A bi-Hamiltonian structure is a pair of Poisson structures P , Q which are compatible, meaning that any linear combination αP+βQ is again a Poisson structure. A biHamiltonian structure (P,Q) is called flat if P and Q can be simultaneously brought to a constant form in a neighborhood of a generic point. We prove that a generic biHamiltonian structure (P,Q) on an odd-dimensional manifold is flat ...

As a NP-Complete problem, multicast routing with delay constraint is a research difficulty in routing problem. Tabu Search is artificial intelligence algorithm, which is an extension of local search algorithm and has simple realization and well properties. In this paper, an efficient algorithm based on Tabu Search for Delay-Constrained Low-Cost Multicast Routing is proposed to solve delay-const...

The Kuratowski–Dugundji theorem that a metrizable space is an absolute (neighborhood) extensor in dimension n iff it is LCn−1&Cn−1 (resp., LCn−1) is extended to a class of non-metrizable absolute (neighborhood) extensors in dimension n. On this base, several facts concerning metrizable extensors are established for non-metrizable ones.

The interplay between maxcliques (maximal cliques) and intersections of closed neighborhoods leads to new types of characterizations of several standard graph classes. For instance, being hereditary clique-Helly is equivalent to every nontrivial maxclique Q containing the intersection of closed neighborhoods of two vertices of Q, and also to, in all induced subgraphs, every nontrivial maxclique...

Perez, F.F. and Fraga, F., 1987. A precise and rapid analytical procedure for alkalinity determination. Mar. Chem., 21: 169-182. A potentiometric analytical method is proposed for the determination of the alkalinity of seawater. The precision is 0.1% and each determination takes 3 min. The technique is very easy to use, even on board ship since it is carried out in open flasks. A polynomial equ...

Generative network models play an important role in algorithm development, scaling studies, network analysis, and realistic system benchmarks for graph data sets. A complex network model gaining considerable popularity builds random hyperbolic graphs, generated by distributing points within a disk in the hyperbolic plane and then adding edges between points with a probability depending on their...

Granular computing aims to develop a granular view for interpreting and solving problems. The model of neighborhood rough sets is one of effective tools for granular computing. This model can deal with complex tasks of classification learning. Despite the success of the neighborhood model in attribute reduction and rule learning, it still suffers from the issue of granularity selection. Namely,...

In this paper, we establish the relationships between spaces with a compact-countable weak-base and spaces with various compact-countable networks, and give two mapping theorems on spaces with compact-countable weakbases. Weak-bases and g-first countable spaces were introduced by A.V.Arhangel’skii [1]. Spaces with a point-countable weak-base were discussed in [5,6], and spaces with a locally co...

This paper introduces a new algorithm Q for optimizing the expected output of a multi input noisy continuous function Q is de signed to need only a few experiments it avoids strong assumptions on the form of the function and it is autonomous in that it re quires little problem speci c tweaking These capabilities are directly applicable to industrial processes and may become in creasingly valuab...

The probability that two spatial objects establish some kind of mutual connection often depends on their proximity. To formalize this concept, we define the notion of a probabilistic neighborhood : Let P be a set of n points in R, q ∈ R a query point, dist a distance metric, and f : R → [0, 1] a monotonically decreasing function. Then, the probabilistic neighborhood N(q, f) of q with respect to...