Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

Worldwide, COVID-19 coronavirus disease is spreading rapidly in a second and third wave of infections. In this context of increasing infections, it is critical to know the probability of a specific number of cases being reported. We collated data on new daily confirmed cases of COVID-19 breakouts in: Argentina, Brazil, China, Colombia, France, Germany, India, Indonesia, Iran, Italy, Mexico, Poland, Russia, Spain, U.K., and the United States, from the 20th of January, 2020 to 28th of August 2021. A selected sample of almost ten thousand data is used to validate the proposed models. Generalized Extreme-Value Distribution Type 1-Gumbel and Exponential (1, 2 parameters) models were introduced to analyze the probability of new daily confirmed cases. The data presented in this document for each country provide the daily probability of rate incidence. In addition, the frequencies of historical events expressed as a return period in days of the complete data set is provided.

Software design is a difficult concept to define when it comes to educational technology. This is reflected in research related to the design, creation, and development of educational technology platforms. Therefore, it was decided to define the concept of software design using the conceptual cartography method through seven different axes of analysis. As a result, it was possible to identify the different conceptual components, as well as the semantic relationships of software design with other similar concepts in the field of educational technology. The need for standardization in the use and application of techniques and models for the development of educational technology platforms is mentioned. Finally, it is proposed that future work include the development of a software tool that allows the creation of platforms considering both technical and pedagogical aspects.

This article addresses the challenges and opportunities that higher education institutions in Mexico face when seeking international accreditation of their programs. educational programs in Information Technology (IT). Using an inclusive approach and considering the integration of artificial intelligence (AI) tools, the study is based on a qualitative synthesis and quantitative analysis of the responses obtained from a form addressed to various educational institutions. The results highlight the importance of social inclusion in the internationalization of educational programs, the challenges anticipated by institutions, and the best practices to ensure compliance with the quality criteria required by international accreditation bodies. Additionally, the ethical and practical implications of using AI in the educational process are explored. The findings underscore the need for inclusive policies and the potential of AI to transform higher education.