Ontologies are widely used as a tool for representing knowledge in Artificial Intelligence more specifically in qualitatively knowledge representation and reasoning, for example, to represent concepts and their relationships. On the other hand, virtual reality has several applications in different fields: such as in medical systems, computer-aided design and education as a virtual learning environment. In both cases, qualitative representations are necessary to perform any qualitative reasoning task. In this paper, we see VRML and Java 3D. Which are formal languages are used to describe objects in 3D. We analyze the similarities between them to define an application ontology with the aim to represent virtual reality environments, independently of the programming language. Then a spatial ontology is defined to describe topological, directional and metric relation which can be used to describe the basic operations in a 3D scene to build a complex environment. Finally, these two ontologies are seen that can be constructed independently but integrated together whether needed.

BackgroundIn order to address the COVID-19 pandemic, health systems have used all their resources, including health care workers in training. Knowing the insights of these workers is of the utmost importance to generate adequate educative/political /administrative strategies. Methods An anonymous cross-sectional online survey was made by the General Directorate of Quality and Health Education in Mexico, in a convenience sample of 6,020 participants who belong to personnel in training for the health area, which included practitioners and professional technologists, undergraduate doctors, nursing, and residents in several specialties.ResultsDifferent positive and negative feelings were identified by the health workers who participated in facing this health emergency; emphasizing elements such as the need for Personal Protection Equipment (PPE) that, when deficient or lacking, generate concerns that raise questions about the medical/epidemiological attention to the pandemic. Based on an analysis of feelings, 8 main feelings were identified, which by frequency of appearing were: distrust(24.83%), fear(21.97%), sadness(12.45%), anticipation(11.65%), anger(10.71%), disgust(9.69%), joy(4.97%) and surprise(3.72%) which influence health workers in training and their wrok performance day to day.Concerning their positive and negative evaluation of their experience facing this health emergency, 13.83% of participants had a positive perception about participating in this kind of health emergency to support the country, 49.94% showed a negative evaluation, and 36.23% kept a neutral evaluation about their participation.ConclusionsThe health workers in training in Mexico gave a negative evaluation of the management of the health emergency. Distrust as a response to the absence of timely information from the education/health institutions, as well as concern about lack of personal protection equipment/inputs, are the main conflicts reported. We must establish a credible globally relevant continuity plan for the education of health care personnel in training, facing emergencies and disasters, so that next time we are properly prepared.

BackgroundIn order to address the COVID-19 pandemic, health systems have used all their resources, including health care workers in training. Knowing the insights of these workers is of the utmost importance to generate adequate educative/political /administrative strategies. Methods An anonymous cross-sectional online survey was made by the General Directorate of Quality and Health Education in Mexico, in a convenience sample of 6,020 participants who belong to personnel in training for the health area, which included practitioners and professional technologists, undergraduate doctors, nursing, and residents in several specialties.ResultsDifferent positive and negative feelings were identified by the health workers who participated in facing this health emergency; emphasizing elements such as the need for Personal Protection Equipment (PPE) that, when deficient or lacking, generate concerns that raise questions about the medical/epidemiological attention to the pandemic. Based on an analysis of feelings, 8 main feelings were identified, which by frequency of appearing were: distrust(24.83%), fear(21.97%), sadness(12.45%), anticipation(11.65%), anger(10.71%), disgust(9.69%), joy(4.97%) and surprise(3.72%) which influence health workers in training and their wrok performance day to day.Concerning their positive and negative evaluation of their experience facing this health emergency, 13.83% of participants had a positive perception about participating in this kind of health emergency to support the country, 49.94% showed a negative evaluation, and 36.23% kept a neutral evaluation about their participation.ConclusionsThe health workers in training in Mexico gave a negative evaluation of the management of the health emergency. Distrust as a response to the absence of timely information from the education/health institutions, as well as concern about lack of personal protection equipment/inputs, are the main conflicts reported. We must establish a credible globally relevant continuity plan for the education of health care personnel in training, facing emergencies and disasters, so that next time we are properly prepared.

La proliferación de las redes sociales y la facilidad que otorgan a sus usuarios para compartir información alienta a compartir lo que ocurre a su alrededor. Las redes sociales ayudan a conocer diferentes acontecimientos, y al estar conscientes de ellos, nos ayuda a tomar decisiones con mayor certeza. Por ejemplo, acerca del tránsito vehicular, el utilizar las redes sociales nos ayuda a saber cuándo están presentes manifestaciones o cuando ha ocurrido un accidente, esta información nos ayuda a eludir un congestionamiento vial desafortunado. Aprovechando la generación de contenido en Twitter y tomando como caso de estudio la Ciudad de México, se recolectó información de usuarios dedicados a publicar eventos viales. Por tanto, se propone una metodología para la geocodificación de textos cortos y un método de aprendizaje automático basado en Máquinas de Soporte Vectorial, con el cual se obtiene un modelo capaz de realizar un análisis espacio temporal de eventos viales. Como caso de estudio se consideró a la Ciudad de México.

El ruido ambiental, es una de las principales formas de contaminación en las ciudades y que afecta la calidad de vida de las personas. En este artículo, se presenta una metodología que utiliza un enfoque basado en la Información Geográfica Voluntaria (VGI, por sus siglas en inglés) para el monitoreo, análisis y predicción del ruido ambiental, lo cual puede resultar muy útil para plantear alternativas que mejoren la  vida en una ciudad. El presente trabajo considera las fases de adquisición de los datos, análisis y procesamiento de los datos, así como la visualización de la información, considerando la temporalidad de los mismos y tomando en cuenta niveles de análisis a nivel macro y micro para la superficie de estudio. Se presentan algunos detalles del diseño y desarrollo de un sistema de información geográfica, compuesto por una aplicación web de mapas, una aplicación para dispositivos móviles denominada “NoiseMonitor”, el análisis geoespacial y los métodos de aprendizaje automático (máquinas de soporte vectorial y redes neuronales artificiales) para la predicción de ruido ambiental; utilizando información contextual; es decir, algunos datos relacionados con la ciudad. Se busca aprovechar la disposición de los ciudadanos de participar colaborativamente para monitorear su entorno y ser considerados como sensores humanos, lo cual a diferencia con los enfoques tradicionales, el costo asociado al desarrollo e implementación de este proyecto es mucho menor.