This paper is concerned with screening features in ultrahigh dimensional data analysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure based on the distance correlation (DC-SIS, for short). The DC-SIS can be implemented as easily as the sure independence screening procedure based on the Pearson correlation (SIS, for short) proposed by Fan and Lv (2008). However, the DC-SIS can significantly improve the SIS. Fan and Lv (2008) established the sure screening property for the SIS based on linear models, but the sure screening property is valid for the DC-SIS under more general settings including linear models. Furthermore, the implementation of the DC-SIS does not require model specification (e.g., linear model or generalized linear model) for responses or predictors. This is a very appealing property in ultrahigh dimensional data analysis. Moreover, the DC-SIS can be used directly to screen grouped predictor variables and for multivariate response variables. We establish the sure screening property for the DC-SIS, and conduct simulations to examine its finite sample performance. Numerical comparison indicates that the DC-SIS performs much better than the SIS in various models. We also illustrate the DC-SIS through a real data example.

The penalised least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which are as efficient as the oracle estimator. However, these attractive features depend on appropriately choosing the tuning parameter. We show that the commonly used the generalised crossvalidation cannot select the tuning parameter satisfactorily, with a nonignorable overfitting effect in the resulting model. In addition, we propose a bic tuning parameter selector, which is shown to be able to identify the true model consistently. Simulation studies are presented to support theoretical findings, and an empirical example is given to illustrate its use in the Female Labor Supply data.

With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis.

In recent years, dual‐atom catalysts (DACs) have attracted extensive attention, as an extension of single‐atom catalysts (SACs). Compared with SACs, DACs have higher metal loading and more complex and flexible active sites, thus achieving better catalytic performance and providing more opportunities for electrocatalysis. This review introduces the research progress in recent years on how to design new DACs to enhance the performance of electrocatalysis. Firstly, the advantages of DACs in increasing metal loading are introduced. Then, the role of DACs in changing the adsorption condition of reactant molecules on metal atoms is discussed. Moreover, the ways in which DACs can reduce the reaction energy barrier of key steps and change the reaction path are explored. Catalytic applications in different electrocatalytic reactions, including the carbon dioxide reduction reaction, oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and nitrogen reduction reaction are followed. Finally, a brief summary is made and the key challenges and prospects of DACs are introduced.

The computer code for the approach developed is available at Penn State Center for Statistical Genetics web site, http://statgen.psu.edu.