Objective: To characterize diabetes care across healthcare facilities in six Peruvian regions. Methods: Cross-sectional study of patients with type 2 diabetes mellitus (T2DM), ranging from primary care facilities to hospital-based facilities, in six Peruvian regions. Data was collected by health staff trained between 2012 and 2016. We studied six diabetes care outcomes and four adequate diabetes care outcomes considering the healthcare facility as the exposure of interest. We estimated prevalence ratios (PR) and their 95% confidence intervals (95% CI) using Poisson regression with robust variance. Results: Data from 8879 patients with T2DM, mean age 59.1 years (SD ± 12.2), 53.6% males, was analyzed. Of these, 8096 (91.2%) were treated at primary care facilities. The proportions of patients who had HbA1c, LDL-c, and creatinine/microalbumin test performed increased with the setting of the...

Classification green coffee beans is one of the main tasks during the quality grading process. This evaluation is normally carried out by specialist doing a visual inspection or using traditional instruments which have some limitations. This work is focused on the implementation of a computer vision system combining a hardware prototype and a software module. The hardware was made to guarantee the controlled conditions to capture the images of green coffee beans, the software is based on computer vision algorithms in order to detect defects of the coffee beans. The novelty of our proposal is the combination of algorithms to enhance the accuracy and the high number of defects detected. We applied a White Patch algorithm as an image enhancement procedure, color histograms as feature extractor and Support Vector Machine (SVM) for the classification task. It was constituted an image beans da...