天宫一号与载人飞船组合体能源管理设计与验证

天宫一号和载人飞船构成组合体期间,载人飞船处于停靠状态,由天宫一号负责组合体能源管理.基于供电安全性和传输功率的要求,组合体进行了近端采样反馈电压控制的并网供电架构设计.经过仿真、地面测试和在轨飞行试验验证,表明天宫一号与载人飞船组合体供电并网设计正确,为载人空间站工程各组合体间供电并网设计提供了有力保证.     

Mutations in the RNA exosome component gene EXOSC3 cause pontocerebellar hypoplasia and spinal motor neuron degeneration

RNA exosomes are multi-subunit complexes conserved throughout evolution and are emerging as the major cellular machinery for processing, surveillance and turnover of a diverse spectrum of coding and noncoding RNA substrates essential for viability. By exome sequencing, we discovered recessive mutations in EXOSC3 (encoding exosome component 3) in four siblings with infantile spinal motor neuron disease, cerebellar atrophy, progressive microcephaly and profound global developmental delay, consistent with pontocerebellar hypoplasia type 1 (PCH1; MIM 607596). We identified mutations in EXOSC3 in an additional 8 of 12 families with PCH1. Morpholino knockdown of exosc3 in zebrafish embryos caused embryonic maldevelopment, resulting in small brain size and poor motility, reminiscent of human clinical features, and these defects were largely rescued by co-injection with wild-type but not mutant exosc3 mRNA. These findings represent the first example of an RNA exosome core component gene that is responsible for a human disease and further implicate dysregulation of RNA processing in cerebellar and spinal motor neuron maldevelopment and degeneration.     

弱激光照射周氏新对虾心脏细胞的显微和超微结构观察

采用He-Ne激光照射周氏新对虾的心脏组织.组织学和生物统计学表明:He-Ne激光照射后,心脏组织具有明显异染色质的细胞数量有所增加.在同一照射强度的情况下,照射的时间越长,其心脏组织具有明显异染色质的细胞数量就越多;电镜观察结果表明:经过2 h照射立即取材的心肌细胞,细胞核形态发生变化,异染色质增加,线粒体出现空泡化、嵴断裂、水肿,粗面内质网出现扩张、颗粒脱落.     

大孔树脂纯化复方中总苷类成分的吸附特征

研究D-101大孔树脂分离纯化金匮肾气丸中总苷类成分的工艺条件及参数.以马钱苷、芍药苷、总苷和总皂苷的质量分数为指标,比较四种大孔树脂对金匮肾气丸苷类成分的吸附性能,筛选出效果较好的D-101大孔树脂.通过对树脂的漏点质量浓度、吸附动力学曲线、药液pH值对树脂吸附的影响,以及对洗脱剂质量浓度、用量、pH值的考察,筛选出最佳的吸附洗脱工艺.D-101大孔树脂能有效地吸附和解吸金匮肾气丸中的苷类成分,最佳的吸附工艺参数苷类样品液质量浓度0.25 g/mL,药液的pH值6,吸附时间为8 h.以pH=8的70%乙醇为洗脱液,洗脱体积为30BV.树脂可重复利用2次.终产品中总苷的质量分数达43.87%,总皂苷的质量分数为4.93%.用大孔树脂对金匮肾气丸中总苷成分类进行纯化是可行的.     

一类新的圈代数和它的Liouville可积演化方程族

基于Lie代数Aa-1的推广,构造了一类新的圈代数,并设计了一个新的谱问题。然后,利用屠格式获得了一个新的可职系统,并推导出它相应的非线性演化方程族,最后,证明了该演化方程族在Liouville意义下是可积的。     

软起动介绍

例举了传统起动设备存在的问题,介绍了软起动技术工作的原理和分类,从异步电动机起动现状出发介绍了几种软起动类别及应用.     

苹果白兰地生产工艺的研究

采用经试验所筛选的菌种，进行了分批发酵生产苹果白兰地的研究，选择了适宜的培养基配方和蒸馏方案，确定了较佳的生产工艺条件，对生产过程中酵母的分离进行了初步探讨。     

宇宙年龄和宇宙物质密度

根据爱因斯坦广义相对论,时空弯曲,宇宙是有限的。现在已被主流物理学家普遍接受的是标准宇宙论,按标准宇宙论经过严格的数学推导,演算得到了宇宙的年龄、宇宙物质密度。作者根据广大天体物理工作者认可的观察数据和结论,通过初等数学的计算,得到的结果大相径庭。根本没天体物工作者寻找的暗物质。     

关于一类包含Euler函数方程的解

对于任意给定的正整数n，ω（n），表示的所有不同素因子的个数．研究了方程φ（n^2）=2^ω^（n^2）的可解性，并给出了该方程的所有正整数解．     

Cooperative Sensor Anomaly Detection Using Global Information

Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves.