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distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including Multiple Linear Regression (MLR) equation and Multiple Nonlinear Regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed.
previous studies performed in our laboratory were used for statistical analyses, which consisted of applying magnetic fields (1 and 2 mT) and electric fields (4 and 8 mV/cm) to chondrocytes. Data from cell proliferation and glycosaminoglycan expression were used to adjust and to validate each mathematical model. Results: The root square model efficiently predicted the chondrocyte dynamics, evidencing determination coefficients of R2 = 92.04 for proliferation and R2 = 70.95 for glycosaminoglycans when magnetic fields were applied, and R2 = 88.19 for proliferation and R2 = 74.79 for glycosaminoglycans when electric fields were applied. Conclusions: The reduced, interactive, quadratic and combined models exhibited lower R2 values, nevertheless, they were useful to predict proliferation and glycosaminoglycan synthesis, as the right-skewed distribution, determined by the F parameter, evidenced a Frejected < Fcomputed. The models are efficient since the prediction of chondrocyte dynamics is comparable to the cell growth and to the molecular synthesis observed experimentally. This novel formulation may be dynamic because the variables that fit the models may be modified to improve in vitro procedures focused on cartilage recovery.
over various indicators whose scope strives to include different views on the industry field. The main point of this paper is to identify the exact relationship between dependent variable (gross domestic product) and a group of independent variables (picked industrial representatives). Such determination offers thereafter the possibility to estimate dependent variable’s value and its next forecast. What is more, the eventual sorting of involved industrial indicators is facilitated according to their importance. The multiple regression analysis is utilized as the method of investigation. Findings answer the stated questions and aims with a suggestion of an appropriate equation.
was based on Indian automotive SMEs. Indian automotive SMEs must develop significant agile capability in order to remain competitive in highly uncertain global environment. One of the objectives of the research was to find various enablers of agility through literature survey. Thereafter questionnaire administered exploratory factor analysis was performed to extract various factors of agility relevant in Indian automotive SMEs environment. Multiple regression analysis was applied to assess the relative importance of these extracted factors. “Responsiveness” was the most important factor followed by “Ability to reconfigure”, “Ability to collaborate”, and “Competency”. Thereafter fuzzy logic bases algorithm was applied to assess the current level of agility of Indian automotive SMEs. It was found as “Slightly Agile”, which was the deviation from the targeted level of agility. Fuzzy ranking methodology facilitated the identification & criticalities of various barriers to agility, so that necessary measures can be taken to improve the current agility level of Indian automotive SMEs. The current research may helpful in finding; key enablers of agility, assessing the level of agility, and ranking of the various enablers of agility to point out the weak zone of agility so that subsequent corrective action may be taken in any industrial environment similar to India automotive SMEs.
which belongs to the socalled Machine Learning. The study used the database of the design, technical condition and potential causes of damage to 199 non-renovated buildings, up to the age of 20 years, of a traditional brick construction, located in the mining area of Legnica-Głogów Copper District (LGOM). The conducted analysis allowed for the qualitative assessment of the influence of mining impacts on the extent of damage to the studied buildings.
tzw. uczenia maszynowego (Machine Learning). W badaniach wykorzystano bazę danych o konstrukcji, stanie technicznym i potencjalnych przyczynach uszkodzeń 199 nieremontowanych budynków w wieku do 20 lat, o tradycyjnej konstrukcji murowanej, usytuowanych na terenie górniczym Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Przeprowadzona analiza pozwoliła na jakościową ocenę wpływu oddziaływań górniczych na zakres uszkodzeń badanych budynków.
Wytworzone paliwo powinno charakteryzować się parametrami spełniającymi kryteria ustanowione przez Europejski Komitet Normalizacyjny (CEN). Technologia energetycznego wykorzystania paliwa typu SRF (Solid Recovered Fuel) determinuje wymogi dotyczące właściwości fizykochemicznych oraz użytkowych paliwa, a tym samym decyduje o wyborze technologii jego przygotowania.
regression analysis of the obtained data, it can be concluded that the efficiency of coal grinding wit use of electromagnetic mill depends on the residence time of the particles in the mill working chamber and a solid phase concentration in the feed directed to the process.
purpose, a total of 270 sirospun yarn samples were produced on the same ring spinning machine under the same conditions at Ege University, by using 11 different cotton blends and three different strand spacing settings, in four different yarn counts and in three different twist coefficients. The sirospun yarn and cotton fibre property interactions were investigated by correlation analysis. For the prediction of yarn quality characteristics, multivariate linear regression methods were performed. As a result of the study, equations were generated for the prediction of yarn tenacity, breaking elongation, unevenness and hairiness by using fibre and yarn properties. After the goodness of fit statistics, very large determination coefficients (R2 and adjusted R2) were observed.
elements belong to the first group of independent variables; their nonlinear combinations (square, square root) belong to the second group. The third group has included complex elements they are functions basing on the relationships between plant growing and environmental conditions among others meteorological conditions. Stations Sulejów has been the source of longterm meteorological, phonological and winter wheat yield data. The station is situated in central part of Poland and winter wheat is grown on medium type of soil. The regression equations including both simple meteorological elements, their nonlinear combinations and complex variables achieves the best statistical characteristics than the equations including simple elements only.
restricting to integer ones for the variables in the mathematical equations gives better results and a minor number of independent variables are needed to achieve a given accuracy degree. Some possible future extensions of the method are pointed out.
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