Examinando por Autor "Ternero, Rodrigo"
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Ítem Framework for the Strategic Adoption of Industry 4.0: A Focus on Intelligent Systems(MDPI, 2023-10) Serey, Joel; Alfaro, Miguel; Fuertes, Guillermo; Vargas, Manuel; Ternero, Rodrigo; Duran, Claudia; Sabattin, Jorge; Gutiérrez, SebastiánDespite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and artificial intelligence systems. This framework will serve as a conceptual guide in the digital transformation processes toward Industry 4.0. This study involved a systematic literature review of the important methodological proposals and identification of thematic axes, research topics, strategic objectives, challenges, drivers, technological trends, models, and design architectures. In total, 160 articles were selected (120 were published between 2017 and 2022). The results provide insights into the prospects for strategic alignment in the adoption of Industry 4.0. The conceptualization of the framework shows that Industry 4.0 needs strategic adjustments mainly in seven objectives (business model, change mindset, skills, human resources, service level, ecosystem, interconnection, and absorption capacity) derived from 10 thematic axes and 28 research topics. Understanding the strategic adoption of Industry 4.0 and artificial intelligence is vital for industrial organizations to stay competitive and relevant in a constantly evolving business landscape.Ítem National Health Systems and COVID-19 Death Toll Doubling Time(Frontiers Media S.A., 2021-07) Alfaro, Miguel; Muñoz-Godoy, Diego; Vargas, Manuel; Fuertes, Guillermo; Duran, Claudia; Ternero, Rodrigo; Sabattin, Jorge; Gutierrez, Sebastian; Karstegl, NataliaCoronavirus disease 2019 (COVID-19) has placed stress on all National Health Systems (NHSs) worldwide. Recent studies on the disease have evaluated different variables, namely, quarantine models, mitigation efforts, damage to mental health, mortality of the population with chronic diseases, diagnosis, use of masks and social distancing, and mortality based on age. This study focused on the four NHSs recognized by the WHO. These systems are as follows: (1) The Beveridge model, (2) the Bismarck model, (3) the National Health Insurance (NHI) model, and (4) the “Out-of-Pocket” model. The study analyzes the response of the health systems to the pandemic by comparing the time in days required to double the number of disease-related deaths. The statistical analysis was limited to 56 countries representing 70% of the global population. Each country was grouped into the health system defined by the WHO. The study compared the median death toll DT, between health systems using Mood's median test method. The results show high variability of the temporal trends in each group; none of the health systems for the three analyzed periods maintain stable interquartile ranges (IQRs). Nevertheless, the results obtained show similar medians between the study groups. The COVID-19 pandemic saturates health systems regardless of their management structures, and the result measured with the time for doubling death rate variable is similar among the four NHSs. © Copyright © 2021 Alfaro, Muñoz-Godoy, Vargas, Fuertes, Duran, Ternero, Sabattin, Gutierrez and Karstegl.Ítem Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research(MDPI, 2023-02) Serey, Joel; Alfaro, Miguel; Fuertes, Guillermo; Vargas, Manuel; Durán, Claudia; Ternero, Rodrigo; Rivera, Ricardo; Sabattin, JorgeThe purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths. © 2023 by the authors.