Abstract
This chapter provides an overview of the procedural modelling methodology that is addressed in this book. With the purpose of pointing out its need, the current issues in Procedural Modelling will be highlighted. In addition, the justification of some strategic decisions made during the development activities will be presented along with a brief enlightenment of the aforementioned methodology. © The Author(s) 2016.

Abstract
This paper describes the development of a novel microfluidic platform for multifactorial analysis integrating four label-free detection methods: electrical impedance, refractometry, optical absorption and fluorescence. We present the rationale for the design and the details of the microfabrication of this multifactorial hybrid microfluidic chip. The structure of the platform consists of a three-dimensionally patterned polydimethylsiloxane top part attached to a bottom SU-8 epoxy-based negative photoresist part, where microelectrodes and optical fibers are incorporated to enable impedance and optical analysis. As a proof of concept, the chip functions have been tested and explored, enabling a diversity of applications: (i) impedance-based identification of the size of micro beads, as well as counting and distinguishing of erythrocytes by their volume or membrane properties; (ii) simultaneous determination of the refractive index and optical absorption properties of solutions; and (iii) fluorescence-based bead counting.

Abstract
This paper addresses the problem of maximising the expected number of transplants in kidney exchange programmes. New schemes for matching rearrangement in case of failure are presented, along with a new tree search algorithm used for the computation of optimal expected values. Extensive computational experiments demonstrate the effectiveness of the algorithm and reveal a clear superiority of a newly proposed scheme, subset-recourse, as compared to previously known approaches.

Abstract
This paper presents an autonomous navigation and position estimation framework which enables an Unmanned Aerial Vehicle (UAV) to possess the ability to safely navigate in indoor environments. This system uses both the on-board Inertial Measurement Unit (IMU) and the front camera of a AR. Drone platform and a laptop computer were all the data is processed. The system is composed of the following modules: navigation, door detection and position estimation. For the navigation part, the system relies on the detection of the vanishing point using the Hough transform for wall detection and avoidance. The door detection part relies not only on the detection of the contours but also on the recesses of each door using the latter as the main detector and the former as an additional validation for a higher precision. For the position estimation part, the system relies on pre-coded information of the floor in which the drone is navigating, and the velocity of the drone provided by its IMU. Several flight experiments show that the drone is able to safely navigate in corridors while detecting evident doors and estimate its position. The developed navigation and door detection methods are reliable and enable an UAV to fly without the need of human intervention.

Abstract
The olive tree (Olea europaea L.) can be affected by Colletotrichum acutatum, causing a loss of yield and quality of the final products, whilst the incidence of this fungal infection depends on several factors, including cultivar susceptibility. Thus, the effect of C. acutatum infection in cultivars displaying different susceptibilities to this fungal disease (Galega Vulgar' - susceptible, Cobrancosa' - moderately susceptible, Picual' - tolerant) has been assessed through spectrophotometric methods and HPLC, while the FTIR spectra of the cuticles have been concomitantly registered, resorting to the ATR accessory. With the support of multivariate analysis, these spectra allowed to discriminate olives with distinct infection times, besides retrieving evidences concerning the different susceptibility of each cultivar, while these observations were reinforced by the spectrophotometric and chromatographic methods. Furthermore, the assessment of the phenolic profile evidenced individual compounds in the distinct cultivars, so as their variations in response to the fungal infection. Distinct olive cultivars were inoculated with Colletotrichum acutatum. The most resistant olive tree cultivars display the highest content in phenolics. FTIR-ATR-based analyses allow to monitor the response of olive fruits to C. acutatum.

Abstract
We introduced a new method for distinguishing two probability ensembles called one from each method, in which the distinguisher receives as input two samples, one from each ensemble. We compare this new method with multi-sample from the same method already exiting in the literature and prove that there are ensembles distinguishable by the new method, but indistinguishable by the multi-sample from the same method. To evaluate the power of the proposed method we also show that if non-uniform distinguishers (probabilistic circuits) are used, the one from each method is not more powerful than the classical one, in the sense that does not distinguish more probability ensembles. Moreover we obtain that there are classes of ensembles, such that any two members of the class are easily distinguishable (a definition introduced in this paper) using one sample from each ensemble; there are pairs of ensembles in the same class that are indistinguishable by multi-sample from the same method.