The COVID-19 pandemic has had a significant impact on various aspects of society, including economic, health, political, and work-related domains. The pandemic has also caused an emotional effect on individuals, reflected in their opinions and comments on social media platforms, such as Twitter. This study explores the evolution of sentiment in Spanish pandemic tweets through a data analysis based on a fine-tuned BERT architecture. A total of six million tweets were collected using web scraping techniques, and pre-processing was applied to filter and clean the data. The fine-tuned BERT architecture was utilized to perform sentiment analysis, which allowed for a deep-learning approach to sentiment classification. The analysis results were graphically represented based on search criteria, such as “COVID-19” and “coronavirus”. This study reveals sentiment trends, significant concerns, relationship with announced news, public reactions, and information dissemination, among other aspects. These findings provide insight into the emotional impact of the COVID-19 pandemic on individuals and the corresponding impact on social media platforms.

Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..

Metric positional encoding supplies physical distances to Transformers, but its contribution to segmentation performance and spacing robustness remains uncertain. We evaluated whether metric coordinates improve performance, produce a more favorable response to resampling, and affect predictions through supplied spacing. A factorial UNETR comparison combined token-index or millimeter-based coordinates with additive Fourier encoding or 3D RoPE. KiTS23 evaluation used three-fold cross-validation on 391 patients; 117 eligible volumes were resampled to through-plane spacings of 3, 4, and 5 mm. Controls altered supplied spacing independently of image content. The RoPE comparison was repeated after retraining on AMOS22. In KiTS23, metric variants responded to supplied spacing but did not yield a more favorable Surface Dice change under resampling (contrast: −0.0029; 95% CI: −0.0104 to 0.0043). Among 38 eligible AMOS22 cases, metric RoPE retained higher absolute scores at every tested spacing, although its advantage narrowed from 0.0512 at native spacing to 0.0216 at 5 mm. Its baseline-relative response was less favorable (contrast: −0.0241; 95% CI: −0.0296 to −0.0186). These findings support evaluating absolute scores alongside changes from baseline and controls of spacing sensitivity. A less favorable change can coexist with higher absolute performance, conditional on the trained models and retrospective perturbations.

En la actualidad, los chatbots se han convertido en una de las aplicaciones más versátiles de la inteligencia artificial (IA), transformando la manera en que las personas interactúan con la tecnología. Estos sistemas conversacionales, basados en procesamiento de lenguaje natural (PLN) y aprendizaje automático, permiten automatizar tareas, personalizar respuestas y brindar asistencia en tiempo real en diversos sectores, como el comercio, la salud y la educación. En el ámbito educativo, los chatbots están revolucionando la enseñanza al ofrecer soporte accesible, aprendizaje adaptativo y retroalimentación instantánea. Su capacidad para guiar a los estudiantes en procesos de aprendizaje estructurados, responder preguntas frecuentes y reforzar conceptos clave los convierte en herramientas innovadoras que complementan la labor docente y fomentan la autonomía del estudiante. El desarrollo de estos chatbots implica la definición de objetivos pedagógicos, el diseño de flujos conversacionales basados en PLN y la integración con plataformas de mensajería instantánea. Este trabajo presenta los resultados de la implementación de chatbots en la enseñanza de Ingeniería, específicamente en Programación Orientada a Objetos (POO) y Estructuras de Datos en el programa de Ingeniería de Sistemas de la Universidad del Magdalena. Se detalla la aplicación de una metodología propia para su desarrollo, abordando tanto los aspectos técnicos como pedagógicos. A través de encuestas, se evaluó la experiencia de los estudiantes, evidenciando que el uso de chatbots les permitió acceder de manera autónoma a conceptos clave, cuestionarios y ejemplos prácticos, reduciendo la necesidad de intervención directa de los docentes. Los resultados reflejan un impacto positivo en el aprendizaje y refuerzan el potencial de la inteligencia artificial en la educación, abriendo nuevas oportunidades para su aplicación en la enseñanza de ciencias de la computación y otras disciplinas.