Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

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Public support or opposition to the expansion of wind energy plays a key role in energy policy and the development of the industry. For more than 30 years, scholars have attempted to understand the nature of public opinion about wind energy. Unfortunately, the largely observational and correlational nature of the evidence limits the abilities of scholars to isolate the causal relationships that shape attitudes about wind energy. Recent summaries of the literature illustrate the need for experimental designs to improve our understanding of the public's view on this growing technology. Using an original survey experiment with a national sample, we test the effectiveness of messages about the economic and environmental implications of the expansion of wind energy. Our results indicate that 1) the public is sensitive to messaging about both the environmental and economic effects of wind energy; 2) the messages have both a persuasive (changing the content of attitudes) and priming (changing the weight applied to existing attitudes) effect on the public; and 3) the environmental messages have a greater effect on public opinions of wind energy than economic messages. Those interested in promoting positive attitudes about alternative energy need to be aware of both the persuasive and priming influences in messages about wind energy.

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.