Nanocomposites and nanomaterials

The neural network Decision Support System for life cycle management of engineering nanomaterials in accordance with the nanosafety norms 
Holyk V.1, Dyadyura K.1, Hrebenyk L.1
1 Sumy State University, 2, Rymskogo-Korsakova St., Sumy, 40007, Ukraine.
E-mail: email.of. corresponding_ vadim-golik@ukr.net

Regularities of the influence of physicochemical parameters of nanoparticles on biological objects and ecosystems are characterized by a high degree of uncertainty, which can be overcome by conducting additional studies. The set of methodical approaches should be determined in accordance with the purpose of the practical use of nanomaterial. The study of the inner properties of engineered nanomaterials is crucial for the methodological search for risk assessment and management strategies in the nanoindustry. In modern conditions of the rapid development of nanotechnologies, the solution to the problem of nanosafety should be coordinated with the processes of development, implementation, and utilization of new nanomaterials. The usage of open-source modeling tools for the design of all stages of the life cycle of nanoscale structures is considered a promising but underdeveloped area of mathematical modeling in addressing issues that take into account the nanosafety norms. The complex structures of the interdependence between the variables of the physicochemical parameters of the nanoparticles create significant problems in the traditional prognostic modeling of the patterns of development of the biological response in living organisms and ecosystems.
The main objective of the study is the development of an effective algorithm for multi-criteria decision making (MCDM) methods for tasks that must be solved in terms of incomplete and uncertain information, starting with the assessment of the safe usage of nanomaterial according to the planned practical goal to successful utilization without environmental and public health hazards.
The paper considers modern Intelligent Decision Support Systems for modeling complex processes and systems. Based on radial basis functions neural network (RBFNN) on accelerated particle swarm optimization (APSO) is proposed, which is designed for nonlinear modeling of decision support system.
The modeling results allow us to optimistically state that the proposed approach has sufficiently high characteristics in terms of network size and estimation accuracy for use in solving nanoethical issues related to the safety of the environment and human health of the use of new nanomaterials for different applications.