Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your perso...

We investigate whether corporations are following the “best disclosure practices” when presenting business reports on their Web sites. As a benchmark, we use the recommendations made by the Jenkins Committee (1994) to improve corporate reporting practices, to evaluate reports presented on corporate Web sites for their value, relevance, and quality of information. We compute a disclosure score u...

We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method for long and short term memory series. Our work was inspired by previously published works that yielded inconsistent results about comparative performance. We have since experimented with 16 time series of di ering complexity using neural netw...

The group method of data handling technique (GMDH) and Box-Jenkins methods are two wellknown time series forecasting of mathematical modeling. In this paper, we introduce a hybrid modeling which combines the GMDH method with the Box-Jenkins method to model time series data. The Box-Jenkins method was used to determine the useful input variables of GMDH method and then the GMDH method which work...

In this paper, we obtain the explicit limit value of the Teichmüller distance between two Teichmüller geodesic rays which are determined by Jenkins-Strebel differentials having a common end point in the augmented Teichmüller space. Furthermore, we also obtain a condition under which these two rays are asymptotic. This is similar to a result of Farb and Masur.

With the increasing number of geographically dis­ tributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency o...

In this study, we introduce and investigate a class of neural architectures of Polynomial Neural Networks (PNNs), discuss a comprehensive design methodology and carry out a series of numeric experiments. PNN is a flexible neural architecture whose structure (topology) is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but becomes generated on t...