RLC串并联电路的等效互换问题探讨

通过对RLC串并联电路的分析 ,推导出串并联电路等效互换后计算电路参数的统一表达式 ,并对两个元件的串并联电路等效互换进行了讨论。     

串联冰蓄冷系统设计中的泵能耗分析

介绍了串联冰蓄冷系统设计中泵能耗的3种节能方法：串联泵，变频泵和制冷机旁通，通过对某工程实例的模拟计算，对比分析了这3种方式的节能效果，提出了有效的节能方式。     

动物遗传育种系列课程双螺旋循环教学模式的建立与实践

根据动物遗传育种系列课程的结构、教学特点、本学科学生的层次性、学科发展和对教师多能性的要求 ,对该系列课程进行了教学内容、教学方式、教学手段的全面改革 ,构建了一套双螺旋循环教学模式 ,即教师与课程的螺旋循环链和学生层次与学习过程的螺旋循环链。这两条“教”、“学”互动的螺旋循环链有机结合 ,每完成一次循环教学过程 ,双螺旋上升一个螺距 ,教师的教学水平提高 ,学生的综合素质和学历提高一个层次。     

递推关系式的建立和应用

通过建立递进关系 ,求解数学中有关数量的问题     

Missing data and forecasting in multivariate time series: An application of the common components dynamic linear model

Summary The paper deals with missing data and forecasting problems in multivariate time series making use of the Common Components Dynamic Linear Model (DLMCC), presented in Quintana (1985), and West and Harrison (1989). Some results are presented and discussed: exploiting the correlation between series, estimated by the DLMCC, the paper shows as it is possible to update state vector posterior distributions for the unobserved series. This is realized on the base of the updating of the observed series state vectors, for which the usual Kalman filter equations can be applied. An application concerning some Italian private consumption series provides an example of the model capabilities.     

Economic Determinants of Net International Migration in Western Europe

This paper aims to estimate theinfluence of economic determinants on netinternational migration in Western Europe inthe period 1960–1998. Net migration rates(i.e., population growth minus natural increase,divided by the midyear population) constitutethe dependent variable. The economicdeterminants used in this study are GDP percapita, unemployment, and average educationallevel (amount of human capital) of thepopulation. Time series regression models havebeen used in country-specific analyses. Inaddition, a pooled cross-sectional time seriesanalysis has been made. The analyses suggestthat GDP per capita has a positive effect andunemployment a negative effect on a country'snet international migration.     

学校早期读写训练对儿童读写成绩的影响

利用儿童家庭一学校早期语言与识字能力系列测验对每个目标儿童语言与识字能力发展进行了评定，并解释了这些测量结果与儿童学校早期语言与识字测量指标之间的关系。结果表明：(1)儿童丰富的同伴关系与词汇量、应变识字能力、下定义能力和理解力有关；(2)教师语言的丰富性有助于儿童语言与识字能力发展；(3)识字课程变量与三岁儿童的应变识字成绩和四岁儿童词汇量和故事理解有关；(4)影响儿童语言与识字能力的因素不是单一的，而是一个因素群。     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast? τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.