Позиционный электропривод механизма перемещения

Объектом исследования является позиционный асинхронный электропривод механизма горизонтального перемещения груза. Цель работы – исследовать основные характеристики асинхронного электропривода с трехконтурной системой управления положением вала двигателя. В процессе исследования проводились выбор асинхронного двигателя для механизма перемещения, расчет параметров двигателя, его статических и динамических характеристик, выбор преобразователя частоты, синтез трехконтурной системы управления следящим электроприводом на базе регулируемого с векторным управлением.The object of the study is a positional asynchronous electric drive mechanism for the horizontal movement of cargo. The purpose of the work is to investigate the basic characteristics of an asynchronous electric drive with a three-circuit control system for positioning the motor shaft. In the process of research, the choice of an asynchronous motor for the displacement mechanism, calculation of the engine parameters, its static and dynamic characteristics, choice of a frequency converter, synthesis of a three-circuit control system for a servomotor drive based on an adjustable vector control were made

Immunonephelometric Quantitation of Apolipoprotein E in Human Serum

Peer Reviewe

Improvement of prediction ability by integrating multi-omic datasets in barley

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors