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This paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin-destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide the results from an experimental program to measure global imperfections and dimensional variations in cold-formed steel-lipped channel members. Measurements were conducted at seven different manufacturing plants in the United States. A total of 210 cross sections were measured for global imperfections. Each cross section was measured at three locations, and every 305 mm (12 in.) along the length. The data is used to characterize global bow, camber, and twist. The majority of the data exhibits global imperfections well approximated by a half sine-wave. Further, global bow imperfections are shown to have a systematic bias in the direction of bow. The measured data is compared with manufacturing tolerances (e.g., L/384) and assumed imperfection sizes from column curves (e.g., L/960) as utilized in design practice in the United States. The measured imperfections served as justification for tightening manufacturing tolerances in the United States (e.g., to L/960). Dimensional variation of typical cold-formed steel members is also measured and compared with tolerances. The measured data provides much needed input for nonlinear analysis of cold-formed steel structures. [ABSTRACT FROM AUTHOR]

This paper extends the work in [Serletis & Shintani, 2003; Elder & Serletis, 2007; Koustas et al.; Hinich & Serletis, 2008] by re-examining the empirical evidence for random walk type behavior in the US stock market. In doing so, it uses daily data on the Dow Jones Industrial Average, over the period from January 3, 1928 to March 15, 2006, and a statistical-physical approach — "detrended fluctuations analysis" — providing a reliable framework for testing the information efficiency in financial markets as shown by Uritskaya [2005a, 2005b] and Uritskaya and Uritsky [2001]. The approach eliminates nonstationary market trends and focuses on the intrinsic correlation structure of stock market fluctuations at different time scales which is studied relative to random walks models. Our results indicate that the US stock market operates close to the state predicted by the efficient markets hypothesis. The observed transient deviations from this state are shown to have a statistical origin, consistent with a purely random geometric Brownian motion. [ABSTRACT FROM AUTHOR]

In this paper, a fast chaos-based pseudo-random number generator (PRNG) is proposed for secured communications. In order to achieve fast throughput and facilitate hardware realization, 32-bit fixed point representation and arithmetic are used. Even under such configuration with quantization errors which will make the normal chaos-based PRNG impractical, our scheme can pass all the statistical tests in the up-to-date National Institute of Standards and Technology (NIST) test suite with the output bit rate up to 134 Mbps in a 2.6 GHz Pentium-4 machine. With such a fast PRNG, a stream cipher is hence designed for the application of online secure voice communication system with User Datagram Protocol (UDP). [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired. [ABSTRACT FROM AUTHOR]

In this paper we propose a simple forecasting strategy which exploits the multiresolution property of the wavelet transform. US aggregate retail sales data have strong trend and seasonal patterns, providing a good testing ground for the proposed forecasting method. First a wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Then a forecasting engine (neural network or fuzzy inference system) is trained on each of the relevant resolution scales, and individual wavelet scale forecasts are recombined to form the overall forecast. Substantial information in both the dynamic nonlinear trend and seasonal patterns of the time series is efficiently exploited: we choose short past windows for the inputs to the forecasting engines at lower scales and long past windows at higher scales. The forecasting engines learn the mapping hierarchically: using a scale-recursive strategy, we combine only those scales where significant events are detected. Univariate simulation results on US aggregate retailing indicate that the proposed method fares favourably in relation to forecasting results obtained by training a neural network on original time series. Multivariate simulation results obtained by including structural components inflation, recession, interest rates, unemployment, show improvement in sales-trend forecast. [ABSTRACT FROM AUTHOR]

This paper presents a simplified approximate analysis of the overall collapse of the towers of World Trade Center in New York on September 11, 2001. The analysis shows that if prolonged heating caused the majority of columns of a single floor to lose their load carrying capacity, the whole tower was doomed. Despite optimistic simplifying assumptions, the structural resistance is found to be an order of magnitude less than necessary for survival. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

This section is maintained by Vladik Kreinovich. Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to: Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

This section is maintained by Vladik Kreinovich. Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to: V. Kreinovich, Department of Computer Science, University of Texas, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

This section is maintained by Vladik Kreinovich. Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@cs.utep.edu, or by regular mail to: Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]