The Surface Urban Heat Island (SUHI) effect refers to the difference in Land Surface Temperature (LST) between an urban area and its surrounding non-urban area. LST can provide detailed information on the variations in different types of land cover. This study, therefore, analyzes the behavior of LST and SUHIs in fourteen cities in the El Bajío Industrial Corridor, Mexico, using Landsat satellite images from 2020, with QGIS software. It utilizes thermal profiles to identify the land uses that intensify LST, which are essentially those that are anthropologically altered. The results show that the increases in LST and SUHI are more pronounced in cities with greater urban conglomeration, as well as those where there are few green areas and a sizeable industrial or mixed area, with few or no bodies of water. In addition, the increase in temperature in the SUHI is due to certain crops such as vegetables, red fruits, and basic grains such as corn, wheat, and sorghum that use fallow as part of agricultural practices, located around urban areas, which minimizes natural areas with arboreal vegetation.

Understanding the mechanisms whereby parks contribute to cooling urban settings is critical to effectively addressing the challenges posed by rising temperatures in densely populated cities and ultimately improving the quality of urban life. This study employs a spatial approach with advanced analytical techniques, including local entropy mapping, to quantify surface temperature changes induced by urban parks across different geographical areas. Using satellite imagery to estimate land surface temperature (LST) during a heat wave in Mexico City, the study provides a practical approach to understanding the complex relationship between urban park size and urban heat island intensity within 300 m. The study’s findings indicate that while parks exert a cooling influence on their immediate vicinity, the extent of this effect varies spatially and depends on factors such as the size and location of the park and the nature of the surrounding terrain. Specifically, the results indicate that this relationship is not randomly distributed across the urban landscape. Instead, there is a clear pattern of spatial clustering within the city. Consequently, this research underlines the complexity of the problem, emphasizing the indispensable role of urban design and planning strategies to harness the full potential of parks as cooling agents within cities.

The development of novel technologies to mitigate the effects of climate change through Smart Grids requires energy related data. Unfortunately, this type of data is not always available in Mexico, especially from non-large urban areas and at the household level. Therefore, we present a dataset that contains electrical demand and consumption time series of 5 households within a small community in Mexico, at various resolutions, as well as weather data. The electrical demand is given in 15 min resolution, while the electrical consumption is presented in both hourly and daily resolutions. The data is contained within 15 separate .csv files; one for each household's resolution. In turn, the weather data is given in two .csv files (for outdoor and indoor variables, respectively) that together contain 24 meteorological variables measured in a 5 min resolution that is not always consistent. The dataset comprises of two separate folders that contain either the electrical demand and consumption files or the weather files. This dataset could aid in the development of novel smart grid methods and algorithms that might be able to push the energy transition in Mexico and other developing countries forward.

This dataset contains hourly-averaged measurements of ozone and meteorological variables collected between March 1 and November 30, 2024, at Universidad Iberoamericana, Mexico City (latitude: 19.372292, longitude:99.263679). The data capture conditions across both the ozone season (March–June) and the rainy season (May–October) in a high-altitude urban environment. Variables include ozone concentration (ppb), temperature ( °C), relative humidity (%), wind direction (°), wind speed (m/s), atmospheric pressure (hPa), accumulated precipitation (mm), and solar radiation (W/m²). The dataset was obtained using a calibrated low-cost sensor system, which transmitted data to the cloud for subsequent download in CSV format. This open-access dataset is intended to support research on the interactions between meteorological conditions and air quality. It can be reused in studies focused on air quality modelling, atmospheric dynamics, environmental monitoring, or urban climate in megacities with similar characteristics.

Robotic strawberry harvesting has achieved significant progress, with decades of research leading to commercial deployments and prototypes capable of autonomous fruit detection, grasping, and stem cutting. However, its development remains limited by the lack of a reusable, realistic crop testbed, making system validation difficult due to limited field access, crop waste, and large sample requirements. Here, we present a reusable strawberry stem made from Hot Melt Adhesives (HMAs) enabling repeated cutting, healing, and detachment. We show that the artificial stem reproduces the static behavior of real stems when its diameter is increased by 2.0 × − 3.0 × , achieving curvature errors between 3.39% and 11.01% of stem length (normalized RMSD), confirming close agreement with their biological counterparts. Furthermore, the healing behavior is analyzed by evaluating the consistency of the cutting force required to sever the stem and the peak tensile load required to detach it across repeated cycles. Fabrication repeatability is assessed by measuring the variability of peak cutting force across independently fabricated stems from the same mold. Although HMA does not fully match the stem’s natural stiffness, the surrogate replicates its static behavior at a larger diameter, providing a practical, low-cost, reusable medium for standardized harvesting tests.