Based on theoretical analysis and numerical calculations, this study systematically investigated the changes in rice tillering dynamics and the simulation of stem tiller growth during the tillering stage using the farm water level as a regulation index for rice irrigation and drainage. Based on pit testing, the results of this study show that both flooding and drought in the tillering stage suppress the tiller output of rice and have a certain compensating effect following rehydration. Heavy drought during the tillering period reduced the effective tiller rate, while flooding and light drought had little effect on the effective tiller rate. Flooding and maintaining a high infiltration rate also increased the effective tiller rate. The primary kinetic model of tiller elongation (DMOR) was a good fit for the tiller elongation process (coefficients of determination of 0.99 or higher). In addition, the growth and extinction rates of the stem tiller extinction curves were fitted. The maximum growth rate of the stem tiller growth segment was ranked as CK > L1 > H1 > L2 > H2, and the maximum extinction rate of the stem tiller extinction segment was ranked as CK > H2 > H1 > L2 > L1, indicating that both flooding and drought during the tillering stage could reduce the growth and extinction rates of the stem tiller. This shows that both flooding and drought can reduce the growth and extinction rates of tillers.
This paper extends the application of Genetic Programming into a new area, automatically splitting video frames based on the content. A GP methodology is presented to show how to evolve a program which can analyse the difference between scenes and split them accordingly. The evolved video splitting programs achieve reasonable performance even when the videos are not easily recognizable by eyes due to the server artificial noises. Moreover, a few different approaches have been investigated in this study. We compare the performance of GP with J48, NaïveBayes and one video splitting software, the experimental results show that GP generated splitters are comparable with two conventional machine learning algorithms and more accurate than human written program.
We present an importance sampling method for the bidirectional scattering distribution function (bsdf) of hair, based on the multi-lobe hair scattering model presented in [Sadeghi et al. 2010]. Our algorithm is efficient, easy to implement and it has no significant memory overhead or need for precomputation. We have integrated our method into both a research raytracer and a micropolygon based production renderer. Figure 1 compares the rendering quality of our method to statified uniform sampling for both direct (environment) lighting rendered with our production renderer and indirect lighting rendered with path tracing. In both cases, our method delivers significantly better image quality than uniform sampling using the same number of samples.
The nonlocal static bending, buckling, free and forced vibrations of graphene nanosheets are examined based on the Kirchhoff plate theory and Taylor expansion approach. The nonlocal nanoplate model incorporates the length scale parameter which can capture the small scale effect. The governing equations are derived using Hamilton's principle and the Navier-type solution is developed for simply-supported graphene nanosheets. The analytical results are proposed for deflection, natural frequency, amplitude of forced vibration and buckling load. Moreover, the effects of nonlocal parameter, half wave number and three-dimensional sizes on the static, dynamic and stability responses of the graphene nanosheets are discussed. Some illustrative examples are also addressed to verify the present model, methodology and solution. The results show that the new nanoplate model produces larger deflection, smaller circular frequencies, amplitude and buckling load compared with the classical model.
This article describes how to construct a integrated wireless clinical information system based on the original information system. It is constructed on the basis of the hospital HIS through the middle ware which provides a data-interaction platform for all the existing data and the data which would be added in future for various information management systems. The software system adopts the Web Service technology in information services. The SOAP protocol is applied to the data interaction between the foreground and the background. The mode of the network structure is Wireless Switchboard + Intelligence antenna. Relying on the wireless network, the handheld data terminal and the barcode, the network information can be extended to the patient bedsides and the mobile medical personnels.
We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper valid instrumental variables (IVs) for the target causal effect by expert knowledge or by assuming that the causal model is a one-directional MR model. As such, in this paper, we first theoretically investigate the identification of the bi-directional MR from observational data. In particular, we provide necessary and sufficient conditions under which valid IV sets are correctly identified such that the bi-directional MR model is identifiable, including the causal directions of a pair of phenotypes (i.e., the treatment and outcome). Moreover, based on the identification theory, we develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest. We theoretically demonstrate the correctness of the proposed algorithm. Experimental results show the effectiveness of our method for estimating causal effects in bi-directional MR.
Since Shrek 2, DreamWorks artists have used the fabric model developed by [Glumac and Doepp 2004] extensively on cloth material shading. Even after we developed the physically based microcylinderical cloth model by [Sadeghi et al. 2013], they continued to prefer the intuitive control of the DreamWorks fabric shading model, which is also a cyindrical shading model, with easy to use artistic controls for highlights, and highlight directions.
The spectral super-resolution (SpeSR) from multispectral images (MSIs) to hyperspectral images (HSIs) can bring rich spectral information. The deep learning-based methods have demonstrated their powerful ability for the SpeSR task, which requires the paired HSI/MSI to train the model. However, HSIs and MSIs are always obtained at different times and under different imaging conditions, covering different areas. To address this issue, in this paper, a framework named BliEstGAN based on the generative adversarial network (GAN) is proposed to estimate the spectral resolution degradation between unpaired HSIs and MSIs that can be used for the blind SpeSR. Specifically, each MSI imaging sensor has its own unique spectral sampling process, which can be modeled as a spectral degradation from its paired HSI. Different spectral degradations can be discriminated by the deep model. Therefore, the generator of the GAN is used to estimate the spectral degradation from HSIs to MSIs, and the discriminator of the GAN is adopted to distinguish whether the estimated and real spectral degradation are similar. The large difference in spatial resolution between MSIs and HSIs makes them easy to discriminate against. Therefore, smooth hyperspectral and multispectral patches are extracted from HSIs and MSIs to eliminate this difference in spatial resolution. Furthermore, according to the imaging sensor mechanism, some special regularization terms are designed for the generator to guarantee its correct convergence. Finally, the estimated spectral resolution degradation can be adopted to generate HSI/MSI pairs for the supervised learning-based SpeSR methods. Experimental results demonstrate the effectiveness of the proposed method.
Photorealistic rendering effects are common in films, but most real time graphics today still rely on scan-line based multi-pass rendering to deliver rich visual experiences. While there have been prior works in distributed path tracing for static scene and objects under rigid motion, real time path tracing of deforming characters has to support per-frame dynamic BVH changes. We present the architecture and implementation of the first real-time production quality cluster path tracing renderer that supports film quality animation and deformation. We build our cluster path tracing system using the open source Blender and its GPU accelerated production quality renderer Cycles. Our system's rendering performance and quality scales linearly with the number of RTX cluster nodes used. It is able to generate and deliver path traced images with global illumination effects to remote light-weight client systems at 15-30 frames per second for a variety of Blender scenes including animated digital human characters with skin deformation and virtual objects.
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