Abstract
Endoscopic capsules are vitamin-sized devices that create 8 to 10 hour videos of the digestive tract. They are the leading diagnosing method for the small bowel, a region not easily accessible with traditional endoscopy techniques. However, these capsules do not provide localization information, even though it is crucial for the diagnosis, follow-ups and surgical interventions. Currently, the capsule localization is either estimated based on scarce gastrointestinal tract landmarks or given by additional hardware that causes discomfort to the patient and represents a cost increase. Current software methods show great potential, but still need to improve in order to overcome their limitations. In this work, a visual odometry method for capsule localization inside the small bowel is proposed.

Abstract
Adaptive optics (AO) restore the angular resolution of ground-based telescopes, but at the cost of delivering a time- and space-varying point spread function (PSF) with a complex shape. PSF knowledge is crucial for breaking existing limits on the measured accuracy of photometry and astrometry in science observations. In this paper, we concentrate our analyses of the anisoplanatism signature only on to the PSF. For large-field observations (20 arcmin) with single-conjugated AO, PSFs are strongly elongated due to anisoplanatism that manifests itself as three different terms for laser guide star (LGS) systems: angular, focal and tilt anisoplanatism. First, we propose a generalized model that relies on a point-wise decomposition of the phase and encompasses the non-stationarity of LGS systems. We demonstrate that it is more accurate and less computationally demanding than existing models: it agrees with end-to-end physical-optics simulations to within 0.1 per cent of PSF measurables, such as the Strehl ratio, FWHM and the fraction of variance unexplained (FVU). Secondly, we study off-axis PSF modelling with respect to the Cn2(h) profile (heights and fractional weights). For 10-mclass telescopes, PSF morphology is estimated at the 1 per cent level as long as we model the atmosphere with at least seven layers, whose heights and weights are known with precisions of 200 m and 10 per cent, respectively. As a verification test, we used the Canada's National Research Council - Herzberg NFIRAOS Optical Simulator (HeNOS) testbed data, featuring four lasers. We highlight the capability of retrieving off-axis PSF characteristics within 10 per cent of the FVU, which complies with the expected range from the sensitivity analysis. Our new off-axis PSF modelling method lays the groundwork for testing on-sky in the near future.

Abstract
The integration of storage systems into smart grids is being widely analysed in order to increase the flexibility of the power system and its ability to accommodate a higher share of wind and solar power. The success of this process requires a comprehensive techno-economic study of the storage technology in contrast with electricity market behaviour. The focus of this work is on lead-acid and vanadium redox flow batteries. This paper presents a novel probabilistic optimization model for managing energy storage systems. The model is able to incorporate the forecasting error of electricity prices, offering with this a near-optimal control option. Using real data from the Spanish electricity market from the year 2016, the probability distribution of forecasting error is determined. The model determines electricity price uncertainty by means of Monte Carlo Simulation and includes it in the energy arbitrage problem, which is eventually solved by using an integer-coded genetic algorithm. In this way, the probability distribution of the revenue is determined with consideration of the complex behaviours of lead acid and vanadium redox flow batteries as well as their associated operating devices such as power converters.

Abstract
Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency-based methods are a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the biobjective version of the algorithm logical analysis of inconsistent data is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, heuristic decomposition uses parallel processing to solve a set covering problem and a cross-validation technique. The biobjective solutions contain the number of reduced features and the accuracy. The algorithm is applied to omics datasets with genome-like characteristics of patients with rare diseases.

Abstract
For oncological patients, the introduction of palliative care in the early stages of the disease’s progression can have great benefits. The Portuguese government recently introduced a program to provide home palliative care support by creating specialized mobile teams, able to track, visit and address the patients’ problems. These teams must be available for the patient, when necessary and if necessary. The teams must also have updated knowledge about the daily evolution of the patients’ health. The Douro Sul Healthcare Centers, together with the University of Trás-os-Montes e Alto Douro, developed and implemented an ICT system to track the status of each and every one of the patients. The system has several components, including: a mobile app for the patients or their caregivers to report daily how the patient’s symptoms have evolved over the last 24 h; a web app for the teams to browser their patients’ status. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
In the context of compiler optimizations, tuning of parameters and selection of algorithms, runtime adaptivity and autotuning are becoming increasingly important, especially due to the complexity of applications, workloads, computing devices and execution environments. For identifying and specifying adaptivity, different phases are required: analysis of program hotspots and adaptivity opportunities, code restructuring, and programming of adaptivity strategies. These phases usually require different tools and modications to the source code that may result in difficult to maintain and error prone code. This paper presents a flexible approach to support the different phases when developing adaptive applications. The approach is based on a single domain-specific language (DSL), able to specify and evaluate multiple strategies and to maintain a separation of concerns. We describe the requirements and the design of the DSL, an accompanying API, and of a Java-to-Java compiler that implements the approach. In addition, we present and evaluate the use of the approach to specify runtime adaptivity strategies in the context of Java programs, especially when considering runtime autotuning of optimization parameters and runtime selection of algorithms. Although simple, the case studies shown truly demonstrate the main advantages of the approach in terms of the programming model and of the performance impact.