Structural-Energy Interpretation of the Friction

The structural-energy model of elastic-plastic deformation is considered as the main mechanism of transformation and dissipation of energy under friction. The equations of friction energy balance are proposed. The energy interpretation of the coefficient of friction is given. A structural-energy diagram of the friction surfaces is proposed. The energy regularities of evolution of tribological contact (elementary tribosystem) are discussed. The idea of the smallest structural element of dissipative friction structures (mechanical (nano) quantum) is discussed. Mechanical quantum is dynamic oscillator of dissipative friction structure. The nano-quantum model of the surfaces damping is proposed. Calculations for some Hertzian heavily loaded contacts of real tribosystems are proposed

THERMAL MODELS OF HD AND EHD LUBRICANT FILM

For the case of the failure of the lubricant film under hydrodynamic lubrication a common thermodynamic theory of strength is considered. According to this theory the failure occurs when the internal energy density (potential and thermal components) in the bulk of material reaches a constant value for a given material. A special case of this theory is when only the density of heat (kinetic) component of internal energy is taken into account. This is due to the kinetic peculiarities of accumulation of internal energy of liquid materials. This condition determines the limit state for liquid lubricants - mineral oils. In the case of hydrodynamic lubrication the practical solution of this power criterion is achieved by using a more convenient criterion - temperature flashes in the lubricant film. When analyzing the regularities of friction under EHD lubrication two separate and possible effects are taken into account. The first one is regularity of plastic deformation (states and properties) at Hertzian contact of solids. The second one is the state and properties of the oil film under irregular and hydrostatic compression. The original structural model of oil film by EHD lubrication in the form of a rotary oscillating cells with elastic interactions is proposed. This is similar to the Rayleigh-Benard cells. It is possible that the size of the cells are an order about nano level (type fullerene’s form or mechanical quantum). The oil film dissipates energy in the direction of relative motion of bodies. This oil film has the highest dissipative properties. Here, the work of the external forces is almost completely dissipated in these oil film structures. The concept of temperature for a such film has almost no meaning. The load of the EHD film is very high because it is elastic and energy dissipation is reversible

Prediction-retrodiction measurements for teleportation and conditional state transfer

Regular measurements allow predicting the future and retrodicting the past of quantum systems. Time-non-local measurements can leave the future and the past uncertain, yet establish a relation between them. We show that continuous time-non-local measurements can be used to transfer a quantum state via teleportation or direct transmission. Considering two oscillators probed by traveling fields, we analytically identify strategies for performing the state transfer perfectly across a wide range of linear oscillator-field interactions beyond the pure beamsplitter and two-mode-squeezing types.Comment: Accepted versio

End-to-end learning of brain tissue segmentation from imperfect labeling

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many potentially faulty intermediate steps. In order to streamline the segmentation, we introduce a deep learning model that is based on volumetric dilated convolutions, subsequently reducing both processing time and errors. Compared to its competitors, the model has a reduced set of parameters and thus is easier to train and much faster to execute. The contrast in performance between the dilated network and its competitors becomes obvious when both are tested on a large dataset of unprocessed human brain volumes. The dilated network consistently outperforms not only another state-of-the-art deep learning approach, the up convolutional network, but also the ground truth on which it was trained. Not only can the incredible speed of our model make large scale analyses much easier but we also believe it has great potential in a clinical setting where, with little to no substantial delay, a patient and provider can go over test results.Comment: Published as a conference paper at IJCNN 2017 Preprint versio

SEASONAL DYNAMICS OF PHYTOPLANKTON AND SOME HYDROCHEMICAL INDICATORS OF THE PEIPSI-PSKOV LAKE

The Peipsi-Pskov Lake is the largest freshwater body in Europe, ranking fourth in area and fifth in volume. It is characterized by shallow water and a high level of trophy. The water in the lake is poorly mineralized and has little transparency due to suspended sediments and the development of plankton. Phytoplankton acts as a primary link in trophic chains, quickly reacts to changes in the aquatic environment and serves as a convenient object in monitoring studies.The average concentrations of total nitrogen in the lake during the observation period were in the range of 525-818 µg/dm3. The content of ammonium, nitrate, and nitrite nitrogen in the samples was mostly below the detection limits. The values of total phosphorus varied from 20 µg/dm3 to 54 µg/dm3, and its concentrations were lower than the sensitivity of the method during the flood recession.The maximum values of total nitrogen and phosphorus were recorded in August: in Lake Peipsi - 1.12 mg/dm3 and 0.09 mg/dm3, in Lake Pskov - 1.59 mg/dm3 and 0.14 mg/dm3, respectively. BOD5 values ranged from 1.96 mg/dm3 in autumn to 4.26 mg/dm3 in summer.During the growing season of 2020, 244 species taxa of phytoplankton from 8 phylums were identified in the Peipsi-Pskov Lake: Chlorophyta, Bacillariophyta, Cyanobacteria, Chrysophyta, Euglenophyta, Cryptophyta, Dinophyta and Xanthophyta. Floristic complex was characterized as сhlorophyta-diatom-cyanobacterial.The number of phytoplankton varied between 2.1 and 16.2 million cells/l depending on the season. The average number was 7.6 million cells/l. The biomass values ranged from 0.9 g/m3 to 3.6 g/m3. The average biomass was 2.3 g/m3.According to the ecological and geographical characteristics of the lake, widespread freshwater forms of microalgae predominated, preferring stagnant-flowing, slightly alkaline waters.Saprobiological analysis showed that the waters of the Peipsi-Pskov Lake were classified as moderately polluted, class III of water purity quality.