弧面分度凸轮机构的分析与设计

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斑节对虾杆状病毒感染与水体理化因子

通过二造虾塘水体理化因子的检测和斑节对虾杆状病毒感染的调查，发现水体理化因子可影响斑节对虾杆状病毒的感染度，其中影响最大的是氨氮，其次是盐度和亚硝酸氮．氨氟和pH值有协同作用，硫化物可诱发斑节对虾杆状病毒病，亚硝酸氮可明显地抑制对虾的生长．     

大学物理实验的不确定度评定

本文介绍了不确定度的有关概念,给出了在大学物理实验中计算直接测量的展伸不确定度的简化公式．     

煤矿瓦斯抽放泵站单片机监测系统

本文采用单片机技术设计了一套经济实用的监测系统,用于煤矿瓦斯抽放泵站参数的连续监测。利用单片机多机通讯的功能,通过 RDS—1型远程数据收发器将泵站的参数传到调度端进行显示、打印和处理。并对泵站的生产进行必要的管理,为改变目前我国煤矿瓦斯抽放泵站采用人工定时检测的现状。     

应用型材料专业本科生科研素养培养体系构建

科研素养的培养是提高应用型本科生综合能力的需要。笔者根据所在学校的现状和材料专业的特点,提出了从特色课程构建、教学内容和方法改革、开放性实验平台搭建、课外科研训练方案和评价机制优化等多方面的应用型材料专业本科生科研素养的培养体系。     

沙参属10个种的染色体研究

 本文报道了我国黑龙江产桔梗科沙参属的10种1变种的染色体数目和核型，对其中     7种作了减数分裂行为的观察。  其中6种1变种为首次报道，并发现2n=68的4x种。该     属染色体基数多为17(x＝17)，但Adenophora trachelioides和A．remotiflora的基数为18     (x=18)，为该属独特基数。核型的共同特征是：小型，以中部(m)、近中部(sm)着丝点     染色体为主，至少具一对近端着丝点染色体和一对随体染色体。该属染色体的演化处于二种     水平：  数目变化(包括多倍化和非整倍体变化)和结构变异。  多倍化是该属物种形成的主要     途径之一。结合其它性状讨论了这些种的分类，并确立1个四倍体新种(A. amurica)和1个新组合(A．pereskiifolia ssp．alternifolia)。     

高等职业教育之教学实训空间模式研究

教学实训空间模式能够有效地处理教学、实训和空间三者之间的关系．有利于提高高等职业教育的课程教学质量和实训水平。因此,分析教学实训空间模式的构建．探讨教学实训空间模式的应用．具有现卖的理论意义和实践价值。     

ASP.NET数据验证机制研究

数据验证是Web程序设计的重要环节,本文针对ASP.NET中验证控件的使用方法进行详细阐述,并结合实例说明在ASP.NET中数据验证的方便性与灵活性.     

基于Linux的内核数据捕获技术研究

以Linux作为数据捕获的平台,以捕获系统的所有活动特别是入侵者的加密会话为目标,提出了一种利用Rootkit技术,通过替换系统调用sys_read,sys_write,sys_open,sys_socketcall等来从内核捕获数据的技术。     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.