8-羟基喹啉-N-氧化物和5,7-二硝基-8-羟基喹啉-N-氧化物与Au(Ⅲ)、Pd(Ⅱ)的配合物之合成

合成4种标题配合物,用元素分析、红外和紫外光谱、摩尔电导、核磁共振氢谱、荧光光谱及X射线粉末衍射物相分析研究了它们的组成和性质及其结构。按结构将配合物分为两类,一是含氢键的离子缔合物;一是含金属—氧键的螯环型配合物。     

水下隧道穿越管道保护效果测试评价

本文通过对四川气田北干线涪江隧道穿越管道阴极保护电参数的现场测试，取得了实际可信的基础数据，验证了处于水下隧道洞内的穿越段管道，能得到陆上强制电流阴极保护系统的有效保护，保护电流所必经的岩石层和锚喷钢筋混凝土层对保护电流没有构成屏蔽的结论，对管道工程设计和运行管理具有指导作用。     

聚铝溶液pH值及盐基度的相关性研究

为聚铝溶液进行了ＰＨ值和盐基度的分析和估算,给出了聚铝溶液的极限ＰＨ值和极限盐基度与总溶解铝浓度的关系曲线,最终计算结果表明：在平衡条件下,聚铝溶液的ＰＨ值和盐基度具有相关性,即当基中一个参数发生变化时。另一个参数必然要发生相应的改变。     

Unipol气相法聚烯烃液体催化剂技术进展

介绍了Unipol气相法聚烯烃工艺液体催化剂的成分、加入反应器的方式和喷嘴结构及控制催化剂液滴尺寸方法等方面的最新进展情况。     

三聚氰胺甲醛树脂合成与性能的研究

本文针对三聚氰胺甲醛树脂合成与性能上存在的几个问题，从理论和实践上进行了讨论。     

基于Flash的关键变量容错存储技术研究

嵌入式系统中往往需要保存少量复位或掉电后依赖的关键变量,利用处理器片内Flash存储参数已经成为常规选择,但与常规Flash存储器件相比,片内Flash擦除次数更为有限,难以满足系统生命周期要求。研究了如何延长微处理器片内Flash使用寿命以及关键系统变量容错存储技术,针对典型嵌入式系统应用,分析Flash擦写管理机制,基于写磨损均衡算法进行改良,进一步提升系统容错能力。该技术在基于STM32单片机硬件平台进行了测试验证。     

梭式窑火焰数字图像的边缘检测初探

本文介绍了几种典型的边缘检测算法,对它们各自的特点进行了比较。对梭式窑内的一幅原始图像,可用边缘检测算法提取火焰图像的边缘。     

集散控制装置的实验功能开发

以一个模拟生产设备为监控对象,介绍了PCS型集散控制装置的硬件和软件组成,并开发了该装置的各种实验功能,可满足实验教学的要求。     

女性护理液抗炎止痒作用的实验研究

女性护理液为中药苦参醇提液加三氯羟基二苯醚复配而成，根据药理方法学以实验动物建立模型，观察护理液的抗炎、止痒作用。结果，单独使用三氯羟基二苯醚，有较好的抗菌消炎作用，但是止痒作用不明显。以苦参醇提液和三氯羟基二苯醚复配，其抗炎、止痒作用均接近地塞米松磷酸钠注射液。     

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition

Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.