Abstract
In microcirculation, the cell-free layer (CFL) is a well-known physiological phenomenon that plays an important role in reducing the flow resistance and in balancing nitric oxide (NO) production by endothelial cells and NO scavenging by red blood cells. To better understand this phenomenon, several blood flow studies have been performed in simple geometries at both in vivo and in vitro environments. However, to date little information is available regarding the effects imposed by a complex branching network on the CFL.The present study shows the CFL layer variation at a microchannel network.The images were captured using a high-speed video microscopy system and the thickness of the CFL was measured using both manual and automatic image analysis techniques. Using this methodology, it was possible to visualise the in vitro blood flowing through the network and to identify several flow phenomena that happen in microcirculation. Overall, the results have shown that the concentration of cells and the geometrical configuration of the network have a major impact on the CFL thickness. In particular, the thickness of the CFL decreases as the fluid flows through a microchannel network composed with successive smaller channels. It was also clear that, for the full length of the network, the CFL thickness tends to decrease with the increase of the concentration of cells. The automatic method developed becomes inaccurate for high haematocrit and needs be calibrated by manual methods for Hcts bigger than 10%. The results obtained from this study could help the development and validation of multiscale numerical models able to take into account the CFL for simulating microvascular blood flow.

Abstract
Scheduling assumes a crucial importance in manufacturing systems, optimizing the allocation of operations to the right resources at the most appropriate time. Particularly in the Flexible Manufacturing System (FMS) topology, where the combination of possibilities for this association exponential increases, the scheduling task is even more critical. This paper presents a heuristic scheduling method based on genetic algorithm for a robotic-centric FMS. Real experiments show the effectiveness of the proposed algorithm, ensuring a reliable and optimized scheduling process. © 2018 by World Scientific Publishing Co. Pte. Ltd.

Abstract
The need for efficient automation methods has prompted the fast development in the field of Robotics. However, most robotic solutions found in industrial environments lack in both flexibility and adaptability to be applied to any generic task. A particular problem arises when robots are integrated in work cells with extra degrees of freedom, such as external axis or positioners. The specification/design of high redundancy systems, including robot selection, tool and fixture design, is a multi-variable problem with strong influence in the final performance of the work cell. This work builds on top of optimisation techniques to deal with the optimal poses reachability for high redundancy robotic systems. In this paper, it will be proposed a poses optimisation approach to be applicable within high redundancy robotic systems. The proposed methodology was validated by using real environment existent infrastructures, namely, the national CoopWeld project.

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to the Multi Agent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

Abstract
Supply chains are complex interdependent structures in which tasks' accomplishment is the result of a compromise between all the entities involved. This complexity is particularly pronounced when dealing with chipping and transportation tasks within a forest-based biomass energy production supply chain. The logistic costs involved are significant and the number of network nodes are usually in a considerable number. For this reason, efficient optimization tools should be used in order to derive cost effective scheduling. In this work, soft computing optimization tools, namely genetic algorithms (GA) and particle swarm optimization (PSO), are integrated within a discrete event simulation model to define the vehicles operational schedule in a typical forest biomass supply chain. The presented simulation results show the proposed methodology effectiveness in dealing with the addressed systems.

Abstract