Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

One of the big threats that human beings confront at present is the risk of extreme weather phenomena caused by climate change. The state of Guerrero is one of the eight states that is most vulnerable to climate change in Mexico. Extreme events due to weather have brought death, destruction, and shortages to the state. With the objective of safeguarding and maintaining quality of life to victims, we present the creation of the Humanitarian Safeguard and Storage Network, or Red de Almacenamiento y Resguardo Humanitario (RARH) in Spanish. Every network node corresponds to a municipality in Guerrero and is evaluated according to its location and level of marginalization. Through the p-median problem, networks of 1, 3, and 12 Humanitarian Safeguard and Storage Nodes, or Nodos de Almacenamiento y Resguardo Humanitario (NARH) in Spanish, were obtained. During the first instance, the minimum distance traveled to the city governments that were not considered as possible sites were evaluated, and during a second evaluation a level of marginalization was added as an additional variable to consider. Feasible solutions were obtained in 67% of the RARH evaluated.

The economic situation and dynamics has led countries to develop policies and strategies to promote their respective developments. Part of them is composed by the creation of Employment Sources through entrepreneurship. The formation of new entrepreneurs in Mexico can begin or be strengthened through higher education institutions. University incubators provide tools to those people, generally students who wish to create their own business, which help them to make business decisions that contribute to the development of their environment.In this qualitative and multi-case phenomenological study, the best practices in the incubation process in the Business Incubators of Public Educational Institutions of Higher Education in Hidalgo state (BIPEI) were identified, the most important item in classifying whether or not the incubators have good practices was also identified, and through a hierarchical grouping algorithm, three groups of incubators with similarities between them were identified as well.

The inventory control and design of a warehouse in the metal-mechanical company in southern Tamaulipas aims to optimize the inventory control and planning process in a metal-mechanical industrial maintenance services company, applying a non-experimental, transversal, quantitative, and qualitative descriptive approach and purposeful, with the ABC inventory classification method, which is obtained by carrying out a diagnosis of the company's inventory control, categorization of materials based on their rotation of use through a Pareto analysis, analysis of the rotation of type A, B, and C materials, respectively and establishing safety stocks, finally making the proposal of new policies for inventory control as well as physical distribution in the warehouse according to the ABC classification.

Irish Sign Language (ISL) stands as a preferred mode of communication used by the deaf and hard-of-hearing community in Ireland. With its unique grammar, syntax, and lexicon, ISL plays a pivotal role in facilitating communication for thousands of individuals, reflecting centuries of cultural heritage and linguistic development. An estimated 5,000 Deaf individuals utilize ISL, with an additional 40,000 hearing individuals, spanning from regular to occasional users, also engaging with Irish Sign Language. Despite its cultural and linguistic importance, ISL faces numerous challenges in terms of technical accessibility. The exclusion of sign languages from modern language technologies places the deaf or hard-of-hearing individuals at a disadvantage, exacerbating the barrier to human-to-human communication and further marginalizing an already under-resourced linguistic subset. This necessitates innovative approaches and technologies to enhance its utilization and promote inclusivity. This research is concerned with evaluating the performance of various deep neural network architectures in the recognition of sign language by utilizing and evaluating state-of-the-art architectures for the recognition of sign language, in particular Irish Sign Language. This research is part of research progress towards the development of an automatic computational annotation system for Irish Sign Language. Notably, the Densenet architecture performed better than other architectures in ISL alphabet recognition with an average accuracy of 99\%. Our findings illustrate the potential of sophisticated deep neural networks to overcome constraints relating to the scarcity of ISL-specific data. This contribution provides the potential to further develop natural language processing tools and technologies for Irish Sign Language, which may alleviate the lack of technical communicative accessibility and inclusion for the deaf and hard-of-hearing community in Ireland.

Cultural and artistic heritage has been depleted by different human and environmental factors, eliminating the vestiges of ancestral cultures. However, Artificial Intelligence has generated new alternatives for the conservation and restoration of the legacy of past generations, whose impact on today's society is reflected in the knowledge that gives rise to new knowledge.

In the field of deep learning, the identification of optimal neural architectures requires not only profound expertise but also a substantial investment of time and effort in the evaluation of the outcomes generated by each proposed model. In this study, we introduce a Single Agent Neuroevolution algorithm, based on the Hill Climbing algorithm for Neural Architecture Search, named Dynamic Neural Network Optimization (DyNNO). This approach focuses on the evaluation of the performance of neural networks optimized for function approximation. Additionally, we have explored the minimization of the number of neurons within the neural network structures. The results demonstrated the feasibility of using this algorithm to automate the neural architecture search process. Furthermore, the reduction in the number of parameters improved the generalization capability of the networks. The findings also suggest that mutation in activation functions can be a factor to explore in achieving a more effective reduction in error rates

This study introduces a novel approach for quantifying and analyzing canine movement through 2D video recordings, aiming to elucidate the relationship between dog movement patterns and their trainability responding to abandonment stimuli. Utilizing an advanced method based on deep learning, we captured and estimated 24 3D-markers of nine Siberian Husky dogs performing the 3-minute task in their daily environments. From the markers captured over the skeletons, the average amount of relative motion was calculated for three sub-phases: “initial”, “intermediate” and “final”, for the two trials performed on each dog. The core objective was to determine how specific motion patterns correlate with different levels of training across sub-phases. Preliminary findings suggest that -the second subphase is more associated with the levels of trainability based on the dispersion between the two trials-, as an indicator of a dogs’ potential for trainability. This research not only contributes to the understanding of canine motion patterns during this specific test but also offers a scalable and non-invasive tool for professionals and dog owners to enhance training outcomes based on individual motion profiles.

According to the European Commission, “Early school leaving is linked to unemployment, social exclusion, poverty, and poor health” (European Commission, 2024). Due to the importance of reducing school drop-out rates, several authors have analyzed this phenomenon. Before the boom of Artificial Intelligence, instead of using or implementing programming methods, researchers applied written formulas and rudimentary graphical visualizations to predict academic completion and the main factors behind it; besides, the development of Machine Learning (ML) algorithms has enhanced the precision and performance of an ample variety of investigations including the educational field. In this paper, we use a dataset of undergraduate-level students at the Universidad Nacional Autónoma de Mexico (UNAM) to predict timely academic completion. We use seven common ML algorithms and propose a novel algorithm based on students' similarities according to the most relevant features in common. This algorithm shows a higher precision than some traditional categorical ML algorithms. This innovative way to predict academic success can support educators, pedagogues, and policymakers make better decisions at UNAM.

The exponential growth of social networking platforms has led many researchers to focus on ways of mining information from them. In this paper, we will use texts from social media in conjunction with techniques of Natural Language Processing to design a system that helps business organizations to identify polarity indicators from customer feedback. In this paper, we analyze tweets related to perceptions of an airline company, and detect the polarity of such tweets, using preprocessing and processing techniques common to the area, and to later incorporate the same techniques, in  a new methodology that consists of the incorporation of lexical resources (LR) and metaclassifiers to support the said task, thereby achieving a decision system with greater precision. In the present work, relevant results are reported in the area of NLP, making use of pre-processing and processing techniques known within the area, the main idea is to find the best classification scenario and increase the classification precision, for this the incorporation of lexical and metaclassifier resources was carried out.

The study presents the results of an investigation into the information behavior of researchers as users of Geographic Information Systems (GIS) at the Institute of Geography of the National Autonomous University of Mexico. These users hold the first position in usage level compared to other users, namely, academic technicians and students. Furthermore, the study object of these researchers is geographical space, where the map constitutes an elemental information source. The problem statement for this research followed the questions: What is the profile of researchers as users of Geographic Information Systems? How is the information behavior of researchers as Geographic Information Systems users at the Institute of Geography of the National Autonomous University of Mexico? What are the variables of information behavior while using Geographic Information Systems software? In which ways library personnel may assist researchers as Geographic Information Systems users? The study applied a qualitative methodology, a focus group technique.