植物在汉英文化中内涵的差异

通过分析比较植物内涵在汉英文化中的不同,了解汉民族与英语民族由于自然环境、文化艺术、历史传统、思维特点及审美观念等方面的差异,有利于深入了解各自不同的文化,也有利于国际文化交流。     

基于光纤光栅和BP神经网络的结构损伤识别

以光纤光栅为传感元件,四边简支板为研究对象,BP神经网络为信号处理手段,研究了光纤光栅传感器和BP神经网络在结构损伤识别中的应用,实验表明,光纤光栅传感器具有灵敏度高、稳定性好的特点,是结构损伤识别中的一种新的信号采集工具。采用光纤光栅作为传感元件,不仅可使BP神经网络成功地实现对四边简支板的损伤识别。而且提高了结构损伤识别的精度。     

毕赤酵母甲醇利用及甲醇蛋白发酵条件优化

利用毕赤酵母(Pichia pastoris)作为甲醇蛋白生产菌种,探究甲醇作为糖类替代碳源时毕赤酵母对甲醇的利用情况,并对甲醇蛋白发酵生产过程中的关键因素进行较为全面的考察和优化.依据试验结果,初步确定了摇瓶水平发酵各因素的最优条件为接种量9%,发酵温度采用30℃,初始p H值为5.0,装液量为40 m L/250 m L摇瓶,转速为210 r/min.在此条件下,得到的甲醇蛋白最高产量(细胞干质量)为1.63 g/L.     

用于认知跳频的归一化谱双向搜索感知算法

认知跳频被认为是消除传统跳频系统用频困扰的有效途径之一。针对认知跳频超宽带和多频隙实时频谱感知的需求,给出基于归一化谱双向搜索(bidirectional search of normalized power spectrum, BSNP)的感知算法,BSNP以跳频频隙内的归一化功率谱作为检验统计量,通过顺序执行正向和反向搜索,感知出跳频带宽中已被占用的所有频隙。利用傅里叶变换的渐进正态性和相互独立性,可推导BSNP单次判决虚警概率的数学表达式和判决门限的闭式表达式。分析和仿真表明,BSNP可以准确地找出频带内被占用的频隙,相比于常规谱估计感知算法,可有效克服噪声不确定度对频谱感知性能的影响。     

Identifying influential spreaders in artificial complex networks

A long-term common belief in complex networks is that,the most connected nodes are the most efficient spreaders.However,recent investigations on real-world complex networks show that the most influential spreaders are those with the highest fc-shell values.It is well-known that,many real-world complex networks have scale free（SF）,small world（SW） properties,therefore,identification of influential spreaders in general artificial SF,SW as well as random networks will be more appealing.This research finds that,for artificial ER and SW networks,degree is more reliable than fc-shell in predicting the outcome of spreading.However,for artificial SF networks,fc-shell is remarkably reliable than degree and betweeness,which indicate that the four recently investigated real-world networks[Kitsak M,Gallos L K,Havlin S,Liljeros F,Muchnik L,Stanley H E,Makse H A,Identification of influential spreaders in complex networks,Nat.Phys.,2010,6：888-893.]are more similar to scale free ones.Moreover,the investigations also indicate us an optimal dissemination strategy in networks with scale free property.That is,starting from moderate-degree-nodes will be ok and even more economical,since one can derive roughly similar outcome with starting from hubs.     

Fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject.Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.     

沃尔夫物理学奖与量子信息的发展

 沃尔夫物理学奖成立于1976年,是世界物理学界最高成就奖之一,获奖者中有不少后来获得诺贝尔物理学奖。通过介绍2010年、2013年和2018年沃尔夫物理学奖获奖者在量子信息领域的突破性成果,展示了该领域近年来的发展轨迹,并对比了这3届获奖者不同的研究思路。     

知识共享视域下的新型产业创新生态系统研究——以科大讯飞成长为例

有别于工业经济时代,互联网大数据时代的知识与技术迅速迭代,因此,必须对基于技术进步的产品与产业生命周期理论进行重新审视.着眼于对国内智能语音产业的分析,按照知识—技术—产品的研究脉络,分析产业内部相关知识与产品的发展过程,结合案例中一些知识专家逐渐发展成为企业家的整个过程,在对产业创新生态系统进行研究的前提下,分析关联企业的发展规律.得出结论如下:在推进新型产业创新生态系统发展的环节中,技术产品将朝着多元化的方向发展.因为技术与知识彼此相互促进和融合,系统内创新将持续深化.