The increase in the number of COVID-19 cases in Mexico has caused an important effort in the analysis of the virus, due to the above, a statistical study of laboratory data of 109 patients from the State Center for Critical Care of Salamanca (CECCS) with or without COVID-19, whose purpose is to analyze in more detail the disease and the reactions in patients. First, a classification was made in men and women, then positive, negative, healthy and deceased patients. It is important to mention that a data cleaning was carried out, in addition to information filling using the mean of the laboratory values, then the clinical data were normalized from 0 to 1, to obtain the BMI of the patients, box plots and graphs days to days of hospitalization with a sample of patients.

Suicide is today one of the leading causes of death in the world. People at risk of suicide commonly leave messages around them, and that often includes social media. Therefore, early detection of suicidal risk is a fundamental prevention factor. At present, the search for suicide risk based on text analysis does not consider fundamental factors such as the analysis based on suicide notes. This research proposes a new text analysis for the detection of suicidal risk based on the study of suicide notes.

This paper describes the design and development of a Graphical User Interface through the virtual instrumentation software NI LabVIEW using the VISA function, to graphically visualize and storage the data of the climatological variables of temperature and relative humidity. The graphical interface offers the option to export the date, time and data of the two variables to text documents with extension “.txt”, which acquires the information of the electronic board wireless monitoring and control, which uses a main device PIC16F877A microcontroller. AMT1001 Precision Analog Sensor was used to sense temperature and relative humidity. The PIC16F877A was programmed using a C programming language in the CCS Compiler compiler, to the data acquisition, and send it via RS232 communication to the computer, using the PL2303 module USB to TTL converter. To check the GUI operation, the electronic wireless monitoring and control card was connected to the computer equipment by wire, however, the monitoring of the climate variables can be done wirelessly by XBEE technology. Future work aims to monitor the climate of a horticultural greenhouse with XBBE technology, so that the data is sent wirelessly to a computer that has the GUI, and is also connected to Ethernet or WIFI, which will have the LabVIEW graphical interface explained in this article, and the data will be displayed / analyzed through the internet.

This article describes the simulation of a unidirectional point-to-point radio frequency (RF) Wireless communication between two electronic circuits with XBEE technology, using the electronic design software PROTEUS V8.8. In the same worksheet in the ISIS graphical interface, the two electronic circuits were implemented, one was called the transmitter circuit and the other the receiver circuit. A Microchip® PIC16F877A microcontroller and an XBEE module are the main devices of each of the electronic circuits, to establish wireless communication by RF. The PIC microcontroller of the transmitter circuit was used as a DAQ data acquisition device, to measure the temperature of two analog sensors, the LM35 and the TMP36, and three digital sensors DS1621. The temperature data was sent from the transmitter circuit to the receiver circuit in character strings, using asynchronous serial communication. A programming algorithm was implemented for the PIC microcontroller of the receiver circuit, to receive and identify the temperature data. The development of this simulation is the basis to implement a wireless sensor network (WSN) with XBEE technology in the future.

Suicide prevention is one of the great issues of the current era. Institutions such as the World Health Organization, have continued to search for all possible alternatives for early detection and timely prevention. Suicide rates have grown more and more in the world, and Mexico, although it is not the country with the most suicides, is one of the countries with the highest growth in recent years. At present, the use of social networks has generated great changes in the way we communicate. Expressing yourself through a social network begins to be more common than expressing ourselves to human beings. Several studies, which will be presented later, show that it is possible to determine from the content of social networks: cases of depression, risk of suicide, and other mental problems. The use of technological tools, such as Natural Language Processing, has served as an effective ally for the early detection of risks, such as abuse, bullying or even detecting emotional problems. The present research seeks to carry out an in-depth analysis in the state of the art of the application of Natural Language Processing as an ally for the detection of suicide risk from the analysis of texts for Mexican Spanish in Social Networks.

Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classification aimed at extreme, class-balancedreduction. A compact genetic algorithm (GA) evolves a fixed number of prototype indices per class drawn from a disjoint design partition; the selected prototypes are then used by a 1-NN classifier, with fitness defined as the number of correctly classified test instances. To address concerns about generality and baseline strength, we evaluate an experimental suite including synthetic 2D Gaussians ($\sigma =0.5$ and $\sigma =1.0$) and a 3D three-moons geometry, as well as public benchmarks spanning binary and multi-class settings and higher-dimensional data (Breast Cancer Wisconsin, Wine, Reduced MNIST/Digits 8 × 8, Forest CoverType with seven classes, and a 10D five-class spiral benchmark). We compare against K-NN baselines with $k\in \{1,3,5,7\}$ using all design samples, and include GA operator ablations (GA1/GA2/GA3). Each scenario is repeated over 30 independent runs, reporting mean ± std, min/max, per-run distributions, win/tie/loss counts, and non-parametric significance tests (paired Wilcoxon with Holm correction; Friedman where applicable). Across datasets, the GA-selected prototype banks—often orders of magnitude smaller than the full design set—match or improve accuracy, with frequent statistically supported wins against strong K-NN baselines, and in the hardest cases provide substantial compression with no loss relative to the best baseline. These results establish a reproducible baseline for extreme, class-balanced prototype reduction suitable for memory- and latency-constrained deployments and for fair comparison against more elaborate prototype selection methods.