Modular Evolution of Coronavirus Genomes

The viral family Coronaviridae comprises four genera, termed Alpha-, Beta-, Gamma-, and Deltacoronavirus. Recombination events have been described in many coronaviruses infecting humans and other animals. However, formal analysis of the recombination patterns, both in terms of the involved genome regions and the extent of genetic divergence between partners, are scarce. Common methods of recombination detection based on phylogenetic incongruences (e.g., a phylogenetic compatibility matrix) may fail in cases where too many events diminish the phylogenetic signal. Thus, an approach comparing genetic distances in distinct genome regions (pairwise distance deviation matrix) was set up. In alpha, beta, and delta-coronaviruses, a low incidence of recombination between closely related viruses was evident in all genome regions, but it was more extensive between the spike gene and other genome regions. In contrast, avian gammacoronaviruses recombined extensively and exist as a global cloud of genes with poorly corresponding genetic distances in different parts of the genome. Spike, but not other structural proteins, was most commonly exchanged between coronaviruses. Recombination patterns differed between coronavirus genera and corresponded to the modular structure of the spike: recombination traces were more pronounced between spike domains (N-terminal and C-terminal parts of S1 and S2) than within domains. The variability of possible recombination events and their uneven distribution over the genome suggest that compatibility of genes, rather than mechanistic or ecological limitations, shapes recombination patterns in coronaviruses.

Reliability Analysis of Toughened Vacuum Glass Based on Gray Relation Decision

The gray system theory was introduced in the design of toughened vacuum glass, and an evaluation mathematical model based on improved gray correlation was established. According to the service performance index of tempered vacuum glass, determine the index system of product evaluation. Based on the improved gray absolute correlation method, the module vacuum glass was analyzed and calculated. The weight of each evaluation index is obtained according to the deviation matrix, and the relevance of the sample data line to the reference data line is elaborated from the perspective of proximity and thus combined with the weight of each evaluation index to get the comprehensive gray correlation. The results of the trial design scheme are designed to prove its effectiveness in vacuum glass product quality evaluation and feasibility.

Vector Quantization for Speech Compression by a New Version of PRSOM

The self-organizing map (SOM) is a popular neural network which was designed for solving problems that involve tasks such as clustering and visualization. Especially, it provides a new strategy of clustering using a competition and co-operation principal. The probabilistic Kohonen network (PRSOM) is the stochastic version of classical one. However, determination of the optimal number of neurons, their initial weights vector and their deviation matrix is still a big problem in the literature. These parameters have a great impact on the learning process of the network, the convergence and the quality of results. Also determination of clusters’ number is a very difficult task. In this paper we propose a new method, called H-PRSOM, which looks for the optimal architecture of the map and determines the suitable codebook for speech compression. According to his hierarchical process, H-PRSOM identifies automatically, in each iteration, new initial parameters of the map. The generated parameters will be used in the learning phase of the probabilistic network. Due to its important propriety of initialization and optimization, we expect that the use of this new version of PRSOM algorithm in the vector quantization might provide good results. In order to evaluate its performance, H-PRSOM model is applied to the problem of speech compression of Arabic digits. The conducted experiments show that the proposed method is able to realize the expected goals.

THE BIAS OPTIMAL K IN THE M/M/1/K QUEUE: AN APPLICATION OF THE DEVIATION MATRIX

We study the optimal buffer capacity K for the M/M/1/K queue under some standard cost and reward structures by comparing various Markov reward processes. Using explicit expressions for the deviation matrix of the underlying Markov chains, we find the bias optimal value for K in the case of a tie between two consecutive optimal gain policies. We show that the bias optimal value depends both on whether the reward is granted upon arrival or departure of the customers, and on the initial queue size. Moreover, we demonstrate that in some specific cases the optimal policy is threshold-based with respect to the initial queue size.

ANALYSIS OF MARKOV-MODULATED INFINITE-SERVER QUEUES IN THE CENTRAL-LIMIT REGIME

This paper focuses on an infinite-server queue modulated by an independently evolving finite-state Markovian background process, with transition rate matrix Q≡(qij)i,j=1d. Both arrival rates and service rates are depending on the state of the background process. The main contribution concerns the derivation of central limit theorems (CLTs) for the number of customers in the system at time t≥0, in the asymptotic regime in which the arrival rates λi are scaled by a factor N, and the transition rates qij by a factor Nα, with α∈ℝ+. The specific value of α has a crucial impact on the result: (i) for α>1 the system essentially behaves as an M/M/∞ queue, and in the CLT the centered process has to be normalized by √N; (ii) for α<1, the centered process has to be normalized by N1−α/2, with the deviation matrix appearing in the expression for the variance.

Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO

This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions.