覆炭载体及镍覆炭催化剂的积炭行为研究

用恒温热重法，以正己烷脱氢反应为指示反应，研究了γ-Al2O3覆炭载体（CCA0，γ-Al2O3，Ni/Al2O3及Ni/CCA催化剂的抗结焦性能；考察了覆炭原料，覆炭量对CCA的抗结焦性能的影响及积炭温度，正己烷浓度和覆炭量对Ni/CCA催化剂的抗结焦性能的影响。结果，CCA，Ni/CCA催化剂的抗结焦性能优于γ-Al2O3，Ni/Al2O3，以环烷烃为覆炭原料得到的CCA的抗结焦性能最好。     

厦门杏林湾排涝泵站跨管线围堰设计方案探讨

针对厦门杏林湾泵站围堰需2次跨过三根市政管线的特点,结合工程区的地质条件、水文条件、围堰结构型式选择、现有施工水平等因素,对土、充灌砂、混凝土、浆砌块石、钢板桩等围堰型式进行分析,经多方案综合比选,提出了"桩基+盖板"的水上管线加固方案,不但可以有效控制管线变形,而且施工可操作性较强。     

环氧化SBS胶粘剂的研制

研究了环氧化SBS橡胶胶粘剂的合成及应用。试验结果表明环氧化SBS胶粘剂在粘接极性材料时的性能要优于SBS胶粘剂。     

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。     

Formation of Polymeric Lewis Acid–Lewis Base Complexes with Well-defined Organoboron Polymers

The binding of Lewis bases to organoboron polymeric Lewis acids has been studied and the parameters that determine the complexation equilibrium have been investigated, which include (i) the strength of the individual Lewis acids and Lewis bases, (ii) concentration, and (iii) temperature. While the strongly Lewis acidic borane polymers poly(4-bis(pentafluorophenyl)borylstyrene) (PS-BPf) and poly(4-(di-2-thienylboryl)styrene) (PS-BTh) form isolable complexes with strong Lewis bases such as 4-t-butylpyridine (^tPy), a temperature dependent equilibrium is established with weaker bases such as THF. Similarly, the weakly Lewis acidic boronate polymer poly(4-diethoxyborylstyrene) (PS-BOEt) undergoes a temperature dependent equilibrium with the strong Lewis base 4-dimethylaminopyridine (DMAP), while poly(4-pinacolatoborylstyrene) (PS-BPin) does not significantly bind to pyridine bases. Decomplexation of PS-BTh· ^t Py is achieved by treatment with the stronger Lewis acid, B(C₆F₅)₃, thereby confirming the reversible nature of the polymeric Lewis acid–base adducts. This paper is dedicated to Professor Ian Manners in gratitude of his guidance throughout the years and recognition of his scientific accomplishments     

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.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.