Analyzing the Conditions for the Electrolytic Formation of Ensembles from Metal Nanowires in the Pores of Track Membranes

Theoretical Foundations of Chemical Engineering - The effect of the electrolyte composition, mass-transfer conditions, pore diameter, interpore distance, and electric parameters on the formation of...     

Information Redundancy in Constructing Systems for Audio Signal Examination on Deep Learning Neural Networks

Cybernetics and Systems Analysis - Preliminary signal processing methods used to create new tools to examine materials and digital sound recording means are described. It is shown that using...     

Optimal Replacement of System Elements Using a Genetic Algorithm

Journal of Computer and Systems Sciences International - The replacement of complex technical system elements is optimized using a genetic algorithm. Its various operators are analyzed and modified...     

Enthalpy Calibration Method for Differential Scanning Calorimeters

Measurement Techniques - A method for enthalpy calibration of differential scanning calorimeters is described which makes it possible to eliminate the existing limits on the temperature range...     

Traceability of Measurements of the Energy Parameters of Laser Beams

Measurement Techniques - A conceptual approach to the traceability of measurements of the energy (power) of laser beams during calibration of means of measurement to standards is formulated,...     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Infrared Spectroscopy Study of Composite Materials Based on Nanoporous High-Silica Glasses Activated with Silver and Lanthanum Ions

Glass Physics and Chemistry - Composite materials (CMs) activated by silver and lanthanum ions based on high-silica porous glass (PG) matrices have been synthesized. The CMs were studied by IR...     

Flash Lamps as Ignition and Initiation Sources of the VS-2 Pyrotechnic Composition

In the present work it is found that the pyrotechnic composition VS-2 can be initiated with flash lamps IFC-500 and EVIS. VS-2 pyrotechnic composition contains 90% of mercury（Ⅱ） 5-hydrazinotetrazolate perchlorate and 10% of optically transparent copolymer of 2-methyl-5-vinyltetrazole and methacrylic acid （PVMT）. We have found that the flash lamps make it possible to initiate combustion of VS-2 composition with its transition to detonation both in cylindrical charges placed in brass caps of 5 mm diameter and 2 mm high， and film charges with 10 mm×80 mm in size and surface weights of 60 mg·cm^-2 and 90 mg·cm^-2， showing ignition delay times 10 μs and 3 μs， respectively. We also measured detonation velocities for VS-2 composition film charges， which were 4375-4505 m·s^-1 （of the charge being surface mass 60 mg·cm^-2） and 4221-4281 m·s^-1 （of the charge being surface mass 90 mg·cm^-2） and their blasting action on the aluminum plate. The depths of the normal shock wave imprints at the charge-barrier interface were 0.6-0.7 mm （for surface mass of the film charges 60 mg·cm^-2） and 1.2-1.3 mm （for surface mass of the film charges 90 mg·cm^-2）.     

Simulation of Silicon Carbide Sputtering by a Focused Gallium Ion Beam

Semiconductors - The precision fabrication of microstructures and nanostructures by the focused-ion-beam method is significantly simplified by the simulation of sputtering of materials by...     

Pigmented flower extracts of plant species from the Geraniaceae and Lamiaceae families as natural food colourants: anthocyanin composition,thermal and oxidative stability

This study determined the anthocyanin profile of pigmented flower extracts from some Geraniaceae and Lamiaceae plant species found in South Africa, and their thermal and oxidative stability to assess their potential food application. Anthocyanins in the Geraniaceae (Pelargonium grandiflorum Willd. and Pelargonium × hortorum L.H. Bailey, Pelargonium zonale hybrid) were 3,5-diglucosides of delphinidin, petunidin, pelargonidin, peonidin and malvidin and acetyl-acylated malvidin, delphinidin and petunidin. The Lamiaceae species (Salvia aurea × dolomitica Bae’s blue, Salvia dolomitica Codd and Plectranthus zuluensis T. Cooke) mainly contained rutinosides of pelargonidin, glucosides of petunidin, pelargonidin and p-coumaric acid- and malonyl-acylated delphinidin and malvidin. Lamiaceae pigments had higher thermal and oxidative stability. This could be due to aromatic malonyl-acylated anthocyanin self-association and strong intermolecular interactions with phenolic acids and derivatives. Flowers from Lamiaceae and Geraniaceae have potential application as natural food colourants, but temperature and oxidising conditions must be considered depending on the particular species being used.