基于马尔可夫决策过程的医疗检查预约优化模型

医技部门的医疗检查如电子计算机断层扫描（CT）、核磁共振成像设备（MRI）、X射线（X－rays）常常有如下三种病人类型：门诊病人、住院病人和急诊病人。针对不同病人类型的需求特点，运用马尔可夫决策过程原理和动态规划方法，建立了医疗检查设备的预约优化模型，并证明了模型的最优性质，得出了不同病人类型的最优预约策略。数值算例的结果表明：本文的预约策略不仅易于实施，而且，通过该模型获得的最大收益比按传统先来先预约的模式所获得的收益要大。     

Mixing time of an unaligned Gibbs sampler on the square

The paper concerns a particular example of the Gibbs sampler and its mixing efficiency. Coordinates of a point are rerandomized in the unit square ${[0,1]}^2$ to approach a stationary distribution with density proportional to $exp(?A^2{(u?v)}^2)$ for $(u,v)\in {[0,1]}^2$ with some large parameter $A$.Diaconis conjectured the mixing time of this process to be $O\left(A^2\right)$ which we confirm in this paper. This improves on the currently known $O(exp\left(A^2\right))$ estimate.     

Continuum versus discrete networks,graph Laplacians,and reproducing kernel Hilbert spaces

Motivated by applications to machine learning, we construct a reversible and irreducible Markov chain whose state space is a certain collection of measurable sets of a chosen l.c.h. space $\mathfrak{X}$. We study the resulting network (connected undirected graph), including transience, Royden and Riesz decompositions, and kernel factorization. We describe a construction for Hilbert spaces of signed measures which comes equipped with a new notion of reproducing kernels and there is a unique solution to a regularized optimization problem involving the approximation of $L^2$ functions by functions of finite energy. The latter has applications to machine learning (for Markov random fields, for example).     

非齐次动力学方程的一种精细积分单步方法

针对非齐次动力学方程■,结合精细积分法和微分求积法,利用同阶的显式龙格-库塔法对计算过程中待求的v_(k+i/s)(i=1,2,…,s)进行预估,提出了一种避免状态矩阵求逆的高效精细积分单步方法。该方法采用精细积分法计算e~(Ht),而Duhamel积分项采用s级s阶的时域微分求积法,计算格式统一且易于编程,可灵活实现变阶变步长。仿真结果表明,与其他单步法及预估校正-辛时间子域法进行数值比较,该方法具有高精度、高效率及良好的稳定性,在求解大规模动力系统时间响应问题中具有较大的优势。     

Optimal strategy of vaccination and treatment in an SIRS model with Markovian switching

Medical treatment and vaccination decisions are often sequential and uncertain. Markov decision process is an appropriate means to model and handle such stochastic dynamic decisions. This paper studies the near‐optimality of a stochastic SIRS epidemic model that incorporates vaccination and saturated treatment with regime switching. The stochastic model takes white noises and color noise into account. We first prove some priori estimates of the susceptible, infected, and recovered populations. Moreover, we establish some sufficient and necessary conditions of the near‐optimality by Pontryagin stochastic maximum principle. Our results show that the two kinds of environmental noises have great impacts on the infectious diseases. Finally, we illustrate our conclusions through numerical simulations.     

The survival probability of critical and subcritical branching processes in finite state space Markovian environment

Let ${\left(Z_n\right)}_{n\ge 0}$ be a branching process in a random environment defined by a Markov chain ${\left(X_n\right)}_{n\ge 0}$ with values in a finite state space $\mathbb{X}$. Let ${\mathbb{P}}_i$ be the probability law generated by the trajectories of ${\left(X_n\right)}_{n\ge 0}$ starting at $X_0=i\in \mathbb{X}.$ We study the asymptotic behaviour of the joint survival probability ${\mathbb{P}}_i\left(Z_n>0,X_n=j\right)$, $j\in \mathbb{X}$ as $n\to +\infty$ in the critical and strongly, intermediate and weakly subcritical cases.     

Persistence of sums of correlated increments and clustering in cellular automata

Let $T$ be the first return time to $(?\infty ,0]$ of sums of increments given by a functional of a stationary Markov chain. We determine the asymptotic behavior of the survival probability, $\mathbb{P}(T\ge t)～C{t}^{?1∕2}$ for an explicit constant $C$. Our analysis is based on a connection between the survival probability and the running maximum of the time-reversed process, and relies on a functional central limit theorem for Markov chains. As applications, we recover known clustering results for the 3-color cyclic cellular automaton and the Greenberg–Hastings model, and we prove a new clustering result for the 3-color firefly cellular automaton.     

Exceptionally Plastic/Elastic Organic Crystals of a Naphthalidenimine-Boron Complex Show Flexible Optical Waveguide Properties

The design of molecular compounds that exhibit flexibility is an emerging area of research. Although a fair amount of success has been achieved in the design of plastic or elastic crystals, realizing multidimensional plastic and elastic bending remains challenging. We report herein a naphthalidenimine–boron complex that showed size-dependent dual mechanical bending behavior whereas its parent Schiff base was brittle. Detailed crystallographic and spectroscopic analysis revealed the importance of boron in imparting the interesting mechanical properties. Furthermore, the luminescence of the molecule was turned-on subsequent to boron complexation, thereby allowing it to be explored for multimode optical waveguide applications. Our in-depth study of the size-dependent plastic and elastic bending of the crystals thus provides important insights in molecular engineering and could act as a platform for the development of future smart flexible materials for optoelectronic applications.