基于西部高校的服装结构设计课程改革

立足西部高校的服装结构设计课程的现状,结合教学实践探索西部服装市场的需求特征,提出强化服装结构设计的师资专业素质,加强服装结构设计与其他学科的交叉渗透,结合西部的民族资源,通过教学手法的改进,从实践出发,提出以人体为核心的服装结构设计课程教学改革措施.     

Seasonal variation of snow microbial community structure in the East Rongbuk glacier, Mt. Everest

Mt. Everest, the zenith of the world, plays an impor- tant role in the global climate change. During the lastdecade, some studies on glacier and snow have been undertaken in this region. The climate and environment changes in the past hundred years have b…     

有限点法在液舱晃荡问题中的应用

液舱晃荡问题是近年来液货船设计中十分关注的问题之一,它涉及自由液面的大幅运动等一系列强非线性问题,因此给基于欧拉观点下空间拓扑网格的数值模拟带来了较大的难度． 文中基于拉格朗日观点下的有限点法(FPM),对不同充容率的液舱晃荡问题进行了数值模拟,并将数值计算结果与试验值进行对比验证． 结果表明:当激励频率与液舱固有频率一致时,舱内流体的晃荡较为剧烈;而当充容率不同时,舱壁对液体流动的抑制作用使得高充容液舱内的流体翻卷变形较为平缓． 通过有限点法有效且准确地模拟了不同充容率液舱在各激励频率下的水面晃荡情况,与试验现象基本吻合． 同时通过对舱壁监测点的压力计算得知,有限点法的数值结果与实测压力曲线基本保持一致． FPM针对不同工况下的二维液舱晃荡情况均给出了较为准确的数值模拟结果,有效验证了有限点法的可靠性．     

New Approach to the Synthesis of C-Glycosides

C-glycosides are important carbohydrate mimetics and have gained considerable attention because ot their attractive biological activities as pharmaceutical targets and their resistance to enzymatic degradation in vivo. Consequently, a large number of C-glycosides have been synthesized for their bioactivity investigation, and various synthetic methodologies have been developed. As a continuation of the synthesis of novel C-glycoside derivatives using exo-glycals as the precursor, we wish to present herein new approaches to the synthesis of C-aryl aminoglycoside via the 1,3-dipolar cycloaddition （Scheme 1 ） and C-disaccharides by Suzuki cross-coupling reaction （Scheme 2）, and the synthesis of dihydropyrimidinonyl （DHPM） glycoside by the three component Biginelli reaction （Scheme 3）.     

Genomic alterations in cultured human embryonic stem cells

Cultured human embryonic stem cell (hESC) lines are an invaluable resource because they provide a uniform and stable genetic system for functional analyses and therapeutic applications. Nevertheless, these dividing cells, like other cells, probably undergo spontaneous mutation at a rate of 10(-9) per nucleotide. Because each mutant has only a few progeny, the overall biological properties of the cell culture are not altered unless a mutation provides a survival or growth advantage. Clonal evolution that leads to emergence of a dominant mutant genotype may potentially affect cellular phenotype as well. We assessed the genomic fidelity of paired early- and late-passage hESC lines in the course of tissue culture. Relative to early-passage lines, eight of nine late-passage hESC lines had one or more genomic alterations commonly observed in human cancers, including aberrations in copy number (45%), mitochondrial DNA sequence (22%) and gene promoter methylation (90%), although the latter was essentially restricted to 2 of 14 promoters examined. The observation that hESC lines maintained in vitro develop genetic and epigenetic alterations implies that periodic monitoring of these lines will be required before they are used in in vivo applications and that some late-passage hESC lines may be unusable for therapeutic purposes.     

A machine learning-based method to detect fluorescent spots and an accelerated,parallel implementation of this method

Under most microscopes, the fluorescent sig- nals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distri- bution. Accurate segmentation of these spots will funda- mentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the inter- esting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm Ada- Boost, which outperforms threshold-based segmentation, wavelets, and FDA under most situations. We also provide a GPU/multi-core CPU implementation of this algorithm; this implementation accelerates the algorithm approxi- mately 10- and 7-fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. Furthermore, we demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection meth- ods outperform the four other previously reported methods.     

Functional Imaging of Breast Tissue and Clinical Application

0Introduction Near infrared(NIR)techniquesarebasedonsensitive,quantitativemeasurementsoffunctionalcontrastbe tweenhealthyanddiseasedtissue.Recently,researchersshowgreatinterestinmeasuringfunctionalpropertiesofbreasttis suesuchashemoglobinconcentrationoroxygensaturationbyNIRspectroscopyandNIRimaging[1,2].NIRlightcanpene trateseveralcentimetersintotissuebeforeitisattenuatedbe lowdetection.ThemainintrinsicmechanismsofNIRlightat tenuationintissuearethescatteringduetoindexofrefractionvariatio…     

A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving

Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters(e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.     

太原地区主要水体汞污染初步调查与评价

<正> 汞属于极有害的环境毒物。由于五十年代日本的不幸事件和六十年代瑞典的鸟类灾难,水环境汞污染对健康的危害问题,引起了人们的普遍注意。1970年秋,在罗马举行的国际海洋污染对策会议上,把日本于1953