Modeling ion implantation of HgCdTe

Ion implantation of boron is used to create n on p photodiodes in vacancy-doped mercury cadmium telluride (MC.T). The junction is formed by Hg interstitials from the implant damage region diffusing into the MC.T and annihilating Hg vacancies. The resultant doping profile is n⁺/n^-/p, where the n⁺ region is near the surface and roughly coincides with the implant damage, the n^- region is where Hg vacancies have been annihilated revealing a residual grown-in donor, and the p region remains doped by Hg vacancy double acceptors. We have recently developed a new process modeling tool for simulating junction formation in MC.T by ion implantation. The interstitial source in the damage region is represented by stored interstitials whose distribution depends on the implant dose. These interstitials are released into the bulk at a constant, user defined rate. Once released, they diffuse away from the damage region and annihilate any Hg vacancies they encounter. In this paper, we present results of simulations using this tool and show how it can be used to quantitatively analyze the effects of variations in processing conditions, including implant dose, annealing temperature, and doping background.     

中国古典园林三维造型研究

论述了采用层次结构化及参数化三维造型方法，建立一个以造型函数库为核心，从而构造各类、各级园林景观构件表面模型的方法和过程。对于某些非建筑类的园林配景构件，采用了Ｆｒａｃｔａｌ方法进行造型。     

实体造型中的几何约束

实体造型是一个基于约束的过程，完成从功能约束到几何约束、再到代数约束的转化而得到实体模型。本文讨论了几何约束的层次性及其表示，并且对几何约束同设计意图的关系进行了研究，提出了基于ＣＳＧ／ＧＣＧ／Ｂ－ｒｅｐ的模型表示。     

有耗传输线的TLM模型法

本文从传输线理论出发，介绍了一种解决传输线问题的新方法－－TLM法。     

CD++: a toolkit to develop DEVS models

The features of a toolkit for modeling and simulation based on the DEVS formalism are presented. The tool is built as a set of independent software pieces running on different platforms. Not only are the main characteristics of the environment presented, a focus on its use is also considered by inclusion of application examples for a variety of problems. Many models can be defined in an automated fashion, simplifying the construction of new models and easing their verification. The use of this formal approach has allowed the development of safe and cost‐effective simulations, significantly reducing development time. Copyright © 2002 John Wiley & Sons, Ltd.     

Thermodynamic modeling of native point defects and dopants of GaN semiconductors

A thermodynamic modeling of GaN was carried out to describe the thermodynamic behavior of native defects, dopants, and carriers (free electrons and holes) in GaN semiconductors. The compound energy model (CEM) was used. An unintentionally doped GaN was taken as an example. Oxygen was introduced into the model as the unintentionally doped impurity, according to the practical experimental phenomena. The energies of component compounds in the model were defined based on the results of the ab initio calculations and adjusted to fit experimental data. The thermodynamic properties of the defects and the oxygen doped were calculated to show the facility of the model.     

Object-oriented DBMS requirements

The requirements on an object-oriented DBMS for management of information in a large, complex enterprise are presented. These requirements aid in the achievement of an environment characterized by data sharing, open architectures, application and data portability, and assurance of data integrity. They were defined from the point of view of a user of the DBMS; therefore they describe the expected functionality of the DBMS and do not specify the method of implementation to achieve this functionality. They encompass requirements on the data model, query and data manipulation languages, the system architecure, interfaces to the system, change management, and transaction management.     

Rate of ammonia oxidation in a synthetic saline wastewater by a nitrifying mixed‐culture

Most of the kinetic studies on nitrification have been performed in diluted salts medium. In this work, the ammonia oxidation rate (AOR) was determined by respirometry at different ammonia (0.01 and 33.5 mg N‐NH₃ L^?1), nitrite (0–450 mg N‐NO₂^? L^?1) and nitrate (0 and 275 mg N‐NO₃^? L^?1) concentrations in a saline medium at 30 °C and pH 7.5. Sodium azide was used to uncouple the ammonia and nitrite oxidation, so as to measure independently the AOR. It was determined that ammonia causes substrate inhibition and that nitrite and nitrate exhibit product inhibition upon the AOR. The effects of ammonia, nitrite and nitrate were represented by the Andrews equation (maximal ammonia oxidation rate, r_AOMAX, = 43.2 [mg N‐NH₃ (g VSS_AO h)^?1]; half saturation constant, K_SAO, = 0.11 mg N‐NH₃ L^?1; inhibition constant K_IAO, = 7.65 mg N‐NH₃ L^?1), by the non‐competitive inhibition model (inhibition constant, K_INI, = 176 mg N‐NO₂^? L^?1) and by the partially competitive inhibition model (inhibition constant, K_INA, = 3.3 mg N‐NO₃^? L^?1; α factor = 0.24), respectively. The r_AOMAX value is smaller, and the K_SAO value larger, than the values reported in diluted salts medium; the K_IAO value is comparable to those reported. Process simulations with the kinetic model in batch nitrifying reactors showed that the inhibitory effects of nitrite and nitrate are significant for initial ammonia concentrations larger than 100 mg N‐NH₄⁺ L^?1. Copyright © 2005 Society of Chemical Industry     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.