Portugal has the sixth highest road fatality rate among European Union members. This is a problem of different dimensions with serious consequences in people’s lives. This study analyses daily data from police and government authorities on road traffic accidents that occurred between 2016 and 2019 in a district of Portugal. This paper looks for the determinants that contribute to the existence of victims in road traffic accidents, as well as the determinants for fatalities and/or serious injuries in accidents with victims. We use logistic regression models, and the results are compared to the machine-learning model results. For the severity model, where the response variable indicates whether only property damage or casualties resulted in the traffic accident, we used a large sample with a small imbalance. For the serious injuries model, where the response variable indicates whether or not there were victims with serious injuries and/or fatalities in the traffic accident with victims, we used a small sample with very imbalanced data. Empirical analysis supports the conclusion that, with a small sample of imbalanced data, machine-learning models generally do not perform better than statistical models; however, they perform similarly when the sample is large and has a small imbalance.

Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance improved considerably, with the random forest algorithm showing the best performance.

In this paper, we have synthesized the information derived from more than 20 papers and PhD theses on the anisotropy of the magnetic susceptibility (AMS) of 19 Variscan granite plutons, spanning the period between 320 Ma and 296 Ma. The AMS data are obtained from 876 sampling sites with more than 7080 AMS measurements and a re-interpretation is proposed. The studied granites exhibit a magnetic susceptibility (Km) ranging from 30 to 10,436 × 10⁻⁶ SI units. Most granites typically exhibit Km values below 1000 × 10⁻⁶ SI, indicative of paramagnetic behavior. Biotite serves as the main carrier of iron (Fe), emphasizing the reduced conditions prevalent during the formation of granite melts in the Variscan orogeny. The AMS fabrics of the studied granite plutons record the magma strain, expressing the chronologic evolution of the stress field during the orogeny. This chronologic approach highlights the magmatic events between around 330 and 315 Ma, occurring in an extensional regime, in which the Borralha pluton is an example of a suite that recorded this extensional AMS fabric. Plutons with ages between 315 and 305 Ma show AMS fabrics, pointing out their emplacement in a compressional tectonic regime related to the Variscan collision. The plutons, younger than 305 Ma, record AMS fabrics indicating that the tectonic setting for emplacement changes from a wrench regime to an extensional one at the end of the collision stage. This is evident as there is a chronological overlap between the granites that exhibit AMS fabrics indicating extension and the ones that have AMS fabrics indicating a wrench regime.

Old and abandoned mines are testimonials of ancient industrial activities, and as such, they are able to convey environmental concerns. A multidisciplinary approach combining ground measurements—i.e., portable X-ray fluorescence and optical spectroradiometry—with airborne multispectral images—i.e., Sentinel-2 and unmanned aerial vehicles—was conducted to define a baseline for the characterization of areas that constitute environmental burdens. The Mostardeira mine, an old copper mine located in the Portuguese Ossa-Morena Zone, was selected as a case study. The results reveal that the soils have toxic metals—e.g., As (mean = 1239 ppm) and Cu (mean = 435 ppm)—above the defined health thresholds. The spectroradiometry provided insights into the soil characterization using data from the vis-NIR spectral region, allowing us to distinguish agricultural soil, mine waste, and bare soils. The spectra obtained are comparable with the USGS soil spectra standards, namely Clinozoisite Epidote HS299, Hematitic Alt. Tuff CU91-223, and Sand DWO-3-DEL2ar1 no oil. The airborne images considered through the lens of principal components analysis and supervised and unsupervised machine learning techniques (random forest and K-means) are found to be effective tools in creating cartographic representations of the contaminated soils. The collected data are used to construct a baseline for characterizing these environmentally challenging areas, whereas the methodological approach is revealed to be successful for tackling the posed environmental problems, allowing us to map the old mine environment passives.