STATEMENT OF THE PROBLEM OPTIMAL CONTROL THE HARDNESS OF THE STEEL PRODUCED BASED ON THE MODEL OF TAKAGI-SUGENO-KANG

This study discusses the problem of mathematical modeling of complex technological systems under uncertainty to obtain the most optimal parameters in the management of the production process in the applied field – metallurgy. In the offered approach one of the most important tasks of management of technological process of steel smelting is considered: maintenance of the set hardness (calcification) of the steel distributed on depth of the smelted product. To minimize the inevitable errors associated with the expert choice of chemical composition, improve the management efficiency and the quality of the produced steel, it is proposed to apply the system of fuzzy production rules Takagi-Sugeno-Kanga (model TSK), based on the modeling of the dependence "composition-hardness". Application of this model will also allow to optimize the choice of the chemical composition of the steel in the conditions of stochasticity of the parameters of the regression models. In addition, in the study of the steel production process there is a need to solve the inverse problem – the determination of the chemical composition of the steel produced at a given hardness value. The proposed model of TSK based on fuzzy production rules for steel smelting prediction and control is presented in matrix form, so one of the possible ways to solve the control problem is to solve the corresponding matrix equation. At the same time, on the basis of experimental data, a significant shift in the estimates of the values of chemical elements was revealed. Therefore, governance must be based on an optimization approach. The proposed formulation of the optimization problem will develop an algorithm for solving the problem of optimal hardness control on the basis of the TSK model, characterized by the ability to automatically determine the required chemical composition of steel by a given distribution of its hardness. In addition, the developed model TSK using the optimal control problem will eliminate errors in determining the calculation model, as well as to determine the hardness of steel for the chemical composition does not fully correspond to a certain set of allowable intervals of changing the mass fractions of chemical elements

The Relationship between Nonprofit Organizations and Cloud Adoption Concerns

Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization\u27s carbon footprint through use of shared data centers for processing

Inside of the Linear Relation between Dependent and Independent Variables

Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors