Physical properties of seeds of 'Gina' and 'Leila' monogerm sugarbeet (Beta vulgaris var. altissima) varieties as a function of moisture content were evaluated. Physical properties of sugarbeet varieties such as unit seed mass, thousand-seed (1000-seed) mass, bulk density, true density, seed volume, angle of repose, and coefficient of friction on various surfaces were determined. The average unit seed mass and 1000-seed mass ranged from 0.0117 to 0.0133 g and from 10.77 to 12.00 g, as the moisture content increased from 8.55% to 17.14% (dry basis) for 'Gina'. The average unit seed mass and 1000-seed mass ranged from 0.0112 to 0.0121 g and from 12.07 to 13.70g, as the moisture content increased from 6.88% to 19.28% (dry basis) for 'Leila'. In the moisture content range, studies on re-wetted sugarbeet seeds showed that the seed volume increased from 0.0122 to 0.0147mm 3 , and from 0.0137 to 0.016mm 3 , for 'Gina' and 'Leila' sugarbeet seeds, respectively. The bulk densities decreased from 125.92 to 120.02 kg/m 3 and from 148.09 to 114.06kg/m 3 and true densities decreased from 916.70 to 827.39kg/m 3 and from 852.81 to 832.20kg/m 3 , for 'Gina' and 'Leila', respectively, whereas the angle of repose increased from 19.31°t o 21.27° and from 21.05° to 21.32°, for 'Gina' and 'Leila' respectively. The static and dynamic coefficients of friction on various surfaces, namely, plywood, mild steel, and galvanised metal also increased linearly with increasing moisture content. The plywood surface offered the maximum friction followed by mild metal and galvanised metal.
In this competitive world, companies need to manage their arrears of debtors to survive. Big companies monthly face thousands of debt cases from their customers and if these debtor customers do not pay their debts within the specified period, these cases will enter the legal stage as legal debt collection that is handled by experienced lawyers. At the legal stage, the cases are sent to the contracted legal debt collection agencies to start a lawsuit. Therefore, assigning which case to which legal debt collection agency is a significant and critical issue in the company's success in getting its debts in the legal stage. So, this study aims to find the legal debt collection agency, which has more capability to close the assigned debt case by predicting case closing probability with machine learning techniques based on past historical data. To predict case closing probability, 8 machine learning algorithms, Catboost Classifier, Extreme Gradient Boosting Classifier, Gradient Boosting Classifier etc., are applied to the processed dataset. The results show us Catboost Classifier has the best accuracy performance 0.87 accuracy. Also, the results show us boosting type ensemble learning algorithms have better performance than other algorithms. Finally, we tune hyper-parameters of Catboost classifier to get better accuracy in the modeling and applied k-fold cross-validation for testing the model's testing stability.
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