Intensive research of nanocomposites contributes to the development of new materials in the fields of medicine, nanoelectronics, energy, biotechnology, information technology. Therefore, the synthesis of new materials by modifying of graphene oxide (GO) with nanocellulose and the study of its properties are of great interest. In this study synthesized nanocomposite material by modifying of graphene oxide (GO) from activated carbon (BAU-A) in a 1:1 volume ratio with nanocellulose (NC) from hemp stems belonging to the annual plant, and their chemical structure was studied by FTIR and UV-spectroscopy. The results of the study showed the absorption of the etheric bond C = O in the UV spectrum at full length 243 nm. The IR spectrum showed all the new etheric bonds O = C - OH at a wavelength of 1625 cm-1. The average particle sizes of GO was 352 nm and NC was 470 nm in length and 80 nm in width. The SEM analysis indicating the NC as a contact layer between ultralow thicknesses of the GO layers. The XRD analysis indicated GO-NC composite film is a substance comprising GO and NC. According to the results, modification of graphene oxide showed that its scope can be expanded as much as possible.

Summary: Solution properties of alternating polyampholytes based on N,N‐dimethyldiallylammonium chloride and maleic acid as well as N,N‐dimethyldiallylammonium and maleamic acid (butylmaleamic acid, phenylmaleamic acid, 4‐butylphenylmaleamic acid) were studied in aqueous and aqueous‐salt solutions. The isoelectric points (IEP) of the amphoteric macromolecules were determined. It was found that the viscosity of equimolar polyampholyte solutions increases at the IEP with an increase in neutral salt concentration, while polyampholyte solutions having an excess of cationic groups exhibit polyelectrolyte character. The influence of pH, neutral salt, and a water–ethanol mixture on the viscosity of polyampholyte solutions was shown. Formation of interpolyelectrolyte complexes between regular polyampholytes based on N,N‐dimethyldiallylammonium chloride–alkyl (aryl) substituted maleamic acids and poly(acrylic acid), poly(styrene sodium sulfonate), as well as the formation of polyampholyte–sodium lauryl sulfate complexes was studied in aqueous solution by potentiometric, conductimetric, turbidimetric, and viscometric methods. The composition of polyampholyte–polyelectrolyte and polyampholyte–surfactant complexes was determined and the influence of temperature and ionic strength on their behavior was studied.

Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

ABSTRACT:A new regular polyampholyte, namely poly(N,N-diallyl-N-octadecylamine-alt-(maleic acid)), was studied as an additive to crude oil. The amphiphilic polyampholyte proved to be an efficient pour point depressant, to inhibit the deposition of wax, and to improve the viscosity of waxy crude oil from the Akshabulak oilfield (Western Kazakhstan). On optimizing the concentration of the polymer, both the kinematical viscosity and the pour point of waxy crude oils were found to be strongly decreased. The morphology of the paraffin aggregates formed was compared before and after heat treatment of the waxy crude oils, in the presence and the absence of the polymer. The rheological characteristics of the waxy crude oil were markedly improved, in particular, by decreasing the plastic viscosity and the yield stress values upon addition the polymer. The inhibition of wax deposits in the presence of the amphiphilic polyampholyte was interpreted in terms of its interference with the wax crystallization process because of the formation of inverse micellar structures. Although the interaction of the cationic and the anionic groups on the polymer backbone stabilizes the smaller size of the aggregates, the hydrophobic side chains of the polymer provide nucleation sites and cocrystallize with the paraffins, thus modifying the paraffin crystal structure.