The paper deals with the language of Russian folklore. Folklore is a unique sphere of existence of the language, the most vivid expression of the national mentality. The folklore word embodied the perception and evaluation of the surrounding world. “What did the word in general mean for the life of the people? The word was equated ... with life itself. The word generated and explained life, it was ... the keeper of memory and the guarantee of the infinity of the future. The folklore text is studied by literary critics, ethnographers, historians, culturologists, and art historians. In the twentieth century, a new science emerged - linguo-folkloristics, the goals and objectives of which were formulated by A.T. Khrolenko only in the seven

Hydrogen sulfide (H₂S) gas sensor based on cobalt–manganese spinel oxide MnCo₂O₄ thin films that were prepared by chemical precipitation and green synthesis methods. Structural, morphological, and compositional characterizations of MnCo₂O₄ thin films were performed using XRD, FE-SEM, EDS, and AFM. It was found through XRD patterns that MnCo₂O₄ was formed in a cubic spinel structure for both methods; however, the chemically precipitated thin films possessed better crystallinity and larger crystallite sizes, whereas the green-synthesized films exhibited slightly lower crystallinities and smaller sizes, as evidenced by the broader peaks observed. In addition, FE-SEM and AFM images revealed that the MnCo₂O₄ films prepared by

Chemical pollution is a very important issue that people suffer from and it often affects the nature of health of society and the future of the health of future generations. Consequently, it must be considered in order to discover suitable models and find descriptions to predict the performance of it in the forthcoming years. Chemical pollution data in Iraq take a great scope and manifold sources and kinds, which brands it as Big Data that need to be studied using novel statistical methods. The research object on using Proposed Nonparametric Procedure NP Method to develop an (OCMT) test procedure to estimate parameters of linear regression model with large size of data (Big Data) which comprises many indicators associated with chemi

  Many production companies suffers from big losses because of  high production cost and low profits for several reasons, including raw materials high prices and no taxes impose on imported goods also consumer protection law deactivation and national product and customs law, so most of consumers buy imported goods because it is characterized by modern specifications and low prices.

  The production company also suffers from uncertainty in the cost, volume of production, sales, and availability of raw materials and workers number because they vary according to the seasons of the year.

  I had adopted in this research fuzzy linear program model with fuzzy figures

In this research, the one of the most important model and widely used in many and applications is linear mixed model, which widely used to analysis the longitudinal data that characterized by the repeated measures form .where estimating linear mixed model by using two methods (parametric and nonparametric) and used to estimate the conditional mean and marginal mean in linear mixed model ,A comparison between number of models is made to get the best model that will represent the mean wind speed in Iraq.The application is concerned with 8 meteorological stations in Iraq that we selected randomly and   then we take a monthly data about wind speed over ten years Then average it over each month in corresponding year, so we g

Abstract

     Machining residual stresses correlate very closely with the cutting parameters and the tool geometries. This research work aims to investigate the effect of cutting speed, feed rate and depth of cut on the surface residual stress of steel AISI 1045 after face milling operation. After each milling test, the residual stress on the surface of the workpiece was measured by using X-ray diffraction technique. Design of Experiment (DOE) software was employed using the response surface methodology (RSM) technique with a central composite rotatable design to build a mathematical model to determine the relationship between the input variables and the response. The results showed that both

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur