In the present work, four nontrivial stages of electrokinetic instability are identified by direct numerical simulation (DNS) of the full Nernst-Planck-Poisson-Stokes system: (i) a stage of the influence of the initial conditions (milliseconds); (ii) one-dimensional (1D) self-similar evolution (milliseconds-seconds); (iii) a primary instability of the self-similar solution (seconds); (iv) a nonlinear stage with secondary instabilities. The self-similar character of evolution at moderately large times is confirmed. Rubinstein and Zaltzman instability and noise-driven nonlinear evolution toward overlimiting regimes in ion-exchange membranes are numerically simulated and compared with theoretical and experimental predictions. The primary instability which happens during this stage is found to arrest a self-similar growth of the diffusion layer. It also specifies its characteristic length as was first experimentally predicted by Yossifon and Chang [G. Yossifon and H.-C. Chang, Phys. Rev. Lett. 101, 254501 (2008)]. A novel principle for the characteristic wave-number selection from the broadband initial noise is established.

No abstract

In the present work linear instability of capillary non-axisymmetric micro-jets of electrolyte solutions in a high-frequency alternating axial electric field is investigated theoretically. The gravity affects are neglected. The problem is described by strongly coupled nonlinear system of PDEs for ion transport, electrical field and fluid flow. Viscous liquid is taken. The problem can be divided into outer and inner ones. Solution for the unsteady double ion layer is obtained in Debye-Huckel approximation provided that the oscillation frequency is sufficiently high while Pecklet number based on the Debye layer thickness is sufficiently small. The unsteady double ion layer produces additional normal and tangential stresses on the liquid-gas interface; the latter can either stabilize or destabilize the flow. It is shown that only axisymmetric mode is unstable while non-axisymmetric perturbations are always stable. It is also shown that in unstable case there is an essential dependence of the main stability characteristics on the parameter proportional to the frequency of external field. There are two threshold values of the parameter The research was partially financed by the Russian Foundation for Basic Research grants No 08-01-00005-à and 06-08-96637-r_ug_a.at which a bifurcation of stability parameters occurs. In particular, the size of the formed drops suffers a jump at increase of amplitude of fluctuation of an electric field. The problem is solved in a broad region of its parameters. There is a qualitative agreement of the theory developed with the available experimental data.

Conical points of a leaky dielectric drop surrounded by a dielectric gas in an external ac electric field are investigated. A novel class of steady conical tips depending on the permittivity ratio and applied signal frequency is presented. It is found that conical solutions with very small angles are possible (angles much smaller than the classical Taylor cone angle 49.3° for a conducting drop in a dc field); this result can be relevant to the observations of small cone angles in Chetwani, Maheshwari, and Chang experiments [N. Chetwani, S. Maheshwari, and H.-C. Chang, Phys. Rev. Lett. 101, 204501 (2008)].

In the article, the authors study the possibility of detecting some fungal diseases of rice using visual computing and machine learning techniques. Leaf blast and brown spot diseases are considered. Modern computer vision methods based on convolutional neural networks are used to identify a particular disease on an image. The authors compare the four most successful and compact convolutional neural network architectures: GoogleNet, ResNet-18, SqueezeNet-1.0, and DenseNet-121. The authors show that in the dataset used for the analysis, the disease can be detected with an accuracy of at least 95%. Testing the algorithm on real data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution. Data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution.