欠发达地区职业院校教师信息技术教学应用分析

目前，欠发达地区职业教育院校教师的信息技术教学技能掌握程度还普遍不熟练，在网络教学、信息化教学模式应用方面课堂教学利用率不高。分析其现状，提高欠发达地区职业院校信息技术教学应用效果，举办教师教育思想改革研讨会，举办教师信息技术教学技能培训班，制定政策鼓励教师多使用信息技术上课，是其目前急需解决的问题。     

Modelling and smoothing parameter estimation with multiple quadratic penalties

Penalized likelihood methods provide a range of practical modelling tools, including spline smoothing, generalized additive models and variants of ridge regression. Selecting the correct weights for penalties is a critical part of using these methods and in the single-penalty case the analyst has several well-founded techniques to choose from. However, many modelling problems suggest a formulation employing multiple penalties, and here general methodology is lacking. A wide family of models with multiple penalties can be fitted to data by iterative solution of the generalized ridge regression problem minimize || W ^1/2 ( Xp − y ) ||²ρ+Σ _{i =1} ^m  θ _i p ' S _i p ( p is a parameter vector, X a design matrix, S _i a non-negative definite coefficient matrix defining the i th penalty with associated smoothing parameter θ _i , W a diagonal weight matrix, y a vector of data or pseudodata and ρ an 'overall' smoothing parameter included for computational efficiency). This paper shows how smoothing parameter selection can be performed efficiently by applying generalized cross-validation to this problem and how this allows non-linear, generalized linear and linear models to be fitted using multiple penalties, substantially increasing the scope of penalized modelling methods. Examples of non-linear modelling, generalized additive modelling and anisotropic smoothing are given.     

经验方法在司法中的地位、作用及其局限性

在适用法律的推理活动中,经验方法与人们惯常所熟悉的形式逻辑的推理之间有着较大的区别,具有范围的广泛性与数量的无限性、结论的或然性和性质的一般性与客观性等特征。经验方法的运用贯穿于司法的全过程,在事实认知和适用法律中被广为运用,对于有效及时地解决纠纷大有裨益。但经验方法的易出错、易受裁判者擅断和主观情感影响等局限应予以克服。司法中,适用法律的方法具有多样性,具体方法的选择,应能够保障不同部门法规范特定目的及其调整方式在当下的实现。     

Improving Voting Systems through Service‐Operations Management

We apply service‐operations‐management concepts to improve the efficiency and equity of voting systems. Recent elections in the United States and elsewhere have been plagued by long lines, excessive waiting times, and perceptions of unfairness. We build models for the waiting lines at voting precincts using both traditional steady‐state queueing methods and simulation models. We develop solution methods to allocate voting machines optimally to precincts. Our objective functions consider both the efficiency and the equity of the voting system. We compare our allocation algorithm to several competing methods, including those used in practice. We examine several different strategies for improving voting operations on both the demand and the capacity side of voting systems, and we present a complete case study of applying our method to data from the 2008 election for Franklin County, Ohio. We conclude that our method is superior to existing polices in terms of efficiency and equity and that it is robust in terms of uncertainties regarding turnout rates on Election Day. We also suggest several operational improvements to the voting process drawn from the service‐operations literature.     

Ridge Estimation under the Stochastic Restriction

In linear programming and modeling of an economic system, there may occur some linear stochastic artificial or unnatural manners, which may need serious attentions. These stochastic unusual uncertainty, say stochastic constraints, definitely cause some changes in the estimators under work and their behaviors. In this approach, we are basically concerned with the problem of multicollinearity, when it is suspected that the parameter space may be restricted to some stochastic restrictions. We develop the estimation strategy form unbiasedness to some improved biased adjustment. In this regard, we study the performance of shrinkage estimators under the assumption of elliptically contoured errors and derive the region of optimality of each one. Lastly, a numerical example is taken to determine the adequate ridge parameter for each given estimator.     

Graphical methods for evaluating ridge regression estimator in mixture experiments

When the component proportions in mixture experiments are restricted by lower and upper bounds, multicollinearity appears all too frequently. Thus, we can suggest the use of ridge regression as a mean for stabilizing the coefficient estimates in the fitted model. We propose graphical methods for evaluating the effect of ridge regression estimator with respect to the predicted response value and the prediction variance.     

Therapeutic Applications of Online Gaming

ABSTRACT

This article discusses online gaming in the twenty-first century, describes online gaming, considers negative and positive aspects of gaming, and proposes several possible research, practice, and educational strategies for subsequent development of this genre into a useful tool for human service interventions. Positive characteristics of online gaming environments such as high levels of social interaction and the potential for transformative experiences strongly suggest that many clients will be utilizing such environments. In addition, practitioners must not only learn to assess client use of gaming but also develop the necessary knowledge and skills to utilize online games as therapeutic tools.     

A Simulation Study of Ridge Regression Estimators with Autocorrelated Errors

In this paper we run a large number of simulations to study the effects of collinearity and autocorrelated disturbances in the performance of several Ridge Regression estimators. The results suggest that with a fair amount of multicollinearity and of autocorrelation the Ridge Regression estimators which take the autocorrelation into account can perform better than the other methods. Also if the error term is only moderately autocorrelated; then the performance of the Ridge Regression estimators built upon ignoring the autocorrelation can outperform the other estimators.     

Risk‐Informed Regulation and Safety Management of Nuclear Power Plants—On the Prevention of Severe Accidents

There are four operating nuclear power plant (NPP) units in Finland. The Teollisuuden Voima (TVO) power company has two 840 MWe BWR units supplied by Asea‐Atom at the Olkiluoto site. The Fortum corporation (formerly IVO) has two 500 MWe VVER 440/213 units at the Loviisa site. In addition, a 1600 MWe European Pressurized Water Reactor supplied by AREVA NP (formerly the Framatome ANP—Siemens AG Consortium) is under construction at the Olkiluoto site. Recently, the Finnish Parliament ratified the government Decision in Principle that the utilities' applications to build two new NPP units are in line with the total good of the society. The Finnish utilities, Fenno power company, and TVO company are in progress of qualifying the type of the new nuclear builds. In Finland, risk‐informed applications are formally integrated in the regulatory process of NPPs that are already in the early design phase and these are to run through the construction and operation phases all through the entire plant service time. A plant‐specific full‐scope probabilistic risk assessment (PRA) is required for each NPP. PRAs shall cover internal events, area events (fires, floods), and external events such as harsh weather conditions and seismic events in all operating modes. Special attention is devoted to the use of various risk‐informed PRA applications in the licensing of Olkiluoto 3 NPP.     

Choosing shrinkage estimators for regression problems

A Bayesian formulation of the canonical form of the standard regression model is used to compare various Stein-type estimators and the ridge estimator of regression coefficients, A particular (“constant prior”) Stein-type estimator having the same pattern of shrinkage as the ridge estimator is recommended for use.