中国高技术制造业创新景气指数研究

以创新周期理论为研究基础,通过运用主成分分析法,完成了中国高技术制造业创新景气指数构造,以考察中国高技术制造业创新的周期性波动特征。指数结果显示,中国高技术制造业创新景气状态在近二十年里先后经历了剧烈波动期、平稳振动期、趋势向上期共三个长周期,景气值有望在2018年达到历史最高位,从繁荣转向低迷的周期转折点可能在2020年末到来。研究结论为：基于主成分法的高技术制造业创新景气指数能够有效地描述中国高技术制造业创新发展的周期状况和波动态势,并能准确地筛选出R D经费内部支出占主营业务收入比重增速、技术引进经费支出增速等关键指标因素,这能为中国政府适时调整产业扶持政策、优化创新资源供给提供有益参考。     

基于PCA-SEDEA的高校协同创新中心科研效率分析与评价——以江苏高校行业产业类协同创新中心为例

利用主成分分析和超效率数据包络分析的组合模型,从投入资源配置和产出效率视角对27个江苏行业产业类协同创新中心建设期内的科研效率进行分析研究,提出进一步推进中心高质量内涵式发展的建议供主管部门、高校和协同创新中心借鉴,为建立科学规范竞争与退出机制提供参考,使得协同创新中心充分发挥有效合理配置创新资源要素的重要作用。     

基于物元理论的创新型人才类型与投入指标的因果分析

以地方高等院校培养创新型人才为视角,通过主成分分析法,找出创新型人才培养的主要投入指标。在关键性指标确定的基础上,引入物元模型分析,定量解决地方高等院校创新型人才培养中投入的定性数据。在研究过程中了解到,地方高等院校培养高层次创新型人才难度较大。但是在问卷调查中发现,生态环境在所有指标中占有重要位置,这为创新型人才培养增加了新视野。     

Principal leadership changes and their consequences for school performance in South Africa

School leadership changes are gaining momentum in South Africa as larger proportions of incumbent principals near retirement age. While this presents opportunities to replace weaker school leaders with better ones, these changes may also destabilize school environments and impede on learning. This study explores this issue using payroll data on public school principals in South Africa linked to national data on schools and matriculation examination outcomes. School fixed effects and propensity score matching with difference-in-difference estimations confirm that principal changes are indeed detrimental to school performance with larger disruptive effects observed in poorer schools.     

中学校长对推进素质教育所应作的理性思考——兼谈牡丹江市第四中学“双主互动课堂模式”课题研究的收获

本文阐述了中国骨干校长在世纪之交的知识经济初期,面对素质教育的职能定位,反思课堂教学,提出新的发展策略,尤其规划了以基础教育为中心的教育格局及构建。     

主成分分析、因子分析、聚类分析的比较与应用

主成分分析、因子分析、聚类分析是三种比较有价值的多元统计方法,但同时也是在使用过程中容易误用或混淆的几种方法。本文从基本思想、数据的标准化、应用上的优缺点等方面,详细地探讨了三者的异同,并且举例说明了三者在实际问题中的应用。     

Becoming a PI: From ‘doing’ to ‘managing’ research

While achieving research independence by becoming a principal investigator (PI) is a key aspiration for many postdocs, little is known of the trajectory from PhD graduation to first PI grant. This interview-based study examined how 16 PIs in science, technology engineering, mathematics or medicine, in the UK and continental Europe, prepared for and dealt with this career transition. Individuals demonstrated commitment to lengthy periods of postdoctoral work in a range of institutions (often involving international mobility) to achieve PI-status. Their emotionally laden journeys required resilience and self-belief, since getting a grant was conceived as partly luck. Once individuals had their grant they faced new challenges that distanced them from actively researching. Still, individuals navigated their intentions in a sustained fashion to create a distinct intellectual profile in the face of challenging circumstances. The results highlight the centrality of emotion in the journey, as well as curricular imperatives for both doctoral and postdoctoral learning.     

Extracting invariable fault features of rotating machines with multi-ICA networks

INTRODUCTIONRotatingmachinessuchaselectromotor,dy namotor,turbocompressor,etc .areimportantequipmentsinmanyindustryfields.Peoplehadbeenpayingconsiderableattentiontotheircon ditionmonitoringandfaultdiagnosis (Xu ,19 98) .Theoretically ,anyfluctuationofforc…     

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.