METHOD OF SYNTHESIS OF THE INTEGRATED ASSESSMENT SYSTEM

Systems of complex estimation (CO) based on a dichotomous tree of criteria and a set of criteria convolution matrices (generalized criteria) are widely used in the evaluation of a wide variety of ob-jects. Purpose of the study. To build a CO system for a given set of criteria, you need to solve two problems: 1. To choose the structure of the dichotomous tree of criteria. 2. To define matrix convolutions of pairs of criteria (generalized criteria) at each vertex of the tree (except for hanging ones). The article deals with the second problem, i.e. the problem of determining matrices the criteria convolution. In practice, this task is often solved based on expert opinions. Materials and methods. Let us assume that there are a set of options (a variant is a set of criteria estimates) and experts have defined complex estimates for each option from this set. The task is to define matrix convolu-tions at each vertex of the tree such that the CO of each variant in the resulting system CO is equal to the EXPERT estimate. The paper defines a class of unified CO mechanisms that meet the following conditions: 1. All matrices of the unified complex estimation mechanism have the same dimension. 2. For any matrix all rows are different and all columns are different. 3. All matrices are monotonous in rows and columns; 4. If all the variant scores are equal to a certain score, then the complex score is equal to this score. Results. Two cases are considered. In the first case, experts can give estimates of options with any set of criteria estimates. In the second case, experts can give a CO of only complete options, that is, options that contain estimates of all criteria. For the first case, an efficient algorithm with an estimate of computational complexity of the order of lm2 is proposed, where l is the number of crite-ria, and m is the number of gradations of the rating scale. The algorithm makes significant use of the 4 property of unified mechanisms. For the second case, we propose a method for solving the problem by constructing “top-down” matrices, i.e. constructing a matrix for the root vertex, then for adjacent ones, and so on. Conclu-sion. Thus, the paper proposes algorithms for the synthesis of unified mechanisms for complex eva-luation, in which the number of required expert options is minimal.

The normalized Laplacian polynomial of rooted product of graphs

Let [Formula: see text] be a simple graph with [Formula: see text] vertices and [Formula: see text] be a sequence of [Formula: see text] rooted graphs [Formula: see text]. The rooted product [Formula: see text], of [Formula: see text] by [Formula: see text] is constructed by identifying the root vertex of [Formula: see text] with the [Formula: see text]th vertex of [Formula: see text]. In this paper, the characteristic polynomial of the normalized Laplacian matrix of [Formula: see text] is obtained. As an application of our results, we obtain the normalized Laplacian polynomial and spectrum of the generalized Bethe trees.

Magic Polygons and Degenerated Magic Polygons: Characterization and Properties

In this work we define Magic Polygons P(n, k) and Degenerated Magic Polygons D(n, k) and we obtain their main properties, such as the magic sum and the value corresponding to the root vertex. The existence of magic polygons P(n, k) and degenerated magic polygons D(n, k) are discussed for certain values of n and k.

The signless Laplacian spectrum of rooted product of graphs

Let [Formula: see text] be a simple graph with [Formula: see text] vertices and [Formula: see text] be a sequence of [Formula: see text] rooted graphs [Formula: see text]. The rooted product [Formula: see text], of [Formula: see text] by [Formula: see text] is constructed by identifying the root vertex of [Formula: see text] with the [Formula: see text]th vertex of [Formula: see text] for [Formula: see text]. In this paper, we introduce a method for computation of the characteristic polynomial of the signless Laplacian matrix of [Formula: see text]. Then the signless Laplacian spectrum and the signless Laplacian energy of some graphs will be computed.

Competing evolutionary paths in growing populations with applications to multidrug resistance

Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represents cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.

Genus Distributions for Iterated Claws

We derive a recursion for the genus distributions of the graphs obtained by iteratively attaching a claw to the dipole $D_3$. The minimum genus of the graphs in this sequence grows arbitrarily large. The families of graphs whose genus distributions have been calculated previously are either planar or almost planar, or they can be obtained by iterative single-vertex or single-edge amalgamation of small graphs. A significant simplifying construction within this calculation achieves the effect of an amalgamation at three vertices with a single root vertex, rather than with multiple roots.

Genus Distributions of 4-Regular Outerplanar Graphs

We present an $O(n^2)$-time algorithm for calculating the genus distribution of any 4-regular outerplanar graph. We characterize such graphs in terms of what we call split graphs and incidence trees. The algorithm uses post-order traversal of the incidence tree and productions that are adapted from a previous paper that analyzes double-root vertex-amalgamations and self-amalgamations.

Degree distribution in random planar graphs

International audience We prove that for each $k \geq 0$, the probability that a root vertex in a random planar graph has degree $k$ tends to a computable constant $d_k$, and moreover that $\sum_k d_k =1$. The proof uses the tools developed by Gimènez and Noy in their solution to the problem of the asymptotic enumeration of planar graphs, and is based on a detailed analysis of the generating functions involved in counting planar graphs. However, in order to keep track of the degree of the root, new technical difficulties arise. We obtain explicit, although quite involved expressions, for the coefficients in the singular expansions of interest, which allow us to use transfer theorems in order to get an explicit expression for the probability generating function $p(w)=\sum_k d_k w^k$. From the explicit expression for $p(w)$ we can compute the $d_k$ to any degree of accuracy, and derive asymptotic estimates for large values of $k$.

Chromatic Solutions

Early in the Seventies I sought the number of rooted λ-coloured triangulations of the sphere with 2p faces. In these triangulations double joins, but not loops, were permitted. The investigation soon took the form of a discussion of a certain formal power series l(y, z, λ) in two independent variables y and z.The basic theory of l is set out in [1]. There l is defined as the coefficient of x2 in a more complicated power series g(x, y, z, λ). But the definition is equivalent to the following formula.1Here T denotes a general rooted triangulation. n(T) is the valency of its root-vertex, and 2p(T) is the number of its faces. P(T, λ) is the chromatic polynomial of the graph of T.