深部互层咸水层二氧化碳分布特征探究

通过构造砂页岩互层结构,并根据渗透率对砂岩层岩性进行分类,建立了多岩相非均质模型,利用多组分多相流数值模拟软件TOUGH2/ECO2N探究二氧化碳注入深部互层咸水层后的分布特征,结果显示二氧化碳聚集在低渗透性的页岩层底部,呈分层结构,砂岩层中优先在渗透率较高的岩相中运移并呈分散分布;超临界二氧化碳不断溶于地层水,含饱和二氧化碳的咸水密度增加并缓慢下沉,无明显分层现象。     

非临氢降凝催化剂的研制与评价

以ZSM-5为主体，加入一定量的自制BCM沸石，采用水热合成法合成新型非临氢降凝催化剂。以催化裂化柴油和减一线馏分油为原料对其反应性能进行评价。结果表明, 该催化剂具有明显的降凝效果。当反应温度为340 ℃、空速为1 h^-1时，凝点降幅为45 ℃左右，液体产品收率均在95%以上。所生产的柴油十六烷值高，硫、氮含量低，凝点低，基本满足了产品调合出厂的质量要求。     

无卤阻燃EPDM胶料的性能预测

用回归分析法对EPDM胶料的阻燃性和物理机械性能进行研究并预测胶料的性能．结果表明：无卤阻燃EPDM胶料的阻燃性随氢氧化铝用量和硼酸锌用量增加而提高，物理机械性能则有所下降，建立的数学模型能较好地拟合胶料各项性能与氢氧化铝用量和硼酸锌用量之间的关系，能够准确地预测含不同氯氧化铝用量和硼酸锌用量胶料的性能。     

光诱导TiO2薄膜超亲水性的研究

介绍了超亲水性TiO2薄膜的亲水性机理及其应用，同时也对研究中存在的问题和将来的发展方向进行了评述。     

连续逆流解离萃取法分离间氯苯胺及对氯苯胺

以萃取酸为连续相，应用填料塔设备对间氯苯胺及对氯苯胺混合物进行连续解离萃取以提纯间氯苯胺。实验考察了不同填料、塔高、停留时间等因素对分离效果的影响，并根据实验数据及成品纯度要求设计了塔的级数，完成了分离试验，分离后间氯苯胺纯度大于99％。     

达岱电厂发电机组定子绝缘盒开裂溢胶原因分析与处理

柬埔寨达岱水电有限公司2号水轮发电机(84.6 MW)检修时发现,发电机定子上下端部共存在3处绝缘盒开裂、溢胶的缺陷,并在定子下端多处绝缘盒灌注胶表层有轻微裂纹。通过原因分析及现场检查与电气试验,确定缺陷是因盒内上下层线棒的铜线接头处焊点过热引起的。按相关工艺要求对3处开裂溢胶的绝缘盒内接头进行了重新焊接,处理后经电气试验证明,处理效果良好。     

阿克苏河径流演变及其对气候变化的响应

气候变化深刻影响着径流变化过程,是造成我国西北干旱区水资源短缺和生态环境荒漠化等问题的重要原因。以阿克苏河流域上游水文站1961-2014年的月径流资料和气象网格数据为基础数据,通过线性回归法、Mann-Kendall非参数检验法和Pearson相关系数法,分析了阿克苏河流域径流演变规律并进一步探讨了径流对气候变化的响应。结果表明:近54年来,阿克苏河流域径流量呈现显著的上升趋势,且研究区内气候增暖增湿趋势明显;径流量和气候要素在时间上有良好的一致性,突变时间均发生在20世纪90年代,径流量的突变时间略滞后于气温和降水量;经相关统计检验分析,阿克苏河流域出山口径流量受到了气温和降水的双重影响,托什干河径流对气温更为敏感,而库玛拉克河则是降水对径流的影响占主导作用。     

水基含铝耐高温防腐蚀涂料

以水溶性的磷酸盐为主要成膜物质,铬酸(盐、酐等)为调节剂,球形铝粉为颜料合成了水基含铝耐高温防腐蚀涂料,该涂料具有优异的耐热、耐湿、耐盐雾、耐高低温冲击等性能。     

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.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.