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An effective linear method, ZUPLS, was developed to improve the accuracy and speed of prokaryotic essential gene identification. ZUPLS only uses the Z-curve and other sequence-based features. Such features can be calculated readily from the DNA/amino acid sequences. Therefore, no well-studied biological network knowledge is required for using ZUPLS. This significantly simplifies essential gene identification, especially for newly sequenced species. ZUPLS can also select necessary features automatically by embedding the uninformative variable elimination tool into the partial least squares classifier. No optimized modelling parameters are needed. ZUPLS has been used, herein, to predict essential genes of 12 remotely related prokaryotes to test its performance. The cross-organism predictions yielded AUC (Area Under the Curve) scores between 0.8042 and 0.9319 by using E. coli genes as the training samples. Similarly, ZUPLS achieved AUC scores between 0.8111 and 0.9371 by using B. subtilis genes as the training samples. We also compared it with the best available results of the existing approaches for further testing. The improvement of the AUC score in predicting B. subtilis essential genes using E. coli genes was 0.13. Additionally, in predicting E. coli essential genes using P. aeruginosa genes, the significant improvement was 0.10. Similarly, the exceptional improvement of the average accuracy of M. pulmonis using M. genitalium and M. pulmonis genes was 14.7%. The combined superior feature extraction and selection power of ZUPLS enable it to give reliable prediction of essential genes for both Gram-positive/negative organisms and rich/poor culture media.

With the rapid development of rubber industry, it becomes more and more important to improve the performance of the quality control system of rubber mixing process. Unfortunately, the large measurement time delay of Mooney viscosity, one of the most important quality parameters of mixed rubber, badly blocks the further development of the issue. The independent component regression‐Gaussian process (ICR‐GP) algorithm is used to solve such typical nonlinear “black‐box” regression problem for the first time to predict Mooney viscosity. In the ICR‐GP method, the non‐Gaussian information is extracted by the independent component regression method firstly, and then the residual Gaussian information is extracted by the Gaussian process method. Meanwhile, both the linear and nonlinear relationships between the input and output variables can be extracted through the ICR‐GP method. With the fact that there is no need to optimize parameters, the ICR‐GP method is especially suitable for “black‐box” regression problems. The highest prediction accuracy was achieved at M = 0.8765 (the root mean square error), which was high enough considering the measuring accuracy (M = ±0.5) of the Mooney viscometer. It is by using the online‐measured rheological parameters as the input variables that the measurement time delay of Mooney viscosity could be dramatically decreased from about 240 to 2 min. Consequently, such Mooney‐viscosity prediction model is very helpful for the development of the rubber mixing process, especially of the emerging one‐step rubber mixing technique. The practical applications performed on the rubber mixing process in a large‐scale tire factory strongly proved the outstanding regression performance of this ICR‐GP Mooney‐viscosity prediction model. Copyright © 2012 John Wiley & Sons, Ltd.

This article is dedicated to examine the impact of social exclusion (i.e., being rejected, isolated, excluded or ignored by other individuals or groups in society) on consumers’ intention of green consumption. Based on Costly Signaling Theory, three experiments have been conducted to explore one main effect and the corresponding mechanism together with two boundary conditions. Specifically, the first study tests the main effect and internal mechanism by manipulating the state of social exclusion. The results show that social exclusion enhances consumers’ intention to buy green products and consumers’ desire for self-sacrifice mediates that relationship. Study 2 manipulates audience state to examine the first boundary condition. The findings show that the effect of social exclusion on green consumption exists only in public purchasing scenarios. Study 3 tests the second boundary condition by manipulating the stability of exclusion causes. The results indicate that the main effect is significant only when causes of exclusion are not stable. The final part discusses theoretical contributions and practical implications of this study in the field of both social exclusion and green consumption.

The results demonstrated that RDS is a robust sampling method in the study of migrant workers. Despite its potential utility, it is also important to recognize and mitigate potential limitations, such as geographic proximity.

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