Zonal flow generation and its feedback on turbulence production in drift wave turbulence

Plasma turbulence described by the Hasegawa-Wakatani equations has been simulated numerically for different models and values of the adiabaticity parameter C. It is found that for low values of C turbulence remains isotropic, zonal flows are not generated and there is no suppression of the meridional drift waves and of the particle transport. For high values of C, turbulence evolves toward highly anisotropic states with a dominant contribution of the zonal sector to the kinetic energy. This anisotropic flow leads to a decrease of a turbulence production in the meridional sector and limits the particle transport across the mean isopycnal surfaces. This behavior allows to consider the Hasegawa-Wakatani equations a minimal PDE model which contains the drift-wave/zonal-flow feedback loop prototypical of the LH transition in plasma devices.Comment: 14 pages, 7 figure

Magnetic order in lightly doped cuprates: Coherent vs. incoherent hole quasiparticles and non-magnetic impurities

We investigate magnetic properties of lightly doped antiferromagnetic Mott insulators in the presence of non-magnetic impurities. Within the framework of the t-J model we calculate the doping dependence of the antiferromagnetic order parameter using the self-consistent diagrammatic techniques. We show that in the presence of non-magnetic impurities the antiferromagnetic order is more robust against hole doping in comparison with the impurity-free host, implying that magnetic order can re-appear upon Zn doping into lightly hole-doped cuprates. We argue that this is primarily due to the loss of coherence and reduced mobility of the hole quasiparticles caused by impurity scattering. These results are consistent with experimental data on Zn-doped LaSrCuO.Comment: 11 pages, 7 figs, (v2) final version as publishe

Psychological and pedagogical peculiarities of teaching foreign undergraduate students at the russian university

The article is devoted to the problem of improvement of professional training of bachelors in Russian language on the basis of psychological and pedagogical features, which is topical nowaday

Research of Influence of Potassium-rich Diets on the Physical Performance of Students

The aim of the work is the scientific substantiation and experimental support of the expedience and use of potassium-cationic water for improving the bread quality and the study of the influence of potassium-rich diets on the physical performance of students. There was studied the influence of potassium cations on the activity of proteolytic enzymes of wheat flour. It was established, that at using potassium-cationic water, the output of wet gluten (35,1 %) essentially increases, at that the output of dry one (8,4 %) decreases to the same extent that is a positive factor in the bakery technology. It was proved that enriching the vital medium of bakery yeast by potassium cations essentially activates their ability to hydrolysis of maltose that favors activation of the process of gassing (Maltase activity – 35,1 min). The process of gassing influences the speed of dough-conduction and ready bread quality, especially volume (357,7, 100 g/ml), porosity (79,1 %) and crumb ability to compression (33,5 c.u.). It was established, that consumption of bread, produced on potassium-cationic water, favors the strengthening of the heart muscle tone, improvement of the general condition of the organism, especially, physical endurance and performance

Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Thermal and Mechanical Characteristics of Polymer Composites Based on Epoxy Resin, Aluminium Nanopowders and Boric Acid

The epoxy polymers are characterized by low thermal stability and high flammability. Nanoparticles are considered to be effective fillers of polymer composites for improving their thermal and functional properties. In this work, the epoxy composites were prepared using epoxy resin ED-20, polyethylene polyamine as a hardener, aluminum nanopowder and boric acid fine powder as flame-retardant filler. The thermal characteristics of the obtained samples were studied using thermogravimetric analysis and differential scanning calorimetry. The mechanical characteristics of epoxy composites were also studied. It was found that an addition of all fillers enhances the thermal stability and mechanical characteristics of the epoxy composites. The best thermal stability showed the epoxy composite filled with boric acid. The highest flexural properties showed the epoxy composite based on the combination of boric acid and aluminum nanopowder

Parenclitic and Synolytic Networks Revisited

Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question-which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with a priori known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not a priori available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the "black-box" nature of other ML approaches

Competition Between Stripes and Pairing in a t-t'-J Model

As the number of legs n of an n-leg, t-J ladder increases, density matrix renormalization group calculations have shown that the doped state tends to be characterized by a static array of domain walls and that pairing correlations are suppressed. Here we present results for a t-t'-J model in which a diagonal, single particle, next-near-neighbor hopping t' is introduced. We find that this can suppress the formation of stripes and, for t' positive, enhance the d_{x^2-y^2}-like pairing correlations. The effect of t' > 0 is to cause the stripes to evaporate into pairs and for t' < 0 to evaporate into quasi-particles. Results for n=4 and 6-leg ladders are discussed.Comment: Four pages, four encapsulated figure

Qualitative understanding of the sign of t' asymmetry in the extended t-J Model and relevance for pairing properties

Numerical calculations illustrate the effect of the sign of the next nearest-neighbor hopping term t' on the 2-hole properties of the t-t'-J model. Working mainly on 2-leg ladders, in the -1.0 < t'/t < 1.0 regime, it is shown that introducing t' in the t-J model is equivalent to effectively renormalizing J, namely t' negative (positive) is equivalent to an effective t-J model with smaller (bigger) J. This effect is present even at the level of a 2x2 plaquette toy model, and was observed also in calculations on small square clusters. Analyzing the transition probabilities of a hole-pair in the plaquette toy model, it is argued that the coherent propagation of such hole-pair is enhanced by a constructive interference between both t and t' for t'>0. This interference is destructive for t'<0.Comment: 5 pages, 4 figures, to appear in PRB as a Rapid Communicatio