In machine learning, a fundamental challenge is the analysis of data to identify feelings using algorithms that allow us to determine the positive or negative emotions that people have regarding a topic. Social networks and microblogging are a valuable source of information, being mostly used to express personal points of view and thoughts. Based on this knowledge we propose a sentiment analysis of English tweets during the pandemic COVID-19 in 2020. The tweets were classified as positive or negative by applying the Logistic Regression algorithm, using this method we got a classification accuracy of 78.5%.

This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thinger.io to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l., where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of −122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events.

El proceso de aprendizaje y enseñanza evoluciona con el pasar del tiempo, debido a esto se implementan distintas metodologías y técnicas para que los estudiantes aprendan de manera óptima; sin embargo, una de las problemáticas más comunes dentro de la salud mental es el estrés y la ansiedad, que pueden generarse al no adaptarse a los nuevos sistemas en la educación, como lo es el caso del aprendizaje virtual obligado por la pandemia por COVID-19. En este contexto, la presente investigación busca describir la percepción de los estudiantes de octavo semestre de la carrera de psicología de la Universidad Tecnológica Indoamérica de la ciudad de Ambato, respecto a su experiencia de aprendizaje en entornos virtuales. Para ello, se recogen las percepciones de una muestra de 50 estudiantes de la Facultad de Ciencias Humanas y de la Salud, basándose en su experiencia con las clases bajo la modalidad virtual en tiempos de pandemia. Los resultados muestran que los estudiantes consideran que el estrés influye en su aprendizaje, y que suelen generar rasgos ansiosos al realizar tareas complejas. La solución hacia los problemas se direcciona al poder conocer los niveles de estrés y ansiedad que pueden tener los estudiantes, el docente influye mucho, por lo que él podrá utilizar metodologías interactivas e incorporar pausas activas, disminuyendo los niveles de estas patologías, con el fin de estimular las habilidades y capacidades de los alumnos.

Society demands accurate, right, quickly and easy access information. Therefore, governments are forced to implements or improve information systems with modern technologies. This research work is focused on the research of responses times on relational and non-relational data base models in an experimental data base system provided by the Función Judicial del Ecuador. The data is migrated for load in NoSQL allotment, and the performance tests is obtained a document-oriented data model. With these tests we could know the best database, focusing all on response time of data queries that have been used on performance tests. In addition, we evaluate the performance before and after the relational database administration system (RDBMS), obtaining as a result that the first test of the queries made to MongoDB substantially surpass the queries made to SQL Server, but in the subsequent tests the times they tend to be similar.