Creep-fatigue interaction properties of nickel-based superalloy 617

High-temperature fatigue properties were investigated for a nickel-based superalloy 617 as a typical candidate material for high-temperature components using an advanced ultra super critical (A-USC) power plant. Creep-fatigue data obtained by strain-controlled tests at 700 ? C was analyzed for the superalloy 617 focused on position of hold at peak strain in comparison with continuously cycled tests. The fatigue life was the shortest with the hold tensile strain wave, which showed mainly intergranular fractu...     

A cautious scheduler for multistep transactions

Given a classC of serializable schedules, a cautiousC-scheduler is an on-line transaction scheduler that outputs schedules in classC and never resorts to rollbacks. Such a scheduler grants the current request if and only if the partial schedule it has granted so far, followed by the current request, can be extended to a schedule inC. A suitable extension is searched among the set of all possible sequences of the pending steps, which are predeclared by the transactions whose first requests have already arrived. If the partial schedule cannot be extended to a schedule inC, then the current request is delayed. An efficient cautiousCPSR-scheduler has been proposed by Casanova and Bernstein.This paper discusses cautiousWRW-scheduling, whereWRW is the largest polynomially recognizable subclass of serializable schedules currently known. Since cautiousWRW-scheduling is, in general, NP-complete as shown in this paper, we introduce, a subclass (namedWRW ^#) ofWRW and discuss an efficient cautiousWRW ^#-scheduler. We also show that the fixed point set of the cautiousWRW ^#-scheduler properly containsCPSR. Therefore, ourWRW ^#-scheduler allows more concurrency than anyCPSR- scheduler.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grant No. A5240 and in part by the Ministry of Education, Science and Culture of Japan under Scientific Research Grant-in-Aid.     

An implicit enumeration program for zero-one integer programming

This paper describes some techniques to improve the speed of the implicit enumeration method for solving zero-one integer programming problems. Among these techniques, the most powerful is the one of using a column vector which works as a tag for each inequality, indicating whether or not the inequality should be checked for the current partial solution. A new condition for underlining a variable and the concept of pseudo-underlining are also proposed. These techniques were implemented in the computer programil lip (ILLinois Integer Programming code). The computational results for different types of problems are discussed.This work is partly supported by NSF Grant No. GJ-503.     

Counting the number of minimum cuts in undirected multigraphs

The problem of counting the number of cuts with the minimum cardinality in an undirected multigraph arises in various applications, such as testing the super-λ-ness of a graph, as described by F.T. Boesch (1986), and calculating upper and lower bounds on the probabilistic connectedness of a stochastic graph G in which edges are subject to failure. It is shown that the number |C( G)| of cuts with the minimum cardinality λ(G) in a multiple graph G=(V,E) can be computed in O(|E|+λ(G)|V|² +λ(G)|C(G)||V|) time     

Generating and approximating nondominated coteries

A coterie, which is used to realize mutual exclusion in a distributed system is a family C of incomparable subsets such that every pair of subsets in C has at least one element in common. Associate with a family of subsets C a positive (i.e., monotone) Boolean function f_c such that f_c(x)=1 if the Boolean vector x is equal to or greater than the characteristic vector of some subset in C, and 0 otherwise. It is known that C is a coterie if and only if f_c is dual-minor, and is a nondominated (ND) coterie if and only if f_c is self-dual. We introduce an operator ρ, which transforms a positive self-dual function into another positive self-dual function, and the concept of almost-self-duality, which is a close approximation to self-duality and can be checked in polynomial time (the complexity of checking positive self-duality is currently unknown). After proving several interesting properties of them, we propose a simple algorithm to check whether a given positive function is self-dual or not. Although this is not a polynomial algorithm, it is practically efficient in most cases. Finally, we present an incrementally polynomial algorithm that generates all positive self-dual functions (ND coteries) by repeatedly applying p operations. Based on this algorithm, all ND coteries of up to seven variables are computed     

Error-Free and Best-Fit Extensions of Partially Defined Boolean Functions

In this paper, we address a fundamental problem related to the induction of Boolean logic: Given a set of data, represented as a set of binary “truen-vectors” (or “positive examples”) and a set of “falsen-vectors” (or “negative examples”), we establish a Boolean function (or an extension)f, so thatfis true (resp., false) in every given true (resp., false) vector. We shall further require that such an extension belongs to a certain specified class of functions, e.g., class of positive functions, class of Horn functions, and so on. The class of functions represents our a priori knowledge or hypothesis about the extensionf, which may be obtained from experience or from the analysis of mechanisms that may or may not cause the phenomena under consideration. The real-world data may contain errors, e.g., measurement and classification errors might come in when obtaining data, or there may be some other influential factors not represented as variables in the vectors. In such situations, we have to give up the goal of establishing an extension that is perfectly consistent with the given data, and we are satisfied with an extensionfhaving the minimum number of misclassifications. Both problems, i.e., the problem of finding an extension within a specified class of Boolean functions and the problem of finding a minimum error extension in that class, will be extensively studied in this paper. For certain classes we shall provide polynomial algorithms, and for other cases we prove their NP-hardness.     

Depth-m search in branch-and-bound algorithms

A new search strategy, called depth-m search, is proposed for branch-and-bound algorithms, wherem is a parameter to be set by the user. In particular, depth-1 search is equivalent to the conventional depth-first search, and depth- search is equivalent to the general heuristic search (including best-bound search as a special case). It is confirmed by computational experiment that the performance of depth-m search continuously changes from that, of depth-first search to that of heuristic search, whenm is changed from 1 to . The exact upper bound on the size of the required memory space is derived and shown to be bounded byO(n^m), wheren is the problem size. Some methods for controllingm during computation are also proposed and compared, to carry out the entire computation within a given memory space bound.     

Catalytic reduction of carbon dioxide. 1. Reduction of carbon dioxide with carbon carrying potassium carbonate

The catalytic behaviour of potassium carbonate doped onto active carbon was investigated with respect to reduction of carbon dioxide. The distribution of potassium on the carbon surface was found by an X-ray microanalyzer to be uniform. The amount of oxygen trapped on the carbon surface during reaction was nearly proportional to the surface concentration of potassium. The catalytic activity with carbon doped with less than 2% potassium carbonate was proportional to the amount of trapped oxygen, while excessive doping by potassium was found to form trapped oxygen independently of the gasification of carbon. Disproportionation of carbon monoxide into carbon dioxide and carbon was found to take place readily at 700 °C, with carbon doped with 4.0 wt % potassium carbonate.     

Finding Essential Attributes from Binary Data

We consider data sets that consist of n-dimensional binary vectors representing positive and negative examples for some (possibly unknown) phenomenon. A subset S of the attributes (or variables) of such a data set is called a support set if the positive and negative examples can be distinguished by using only the attributes in S. In this paper we study the problem of finding small support sets, a frequently arising task in various fields, including knowledge discovery, data mining, learning theory, logical analysis of data, etc. We study the distribution of support sets in randomly generated data, and discuss why finding small support sets is important. We propose several measures of separation (real valued set functions over the subsets of attributes), formulate optimization models for finding the smallest subsets maximizing these measures, and devise efficient heuristic algorithms to solve these (typically NP-hard) optimization problems. We prove that several of the proposed heuristics have a guaranteed constant approximation ratio, and we report on computational experience comparing these heuristics with some others from the literature both on randomly generated and on real world data sets.