The province of Bolivar is located in the central-west region of Ecuador. The main beverages produced are spirits and chichas; among them “rice chicha”. In a first stage, samples of rice chicha (at points of sale) from different producers of this province were evaluated; microbial counts and physicochemical analyzes were performed. In the second stage, the most representative enterobacteria, lactic acid bacteria, yeasts and molds were isolated form “rice chicha” during three phases of the elaboration process: initial, fermentative and final. For all microorganisms, macroscopic and microscopic descriptions were performed. The identification of enterobacteria and lactic acid bacteria was carried out with biochemical tests, for mold a microscopic identification was applied by staining with lactophenol blue solution and for yeast the APIC AUX 20 kit was used. Count values in average were 2.7 and 2.5 log CFU / ml of total coliforms and enterobacteria, 1.8 log CFU / ml of lactic acid bacteria and 2.5 log CFU / ml for yeasts and molds. During the three fermentation phases the most representative species were partially identified: 13 strains of Enterobacteriaceae, 9 strains of lactic acid bacteria, 15 strains of mold and 6 strains of yeast.

World energy matrix is changing due to the increase of energy consumption in the last years. Installation of unconventional renewable energy plants in many countries has become a solution to this event. Integration of this type of energy to the electric network causes interest in the study of phenomena that affects energy production. This document is a review of concepts and events occurring on energy quality and how they interact in a wind farm. The efficiency of wind turbines is affected by many factors, the most outstanding are: substations activities and weather issues such as wind gusts, which causes variations in the power curve. Constant monitoring on electric systems and data processing offers a high potential in improving the process of production and distribution of energy.

This work focuses on developing a fast coral reef detector, which is used for an autonomous underwater vehicle, AUV. A fast detection secures the AUV stabilization respect to an area of reef as fast as possible, and prevents devastating collisions. We use the algorithm of Purser et al. (2009) because of its precision. This detector has two parts: feature extraction that uses Gabor Wavelet filters, and feature classification that uses machine learning based on Neural Networks. Due to the extensive time of the Neural Networks, we exchange for a classification algorithm based on Decision Trees. We use a database of 621 images of coral reef in Belize (110 images for training and 511 images for testing). We implement the bank of Gabor Wavelets filters using C++ and the OpenCV library. We compare the accuracy and running time of 9 machine learning algorithms, whose result was the selection of the Decision Trees algorithm. Our coral detector performs 70ms of running time in comparison to 22s executed by the algorithm of Purser et al. (2009).

One of the largest areas of the food industry is the production of snacks, but these have generally an inadequate nutritional profile in healthy eating. The vacuum frying technology is presented as a clear option processing for the development of new products and existing ones. This review article presents the advantages of using technology in Vacuum frying different types of food, especially in relation to the contents of some micronutrients and fat. They also mention the work done in Ecuador, especially in local products.