Lotus@Runtime: A Tool for Runtime Monitoring and Verification of Self-adaptive Systems (Artifact)

This paper presents Lotus@Runtime, an extensible tool that uses models@runtime to monitor and verify self-adaptive systems. The tool monitors the execution traces generated by a self-adaptive system and annotates the probabilities of occurrence of each system action on their respective transition on the system model, which is created at design time in the tool as a Labelled Transition System (LTS). Then, runtime checks of a set of reachability properties are performed against the updated probabilistic model. If a property is violated, the self-adaptive system can be informed by a notification mechanism provided by Lotus@Runtime. The applicability of the proposed tool has been demonstrated by two service-based self-adaptive systems taken and adapted from the literature

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Heterofucans from the Brown Seaweed Canistrocarpus cervicornis with Anticoagulant and Antioxidant Activities

Fucan is a term used to denominate a family of sulfated polysaccharides rich in sulfated l-fucose. We extracted six fucans from Canistrocarpus cervicornis by proteolytic digestion followed by sequential acetone precipitation. These heterofucans are composed mainly of fucose, glucuronic acid, galactose and sulfate. No polysaccharide was capable of prolonging prothrombin time (PT) at the concentration assayed. However, all polysaccharides prolonged activated partial thromboplastin time (aPTT). Four sulfated polysaccharides (CC-0.3/CC-0.5/CC-0.7/CC-1.0) doubled aPTT with only 0.1 mg/mL of plasma, only 1.25-fold less than Clexane®, a commercial low molecular weight heparin. Heterofucans exhibited total antioxidant capacity, low hydroxyl radical scavenging activity, good superoxide radical scavenging efficiency (except CC-1.0), and excellent ferrous chelating ability (except CC-0.3). These results clearly indicate the beneficial effect of C. cervicornis polysaccharides as anticoagulants and antioxidants. Further purification steps and additional studies on structural features as well as in vivo experiments are needed to test the viability of their use as therapeutic agents

Constrained mixture estimation for analysis and robust classification of clinical time series

Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-β (IFNβ) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNβ treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

The big five personality traits, perfectionism and their association with mental health among UK students on professional degree programmes

Background In view of heightened rates of suicide and evidence of poor mental health among healthcare occupational groups, such as veterinarians, doctors, pharmacists and dentists, there has been increasing focus on the students aiming for careers in these fields. It is often proposed that a high proportion of these students may possess personality traits which render them vulnerable to mental ill-health. Aim To explore the relationship between the big five personality traits, perfectionism and mental health in UK students undertaking undergraduate degrees in veterinary medicine, medicine, pharmacy, dentistry and law. Methods A total of 1744 students studying veterinary medicine, medicine, dentistry, pharmacy and law in the UK completed an online questionnaire, which collected data on the big five personality traits (NEO-FFI), perfectionism (Frost Multidimensional Perfectionism Scale), wellbeing (Warwick-Edinburgh Mental Well-being Scale), psychological distress (General Health Questionnaire-12), depression (Beck Depression Inventory-II) and suicidal ideation and attempts. Results Veterinary, medical and dentistry students were significantly more agreeable than law students, while veterinary students had the lowest perfectionism scores of the five groups studied. High levels of neuroticism and low conscientiousness were predictive of increased mental ill-health in each of the student populations. Conclusions The study highlights that the prevailing anecdotal view of professional students possessing maladaptive personality traits that negatively impact on their mental health may be misplaced