Abstract
In this work, we address through model predictive control (MPC) a constrained nonlinear plant described by a continuous-time dynamical model, which naturally leads to a sampled data control system. The numerical solution of the optimal control problems involved in MPC must utilize, eventually, some form of discretization. Nevertheless, there are several advantages in maintaining a continuous-time model until later stages. One advantage is that we can devise numerical procedures which, by exploiting additional freedom in selecting the discretization points, are more efficient when continuous-time models are used. Here, we discuss an extension to MPC of an Adaptive Mesh Refinement (AMR) algorithm, which has shown to be efficient in solving nonlinear optimal control problems. We derive a sufficient condition that guarantees that an MPC scheme using an adaptive time mesh refinement algorithm preserves stability.

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.