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A systematic analysis of the dynamic behavior of a gear-bearing system with nonlinear suspension, couple-stress fluid flow effect, nonlinear oil-film force, and nonlinear gear mesh force is performed in the present study. The dynamic orbits of the system are observed using bifurcation diagrams plotted using the dimensionless rotational speed ratio as a control parameter. The onset of chaotic motion is identified from the phase diagrams, power spectra, Poincaré maps, Lyapunov exponents and fractal dimension of the gear-bearing system. The numerical results reveal that the system exhibits a diverse range of periodic, subharmonic, quasiperiodic, and chaotic behaviors. The couple-stress fluid would be a useful lubricating fluid to suppress nonlinear dynamic responses and improve the steady of the systems. The results presented in this study provide some useful insights into the design and development of a gear-bearing system for rotating machinery that operates in highly rotational speed and highly nonlinear regimes.

Malicious JavaScript code in webpages on the Internet is an emergent security issue because of its universality and potentially severe impact. Because of its obfuscation and complexities, detecting it has a considerable cost. Over the last few years, several machine learning-based detection approaches have been proposed; most of them use shallow discriminating models with features that are constructed with artificial rules. However, with the advent of the big data era for information transmission, these existing methods already cannot satisfy actual needs. In this paper, we present a new deep learning framework for detection of malicious JavaScript code, from which we obtained the highest detection accuracy compared with the control group. The architecture is composed of a sparse random projection, deep learning model, and logistic regression. Stacked denoising auto-encoders were used to extract high-level features from JavaScript code; logistic regression as a classifier was used to distinguish between malicious and benign JavaScript code. Experimental results indicated that our architecture, with over 27 000 labeled samples, can achieve an accuracy of up to 95%, with a false positive rate less than 4.2% in the best case. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Genetic improvement using the mass selection method of growth performance and stripe pattern on caudal fin in selected F6, F7 and F8 generations from genetically improved farmed tilapia strain Nile tilapia was evaluated at the National Tilapia Seed Farm of Qingdao, north China. In experiment I (2003), the average of daily body-weight gain of F7 was 12.5% higher than F6, and 28.6% higher than F5 ( P<0.05). In experiment II (2004), the average daily body-weight gain of F8 was 12.9% larger than F7, and 56.9% larger than F6 ( P<0.05). Coefficient of variation [(SD/mean)100] of body weight of F8 decreased significantly ( P<0.05) to 79.7% of F6 and 72.9% of F7, respectively, indicating that the growth became more synchronous after continuous selection. The percentage of fish with ideal vertical and parallel stripes on caudal fins in F5, F6, F7 and F8 reached 24.8%, 28.8%, 32.2% and 37.6%, respectively, significantly higher than that of the base population (17.6%) ( P<0.05). [ABSTRACT FROM AUTHOR]