中国上市公司股份回购分析

在证券市场发达的国家，股份回购是一种成熟的资本运作方式，自从二十世纪八十年代以来被越来越多的公司使用。在我国，回购仍处于探索和尝试阶段。对于资本运作方式，大家比较熟悉的是收购、兼并、增发新股等资本扩张型的运作，股份回购等资本收缩型运作并不常见。然而，股份回购在优化资本结构、国有股减持和推进现代激励机制方面都具有现实意义。本文对我国回购案例进行分析，进而对我国股份回购制度安排提出建议。 本文的研究结构如下： 第一章 对股份回购作出一个概括性介绍。说明当前西方资本市场中常 见的股份回购类型、回购的主要目的、主流的回购理论，进而简要介绍国外相关法律及发展趋势。 第二章 从上市公司、股东及证券...Stock repurchase is a mature method of capital operation in developed capital markets and is frequently used since 1980s. In China, it’s still under trial stage. We are familiar with capital-widening operations such as acquisition, merger and issuing new shares, but not with stock repurchase. It has important functions of capital structure optimization, State-owned stock lessening and modern incen...学位：工商管理硕士院系专业：管理学院工商管理教育中心_工商管理硕士(MBA)学号：19981501

Extract Feature Lines from Building LiDAR Point Cloud Based on Multi-structure Estimators

建筑物激光雷达(lIgHT dETECTIOn And rAngIng,lIdAr)点云特征线对于多视角点云配准、建筑物对称性检测、建筑物三维重建等应用具有十分重要的意义.由于lIdAr点云具有数据量庞大的特点,传统的算法难以实现建筑物特征线的快速提取.针对这个问题,提出一种基于多结构鲁棒估计的建筑物特征线提取算法,该算法利用历史模型信息进行条件采样,并通过迭代搜索符合所有特征线性质的模型.根据建筑物lIdAr数据的实验结果表明,该方法与传统的rAnSAC(rAndOM SAMPlE COnSEnSuS)、MlESAC(MAXIMuM lIkElIHOOd ESTIMATIOn SAMPlE COnSEnSuS)等算法相比,避免了无效、重复的特征线采样过程,在相同时间内可获取更多的直线内点,从而有效提高了建筑物特征线的提取效率.Feature lines extracted from building LiDAR(Light Detection and Ranging)point cloud data are of great significance in multiple views registration,building symmetry detection,3Dsurface reconstruction,among others.Since the LiDAR data are generally associated with a huge amounts of 3Dpoints,traditional algorithms suffer from the time complexity of rapidly extracting feature lines from building point cloud.In order to solve this problem,we present a feature lines extracted algorithm based on multi-structure robust estimation.In the proposed method,historical models generated by random strategy have been used for conditional sampling new models.Consequently,the searching process aims at extracting all feature lines from the model set.In the section of experiments,the multi-structure algorithm has been compared with the RANSAC(random sample consensus)and MLESAC(maximum likelihood estimation sample consensus).Results acquired from our LiDAR dataset indicate that the proposed method improves the efficiency of building feature lines extraction,since the multi-structure algorithm avoids many invalid and repeated sampling processes.Therefore,we can generate more feature lines at the same time.国家自然科学基金(61103052); 国家科技支撑计划(201309110001); 国家高技术研究发展计划(863计划)(2012AA12A208-06); 福建省产学重大科技项目(2011H6020); 福建省自然科学基金(2012J01013;2013J01245); 福建省教育厅专项课题(JK2012025); 厦门市科技计划项目(3502Z20110010

生物絮凝剂产生菌的筛选及絮凝性能研究

从活性污泥中筛选出絮凝剂产生菌,并对其絮凝条件进行絮凝性能研究。经絮凝实验筛选得到4株絮凝活性较高且稳定的菌株,分别命名为M1、M2、M3、n5。对其中1株进行絮凝活性及絮凝条件的研究,其絮凝活性物质主要为菌体分泌物,该菌可产生高絮凝活性的最佳絮凝条件:对于浓度为1--9g/l的高岭土,最佳助凝剂为1%的CAC l2,投入量为40 Mg/l,PH值为9,絮凝率可达98%,具有良好的热稳定性,适于工业化生产。国家大学生创新性实验计划项目(091022008);黑龙江省教育厅科学技术研究项目(1153005