Distribuição de temperatura de superfície e sua relação com indicador socioeconômico – Santos/SP

Mudanças da cobertura da terra ocasionadas pelo crescimento urbano e atividades humanas geram condições que afetam a capacidade e a emissão térmica, modificando o clima local. Pesquisas mostram relações entre dados socioeconômicos e distribuição espacial da temperatura de superfície terrestre. Assim, o objetivo deste trabalho é explorar a correlação entre a temperatura e os dados socioeconomicos de Santos-SP. Para atingir esse objetivo, utilizamos imagens Landsat 5 TM de 2010 para estimar a temperatura da superfície terrestre. Os dados do Censo de 2010 foram utilizados para avaliar a correlação entre a temperatura e a renda familiar mensal. Os resultados mostraram moderada correlação entre essas variáveis, onde bairros com renda salarial menor apresentam elevada temperatura superficial do que os bairros mais ricos, próximos à orla. Esta realidade pode ser explicada através dos materiais construtivos, ausência de vegetação e áreas verdes, maior densidade demográfica e impermeabilização do solo.

'Space to talk': a Portuguese focus group study of parents' experiences, needs and preferences in parenting support during prenatal and well-child care

OBJECTIVE: To explore the experiences, needs and preferences of a group of parents regarding the parenting support received during prenatal and well-child care in the Portuguese National Health Service. DESIGN AND SETTING: We undertook descriptive-interpretive qualitative research running multiple focus groups in Porto, Northern Portugal. PARTICIPANTS, DATA COLLECTION AND ANALYSIS: Purposive sampling was used between April and November 2018. Focus groups were conducted with 11 parents of a 0-3 years old with well-child visits done in primary care units. Thematic analysis was performed in a broadly inductive coding strategy and findings are reported in accordance with Consolidated Criteria for Reporting Qualitative Research guidelines. RESULTS: Three main themes were identified to describe parents' experience when participating in their children's healthcare: (1) logistics/delivery matter, including accessibility, organisation and provision of healthcare activities, unit setting and available equipment; (2) prenatal and well-child care: a relational place to communicate, with parents valuing a tripartite space for the baby, the family and the parent himself, where an available and caring health provider plays a major role and (3) parenting is challenging and looks for support, based on key points for providers to watch for and ask about, carefully explained and consensual among health providers. CONCLUSION: This study provides insight into parents' needs and healthcare practices that affect the parenting experience. To meet parents' preferences, sensitive health providers should guarantee a relational place to communicate and person-centredness, accounting for the whole family system to support healthy parenting collaboratively. Future studies are warranted to further strengthen the knowledge in the field of a population-based approach for parenting support.info:eu-repo/semantics/publishedVersio

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage

An Automated Machine Learning Framework in Unmanned Aircraft Systems:New Insights into Agricultural Management Practices Recognition Approaches

The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant–environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years

Ischemic stroke caused by large-artery atherosclerosis: a red flag for subclinical coronary artery disease

BackgroundThe coronary calcium score (CAC) measured on chest computerized tomography is a risk marker of cardiac events and mortality. We compared CAC scores in two multiethnic groups without symptomatic coronary artery disease: subjects in the chronic phase after stroke or transient ischemic attack and at least one symptomatic stenosis ≥50% in the carotid or vertebrobasilar territories (Groupathero) and a control group (Groupcontrol).MethodsIn this cross-sectional study, Groupathero included two subgroups: GroupExtraorIntra, with stenoses in either cervical or intracranial arteries, and GroupExtra&Intra, with stenoses in at least one cervical and one intracranial artery. Groupcontrol had no history of prior stroke/transient ischemic attacks and no stenoses ≥50% in cervical or intracranial arteries. Age and sex were comparable in all groups. Frequencies of CAC ≥100 and CAC > 0 were compared between Groupathero and Groupcontrol, as well as between GroupExtraorIntr, GroupExtra&Intra, and Groupcontrol, with bivariate logistic regressions. Multivariate analyses were also performed.ResultsA total of 120 patients were included: 80 in Groupathero and 40 in Groupcontrol. CAC >0 was significantly more frequent in Groupathero (85%) than Groupcontrol (OR, 4.19; 1.74–10.07; p = 0.001). Rates of CAC ≥100 were not significantly different between Groupathero and Groupcontrol but were significantly greater in GroupExtra&Intra (n = 13) when compared to Groupcontrol (OR 4.67; 1.21–18.04; p = 0.025). In multivariate-adjusted analyses, “Groupathero” and “GroupExtra&Intra” were significantly associated with CAC.ConclusionThe frequency of coronary calcification was higher in subjects with stroke caused by large-artery atherosclerosis than in controls

The HLA-DRB1 Alleles Effects on Multiple Sclerosis:a Systematic Review

Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system that affects sensitive and motor functions. Many population studies were made with the intent of knowing better the most affected groups and the disease manifestations. These review analyses some of those studies, evaluating risk factors, especially genetic relations of Human Leukocyte Antigen DRB1 (HLADRB1) gens, for developing clinical disease.Method: We have analyzed 57 articles, published between 2009 and 2014, with the key words “multiple sclerosisâ€, “genetic association studies†and “HLA-DRB1 chainsâ€, through the Scopus database. Only 18 articles were eligible for our study; they were read entirely and included in the fial analysis.Results: Most studies imply genetic and environmental factors for the incidence of MS, its age of starting and prognosis. Previous studies have shown that many gens are related in MS pathogenesis and that interactions between them are important in determining clinicalmanifestations.Limitations: Different results were observed when different populations were targeted in the studies.Conclusion: There is an important relation between HLA-DRB1 and MS in diverse population groups. Complementary studies are needed to know better the importance of environmental factors and its interaction with gens in the development of MS

A História da Alimentação: balizas historiográficas

Os M. pretenderam traçar um quadro da História da Alimentação, não como um novo ramo epistemológico da disciplina, mas como um campo em desenvolvimento de práticas e atividades especializadas, incluindo pesquisa, formação, publicações, associações, encontros acadêmicos, etc. Um breve relato das condições em que tal campo se assentou faz-se preceder de um panorama dos estudos de alimentação e temas correia tos, em geral, segundo cinco abardagens Ia biológica, a econômica, a social, a cultural e a filosófica!, assim como da identificação das contribuições mais relevantes da Antropologia, Arqueologia, Sociologia e Geografia. A fim de comentar a multiforme e volumosa bibliografia histórica, foi ela organizada segundo critérios morfológicos. A seguir, alguns tópicos importantes mereceram tratamento à parte: a fome, o alimento e o domínio religioso, as descobertas européias e a difusão mundial de alimentos, gosto e gastronomia. O artigo se encerra com um rápido balanço crítico da historiografia brasileira sobre o tema