大斯托克斯位移远红光至近红外荧光探针用于检测肝损伤过程中过氧化亚硝酸盐的动态变化

肝损伤是影响公众健康的重大问题之一, 已经引起了人们越来越多的关注. 而过表达的过氧化亚硝酸盐(ONOO^?)在肝损伤等疾病的发病机制中起着重要作用, 被认为是一种与早期肝损伤密切相关的生物活性分子. 因此, 为了探究ONOO^?在肝损伤过程中的作用, 开发可以实现肝损伤过程中ONOO^?高选择性和实时检测的分析方法具有重要意义. 本文报道了一种具有大斯托克斯位移的远红光至近红外(FR-NIR)ONOO^?荧光探针. 由于该探针具有大的斯托克斯位移, 可以有效消除光谱重叠和自吸收的干扰, 从而显著提高成像的信噪比. 此外, 该探针对ONOO^?具有高的灵敏度(检出限为25.8 nmol/L)和良好的选择性, 被成功用于药物诱导肝损伤过程中ONOO^?信号的成像检测.     

A discontinuous Galerkin method for Stokes equation by divergence-free patch reconstruction

A discontinuous Galerkin method by patch reconstruction is proposed for Stokes flows. A locally divergence-free reconstruction space is employed as the approximation space, and the interior penalty method is adopted which imposes the normal component penalty terms to cancel out the pressure term. Consequently, the Stokes equation can be solved as an elliptic system instead of a saddle-point problem due to such weak form. The number of degree of freedoms of our method is the same as the number of elements in the mesh for different order of accuracy. The error estimations of the proposed method are given in a classical style, which are then verified by some numerical examples.     

Some methods of training radial basis neural networks in solving the Navier‐Stokes equations

The purpose of this research is to analyze the application of neural networks and specific features of training radial basis functions for solving 2‐dimensional Navier‐Stokes equations. The authors developed an algorithm for solving hydrodynamic equations with representation of their solution by the method of weighted residuals upon the general neural network approximation throughout the entire computational domain. The article deals with testing of the developed algorithm through solving the 2‐dimensional Navier‐Stokes equations. Artificial neural networks are widely used for solving problems of mathematical physics; however, their use for modeling of hydrodynamic problems is very limited. At the same time, the problem of hydrodynamic modeling can be solved through neural network modeling, and our study demonstrates an example of its solution. The choice of neural networks based on radial basis functions is due to the ease of implementation and organization of the training process, the accuracy of the approximations, and smoothness of solutions. Radial basis neural networks in the solution of differential equations in partial derivatives allow obtaining a sufficiently accurate solution with a relatively small size of the neural network model. The authors propose to consider the neural network as an approximation of the unknown solution of the equation. The Gaussian distribution is used as the activation function.     

The improvement of numerical modeling in the solution of incompressible viscous flow problems using finite element method based on spherical Hankel shape functions

In this paper, the finite element method with new spherical Hankel shape functions is developed for simulating 2‐dimensional incompressible viscous fluid problems. In order to approximate the hydrodynamic variables, the finite element method based on new shape functions is reformulated. The governing equations are the Navier‐Stokes equations solved by the finite element method with the classic Lagrange and spherical Hankel shape functions. The new shape functions are derived using the first and second kinds of Bessel functions. In addition, these functions have properties such as piecewise continuity. For the enrichment of Hankel radial basis functions, polynomial terms are added to the functional expansion that only employs spherical Hankel radial basis functions in the approximation. In addition, the participation of spherical Bessel function fields has enhanced the robustness and efficiency of the interpolation. To demonstrate the efficiency and accuracy of these shape functions, 4 benchmark tests in fluid mechanics are considered. Then, the present model results are compared with the classic finite element results and available analytical and numerical solutions. The results show that the proposed method, even with less number of elements, is more accurate than the classic finite element method.     

Shape morphing and topology optimization of fluid channels by explicit boundary tracking

An integrated shape morphing and topology optimization approach based on the deformable simplicial complex methodology is developed to address Stokes and Navier‐Stokes flow problems. The optimized geometry is interpreted by a set of piecewise linear curves embedded in a well‐formed triangular mesh, resulting in a physically well‐defined interface between fluid and impermeable regions. The shape evolution is realized by deforming the curves while maintaining a high‐quality mesh through adaption of the mesh near the structural boundary, rather than performing global remeshing. Topological changes are allowed through hole merging or splitting of islands. The finite element discretization used provides smooth and stable optimized boundaries for simple energy dissipation objectives. However, for more advanced problems, boundary oscillations are observed due to conflicts between the objective function and the minimum length scale imposed by the meshing algorithm. A surface regularization scheme is introduced to circumvent this issue, which is specifically tailored for the deformable simplicial complex approach. In contrast to other filter‐based regularization techniques, the scheme does not introduce additional control variables, and at the same time, it is based on a rigorous sensitivity analysis. Several numerical examples are presented to demonstrate the applicability of the approach.     

Weak and Strong Solutions for the Stokes Approximation of Non-homogeneous Incompressible Navier-Stokes Equations

In this paper,the Dirichlet problem of Stokes approximate of non-homogeneous incompressibleNavier-Stokes equations is studied.It is shown that there exist global weak solutions as well as global andunique strong solution for this problem,under the assumption that initial density ρ_0(x)is bounded away from0 and other appropriate assumptions(see Theorem 1 and Theorem 2).The semi-Galerkin method is applied toconstruct the approximate solutions and a prior estimates are made to elaborate upon the compactness of theapproximate solutions.     

NaYF4∶Eu3+, Tm3+, Yb3+材料中Stokes和反Stokes发光研究

合成了Eu3+,Tm3+和Yb3+掺杂的NaYF₄材料。360 nm光激发呈蓝色发光,峰值位于452 nm,对应Tm3+的1D₂→3F₄跃迁;395 nm光激发呈橙色发光,峰值位于591 nm,对应Eu3+的5D₀→7F₁跃迁;409 nm光激发呈红色发光,峰值位于613 nm,对应Eu3+的5D₀→7F₂跃迁;980 nm光激发呈蓝色和红色发光,发光峰位于474和646 nm。蓝光来源Tm3+的1G₄ →3H₆跃迁,红光来源Tm3+的1G₄→3F₄跃迁。在双对数曲线中,蓝光474 nm和红光646 nm的斜率分别为2.1和2.4,在980 nm光激发下,蓝光和红光发射都是双光子过程。还研究了材料的吸收光谱,并利用X射线衍射,扫描电镜测试了材料的物相结构和微观形貌。结果表明：NaYF₄∶Eu3+, Tm3+, Yb3+材料具有较规则的六方相结构,结晶良好。     

固壁近旁Stokes流中粘性液滴的运动和变形

发展了一种模拟固壁近旁轴对称Stokes流中粘性液滴的运动和变形及直接计算固壁上应力的边界积分方法．用此方法对不同的液滴-固壁初始相对间距、粘度比、表面张力和浮力联合参数以及环境流动参数情况进行了数值实验．数值结果显示,由于环境流动和浮力的作用,随着时间的推进,液滴在轴向压缩,在径向拉伸．当环境流动的作用弱于浮力作用时,随着时间的推移,液滴上升并向上弯,固壁上由液滴运动所引起的应力不断减小．当环境流动的作用强于浮力作用时,随着时间的推移,液滴变得越来越扁．在这种情形,当大初始间距时,壁面上的应力随液滴的演变而增大;当小初始间距时,由环境流动、浮力及壁面对流动的较强作用的联合影响,此应力随液滴的演变而减小．由于液滴运动所引起的壁面应力的有效作用仅限于对称轴附近的一个小范围内,且此范围随液滴与固壁的初始间距增大而增大．应力的大小随初始间距增大而大为减小．表面张力对液滴变形有阻止作用．液滴粘性会减小液滴的变形和位置迁移．     

激光与材料参数对瞬态SBS 诱导的激光超声破坏效果的影响

 通过数值求解一维瞬态受激布里渊散射（SBS）声光耦合波方程，从理论上分析了泵浦激光参数及光学材料参数对SBS过程发生阈值的影响。以SBS过程中建立起来的应力场抗拉（压）强度和散射场的反射率为判据，分析了激光脉宽及作用区域长度对激光超声破坏材料效果的影响，探讨了短脉冲激光（约ns）引起的激光超声对材料的破坏机理；讨论了通过参数配置有效遏制SBS过程激光超声对光学材料的破坏问题。     

粘滑混合边界条件下平面边界前的Stokes流动

对具有粘滑混合边界条件的平面边界,建立一个Stokes流动的一般性定理,利用双调和函数A与调和函数B,表示了3维Stokes流动的速度场和压力场.关于无滑动平面边界前Stokes流动的早期定理,成为该一般性定理的一个特例.进一步地,从一般性定理导出了一个推论,根据该Stokes流函数,给出了粘滑边界条件时刚性平面轴对称Stokes流动问题的解,得到了流体作用在边界上的牵引力和扭矩公式.给出了一个说明性的例子.