An LP-based heuristic procedure for the generalized assignment problem with special ordered sets

The generalized assignment problem with special ordered sets (GAPS2), is the problem of allocating n tasks to m time-periods, where each task must be assigned to a time-period, or shared between two consecutive time-periods. For reasonably large values of m and n the NP-hard combinatorial problem GAPS2 becomes intractable for standard mathematical programming software, hence there is a need for heuristic algorithms to solve such problems. It will be shown how an LP-based heuristic developed previously for the well-established generalized assignment problem can be modified and extended to solve GAPS2. Encouraging results, in terms of speed and accuracy, in particular when compared to an existing heuristic for GAPS2, are described.     

高度有序多孔氧化铝模板的制备工艺与生长机制的研究

采用二次氧化法制备出高度有序的多孔氧化铝模板，结合扫描电镜和原子力显微镜对其结构、形貌进行观察和表征。研究了铝箔预处理和温度等对多孔氧化铝模板孔洞有序性的影响，讨论了有序孔洞的自组织生长机理。     

Linking of serially ordered lists by macaque monkeys (Macaca mulatta): List position influences.

Five monkeys (Macaca mulatta) were trained on 2 sets of 3 5-item serially ordered lists. Then, each set was either linked or not in a counterbalanced, within-subject design. Linking entailed training on the 2 pairs that ordered the 3 5-item fists into a single overall 15-item series. Choices on novel pairings after linking conditions attempted to define the unique contributions of knowledge of within-list ordinal position and between-lists link training. With linkage, the series was immediately treated as a 15-item ordered list. Without linkage, choices reflected list positions from initial learning, but continued testing with directional reward yielded gradual ordering into a 15-itern list. Apparently, monkeys remembered and used initial list-position information, but linkage allowed inference of an integrated serial relationship among items. Results supported primate list memory as an organizational process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Parametric and nonparametric Bayesian model specification: A case study involving models for count data

In this paper we present the results of a simulation study to explore the ability of Bayesian parametric and nonparametric models to provide an adequate fit to count data of the type that would routinely be analyzed parametrically either through fixed-effects or random-effects Poisson models. The context of the study is a randomized controlled trial with two groups (treatment and control). Our nonparametric approach uses several modeling formulations based on Dirichlet process priors. We find that the nonparametric models are able to flexibly adapt to the data, to offer rich posterior inference, and to provide, in a variety of settings, more accurate predictive inference than parametric models.     

沉积环境与多元统计综合分析法

本文首先根据地震资料提取了十二种地震参数，然后通过对ＬＨ油田ＴＺ地区沙三上段下部地层进行沉积环境分析，确定了可能存在的沉积相类型，并用有序样品聚类的方法对过井和井旁地震测线按沉积相个数进行分割，由此而建立了该区各类沉积相的判别函数。通过这些判别函数，用逐步判别分析的方法，对该区其它地震测线的参数进行判别分类，最后搞清了研究区目的层的沉积相展布。通过分析和计算证明，这一方法对有地震资料的盆地或地区进行沉积相的划分，是行之有效的     

The induced generalized aggregation operators for intuitionistic fuzzy sets and their application in group decision making

In this paper, we present the induced generalized intuitionistic fuzzy ordered weighted averaging (I-GIFOWA) operator. It is a new aggregation operator that generalized the IFOWA operator, including all the characteristics of both the generalized IFOWA and the induced IFOWA operators. It provides a very general formulation that includes as special cases a wide range of aggregation operators for intuitionistic fuzzy information, including all the particular cases of the I-IFOWA operator, GIFOWA operator and the induced intuitionistic fuzzy ordered geometric (I-IFOWG) operator. We also present the induced generalized interval-valued intuitionistic fuzzy ordered weighted averaging (I-GIIFOWA) operator to accommodate the environment in which the given arguments are interval-valued intuitionistic fuzzy sets. Further, we develop procedures to apply them to solve group multiple attribute decision making problems with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. Finally, we present their application to show the effectiveness of the developed methods.     

Inference rules using local contexts

We construct equivalent localized versions of a formula, adding assumptions simultaneously to various locations, where the particular location determines what is added. Inference rules that take advantage of localized formulas are presented for sequent calculi in which the left hand side of sequents can be used to accumulate the background assumptions (or contexts) of assertions. The intended application is to the automatic generation of tractable justifying lemmas for substitution operations for interactive proof development systems, especially those concerned with mathematical topics where manipulation of deeply embedded terms is desirable.