Delay-decomposing approach to robust stability for switched interval networks with state-dependent switching

This paper is concerned with a class of nonlinear uncertain switched networks with discrete time-varying delays . Based on the strictly complete property of the matrices system and the delay-decomposing approach, exploiting a new Lyapunov–Krasovskii functional decomposing the delays in integral terms, the switching rule depending on the state of the network is designed. Moreover, by piecewise delay method, discussing the Lyapunov functional in every different subintervals, some new delay-dependent robust stability criteria are derived in terms of linear matrix inequalities, which lead to much less conservative results than those in the existing references and improve previous results. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.     

The Gill-Oxygen Limitation Theory and size at maturity/maximum size relationships for salmonid populations occupying flowing waters

The slowing of growth as fish age has long been believed to be related to energy expenditure for maturation, and this rationalization has been used to explain why, across nearly all fish species, the relationship between size at first maturity (L_m) and maximum (L_max) or asymptotic length (L_∞) is relatively constant. In contrast, the Gill-Oxygen Limitation Theory (GOLT) postulates that (a) fish growth slows because as they grow, their two-dimensional ability to extract oxygen from the water diminishes relative to their three-dimensional weight gain, and (b) they can only invest energy for maturation if oxygen supply at their size at first maturity (Q_m) exceeds that needed for maintenance metabolism (Q_∞). It has been reported previously across dozens of marine fish species that the relationship between Q_m and Q_∞ is linear and, further, it can be mathematically converted to L_m vs. L_∞ by raising both terms to the power of D (the gill surface factor), resulting in a slope of 1.36. If the GOLT is universal, a similar slope should exist for L_m^D vs. L_∞^D relationships for freshwater species across multiple individual populations that reside in disparate habitats, although to our knowledge this has never been evaluated. For analysis, we used existing data from previous studies conducted on 51 stream-dwelling populations of redband trout Oncorhynchus mykiss gairdneri, Yellowstone cutthroat trout O. clarkii bouvieri and mountain whitefish Prosopium williamsoni. The resulting L_m^D vs. L_∞^D slopes combining all data points (1.35) or for all species considered separately (range = 1.29–1.40) were indeed equivalent to the slope originally produced for the marine species from which the GOLT-derived relationship was first reported. We briefly discuss select papers both supporting and resisting various aspects of the GOLT, note that it could potentially explain shrinking sizes of marine fish, and call for more concerted research efforts combining laboratory and field expertise in fish growth research.     

Sample size and power for the weighted log-rank test and Kaplan-Meier based tests with allowance for nonproportional hazards

Asymptotic distributions under alternative hypotheses and their corresponding sample size and power equations are derived for nonparametric test statistics commonly used to compare two survival curves. Test statistics include the weighted log-rank test and the Wald test for difference in (or ratio of) Kaplan-Meier survival probability, percentile survival, and restricted mean survival time. Accrual, survival, and loss to follow-up are allowed to follow any arbitrary continuous distribution. We show that Schoenfeld's equation—often used by practitioners to calculate the required number of events for the unweighted log-rank test—can be inaccurate even when the proportional hazards (PH) assumption holds. In fact, it can mislead one to believe that 1:1 is the optimal randomization ratio (RR), when actually power can be gained by assigning more patients to the active arm. Meaningful improvements to Schoenfeld's equation are made. The present theory should be useful in designing clinical trials, particularly in immuno-oncology where nonproportional hazards are frequently encountered. We illustrate the application of our theory with an example exploring optimal RR under PH and a second example examining the impact of delayed treatment effect. A companion R package npsurvSS is available for download on CRAN.     

Interpreting Statistical Evidence with Empirical Likelihood Functions

There has been growing interest in the likelihood paradigm of statistics, where statistical evidence is represented by the likelihood function and its strength is measured by likelihood ratios. The available literature in this area has so far focused on parametric likelihood functions, though in some cases a parametric likelihood can be robustified. This focused discussion on parametric models, while insightful and productive, may have left the impression that the likelihood paradigm is best suited to parametric situations. This article discusses the use of empirical likelihood functions, a well‐developed methodology in the frequentist paradigm, to interpret statistical evidence in nonparametric and semiparametric situations. A comparative review of literature shows that, while an empirical likelihood is not a true probability density, it has the essential properties, namely consistency and local asymptotic normality that unify and justify the various parametric likelihood methods for evidential analysis. Real examples are presented to illustrate and compare the empirical likelihood method and the parametric likelihood methods. These methods are also compared in terms of asymptotic efficiency by combining relevant results from different areas. It is seen that a parametric likelihood based on a correctly specified model is generally more efficient than an empirical likelihood for the same parameter. However, when the working model fails, a parametric likelihood either breaks down or, if a robust version exists, becomes less efficient than the corresponding empirical likelihood.     

A Generalization of the Intraclass Tau Correlation for Tied and Censored Data

Intraclass tau correlations and a corresponding asymptotic permutation test have already been derived by WHITFIELD (1949), though for continuous data only. WHITFIELD'S concepts are now generalized for use with tied and censored data and a suggestion is made for the application of the methods to arbitrary and varying group sizes. Special attention is given to their application to dichotomous data. Extensive simulations confirming the safe use of the asymptotic test even for samples of moderate size are reported and calculated examples with data from twin-births and kidney transplantations presented.     

一类S-I传染病模型行波解的存在性

本文研究了一类具有扩散且是非线性传染率的SI传染病模型,分析了模型的行波解的存在性条件,给出了最小波速与产生单调和振荡行波解的条件,并且进行了计算机仿真．     

单种群扩散时滞系统的持久性和全局渐近稳定性

在这篇文章中,我们证明了一个单种群扩散时滞系统的持久性,同时,给出它的全局渐近稳定性的充分条件。     

具有三个年龄阶段的单种群自食模型

建立并研究了两个具有三个年龄阶段的单种群自食模型．这篇文章的主要目的是研究时滞对种群生长的作用,对于没有时滞的的模型,我们利用Liapunov函数,得到了系统平衡点全局渐近稳定的充分条件;而具有时滞的的模型,我们得到,随着时滞T增加,当系数满足一定条件时,正平衡点的稳定性可以改变有限次,最后变成不稳定;否则,时滞模型的正平衡点的稳定性不改变。     

Application of a new method of nonlinear dynamical system identification to biochemical problems

The system identification method for a variety of nonlinear dynamic models is elaborated. The problem of identification of an original nonlinear model presented as a system of ordinary differential equations in the Cauchy explicit form with a polynomial right part reduces to the solution of the system of linear equations for the constants of the dynamical model. In other words, to construct an integral model of the complex system (phenomenon), it is enough to collect some data array characterizing the time-course of dynamical parameters of the system. Collection of such a data array has always been a problem. However difficulties emerging are, as a rule, not principal and may be overcome almost without exception. The potentialities of the method under discussion are demonstrated by the example of the test problem of multiparametric nonlinear oscillator identification. The identification method proposed may be applied to the study of different biological systems and in particular the enzyme kinetics of complex biochemical reactions.     

Unconditional inferences on the difference of two proportions in cross-sectional studies

In order to analyse a 2 x 2 table it is usual to perform inferences (hypothesis test or interval of confidence) on the difference d = p2 - p1 between two independent proportions. To this end it has been customary to adopt the Fisher conditional method, but nowadays the unconditional method of Barnard is increasingly adopted. However, all the present unconditional inferences are based on a double-binomial model. This article performs these inferences - exact and asymptotic - under a multinomial model, which is the appropriate one when the data proceed from a cross-sectional survey. At http://www.ugr.es/-bioest/SG_ASO.EXE there is a program for performing the said unconditional tests that may be copied.