Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

The cultivation of sugar beet (Beta vulgaris L.) for table or horticultural purposes is largely carried out in the conventional way which is characterized by intense mechanization causing soil degradation and high labor costs. New cultivation techniques are being employed in the production of vegetables aiming to ensure improvements in environmental and economic conditions, such as the no-till farming system. Thus, the objective of this work was to evaluate the vegetable classification and physicochemical characteristics of beets from different corn planting densities. The experiment was conducted in the period from October 2018 to June 2019 in the municipality of Nova Laranjeiras (PR). Corn was used as a cover plant and the vegetable used was beet cultivar Early Wonder Tall Top. The experimental design used was in interspersed blocks in unifactorial scheme (corn densities 40, 60, 80, 100 thousand plants/ha and control) with four blocks, with plots 3.60 m long and 1.20 m wide. The parameters evaluated 60 days after planting were: commercial classification (class, group, subgroup, category), length, diameter, mass, pulp firmness, soluble solids, titratable acidity, pH and ratio, phenolic compounds. Of which the variables that were not significant at 0.5 probability were length, category (defects), firmness, subgroup (flesh color), soluble solids and phenolic compounds. It is concluded that high densities of corn as mulch for SPDH of sugar beet crop negatively affect the grade and physicochemical characterization of the products.

I summarize the current regulatory decisions aimed at combating the debt load of the population in Russia. Further, I show that the level of delinquency of the population on loans is growing despite the regulatory measures taken. In my opinion, the basis of regulatory policy should move from de facto pushing personal bankruptcies to preventing them. I put forward a hypothesis and statistically prove the expediency of quantitative restrictions on one borrower. It is necessary to introduce reports to the credit bureaus of some types of overdue debts, which are not actually reported now. It is also necessary to change the order of debt repayment established by law, allowing the principal and current interest to be paid first, which will prevent the expansion of the debt.

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.