Harnessing deep learning to forecast local microclimate using global climate data.

Microclimate is a complex non-linear phenomenon influenced by both global and local processes. Its understanding holds a pivotal role in the management of natural resources and the optimization of agricultural procedures. This phenomenon can be effectively monitored in local areas by employing models that integrate physical laws and data-driven algorithms relying on climate data and terrain conformation. Climate data can be acquired from nearby meteorological stations when available, but in their absence, global climate datasets describing 10 km-scale areas are often utilized. The present research introduces an innovative microclimate model that combines physical laws and deep learning to reproduce temperature and relative humidity variations at the meter-scale within a study area located in the Lombardian foothills. The model is exploited to perform a comparative study investigating whether employing the global climate dataset ERA5 as input reduces model's accuracy in reproducing the microclimate variations compared to using data collected by the Lombardy Regional Environment Protection Agency (ARPA) from a nearby meteorological station. The comparative analysis shows that using local meteorological data as inputs provides more accurate results for microclimate modeling. However, in situations where local data is not available, the use of global climate data remains a viable and reliable approach.

A pipeline for monitoring water pollution: The example of heavy metals in Lombardy waters.

Time-dependent geolocalized analysis of pollution data allows to better understand their dynamics over time and could suggest strategies to restore a good ecological status of contaminated area. This research analyzes concentrations of pollutants in surface waters and groundwater monitored by the Regional Environment Protection Agency of Lombardy from 2017 to 2020. Lombardy is one of the richest and populous region of Europe, providing an interesting example of the impact of environmental pollutants due to anthropogenic and industrial activities, not only for Italy but also for all Europe. Results show that groundwater displays more sites with heavy metals above the legal limit with respect to surface waters, including As, Ni, Cr and Zn. Furthermore, the spatio-temporal analysis of the data clearly shows that the introduction of more restrictive laws is a proper policy to improve the ecological status of the water.

Predicting the failure of two-dimensional silica glasses.

Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate failure predictions are becoming possible even for strongly disordered solids, but the sheer number of parameters used in the process renders a physical interpretation of the results impossible. Here we address this issue and use machine learning methods to predict the failure of simulated two dimensional silica glasses from their initial undeformed structure. We then exploit Gradient-weighted Class Activation Mapping (Grad-CAM) to build attention maps associated with the predictions, and we demonstrate that these maps are amenable to physical interpretation in terms of topological defects and local potential energies. We show that our predictions can be transferred to samples with different shape or size than those used in training, as well as to experimental images. Our strategy illustrates how artificial neural networks trained with numerical simulation results can provide interpretable predictions of the behavior of experimentally measured structures.

Saúde pública em Caxias do Sul: noventa anos de evolução e os desafios na modernidade / Public health in Caxias do Sul: ninety years of evolution and the challenges of modernity

Noventa anos da saúde pública em Caxias do Sul. O médico Marco Túlio Zanchi avalia, em seu novo livro, aspectos econômicos, políticos e sociais, bem como instituições, leis e pessoas que participaram ativamente desta história.

Análise retrospectiva da gestaçäo na adolescência no Hospital Geral de Caxias do Sul / The retrospective study about young pregnants who were assisted in the Hospital Geral de Caxias do Sul

Tendo em vista o início das atividades do Hospital Geral de Caxias do Sul (HG), os autores realizaram uma pesquisa retrospectiva em prontuários de gestantes, com menos de 19 anos, que receberam assistência no Centro Obstétrico do referido hospital. O objetivo foi identificar o perfil dessas gestantes adolescentes e os resultados dessas gestaçöes para o binômio mäe-filho. A incidência de gestaçöes abaixo de 19 anos, na populaçäo estudada, foi de 20,4 por cento, sendo que a maioria era primigesta (77,94 por cento). Dentre as mesmas, 54,90 por cento näo apresentaram intercorrências durante a gestaçäo. Ocorreu parto normal em 81,86 por cento dos casos. Quanto aos recém-nascidos, a grande maioria apresentou índice de APGAR entre 8-10 no quinto minuto. Os resultados säo discutidos e comparados com a literatura médica.