Currently, energy demand and environmental pollution have boosted the evolution of different environment friendly technologies for the development of clean and renewable energies. That is why scientific research allows us to demonstrate the use of solid state based devices implementing temperature changes by electric induction. Given the above, a combined thermic generation system is taken into consideration, consisting in an environmentally friendly thermoelectric generator and refrigerator, wich is fueled and controlled by an electric fluid. Therefore, this study gives an approch and mathematic demonstrations to the concepts of greater relevance about the thermoelectric device and the effects of greater incidence, responsible for giving advantageous characteristics to the implementation of Peltier cells as a basic device in a cooling system for smaller applications, maintaining an ecological system, clean and without detrimental effects to the ozone layer, since there is no C02 emissions into the atmosphere.

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.

Dado que la información se ha constituido en uno de los activos más valiosos de las organizaciones, es necesario salvaguardarla a través de diferentes estrategias de protección, con el fin de evitar accesos intrusivos o cualquier tipo de incidente que cause el deterioro y mal uso de la misma. Precisamente por ello, en este artículo se evalúa la eficiencia de un modelo de detección de intrusiones de red, utilizando métricas de sensibilidad, especificidad, precisión y exactitud, mediante un proceso de simulación que utiliza el DATASET NSL-KDD DARPA, y en concreto las características más relevantes con CHI SQUARE. Esto último a partir de una red neuronal que hace uso de un algoritmo de aprendizaje no supervisado y que se basa en mapas auto organizativos jerárquicos. Con todo ello se clasificó el tráfico de la red BI-CLASE de forma automática. Como resultado se encontró que el clasificador GHSOM utilizado con la técnica CHI SQUARE genera su mejor resultado a 15 características con precisión, sensibilidad, especificidad y exactitud