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Rosstat data on the dynamics of major socially significant diseases have been studied. The relationship between such diseases has been investigated. The influence of the main socially significant diseases and the main indicators of socio-economic development of the regions of the Russian Federation on the number of abortions has been studied. The most informative signs related to socially significant diseases and socio-economic development in all countries of the world have been selected. The influence of these signs on infant mortality has been studied. Machine learning methods collected in the Data Master Azforus (DMA) program were applied. The conducted research has demonstrated the effectiveness of using machine learning methods to identify patterns linking the frequency of socially significant diseases and indicators of socio-economic development.

The paper considers cold rolling simulation during the continuous annealing process. The development of a simulation model of the considered process is relevant as it makes it possible to predict the mechanical properties of steel, such as yield stress, ultimate resistance, tensile strain, without a natural experiment. Dynamic neighborhood models are applied for the simulation of complex distributed production processes and systems. They reflect the dynamics of the considered process and make it possible to control its results at the intermediate production stages. A general definition of dynamic neighborhood models with a fuzzy hierarchical structure is given, the models being characterized by fuzzy neighborhood two-level links between the model nodes of the first and second level. In the paper, linear fuzzy hierarchical dynamic neighborhood models of the cold rolling process for calculating the mechanical properties of steel are developed. The simulated and production data on the basis of relative identification errors are analyzed and conclusions are made. [ABSTRACT FROM AUTHOR]

The paper considers and simulates a neural network-based strip cooling process in a hot-strip mill. This process is outlined in a figure highlighting the main functional areas. A hybrid algorithm for training a radial basis neural network is described. A program was developed in the Mathcad programming unit to implement the mathematical model of the researched neural network. The input and output variables of the strip cooling process were selected, the initial data were normalized. Training and validation samples are drawn using Microsoft Access queries. A number of computational experiments were conducted for this model to obtain the optimum structure and parameters of the radial basis neural network. The results of neural network training, graphs of deviations of model values from the set ones in training and validation samples are given. The root-mean-square and relative network errors were found using the unnormalized output data. [ABSTRACT FROM AUTHOR]

Background: Rfx winged‐helix transcription factors, best known as key regulators of core ciliogenesis, also play ciliogenesis‐independent roles during neural development. Mammalian Rfx4 controls neural tube morphogenesis via both mechanisms. Results: We set out to identify conserved aspects of rfx4 gene function during vertebrate development and to establish a new genetic model in which to analyze these mechanisms further. To this end, we have generated frame‐shift alleles in the zebrafish rfx4 locus using CRISPR/Cas9 mutagenesis. Using RNAseq‐based transcriptome analysis, in situ hybridization and immunostaining we identified a requirement for zebrafish rfx4 in the forming midlines of the caudal neural tube. These functions are mediated, least in part, through transcriptional regulation of several zic genes in the dorsal hindbrain and of foxa2 in the ventral hindbrain and spinal cord (floor plate). Conclusions: The midline patterning functions of rfx4 are conserved, because rfx4 regulates transcription of foxa2 and zic2 in zebrafish and in mouse. In contrast, zebrafish rfx4 function is dispensable for forebrain morphogenesis, while mouse rfx4 is required for normal formation of forebrain ventricles in a ciliogenesis‐dependent manner. Collectively, this report identifies conserved aspects of rfx4 function and establishes a robust new genetic model for in‐depth dissection of these mechanisms. Developmental Dynamics 247:650–659, 2018. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this paper we define a non-deterministic dynamic neighborhood model. As a special case, a linear neighborhood model is considered. When a non-deterministic neighborhood model functions, it is possible to introduce a restriction on the number of active layers, which will allow the variation of the non-determinism of the model at each moment of time. We give the notion of the non-determinacy measure and prove that it has the properties of a probability measure. We formulate the problem of reachability with partially specified parameters, layer priorities, and the non-determinacy measure. An algorithm for solving the attainability problem for a neighborhood model with variable indeterminacy and layer priorities is presented. An example of its solution is shown, which shows that when the priorities are compared and the measure of non-determinism is used, the solution of the problem can be obtained more quickly than by a method that does not use priorities. [ABSTRACT FROM AUTHOR]