An Application of MCDM in Water Resources Problems

In this paper a practical application of MCDM in water resources problems is presented. Based on a real project for Qinhuangdao water resources management sponsored by Qinhuangdao Municipality, we construct a set of models for inflow forecast, reservoir operations, water supply and allocation, and flood routing for system optimal operation and flood management. A stochastic dynamic programming (DP) model with a fuzzy criterion is proposed for monthly reservoir operations. A series of goal programming (GP) models is built for water supply and allocation on different planning and operating levels. The DP–GP models fulfil the optimal operation tasks of a water resources management decision support system (WRMDSS) for Qinhuangdao water resources management.     

Indifference sets of reference points in multi‐objective integer linear programming

Reference point approaches for multi‐objective problems rely on the definition of an achievement scalarizing function that projects reference points onto the non‐dominated solution set. In this paper, we investigate the behaviour of reference points using a Tchebycheff metric‐based scalarizing function in multi‐objective pure integer linear programming (MOILP). Since the non‐dominated solutions are discrete in MOILP, there are multiple reference points that lead to the same solution, i.e. there are indifference sets on the reference point space. We investigate some properties of the reference points in MOILP and also the graphical representation of indifference sets for tri‐objective problems. We further investigate properties of the reference points when additional limitations on the objective function values are introduced. Copyright © 2001 John Wiley & Sons, Ltd.     

Material handling device selection in cellular manufacturing

This paper presents a new multi‐criteria decision model for the material handling device (MHD) selection problem in cellular manufacturing systems. Given a set of manufacturing cells based on several automatic work‐centres, the technique makes it possible to select a particular MHD for each cell in an integrated way, with different constraints being taken into consideration. The approach is based on two different multi‐attribute analyses executed with analytic hierarchy process (AHP) methodology, and a final integer linear programming including important limitations faced by the designer when making MHD investment decisions. An example using real data is provided to illustrate this methodology. Copyright © 2002 John Wiley & Sons, Ltd.     

The operation of Lake Kariba: A multiobjective decision analysis

The paper describes a successful application of Bayesian decision analysis to the operation of the Lake Kariba hydropower system. This management problem is complicated by the high uncertainty of the inflow process, multiple and conflicting objectives and the influence of time on some of the parameters in the management task. Inflows to the reservoir are forecast through dynamic linear models. Managerial preferences are modelled through a multiattribute utility function. Since the solution of the exact model is computationally too demanding, a heuristic method is applied to find a feasible control strategy. A comparison with results obtained by methods used previously demonstrates the superiority of the methodology presented here.     

Motor Improvement Using Motion Sensing Game Devices for Cerebral Palsy Rehabilitation

The authors aimed to investigate the effects of an intervention based on interactive game set with the movement sensor Kinect on children with cerebral palsy (CP). Twenty-eight participants were recruited. Their age was between 3 and 12 years old, and rated as level I, II, or III on the Gross Motor Function Classification System. They played two games from the Xbox 360 Kinect system and were evaluated using the Gross Motor Function Measure (GMFM) after a period of 8 weeks. The intervention led to significant motor function improvement as increase of the global scores on the GMFM (p < .001). Rehabilitation interventions using the Xbox 360 Kinect interactive games may represent useful tools for children with CP.     

MATERNAL INTERACTION QUALITY MODERATES EFFECTS OF PRENATAL MATERNAL EMOTIONAL SYMPTOMS ON GIRLS’ INTERNALIZING PROBLEMS

The role of mother–infant interaction quality is studied in the relation between prenatal maternal emotional symptoms and child behavioral problems. Healthy pregnant, Dutch women (N = 96, M = 31.6, SD = 3.3) were allocated to the “exposed group” (n = 46), consisting of mothers with high levels of prenatal feelings of anxiety and depression, or the “low‐exposed group” (n = 50), consisting of mothers with normal levels of depressive or anxious symptoms during pregnancy. When the children (49 girls, 47 boys) were 23 to 60 months of age (M = 39.0, SD = 9.6), parents completed the Child Behavior Checklist (T.M. Achenbach & L.A. Rescorla, 2000 ), and mother–child interaction quality during a home visit was rated using the Emotional Availability Scales. There were no differences in mother–child interaction quality between the prenatally exposed and low‐exposed groups. Girls exposed to high prenatal emotional symptoms showed more internalizing problems, if maternal interaction quality was less optimal. No significant effects were found for boys.     

Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach

Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one‐step‐ahead strategies in which the stimulus on each trial is selected to maximize inference on the next trial only. A lingering question in the field has been how much additional benefit would be gained by optimizing beyond the next trial. A range of technical challenges has prevented this important question from being addressed adequately. This study applies dynamic programming (DP), a technique applicable for such full‐horizon, “global” optimization, to model‐based perceptual threshold estimation, a domain that has been a major beneficiary of adaptive methods. The results provide insight into conditions that will benefit from optimizing beyond the next trial. Implications for the use of adaptive methods in cognitive science are discussed.     

Order-Constrained Solutions in <Emphasis Type="Italic">K</Emphasis>-Means Clustering: Even Better Than Being Globally Optimal

This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that although the usual K-means sum-of-squared-error criterion is not guaranteed to be minimal, a true underlying cluster structure may be more accurately recovered. Also, substantive interpretability seems generally improved when constrained solutions are considered. We illustrate the procedure with several data sets from the literature.     

Hilbert's epsilon as an operator of indefinite committed choice

Hilbert and Bernays avoided overspecification of Hilbert's ε-operator. They axiomatized only what was relevant for their proof-theoretic investigations. Semantically, this left the ε-operator underspecified. After briefly reviewing the literature on semantics of Hilbert's epsilon operator, we propose a new semantics with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the ε simplifies proof search and is natural in the sense that it mirrors some cases of referential interpretation of indefinite articles in natural language.     

交互进化计算对焦虑测量的适用性探析

大量的研究用stroop色词命名任务和点探测任务等经典的情绪研究方法证明了社交焦虑个体情绪识别的特殊性。该研究采用IEC(交互进化计算)的方法, 研究了焦虑组和对照组(低焦虑组)在情绪感知动态范围上的差异性。实验要求第一组被试利用给出的IEC情绪识别光影系统设计出符合实验维度(高兴、恐惧)的图片, 并让第二组被试对图片进行评估, 得出每幅图片的心理数值范围。实验结果说明IEC可以适用于常规优化系统之外的一个新领域—— 心理健康的测量,同时也表明社交焦虑个体的情绪感知动态范围窄于正常人。