国外加氢裂化技术的最新进展

介绍了国外加氢裂化技术在催化剂、生产工艺和反应器内构件设计方面的新进展，对我国加氢变化技术的发展提出了建议。     

R&D activities on wireless systems in China

Various wireless systems and the research preceding their practical use in China are described. The topics discussed include research establishments, research and development projects underway, challenges inherent to digital radio, and expansion opportunities afforded by satellite communications     

预处理工艺对硬质合金与金刚石膜间粘结力的影响

在两种经不同预处理的硬质合金YG8基底上，采用微波等离子体化学气相沉积法，在微波功率2kW，压强4.0kPa和6.5kPa，CH4和H2流量分别为1.6cm/s和100.0cm/s的条件下生长金刚石薄膜。利用X射线衍射检测了金刚石薄膜是否存在，用拉曼光谱分析了薄膜的质量，用金相显微镜观察了薄膜的洛氏硬度压痕，标定并比较了不同预处理工艺膜与基底的结合力。实验结果表明，不同的预处理方法对于粘结力的影响不大，最主要的因素是钴含量的多少。     

浓香型瓶装酒库存过程中理化指标的变化

经过对1996年及1999年灌装的种子牌系列浓香型白酒的理化指标检测，发现在贮存过程中理化指标变化很大，总酸，固形物呈上升趋势，总酯，己酸乙酯呈下降趋势,其升降幅度与贮存时间相关，时间越长变化越大，贮存3年的高低度5个样平均总酸上升18．2％，28度样总酸上升28．3％，50度样总酸仅上升11％，固形物平均上升39．2％，28度样上升43％，50度样上升18％，总酯平均下降24％，28度样下降34％，50度样下降31％，己酸己酯平均下降32．4％，28度样下降59％，50度样下降32％，贮存6年的5个样总酸平均上升39．8％，28度样上升52％，50度样上升41％，固形物平均上升71．8％，28度样上升635＜50度样上升77％，总酯平均下降42．4％，28度样下降54％，50度样下降31％，己酸乙酯平均下降51．8％，28度样下降69％，50度样下32%.     

CuO/ZnO甲醇合成催化剂的正电子湮没谱学研究

发展了用正电子湮没寿命参数测定用共沉淀方法制备的金属氧化物系固溶度的新方法。测量了一系列用共沉淀方法制备的不同CuO at％含量的CuO/ZnO甲醇合成催化剂粉末(压制成片)还原前后的正电子寿命谱。分析该种催化剂的固溶特性及其与正电子寿命参数的关系,得出还原后样品的固溶度为12CuOat％.支持了CuO/ZnO甲醇合成催化剂以Cu~+离子为活性中心的观点。     

基于充放电原理实现的微电容测量电路

具有抗分布电容以及简单实用等特性的充放电电路是目前微电容测量中广泛采用的一种测量电路。本文对基于充放电原理的微电容测量电路进行了深入研究，并介绍一种基于充放电原理的实用电路。     

φ1m氨合成塔20钢异径管失效分析

分析了氨合成塔底部副线20钢异径管爆破的原因。结果表明：异径管长期处于200℃以上工作，管内H2与管材中的碳或Fe3发生化学反应，生成CH4致使管内脱碳并产生裂纹，异径管强度降低脆性增加，导致爆破。     

Improving signal prediction performance of neural networks through multiresolution learning approach.

In this paper, we extend the original work on multiresolution learning for neural networks, and present new developments on the multiresolution learning paradigm. The contributions of this paper include: (1) proposing a new concept and method of adjustable neural activation functions in multiresolution learning to improve neural network learning efficacy and generalization performance for signal predictions; (2) providing new insightful explanations for the multiresolution learning paradigm from a multiresolution optimization perspective; (3) exploring underlying ideas why the multiresolution learning scheme associated with adjustable activation functions would be more appropriate for the multiresolution learning paradigm; and (4) providing rigorous validations to evaluate the multiresolution learning paradigm with adjustable activation functions and comparing it with the schemes of multiresolution learning with fixed activation functions and traditional learning. This paper presents systematically new analytical and experimental results on the multiresolution learning approach for training an individual neural network model, demonstrates our integral solution on neural network learning efficacy, and illustrates the significant improvements on neural networks' generalization performance and robustness for nonlinear signal predictions.