In silico profiling of cell growth and succinate production in Escherichia coli NZN111

Additional file 6. Reactions histograms for NZN111 at distinct upper and lower bounds of reaction EX_succ(e)

On the analysis of a piecewise nonlinear-linear vibration isolator with high-static-low-dynamic-stiffness under base excitation

A piecewise nonlinear vibration isolator with high-static-low-dynamic-stiffness (HSLDS) is presented in this paper. This nonlinear vibration isolator is comprised of a vertical spring and two pre-compressed cam-roller-spring mechanisms used as the stiffness correctors. Firstly, the static analysis of the vibration isolator is analyzed. The primary resonance of the system under harmonic base excitation is derived by applying the averaging method and further verified by the direct numerical integration. The effect of base excitation amplitude and damping ratio on the resonance frequency is considered. The stability analysis of the primary resonance is also studied. Then, the frequency island phenomenon is found and confirmed by numerical method. The parameter analysis on the appearance of frequency island is also considered. Finally, the absolute displacement transmissibility of the vibration isolator is defined and compared with the conventional HSLDS vibration isolator and the equivalent linear one. The results show that it exhibits a wider frequency range of vibration isolation than the equivalent linear vibration isolator. When the base excitation amplitude takes a larger value, the unbounded response which occurs in the conventional HSLDS vibration isolator can also be avoided, then a better isolation performance can be achieved

Response and performance of a nonlinear vibration isolator with high-static-low-dynamic-stiffness under shock excitations

A nonlinear vibration isolator with High-Static-Low-Dynamic-Stiffness (HSLDS) characteristic comprised of vertical spring and horizontal spring is presented in this paper. Response of the nonlinear vibration isolator under three different kinds of base shock excitations is considered, the dynamic motion can be approximately described by the classic Duffing equation. A transformation function and ultra-spherical polynomial approximation method are employed to determine the shock response and compared with numerical method. Then performance of the nonlinear vibration isolator under shock excitations is evaluated by three performance indicies (Maximum Absolute Displacement Ratio (MADR), Maximum Relative Displacement Ratio (MRDR) and Maximum Acceleration Ratio (MAR)), and also compared with a linear one. Results show that the analytic method suits for weak nonlinearity and the performance of the nonlinear vibration isolator under shock excitations is greatly influenced by the input shock magnitude and structural parameters

Fewer is More: Efficient Object Detection in Large Aerial Images

Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN