Gases e ruídos em edificações para suínos: aplicação das normas NR-15, CIGR e ACGIH

Swine production represents an important segment of Brazilian economy, and the possibility of increasing production is eminent mainly if considered the low pork consumption when compared to other meat and the consumption of other countries. The increasing need in the international market demands show that in a near future the commercial barriers will be based on welfare, in the protection of the environment as well as in the worker's legislation. Little knowledge is available in the subject of worker's standards in the environmental agents in rural activities as well as the air quality under Brazilian conditions. The objectives of this research were to apply the main used standards related to noise and gases and to estimate occupational risk using measurements of noise level, hydrogen sulfide, methane and oxygen in swine housing, in piglet's nursery and finishing. The results showed that the continuous noise level were below the one found in the standards, however there were observed differences (P < 0.05) in relation to the noise level measured in piglet's nursing cages and in semi-slatted floor. The respective concentrations of hydrogen sulfide and methane were less than 1 ppm and less than 0,1% by volume, which was lower than the recommended limits in NR-15, CIGR and ACGIH. The oxygen level was 21% in average.

Cinética de remoção de lactato na definição de pausas para treinamento intervalado de alta intensidade

Universidade Estadual de Campinas . Faculdade de Educação Física

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.