Abstract
Probiotics benefits in fish farming have been usually inferred appraising the effects observed on the host and not through the direct assessment of probiotic dynamics in the host gut microbiota. To overcome this gap, quantitative PCR (qPCR) can be a powerful approach to study the bacterial dynamics in fish gut microbiota. The presented work proposes four B. licheniformis-specific DNA markers and details a qPCR method to track putative probiotics B. licheniformis on fish gut. The four B. licheniformis-specific DNA markers - BL5B (hypothetical protein BL00303), BL8A (serA2), BL13C (rfaB) and BL18A (ligD) - were selected and validated by PCR and multiplex-PCR with 20 B. licheniformis isolates and a broad range of non-target bacteria. To assess the dynamics of B. licheniformis in the digesta of farmed fish, a qPCR was validated using markers BL8A and BL18A and calibration curves obtained for both markers with digesta samples spiked with B. licheniformis cells showed a high correlation (R-2 > 0.99) over 6 log units (CFU/ reaction), and a limit of detection (LOD) as low as 247 CFUs/reaction. Furthermore, the consistent qPCR repeatability and reproducibility underline the specificity and reliability of the qPCR proposed. Ultimately, the possibility to monitor the dynamics of B. licheniformis probiotics in the gut microbiota of farmed fish might be instrumental to optimize best practices in aquaculture.

Abstract
In this paper, a function-based modulation control strategy for modular multilevel converters (MMCs) in a distributed generation (DG) system is proposed. Two novel modulation functions are introduced in this paper for the switching state functions of the lower and upper sub-modules of the interfaced MMC, which is considered as the main contribution of this control technique over other control methods. The amplitude and phase angle of the output current of the interfaced MMC can be easily applied to the proposed modulation functions to prepare a specific active and reactive power injection into the demand side. In addition, the equivalent capacitors of the lower and upper sub-modules are defined by taking into account the introduced modulation functions to guarantee an appropriate operation for the interfaced MMC in DG systems. Simulation results validate the capability of the proposed control method for MMCs under load variations.

Abstract
Air temperature data from many locations worldwide are only available as series of daily minima and maxima temperatures. Historically, several different approaches have been used to estimate the actual daily mean temperature, as only in the last two or three decades automatic thermometers are able to compute its actual value. The most common approach is to estimate it by averaging the daily minima and maxima. When only daily minima and maxima are available, an alternative approach, proposed by Dall'Amico and Hornsteiner in 2006, uses the two daily extremes together with next day minima temperature and a coefficient related to the local daily astronomical sunset time. Additionally, the method uses two optimizable coefficients related to the region's temperature profile. In order to use this approach it is necessary to optimize the region's unknown parameters. For this optimization, it is necessary a dataset containing the maxima, minima, and the actual daily mean temperatures for at least one year. In this research, for the period 2007-2014, we used three datasets of minima, maxima and actual mean temperatures obtained at three automatic meteorological stations located in the Douro Valley to optimize the two unknown parameters in the Dall'Amico and Hornsteiner approach. Moreover, we compared the actual mean daily temperatures available from the three datasets with the correspondent values estimated by using i) the usual approach of averaging the daily maxima and minima temperatures and ii) the Dall'Amico and Hornsteiner approach. Results show that the former approach overestimates, on average, the daily mean temperatures by 0.5 degrees C. The Dall'Amico and Hornsteiner approach showed to be a better approximation of mean temperatures for the three meteorological stations used in this research, being unbiased relative to the actual mean values of daily temperatures. In conclusion, this research confirms that the Dall'Amico and Hornsteiner is a better approach to estimate the mean daily temperatures and provides the optimized parameters for three sites located at each of the three sub-regions of the Douro Valley (Baixo Corgo, Cima Corgo and Douro Superior).

Abstract
Current market demands require several degrees of flexibility, speed, and repetitiveness of manufacture and logistic processes. Considering that a fourth industrial revolution is to be expected in a near future - which is highly based on smart machines, storage systems, and production facilities that cooperate to allow dynamic businesses and engineering processes - robotics presents itself as an increasingly sought-after solution, since it is often associated with such concepts. Hence, it is of no wonder that the worldwide operational stock of industrial robots has been increasing in a steady pace for the past decades and is expected to progress in such a trend. Within the several activities for robots on industrial applications, handling operations are regarded as predominant on the European market. Subsequently, palletizing applications are amongst the handling operations that have played an important role in the end stages of modern supply chains. In this context, this work aims to contextualise and develop an application for palletizing robots. This application, together with an off-line programming software (RobotStudio), allows for automatic programming of a robot's palletizing functions. Developed in the robot's native language (RAPID), the application has a basic user interface written in XML and can provide different pallet patterns. © 2018 IEEE.

Abstract
In this work a fully automatic system to identify the extensor tendon on ultrasound images of the metacarpophalangeal joint is proposed. These images are used to diagnose rheumatic diseases which are one of the main causes of impairment and pain in developed countries. The early diagnosis of these conditions is crucial to a proper treatment and follow-up and so, a system such as the one proposed here, could be useful to automatically extract relevant information from the resulting images. This work is an extension of a previous published work which uses manual annotations of the skin line, metacarpus and phalange to guide the extensor tendon segmentation. By introducing automatic segmentations of all structures, we expect to create a fully automatic system, which is more interesting to the possible end-users. Results show that, despite an expected loss in the performance, it is still possible to correctly identify the extensor tendon with a Confidence of 88% considering a maximum allowed Modified Hausdorff Distance of 0.5mm.

Abstract
Habitat mapping is an important task to manage ecosystems. This task becomes most challenging when it comes to marine habitats as it is hard to get good images in underwater conditions and to precisely locate them. In this paper we present a novel technique for performing habitat mapping automating all phases, from data collection to classification, lowering costs and increasing efficiency throughout the process. For mapping habitats in a vast coastal region, we use visible light cameras mounted on autonomous underwater vehicles, capable of collecting and geo-locating all acquired data. The optic images are enhanced using Computer Vision techniques, to help specialists identify the habitats they contain (during training phase). In a later stage, we employ convolutional neural networks to automatically identify habitats in all imagery. Habitats are classified according to the European Nature Information System, an European classification standard for habitats.