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关键词： 包装材料   静电现象   绿色包装   科研人员   复合功能   面电阻率   金属系   经济环保   表面改性   导电高分子材  

摘要： 包装这门学科,对其材料的性能要求有时较苛刻,物理的、化学的、力学的、电学的等性能往往要同时满足,而且还要赋予特定的多功能,例如防静电、防电磁波等所谓的"双防"材料,甚至多防材料等。随着抗静电包装材料需求的增长以及社会对绿色包装的重视,研制具有高能动化、稳定性好、经济环保的复合功能材料给科研人员提出了更高的要求。

关键词： 样品   采集   分类  

摘要： 区疾控中心卫生科承担着卫生行政部门、食品药品监管部门、上级疾控部门委托或布置的食品流通领域的食品样品、餐茶具消毒样品,农村饮用水安全工程监测和辖区集中式或分散式供水水质样品、医疗机构放射设备的放射防护监测及放射从业人员的体质监测样品、公共场所卫生监测样品的采样与送检和监测评价与检测报告书发送工作。

关键词： 跨境贸易   人民币结算   国际计价结算  

摘要： 随着我国对外贸易在不断的扩大,针对国际贸易中货币计价和结算职能的影响及决定因素做出了具体的分析,本文主要采用的是主成分分析方法来探索跨境贸易人民币结算的影响因素。国际环境下,多边贸易在不断发展,国际贸易结算货币的选择对于汇率风险和交易成本的降低,再加上经济效益的提高越老越受贸易商的重视,这就需要重点对跨境贸易人民币结算影响的因素做出具体分析,调整国际货币环境,促进经济贸易的健康流通。

关键词： 肝硬化   中医病因探析   疾病诊断   生化检验项目   作用  

摘要： 目的探讨生化检验项目在肝硬化疾病诊断中的作用。方法本次共选择50例肝硬化患者(肝硬化组),均为我院2013年1月至2014年1月收治,采用生化检验,就结果与同期收集的50例健康体检者(健康组)进行比较。结果观察组选取病例ASTm、ADA、GIDH、AFU、NAC、LAP值均高于对照组,差异均有统计学意义(P<0.05),前白蛋白(PAB)相对降低。结论加强生化检验项目监测,对早期诊断肝硬化疾病意义重大,可为临床治疗提供准确的参考依据。

关键词： 基层检验专业质控组   区域性   质量控制  

摘要： 目的探讨基层检验专业质控组在区域性质量控制中的作用。方法在我市成立医疗机构基层检验专业质控组,选取从2011年3月至2013年3月之间在我市医疗卫生机构进行治疗的病患5000名,在对治疗结束之后向患者发放治疗情况问卷。对我市的市属、社区、乡镇医疗卫生机构的工作质量进行跟踪监督检查,对各个市属、社区、乡镇卫生医疗机构中出现的问题进行分析和总结,组织个别医疗卫生机构的人员进行单独的医疗培训。结果在建立基层检验专业质控组之后各个卫生医疗机构的医疗事故发生率明显下降,从2011年3月至2013年3月之间各个卫生医疗机构的合格率从60.5%上升到87.5%,优良率从原来的55.7%提升到了80%,各个环节中的事故发生明显减少。结论基层检验专业质控组的介入在区域性的质量控制的作用明显,能够帮助各个医疗卫生机构提升工作水平。制定统一的标准和规范进行质量的监督对于推动整个医疗卫生行业的发展是一件十分重要的举措。

关键词： 第三人   违约责任   合同相对性  

摘要： 我国司法实践中存在着大量涉及第三人的合同,原则上强调合同的相对性,合同关系以外的第三人不享有合同权利也不承担合同义务。但在第三人原因造成违约时,实务处理中对第三人的原因范围如何确定,如何处理与侵权责任的关系等,由于相关法律规范较为简单,学界存在较多争议。因此有必要对这一问题进行全面梳理,明确相关的责任处理问题,以彰显现代合同法自由与秩序理念,完善合同制度。

关键词： Supervised clustering   Data mining   Gene expression   attributes  

摘要： Clustering is a useful technique for the analysis of gene expression data. Clustering algorithms based on probability model offers an alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a "good" clustering method and determining the "correct" number of clusters are reduced to model selection problems in the probability framework. This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, and classification. By clustering attributes, the search dimension of a data mining algorithm is reduced. The reduction of search dimension is especially important to data mining in gene expression data because such data typically consist of a huge number of genes (attributes) and a small number of gene expressions. It is for the aforementioned reasons that gene grouping and selection are important preprocessing steps for many data mining algorithms to be effective when applied to gene expression data. This project defines the problem of attribute clustering and introduces a methodology to solving it. Our proposed method group"s interdependent attributes into clusters by optimizing a criterion function derived from an information measure that reflects the interdependence between attributes. By applying our algorithm to gene expression data, meaningful clusters of genes are discovered. The grouping of genes based on attribute interdependence within group helps to capture different aspects of gene association patterns in each group. Significant genes selected from each group then contain useful information for gene expression classification and identification.

关键词： Mycobacterium tuberculosis   Microscopic imaging   Pattern recognition   Random forests   Support vector machines  

摘要： The World Health Organization suggests visual examination of stained sputum smear samples as a preliminary and basic diagnostic technique for diagnosing tuberculosis. The visual examination process requires much time of laboratorian, and also, it is prone to mistakes. For this purpose, this paper proposes a novel random forest (RF)-based segmentation and classification approaches for the automated classification of Mycobacterium tuberculosis in microscopic images of Ziehl-Neelsen-stained sputum smears obtained using a light-field microscope. The RF supervised learning method is improved to classify each pixel depending on local color distributions as a part of candidate bacilli regions. Therefore, each pixel is labeled as either a candidate tuberculosis (TB) bacilli pixel or not. The candidate pixels are grouped together using connected component analysis. Each pixel group is then rotated, resized and centrally positioned within a bounding box, respectively, in order to utilize appearance-based tuberculosis bacteria identification algorithms. Finally, each region is classified by using the proposed RF learning algorithm trained on manually marked TB bacteria regions in the training images. The algorithm produces results that agree well with manual segmentation and identification. Different two-class pixel and object classifiers are also compared to show the performance of the proposed RF-based pixel segmentation and bacilli objects identification algorithm. The sensitivity and specificity of the proposed classifier are above 75.77 and 96.97% for the segmentation of the pixels, respectively. It is also revealed that the sensitivity increases over 93% when the staining is performed in accordance with the procedure. Moreover, these measures are above 89.34 and 62.89% for the identification of bacilli objects. The results show that the proposed novel method is quite successful when compared to the other applied methods.

关键词： geographic object-based image analysis   segmentation   classification   sample supervised   spatial metrics   metaheuristics  

摘要： Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

关键词： Word Choice   Performance validity   Recognition Memory Test.  

摘要： Word Choice (WC), a test in the Advanced Clinical Solutions package for Wechsler measures, was examined in two studies. The first study compared WC to the Recognition Memory Test-Words (RMT-W) in a clinical sample (N = 46). WC scores were significantly higher than RMT-W scores overall and in sample subsets grouped by separate validity indicators. In item-level analyses, WC items demonstrated lower frequency, greater imageability, and higher concreteness than RMT-W items. The second study explored WC classification accuracy in a different clinical sample grouped by separate validity indicators into Pass (n = 54), Fail-1 (n = 17), and Fail-2 (n = 8) groups. WC scores were significantly higher in the Pass group (M = 49.1, SD = 1.9) than in the Fail-1 (M = 46.0, SD = 5.3) and Fail-2 (M = 44.1, SD = 4.8) groups. WC demonstrated area under the curve of .81 in classifying Pass and Fail-2 participants. Using the test manual cutoff associated with a 10% false positive rate, sensitivity was 38% and specificity was 96% in Pass and Fail-2 groups with 24% of Fail-1 participants scoring below cutoff. WC may be optimally used in combination with other measures given observed sensitivity.