Sliding mode control of time‐varying delay Markov jump with quantized output

This paper investigates the quantized sliding mode control of Markov jump systems with time‐varying delay. A dynamical adjustment law is explored to quantize the system output. By constructing an observer‐based integral sliding surface, a sliding mode controller is designed to take over the dynamical motion of state estimation and ensure the reachability of sliding surface. A new scaling manner is developed to build the bound between the system output and quantized error. With the help of separation strategies for controller synthesis and general transition probabilities and a lower bound theorem for nonlinear integral terms, a new synthesis method to ensure the required stability and meet the required performance is proposed in the form of linear matrix inequalities. The validity of the proposed control method is illustrated by a numerical example.     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

Stochastic models for geriatric in-patient behaviour

Departments of geriatric medicine engage in two distinct formsof clinical activity: acute/rehabilitative and long-stay care.These are organizationally distinct and have very differentresource needs. Current hospital planning models, however, assumethat patients all move through the system at the same rate,thereby ignoring this effect of inherent heterogeneity in patientbehaviour. The present paper describes the movement of patientsthrough geriatric hospitals by a two-stage continuous-time Markovmodel, where the stages represent acute/rehabilitative and long-staypatients respectively. Patients are initially admitted to thefirst stage, from which they may depart from the system, bydeath or discharge, or move into the second stage, from whichthey eventually depart by death or discharge (unlikely). Admissionsare modelled in two ways: either as replacements for departuresor as a Poisson stream. Expressions for the distribution andmovement of numbers of patients are derived and evaluated fordata from a number of hospitals. Such an approach has the advantage,over previous crude models, of taking into account differenttypes of patients and introducing variability, thus making itpossible to extract variances as well as means of numbers ofgeriatric patients requiring hospital care.     

Multivariate Markov chain analysis of the probability of pregnancy in infertile couples undergoing assisted reproduction.

BACKGROUND: Estimating the probability of pregnancy leading to delivery and the influence of clinical factors on that probability is of fundamental importance in the treatment counselling of infertile couples. A variety of statistical techniques have been used to analyse fertility data, many borrowed from survival analysis. METHODS AND RESULTS: We propose an alternative method of analysis which is based on a discrete time Markov chain approach, with states 'pregnancy (leading to a delivery)', 'not pregnant', and 'censored' and in which the transition probabilities are dependent both on the clinical characteristics of the patient and the treatment given. CONCLUSIONS: We believe that the method of analysis presented here may be preferable to standard analyses in that it better reflects the clinical situation, it is a truly discrete time analysis applied to a discrete time situation, it explicitly models the censoring process (a process which in itself provides information of interest to the physician) and can be readily extended to a variety of clinical situations.     

面向残疾人的汉语可视语音数据库

将人机交互领域中研究的唇读技术应用于康复工程之中,设计了一个基于视觉语言的语音合成系统。该系统特别针对后天致残,丧失语音能力的人设计,采用了一种特定条件下的汉语可视语音数据库。不同于现有的数据库,该数据库的设计具有以下特点：采用了非对称唇形轮廓模型,提取了嘴唇突出度的信息;针对汉语音节的特点,增强了汉字音节中信息变化过程;兼顾未来唇读技术的发展,以音节为基本元素,具有可扩充性。采用运动检测和数学形态学的办法提取唇动图像序列中的唇形区域,并从中提取非对称唇形轮廓模型特征参数,同时通过计算部分参数对时间的差分,来获得唇形轮廓的动态信息。基于隐马尔可夫模型的学习和识别实验表明,该数据库的设计方法合理,所选的唇动特征用能够将识别效果平均提高25%。     

Insulin Minimal Model Indexes and Secretion: Proper Handling of Uncertainty by a Bayesian Approach

The identification of the insulin minimal model (MM) for the estimation of insulin secretion rate (ISR) and physiological indexes (e.g. beta-cell sensitivity) requires the knowledge of C-peptide (CP) kinetics. The four parameters of the two-compartment model of CP kinetics in a given individual can be derived either from an additional bolus experiment or, more frequently, from a population model. However, in both situations, the CP kinetics is uncertain and, in MM identification, it should be treated as such. This paper shows how to handle CP kinetics uncertainty by using a Bayesian methodology. In seven subjects, MM indexes and ISR were estimated together with their confidence intervals, using either the bolus data or the population model to assess CP kinetics. The two main results that arise from the application of the new methodology are: (i) the use of the population model in place of the bolus data to determine CP kinetics does not affect, on average, the point estimates of ISR profile and MM parameters but only the confidence intervals which becomes wider (less than 50%); (ii) in both the bolus and population situation neglecting the uncertainty of CP kinetics, as done in MM literature so far, introduces no bias, on average, on point estimates of MM indexes but only an underestimation of confidence intervals.     

我国消除丙型肝炎的普通人群HCV检测策略的成本效果分析

目的 分析我国消除丙型肝炎（丙肝）的普通人群HCV检测策略的成本效果,明确最佳成本效果的HCV检测年龄。方法 运用TreeAge pro 2019软件构建决策树马尔科夫模型,以1年为周期,模拟10万名20~59岁各年龄组人群HCV检测和治疗结果,以全社会角度分析策略间比较的成本效果和效益。效果指标为增量成本效果比（ICER）,效益指标为净货币效益（NMB）,以我国2022年人均国内生产总值（85 698元）为意愿支付阈值。通过单因素敏感性分析和概率敏感性分析评估结果可靠性。结果 在20~59岁人群HCV检测有成本效果,在40~49岁年龄组进行HCV检测成本效果最佳。20~59岁年龄组人群HCV检测策略与未HCV检测策略比较,增量成本为161.24元/人,增量效用为0.003 6质量调整寿命年（QALYs）/人,ICER为45 197.26元/QALY,ICER小于意愿支付阈值,具有成本效果。各年龄组人群HCV检测策略与未HCV检测策略比较,ICER为42 055.06~53 249.43元/QALY,NMB为96.52~169.86元/人,其中40~49岁年龄组的ICER最低,NMB最高。单因素敏感性分析结果显示,贴现率、丙肝抗体（抗-HCV）检测成本、人群抗-HCV阳性率和直接抗病毒药物治疗成本对经济学评价影响较大,但改变参数取值,结论不变。概率敏感性分析结果表明模型分析结果稳定。结论 医疗机构探索动员20~59岁普通人群进行HCV检测具有较好的成本效果,以40~49岁年龄组人群的HCV检测成本效果最佳。在我国普通人群中实施HCV检测的“愿检尽检”策略,能降低人群丙肝疾病负担。     

离子通道门控动力学研究

报道作者近期在离子通道动力学方面所作的研究工作。以膜片钳记录信号的自相关函数为基础,证实了离子通道记忆性的存在,并提出两状态非线性随机模型和镶嵌点过程模型,用于描述记忆性和门控动力学的特征,这样做可以克服Ｍａｒｋｏｖ模型和分形模型所遇到的３项困难,即状态不可辨认性、开关判定的主观性和时间间隔疏漏。另外,作者还提出了连续分组平均时间检测法,帮助确定Ｍａｒｋｏｖ模型中状态的个数,与多指数拟合法相比,此方法更直观和易于操作     

临床随访研究中样本含量的估计

目的:介绍临床随访研究中生存分析资料的log-rank检验所需样本含量的估计法.方法:以离散性Markov链拟合生存过程,据此计算log-rank统计量的数学期望和方差,导出样本含量估计公式.结果:实例分析表明,该法能较好反映实际情况,应用灵活.结论:本法是一种有效、可行的样本含量估计法,值得推荐.     

A new approach to the limiting dilution assay

Improved methods of human immunodeficiency virus isolation have sparked an increased interest in determination of the number and proportion of infected cells in plasma samples. The limiting dilution assay is a popular method of determining the proportion of infected cells. We propose a new estimator of the proportion of infected cells based on an underlying Markov chain model for the progression of infected cells to further flasks in the series. This method should improve both design and analyses of these assays.