模块化的微观结构与风险特性：基于产业集群的分析/FONT

模块化是产业集群发展的“秘笈”和演进的主流趋势,对产业集群的发展和升级具有重要的促进作用。但由于产业集群的发展存在着诸多不确定性和激烈的竞争,模块化过程中也隐藏着不容忽视的风险。本文探讨了模块化的微观结构,分析了产业集群模块化过程中的相关风险,包括兼容性风险、网络性风险、生态风险、信息风险和锁定风险,这些风险影响着模块化产品的市场结构和收益特征。因此,在产业集群模块化过程中,必须采取相应措施来控制和消除风险,降低其危害和冲击,进一步促进价值模块整合和产业融合。     

杰出人才是怎样炼成的?——下村修荣获诺贝尔化学奖案例研究

日本学者下村修作为绿色荧光蛋白质的发现者及其结构研究的开创者,与查非、钱永健一同荣获2008年度诺贝尔化学奖。对下村修投身生物发光研究的经过以及取得多项重大科学突破的经历进行了梳理,并结合"钱学森之问"从多个角度对下村修的成才之路展开了分析。最后就下村修这个案例对杰出人才的培养进行了讨论。     

CDRS:云存储中一种代价驱动的自适应副本策略

针对云存储环境下突出的一些新问题,如网络的广域性与动态性、商业利益的追求、一致性与可用性的权衡等,提出了一种代价驱动的自适应副本策略CDRS.该副本策略通过引 入市场机制中的代价,综合考虑负载平衡及一致性与可用性的均衡,对副本进行自适应的操作,达到最小化副本开销和最大化副本收益的目标.实验结果表明,与传统的副本策略相比,该策略在副本收益以及负载均衡方面有着更大的优势.     

影响我国产学研合作创新绩效的行业特征

收集了珠江三角地区各行业中参与产学研合作的399家企业客观数据,从行业的角度对产学研合作绩效进行评价,并将表征行业特征的行业中企业规模、技术水平及资本密集度作为研究变量,利用相关理论模型分析验证以上三个变量对产学研合作创新绩效的影响.分析结果表明,企业规模小、技术水平和资本密集度较高的行业其产学研合作创新绩效较为明显.     

图像识别在考生身份认证系统中的应用

针对传统考生身份认证方法的缺陷,提出一种基于人脸识别的考生身份认证系统。首先利用图像采集系统采集考生人脸图像,然后对人脸图像进行特征提取和特征选择,并将人脸特征输入到人脸特征库进行匹配,最后采用支持向量机算法对人脸进行分类识别。实验结果表明,该系统提高了考生身份识别的正确率,减少了识别时间,能够很好满足实际考试的要求。     

行业划转高校教学科研管理对策研究

地矿特色鲜明的行业划转高校是否有科学合理的管理机制,是否能保障教学科研持续发展的重要支撑。高校的教学与科研管理两者密切相关,完善的科研管理制度,教师从事科研的积极性,解决好科研教学内在矛盾,加强教师和科研团队建设,是提高高校教育与科研管理水平的重要手段。     

非线性MIMO级联数据库频繁访问特征分区技术

多输入输出(MIMO)级联数据库广泛应用在大型电子设备故障诊断数据库架构中。对MIMO级联数据库频繁访问过程中,进行特征分区有利于提高数据库访问性能。提出一种基于属性集合幂集的区间概念格的非线性MIMO级联数据库频繁访问特征分区算法,进行MIMO级联数据库的非线性特征分析及区间概念格构造,对数据库子系统的状态矢量进行临界状态数学建模,构建数据库的特征分区目标函数,改进MIMO级联数据库频繁访问特征分区性能。仿真实验表明,该算法能准确实现对非线性MIMO级联数据库频繁访问特征的提取和分区处理,有效提高了对MIMO级联数据库的内部访问特征的分析能力,有利于提高数据库访问性能,大幅提高数据检测性能,为构建专家系统奠定基础。     

高职院校生产性实训基地建设与管理机制研究

实训是高职教育的重要环节,是高职院校提高学生专业技能,加强生产性实训室建设、提高实践教学质量的重要措施,是高职院校发展的必由之路。     

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��[Purpose/significance] The abstract of scientific papers is a vital indexing object within information organization. Meanwhile, indexing the abstract according to certain rules is conducive for not only scientific communication or knowledge discovery, and intelligence analysis as well. Thus, how to realize auto-index accurately and quickly, for millions of unstructured abstracts existed nowadays is a crucial problem to be addressed.[Method/process] This study assumed that different categories of abstract are inherently consistent, that is, the study of structured abstract can provide a method and technical reference for unstructured abstract auto-indexing. Acting in accordance with this assumption and based on the US National Library of Medicine's structural element labeling terminology, this study accomplished mapping across abstract element classifications and proposed BOMRC system, a normalization indexing method for structured abstract. Then we collected research sample and used text mining method to analyze multiple features of structured abstract quantitatively and statistically, such as word frequency, TF-IDF value, as for dimension of words, verbs, three-word lexical chunks and four-word lexical chunks, which enabled us propose a semantic feature dictionary for structured elements. Finally, we used unstructured abstract to test the validity of the semantic feature dictionary.[Result/conclusion] The results show that the semantic feature dictionary method can effectively identify various structural elements of scientific paper abstract, and it can be used to optimize the automatic recognition model, which may be based on machine learning methods.     

Psychometric characteristics of the Persian version of the Multidimensional School Anger Inventory–Revised

The Multidimensional School Anger Inventory–Revised (MSAI-R) is a measurement tool to evaluate high school students' anger. Its psychometric features have been tested in the USA, Australia, Japan, Guatemala, and Italy. This study investigates the factor structure and psychometric quality of the Persian version of the MSAI-R using data from an administration of the inventory to 585 Iranian high school students. The study adopted the four-factor underlying structure of high school student anger derived through factor analysis in previous validation studies, which consists of: School Hostility, Anger Experience, Positive Coping, and Destructive Expressions. Confirmatory factor analysis of this four-factor model indicated that it fit the data better than a one-factor baseline model, although the fit was not perfect. The Rasch model showed a very high internal consistency among items, with no item misfitting; however, our results suggest that to represent the construct sufficiently some items should be added to Positive Coping and Destructive Expression. This finding is in agreement with Boman, Curtis, Furlong, and Smith's Rasch analysis of the MSAI-R with an Australian sample. Overall, the results from this study support the psychometric features of the Persian MSAI-R. However, results from some test items also point to the dangers inherent in adapting the same test stimuli to widely divergent cultures.