We report a new metallization process for achieving low resistance ohmic contacts to molecular beam epitaxy grown n-GaN (∼1017 cm−3) using an Al/Ti bilayer metallization scheme. Four different thin-film contact metallizations were compared during the investigation, including Au, Al, Ti/Au, and Ti/Al layers. The metals were first deposited via conventional electron-beam evaporation onto the GaN substrate, and then thermally annealed in a temperature range from 500 to 900 °C in a N2 ambient using rapid thermal annealing techniques. The lowest value for the specific contact resistivity of 8×10−6 Ω cm2, was obtained using Ti/Al metallization with anneals of 900 °C for 30 s. X-ray diffraction and Auger electron spectroscopy depth profile were employed to investigate the metallurgy of contact formation.

Transmission electron microscopy has been applied to characterize the structure of Ti/Al and Ti/Al/Ni/Au Ohmic contacts on n-type GaN (∼1017 cm−3) epitaxial layers. The metals were deposited either by conventional electron-beam or thermal evaporation techniques, and then thermally annealed at 900 °C for 30 s in a N2 atmosphere. Before metal deposition, the GaN surface was treated by reactive ion etching. A thin polycrystalline cubic TiN layer epitaxially matched to the (0001) GaN surface was detected at the interface with the GaN substrate. This layer was studied in detail by electron diffraction and high resolution electron microscopy. The orientation relationship between the cubic TiN and the GaN was found to be: {111}TiN//{00.1}GaN, [110]TiN//[11.0]GaN, [112]TiN//[10.0]GaN. The formation of this cubic TiN layer results in an excess of N vacancies in the GaN close to the interface which is considered to be the reason for the low resistance of the contact.

Traditional methods for item selection in computerized adaptive testing only focus on item information without taking into consideration the time required to answer an item. As a result, some examinees may receive a set of items that take a very long time to finish, and information is not accrued as efficiently as possible. The authors propose two item-selection criteria that utilize information from a lognormal model for response times. The first modifies the maximum information criterion to maximize information per time unit. The second is an inverse time-weighted version of a-stratification that takes advantage of the response time model, but achieves more balanced item exposure than the information-based techniques. Simulations are conducted to compare these procedures against their counterparts that ignore response times, and efficiency of estimation, time-required, and item exposure rates are assessed.

Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at .

The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the advantage of conciseness, but may suffer from reduced flexibility to fit real data. We propose a semiparametric approach, specifically, the Cox proportional hazards model with a latent speed covariate to model the RTs, embedded within the hierarchical framework proposed by van der Linden to model the RTs and response accuracy simultaneously. This semiparametric approach combines the flexibility of nonparametric modeling and the brevity and interpretability of the parametric modeling. A Markov chain Monte Carlo method for parameter estimation is given and may be used with sparse data obtained by computerized adaptive testing. Both simulation studies and real data analysis are carried out to demonstrate the applicability of the new model.