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Numerous experiments on the effect of acoustic fields on oil media have shown the changing nature of oil physicochemical properties. In the present paper, we present a concept of internal airlift for oil medium with dissolved gas which could be propelled by external acoustic field. The mechanism determining gas bubble size as a function of pressure change is discussed. Model of interaction for the growing bubbles with acoustic fields is presented. Relationships specifying the characteristics of both the required acoustic field and oil medium are derived. The use of these relations makes it possible to define the available range of parameters for the system under consideration where one can obtain the expected effect on oil medium. It is demonstrated how the change in pressure and oil saturation (namely, the density of oil particles in the entire flow) of the medium is associated with temperature fields in the system. In particular, it is shown that the maximum deviation between the temperature change in oil and gas and gas–liquid media reaches a significant value, namely 10−2 K for a gas–liquid medium, while this difference is −0.1 K in an oil-and-gas medium. Using this approach, thermograms of oil producing wells have been analysed at a qualitative level.

The formation of nonlinear, nonstationary structures in weakly collisional media with collective interactions are investigated analytically within the framework of the kinetic description. This issue is considered in one-dimensional geometry using collision integral in the Bhatnagar-Gross-Krook form and some model forms of the interparticle interaction potentials that ensure the finiteness of the energy and momentum of the systems under consideration. As such potentials, we select the Yukawa potential, the δ-potential, which describes coherent structures in a plasma. For such potentials we obtained a dispersion relation which makes it possible to estimate the size and type of the forming structures.

Deep neural networks show high accuracy in theproblem of semantic and instance segmentation of biomedicaldata. However, this approach is computationally expensive. Thecomputational cost may be reduced with network simplificationafter training or choosing the proper architecture, which providessegmentation with less accuracy but does it much faster. In thepresent study, we analyzed the accuracy and performance ofUNet and ENet architectures for the problem of semantic imagesegmentation. In addition, we investigated the ENet architecture by replacing of some convolution layers with box-convolutionlayers. The analysis performed on the original dataset consisted of histology slices with mast cells. These cells provide a region forsegmentation with different types of borders, which vary fromclearly visible to ragged. ENet was less accurate than UNet byonly about 1-2%, but ENet performance was 8-15 times faster than UNet one., Comment: 4 pages, 5 figures, 1 table

Mast cells are an important part of the human immune system. They are also found in pathological pathways of various diseases. Their activity in tissues may be analyzed with a histopathological examination. In this preliminary study, we apply a deep learning approach for analysis of an original dataset of mast cells stained by toluidine blue. UNet and a convolutional neural network are applied for cell segmentation and classification, respectively. The result allows for the proposal of a methodological consideration for the subsequent full study. Basic deep learning approaches reach 66.64% segmentation accuracy when measured with the Dice coefficient and 81.36% class accuracy when the training dataset is measured with the categorical accuracy metric.