalization rather than merely validating institutional compliance.

López Gonzales, Javier Linkolk

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