感光材料内包装黑色膜的制造工艺与性能

感光材料包装膜分为内包装膜和外包装膜。本文主要介绍内包装用黑色膜的技术现状,制造工艺,包括原材料的选择,工艺技术路线及关键设备,并列举了产品主要规格与性能指标以及加工应用。     

煤气废水在不同生化环境中的处理效果分析及工艺优化

生物处理方法是煤气废水处理的核心工艺,生物处理的功效决定了煤气废水处理的经济可行性。通过研究煤气废水中主要污染物在厌氧和好氧生物处理系统中的去除效果,分析污染物在不同的生化环境中的降解特征,进而提出一种新型煤气废水生物处理工艺。     

电化学氧化法处理生活污水的研究

采用电化学氧化法处理生活污水,探讨了电解质浓度、电流密度、pH值、反应温度、不同电极等对COD、浊度的影响。实验表明,处理水中COD、浊度的最佳条件是:电解质浓度为45 g/L,pH值为8,电流密度为60 mA/m2,温度为22℃,阳极为RuSn电极,阴极使用不锈钢电极。此时COD去除率达86.22%,浊度去除率达80%,悬浮固体去除率和细菌去除率94%和99%。     

稳定剂对环保型无铅无镉釉上彩熔剂性能影响的研究

在碱-硼-硅系统基础熔剂中引入稳定剂，运用正交实验方法对无铅无镉釉上彩熔剂性能进行研究。通过化学稳定性、堆烧、粘度测试、印花彩烤等一系列对比试验，研究了稳定剂ZrO2、ZnO、SnO2和TiO2的最佳引入量。实验结果表明：在基础熔剂配方的基础上。稳定剂ZrO2的最佳引入量为6％；SnO2引入量不应超过5％；ZnO引入量10％为最好；TiO2引入1％的最低水平即可，8#为本研究中的最佳配方。     

水解-水热体系中同质异构TiO2纳米晶生成机理的分子模拟研究

在实验研究的基础上,采用分子模拟的方法,对在水解-水热体系中TiO2纳米颗粒晶相生成原理进行了系统的模拟计算和建模研究,文章介绍了分子模拟软件Hyperchem的计算方法,建立了钛离子水解过程和能量分布模型,利用模拟计算和模型,直观和定量分析了水解中间产物、水热晶化过程以及最终产物间的关系。研究了pH值对同质异构相成因的影响机理;静电场作用对同质异构相生成的影响。考察了水合络离子的连接方式,指出顶角连接与棱边连接保持到最终产物的结构中。从能量的角度揭示了同质异构TiO2晶型成因。     

催化裂化再生器中铂助燃剂催化活性的测定(Ⅰ)——在新助燃剂上CO氧化的反应动力学

研究了催化裂化再生条件下铂助燃剂上ＣＯ氧化反应动力学。在排除了反应管及稀释剂的催化氧化作用后，测定了铂含量为１０－６ｇ／ｇ的国产新助燃剂上的反应动力学数据，得出了在氧过量时一氧化碳和氧的反应级数分别为１和０．５。给出了６６０℃下的反应速度常数值及其置信区间。     

氢化物发生的机理及其在原子光谱中的应用

详细介绍了氢化物发生的反应体系和氢化反应的两种机理,并讨论了氢化物发生法在原子光谱中的应用现状和发展趋势。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.