An Analysis of the Current Situation of the Employed Youth in China on Wealth Concept and Its Influencing Factors —Based on a Nationwide Survey of 439 Young Employees

This research conducted a sample survey of 439 young employees in different industries and fields in 7 provinces and municipalities in North China, Central China, East China, South China, Northeast China, Northwest China, and Southwest China. Through data analysis, it found that the mainstream of the employed youth held a positive concept of wealth, while a few of them had issues such as money worship, hedonism, and hatred or envy towards the rich. There are significant differences in wealth cognition, wealth creation, wealth consumption, and wealth distribution among young employees of different genders, regions and units as well as with different numbers of children. And the main influencing factors of their wealth concept appear to be the complex social environment, the differences in personal cognition and thinking modes, the absence of wealth education, and the impact of internet culture

Construction and Application of Passenger Flow Simulation Evaluation Index System in Urban Rail Transit Transfer Stations

To popularize the efficient application of the passenger flow simulation technology in urban rail transit transfer stations, and make the simulation evaluation systematic, comprehensive and reasonable, a passenger flow simulation evaluation index system of transfer stations was constructed, based on the characteristics of transfer stations. Then the calculation method, evaluation scope and evaluation standard of each evaluation index were proposed. Finally, through Anhuaqiao station of Beijing urban rail transit, the evaluation index system and evaluation standards were verified feasible, comprehensive and effective

Arabidopsis DRB4, AGO1, AGO7, and RDR6 participate in a DCL4-initiated antiviral RNA silencing pathway negatively regulated by DCL1

Plant RNA silencing machinery enlists four primary classes of proteins to achieve sequence-specific regulation of gene expression and mount an antiviral defense. These include Dicer-like ribonucleases (DCLs), Argonaute proteins (AGOs), dsRNA-binding proteins (DRBs), and RNA-dependent RNA polymerases (RDRs). Although at least four distinct endogenous RNA silencing pathways have been thoroughly characterized, a detailed understanding of the antiviral RNA silencing pathway is just emerging. In this report, we have examined the role of four DCLs, two AGOs, one DRB, and one RDR in controlling viral RNA accumulation in infected Arabidopsis plants by using a mutant virus lacking its silencing suppressor. Our results show that all four DCLs contribute to antiviral RNA silencing. We confirm previous reports implicating both DCL4 and DCL2 in this process and establish a minor role for DCL3. Surprisingly, we found that DCL1 represses antiviral RNA silencing through negatively regulating the expression of DCL4 and DCL3. We also implicate DRB4 in antiviral RNA silencing. Finally, we show that both AGO1 and AGO7 function to ensure efficient clearance of viral RNAs and establish that AGO1 is capable of targeting viral RNAs with more compact structures, whereas AGO7 and RDR6 favor less structured RNA targets. Our results resolve several key steps in the antiviral RNA silencing pathway and provide a basis for further in-depth analysis

Effects of cloud overlap in photochemical models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95365/1/jgrd10939.pd

Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

Proximal gradient-based optimization is one of the most common strategies for solving image inverse problems as well as easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high/low frequency image features,but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction.Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration and post-processing. In the image restoration step,a cascade geometric incremental learning module is designed to compensate for the missing texture information from different geometric spectral decomposition domains. Inspired by overlap-tile strategy, we also develop a post-processing module to remove the block-effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable,an adaptive initialization technique of physical-parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://github.com/fanxiaohong/Nest-DGIL.Comment: 15 page