The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions

This study examines the role of regulatory processes in medical students as they learn to deliver bad news to patients in the context of an international web-based problem based learning environment (PBL). In the PBL a medical facilitator and students work together to examine video cases on giving bad news and share their perspectives on what was done effectively and what could be done differently. We examine how regulation occurs within this collaboration. A synchronous computer-supported collaborative learning environment (CSCL) facilitated peer discussion at a distance using a combination of tools that included video-conferencing, chat boxes, and a shared whiteboard to support collaborative engagement. We examine regulation along a continuum, spanning from self- to co-regulation, in situations where medical students learn how to manage their own emotions and adapt their responses to patient reactions. We examine the nature of the discourse between medical students and facilitators to illustrate the conditions in which metacognitive, co-regulation and social emotional activities occur to enhance learning about how to communicate bad news to patients.     

Total-FETI domain decomposition method for solution of elasto-plastic problems

In this paper we present an algorithm for the parallel solution of the rate-independent elasto-plastic problems with kinematic hardening. We assume the von Mises plastic criterion and the associated plastic flow rule. The time discretization is based on the implicit Euler method. The corresponding one-time-step problem is formulated in the incremental form with respect to the unknown displacement and discretized spatially by the finite element method. We use an ‘external’ algorithm based on a linearization of the elasto-plastic stress–strain relation by the corresponding tangential operator and we parallelize the arising linearized problem by the Total-FETI method. The numerical experiments were carried out using our novel C/C++ library FLLOP (FETI Light Layer On top of PETSc) at HECToR supercomputer located at EPCC, UK.     

Economic selection of process mean and lot‐sizing decisions for multi‐newsvendor supply chain

Two related problems are integrated in this paper, the first is the targeting problem and another is production/inventory decisions in a supply chain. The supply chain under consideration consists of a supplier of raw material, a single producer, and multiple newsvendors. The producer can adjust the process mean before starting the production run. Once set to a certain target value, the process mean is not changed until the production lot is completed. At the end of a production run, the producer screens the lot and uses specification limits to evaluate the quality of the item. Nonconforming items are scrapped with no salvage value; however, conforming items are delivered to newsvendors who are subjected to random demand. If demand on a newsvendor in a season is lower than anticipated, surplus items will be returned to the producer at a certain transportation cost. We first develop a mathematical model that maximizes the expected total profit of the supply chain. Then, a table for two special functions is devised to simplify the solution method and is used to find the optimal solution of the proposed model. We also study the significance of this integration by comparing the performance of the proposed model with an independent model where the process mean selection and lot‐sizing decisions are found separately.     

一种混合局部搜索算法的遗传算法求解旅行商问题

针对遗传算法容易产生早熟现象以及局部寻优能力较差的缺点,提出一种求解旅行商问题的高效混合遗传算法。该算法首先用加权最近邻法产生初始种群,对种群中相同的个体,用K-近邻法产生新的个体代替相同的个体,然后淘汰适应性较差的个体,用交叉操作产生新的个体,最后,对部分个体进行3-opt优化变异,对种群中优秀个体用改进的Lin-Kernighan算法进行优化。对TSPLIB中部分实例的仿真结果表明,所提出的混合局部搜索算法的改进遗传算法在求解TSP问题时可以高效地获得高质量的解。     

网络链路性能参数估计的层析成像方法综述

网络层析成像能够在网络内部节点不提供测量协作的情况下,根据端到端的测量结果,间接地估计网络内部链路性能参数,是一种重要的网络测量手段,能直接指导网络管理和网络优化,目前受到国内外学术界和工业界广泛的关注.在广泛收集国内外资料的基础上,首先总结了目前网络层析成像使用的主要端到端测量方法和技术;再根据不同参数对链路性能刻画程度的不同,将链路性能参数的网络层析成像方法分为两类:定量参数推断方法和定性参数推断方法;然后,针对不同类型参数的估计问题,概括分析了现有算法的特点;最后指出该类方法未来的研究方向与潜在的应用前景.     

概率最大受限路径相容算法

研究了可用于求解约束满足问题的最大受限路径相容算法(maxRPC).maxRPC算法执行过程中有大量无效的寻找路径相容证明(PC-witness)的操作,有效地识别和避免这些无效的寻找PC-witness的操作,可以提高maxRPC算法的求解效率.首先,提出了在一条约束上任意两个相容的值在任意路径上存在PC-witness的概率;然后,基于这一概率提出了一种概率最大受限路径相容算法(PmaxRPC),并将新算法成功应用于求解约束满足问题的回溯搜索.实验结果显示:PmaxRPC可以避免一部分无效的寻找PC-witness的操作,在求解约束满足问题时,PmaxRPC效率高于maxRPC.在某些测试用例上,PmaxRPC比maxRPC和最流行的弧相容算法效率更高.     

基于网络图的产品生产、运输、定价综合决策模型

针对大型垄断厂商在商品生产、运输、定价决策中独立考虑生产定价和运输两个环节的弊端, 提出一种将产量决策、运输决策和定价决策3 个环节作为一个系统进行考虑的综合决策模型. 借助利润网络图, 构建同时考虑生产及运输成本以及市场需求的商品生产、运输和定价综合决策模型, 并探讨模型的解法及其理论基础. 通过给出的应用案例表明, 相比传统决策模型, 利用综合决策模型可以提高15.50% 的利润.

     

MSP问题NP完全性研究

针对文献[1,2]提出的MSP问题,研究了MSP问题与着色问题、子图同构问题的对应关系,揭示了MSP问题所反映的NP完全问题的共性;分析了MSP问题的相变现象,为文献[1,2]提出的多项式时间算法框架的测试提供了难例产生方法。     

Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems

Evolutionary algorithms (EAs) are often employed to multiobjective optimization, because they process an entire population of solutions which can be used as an approximation of the Pareto front of the tackled problem. It is a common practice to couple local search with evolutionary algorithms, especially in the context of combinatorial optimization. In this paper a new local search method is proposed that utilizes the knowledge concerning promising search directions. The proposed method can be used as a general framework and combined with many methods of iterating over a neighbourhood of an initial solution as well as various decomposition approaches. In the experiments the proposed local search method was used with an EA and tested on 2-, 3- and 4-objective versions of two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the quadratic assignment problem (QAP). For comparison two well-known local search methods, one based on Pareto dominance and the other based on decomposition, were used with the same EA. The results show that the EA coupled with the directional local search yields better results than the same EA coupled with any of the two reference methods on both the TSP and QAP problems.     

Gain‐scheduled filtering for linear parameter‐varying systems using inexact scheduling parameters with bounded variation rates

This paper is concerned with the problem of full‐order H₂ linear parameter‐varying filter design for continuous‐time systems with bounded rate of variations under the condition that the scheduling parameters do not exactly fit the real ones. The scheduling parameters and their derivatives are supposed to belong to polytopes with known vertices. The synthesis conditions are formulated in terms of parameter‐dependent bilinear matrix inequalities by means of parameter‐dependent Lyapunov function and introducing some extra variables for the filter design. An iterative procedure is presented to cast the bilinear matrix inequalities problem into a linear matrix inequality optimization problem. The design of robust filters for both time‐varying and time‐invariant systems can be viewed as particular cases of the proposed method. The merit of the method presented in this paper lies in two fields. The first pertains to dealing with the measurement uncertainty in a less conservative manner than available approaches in the gain‐scheduled filtering problem. The second is to provide more efficient methods than the existing ones in the literature for the robust filter design. Copyright © 2015 John Wiley & Sons, Ltd.