Engineering of multiferroic BiFeO3 grain boundaries with head-to-head polarization configurations

Confined low dimensional charges with high density such as two-dimensional electron gas (2DEG) at interfaces and charged domain walls in ferroelectrics show gre...     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Germline-specific dgcr8 knockout in zebrafish using a BACK approach

Zebrafish is an important model to study developmental biology and human diseases. However, an effective approach to achieve spatial and temporal gene knockout in zebrafish has not been well established. In this study, we have developed a new approach, namely bacterial artificial chromosome-rescue-based knockout (BACK), to achieve conditional gene knockout in zebrafish using the Cre/loxP system. We have successfully deleted the DiGeorge syndrome critical region gene 8 (dgcr8) in zebrafish germ line and demonstrated that the maternal-zygotic dgcr8 (MZdgcr8) embryos exhibit MZdicer-like phenotypes with morphological defects which could be rescued by miR-430, indicating that canonical microRNAs play critical role in early development. Our findings establish that Cre/loxP-mediated tissue-specific gene knockout could be achieved using this BACK strategy and that canonical microRNAs play important roles in early embryonic development in zebrafish.     

Overactive bone morphogenetic protein signaling in heterotopic ossification and Duchenne muscular dystrophy

Bone morphogenetic proteins (BMPs) are important extracellular cytokines that play critical roles in embryogenesis and tissue homeostasis. BMPs signal via transmembrane type I and type II serine/threonine kinase receptors and intracellular Smad effector proteins. BMP signaling is precisely regulated and perturbation of BMP signaling is connected to multiple diseases, including musculoskeletal diseases. In this review, we will summarize the recent progress in elucidation of BMP signal transduction, how overactive BMP signaling is involved in the pathogenesis of heterotopic ossification and Duchenne muscular dystrophy, and discuss possible therapeutic strategies for treatment of these diseases.     

杭州人工智能企业集群发展研究

基于人工智能技术、产品、服务对“人”“城市”“平台”的作用和影响,分析了杭州三类人工智能企业集群的发展情况和存在短板,提出对策建议。     

浅议工程施工成本控制方法

加强成本管理是市政施工企业提高管理水平、经济效益和持续稳定发展的重要保证,是直接涉及影响企业信誉和生存的关键因素,是需要我们实实在在地面对和解决的问题.     

感应式静电引信探测技术

目的　研究直接检测探测电极的感应电荷获得静电目标信息的静电近炸引信探测技术; 方法　采用短路轴向探测电极感应引信接近静电目标过程中电荷量的变化,并采用电荷放大器检测信号; 利用静电学的基本原理,对其进行了理论推导; 结果　得出探测方程和定距公式,以及增大探测灵敏度的设计准则; 阐述了在该种电极设置下对检测电路的要求以及环境变化对检测结果稳定性的影响; 结论　感应式静电引信探测技术适用于静电目标探测且探测距离远、不受非带电物体的干扰;     

MIL-101(Fe)对草甘膦的吸附行为研究

采用溶剂热法制备MIL-101(Fe)用作吸附材料,借助XRD、SEM、XPS及氮气吸脱附测试对所制材料的形貌及结构进行表征,研究了溶液pH值、反应时间、溶液浓度以及温度对其吸附草甘膦行为的影响。结果表明,当pH为3时,MIL-101(Fe)对草甘膦的吸附效果最佳,且随着温度的升高,MIL-101(Fe)对草甘膦的吸附速率明显增加,吸附过程符合拟二级动力学,等温吸附实验数据符合Langmuir和Temkin模型。MIL-101(Fe)吸附草甘膦属自发吸热的化学吸附反应,MIL-101(Fe)的中心金属Fe与草甘膦中O的配位在吸附过程中起主要作用。     

面向VHDL语言的专用智能编辑器

目的设计面积超高速集成电路硬件描述语言的专用智能编辑器。方法使用散列表记忆各类标识符方法,动态地分析当前输入状态,完成ＶＥｄｉｔｏｒ的功能设置。结果该编辑器汇聚了目前流行的各类通用程序编辑器的优点,同时具有其独特的功能－标识符自动记忆,语句联想式输入及简单词法,语法错误的报错功能,结论实现了一个功能齐全的编辑器。