Abstract
Purpose: To study the impact of shielding elements in the proximity of Intra-Operative Radiation Therapy (IORT) irradiation fields, and to generate graphical and quantitative information to assist radiation oncologists in the design of optimal shielding during pelvic and abdominal IORT. Method: An IORT system was modeled with BEAMnrc and EGS++ Monte Carlo codes. The model was validated in reference conditions by gamma index analysis against an experimental data set of different beam energies, applicator diameters, and bevel angles. The reliability of the IORT model was further tested considering shielding layers inserted in the radiation beam. Further simulations were performed introducing a bone-like layer embedded in the water phantom. The dose distributions were calculated as 3D dose maps. Results: The analysis of the resulting 2D dose maps parallel to the clinical axis shows that the bevel angle of the applicator and its position relative to the shielding have a major influence on the dose distribution. When insufficient shielding is used, a hotspot nearby the shield appears near the surface. At greater depths, lateral scatter limits the dose reduction attainable with shielding, although the presence of bone-like structures in the phantom reduces the impact of this effect. Conclusions: Dose distributions in shielded IORT procedures are affected by distinct contributions when considering the regions near the shielding and deeper in tissue: insufficient shielding may lead to residual dose and hotspots, and the scattering effects may enlarge the beam in depth. These effects must be carefully considered when planning an IORT treatment with shielding.

Abstract
A curvature sensor based on an Fabry-Perot (FP) interferometer was proposed. A capillary silica tube was fusion spliced between two single mode fibers, producing an FP cavity. Two FP sensors with different cavity lengths were developed and subjected to curvature and temperature. The FP sensor with longer cavity showed three distinct operating regions for the curvature measurement. Namely, a linear response was shown for an intermediate curvature radius range, presenting a maximum sensitivity of 68.52 pm/m(-1). When subjected to temperature, the sensing head produced a similar response for different curvature radii, with a sensitivity varying from 0.84 pm/degrees C to 0.89 pm/degrees C, which resulted in a small cross-sensitivity to temperature when the FP sensor was subjected to curvature. The FP cavity with shorter length presented low sensitivity to curvature.

Abstract
This work focus on the simulation of gas pipeline dynamic models in view to develop a leakage detection tool. The gas dynamics in the pipes is represented by a system of nonlinear partial differential equations. The linear partial differential equations is reduced to a transfer function model. Taking advantage of an electrical analogy, a pipeline can be represented by a two port network where gas mass flows behave like electrical currents and pressures like voltages. Thence, four transfer functions quadripole models are found to describe the gas pipeline dynamics, depending on the variable of interest at the boundaries. These models are simple enough to be used in the control and management of the network. These models have been validated using operational data and used to simulate a leakage. © Copyright 2016, PSIG, Inc.

Abstract
This paper describes a robot with 12 degrees of freedom for pick-and-place operations using bricks. In addition, an optimization approach is proposed, which determines the state of each joint (that establishes the pose for the robot) based on the target position while minimizing the effort of the servomotors avoiding the inverse kinematics problem, which is a hard task for a 12 DOF robot manipulator. Therefore, it is a multi-objective optimization problem that will be solved using two optimization methods: the Stretched Simulated Annealing method and the NSGA II method. The experiments conducted in a simulation environment prove that the proposed approach is able to determine a solution for the inverse kinematics problem. A real robot formed by several servomotors and a gripper is also presented in this research for validating the solutions.

Abstract
Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.

Abstract
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.