1,045,693 research outputs found

    Understanding Supply Chain Complexity with Performance Measurement

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    Abstract: Despite the great number of complex systems existing in the real world, complexity is currently a poorly explored topic. In organizational settings, managers regularly apply to complex contexts classical approaches developed for simple systems, just because they do not know how to take into account companies' internal and external complexity. Nevertheless, before developing new managerial models, a deep knowledge about drivers and effects of complexity is needed. After defining the characteristics making supply chains complex systems, this paper discusses performance measurement as a methodology to analyze the effects of complexity on supply chain behavior. The results of a survey highlight that manufacturing companies usually evaluate isolated aspects of their supply chains, without considering the relationships between different performance indicators or dimensions. This work suggests System Dynamics as a valuable approach to understand the cause and effect connections among metrics and system elements affecting their values, thus clarifying the structure leading to a complex behavior. This research is the first step of a larger project aimed at providing companies with innovative tools to understand and manage supply chain complexit

    Using a Combination of Measurement Tools to Extract Metrics from Open Source Projects

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    Software measurement can play a major role in ensuring the quality and reliability of software products. The measurement activities require appropriate tools to collect relevant metric data. Currently, there are several such tools available for software measurement. The main objective of this paper is to provide some guidelines in using a combination of multiple measurement tools especially for products built using object-oriented techniques and languages. In this paper, we highlight three tools for collecting metric data, in our case from several Java-based open source projects. Our research is currently based on the work of Card and Glass, who argue that design complexity measures (data complexity and structural complexity) are indicators/predictors of procedural/cyclomatic complexity (decision counts) and errors (discovered from system tests). Their work was centered on structured design and our work is with object-oriented designs and the metrics we use parallel those of Card and Glass, being, Henry and Kafura's Information Flow Metrics, McCabe's Cyclomatic Complexity, and Chidamber and Kemerer Object-oriented Metrics

    Overcoming Problems in the Measurement of Biological Complexity

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    In a genetic algorithm, fluctuations of the entropy of a genome over time are interpreted as fluctuations of the information that the genome's organism is storing about its environment, being this reflected in more complex organisms. The computation of this entropy presents technical problems due to the small population sizes used in practice. In this work we propose and test an alternative way of measuring the entropy variation in a population by means of algorithmic information theory, where the entropy variation between two generational steps is the Kolmogorov complexity of the first step conditioned to the second one. As an example application of this technique, we report experimental differences in entropy evolution between systems in which sexual reproduction is present or absent.Comment: 4 pages, 5 figure


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    Measurement is one of the problems in the area of software engineering. Since traditional measurement theory has a major problem in defining empirical observations on software entities in terms of their measured quantities, Morasca has tried to solve this problem by proposing Weak Measurement theory. In this paper, we tried to evaluate the applicability of weak measurement theory by applying it on a newly proposed Modified Cognitive Complexity Measure (MCCM). We also investigated the applicability of Weak Extensive Structure for deciding on the type of scale for MCCM. It is observed that the MCCM is on weak ratio scale

    Parallelizing Quantum Circuits

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    We present a novel automated technique for parallelizing quantum circuits via forward and backward translation to measurement-based quantum computing patterns and analyze the trade off in terms of depth and space complexity. As a result we distinguish a class of polynomial depth circuits that can be parallelized to logarithmic depth while adding only polynomial many auxiliary qubits. In particular, we provide for the first time a full characterization of patterns with flow of arbitrary depth, based on the notion of influencing paths and a simple rewriting system on the angles of the measurement. Our method leads to insightful knowledge for constructing parallel circuits and as applications, we demonstrate several constant and logarithmic depth circuits. Furthermore, we prove a logarithmic separation in terms of quantum depth between the quantum circuit model and the measurement-based model.Comment: 34 pages, 14 figures; depth complexity, measurement-based quantum computing and parallel computin

    Robust Inference for State-Space Models with Skewed Measurement Noise

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    Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.Comment: 5 pages, 7 figures. Accepted for publication in IEEE Signal Processing Letter
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