Teacher Questioning in College English Class: A Guide to Critical Thinking

Amidst the great revolution in China s education system promoting critical thinking in school education to prepare students for the needs of modern world has been advocated by more and more educators Critical thinking is a learned skill that needs to be cultivated by effective instruction Research suggest that teacher questioning plays an important role in promoting students critical thinking through classroom interaction This article reviews literature on how critical thinking relates to teacher questioning instructional approach and advocates effective use of teacher questioning technique in college English class to actively engage students in the learning process and guide them to critical thinkin

Advanced Control Strategy of DFIG Wind Turbines for Power System Fault Ride Through

This paper presents an advanced control strategy for the rotor and grid side converters of the doubly fed induction generator (DFIG) based wind turbine (WT) to enhance the low-voltage ride-through (LVRT) capability according to the grid connection requirement. Within the new control strategy, the rotor side controller can convert the imbalanced power into the kinetic energy of the WT by increasing its rotor speed, when a low voltage due to a grid fault occurs at, e.g., the point of common coupling (PCC). The proposed grid side control scheme introduces a compensation term reflecting the instantaneous DC-link current of the rotor side converter in order to smooth the DC-link voltage fluctuations during the grid fault. A major difference from other methods is that the proposed control strategy can absorb the additional kinetic energy during the fault conditions, and significantly reduce the oscillations in the stator and rotor currents and the DC bus voltage. The effectiveness of the proposed control strategy has been demonstrated through various simulation cases. Compared with conventional crowbar protection, the proposed control method can not only improve the LVRT capability of the DFIG WT, but also help maintaining continuous active and reactive power control of the DFIG during the grid faults

On the Covariate-adjusted Estimation for an Overall Treatment Difference with Data from a Randomized Comparative Clinical Trial

To estimate an overall treatment difference with data from a randomized comparative clinical study, baseline covariates are often utilized to increase the estimation precision. Using the standard analysis of covariance technique for making inferences about such an average treatment difference may not be appropriate, especially when the fitted model is nonlinear. On the other hand, the novel augmentation procedure recently studied, for example, by Zhang and others (2008. Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics 64, 707–715) is quite flexible. However, in general, it is not clear how to select covariates for augmentation effectively. An overly adjusted estimator may inflate the variance and in some cases be biased. Furthermore, the results from the standard inference procedure by ignoring the sampling variation from the variable selection process may not be valid. In this paper, we first propose an estimation procedure, which augments the simple treatment contrast estimator directly with covariates. The new proposal is asymptotically equivalent to the aforementioned augmentation method. To select covariates, we utilize the standard lasso procedure. Furthermore, to make valid inference from the resulting lasso-type estimator, a cross validation method is used. The validity of the new proposal is justified theoretically and empirically. We illustrate the procedure extensively with a well-known primary biliary cirrhosis clinical trial data set

Coronary heart disease risks associated with high levels of HDL cholesterol.

BackgroundThe association between high-density lipoprotein cholesterol (HDL-C) and coronary heart disease (CHD) events is not well described in individuals with very high levels of HDL-C (>80 mg/dL).Methods and resultsUsing pooled data from 6 community-based cohorts we examined CHD and total mortality risks across a broad range of HDL-C, including values in excess of 80 mg/dL. We used Cox proportional hazards models with penalized splines to assess multivariable, adjusted, sex-stratified associations of HDL-C with the hazard for CHD events and total mortality, using HDL-C 45 mg/dL and 55 mg/dL as the referent in men and women, respectively. Analyses included 11 515 men and 12 925 women yielding 307 245 person-years of follow-up. In men, the association between HDL-C and CHD events was inverse and linear across most HDL-C values; however at HDL-C values >90 mg/dL there was a plateau effect in the pattern of association. In women, the association between HDL-C and CHD events was inverse and linear across lower values of HDL-C, however at HDL-C values >75 mg/dL there were no further reductions in the hazard ratio point estimates for CHD. In unadjusted models there were increased total mortality risks in men with very high HDL-C, however mortality risks observed in participants with very high HDL-C were attenuated after adjustment for traditional risk factors.ConclusionsWe did not observe further reductions in CHD risk with HDL-C values higher than 90 mg/dL in men and 75 mg/dL in women

Oscillatory Stability and Eigenvalue Sensitivity Analysis of A DFIG Wind Turbine System

2010-2011 > Academic research: refereed > Publication in refereed journa

Effectively Selecting a Target Population for a Future Comparative Study

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. Using this system, we specify a desired level of treatment difference and create a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated group-specific treatment difference curve across a range of threshold values is constructed. The population of patients with any desired level of treatment benefit can then be identified accordingly. To avoid any ``self-serving\u27\u27 bias, we utilize a cross-training-evaluation method for implementing the above two-step procedure. Lastly, we show how to select the best scoring system among all competing models. The proposals are illustrated with the data from two clinical trials in treating AIDS and cardiovascular diseases. Note that if we are not interested in designing a new study for comparing similar treatments, the new procedure can also be quite useful for the management of future patients who would receive nontrivial benefits to compensate for the risk or cost of the new treatment