Abstract
Objectives/Hypothesis. The aim of this article was to describe the results of an audio-perceptual evaluation carried out by 10 judges, on a database comprising 90 voice recordings plus 10 samples repetition, with the purpose of characterizing the intra-and interrater reliability. Study Design. Exploratory, transversal. Methods. The classification of the GRBAS parameters was obtained for each one of the 10 experts, concerning the 90 voice samples. The intraclass correlation coefficient determined the interrater reliability. For the 10 repeated voices, the intrarater reliability was assessed by means of a dispersion analysis. Results. The average judges' classification for each of the GRBAS parameters differs (P < 0.05). The values of the correlations, with confidence intervals of 95%, between the average scores for all components of the GRBAS scale lie, in general, between 0.838 and 0.966. The first three parameters of the scale (G, R, and B) have the higher interrater reliability. Differences were statistically significant (P < 0.05) for experts 1, 6, 9, and 10, which means a poor intrarater reliability for 40% of the judges. Conclusions. All the experts had similar evaluation criteria for the assessment of the five parameters of the GRBAS scale (the values of the confidence intervals at 95% of the experts average ratings of the GRB were above 0.8). However, its quantification is not statistically similar. Asthenia and Strain have lower reliability. Most experts do not reveal statistically significant differences between the values assigned to the GRB parameters (P > 0.05).

Abstract
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization (PSO) using natural selection, or survival-of-the-fittest, to enhance the ability to escape from local optima. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots. Therefore, the RDPSO decreases the amount of required information exchange among robots, and is scalable to large populations of robots. This paper presents a stability analysis of the RDPSO to better understand the relationship between the algorithm parameters and the robot's convergence. Moreover, the analysis of the RDPSO is further extended for real robot constraints (e.g., robot dynamics, obstacles and communication constraints) and experimental assessment with physical robots. The optimal parameters are evaluated in groups of physical robots and a larger population of simulated mobile robots for different target distributions within larger scenarios. Experimental results show that robots are able to converge regardless of the RDPSO parameters within the defined attraction domain. However, a more conservative parametrization presents a significant influence on the convergence time. To further evaluate the herein proposed approach, the RDPSO is further compared with four state-of-the-art swarm robotic alternatives under simulation. It is observed that the RDPSO algorithm provably converges to the optimal solution faster and more accurately than the other approaches.

Abstract
Transcriptional regulation plays an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We try to discover how genes interact when submitted to stress by exploring techniques of gene expression data analysis. We use several types of data, including high-throughput data. These results will help us recreate plausible regulatory networks by using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.

Abstract
Alzheimer's disease is a major cause of disability in older people and because of its nature and symptoms it is a huge weight on the caregivers and health system. The aim of this project, developed at Fraunhofer Portugal, is to create a remote monitoring system for patients suffering from Alzheimer's disease. This system comprises a device able to monitor the environment temperature and humidity, the patients location, using GPS, as well as the patient's movements, including falls. The device sends this information to the caregiver via SMS, and also to a server, where it is stored in a database developed in this work, and which the caregiver can access via a smartphone, using an application also developed in this project. This system therefore affords a close surveillance of the patient's status as well as access to historical data of the patient's information.

Abstract
In the end of a requirements elicitation phase, it is expectable that all information can be properly perceived by the implementation teams. In our work, we faced the problem of dealing with a large set of requirements that need to be implemented by different teams, to ensure on-time delivery. The teams are physically separated and with different working cultures and skills. The system requirements were (semi-) automatically derived from user requirements, are framed within a logical architecture (of the intended system) and the desired high-level execution scenarios are defined and included in the requirements description. Due to the large size of the architecture and to the nature of the working environment, the architecture must be divided before being delivered. In this paper, we present our approach for dividing the logical architecture of the intended system into modules to be delivered for implementation. The division in constructed upon requirement views that provide information about the modules, their interfaces, the intended execution scenarios, and the interfaces with other modules or systems. The approach is evaluated using the ISOFIN project.

Abstract
In this work, an all-fiber loop mirror using a four-bridge silica fiber with a double-Y-shaped suspended-core is presented for the measurement of strain and torsion. The sensing head is formed by a section of the microstructured fiber with 90 mm in length. The fiber loop sensor allowed observing a distinct interference pattern as a result of the geometry of the core fiber. Different sensitivities to strain and torsion were obtained, namely, -5.11 pm/mu e and +/- 1.34 pm/degree.