Due to the COVID-19 pandemic, the world's population has undergone different changes in which an impact on the use of the internet stands out from daily aspects such as education, commerce, health, among others; which has made us highly dependent on its usage. In this regard, the term Internet of Things (IoT) has been increasingly recognized for its contribution to improving people's life quality, through objects that are integrated and connected to the Internet. This document aims to introduce alternatives for IoT-based home automation making use of NodeMCU, Sinric Pro, and smart voice assistants such as Google Home and Alexa. The proposals are efficient in terms of easy implementation and reduce electricity consumption by around 30%. This document is research that helps families improve their energy efficiency and daily productivity through IoT.

Due to the problem of drinking water scarcity in different cities around the world, there are innovative proposals to automate garden irrigation in homes, to reduce drinking water consumption. For this research, a sample of 68 inhabitants of the Region of Arequipa - Peru has been surveyed to know the common habits in the irrigation of the gardens. From this data, two systems have been implemented in two average gardens using the Arduino UNO board (integrating with the Ethernet Shield) and the NodeMCU, each proposal integrates soil moisture sensors, water flow sensor, and actuators, such as the solenoid valve and the relay, besides centralizing the information through an IoT System (Home Assistant or Adafruit IO). This has managed to establish a comparison of both, generating a discussion according to the advantages and disadvantages addressed by each proposal and obtaining a saving of potable water in the irrigation of plants.

Due to the indiscriminate use of drinking water through the system of flooding in an irregular way to the different plants in the gardens of the population of Arequipa; this research is presented, that integrates an IoT system that is executed in a Raspberry Pi and through its GPIO allows to control the electric solenoid valve; and with the Arduino Uno with its Shield Ethernet it achieves the monitoring in real-time, of the humidity of the soil and the water consumption of the gardens. Finally, with the integration of the Telegram instant messaging application, alerts are managed when the garden is dry or wet. The proposal generated significant savings in drinking water consumption for the Arequipa population.

It is increasingly important to analyze electrical systems, in different situations for academic purposes or real situations at work. There are simulators from different sources, however, many of them do not offer a friendly interface for users with little experience in the area of electricity. This research is presented, where an interactive interface is developed using Digsilent PowerFactory and LabVIEW, for connectivity MatrikonOPC was used; The interface was developed in LabVIEW with two menus, one for diagram, calculation configuration and switch control and the second for control of loads and to visualize the variation of the power values of the reference generator. Finally, the transmission of the measurement data and the control of the switches is done through the bidirectional connectivity of Digsilent and the graphical interface in LabVIEW, obtaining a friendly interface for the analysis of load flow in electrical systems. This proposal made it easy for users with little experience to carry out the analysis of a model of the electrical network of the city of Arequipa.

The aim of the article is to classify the levels of gross motor function measurement (GMFCS) in minors using machine learning techniques. The study elements were 16 patients, boys, and girls between 2 and 9 years of age from a rehabilitation and physiotherapy institution suffering from cerebral palsy in gross motor function. The clinical analysis, the application of therapy and its measurement of gross motor function were collected, then the classification of nine machine learning algorithms was applied: k-Nearest Neighbor (k-NN), Gradient Boosted tree, Decision Stump, Random Tree, Rule Induction, Improved Neural Net, Generalized Linear Model, SVM, and Linear Discriminant Analysis, which were compared based on accuracy. The results obtained showed that the Linear Discriminant Model was the one that gave the best result with a 96.88 classification accuracy. Therefore, it is concluded that the use of machine learning techniques allows obtaining good accuracy in the classification of the measured level of gross motor function in boys and girls that can be used by specialists to carry out this task.