Run Time Models in Adaptive Service Infrastructure

Software in the near ubiquitous future will need to cope with vari- ability, as software systems get deployed on an increasingly large diversity of computing platforms and operates in different execution environments. Heterogeneity of the underlying communication and computing infrastruc- ture, mobility inducing changes to the execution environments and therefore changes to the availability of resources and continuously evolving requirements require software systems to be adaptable according to the context changes. Software systems should also be reliable and meet the user's requirements and needs. Moreover, due to its pervasiveness, software systems must be de- pendable. Supporting the validation of these self-adaptive systems to ensure dependability requires a complete rethinking of the software life cycle. The traditional division among static analysis and dynamic analysis is blurred by the need to validate dynamic systems adaptation. Models play a key role in the validation of dependable systems, dynamic adaptation calls for the use of such models at run time. In this paper we describe the approach we have un- dertaken in recent projects to address the challenge of assessing dependability for adaptive software systems

Immunogenicity of viral vaccines in the italian military

Military personnel of all armed forces receive multiple vaccinations and have been doing so since long ago, but relatively few studies have investigated the possible negative or positive interference of simultaneous vaccinations. As a contribution to fill this gap, we analyzed the response to the live trivalent measles/mumps/rubella (MMR), the inactivated hepatitis A virus (HAV), the inactivated trivalent polio, and the trivalent subunits influenza vaccines in two cohorts of Italian military personnel. The first cohort was represented by 108 students from military schools and the second by 72 soldiers engaged in a nine-month mission abroad. MMR and HAV vaccines had never been administered before, whereas inactivated polio was administered to adults primed at infancy with a live trivalent oral polio vaccine. Accordingly, nearly all subjects had baseline antibodies to polio types 1 and 3, but unexpectedly, anti-measles/-mumps/-rubella antibodies were present in 82%, 82%, and 73.5% of subjects, respectively (43% for all of the antigens). Finally, anti-HAV antibodies were detectable in 14% and anti-influenza (H1/H3/B) in 18% of the study population. At mine months post-vaccination, 92% of subjects had protective antibody levels for all MMR antigens, 96% for HAV, 69% for the three influenza antigens, and 100% for polio types 1 and 3. An inverse relationship between baseline and post-vaccination antibody levels was noticed with all the vaccines. An excellent vaccine immunogenicity, a calculated long antibody persistence, and apparent lack of vaccine interference were observed

Min-Max Coverage in Multi-interface Networks

International audienceWe consider devices equipped with multiple wired or wireless interfaces. By switching among interfaces or by combining the available interfaces, each device might establish several connections. A connection is established when the devices at its endpoints share at least one active interface. Each interface is assumed to require an activation cost. In this paper, we consider the problem of establishing the connections defined by a network G = (V,E) while keeping as low as possible the maximum cost set of active interfaces at the single nodes. Nodes V represent the devices, edges E represent the connections that must be established. We study the problem of minimizing the maximum cost set of active interfaces among the nodes of the network in order to cover all the edges. We prove that the problem is NP-hard for any fixed Δ ≥ 5 and k ≥ 16, with Δ being the maximum degree, and k being the number of different interfaces among the network. We also show that the problem cannot be approximated within Ω(ln Δ). We then provide a general approximation algorithm which guarantees a factor of O((1 + b)ln (Δ)), with b being a parameter depending on the topology of the input graph. Interestingly, b can be bounded by a constant for many graph classes. Other approximation and exact algorithms for special cases are presented

A Feedback-Based Adaptive Service-Oriented Paradigm for the Internet of Things

© Springer International Publishing AG, part of Springer Nature 2018. With integrating physical devices into digital world, Internet of Things (IoT) have presented tremendous potential in various different application domains such as smart cities, intelligent transportation, smart home, healthcare and industrial automation. However, current IoT solutions and usage scenarios are still very limited because of the difficulty in sensing the context in continuously changing environments and adaptation to the changes accordingly. The complex dynamic interactions between system components and physical environments are a bit challenging especially when there are other concerns such as scalability and heterogeneity. To solve this problem, a novel adaptive service-oriented paradigm is proposed to support IoT from a low-level viewpoint. The paradigm can overcome some disadvantages of REST (Representational State Transfer) architecture style in the IoT. Two classical examples are illustrated using the proposed paradigm by adding an extra constraint based on REST to improve system states verification and enhance the functionality in modelling physical processes

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques