The Oscillation Inequality of Harmonic Functions on Post Critically Finite Self-Similar Sets

In this paper we establish the oscillation inequality of harmonic functions and Hlder estimate of the functions in the domain of the Laplacian on connected post critically finite(p.c.f.) self-similar sets.     

$H<1/6$时次分数布朗运动赋权立变差的一个注记

本文中我们利用Malliavin计算的技巧研究了$H<1/6$时次分数布朗运动赋权立变差的$L^2$收敛性.     

Robust synchronization of delayed neural networks based on adaptive control and parameters identification

This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique.     

自然联系函数下自适应设计广义线性模型的渐近性质(英文)

本文在一些弱的条件下,对自然联系函数和自适应设计下广义线性模型的极大拟似然估计渐近性进行研究,获得了极大拟似然估计的渐近存在性、弱相合性、收敛速度及渐近正态性.并通过蒙特卡罗数值模拟的方法对所得结果进行验证.     

The abelian anti-ghost equation for the standard model in the 't Hooft background gauge

In this paper we study the abelian anti-ghost equation for the Standard Model quantized in the 't Hooft background gauge. We show that this equation assures the non-renormalization of the abelian ghost fields and prevents possible abelian anomalies.     

Global asymptotic stability analysis for neutral stochastic neural networks with time-varying delays

In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criteria are derived to ensure the global, robust, asymptotic stability of the addressed system in the mean square for all admissible parameter uncertainties. The criteria can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results.     

电镜-能谱仪在柳树叶重金属污染研究中的应用

采用自然干燥、化学处理、冷冻处理3种方法制备待测样本,运用扫描电镜和透射电镜结合X射线能谱仪研究柳树叶肉组织对重金属的累积状况,并定位到栅栏组织的细胞壁,为定性定量以及定位分析组织细胞固定、转移重金属污染探索合适的制样方法.结果表明:(1)重金属Pb、Cu、Cd、Zn在柳树叶肉组织被检测到,并已进入到细胞壁内,同时定量结果显示污染越严重,重金属累积量越多,证明了柳树叶对重金属污染的吸收能力;(2)通过综合比较,认为冷冻处理的样本比较适合进行植物组织微区的元素定性定量以及定位分析,因此推测冷冻制样加冷冻电镜的方法将会最为理想.     

宽象限相依随机变量部分和的中偏差及其在保险中的应用

研究了控制变换尾分布的宽象限相依实值随机变量部分和的中偏差.相应于所得到的理论结果,进一步给出了在相依保险风险模型中的两个应用;一是在基于顾客到达过程的保险风险模型中,保险公司盈余的渐近估计;二是在复合更新风险模型中,有限时和无限时破产概率的一致渐近估计.     

A risk-based modeling approach for radiation therapy treatment planning under tumor shrinkage uncertainty

Robust optimization approaches have been widely used to address uncertainties in radiation therapy treatment planning problems. Because of the unknown probability distribution of uncertainties, robust bounds may not be correctly chosen, and a risk of undesirable effects from worst-case realizations may exist. In this study, we developed a risk-based robust approach, embedded within the conditional value-at-risk representation of the dose-volume constraint, to deal with tumor shrinkage uncertainty during radiation therapy. The objective of our proposed model is to reduce dose variability in the worst-case scenarios as well as the total delivered dose to healthy tissues and target dose deviations from the prescribed dose, especially, in underdosed scenarios. We also took advantage of adaptive radiation therapy in our treatment planning approach. This fractionation technique considers the response of the tumor to treatment up to a particular point in time and reoptimizes the treatment plan using an estimate of tumor shrinkage. The benefits of our model were tested in a clinical lung cancer case. Four plans were generated and compared: static, nominal-adaptive, robust-adaptive, and conventional robust (worst-case) optimization. Our results showed that the robust-adaptive model, which is a risk-based model, achieved less dose variability and more control on the worst-case scenarios while delivering the prescribed dose to the tumor target and sparing organs at risk. This model also outperformed other models in terms of tumor dose homogeneity and plan robustness.