Rodríguez Aguilar, Román

Main Affiliation

Facultad de Ciencias Económicas y Empresariales - CampCM

Preferred name

Official Name

Rodríguez Aguilar, Román

ORCID

0000-0002-6496-4453

Researcher ID

AAO-6158-2020

Scopus Author ID

56717663000

Now showing 1 - 10 of 96

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Corporate Social Responsibility and Financial Performance: Evidence from Public Companies Listed on the Mexican Stock Exchange
(Springer Nature Switzerland, 2026)
Jiménez-Casillas, Laura Elena
;
Velázquez-Salazar, Marisol
;
Rodríguez Aguilar, Román
The research on how corporate social responsibility (CSR) affects the financial performance (FP) of companies is limited, especially in the case of Mexico. There is a lack of studies that measure the financial impact of socially responsible strategies in Mexican companies. This study introduces an innovative approach by proposing a hybrid model that combines three quantitative methodologies: principal component analysis (PCA), cluster analysis, and an ordered logit model. The goal is to assess the impact of sustainability practices on financial performance. The study considers financial data from the past decade for 91 companies listed on the Mexican Stock Exchange. The results indicate that CSR practices have a positive effect on FP. The proposed hybrid model serves as a valuable methodological tool for evaluating the impact on FP of Mexican public companies listed on the BMV and holding the ESR seal granted by CEMEFI (Mexican Center for Philanthropy). This approach will help monitor the evolution of FP through the adoption of various CSR-related practices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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The Sustainable Industrial Site of the Future Based on Energy Efficiency, Renewable Energy, Artificial Intelligence, and Energy Policy
(Springer Nature Switzerland, 2026)
M’Baye, Abaubakry
;
Rodríguez Aguilar, Román
The world’s high emissions of carbon dioxide are the primary cause of climate change and its harmful consequences, such as floods, storms, and droughts. According to the Intergovernmental Panel on Climate Change, human-induced warming reached approximately 1 °C above preindustrial levels in 2017, increasing at 0.2 °C per decade. The industrial sector contributes greatly to this global warming, as it is among the largest energy consumers. Indeed, industrial processes require a lot of energy, for example, for cooling, venting, pumping, producing steam, and hot water. This chapter focuses on industrial sites and aims to find a global solution to accelerate their transition toward sustainability. The study defines a clear and robust framework to obtain a sustainable industrial site and to keep it sustainable. Technologies (both well-proven technologies and new technologies) and policies are examples of tools used during the research. The method used is also supported by the current state-of-the-art on carbon emissions reduction and energy savings. The framework is based on four levers: energy efficiency, artificial intelligence, renewable energy, and energy policy and management. This framework can be used by any organization, stakeholders, and policymakers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Preface: Intelligent Computing and Optimization : Proceedings of the 6th International Conference on Intelligent Computing and Optimization 2023 (ICO2023) : Volume 4
(Springer, 2023)
Vasant, Pandian
;
Weber, Gerhard-Wilhelm
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Arefin, Mohammad Shamsul
;
Rodríguez Aguilar, Román
;
Panchenko, Vladimir
The sixth edition of the International Conference on Intelligent Computing and Optimization (ICO’2023) was held during April 27–28, 2023, at G Hua Hin Resort and Mall, Hua Hin, Thailand. The objective of the international conference is to bring the global research scholars, experts and scientists in the research areas of intelligent computing and optimization from all over theworld to share their knowledge and experiences on the current research achievements in these fields. This conference provides a golden opportunity for global research community to interact and share their novel research results, findings and innovative discoveries among their colleagues and friends. The proceedings of ICO’2023 is published by SPRINGER (in the book series Lecture Notes in Networks and Systems) and indexed by SCOPUS. ©2023 Springer, ©2023 The authors.
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Preface: Intelligent Computing and Optimization : Proceedings of the 6th International Conference on Intelligent Computing and Optimization 2023 (ICO2023) : Volume 3
(Springer, 2023)
Vasant, Pandian
;
Weber, Gerhard-Wilhelm
;
Arefin, Mohammad Shamsul
;
Rodríguez Aguilar, Román
;
Panchenko, Vladimir
The sixth edition of the International Conference on Intelligent Computing and Optimization (ICO’2023) was held during April 27–28, 2023, at G Hua Hin Resort and Mall, Hua Hin, Thailand. The objective of the international conference is to bring the global research scholars, experts and scientists in the research areas of intelligent computing and optimization from all over theworld to share their knowledge and experiences on the current research achievements in these fields. This conference provides a golden opportunity for global research community to interact and share their novel research results, findings and innovative discoveries among their colleagues and friends. The proceedings of ICO’2023 is published by SPRINGER (in the book series Lecture Notes in Networks and Systems) and indexed by SCOPUS. ©2023 Springer, ©2023 The authors.
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Short-term generation planning by primal and dual decomposition techniques
(Universidad Nacional de Colombia, Facultad de Minas, 2015)
Marmolejo Saucedo, José Antonio
;
Rodríguez Aguilar, Román
This paper addresses the short-term generation planning (STGP) through thermoelectric units. The mathematical model is presented as a Mixed Integer Non Linear Problem (MINLP). Several works on the state of art of the problem have revealed that the computational effort of this problem grows exponentially with the number of time periods and number of thermoelectric units. Therefore, we present two alternatives to solve a STGP based on Benders’ partitioning algorithm and Lagrangian relaxation in order to reduce the computational effort. The proposal is to apply primal and dual decomposition techniques, which exploit the structure of the problem to reduce solution time by decomposing the STGP into a master problem and a subproblem. For Benders’ algorithm, the master problem is a Mixed Integer Problem (MIP) and for the subproblem, it is a Non Linear Problem (NLP). For Lagrangian relaxation, the master problem and the subproblem are MINLP. The computational experiments show the performance of both decomposition techniques applied to the STGP. These techniques allow us to save computation time when compared to some high performance commercial solvers. ©Universidad Nacional de Colombia: Facultad de Minas, Los autores.
Scopus© Citations 2 29
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El coeficiente de Hurst y el parámetro α-estable para el análisis de series financieras Aplicación al mercado cambiario mexicano
(Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración, 2014)
Rodríguez Aguilar, Román
Este trabajo aborda la utilidad de estimar, previo a cualquieranálisis, el parámetro de la distribución -estable y el coeficientede Hurst para una serie financiera en periodos de altavolatilidad. Mediante la estimación del coeficiente de Hurst yel parámetro se busca explorar la violación de dos grandessupuestos en la modelación de series financieras: suponer quelas series presentan una distribución normal y que los rendimientossucesivos son independientes; asimismo, se analiza elcaso del tipo de cambio Fix peso-dólar en México en el periodo1992-2011. Uno de los principales resultados es la identificaciónde características fractales y colas pesadas en la seriepara algunos periodos en magnitudes diferenciadas; dichasdiferencias se acentúan en periodos de crisis. Caracterizar laserie mediante estos parámetros a través de un índice permitirámejorar la toma de decisiones sobre el tipo de análisis que esmetodológicamente correcto aplicar en una ventana de tiempoespecífica, ya sea para valuación de activos o para la gestiónde riesgos. ©2012 Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración © El autor.
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Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System
(2018)
Rodríguez Aguilar, Román
;
Marmolejo Saucedo, José Antonio
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Vasant, Pandian
To propose a satisfaction indicator of users of health services affiliated to the Social Protection System in Health (SPSS). Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A machine-learning model based on Logistic Models and Principal Components was developed to estimate a satisfaction index. The survey data collected for the “SPSS 2014 User’s Satisfaction Study” was used, considering a sample of 28,290 users. The proposed model shows, in general, the positive perception of quality of health services (national average 0.0756). There are factors statistically significant that influence these results, the good perception of infrastructure (OR:2.12; CI 95%:1.9–2.36); the gratuity of the service provided (OR:1.98; CI 95%: 1.42–2.76); and full medicines supply (OR:1.81; CI 95%:1.91–2.36). The proposed index can be used as an indicator for improving health care quality of the population covered by the SPSS. © 2019, Springer Nature Switzerland AG.
Scopus© Citations 1 1 13
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Sales predictive analysis for improving supply chain drug sample
(Elsevier BV, 2025)
Téllez-Ballesteros, Susana Casy
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Torres-Mendoza, Ricardo
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Marmolejo-Saucedo, José Antonio
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Rodríguez Aguilar, Román
The delivery of drug samples allows increasing sales of pharmaceutical products [6]. However, we discovered some problems that can be improved in the supply chain that delivers drug samples (used for the treatment of excess glucose). Databases were integrated; then we apply data extraction and transformation; and finally we apply multiple regression analysis to explain drug sales. The first analysis evaluates the integration of regional data and the second analysis refers to data dis-aggregated by region. We identify the region with the greatest impact on sales and the impact of the delivery of drug samples in the Mexican market. ©The authors ©Elsevier ©Procedia Computer Science.
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Modeling responsible technologies using multiagent system for climate crisis and sustainability
(Elsevier, 2025)
Nanda, Pragyan
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Sahoo, Sipra
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Rodríguez Aguilar, Román
;
Marmolejo-Saucedo, José Antonio
;
Behera, Itishree
This chapter delves into the dynamic landscape of responsible technologies and their crucial role in addressing the climate crisis and advancing sustainability. It highlights the need to understand the intricate connections between technology adoption, environmental impact, and societal behavior across various dimensions of sustainability, including environmental, social, economic, cultural, technological, political, ethical, health, educational, and adaptive aspects. The chapter introduces the concept of responsible technologies and their key principles, emphasizing real-world experimentation limitations and simulation advantages, particularly through a multiagent system (MAS). It explores different modeling approaches, focusing on the suitability of MAS for studying responsible technologies, along with considerations in its implementation. A step-by-step guide is provided for constructing a tailored MAS model for responsible technologies, incorporating real-world data and environmental factors. Findings and insights from MAS simulations are analyzed, shedding light on the implications of responsible technology adoption across these sustainability dimensions. The chapter also underscores the pivotal role of a MAS in comprehensively modeling responsible technologies and invites further research exploration in this expansive domain, serving as a comprehensive guide for researchers, policymakers, and stakeholders committed to leveraging responsible technologies for climate change mitigation and sustainability advancement. ©The authors ©Elsevier.
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Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators
(MDPI, 2024)
Rodríguez Aguilar, Román
;
Marmolejo Saucedo, José Antonio
;
Köse, Utku
The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration. ©The authors ©MDPI.
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Rodríguez Aguilar, Román

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