高分辨雷达距离-多普勒扩展目标广义自适应子空间检测器

本文提出了一种非高斯杂波中的距离-多普勒扩展目标广义自适应子空间检测器(RDST-GASD).采用了频率-慢时间子空间模型来描述宽带雷达回波信号序列.杂波的统计模型采用球不变随机向量描述,各距离单元内的杂波具有相同的归一化协方差矩阵和不同的功率(非均匀),并且假设可以得到只含有杂波的参考单元的数据以估计杂波的协方差矩阵.通过理论分析证明了RDST-GASD对杂波的功率和协方差矩阵都具有恒虚警的性质.对于宽带雷达,在相参处理时间内,目标回波通常存在越距离单元走动,RDST-GASD进行了距离对齐处理,从而实现了有效的相参积累,提高了检测性能.     

新疆膜下滴灌土壤粒径分布及与水盐含量的关系

土壤粒径分布及空间变化是土壤重要的物理特性之一,对土壤水分和溶质运移、土壤侵蚀等有重要意义．研究选用新疆膜下滴灌试验田的563个土壤样品,利用激光衍射粒径分析仪分析土壤粒径分布,采用质地三角分类、分形和多重分形对土壤粒径分布进行研究,探讨土壤粒径特征及与水盐含量的关系．研究区土壤颗粒中粘粒含量偏低,平均值为1．52％,砂粒含量最大,粒径分布呈现复杂的单、双峰分布特征;土壤粒径分布的分形和多重分形与土壤颗粒含量的标准差及最大值有良好的相关性,可以较好地反映土壤粒径分布曲线．膜下滴灌对表层土壤盐分空间分布有较大影响,随着深度增加影响减弱;土壤质地与稳定状态下土壤水盐含量关系密切,特别是对土壤表层盐分聚集和深层土壤水分分布有显著影响．     

基于DSP＆CPLD的便携式振动信号处理系统

为了更准确方便地诊断设备运行特性,结合FFT和小波分析原理设计了一种基于DSP＆CPLD的便携式振动信号处理系统。系统的硬件电路由信号调理电路、数据采集处理模块和外围电路等组成,其软件部分由主程序模块、数据采集处理程序和LCD显示程序等组成,能够实时采集振动信号,显示并存储静态特性、振动波形和频谱等数据。     

淮河流域近50年来气候变化及突变分析

基于淮河流域1961~2015年的主要气象数据,采用趋势分析、M-K检验和R/S方法,对淮河流域的气温、降水和日照时数3个要素的时间序列进行趋势分析和突变点检验,结果显示:1淮河流域多年平均气温在时间上呈现明显突变上升趋势,其中大部分站点气温突变出现在20世纪90年代;流域南部的气温上升程度高于北部地区;从整个流域的Hurst指数分析来看,未来淮河流域的年平均气温仍将持续升高,但上升程度不显著。2淮河流域的年降水量在时间上呈现一定的减少趋势,并且发生多次降水突变现象;各站点的降水量变化不大,但均出现多次突变情况;从Hurst指数来看,流域未来的年平均降水量有上升趋势。3流域的年平均日照时数呈现下降趋势,但变化并不显著;各站点均出现明显的日照时数下降现象;从Hurst指数来看,流域未来的日照时数将继续下降。     

非高斯杂波背景中距离扩展目标的自适应积累检测器

针对SIRV建模的非高斯杂波背景中距离扩展目标检测问题,首先,假设杂波协方差矩阵结构已知,利用点目标检测器NMF对每个距离单元多个脉冲回波进行相干积累,再对目标所占距离单元能量进行非相干积累,提出了距离扩展目标的积累检测器NMFI,并推导了NMFI虚警概率与检测门限的关系表达式.其次,利用不含目标的辅助单元数据提出了修正的协方差矩阵迭代估计器MRE,再将估计矩阵代替NMFI中的真实协方差矩阵得到自适应检测器MRE-ANMFI,并证明了MRE-ANMFI对杂波纹理分量和协方差矩阵结构的CFAR特性.最后,利用仿真实验验证了本文方法的有效性.     

基于FPGA的音频分析系统实现

采用Altera公司的FPGA芯片EP2C20作为控制和运算核心，实现对频率范围在20Hz～20kHz的音频信号分析和测试。采用FFT算法对音频信号进行频谱分析和计算处理，实现失真度分析，并完成信号总功率、各分量频率、周期等参数分析测量。采用TFT-LCD作为显示终端，能够直观显示功率谱曲线和各测试参数值。通过软件分析对比测试发现，该系统实时性强，测量准确度高，误差优于0．3％，可以有效完成音频信号分析和处理。     

杂交应力实壳单元对压电结构的非线性分析

给出8节点实壳单元对压电结构的几何非线性分析. 为了克服剪切、梯形和厚度锁定, 采用了假设自然应变方法, 特别是修正广义层合刚度矩阵. 由修正广义层合刚度矩阵得到的广义应力被单独假设, 导出了推广的Hellinger-Reissner 泛函, 通过选择广义应力假设实体单元的应力假设, 导出了杂交应力实壳单元的表达式. 非线性数值算例说明给出的单元模型对分析非线性压电结构的有效性.     

致密砂岩气水相对渗透率模型

致密砂岩富含微米级孔隙,其气、水两相流动复杂,如何建立可靠的相对渗透率模型是目前亟待解决的问题.基于流体单管流动方程耦合二阶滑移模型,结合分形理论,建立了致密砂岩储层气、水相对渗透率模型,该模型考虑了气体滑脱效应、孔喉结构参数、含水饱和度分布,模型的可靠性通过多个已发表的气水相对渗透率实验数据验证.结果表明:1)建立的模型可用以描述致密砂岩储层微米尺度孔隙中的流体流动行为; 2)压力和含水饱和度增加都会减小滑脱效应对气相相对渗透率的提高作用,当压力小于1 MPa时,气相相对渗透率对压力极为敏感;温度对气相相对渗透的影响有限,可忽略; 3)孔径分布分形维数和孔隙迂曲度分形维数增加,有利于气相相对渗透率的提高,但会降低水相相对渗透率,且孔径分布分形维数的影响更大.     

梯度功能材料结构的广义当量反对称弯曲理论

根据结构与材料的特性, 将梯度功能材料(FGM)结构定义为一个广义当量反对称结构, 这是为将成熟的板壳理论和复合材料层合理论应用于FGM结构而提出的一种新概念和新思想. 在此基础上, 提出一种用于梯度功能材料结构特性分析的新理论——广义当量反对称弯曲理论, 结合广义双重Fourier级数解法, 对有着实际应用背景的FGM结构进行具体的分析计算, 并与现有的理论和实验进行对比.     

全相位FFT相位测量法

为精确测出采样序列各样点的相位值,本文深入研究了全相位FFT（简称apFFT）的相位谱特性,严格证明了apFFT的相位不变性,基于此提出了apFFT相位测量法.此法无需借助任何频谱校正措施即可精确测量出相位值,因而计算量小.仿真实验证明此法在无噪时的精度比第1类相位差法高出5个数量级,在信噪比较大时的均方根误差仅为第1类相位差法的1/4～1/3.     

L1Tc non-LTR retrotransposons from Trypanosoma cruzi contain a functional viral-like self-cleaving 2A sequence in frame with the active proteins they encode

A comparative analysis of 40 Trypanosoma cruzi L1Tc elements showed that the 2A self-cleaving sequence described in viruses is present in them. Of these elements, 72% maintain the canonical 2A motif (DxExNPGP). A high percentage has a conserved point mutation within the motif that has not been previously described. In vitro and in vivo expression of reporter polyproteins showed that the L1Tc2A sequence is functional. Mutations within certain L1Tc2A sequences affect the efficiency of the cleavage. The data indicate that the L1Tc2A sequence may be influencing the L1Tc enzymatic machinery determining the composition and level of the translated products. The residues located immediately upstream of the 2A consensus sequence increase the cleaving efficiency and appear to stabilize the relative amount of translated products. These authors contributed equally to this work. Received 26 January 2006; received after revision 11 April 2006; accepted 21 April 2006     

Volatility forecasting with bivariate multifractal models

This paper examines volatility linkages and forecasting for stock and foreign exchange markets from a novel perspective by utilizing a bivariate Markov-switching multifractal model that accounts for possible interactions between stock and foreign exchange markets. Examining daily data from major advanced and emerging nations, we show that generalized autoregressive conditional heteroskedasticity models generally offer superior volatility forecasts for short horizons, particularly for foreign exchange returns in advanced markets. Multifractal models, on the other hand, offer significant improvements for longer horizons, consistently across most markets. Finally, the bivariate multifractal model provides superior forecasts compared to the univariate alternative in most advanced markets and more consistently for currency returns, while its benefits are limited in the case of emerging markets.     

RNA recognition by double-stranded RNA binding domains: a matter of shape and sequence

The double-stranded RNA binding domain (dsRBD) is a small protein domain of 65–70 amino acids adopting an αβββα fold, whose central property is to bind to double-stranded RNA (dsRNA). This domain is present in proteins implicated in many aspects of cellular life, including antiviral response, RNA editing, RNA processing, RNA transport and, last but not least, RNA silencing. Even though proteins containing dsRBDs can bind to very specific dsRNA targets in vivo, the binding of dsRBDs to dsRNA is commonly believed to be shape-dependent rather than sequence-specific. Interestingly, recent structural information on dsRNA recognition by dsRBDs opens the possibility that this domain performs a direct readout of RNA sequence in the minor groove, allowing a global reconsideration of the principles describing dsRNA recognition by dsRBDs. We review in this article the current structural and molecular knowledge on dsRBDs, emphasizing the intricate relationship between the amino acid sequence, the structure of the domain and its RNA recognition capacity. We especially focus on the molecular determinants of dsRNA recognition and describe how sequence discrimination can be achieved by this type of domain.     

Dynamic harmonic regression

This paper describes in detail a flexible approach to nonstationary time series analysis based on a Dynamic Harmonic Regression (DHR) model of the Unobserved Components (UC) type, formulated within a stochastic state space setting. The model is particularly useful for adaptive seasonal adjustment, signal extraction and interpolation over gaps, as well as forecasting or backcasting. The Kalman Filter and Fixed Interval Smoothing algorithms are exploited for estimating the various components, with the Noise Variance Ratio and other hyperparameters in the stochastic state space model estimated by a novel optimization method in the frequency domain. Unlike other approaches of this general type, which normally exploit Maximum Likelihood methods, this optimization procedure is based on a cost function defined in terms of the difference between the logarithmic pseudo‐spectrum of the DHR model and the logarithmic autoregressive spectrum of the time series. The cost function not only seems to yield improved convergence characteristics when compared with the alternative ML cost function, but it also has much reduced numerical requirements. Copyright © 1999 John Wiley & Sons, Ltd.     

从文献计量分析看世界合成生物学研究现状

合成生物学是生物学的一个新兴分支学科.以美国科学引文索引数据库扩展版数据库(SCI-EXPANDED)收录入库,发表于1990～2010年的3 949篇与合成生物学相关的论文为研究对象,通过对论文的产出规模、地域分布、机构分布、研究热点、学科关联性、知识基础等方面的计量分析,在一定程度上揭示出近年来世界合成生物学的发展是一种成果产出不断丰富,理论研究与应用研究均得到积极推进,与众多学科发展联系紧密,各国科研投入力量在显著加强以及美国在全球一枝独秀的局面.     

Optimal sampling frequency for volatility forecast models for the Indian stock markets

This paper evaluates the performance of conditional variance models using high‐frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non‐normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd.     

Foreign exchange market prediction with multiple classifiers

Foreign exchange market prediction is attractive and challenging. According to the efficient market and random walk hypotheses, market prices should follow a random walk pattern and thus should not be predictable with more than about 50% accuracy. In this article, we investigate the predictability of foreign exchange spot rates of the US dollar against the British pound to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto‐mutual information and false nearest‐neighbor methods. Some inductive machine‐learning classifiers—artificial neural network, decision tree, k‐nearest neighbor, and naïve Bayesian classifier—were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved a prediction accuracy of up to 67%. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting Daily Variations of Stock Index Returns with a Multifractal Model of Realized Volatility

Multifractal models have recently been introduced as a new type of data‐generating process for asset returns and other financial data. Here we propose an adaptation of this model for realized volatility. We estimate this new model via generalized method of moments and perform forecasting by means of best linear forecasts derived via the Levinson–Durbin algorithm. Its out‐of‐sample performance is compared against other popular time series specifications. Using an intra‐day dataset for five major international stock market indices, we find that the the multifractal model for realized volatility improves upon forecasts of its earlier counterparts based on daily returns and of many other volatility models. While the more traditional RV‐ARFIMA model comes out as the most successful model (in terms of the number of cases in which it has the best forecasts for all combinations of forecast horizons and evaluation criteria), the new model performs often significantly better during the turbulent times of the recent financial crisis. Copyright © 2014 John Wiley & Sons, Ltd.     

分枝杆菌噬菌体 D29的生物学特性研究及其抗耐药结核潜力的探讨

目的研究分枝杆菌噬菌体 D29的生物学特性,为 D29抗耐药结核治疗奠定基础.方法观察噬菌体电镜结构和噬菌斑形态;测定 D29最佳感染复数(MOI);一步生长实验;检测 pH值对 D29活力的影响;斑点法测定裂解谱;中和实验检测抗原性.结果 D29噬菌斑圆形透明,边界清楚;D29尾长129nm,最佳 MOI为10-4;D29感染宿主菌的潜伏期约为50min,裂解量为10;pH值对 D29存活率影响大,酸性环境不影响 D29裂解能力;D29能裂解分枝杆菌临床耐药株;D29K值为1069.50.结论 D29属于长尾噬菌体科(siphoviridae),裂解谱广,抗原性较高,具有抗耐药结核潜力