Team automata for security analysis

We show that team automata (TA) are well suited for security analysis by reformulating the Generalized Non-Deducibility on Compositions (GNDC) schema in terms of TA. We then use this to show that integrity is guaranteed for a case study in which TA model an instance of the Efficient Multi-chained Stream Signature (EMSS) protocol

Disease-related malnutrition and nutritional assessment in clinical practice

Malnutrition has been defined as “a state resulting from lack of intake or uptake of nutrition that leads to altered body composition (decreased fat-free mass) and body cell mass leading to diminished physical and mental function and impaired clinical outcome from disease”. The prevalence of malnutrition in hospital populations is reported to vary between 11-45%. To prevent or treat malnutrition, early recognition of the (risk for) malnutrition is necessary. The prevalence of malnutrition, its characteristics, and the subsequent necessary interventions may vary in different patient populations. This thesis aimed to provide new insights with regard to the (risk) assessment of disease-related malnutrition and its implications for healthcare professionals in order to improve their care for patients in daily clinical practice. This thesis shows that a substantial part of the patients prior to vascular surgery, or patients with COPD following a pulmonary rehabilitation program, is malnourished or at risk for malnutrition, whereas these patients may be unrecognized and thus not treated. Therefore screening and assessment are important, and should be performed in such a way that all domains of malnutrition are represented, as well as the underlying factors that give guidance to interventions. Insight in what motivates people to eat healthy and new methods to measure body composition can be helpful to the nutrition care process. This is important as (risk for) malnutrition is a predictor of worse clinical outcome and is associated with frailty. To improve recognition of malnutrition and nutrition-related disorders, more knowledge and awareness is needed

Supporting reading comprehension in history education:the use and usefulness of a digital learning environment

Reading comprehension is an essential skill for processing textual information and acquiring knowledge. Therefore, being able to read and comprehend informational texts is a crucial prerequisite for academic success in textual subjects such as history. Over the last decade, technology-enhanced learning environments have often been used to support students’ reading and learning processes, which has proven to be effective for reading comprehension in general and for history education in particular. The research in this dissertation aimed at stimulating students’ cognition, metacognition and motivation with the help of a digital learning environment (DLE) in the context of the self-regulated reading of expository texts for the subject of history. Cognitive, metacognitive, and motivational scaffolds, called ‘hints’, were incorporated in the DLE to support students’ comprehension of expository texts. The DLE measured students’ reading comprehension performance, cognitive and behavioural engagement, and subject-specific reading skills such as identifying cause and effect. Additionally, students’ results were presented to teachers trough basic and extended data output in the DLE, and teachers were trained to translate these data into effective reading strategy instruction. This dissertation analyses the practical implementation of the DLE and its effects on students’ text comprehension, self-regulation, motivation, engagement, historical content knowledge, and historical reasoning ability. It also describes the effects of using the DLE on teachers’ use of data and their instructional practice. By focusing on all these interrelated aspects, the results described provide a comprehensive overview of the use and usefulness of a DLE to support students’ reading comprehension in history education

Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLan with action rates, which specify the likelihood of exhibiting particular behaviour or of installing features at a specific moment or in a specific order. The enriched language (called PFLan) allows us to specify models of software product lines with probabilistic configurations and behaviour, e.g. by considering a PFLan semantics based on discrete-time Markov chains. The Maude implementation of PFLan is combined with the distributed statistical model checker MultiVeStA to perform quantitative analyses of a simple product line case study. The presented analyses include the likelihood of certain behaviour of interest (e.g. product malfunctioning) and the expected average cost of products.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301