Entropy of multidimensional cellular automata

Since the topological entropy of a vast class of two-dimensional cellular automata (CA) is infinite, of interest is the possibility to renormalize it so that to obtain a positive finite value. We find the asymptotics of the information function of a multidimensional CA and, accordingly, introduce the renormalized topological entropy as a coefficient of this asymptotics. We describe some properties of the introduced quantity, in particular, its positivity for CA of the type of “The Game of Life.” Also, we give an example of an explicit evaluation of this parameter for a particular cellular automaton.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.     

Second-order stresses and strains in heterogeneous steels: Self-consistent modeling and X-ray diffraction analysis

Theoretical and experimetal methods have been developed to characterize the effect of mechanical loading on the mesoscopic and macroscopic mechanical state of polycrystalline materials. Ferritic and austenitic single-phase materials were first analyzed, then phase interaction was studied in a multiductile phase material (austeno-ferritic duplex steel) and a natural reinforced composite (pearlitic steel). The theoretical method is based on the self-consistent approach in which elastic and plastic characteristics of the phases have been applied through the micromechanical behavior of single-crystal-using slip systems and microscopic hardening. The effects of a crystallographic texture and phase interaction during loading and after unloading were studied. The elastic and plastic anisotropy of the grains having the same crystallographic orientation were assessed by diffraction strain analysis. The simulation was compared with the experiments performed using the X-ray diffraction technique. In the considered duplex and pearlitic steels, it was observed that the ferrite stress state is much lower than the austenite and cementite ones. The results of diffraction strain distribution have showed the pertinence of the models and give valuable information, for example, for the yield stress and the hardening parameters of each phase in a two-phase material.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Sequential Bayesian bit error rate measurement

As bit error rates decrease, the time required to measure a bit error rate (BER) or perform a BER test (i.e., to determine that a particular communications device's BER is less than some acceptable limit) increases dramatically. One cause of long measurement times is the difficulty of deciding a priori how many bits to measure to establish the BER to within a predetermined confidence interval width. This paper explores a new approach to deciding how many bits to measure, namely a sequential Bayesian approach. As measurement proceeds, the posterior distribution of BER is checked to see if the conclusion can be made that the BER rate is known to be within the desired range with high enough probability. Desired properties of the posterior distribution such as the maximum a postiori estimate and confidence limits can be computed quickly using off-the-shelf numerical software. Examples are given of using this method on bit error data measured with an Agilent 81250 parallel BER tester.     

A XRD study of Co/Au multilayers using a laboratory microdiffractometer

In this paper we show that by the analysis of 2D images collected with a laboratory X-ray microdiffractometer it is possible to non-destructively evaluate the structure, the microstructure, and the preferred orientation of films. In particular, the structural analysis of Co/Au multilayers on Si(1 0 0) deposited at different Ar pressures are reported and discussed.     

Formaldehyde content of milk in goats fed formaldehyde-treated soybean oil-meal

Formaldehyde is used in ruminant feeding for different purposes including the protection of dietary proteins from ruminal degradation. The formaldehyde content of milk of goats fed various levels of formaldehyde-treated soybean oil-meal has been determined by using a sensitive HPLC method. Results showed a significant linear correlation between ingested formaldehyde and formaldehyde concentration in milk. About 0.02% of ingested formaldehyde was excreted in milk, as free formaldehyde.