The existence of quantum computers and Shor’s algorithm poses an imminent threat to classical public-key cryptosystems. These cryptosystems are currently used for the exchange of keys between servers and clients over the Internet. The Internet of Things (IoT) is the next step in the evolution of the Internet, and it involves the connection of millions of low-powered and resource-constrained devices to the network. Because quantum computers are becoming more capable, the creation of a new cryptographic standard that cannot be compromised by them is indispensable. There are several current proposals of quantum-resistant or post-quantum algorithms that are being considered for future standards. Given that the IoT is increasing in popularity, and given its resource-constrained nature, it is worth adapting those new standards to IoT devices. In this work, we study some post-quantum cryptosystems that could be suitable for IoT devices, adapting them to work with current cryptography and communication software, and conduct a performance measurement on them, obtaining guidelines for selecting the best for different applications in resource-constrained hardware. Our results show that many of these algorithms can be efficiently executed in current IoT hardware, providing adequate protection from the attacks that quantum computers will eventually be capable of.

In large and densely populated cities, the concentration of pollutants such as ozone and its dispersion is related to effects on people’s health; therefore, its forecast is of great importance to the government and the population. Given the increased computing capacity that allows for processing massive amounts of data, the use of machine learning (ML) as a tool for air quality analysis and forecasting has gotten a significant boost. This research focuses on evaluating different models, such as Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB), to forecast ozone ($O_3$) concentration 24 h in advance, using data from the Mexico City Atmospheric Monitoring System using meteorological variables that influence the phenomenon of ozone dispersion and formation.

Accurate detection and classification of wind states is crucial for accurately assessing and predicting wind energy production, fire spread behavior, air quality monitoring, and understanding complex meteorological phenomena. The complex nature of wind necessitates advanced data analysis techniques to extract meaningful patterns from meteorological datasets. This study presents a stochastic wind classification and identification analysis based on a Gaussian Mixture Model (GMM) clustering method applied to a case study in La Rumorosa'', Mexican California. By analyzing four meteorological variables (relative humidity, atmospheric pressure, wind speed, and wind direction) over five years, the method automatically identifies distinct wind conditions that can be defined as climate states, including well-known regional phenomena like Santa Ana winds and local orographic winds. Accurate detection of wind states enables better forecasting of wind energy potential at favorable sites, wildfire risk management through predicted fire behavior and monitoring pollutant/allergen dispersal patterns. The proposed approach offers a reliable, computationally efficient method for detecting wind patterns, extending to different geographical regions impacted by diverse wind phenomena.

This study investigates the combined effects of air pollution and socioeconomic factors on disease incidence and severity, addressing gaps in prior research that often analyzed these factors separately. Using data from 86,170 hospitalizations in Mexico City (2015–2019), we employed multivariate statistical methods (PCA and factor analysis) to construct composite measures of social and economic status and grouped correlated pollutants. Logistic and negative binomial regression models assessed their associations with hospitalization risk and frequency. Results showed that economic status significantly influenced diabetes complications, while social factors affected prenatal care-related diseases and hypertension. The ${\mathrm{PM}}_{10}$–${\mathrm{PM}}_{2.5}$–CO group increased the incidence of asthma, influenza, and epilepsy, whereas ${\mathrm{NO}}_2$–${\mathrm{NO}}_x$ impacted diabetes complication severity and influenza. Nonlinear effects and interactions (e.g., age and weight) were also identified, highlighting the need for integrated analyses in environmental health research.