Currently, research on microplastics (MPs) has increased due to their rapid distribution throughout the world and their harmful effects on the ecosystem. However, a detailed description of their dispersion and the methods for both detection and removal has not been given. The objective of this research is to carry out a bibliographic review that allows for a multidisciplinary analysis of microplastic contamination and current detection and removal methods. The method used is PRISMA in which articles from reliable databases such as Scopus, Web of science, and Google Scholar were collected and analyzed to finally provide details on the physical and chemical methods for detecting MPs, in addition to presenting the technologies for their removal. As a result of the analysis, critical information was obtained from the different studies on the impact of MPs on the ecosystem and the variation in detection and removal efficiency according to the type of pretreatment and methods applied to the sample. It is concluded that this research is essential to understand the consequences that MPs have on the ecosystem and provide tools to evaluate and improve current technologies, mainly detection and removal.

There are currently several industries, including the industries of maritime containers, gas, oil, among others, in these we find areas of difficult access, for example, confined environments, in these environments it is common that fatal accidents occur due to the presence of toxic gases, because they don't present a constant monitoring of the environment, to solve this, we propose a system that uses a local network that, with image processing, can detect toxic gases remotely. In this project, the reagents that adapt their properties and detection principles will be used to vary their color with respect to a specific gas, these colors will intensify according to the concentration of the gas in the environment, this will be captured by a chamber and processed in a microprocessor, which will allow the color of the reagent to be related to the percentage of the gas present in the environment.

Water quality assessment presents challenges, primarily the paucity of available data and ongoing system maintenance. This research develops an automated monitoring and control of water quality parameters in aquaponic systems with internet of thing (IoT) technology. Proper fish feeding management is important, which is why the fish were fed at 12:00, 16:00 and 07:00. The most significant relative error recorded during the validation of the DS18B20, PH-4502C, SEN0244, SEN0237-A, SEN0189 and DFR0300 sensors is 5.0%. The maximum standard deviation between the mentioned sensors was 1.96, and the highest coefficient of variation reached 7.24%. Before the installation of the aquaponic system, the specific growth rate (SGR) of fish was 4.89±0.17% and after implementing the automated aquaponics system, the SGR of fish increased to 6.21±0.24%. The feed conversion ratio values of the fish, both before and after the installation of the control system, were 1.98±0.14% and 1.53±0.09%, respectively. In addition, an improvement in plant growth was observed, evidenced by the difference in the values of height, number of leaves, leaf length, and weight of the plants before and after the installation of the control system, which was 7.74 cm, 5 leaves, 5.6 cm, and 41.6 g respectively.

Several patients who have overcome coronavirus disease (COVID-19) have been left with cardiovascular and pulmonary sequelae and most medical centers lack a remote monitoring system for each patient that notifies them of any complications during rehabilitation. The objective of this research was to implement a Wearable that monitors the patient's health and alerts in case of detecting any anomaly. For this reason, a Wearable was developed that displays the patient's heart rate, oxygen saturation level and body temperature on the Light Emitting Diode (LCD) and the application mobile, sending an alert and geolocation message if anomalies are detected in vital signs. The standard deviation of heart rate, temperature and oxygen saturation was obtained, which was 1.4930, 0.1558 and 0.4364 in the rest stage, respectively, and 6.3442, 0.2365 and 0.9186 in the physical activity stage respectively with a maximum duration of 42 hours and 52 minutes of battery, managing to send alert messages and store the information in the cloud, which allows to conclude that the Wearable can facilitate the management of the database and the location of the patient, that the measurement error increases with physical activity, and that battery life varies with the number of biosignal readings per hour.

Alzheimer's is a progressive disease that affects memory, causing disorientation in the patient, which causes them to lose themselves, generating anguish in families who have to resort to expensive searches. The objective of this research was to implement a device that can remotely provide the location of the Alzheimer's patient over a long period to relatives for greater security. For this, in this research, a mobile application was developed that receives information from a wearable that applies the internet of things using ong-range wide area technology to show the patient's real-time location and uses piezoelectrics for greater battery autonomy. The real-time location of the person and the radius of the safe zone in the application were obtained as results, the received signal strength indicator value where the signal was excellent or good had a value of -30 to -89 dB between 0 to 400 meters and the battery discharge time was 11 hours and 44 minutes. It was concluded that the application is interactive, that the piezoelectric system increased the autonomy of the wearable, and that the long-range wide area (LoRa) technology allowed monitoring of the patient's location with great precision at 400 meters.