Using a partially observable Markov chain model to assess colonoscopy screening strategies – A cohort study

Colorectal cancer (CRC) is notoriously hard to combat for its high incidence and mortality rates. However, with improved screening technology and better understanding of disease pathways, CRC is more likely to be detected at early stage and thus more likely to be cured. Among the available screening methods, colonoscopy is most commonly used in the U.S. because of its capability of visualizing the entire colon and removing the polyps it detected. The current national guideline for colonoscopy screening recommends an observation-based screening strategy. Nevertheless, there is scant research studying the cost-effectiveness of the recommended observation-based strategy and its variants. In this paper, we describe a partially observable Markov chain (POMC) model which allows us to assess the cost-effectiveness of both fixed-interval and observation-based colonoscopy screening strategies. In our model, we consider detailed adenomatous polyp states and estimate state transition probabilities based on longitudinal clinical data from a specific population cohort. We conduct a comprehensive numerical study which investigates several key factors in screening strategy design, including screening frequency, initial screening age, screening end age, and screening compliance rate. We also conduct sensitivity analyses on the cost and quality of life parameters. Our numerical result demonstrates the usability of our model in assessing colonoscopy screening strategies with consideration of partial observation of true health states. This research facilitates future design of better colonoscopy screening strategies.     

Global Priority Estimation in Multiperson Decision Making

The analytic hierarchy process generalized to synthesizing local preferences across a complex hierarchy structure into global priorities in multiperson decision making is considered in various approaches. They include classic eigenvector synthesis and multiplicative method, and several other suggested techniques, such as three-dimensional eigenvectors, and simultaneous linear and nonlinear estimations by whole hierarchy structure in synthetic optimizing procedures. A numerical example from marketing research with many criteria, subcriteria, alternatives, and respondents is presented. The quality of approximation compared by different approaches shows that the best results are produced by the nonlinear synthetic priority techniques. The techniques described have been successfully used in dozens of real-world projects and are very helpful for practical managerial decision making in complex hierarchies. Dr. Stan Lipovetsky is Senior Research Director, GfK Custom Research North America, Marketing Science, Research Center for Excellence. The author thanks Professor T. Rapcsak and two reviewers for insightful suggestions that improved the paper.     

An in silico expert system for the identification of eye irritants

This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient chemicals for eye irritancy potential, which is based upon exclusion rules of five physicochemical properties – molecular weight (MW), hydrophobicity (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA) and polarizability (Pol). These rules were developed using the ADMET Predictor software and a dataset of 917 eye irritant chemicals. The dataset was divided into 826 (90%) chemicals used for training set and 91 (10%) chemicals used for external validation set (every 10th chemical sorted by molecular weight). The sensitivity of these rules for the training and validation sets was 72.3% and 71.4%, respectively. These rules were also validated for their specificity using an external validation set of 2011 non-irritant chemicals to the eye. The specificity for this validation set was revealed as 77.3%. This method facilitates rapid screening and prioritization of data poor chemicals that are unlikely to be tested for eye irritancy in the Draize test.     

超临界水临界区域判定方法研究

对超临界水在临界区域进行合理的判定和区域划分,对于深入理解超临界水在临界过渡区域的流动和换热相关特征具有重要的作用。本文分析了超临界水从拟液态区向拟汽态区过渡的过程中,其导热系数、动力粘度、定压比热和膨胀系数等相关参数的变化规律特征,并归纳了已有超临界水在临界区域的划分判定模型。分析结果表明,在临界过渡区域,超临界水的流动特征参数和换热特征参数均会发生一系列连续剧烈的变化;只有同时考虑超临界水的膨胀特性和最大比热特性,才能更加合理地对临界区域进行划分。在此分析基础上,本工作完善了超临界水的三区分析判定模型,得到了新的超临界水在临界区域的判定划分数据,并由此拟合得到了新的超临界水分区边界计算关系式。新的计算关系式的误差范围在±0. 3℃之内,满足计算分析的要求。     

Stabilization of switched systems with polytopic uncertainties via composite quadratic functions

This paper studied the stabilization of switched linear systems with polytopic uncertainties by employing the methods of nonsmooth analysis and the composite quadratic Lyapunov functions. Above all, the minimum quadratic functions and the directional derivatives along the vertex directions of subsystems are applied to construct the new switching law. Then, some sufficient conditions for stabilization of switched linear systems are established considering the sliding modes and the directional derivatives along sliding modes. Finally, numerical examples are given to demonstrate the effectiveness of the synthesis results.     

Some interval-valued intuitionistic uncertain linguistic Choquet operators and their application to multi-attribute group decision making

In this study a generated admissible order between interval-valued intuitionistic uncertain linguistic numbers using two continuous functions is introduced. Then, two interval-valued intuitionistic uncertain linguistic operators called the interval-valued intuitionistic uncertain linguistic Choquet averaging (IVIULCA) operator and the interval-valued intuitionistic uncertain linguistic Choquet geometric mean (IVIULCGM) operator are defined, which consider the interactive characteristics among elements in a set. In order to overall reflect the correlations between them, we further define the generalized Shapley interval-valued intuitionistic uncertain linguistic Choquet averaging (GS-IVIULCA) operator and the generalized Shapley interval-valued intuitionistic uncertain linguistic Choquet geometric mean (GS-IVIULCGM) operator. Moreover, if the information about the weights of experts and attributes is incompletely known, the models for the optimal fuzzy measures on expert set and attribute set are established, respectively. Finally, a method to multi-attribute group decision making under interval-valued intuitionistic uncertain linguistic environment is developed, and an example is provided to show the specific application of the developed procedure.     

A complex multi-attribute large-group PLS decision-making method in the interval-valued intuitionistic fuzzy environment

In the complex multi-attribute large-group decision-making (CMALGDM) problems in interval-valued intuitionistic fuzzy (IVIF) environment, attributes of the alternatives are often stratified and correlated. This paper proposes a decision-making method for these problems based on partial least squares (PLS) path modelling, which not only fully exploits the decision information of decision makers (DMs), but also effectively addresses the relativity problem in the decision attributes and objectively assigned weights to the primary decision attributes (i.e., “latent variables for decision making”). The method can be outlined in three steps. First, a two-stage method is proposed to transform the interval-valued intuitionistic fuzzy number (IVIFN) samples into single-valued samples. In this step, an improved C-OWA operator is first given to transform the IVIFN samples into intuitionistic fuzzy number (IFN) samples, which makes the preference information of the DMs more objectively aggregated. Then a proposed membership-based method is applied to reduce the information loss and transform the IFN samples into single-valued samples. Second, the estimated values and weights of the “latent variables for decision-making” are obtained by means of the PLS path modelling algorithm. Finally, a multi-alternative sorting method is devised in accordance with the estimated values and weights. An example is provided to illustrate the proposed technique and evaluate its feasibility and validity.     

Integrated Fuzzy AHP–TOPSIS for selecting the best plastic recycling method: A case study

Due to the rapid depletion of natural resources and undesired environmental changes in a global scale, it is necessary to conserve the natural resources and protect the environment. Industries which manufacture plastic based products have the necessity to recycle plastics. There are number of methods to recycle plastics. Since the selection of the best recycling method involves complex decision variables, it is considered to be a multiple criteria decision-making (MCDM) problem. This article develops an evaluation model based on the fuzzy Analytic Hierarchy Process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) to enable the industry practitioners to perform performance evaluation in a fuzzy environment. The purpose of the study is to determine the best method for recycling plastics among the various plastic recycling processes. By observing the results, it is identified that mechanical recycling process is found to be the best plastic recycling process using the integrated approach.