A unique method for producing Polikon cation-anion exchange mosaic materials has been proposed. The materials were prepared by the polycondensation filling of polymer composites and via synthesis and curing of a weakly basic anion exchanger and a strongly acidic sulfonated cation exchanger on the surface and in the structure of a fibrous novolac phenol-formaldehyde matrix. Molecular models that characterize the chemical composition of the developed composites are given. The effect of the composition that facilitates the formation of a material with stable performance characteristics has been studied. The cause-and-effect relationships of the structural, physicochemical, and operational properties of the heterogeneous cationanion-exchange mosaic materials Polikon under thermomechanical action at the stage of polymer matrix curing were investigated. Empirical equations that refine these relationships were obtained. The morphology and internal structure of the test heterogeneous membranes were characterized by scanning electron microscopy and energy-dispersive elemental analysis. The viscoelastic properties of the samples were studied by dynamic thermomechanical analysis.
The approach to solving the problems of diagnosis and prognosis of diseases of agricultural crops using machine learning methods is described. To solve the problem of forecasting diseases of agricultural crops, it is proposed to use a genetic algorithm in the work. The analysis of the effectiveness of the proposed method is carried out depending on the convergence rate of such parameters as the mutation coefficient and population size. To solve the problem of diagnostics of agricultural crops, it is proposed to use a recurrent type of neural network. A software modelling complex has been developed that allows solving the problems of plant diseases diagnostics and making forecasts. The results obtained can reduce the costs of agricultural enterprises by reducing the cost of diagnosing agricultural diseases.
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