Computation and visualization of periodic orbits in the circular restricted three-body problem

In this thesis, the continuation and bifurcation software AUTO is used to compute periodic solutions of the circular restricted Three-Body problem (CR3BP). Periodic solution families for the Sun-Earth, the Earth-Moon, and the Sun-Jupiter system are studied in detail. Bifurcation diagrams for these systems are presented. Corresponding periodic orbits are also shown. To understand the solution structure better, a new data visualization package, PLAUT04, has been developed for AUTO. It reads AUTO data files and creates solution diagrams and bifurcation diagrams. This new package can also be used to animate solutions. A special version of PLAUT04, called PLAUT04/r3b, has been developed for the CR3BP. Using PLAUT04/r3b, we can animate solutions both in a rotating frame and in an inertial frame. These new graphics packages for AUTO have good rendering speed, flexibility, and display quality. A user-friendly interface makes both easy to learn and use

Nano-structured interpenetrating composites with enhanced Young's modulus and desired Poisson's ratio

This paper has demonstrated that interpenetrating composites could be designed to not only have an significantly enhanced Young�s modulus, but also have a Poisson�s ratio at a desired value (e.g. positive, or negative, or zero). It is found that when the effect of the Poisson�s ratio is absent, the Young�s modulus of interpenetrating composites is closer to the Hashin and Shtrikman�s upper limit than to their lower limit, and much larger than the simulation and experimentally measured results of the conventional isotropic particle or fibre composites. It is also illustrated that at the nanoscale, the interphase can either strengthen or weaken the stiffness, and the elastic properties of interpenetrating composites are size-dependent

Features of the Three Dimensional Structure in the Pacific Sub-surface Layer in Summer

The anomaly of the summer sea temperature is analyzed by a spatial-temporal synthetically rotated orthogonal function (REOF) at three different depths (0 m, 40 m, and 120 m) over the area 110°E~100°W and 30°S~60°N. The spatial-temporal distribution shows that the “signal” of annual anomaly is stronger in the sub-surface layer than the surface layer, and it is stronger in the eastern equatorial Pacific than in the western area. The spatial structure of the sea temperature anomaly at different layers is related to both the ocean current and the interaction of ocean and atmosphere. The temporal changing trend of the sub-surface sea temperature in different areas shows that the annual mean sea temperature increases and the annual variability evidently increases from the 1980s, and these keep the same trend with the increasing El Nino phenomenon very well

Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection,Window Selection, and Histogram Specification

In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Functional importance of different patterns of correlation between adjacent cassette exons in human and mouse

<p>Abstract</p> <p>Background</p> <p>Alternative splicing expands transcriptome diversity and plays an important role in regulation of gene expression. Previous studies focus on the regulation of a single cassette exon, but recent experiments indicate that multiple cassette exons within a gene may interact with each other. This interaction can increase the potential to generate various transcripts and adds an extra layer of complexity to gene regulation. Several cases of exon interaction have been discovered. However, the extent to which the cassette exons coordinate with each other remains unknown.</p> <p>Results</p> <p>Based on EST data, we employed a metric of correlation coefficients to describe the interaction between two adjacent cassette exons and then categorized these exon pairs into three different groups by their interaction (correlation) patterns. Sequence analysis demonstrates that strongly-correlated groups are more conserved and contain a higher proportion of pairs with reading frame preservation in a combinatorial manner. Multiple genome comparison further indicates that different groups of correlated pairs have different evolutionary courses: (1) The vast majority of positively-correlated pairs are old, (2) most of the weakly-correlated pairs are relatively young, and (3) negatively-correlated pairs are a mixture of old and young events.</p> <p>Conclusion</p> <p>We performed a large-scale analysis of interactions between adjacent cassette exons. Compared with weakly-correlated pairs, the strongly-correlated pairs, including both the positively and negatively correlated ones, show more evidence that they are under delicate splicing control and tend to be functionally important. Additionally, the positively-correlated pairs bear strong resemblance to constitutive exons, which suggests that they may evolve from ancient constitutive exons, while negatively and weakly correlated pairs are more likely to contain newly emerging exons.</p

3D Model-Based Simulation Analysis of Energy Consumption in Hot Air Drying of Corn Kernels

To determine the mechanism of energy consumption in hot air drying, we simulate the interior heat and mass transfer processes that occur during the hot air drying for a single corn grain. The simulations are based on a 3D solid model. The 3D real body model is obtained by scanning the corn kernels with a high-precision medical CT machine. The CT images are then edited by MIMICS and ANSYS software to reconstruct the three-dimensional real body model of a corn kernel. The Fourier heat conduction equation, the Fick diffusion equation, the heat transfer coefficient, and the mass diffusion coefficient are chosen as the governing equations of the theoretical dry model. The calculation software, COMSOL Multiphysics, is used to complete the simulation calculation. The influence of air temperature and velocity on the heat and mass transfer processes is discussed. Results show that mass transfer dominates during the hot air drying of corn grains. Air temperature and velocity are chosen primarily in consideration of mass transfer effects. A low velocity leads to less energy consumption