Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

/pdf/principles-of-model-checking-249iimickz.pdf

Principles of Model Checking

This study surveys the literature examining the privatization of state-owned enterprises (SOEs) We review the history of privatization, the theoretical and empirical evidence on the relative performance of state owned and privately owned firms, the types of privatization, if and by how much privatization has improved the performance of former SOEs in non-transition and transition countries, how investors in privatizations have fared, and the impact of privatization on the development of capital markets and corporate governance. In most settings privatization "works" in that the firms become more efficient, more profitable, and financially healthier, and reward investors.

/pdf/from-state-to-market-a-survey-of-empirical-studies-on-54vi9aent6.pdf

From State to Market: A Survey of Empirical Studies on Privatization

This paper describes a major new release of the PRISMprobabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.

http://www.prismmodelchecker.org/papers/cav11.pdf

PRISM 4.0: verification of probabilistic real-time systems

The effects of solution pH and temperature on the adsorption of fluoride onto bone char made from cattle bones were investigated in this work. It was found that the maximum adsorption took place at pH 3 and the adsorption capacity decreased nearly 20 times augmenting the pH from 3 to 12. This behavior was attributed to the electrostatic interactions between the surface of bone char and the fluoride ions in solution. The adsorption capacity was not influenced by temperature in the range from 15 to 35 °C. A comparison of fluoride adsorption capacities among several adsorbents revealed that the adsorption capacity of the bone char was 2.8 and 36 times greater than those of a commercial activated alumina (F-1) and a commercial activated carbon (F-400). The adsorption capacity is considerably dependent upon the physicochemical properties of the bone char surface and the solution pH.

/pdf/adsorption-of-fluoride-from-water-solution-on-bone-char-2xjff2bkrx.pdf

Adsorption of Fluoride from Water Solution on Bone Char

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Probabilistic model checking is an automatic formal verification technique for analysing quantitative properties of systems which exhibit stochastic behaviour. PRISM is a probabilistic model checking tool which has already been successfully deployed in a wide range of application domains, from real-time communication protocols to biological signalling pathways. The tool has recently undergone a significant amount of development. Major additions include facilities to manually explore models, Monte-Carlo discrete-event simulation techniques for approximate model analysis (including support for distributed simulation) and the ability to compute cost- and reward-based measures, e.g. the expected energy consumption of the system before the first failure occurs. This paper presents an overview of all the main features of PRISM. More information can be found on the website: www.cs.bham.ac.uk/∼dxp/prism.

PRISM : A tool for automatic verification of probabilistic systems

In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports three probabilistic models: discrete-time Markov chains, Markov decision processes and continuous-time Markov chains. Analysis is performed through model checking such systems against specifications written in the probabilistic temporal logics PCTL and CSL. The tool features three model checking engines: one symbolic, using BDDs (binary decision diagrams) and MTBDDs (multi-terminal BDDs); one based on sparse matrices; and one which combines both symbolic and sparse matrix methods. PRISM has been successfully used to analyse probabilistic termination, performance, and quality of service properties for a range of systems, including randomized distributed algorithms, manufacturing systems and workstation clusters.

/pdf/prism-probabilistic-symbolic-model-checker-25ublbrkvl.pdf

PRISM: Probabilistic Symbolic Model Checker

This tutorial presents an overview of model checking for both discrete and continuous-time Markov chains (DTMCs and CTMCs). Model checking algorithms are given for verifying DTMCs and CTMCs against specifications written in probabilistic extensions of temporal logic, including quantitative properties with rewards. Example properties include the probability that a fault occurs and the expected number of faults in a given time period. We also describe the practical application of stochastic model checking with the probabilistic model checker PRISM by outlining the main features supported by PRISM and three real-world case studies: a probabilistic security protocol, dynamic power management and a biological pathway.

/pdf/stochastic-model-checking-3cb969wqw5.pdf

Stochastic model checking

http://edge.rit.edu/edge/P13418/public/Reference%20Documents/Fluoride%20Removal/Adsorption%20kinetics%20of%20fluoride%20on%20low%20cost%20materials.pdf

David Parker

Papers

PRISM 4.0: verification of probabilistic real-time systems

PRISM : A tool for automatic verification of probabilistic systems

PRISM: Probabilistic Symbolic Model Checker

Stochastic model checking

Adsorption kinetics of fluoride on low cost materials