http://hdl.handle.net/20.500.11799/40910 2026-04-10T11:53:32Z 2026-04-10T11:53:32Z Colín, Noé Armando Algara, Marcos http://hdl.handle.net/20.500.11799/143862 2026-02-28T01:52:22Z 2024-09-12T00:00:00Z

La agenda 2030 en la UAEMéx desde el ODS 2: Hambre Cero Colín, Noé Armando; Algara, Marcos La Universidad Autónoma del Estado de México (UAEMéx) se compromete con la Agenda 2030, enfocándose en el segundo Objetivo de Desarrollo Sostenible (ODS): erradicar el hambre y promover la seguridad alimentaria. La institución contribuye a este objetivo a través de diversas iniciativas, como la generación de empleo, el otorgamiento de becas, la provisión de alimentación gratuita o accesible en sus cafeterías, y la implementación de huertos urbanos educativos. Además, ofrece carreras relacionadas con la agricultura y nutrición y realiza investigaciones para abordar problemas alineados con este ODS. Estos esfuerzos buscan no solo apoyar a la comunidad universitaria sino también inspirar a las nuevas generaciones a contribuir a la sostenibilidad global. Articulo de investigación sobre la Agenda 2030 en la UAEMéx

2024-09-12T00:00:00Z Reynold, Osuna-González Guillermo, De Ita Luna Rosa María Valdovinos Rosas, / Yagul, Pedraza-Pérez http://hdl.handle.net/20.500.11799/143770 2026-02-27T04:46:08Z 2025-10-09T00:00:00Z

Structured RDF Dataset for Genomic Feature Extraction and Detection of Biotechnological Microorganisms of the Burkholderia Genus Reynold, Osuna-González; Guillermo, De Ita Luna; Rosa María Valdovinos Rosas, /; Yagul, Pedraza-Pérez The current dataset has been built from 200 genomes of Burkholderia genus microorganisms in GenBank Flat File (GBFF) format, with information obtained from the National Center for Biotechnology Information (NCBI). Python scripts were developed to automate the extraction of genomic fea- tures such as coding sequences (CDS) and different types of RNA, extracting the descriptive information of each one. The extracted features were transformed into RDF (Resource Description Framework) format as initial knowledge graphs with Turtle syntax, and later, they were merged into a unified knowledge graph (KG) to facilitate their access via queries written in the SPARQL system. This dataset is publicly available to researchers through the Mendeley repository, allowing them to perform com- plex searches, looking for composite features coming from the dataset’s source organisms. Choosing the RDF format for the knowledge graph also promotes interoperability with other biological datasets that use the same structure and are widely accessible through SPARQL Endpoints. Artículo cientifico

2025-10-09T00:00:00Z Zepeda-Martínez, Diana Angélica, Guzmán Ponce David Joaquín Delgado Hernández, / Rosa María Valdovinos Rosas, / http://hdl.handle.net/20.500.11799/143760 2026-02-27T04:13:22Z 0017-06-20T00:00:00Z

Pattern Recognition in Road Safety: Uncovering the Latent Causes of Accidents on Mexico’s Federal Highways Zepeda-Martínez, Diana; Angélica, Guzmán Ponce; David Joaquín Delgado Hernández, /; Rosa María Valdovinos Rosas, / Land transportation in Mexico plays a crucial role in ensuring connectivity and facilitating the mobility of both people and commodity. Nevertheless, this sector confronts substantial challenges, predominantly related with road accidents. Understanding the factors that contribute to these accidents is essential to developing and implementing effective safety strategies to reduce their frequency and severity. This research uses two unsupervised methods: latent Dirichlet allocation analysis (LDA) and the K-means algorithm, to identify the underlying factors responsible for road accidents in Mexico. LDA uncovers latent thematic structures in accident reports, revealing patterns in textual descriptions, and K-means identifies groups of accidents that share common attributes. The study period is from the years 2015 and 2019. The results suggest that traffic accidents are significantly influenced by a combination of factors such as driver behavior, road conditions, weather conditions and weather patterns. Artículo científico

0017-06-20T00:00:00Z Angélica, Guzmán Ponce Rosa María Valdovinos Rosas, / Jacobo Leonardo, González-Ruiz Iván, Francisco-Valencia José Raymundo, Marcial-Romero http://hdl.handle.net/20.500.11799/143759 2026-02-27T04:09:32Z 2024-07-01T00:00:00Z

Exploring COVID-19 Trends in Mexico During the Winter Season with Explainable Artificial Intelligence (XAI) Angélica, Guzmán Ponce; Rosa María Valdovinos Rosas, /; Jacobo Leonardo, González-Ruiz; Iván, Francisco-Valencia; José Raymundo, Marcial-Romero COVID-19 has become the most significant pandemic in recent years. Today, Mexico has recorded millions of infections and deaths since the pandemic started. Around the world, machine learning methods have been used to understand, predict or develop strategies to manage the virus and the pandemic. Although algorithms provide good results, it is necessary to understand why a model makes specific predictions with a particular data set. To explain this question, we apply Explainable Artificial Intelligence (XAI) in this paper. With this, it is possible to understand the characteristics that influence the model decisions when denoting between deaths and survivors. As a case of study, the positive cases detected during the winter season of 2020-2021 and 2021-2022 were considered. In this season, respiratory diseases increased considerably, and in the study period, they influenced the increase in positive cases and the spread of COVID-19. Preliminary results suggest that age is essential when using a Random Forest model. Preliminary results suggest that age is essential when determining the prognosis of a patient infected by COVID-19 in winter seasons. Artículo científico

2024-07-01T00:00:00Z