Search

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. [ABSTRACT FROM AUTHOR]

Road Traffic Accidents (RTA) cause human losses and irreparable physical and psychological damage to many of the victims. They also involve a very relevant economic dimension. It is urgent to improve the management of human and material resources for more effective prevention. This work makes an important contribution by presenting a methodology that allowed for achieving a predictive model for the occurrence of RTA on a road with a high RTA rate. The prediction is obtained for each road segment for a given time and day and combines results from statistical methods, spatial analysis, and artificial intelligence models. The performance of three Machine Learning (ML) models (Random Forest, C5.0 and Logistic Regression) is compared using different approaches for imbalanced data (random sampling, directional sampling, and Random Over-Sampling Examples (ROSE)) and using different segment lengths (500 m and 2000 m). This study used RTA data from 2016–2019 (training) and from May 2021–June 2022 (test). The most effective model was an ML logistic regression with the ROSE approach, using segments length 500 m (sensitivity = 87%, specificity = 60%, AUC = 0.82). The model was implemented in a digital application, and a Portuguese security force is already using it. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a major concern for modern society with a high toll on human life and involve hard to account economic consequences. New knowledge can be obtained from combining GIS tools with machine learning and artificial intelligence, developing what is, in this work, identified as spatial intelligence. This approach is tested in a case study of Setúbal district, Portugal, for the period of 2016 to 2019. Departing from a heatmap analysis, and applying kernel density estimation, new spatial approaches were used, namely DBSCAN and Getis-Ord. The results obtained allowed the identification of novel meaningful locations of road traffic accidents. Consequently, the knowledge built from the underlying patterns is considered the key to developing new strategies to solve this modern social curse. The methodology proposed in this study demonstrates that the combination of expertise built from the different spatial analyses can provide a better understanding of the determinants of road traffic accidents. This approach is expected to be valuable for data analysts and decision-makers, contributing to diminishing human losses related to road traffic accidents. [ABSTRACT FROM AUTHOR]

The goal of this study was to assess ectoparasite-small mammal host associations in central-west Portugal in native and exotic habitats, respectively, including a cork oak woodland and a Eucalyptus plantation. Mammals were sampled with Sherman and pitfall traps, and ectoparasites were collected and identified in all samples. We trapped 681 small mammals: 5 rodent species - wood mouse, Apodemus sylvaticus (N = 527); the Algerian mouse, Mus spretus (N = 67); Cabrera's vole, Microtus cabrerae (N = 8); the Lusitanian pine vole, Microtus lusitanicus (N = 2); and the black rat, Rattus rattus (N = 1) - and 1 insectivore species - the greater white-toothed shrew, Crocidura russula (N = 76). We identified 4 flea species (Nosopsyllus fasciatus, Ctenophthalmus sp., Typhloceras favosus, and Typhloceras poppei); 5 tick species (Haemaphysalis punctata, Ixodes ricinus, Ornithodoros sp., Rhipicephalus pusillus, and Rhipicephalus sanguineus); and 1 chigger species (Neotrombicula sp.) from hosts. The wood mouse was the small mammal species with the highest overall ectoparasite prevalence, although ticks were the least prevalent taxon. We found that ectoparasite guilds represented a somewhat generalist host pattern, and chiggers presented some host specificity. The data presented herein increase the knowledge regarding ectoparasite prevalence and distribution among small mammal hosts in the Mediterranean. Although surveys of ectoparasites of small mammals in central-west Portugal are not novel, this is the first time that a species belonging to the genus Ctenophthalmus is reported from M. cabrerae (Cabrera's vole) in Portugal. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Biodiversity is recognizably affected by land conversion for agriculture. However, the assessment of impacts on wildlife often lacks information on populations structure and individuals' condition, allowing only a limited view of the problem. Individual body condition/health can influence animal's reproductive success or survival. Eucalyptus globulus plantations are important forestry systems in Iberia, being the widest forested‐land use in Portugal, which hosts the broadest European E. globulus coverage. We tested Eucalyptus plantations' effects on small mammals' body condition (using the Scaled Mass Index‐SMI as surrogate) and ectoparasite abundance (number of mites, ticks and fleas per host). We applied a Generalized Linear Mixed Model (GLMM) approach for testing the influence of gender, habitat, species, season and understory cover on SMI and ectoparasites abundance. We captured 681 individuals, hosting overall 521 fleas (n = 305), mites (n = 159) and ticks (n = 57). Animal's body condition was not affected by plantations, but was lower for males and higher in spring. However, animals captured in Eucalyptus plantations and spring had higher parasite abundance (namely ticks and fleas), as well as males. Mites results were inconclusive. Body condition patterns seem to be determined by males demanding spatial/reproductive behavior (i.e., wider home‐ranges and search for females), while plantations promote ectoparasites abundance (e.g., induced by habitat disturbance and dilution effect). Results highlight that plantations may have negative effects on wildlife, but the evaluation of anthropic landscapes impact upon wildlife should encompass several bio‐ecological indicators, since the use of a restricted set (e.g., body condition) may induce a biased view. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Kineanthropometry & Human Performance is the property of Brazilian Journal of Kineanthropometry & Human Performance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper quantifies green roofs energy savings which is a challenging topic, namely in Mediterranean climate with distinct heating and cooling seasons. The thermal behavior of a green roof case study in Lisbon, Portugal, was assessed through an experimental campaign during heating and cooling periods of the year of 2013. These experimental results were then used to calibrate a building energy simulation in EnergyPlus. After validation, the numerical model was used to compare the energy performance of intensive, semi-intensive and extensive green roofs. The three green roof types lead to similar heating energy needs but extensive green roof solution shows higher cooling energy needs than semi-intensive and intensive ones, of 2.8 and 5.9 times more, respectively. Furthermore, the performance of each type of green roof and different insulation properties was compared to traditional roof solutions. With no thermal insulation, extensive green roofs require 20% less energy use than black roofs and show a similar annual behavior than white roofs. Semi-intensive and intensive green roofs energy use is 60–70% and 45–60% lower than black and white roofs, respectively. Well insulated roofs do not take full advantage of evapotranspiration cooling effects, which is particularly noticeable when comparing with high reflective white roofs. [ABSTRACT FROM AUTHOR]

• Back-PAQ-Br is a questionnaire that evaluates attitudes and beliefs about back pain • Back-PAQ-Br has measurement properties that are similar to the original version • Back-PAQ-Br is reliable and valid to assess attitudes and beliefs about back pain • Back-PAQ-Br can assess people with and without back pain, and healthcare professionals • Back-PAQ-Br can be used in research and in the clinical setting The Back Pain Attitudes Questionnaire (Back-PAQ) was developed to evaluate attitudes and beliefs of the general public, people with back pain, and healthcare professionals about the spine. To translate and cross-culturally adapt the Back-PAQ (34-item and 10-item versions) into Brazilian-Portuguese (Back-PAQ-Br) and test its measurement properties in a Brazilian sample. The cross-cultural adaptation and testing of the measurement properties followed the recommendations of international guidelines. Members of the general public, people with back pain, and healthcare professionals, for a total of 139 individuals, took part in the assessment of internal consistency, construct validity, and ceiling and floor effects. The Hospital Anxiety and Depression Scale (HADS) and the Brazilian-Portuguese version of the Tampa Scale of Kinesiophobia (TSK) were used to evaluate construct validity. Test-retest reproducibility was determined on 77 participants. Retest was performed a minimum of 1 week and a maximum of 2 weeks from the original test. There was very high agreement between translators (88.2%). The Back-PAQ-Br showed excellent internal consistency (Cronbach's alpha 0.92) and excellent reproducibility (ICC 0.94; SEM 5.14 points on a 136 point scale), with a smallest detectable change (90% confidence level) of 11.93 points. There was strong correlation between Back-PAQ-Br and TSK (r = -0.72) and very weak correlation between Back-PAQ-Br and HADS (r = -0.23 for both depression and anxiety domains). No ceiling/floor effects were observed. The translation process and cross-cultural adaptation had very high agreement between translators. The Back-PAQ-Br has excellent measurement properties that are similar to the properties of the original version. [ABSTRACT FROM AUTHOR]

Background: Chagas disease is caused by Trypanosoma cruzi, and humans acquire the parasite by exposure to contaminated feces from hematophagous insect vectors known as triatomines. Triatoma virus (TrV) is the sole viral pathogen of triatomines, and is transmitted among insects through the fecal-oral route and, as it happens with T. cruzi, the infected insects release the virus when defecating during or after blood uptake., Methods: In this work, we analysed the occurrence of anti-TrV antibodies in human sera from Chagas disease endemic and non-endemic countries, and developed a mathematical model to estimate the transmission probability of TrV from insects to man, which ranged between 0.00053 and 0.0015., Results: Our results confirm that people with Chagas disease living in Bolivia, Argentina and Mexico have been exposed to TrV, and that TrV is unable to replicate in human hosts., Conclusions: We presented the first experimental evidence of antibodies against TrV structural proteins in human sera.