Abstract
One of the most common ways of collecting ocean data is to deploy sensors from the surface, allowing to understand the variation of water properties with depth. Autonomous vertical profilers are robotic vehicles that replace human operators in this task. They form a particular class of autonomous underwater vehicles that move predominantly along the vertical axis, typically with reduced control on the horizontal axis. This paper describes a propeller driven autonomous underwater profiler, optimized for shallow waters. The vehicle has no fins or other control surfaces, and uses four independent thrusters to provide both vertical and horizontal motion, including hovering in the water column. The paper describes the main subsystems, including the hardware implementation, the software structure, and the motion controllers, with experimental data from the first trials. © 2017 Marine Technology Society.

Abstract
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Abstract
Entropy and compression have been used to distinguish fetuses at risk of hypoxia from their healthy counterparts through the analysis of Fetal Heart Rate (FHR). Low correlation that was observed between these two approaches suggests that they capture different complexity features. This study aims at characterizing the complexity of FHR features captured by entropy and compression, using as reference international guidelines. Single and multi-scale approaches were considered in the computation of entropy and compression. The following physiologic-based features were considered: FHR baseline; percentage of abnormal long (% abLTV) and short (% abSTV) term variability; average short term variability; and, number of acceleration and decelerations. All of the features were computed on a set of 68 intrapartum FHR tracings, divided as normal, mildly, and moderately-severely acidemic born fetuses. The correlation between entropy/compression features and the physiologic-based features was assessed. There were correlations between compressions and accelerations and decelerations, but neither accelerations nor decelerations were significantly correlated with entropies. The % abSTV was significantly correlated with entropies (ranging between 0.54 and 0.62), and to a higher extent with compression (ranging between 0.80 and 0.94). Distinction between groups was clearer in the lower scales using entropy and in the higher scales using compression. Entropy and compression are complementary complexity measures.

Abstract
Distributed Generation (DG) using renewable technologies is increasing due to their benefits including energy security and emission reduction. However, installing new DGs in distributed networks is limited due to network constraints such as feeder capacity and short circuit level, as well as higher investment costs. In this paper, network reconfiguration and reactive power planning are used to maximize DG penetration level and to minimize annual loss for DGs with biomass technologies. In order to model the problem uncertainties, 96 scenarios considering ten different network load levels are studied. A multi objective method is applied for solving this optimization problem by using Pareto front. The numerical results indicate the positive impacts of the proposed approach on improving the network security.

Abstract
The quality and composition of biomass as used in Portugal's thermal power plants is highly variable. The biomass consists mainly of residual forest biomass derived from forestry operations and wood waste from industrial processes, in particular paper and pulp industry. Its quality and composition is influenced by the presence of moisture and inert fragments, the latter being incorporated during collection or as a consequence of adherence to the biomass prior to collection. This variability presents difficulties for the thermal power plants; besides being an additional operational cost, the presence of large amounts of water and inerts in biomass used as a fuel, can result in problems related to the instability of the combustion and the accumulation of ash or rock that have to be removed and discarded. The objective of this paper is to review the main parameters that influence the quality of biomass, while analysing the current state-of-the-art power generation from the biomass sector in Portugal, as a new contribution to earlier studies.

Abstract
A compact sensing structure using two distinct optical devices, a microfiber knot resonator and an abrupt taper-based Mach-Zehnder interferometer (MZI), is presented. The device was fabricated using only CO2 laser processing. The transmission spectrum presents two different components with different sensitivities to different physical and chemical parameters. The sensor was characterized in temperature and refractive index. For temperature sensing in water, the MZI component presents a sensitivity of -196 +/- 2 pm/degrees C while the microfiber knot resonator (MKR) component shows a sensitivity of 25.1 +/- 0.9 pm/degrees C, for water temperature variations of 12 degrees C. Sensitivities of 1354 +/- 14 nm/RIU and -43 +/- 4 nm/RIU were achieved for refractive index sensing for the MZI and the MKR components, respectively, in a refractive index range from 1.32823 to 1.33001. The matrix method was used for the simultaneous measurement of temperature and refractive index.