均匀性度量中的密集性偏差与稀疏性偏差

该文根据王元，方开泰［２］的近似偏差（discrepancy）的均匀性准则，定义了理想布点情况下的标准半径，定义了ｍ 维单位子空间Ｃｍ＝［０，１］中两点间的ｆ距离和ｇ距离，由此定义了最大空穴半径和最小空穴半径，提出了均匀性度量的密集性偏差与稀疏性偏差．给出了二维情况 下的计算结果．我们的方法计算量不大，不仅能较好地度量布点的均匀性以及布点在低维投影的均匀性，而且能指导如何调整布点使之尽可能与理想布点接近．     

线性系统的输出反馈特征结构配置——具有不可控不可观模态的情形

本文考虑具有不可控不可观模态的线性系统的输出反馈特征结构配置问题。根据文中给出的矩阵方程AV＋BW＝VF的一种显式参数解，得到了线性系统的输出反馈特征结构配置的一种参数化方法，本文没有对系统附加能控能观条件，在[AB]能控的条件下，本文得到的矩阵方程的解是文[5]中定理2的结果，在[AB]能控且[AC]能观的条件下，本文得到的输出反馈特征结构配置方法是文[6]在系统为定常时算法Ⅱ的结果。     

总延误问题顺时安排法的性能比

给定一组工件的加工时间与工期，要求确定这些工件在一台机器上的加．工排列，使相应的总延误达到最小，这就是总延误问题．该问题在近年已被证明是NP困难的．由Wilkermn和Irwin（1971），林勋（1983）等所研究的顺时安排法能得到相邻交换意义下的局部解．在本文中，我们进一步证明该算法能得到前移邻域意义下的局部解，并确定了该算法的性能比．     

微分学中不等式的证明

针对微分学不等式列出五种常用证明方法,即利用单调性证明法,利用拉格朗日中值定理证明法,利用最值证明法,利用泰勒公式证明法,和利用凹凸性证明法.实例说明每种方法的使用细节,以达到使初学者能尽快掌握微分学不等式证明的目的.     

浅谈珠算集训

珠算集训是指在参加珠算比赛或珠算晋级前，为了快速提高珠算技术永平，而将选手集中起来，在一定时间里所进行的系统性强化训练方法．这种方法能使选手们在短期内迅速提高珠算技术水平，达到平时训练所无法比拟的效果．并且通过在集训中的模拟练习，能增强选手对比赛和晋级的适应性。使其在心理上达到实战的最佳状态；     

标准型方案与链型方案的等效性转换

本文通过对标准型与链型统计抽样方案的分析和研究，在等效性和可操作性原则下，使标准型方案和链型方案可以相互转换使用，并在计算机上实现程序化。     

一类非线性椭圆方程组很弱解的正则性

本文在可控增长条件（１．２）－（１．４）下，对一类非线性椭圆方程组（１．１）改进其很弱解偏微商的可积性，使其为经典意义下的弱解。     

关于有理插值函数存在性的确定

在本中，我们利用Newton插值多项式，改进了[1]中的方法，使其能更简便，快速，严谨地判别有理插值函数的存在性，并在其存在时给出相应的插值有理函数的具体表达式。     

一维抛物型系统切换控制下的近似能控性

考虑一维热传导方程在两个边界控制切换作用情形下的近似能控性.采用变分方法,通过某个二次泛函的极小值点构造合适的边界控制函数,使其满足切换条件,而且使得系统是近似能控的.     

关于Ladas等人振动性定理的一点评注

本文对Ｇ．Ｌａｄｏｓ等［１］中的主要结果，在一定条件下给出了一个改进，使结果应用更为方便     

Bayesian doubly adaptive randomization in clinical trials

Bayesian adaptive randomization has attracted increasingly attention in the literature and has been implemented in many phase II clinical trials. Doubly adaptive biased coin design (DBCD) is a superior choice in response-adaptive designs owing to its promising properties. In this paper, we propose a randomized design by combining Bayesian adaptive randomization with doubly adaptive biased coin design. By selecting a fixed tuning parameter, the proposed randomization procedure can target an explicit allocation proportion, and assign more patients to the better treatment simultaneously. Moreover, the proposed randomization is efficient to detect treatment differences. We illustrate the proposed design by its applications to both discrete and continuous responses, and evaluate its operating features through simulation studies.     

Trade reduction vs. multi-stage: A comparison of double auction design approaches

With the growth of electronic markets, designing double auction mechanisms that are applicable to emerging market structures has become an important research topic. In this paper, we investigate two truthful double auction design approaches, the Trade Reduction Approach and the Multi-Stage Design Approach, and compare their resulting mechanisms in various exchange environments. We find that comparing with the Trade Reduction Approach, the Multi-Stage Design Approach offers mechanisms applicable to more complicated exchange environments. Furthermore, for the known trade reduction mechanisms, we prove that the corresponding mechanisms under the multi-stage design approach are superior in terms of both social efficiency and individual payoffs, in each exchange environment of interest. Our computational tests show that the mechanisms under the multi-stage design approach achieve very high efficiency in various scenarios.     

A self-tuning heuristic for the design of communication networks

This paper addresses the design of communication networks that has a large application area. The problem is to design a minimum cost network subject to a given reliability level. Complexity of the problem is twofold: (1) finding a minimum-cost network topology that every pair of nodes can communicate with each other and (2) computing overall reliability to provide the reliability constraint. Over the last two decades, metaheuristic algorithms have been widely applied to solve this problem due to its NP-hardness. In this study, a self-tuning heuristic (STH), which is a new approach free from parameter tuning, is applied to the design of communication networks. Extensive computational results confirm that STH generates superior solutions to the problem in comparison to some well-known local search metaheuristics, and also more sophisticated metaheuristics proposed in the literature. The practical advantage of STH lies in both its effectiveness and simplicity in application to the design problem.     

Using simulation modelling for evaluating screening services for diabetic retinopathy

In using discrete event simulation for planning services in the health sector, epidemiologists and clinicians were closely involved in model design, data collection, analysis, validation and experimentation. For patients with diabetes, loss of sight can be prevented by timely treatment if detected sufficiently early. Simulation models, using the patient oriented simulation technique, POST, have been developed to assist policy makers in the choice of screening strategy in terms of operator, equipment, frequency of screening and target population. The models describe the progress of a population of diabetic patients, including new arrivals, over 25 years. The initial population were given characteristics retrospectively and the parameters were derived from peer reviewed publications. The results from the models show that the interval between screening is more important than screening sensitivity. The simulation can determine the expected workload and the amount of vision loss prevented for any population group.     

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It is difficult to get an accurate optimum design when the experimental design area is very irregular under complex constraints. This paper constructs a random search algorithm for mixture experiments designed (MDRS). Firstly, generating an initial points set in areas with complex constraints by the Monte-Carlo method, then use MDRS algorithm iterative to approximate optimum set. By way of example verification, this method is effective. It can be used as a standard measure of other designs, that is the only effective when given superior to other designs approximate optimal solution.     

How to choose the simulation model for computer experiments: a local approach

In many scientific areas, non‐stochastic simulation models such as finite element simulations replace real experiments. A common approach is to fit a meta‐model, for example a Gaussian process model, a radial basis function interpolation, or a kernel interpolation, to computer experiments conducted with the simulation model. This article deals with situations where more than one simulation model is available for the same real experiment, with none being the best over all possible input combinations. From fitted models for a real experiment as well as for computer experiments using the different simulation models, a criterion is derived to identify the locally best one. Applying this criterion to a number of design points allows the design space to be split into areas where the individual simulation models are locally superior. An example from sheet metal forming is analyzed, where three different simulation models are available. In this application and many similar problems, the new approach provides valuable assistance with the choice of the simulation model to be used. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal design of personalized prostate cancer therapy using Infinitesimal Perturbation Analysis

The standard treatment for advanced prostate cancer is hormone therapy in the form of continuous androgen suppression (CAS), which unfortunately frequently leads to resistance and relapse. An alternative scheme is intermittent androgen suppression (IAS), in which patients are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. In spite of extensive recent clinical experience with IAS, the design of ideal protocols for any given patient remains a challenge. The level of prostate specific antigen (PSA) is frequently monitored to determine when patients will be taken off therapy and when therapy will resume. In this work, we propose a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and perform Infinitesimal Perturbation Analysis (IPA) to adaptively adjust PSA threshold values so as to improve therapy outcomes. We also apply this methodology to clinical data from real patients, and obtain promising results and valuable insights for personalized IAS therapy design.     

Approximate and Widespread Pareto Solutions in the Structure-Control Design of Mechatronic Systems

The structure-control design approach of mechatronic systems requires a different design formulation where the mechanical structure and control system are simultaneously designed. Optimization problems are commonly stated to confront the structure-control design formulation. Nevertheless, these problems are often very complex with a highly nonlinear dependence between the design variables and performance functions. This fact has made the use of evolutionary algorithms, a feasible alternative to solve the highly nonlinear optimization problem; the method to find the best solution is an open issue in the structure-control design approach. Hence, this paper presents a mechanism to exhaustively exploit the solutions in the differential evolution (DE) algorithm in order to find more non-dominated solutions with uniformly distributed Pareto front and better trade-offs in the structure-control design framework. The proposed approach adopts an external population to retain the non-dominated solutions found during the evolutionary process and includes a mechanism to mutate the individuals in their corresponding external population region. As a study case, the structure-control design of a serial-parallel manipulator with its control system is stated as a dynamic optimization problem and is solved by using the proposed approach. A comparative analysis shows that the multi-objective exhaustive exploitation differential evolution obtained a superior performance in the structure-control design framework than a DE algorithm which did not consider the proposal. Hence, the resulting designs provide better trade-offs between the structure-control performance functions.     

Generalized incomplete Trojan-type designs

Generalized incomplete Trojan-type designs are defined to be row–column designs in which each cell, corresponding to the intersection of row and column, contains more than one treatment and the rows are incomplete. A method of constructing generalized incomplete Trojan-type design has been developed and some properties of this class of designs are discussed.     

A two-stage hybrid optimization for honeycomb-type cellular structures under out-of-plane dynamic impact

Honeycomb structures with better balance between lightweight and crashworthiness have aroused growing attentions. However, structural parameters design by traditional optimization algorithm in small design space is not sufficient to significantly enhance the specific energy absorption (SEA) with the lower peak acceleration (a_max). In this paper, a two-stage hybrid optimization for honeycomb-type cellular parameters is proposed to achieve rapid positioning of design space and significantly increase crashworthiness in a larger variable domain under out-of-plane dynamic impact. In stage I, a Taguchi-based grey correlation discrete optimization, combining Taguchi analysis, grey relational analysis, analysis of variance (ANOVA) with grey entropy measurement, is performed to determine the initial optimal value with a higher robustness and the significant influence variables. In stage II, a multi-objective design technique, namely non-nominated sorting genetic algorithm II based on surrogated model, is adopted to maximize the SEA and minimize the a_max in a relatively small design domain. And it is found that the proposed two-stage hybrid method can broaden the optimal design space compared to that of traditional method attributable to its center point positioned by stage I. And the final optimization based on the proposed strategy is superior to the original structure, i.e., the SEA is increased by 47.55% and the a_max is decreased by 80.8%. Therefore, the proposed algorithm can also be used to solve other more complicated engineering problems in a large design space with insightful design data.