Abstract
Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences in oil and gas exploration, not only because it has a direct impact on the following decisions, but also due to the subsequent costs inherent to drilling wells, and the potential return of oil deposits. These logs frequently present gaps of varied sizes in the sensor recordings, that happen for diverse reasons. These gaps result in less information used by the interpreter to build the stratigraphic models, and consequently larger uncertainty regarding what will be encountered when the next well is drilled. The main goal of this work is to compare Gradient Tree Boosting, Random Forests, Artificial Neural Networks, and three algorithms of Linear Regression on the prediction of the gaps in well log data. Given the logs from a specific well, we use the intervals with complete information as the training data to learn a regression model of one of the sensors for that well. The algorithms are compared with each other using a few individual example wells with complete information, on which we build artificial gaps to cross validate the results. We show that the ensemble algorithms tend to perform significantly better, and that the results hold when addressing the different examples individually. Moreover, we performed a grid search over the ensembles parameters space, but did not find a statistically significant difference in any situation.

Abstract
This paper investigates the performance of the largest Brazilian hydropower plants. This study covers 78% of the total installed capacity from hydros in the country, and considers indicators reflecting operational and maintenance costs as well as quality of service. The assessment was conducted using a new approach for the construction of composite indicators, based on a directional distance function model. First, we assessed the hydropower plants allowing for complete flexibility in the definition of weights, enabling the identification of underperforming plants, and quantification of their potential for improvement. Next, we assessed the plants considering different perspectives regarding the importance attributed to each indicator. This allowed reflecting different points of view, focusing primarily on operation and maintenance costs or quality issues. The results identify the hydropower plants that can be considered benchmarks in different scenarios, and allow testing the robustness of plants' classification as benchmarks in the unrestricted model.

Abstract
This work presents a technique that uses the immersion of patients in an interactive 3D virtual environment in the orthoptic treatment of strabismus. The most important part of this work is the act of forcing the eyes to cooperate, increasing the level of adaptation of the nervous system to the binocular vision, allowing the diverted eye to be rehabilitated. Returning to the patient better visual comfort and quality of life. The virtual environment, because it is attractive, has the function of entertainment, possessing as its property the ability to propose challenges directed towards specific objectives. In addition to offering real-time biological feedback to the healthcare professional who is making use of this product. Another point is that this interface has ideal approaches to be used in orthoptic treatment. And all of it was developed with free software and made by a low-cost virtual reality glasses, Google Cardboard, which uses a smartphone as a display for its display.

Abstract
The stochastic nature of the renewable generators and price-responsive loads, as well as the high computational burden and violation of the generators' and load aggregators' privacy can make the centralized energy market management a big challenge for distribution network operators. In this paper, we first formulate the centralized energy trading as a bilevel optimization problem, which is nonconvex and includes the entities' optimal strategy to the price signals. We tackle the uncertainty issues by proposing a probabilistic load model and studying the down-side risk of renewable generation shortage. To address the nonconvexity of the centralized problem, we apply convex relaxation techniques and design proper price signals that guarantee zero relaxation gap. It enables us to address the privacy issue by developing a decentralized energy trading algorithm. For the sake of comparison, we use the dual decomposition and proximal Jacobian alternating direction method of multipliers for the algorithm design. Extensive simulations are performed on different standard test feeders to compare the CPU time of the proposed algorithm with the centralized approach and evaluate its performance in increasing the load aggregators' and generators' profit. Finally, we compare the impact of load and generation uncertainties on the optimality of the results.

Abstract
The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage (here-and-now) solutions where uncertainty has not yet been revealed. This study extends the authors' previous work addressing the stochastic product-launch planning problem, by developing a new Multi-Objective Integer Programming model, embedded in a unified decision-making framework, to obtain the final design strategy that "maximizes" productivity while considering the decision-maker preferences. An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that "maximize" productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved.

Abstract
For elderly people, social isolation is one significant factor in the deterioration of their life's quality. It has a profound impact on the general health and is produced by the diminution of social interactions. Nowadays, there is technology that can retrieve contextual data from the user's environment and interact with him in some simple, yet effective manners. The intelligent personal assistants can interact with the person by means of natural voice language. Previously, it was created a model for the adoption of electronic intelligent assistants by the elderly, as well a preliminary evaluation of the features of the intelligent personal assistants, currently available in the consumer market. In this article, it is evaluated the option of using the current consumer digital assistants to implement the proposed model. Several assistants were examined (Amazon, Google, Microsoft, and Apple), and their functionalities evaluated by creating four interaction scenarios and assessing the assistants' compliance with these scenarios.