In addition to satisfying data consistency requirements as in conventional database systems, concurrency control in real-time database systems must also satisfy timing constraints, such as deadlines associated with transactions. Concurrency control for a real time database system can be studied from several different perspectives. This largely depends on how the system is specified in terms of data consistency requirements and timing constraints. The objective of this research is to investigate and propose concurrency control algorithms for real time database systems, that not only satisfy consistency requirements but also meet transaction timing constraints as much as possible, minimizing the percentage and average lateness of deadline-missing transactions.

This paper explains the building of robust software using multiagent reputation. One of the major goals of software engineering is to achieve robust software. Our hypothesis is that robustness can be increased through redundancy. We achieve redundancy by using agents, with each agent wrapping a different algorithm with similar functionality. The agents build trust in each other using reinforcement learning. Two types of reputation management are simulated: one in which the reputations of all agents are maintained centrally and a second, which is distributed, where an agent maintains locally the reputations of the agents it knows and each agent can have its own evaluation of its known agents’ performances. We simulated and compared two ways of achieving distributed reputation management. A probabilistic function is used as a preprocessing technique for selecting a set of agents based on reinforcement values of the agents. The values are obtained based on the correctness of the results the agent produces in performing the task it is given. Voting is used as a post-processing technique for judging the correctness of the output generated by the agents. 1.

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Safety regulations and standards imposed by national regulators on nuclear power plant systems provide highlevel requirements, recommendations and/or guidance expressed in natural language. In many cases, this leaves a large margin for interpretation, not all of which are acceptable to a given regulator. Currently the elements that lead to the establishment of acceptable/accepted practices are not always documented, nor are these practices formally modeled. When a new standard appears or when Electricité de France (EDF) has to discuss a standard with another regulator, there is no systematic process to build a practice. Domain-specific modeling, traceability and variability modeling are Model-Driven Engineering (MDE) techniques that could address various aspects of practice formalization. This paper precisely defines the modeling issues that are currently faced by EDF when managing regulatory safety requirements, standards and practices. Then we review existing requirements modeling techniques to understand their benefits and limits according to EDF’s needs.

ABSTRACT: The notion of an integrated project database (IPDB) has existed for decades. Over that time many projects have been undertaken to develop the technologies and frameworks required to implement an IPDB. Also over that time, there has been promotion of the benefits and impacts that IPDB systems will have on the industry. As there are still no industrially stable IPDB systems in existence, the industry's perception of what they are and what they can do has diverged from many of the original presentations. It is also clear that researchers and developers involved in IPDB development have many different ideas about what constitutes an IPDB and what is, or is not, possible to create. This paper aims to describe misconceptions which are growing up around IPDB systems, and presents the authors ' view of reality. Consensus in this area is currently being sought through the majority opinion of the UK network of experts in objects and integration (URL-1 1999) which is run by the DETR. 1

Modeling social media support for the elicitation of citizen opinion

Ionic solutions are mixtures of interacting anions and cations. They hardly resemble dilute gases of uncharged non‐interacting point particles described in elementary textbooks. Biological and electrochemical solutions have many components that interact strongly as they flow in concentrated environments near electrodes, ion channels, or active sites of enzymes. Flows are driven by a combination of electrical and chemical potentials that depend on the charges, concentrations, and sizes of all ions, not just the same type of ion. We use a variational method EnVarA that combines Hamilton’s least action and Rayleigh’s dissipation principles to create a variational field theory that includes flow, friction, and complex structure with physical boundary conditions. EnVarA optimizes both the action integral functional of classical mechanics and the dissipation functional. These functionals can include entropy and dissipation as well as potential energy. The stationary point of the action is determined with respect to the trajectory of particles. The stationary point of the dissipation is determined with respect to rate functions (such as velocity). Both variations are written in one Eulerian (laboratory) framework. In variational analysis, an ‘extra layer ’ of mathematics is used to derive partial differential equations. Energies and dissipations of different components are combined in EnVarA and Euler

Abstract The i * framework is a widespread formalism in the software engineering discipline that allows expressing intentionality of system actors. From the time it was issued, in the mid-nineties, a growing research community has adopted it either in its standard form or formulating variations in order to adapt it to some particular purpose. New methods, techniques and tools have made evolve the framework in a way that it may be currently considered quite mature from the scientific perspective. However, the i * framework has not been transferred to practitioners at the same extent yet: industrial experiences using i * are not many and have been mainly conducted by i * experts that are part of that very research community. Therefore, it may be argued that some steps are needed for boosting the adoption of i * by practitioners. In this chapter, we identify some scientific challenges whose overcoming could represent a step towards this goal. For each challenge, we present the problem that is addressed, its current state of the art and some envisaged lines of research. 1