Who is the Real Author of A Dream of Red Mansions

Hu Shi considers that the author of A Dream of Red Mansions is Cao Xueqin of Qing Dynasty. The opinion in this paper is that the author of the novel A Dream of Red Mansions is Emperor Jianwen, Zhu Yunwen,of the Great Ming Empire(大明帝国建文帝朱允炆). And “Cao Xueqin”(曹雪芹)in the novel is，in fact, Zhu Yunwen(朱允炆).

Gaseous plume flows in space propulsion

AbstractThis paper presents a gaskinetic study on high-speed, highly rarefied jets expanding into a vacuum from a cluster of planar or annular exits. Based on the corresponding exact expressions for a planar or annular jet, it is convenient to derive the combined multiple jet flowfield solutions of density and velocity components. For the combined temperature and pressure solutions, extra attention is needed. Several direct simulation Monte Carlo simulation results are provided to validate these analytical solutions. The analytical and numerical solutions are essentially identical for these high Knudsen number jet flows

Interior derivative estimates and Bernstein theorem for Hessian quotient equations

In this paper, we obtain the interior derivative estimates of solutions for elliptic and parabolic Hessian quotient equations. Then we establish the Bernstein theorem for parabolic Hessian quotient equations, that is, any parabolically convex solution $u=u(x,t)\in C^{4,2}(\mathbb{R}^n\times (-\infty,0])$ for $-u_t\frac{S_n(D^2u)}{S_l(D^2u)}=1$ in $\mathbb{R}^n\times (-\infty,0]$ must be the form of $u=-mt+P(x)$ with $m>0$ being a constant and $P$ being a convex quadratic polynomial

Persistence of Prolonged C-peptide Production in Type 1 Diabetes as Measured With an Ultrasensitive C-peptide Assay

Objective: To examine persistence of C-peptide production by ultrasensitive assay years after onset of type 1 diabetes and factors associated with preserving β-cell function. Research Design and Methods: Serum C-peptide levels, a marker of insulin production and surviving β-cells, were measured in human subjects (n = 182) by ultrasensitive assay, as was β-cell functioning. Twenty-two times more sensitive than standard assays, this assay’s lower detection limit is 1.5 pmol/L. Disease duration, age at onset, age, sex, and autoantibody titers were analyzed by regression analysis to determine their relationship to C-peptide production. Another group of four patients was serially studied for up to 20 weeks to examine C-peptide levels and functioning. Results: The ultrasensitive assay detected C-peptide in 10% of individuals 31–40 years after disease onset and with percentages higher at shorter duration. Levels as low as 2.8 ± 1.1 pmol/L responded to hyperglycemia with increased C-peptide production, indicating residual β-cell functioning. Several other analyses showed that β-cells, whose C-peptide production was formerly undetectable, were capable of functioning. Multivariate analysis found disease duration (β = −2.721; P = 0.005) and level of zinc transporter 8 autoantibodies (β = 0.127; P = 0.015) significantly associated with C-peptide production. Unexpectedly, onset at >40 years of age was associated with low C-peptide production, despite short disease duration. Conclusions: The ultrasensitive assay revealed that C-peptide production persists for decades after disease onset and remains functionally responsive. These findings suggest that patients with advanced disease, whose β-cell function was thought to have long ceased, may benefit from interventions to preserve β-cell function or to prevent complications

Spatiotemporal patterns and determinants of renewable energy innovation: Evidence from a province-level analysis in China

China’s renewable energy innovation is essential for realizing its carbon neutrality targets and the low-carbon transition, but few studies have spatially examined its characteristics and spillover effects. To fill the research gap, this study investigates its distribution and trends from a spatiotemporal dimension and focuses on the spatial effects of the influencing factors to identify those that have a significant impact on renewable energy innovation by using China’s provincial panel data from 2006 to 2019. The results show the following findings. (1) Renewable energy innovation shows distinct spatial differences across China’s provinces such that it is high in the east and south and low in the west and north, which exhibits spatial locking and path-dependence. (2) There is a positive spatial correlation with renewable energy innovation. (3) R&D investment and GDP per capita significantly promote renewable energy innovation, but the former effect is mainly observed in the local area, whereas the latter shows spatial effects. More market-oriented policies should be taken for the improvement of renewable energy innovation and the establishment of regional coordination mechanisms are proposed

Homogeneous Expansion of Human T-Regulatory Cells Via Tumor Necrosis Factor Receptor 2

T-regulatory cells (Tregs) are a rare lymphocyte subtype that shows promise for treating infectious disease, allergy, graft-versus-host disease, autoimmunity, and asthma. Clinical applications of Tregs have not been fully realized because standard methods of expansion ex vivo produce heterogeneous progeny consisting of mixed populations of CD4 + T cells. Heterogeneous progeny are risky for human clinical trials and face significant regulatory hurdles. With the goal of producing homogeneous Tregs, we developed a novel expansion protocol targeting tumor necrosis factor receptors (TNFR) on Tregs. In in vitro studies, a TNFR2 agonist was found superior to standard methods in proliferating human Tregs into a phenotypically homogeneous population consisting of 14 cell surface markers. The TNFR2 agonist-expanded Tregs also were functionally superior in suppressing a key Treg target cell, cytotoxic T-lymphocytes. Targeting the TNFR2 receptor during ex vivo expansion is a new means for producing homogeneous and potent human Tregs for clinical opportunities

An efficient background modeling approach based on vehicle detection

The existing Gaussian Mixture Model(GMM) which is widely used in vehicle detection suffers inefficiency in detecting foreground image during the model phase, because it needs quite a long time to blend the shadows in the background. In order to overcome this problem, an improved method is proposed in this paper. First of all, each frame is divided into several areas(A, B, C and D), Where area A, B, C and D are decided by the frequency and the scale of the vehicle access. For each area, different new learning rate including weight, mean and variance is applied to accelerate the elimination of shadows. At the same time, the measure of adaptive change for Gaussian distribution is taken to decrease the total number of distributions and save memory space effectively. With this method, different threshold value and different number of Gaussian distribution are adopted for different areas. The results show that the speed of learning and the accuracy of the model using our proposed algorithm surpass the traditional GMM. Probably to the 50th frame, interference with the vehicle has been eliminated basically, and the model number only 35% to 43% of the standard, the processing speed for every frame approximately has a 20% increase than the standard. The proposed algorithm has good performance in terms of elimination of shadow and processing speed for vehicle detection, it can promote the development of intelligent transportation, which is very meaningful to the other Background modeling methods. (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only