MathMC: A mathematica-based tool for CSL model checking of deterministic and stochastic Petri nets

Deterministic and Stochastic Petri Nets (DSPNs) are a widely used high-level formalism for modeling discreteevent systems where events may occur either without consuming time, after a deterministic time, or after an exponentially distributed time. CSL (Continuous Stochastic Logic) is a (branching) temporal logic developed to express probabilistic properties in continuous time Markov chains (CTMCs). In this paper we present a Mathematica-based tool that implements recent developments for model checking CSL style properties on DSPNs. Furthermore, as a consequence of the type of process underlying DSPNs (a superset of Markovian processes), we are also able to check CSL properties of Generalized Stochastic Petri Nets (GSPNs) and labeled CTMCs

Why the adaptation-selection debate is misconstrued: aDarwinian view of organizational change

This paper is motivated by the belief that the notion of ‘generalized Darwinism’ offers ameta-theory that provides a logical foundation for the integration of the different theoreticalperspectives in the extensive literature on the selection and adaptation of organizations. We clarify thenotion of generalized Darwinism, and argue that its recursive causal logic provides the only possibleexplanation for the way in which organizations achieve adaptive fit. The crucial insights that can bederived from a generalization of Darwinism are twofold. First, contrary to a widely held belief, there isno logical contradiction between Darwinian and Lamarckian evolution. Second, environmentalselection processes and intentional adaptation are part of one and the same process. We conclude thatthe explanatory logic of generalized Darwinism demonstrates that the adaptation-selection debate ismisconstrued. Adaptation and selection are simply different aspects of the same evolutionary processand complement each other in a Darwinian explanation of how organizational change can lead toenvironmental fit

Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project DURABROADS (No. 605404).The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 605404

Organizational Learning as Evolution: The Promise of GeneralizedDarwinism for Organization Science

The purpose of this paper is to provide a rigorous treatment of organizational learning as anevolutionary process. The paper is motivated by the believe that the notion of ‘generalized Darwinism’offers a meta-theory that can draw together the diverse theoretical perspectives that comprise theextensive literature on the selection and adaptation of organizations. We clarify the notion ofgeneralized Darwinism, and show that it provides a theoretical framework that can accommodateadaptation, selection and learning processes. This is done in three steps. First, we specify the generalnature of a Darwinian explanation. Secondly, we demonstrate that learning can be understood as aDarwinian process. Thirdly, we show how the resulting theoretical framework can be applied to bothorganizational selection and adaptation. We discuss the implications of this view of organizationallearning and conclude that the explanatory logic of generalized Darwinism demonstrates that theadaptation-selection debate is misconstrued. Adaptation and selection are simply differentmanifestations of the same Darwinian process

Theoretical Setting of Inner Reversible Quantum Measurements

We show that any unitary transformation performed on the quantum state of a closed quantum system, describes an inner, reversible, generalized quantum measurement. We also show that under some specific conditions it is possible to perform a unitary transformation on the state of the closed quantum system by means of a collection of generalized measurement operators. In particular, given a complete set of orthogonal projectors, it is possible to implement a reversible quantum measurement that preserves the probabilities. In this context, we introduce the concept of "Truth-Observable", which is the physical counterpart of an inner logical truth.Comment: 11 pages. More concise, shortened version for submission to journal. References adde

Generalized disjunction decomposition for the evolution of programmable logic array structures

Evolvable hardware refers to a self reconfigurable electronic circuit, where the circuit configuration is under the control of an evolutionary algorithm. Evolvable hardware has shown one of its main deficiencies, when applied to solving real world applications, to be scalability. In the past few years several techniques have been proposed to avoid and/or solve this problem. Generalized disjunction decomposition (GDD) is one of these proposed methods. GDD was successful for the evolution of large combinational logic circuits based on a FPGA structure when used together with bi-directional incremental evolution and with (1+ë) evolution strategy. In this paper a modified generalized disjunction decomposition, together with a recently introduced multi-population genetic algorithm, are implemented and tested for its scalability for solving large combinational logic circuits based on Programmable Logic Array (PLA) structures