Abstract-In this paper, we reported an E-plane horn antenna incorporating a metamaterial. Such a metamaterial is made up of metallic cylinders organized in a two-dimensional square lattice. After properly designing the lattice constant and unit cell pattern, we synthesized a medium with the effective refractive index smaller than unity. Therefore, once the waves were excited within the metamaterial, the refractive waves tend to be perpendicular to the interface between the metamaterial and uniform medium. Based on this concept, a 4-way beam splitter was designed to equally distribute the input power into 4 different directions. We then guide each of the power into individual E-plane flared opening to radiate a directional beam pattern in each sector. We have fabricated this antenna and measured its radiation characteristics including the return loss and far-field pattern. The excellent agreement between the measured and simulated results was obtained. Due to the properties of robust, low-loss, and low-cost, this antenna may have promising application in a point-to-multiple-point downlink system.

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Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case-control (choice-based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out-of-sample error rates. Copyright © 2007 John Wiley & Sons, Ltd.

For multiple-class prediction, a frequently used approach is based on ordered probit model. We show that this approach is not optimal in the sense that it is not designed to minimize the error rate of the prediction. Based upon the works by Altman (J. two-class prediction, we propose a modified ordered probit model. The modified approach depends on an optimal cutoff value and can be easily applied in applications. An empirical study is used to demonstrate that the prediction accuracy rate of the modified classifier is better than that obtained from usual ordered probit model. In addition, we also show that not only the usual accounting variables are useful for predicting issuer credit ratings, market-driven variables and industry effects are also important determinants.Currently, there are many widely recognized rating agencies, such as Moody's Investors Service, Standard and Poor's Ratings Services (S&P's), etc. They routinely provide credit ratings for bonds and companies.This study focuses on the S&P's long-term issuer credit rating (LTR). According to the definition given by S&P's, the LTR focuses on the obligor's capacity and willingness to meet its long-term financial commitments. To determine the LTR of a company, S&P's examines a profile called the corporate rating analysis and methodology profile. The profile contains two types of information of the company. The first type of information is available publicly, for example, public financial data. The second type of information is collected through a proprietary process from industry characteristics, competitive bargain positions, interviews with management team, etc. Thus, S&P's rating procedure is in part common knowledge. However, the major determinants of S&P's LTR are basically not clear. In this paper, we propose to first identify important predictors of S&P's LTR, selected from publicly available market data, accounting data, and industry classification.Based on the Compustat North America (COMPUSTAT) database, there were 8039 companies in the year 2005 having stock traded on the New York Stock Exchange, American Stock Exchange, or NASDAQ. However, among those 8039 companies, there were only 20.46% (1645) companies having S&P's LTRs. This means that most of companies do not have their S&P's LTRs. In this paper, our next focus is to forecast ratings of those companies 'without' S&P's LTRs. Pettit et al. [1] reported that the new faces in the pool of companies with S&P's LTRs have lower rating category on the average. Blume et al. [2]also found similar results that bond ratings have declined, but the decline could be due to the use of more stringent rating standards in assigning ratings. On the other hand, we do not pursue the issue of rating forecast for companies 'with' S&P's LTRs for two reasons. First, if a company is once rated by S&P's, then it will be continuously rated unless a special event happens to the company, for example, bankruptcy. Second, the continuously rated companies have relatively unchanged rating categories in general [1]. Thus...

A surface plasmon resonance (SPR) sensor based on gate-controlled periodic graphene ribbons array is reported. Different from the conventional methods by monitoring reflectivity variations with respect to incident angle or wavelength, this approach measures the change in SPR curve against the variation of graphene chemical potential (via dynamically tuning the gate voltage) at both fixed incident angle and wavelength without the need of rotating mirror, tunable filter or spectrometer for angular or wavelength interrogation. Theoretical calculations show that the sensitivities are 36,401.1 mV/RIU, 40,676.5 mV/RIU, 40,918.2 mV/RIU, and 41,160 mV/RIU for analyte refractive index (RI) equal to 1.33, 1.34, 1.35 and 1.36; their figure of merit (1/RIU) are 21.84, 24, 23.74 and 23.69, respectively. Significantly, the enhancement in the non-uniform local field due to the subwavelength graphene ribbon resonator can facilitate the detection in redistribution of protein monolayers modeled as dielectric bricks.