Abstract
One of the aspects that affects the reliability of a software application is the programming language used for its development. In industrial automation applications, the most commonly used languages are those defined in the IEC 61131-3 standard. This work contains an analysis of the semantics of ST and IL expressions, and highlights ambiguities in the standard that might lead to seemingly correct code being executed and evaluated differently in distinct IEC 61131-3 execution environments. Examples of code that may be evaluated to different results are given, and an evaluation of several commercially available IEC 61131-3 compilers is made and compared. A static code analyzer was developed capable of identifying legal IEC 61131-3 code that may result in ambiguous behavior, and this analyzer was used to test source code currently in use in real-world control applications.

Abstract
Schedulability analyses, while valuable in theoretical research, cannot be used in practice to reason about the timing behaviour of a real-time system without including the overheads induced by the implementation of the scheduling algorithm. In this paper, we provide an overhead-aware schedulability analysis based on demand bound functions for two hard real-time semi-partitioned scheduling algorithms, EDFWM- and C=D. This analysis is based on a novel implementation that uses a global clock to reduce the overheads incurred due to the release jitter of migrating subtasks. The analysis is used to guide the respective off-line task assignment and splitting procedures. Finally, results of an evaluation are provided highlighting how the different algorithms perform with and without a consideration of overheads.

Abstract
Data analysis and monitoring is currently carried out within enterprises using Business Intelligence tools that are subject to major limitations (as outlined in the state of the art analysis that we perform). Effective visualization support is a very much needed feature in Big Data applications. In this paper we examine the visualisation requirements of a real world banking application, and identify generic visualisation tasks that are essential for doing effective analysis of a complex process that produces amazingly large amounts of data. The requirements for the visualization support that we propose are modelled using an application wireframe that acts a story-board. The effectiveness of the visualization facilities that we propose is demonstrated through their application to the Big Data banking use-case.

Abstract

Abstract
Introduction: Colectomy is a major event that may significantly affect the outcome of ulcerative colitis (UC) in terms of both quality of life and mortality. This paper aims to identify clinical prognostic factors that may be significantly associated with this event. Methods: PubMed, ISI Web of Knowledge and Scopus were searched to identify studies investigating the association between clinical factors in adult patients with UC and studied events. The clinical factors evaluated in this meta-analysis were gender, smoking habits, disease extent, use of corticosteroids, and episodes of hospitalization. Results: Of the 3753 initially selected papers, 20 were included. The analysis showed a significantly lower risk of colectomy for female patients (odds ratio [OR] 0.78 [95% CI 0.68, 0.90]) and for smoking patients (OR 0.55 [0.33, 0.91]), and a higher risk for patients with extensive disease (OR 3.68 [2.39, 5.69]), for patients who took corticosteroids at least once (OR 2.10 [1.05, 4.22]), and for patients who were hospitalized (OR 4.13 [3.23, 5.27]). Conclusion: Gender, smoking habits, disease extent, need for corticosteroids, and hospitalization were all significantly associated with UC prognosis. These results may clarify the relative influences of these and other prognostic factors in the natural course of the disease and therefore help improve the management approach, thus improving the follow-up of patients.

Abstract
Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.