Rodríguez Aguilar, Román
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Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
Rodríguez-Aguilar, Román
Rodríguez-Aguilar, R.
Aguilar, R.R.
Rodriguez-Aguiler, R.
Rodríguez, R.
Roman, R.A.
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ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

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The mahalanobis distance between the hurst coefficient and the Alpha-Stable parameter: An early warning indicator of crises

2016 , Rodríguez Aguilar, Román , Cruz-Aké, Salvador , Venegas-Martínez, Francisco

The Hurst coefficient and the alpha-stable parameter are useful indicators in the analysis of time series to detect normality and absence of self-similarity. In particular, when these two features met simultaneously the series is driven by white noise. This paper is aimed at developing an index to measure the degree to which a time series departs from white noise. The proposed index is built by using the principal component analysis of the Mahalanobis distances between the Hurst coefficient and the alpha-stable parameter from theoretical values of normality and absence of self-similarity. The proposed index is applied to examine the Mexican Peso exchange rate against the US Dollar. The distinctive characteristic of the index is that it can be used as an early warning indicator of crises, as it is shown for the Mexican case. © 2010, Academic Publications, Ltd.; https://ijpam.eu ©The authors.

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Catastrophic Health Spending by COVID-19 in the Mexican Insurance Sector

2024-01-01 , Domínguez-Gutiérrez, Ulises , Rodríguez Aguilar, Román

The COVID-19 pandemic that the world has been suffering for 3 years has generated major impacts worldwide, both in public health systems and in the private insurance industry. The high costs of care derived from cases with complications have likewise generated a great impact on the private insurance industry. In the case of Mexico, the mortality rates observed are among the first places, in addition to generating a great impact on private insurance. This work deals with the measurement of the impact of catastrophic expenses derived from COVID-19 in an insurance company; using a set of machine learning models, the key variables in the estimation of patients with potential catastrophic expenses were determined. The results show that the estimated classification model has a positive performance in addition to allowing the identification of the main risk factors of the insured as well as their potentially catastrophic impact on insurance companies.© 2024 Springer Nature

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Backbone Distribution Network Design for the Mexican Automotive Industry

2020 , Retana-Blanco, Brenda , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román , Pedraza-Arroyo, Erika

The logistics network of an automotive company in Mexico was analyzed to propose a better logistics network in the country to improve delivery times to customers. The analysis was conducted with Greenfield Analysis and Network Optimization. Taking into account the information given by the company, it was possible to obtain optimal scenarios for better operation, which involved the construction of distribution centers in the Hidalgo state in Mexico. © Springer Nature Switzerland AG 2020.

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Machine learning models in health prevention and promotion and labor productivity: A co-word analysis

2024 , Domínguez Miranda, Sergio Arturo , Rodríguez Aguilar, Román

Objective: The objective of this article is to carry out a co-word study on the application of machine learning models in health prevention and promotion, and its effect on labor productivity. Methodology: The analysis of the relevant literature on the proposed topic, identified in the last 15 years in Scopus, is considered. Articles, books, book chapters, editorials, conference papers and reviews refereed publications were considered. A thematic mapping analysis was performed using factor analysis and strategy diagrams to derive primary research approaches and identify frequent themes as well as thematic evolution. Results: The results of this study show the selection of 87 relevant publications with an average annual growth rate of 23.25% in related production. The main machine learning algorithms used, the main research approaches and key authors, derived from the analysis of thematic maps, were identified. Conclusions: This study emphasizes the importance of using co-word analysis to understand trends in research on the impact of health prevention and promotion on labor productivity. The potential benefits of using machine learning models to address this issue are highlighted and anticipated to guide future research focused on improvements in labor productivity through prevention and promotion of health. Originality: The identification of the relationship between work productivity and health prevention and promotion through machine learning models is a relevant topic but little analyzed in recent literature. The analysis of co-words allows us to establish the reference point of the state of the art in this regard and future trends.

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An Asset Index Proposal for Households in Mexico Applying the Mixed Principal Components Analysis Methodology

2021 , Rodríguez Aguilar, Román , DelaTorre-Diaz, Lorena

The development of assets indices has grown as an alternative to measure wealth from different generations in the evaluation of social mobility. A proposal of the development of an asset index is presented using the GSVD-based mixed principal components analysis (PCAMix package in R). The contribution rests in the combination of both numerical and categorical data and the integration of the simultaneous effect of these variables in the index. It was used in profiling the Mexican households according to the information from the 2018 National Household Income and Expenditure and the determination of the Gini coefficient to evaluate the inequality of distribution at the state level. Results show a high level of disparity in the distribution of assets with only 0.01% of the households possessing 40% or more of the assets included in the index, being the southern region where greatest challenges for ascending social mobility.© Springer Nature

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Machine learning models in health prevention and promotion and labor productivity: A co-word analysis

2024 , Sergio Arturo Dominguez Miranda , Rodríguez Aguilar, Román

Objective: The objective of this article is to carry out a co-word study on the application of machine learning models in health prevention and promotion, and its effect on labor productivity. Methodology: The analysis of the relevant literature on the proposed topic, identified in the last 15 years in Scopus, is considered. Articles, books, book chapters, editorials, conference papers and reviews refereed publications were considered. A thematic mapping analysis was performed using factor analysis and strategy diagrams to derive primary research approaches and identify frequent themes as well as thematic evolution. Results: The results of this study show the selection of 87 relevant publications with an average annual growth rate of 23.25% in related production. The main machine learning algorithms used, the main research approaches and key authors, derived from the analysis of thematic maps, were identified. Conclusions: This study emphasizes the importance of using co-word analysis to understand trends in research on the impact of health prevention and promotion on labor productivity. The potential benefits of using machine learning models to address this issue are highlighted and anticipated to guide future research focused on improvements in labor productivity through prevention and promotion of health. Originality: The identification of the relationship between work productivity and health prevention and promotion through machine learning models is a relevant topic but little analyzed in recent literature. The analysis of co-words allows us to establish the reference point of the state of the art in this regard and future trends.

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A Timetabling Application for the Assignment of School Classrooms

2019 , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román

In this manuscript a group of students from an university of Mexico develop a user-friendly university timetabling tool based on a spreadsheet and using Open-Solve optimization software. The developed tool uses 0–1 integer programming to maximize the number of classes with their respective teacher allocated to a classroom in a certain time schedule. It does not solve only one specific case, the user can introduce each semester’s specific information to the spreadsheet and the tool will automatically generate a schedule that maximizes the number of classes assigned using the given time and classroom resources. Each semester the available times and the teacher can be changed for each class according to the needs for that specific semester. The user does not need to be someone who understands linear programming. The tool is developed on a user-friendly way so that the staff of the school can use it without help from the developers. In this paper we will show how this tool was developed for a smaller example and how it works with a real case. © 2020, Springer Nature Switzerland AG.

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Household Expenditure in Health in Mexico, 2016

2019 , Rodríguez Aguilar, Román , Ramírez Pérez, Héctor Xavier

The aim of this research is to evaluate the financial protection of public health insurance by analyzing the percentage of households with catastrophic expenditure in health (HCEH) in Mexico and its relationship with the condition of poverty, the state, the condition of insurance, and the items of health expenditure. A special emphasis was placed on the poorest households (income quintile I). Method: The National Household Income and Expenditure Survey 2002–2016 was used to estimate the percentage of HCEH. The analysis was carried out with Stata-SE 12. Results: in 2016 there was 2.13% of HCEH (1.82–2.34%, N = 657,474). Conclusions: the percentage of HCEH decreased in recent years, although in 2016 it increased slightly, improving financial protection in health. This decrease seems to have stagnated, maintaining inequities in access to health services, especially in the rural population without affiliation to any health institution. © 2020, Springer Nature Switzerland AG.

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Demand prediction using a soft-computing approach : a case study of automotive industry

2020 , Salais-Fierro, Tomás Eloy , Saucedo-Martínez, Jania , Rodríguez Aguilar, Román , Vela-Haro, Jose Manuel

According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.

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Correction to: Data Analysis and Optimization for Engineering and Computing Problems

2020 , Vasant, Pandian , Litvinchev, Igor , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román , Martínez Ríos, Félix Orlando

This book was inadvertently published without updating the following (or with the following error) © Springer Nature Switzerland AG 2020

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