Abstract
Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81 +/- 0.59 and 1.98 +/- 0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.

Abstract
In this manuscript a novel method is presented for left ventricle (LV) tracking in three-dimensional ultrasound data using a hybrid approach combining segmentation and tracking-based clues. This is accomplished by coupling an affine motion model to an existing LV segmentation framework and introducing an energy term that penalizes the deviation to the affine motion estimated using a global Lucas–Kanade algorithm. The hybrid nature of the proposed solution can be seen as using the estimated affine motion to enhance the temporal coherence of the segmented surfaces, by enforcing the tracking of consistent patterns, while the underlying segmentation algorithm allows to locally refine the estimated global motion. The proposed method was tested on a dataset composed of 24 4D ultrasound sequences from both healthy volunteers and diseased patients. The proposed hybrid tracking platform offers a competitive solution for fast assessment of relevant LV volumetric indices, by combining the robustness of affine motion tracking with the low computational burden of the underlying segmentation algorithm. © 2017 Elsevier Ltd

Abstract
Fast volumetric cardiac imaging requires reducing the number of transmit events within a single volume. One way of achieving this is by limiting the field of view (FOV) of the recording to the myocardium when investigating cardiac mechanics. Although fully automatic solutions towards myocardial segmentation exist, translating that information in a fast ultrasound scan sequence is not trivial. In particular, multi-line transmit (MLT) scan sequences were investigated given their proven capability to increase frame rate (FR) while preserving image quality. The aim of this study was therefore to develop a methodology to automatically identify the anatomically relevant conically shaped FOV, and to translate this to the best associated MLT sequence. This approach was tested on 27 datasets leading to a conical scan with a mean opening angle of 19.7° ± 8.5°, while the mean "thickness" of the cone was 19° ± 3.4°, resulting in a frame rate gain of about 2. Then, to subsequently scan this conical volume, several MLT setups were tested in silico. The method of choice was a 10MLT sequence as it resulted in the highest frame rate gain while maintaining an acceptable cross-talk level. When combining this MLT scan sequence with at least four parallel receive beams, a total frame rate gain with a factor of approximately 80 could be obtained. As such, anatomical scan sequences can increase frame rate significantly while maintaining information of the relevant structures for functional myocardial imaging. © 2017 by the authors.

Abstract
Application frameworks are a powerful technique for large-scale reuse, but require a considerable effort to understand them. Good documentation is costly, as it needs to address different audiences with disparate learning needs. When code and documentation prove insuficient, developers turn to their network of experts. Nevertheless, this proves difficult, mainly due to the lack of expertise awareness (who to ask), wasteful interruptions of the wrong people and unavailability ( either due to intrusion or time constraints). The DRIVER platform is a collaborative learning environment where framework users can, in a non-intrusive way, store and share their learning knowledge while following the best practices of framework understanding (patterns). Developed by the authors, it provides a framework documentation repository, mounted on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended. Combining these social activities, the DRIVER platform promotes collaborative learning, mitigating intrusiveness, unavailability of experts and loss of tacit knowledge. This paper presents the assessment of DRIVER using a controlled academic experiment that measured the performance, effectiveness and framework knowledge intake of MSc students. The study concluded that, especially for novice learners, the platform allows for a faster and more effective learning process.

Abstract
The GReSBAS project (2016-2019) aims to enable the active participation of buildings in DR programs through gamified competition between building owners. In case of large buildings, gamified competition can be established within the building for its occupants, for instance having different floors of the building competing between them. This approach will allow building owners to reduce electricity costs and increase energy efficiency by motivating/rewarding building occupants for participating in DR programs. The concepts and tools developed under GReSBAS will be tested in two demonstration sites: a corporate building in Portugal and a residential building in Turkey. This paper presents the Portuguese demonstration site and describes how the energy consumption, temperature and building occupancy data will be collected, processed and used by the tools developed in GReSBAS.

Abstract
Software Product Lines (SPLs) are families of systems that share a high number of common assets while differing in others. In component-based systems, components themselves can be SPLs, i.e., each component can be seen as a family of variations, with different interfaces and functionalities, typically parameterized by a set of features and a feature model that specifies the valid combinations of features. This paper explores how to safely replace such families of components with more refined ones. We propose a notion of refinement for Interface Featured Timed Automata (IFTA), a formalism to model families of timed automata with support for multi-action transitions. We separate the notion of IFTA refinement into behavioral and variability refinement, i.e., the refinement of the underlying timed automata and feature model. Furthermore, we define behavioral refinement for the semantic level, i.e., transition systems, as an alternating simulation between systems, and lift this definition to IFTA refinement. We illustrate this notion with examples throughout the text and show that refinement is a pre-order and compositional.