Frozen Gaussian Sampling for Scalar Wave Equations

In this article, we introduce the frozen Gaussian sampling (FGS) algorithm to solve the scalar wave equation in the high-frequency regime. The FGS algorithm is a Monte Carlo sampling strategy based on the frozen Gaussian approximation, which greatly reduces the computation workload in the wave propagation and reconstruction. In this work, we propose feasible and detailed procedures to implement the FGS algorithm to approximate scalar wave equations with Gaussian initial conditions and WKB initial conditions respectively. For both initial data cases, we rigorously analyze the error of applying this algorithm to wave equations of dimensionality $d \geq 3$. In Gaussian initial data cases, we prove that the sampling error due to the Monte Carlo method is independent of the typical wave number. We also derive a quantitative bound of the sampling error in WKB initial data cases. Finally, we validate the performance of the FGS and the theoretical estimates about the sampling error through various numerical examples, which include using the FGS to solve wave equations with both Gaussian and WKB initial data of dimensionality $d = 1, 2$, and $3$

D-box-binding protein alleviates vascular calcification in rats with chronic kidney disease by activating microRNA-195-5p and downregulating cyclin D1

Vascular calcification (VC) is a critical complication in chronic kidney disease (CKD), where transcription factors (TFs) and microRNAs (miRs) could potentially play a pivotal role in its pathogenesis and progression. To explore the potential molecular mechanism by which the TF D-box-binding protein (DBP) regulates the miR-195-5p/cyclin D1 (CCND1) axis and its impact on aortic VC in CKD rats, we established a rat model of CKD with VC through a 5/6 nephrectomy procedure. This model was treated with lentivirus overexpressing DBP or CCND1 to analyze their roles in aortic VC. Additionally, an in vitro cell model of VC was induced by high phosphorus. This model underwent transfection with lentivirus overexpressing DBP or miR-195-5p mimic/inhibitor to confirm their regulatory roles in aortic VC in vitro. We assessed the interactions between DBP and miR-195-5p, as well as between miR-195-5p and CCND1. Our results indicated that the expression of DBP and miR-195-5p was reduced, while CCND1 levels were elevated in both the rat and cell models.  Overexpression of miR-195-5p inhibited VC in vascular smooth muscle cells (VSMCs). Bioinformatics prediction and dual luciferase assays confirmed that DBP could act as a TF to enhance miR-195-5p expression, with Ccnd1 identified as a downstream target gene of miR-195-5p. Overexpression of DBP inhibited aortic calcification in CKD rats, whereas overexpression of CCND1 produced the opposite effect. In conclusion, the TF DBP can inhibit CCND1 expression through transcriptional activation of miR-195-5p, thereby preventing VC in rats with CKD

Perceived organizational career management and career adaptability as predictors of success and turnover intention among Chinese employees

Based on the theories of career construction and of social exchange, the current research examined the joint and interactive effects of perceived organizational career management and career adaptability on indicators of career success (i.e., salary and career satisfaction) and work attitudes (i.e., turnover intention) among 654 Chinese employees. The results showed that career adaptability played a unique role in predicting salary after controlling for the effects of demographic variables and perceived organizational career management. It was also found that both perceived organizational career management and career adaptability correlated negatively with turnover intention, with these relationships mediated by career satisfaction. The results further showed that career adaptability moderated the relationship between perceived organizational career management and career satisfaction such that this positive relationship was stronger among employees with a higher level of career adaptability. In support of the hypothesized moderated mediation model, for employees with a higher level of career adaptability, the indirect effect of perceived career management on turnover intention through career satisfaction was stronger. These findings carry implications for research on career success and turnover intentio

Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in different organs of a Larix principis-rupprechtii Mayr. plantation in the Qinling Mountains, China

Understanding how concentrations of elements and their stoichiometry change with plant growth and age is critical for predicting plant community responses to environmental change. Weusedlong-term field experiments to explore how the leaf, stem and root carbon (C), nitrogen (N) and phosphorous (P) concentrations and their stoichiometry changed with growth and stand age in a L.principis-rupprechtii Mayr. plantation from 2012–2015 in the Qinling Mountains, China. Our results showed that the C, N and P concentrations and stoichiometric ratios in different tissues of larch stands were affected by stand age, organ type andsampling month and displayed multiple correlations with increased stand age in different growing seasons. Generally, leaf C and N concentrations were greatest in the fast-growing season, but leaf P concentrations were greatest in the early growing season. However, no clear seasonal tendencies in the stem and root C, N and P concentrations were observed with growth. In contrast to N and P, few differences were found in organ-specific C concentrations. Leaf N:P was greatest in the fast-growing season, while C:N and C:P were greatest in the late-growing season. No clear variations were observed in stem and root C:N, C:P andN:Pthroughout the entire growing season, but leaf N:P was less than 14, suggesting that the growth of larch stands was limited by N in our study region. Compared to global plant element concentrations and stoichiometry, the leaves of larch stands had higher C, P, C:NandC:PbutlowerNandN:P,andtherootshadgreater PandC:NbutlowerN,C:Pand N:P. Our study provides baseline information for describing the changes in nutritional elements with plant growth, which will facilitates plantation forest management and restoration, and makes avaluable contribution to the global data pool on leaf nutrition and stoichiometry

Conformation of hydrogen in deep earth

【Abstract】Hydrogen is the simplest atom and the most abundant element in the universe.Molecular hydrogen is a common form of hydrogen,The phase transition of molecular hydrogen at the ultrahigh pressure is the subject of great theoretical and experimental interest in the 1980's.However,whether there exists molecular hydrogen in deep earth is an important problem.This paper presents our recent results

Effect of Initial State of Air-launch on Payload of Launch Vehicle

At present, the research on air launch technology at home and abroad focuses on the numerical simulation and aerodynamic characteristics in the process of rocket separation, while the research on the impact of air launch on the payload of launch vehicle is rarely involved. The influence of the initial state of air-launch on the payload of the launch vehicle is studied. By simplifying the model of the flight orbit, the mathematical model of the rocket flight is first established and then the influence of the launch height and the launch velocity on the payload of the rocket after entering the orbit is quantitatively analyzed. The results show that increasing the launch height can effectively increase the payload, but if the launch height is increased beyond 10 km, the increase in payload is not as significant as before. Increasing the launch velocity can significantly increase the payload, and the launch velocity is more significant for the payload increase. Therefore, considering the economics of air-launch, it is more appropriate to set an initial launch height of 10 km and to increase the initial launch speed as much as possible

Application of machine learning on the modelling of diffusion Magnetic Resonance Imaging signal

International audienceAbstract The modelling of diffusion Magnetic Resonance Imaging (dMRI) signals is very important for medical clinical application. However, the traditional method is to use a fixed mathematical model to make assumptions about the diffusion-weighted (DW) signals of all regions of human organ, which is unreasonable. In this paper, Convolutional Neural Network (CNN), a machine learning based method is used for learning the different characteristics of the signals, and finally intelligently give multi-model predictions for different regions of human livers. The performance of the proposed method is verified on both simulation and real liver data. The results show that the multi-model predicted by CNN method has high performance in distinguishing normal liver from diseased liver, and has great clinical application prospect

Application of machine learning in optimizing b-value acquisition strategy of diffusion Magnetic Resonance Imaging

International audienceAbstract The b-value acquisition strategy of diffusion Magnetic Resonance Imaging (dMRI) is very important for medical clinical application, especially the low b-value strategy. However, the choice of b-values is affected by several factors: for example, different tissue, different regions of tissue, the dependence of dMRI signals on b-values are different. Specifically, dMRI signals in areas with faster blood circulation may be more sensitive to low b-values (b<50 s/mm 2 ); in addition, to obtain the diffusion or perfusion information from the diffusion-weighted (DW) signal, fitting methods are required, which also affected by low b-values. In this paper, Convolutional Neural Network (CNN), a machine learning based method is first used for learning the different characteristics of the DW signals in different regions of tissue and generated by different b-value acquisition strategy, and then analyse the dependence of DW signals on low b-values in different regions of the tissue. Finally, to study the dependence of the fitting methods on low b-values, which to determine the b-value acquisition strategy. The results show that the b-value acquisition strategy are different in different perfusion regions and using different fitting methods