微区X射线荧光成像技术在岩心分析中的应用

微区X射线荧光（Micro-XRF）分析技术是通过微小的X射线光束照射样品，对样品进行原位成分观测的无损分析手段之一，具有灵敏度高、速度快和准确性高的特点。采用微区X射线荧光光谱仪（M6 JETSTREAM）对安徽铜陵冬瓜山铜矿床四段岩心样品进行面扫描，分析不同矿层共17种元素区域分布特征、空间分布规律及组合关系等，结果表明：（1）Cu和Fe两种成矿元素高值空间分布区域基本不重叠，S与Fe分布范围高度重叠，关系密切，微量元素Ni，Bi，Pb，Zn，Si，Na与Cu密切相关，而Ti，Al，K与Fe具有弱相关性；（2）垂向上，Fe元素含量随深度增加逐步增大，而Cu元素含量呈降低趋势，其他元素也随深度呈下降趋势；（3）元素分布受石炭纪中期海底喷流沉积成矿作用和岩浆热液成矿作用叠加改造作用明显；（4）该钻孔矿石矿物以磁黄铁矿、黄铜矿和黄铁矿为主，垂向上组合规律明显，脉石矿物以石英、石榴子石和透辉石为主。该技术通过分析元素空间分布规律、相关性以及矿物组合和分配关系等可对元素富集和运移以及对矿床的成矿机制、成因模式等地质环境和地质过程提供新认识和新证据。结合矿床地球化学特征的分布模式，微量元素可作为寻找主矿种的指示元素，为深部找矿提供依据。此外，该技术能作为预分析技术快速筛选出感兴趣的信息和位置，为后期各种更高精度的微区分析提供不同尺度、不同层次的元素分布信息。     

Ergodic stationary distribution and practical application of a hybrid stochastic cholera transmission model with waning vaccine-induced immunity under nonlinear regime switching

Considering the effect of stochasticity including white noise and colored noise, this paper aims to study a hybrid stochastic cholera epidemic model with waning vaccine-induced immunity and nonlinear telegraph perturbations. First, we derive a critical value ${?}_0^C$ related to the basic reproduction number ${?}_0$ of the deterministic model. The key aim of this paper is to generalize the θ-stochastic criterion method proposed by the recent work (Han et al. in Chaos Solit Fract 140:110238, 2020) to eliminate nonlinear telegraph perturbations. Next, via constructing several θ-stochastic Lyapunov functions and using the generalized method, we further prove that the stochastic model have a unique ergodic stationary distribution under ${?}_0^C>1$. Results show that the prevention and control of cholera epidemic depend on low transmission rate and small telegraph perturbations. Finally, the corresponding numerical simulations are performed to illustrate our analytical results and a practical application on the Somalia cholera outbreak is shown at the end of this paper.     

全球海洋潜水旅游目的地分布特征及其自然影响因素研究

以全球海洋潜水旅游目的地为研究对象, 借助地理信息系统(GIS), 运用基尼系数、层次分析法(AHP)等方法, 研究全球海洋潜水旅游目的地的分布特征及其自然影响因素. 结果显示: (1)全球海洋潜水旅游目的地的分布数量由低纬向高纬递减; 洲际分布呈现高度集中, 主要聚集区是东亚与东南亚、地中海-红海以及加勒比海沿岸, 太平洋、印度洋与大西洋为散点分布. (2)影响海洋潜水旅游目的地的自然因素主要为海洋地形、海水温度、陆地气温、洋流、潮汐、海水能见度、珊瑚礁等, 相关因素影响程度与广度因海域呈现地域综合导控, 使得海洋潜水旅游目的地主要聚集在热带海域等地.     

The value function of a transportation problem

We investigate the value of an optimal transportation problem with the maximization objective as a function of costs and vectors of production and consumption. The value is concave in production. For generic costs, the numbers of linearity domains and peak points are independent of costs and consumption. The peak points are determined by an auxiliary assignment problem. The volumes of the linearity domains are independent of costs while their dependence on consumption can be expressed via the multinomial distribution.     

On the Brauer p-dimension of Henselian discrete valued fields of residual characteristic p > 0

Let $(K,v)$ be a Henselian discrete valued field with residue field $\stackrel{?}{K}$ of characteristic $p>0$, and ${\text{Brd}}_p(K)$ be the Brauer p-dimension of K. This paper shows that ${\text{Brd}}_p(K)\ge n$ if $[\stackrel{?}{K}:{\stackrel{?}{K}}^p]=p^n$, for some $n\in \mathbb{N}$. It proves that ${\text{Brd}}_p(K)=\infty$ if and only if $[\stackrel{?}{K}:{\stackrel{?}{K}}^p]=\infty$.     

NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

NIFTy , “Numerical Information Field Theory,” is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high‐dimensional real‐world problems. This paper introduces NIFTy  3, a major upgrade to the original NIFTy  framework. NIFTy  3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n‐dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy  3 is able to handle non‐scalar fields, such as vector or tensor fields. The functionality and performance of the software package is demonstrated with example code, which implements a mock inference inspired by a real‐world algorithm from the realm of information field theory. NIFTy  3 is open‐source software available under the GNU General Public License v3 (GPL‐3) at https://gitlab.mpcdf.mpg.de/ift/NIFTy/tree/NIFTy_3 .     

A bound on the Wasserstein-2 distance between linear combinations of independent random variables

We provide a bound on a distance between finitely supported elements and general elements of the unit sphere of ${?}^2\left({\mathbb{N}}^1\right)$. We use this bound to estimate the Wasserstein-2 distance between random variables represented by linear combinations of independent random variables. Our results are expressed in terms of a discrepancy measure related to Nourdin–Peccati’s Malliavin–Stein method. The main application is towards the computation of quantitative rates of convergence to elements of the second Wiener chaos. In particular, we explicit these rates for non-central asymptotic of sequences of quadratic forms and the behavior of the generalized Rosenblatt process at extreme critical exponent.     

Mean field limits for nonlinear spatially extended Hawkes processes with exponential memory kernels

We consider spatially extended systems of interacting nonlinear Hawkes processes modeling large systems of neurons placed in ${\mathbb{R}}^d$ and study the associated mean field limits. As the total number of neurons tends to infinity, we prove that the evolution of a typical neuron, attached to a given spatial position, can be described by a nonlinear limit differential equation driven by a Poisson random measure. The limit process is described by a neural field equation. As a consequence, we provide a rigorous derivation of the neural field equation based on a thorough mean field analysis.