The content and fatty acid (FA) composition of FA neutral acylglycerols (NAG), a mixture of 1,2,3‐triacyl‐sn‐glycerols (TAG) and 3‐acetyl‐1,2‐diacyl‐sn‐glycerols (acDAG), were determined in the seeds and arils of fruits of 14 Euonymus L. species. On the average, the seeds exceeded the arils in the absolute and relative dry matter content 2.9‐ and 1.9‐fold, respectively, and separate plant species greatly differed in NAG composition. The proportions of TAG in the NAG of seeds and arils were 4–5 and ~98 %, respectively. The degree of FA unsaturation in aril NAG was higher than in the seed NAG, and in acDAG—higher, than in TAG. In the NAG, 14 major FA molecular species (excluding minor FA) were found, and linoleic, oleic, palmitic, and linolenic acids were predominant. NAG of separate taxonomic units of the genus Euonymus L. differed from each other in the concentration of major FA as well as other FA. So, by using statistical analysis, it was definitely established that the species from the subgenus Euonymus were characterized by an increased content of linoleic acid, while those from the subgenus Kalonymus, by the predominance of oleic acid. Meanwhile, the species of the section Euonymus were marked by an enhanced concentration of a number of hexa‐ and octadecenoic FA positional isomers.

Abstract. Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with оriginal random forest with incorporated "replace-thelooser" forgetting andother state-of-the-art concept-drfit classifiers like AWE2.

Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal’s banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the valuable information at different stages of AC/DC hybrid power systems projects development with stand-alone hybrid solar-wind power generation systems.

One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable to build a reliable decision rules for feature space classification in the presence of many possible states of the system. In this paper, novel techniques based on decision trees are used for evaluation of the reliability of the regime of electric power systems. We proposed hybrid approach based on random forests models and boosting models. Such techniques can be applied to predict the interaction of increasing renewable power, strage devices and swiching of smart loads from intelligent domestic appliances, storage heaters and air-conditioning units and electric vehicles with grid for enhanced decision making. The ensemble classification methods were tested on the modified 118-bus IEEE power system showing that proposed technique can be employed to examine whether the power system is secured under steady-state operating conditions.

The content of palmitic acid (PA) in the fractions of plant lipids of different polarity were investi gated. The following relationship was revealed: when polarity of lipid fraction rose, the content of PA in total fatty acids of this fraction increased. It was shown that in a number of consecutive extracts of total lipids iso lated from plant tissue with the same solvent each next extract containing more tightly bound lipids carries more PA. Predominance of this fatty acid within high polar lipids that may be compared to annular lipids and are indispensable for operation of numerous enzymatic systems are discussed. A considerable quantity of PA was shown in wheat mitochondrial membranes; under cold stress, its level rose even higher. Among phospho lipids, the greatest content of PA is usually associated with phosphatidyl inositols, while that among glycolip ids is with sulfoquinovosyl diacylglycerols.