International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Firms progressively search for new technologies and methods to develop sustainable competitive advantage. This article is aimed to investigate the pros and cons of integrating human RFID chip implants and Artificial Intelligence in the new digital global environment. The paper focuses on the new digital communication and machine culture, which rises between human and machine. It represents the unlimited industrial possibilities and potential of Artificial Intelligence. It also assesses the future risk that the world of human work being run by artificial management and the social impact to the public.

In the past one hundred years, there were more global changes than in the previous one thousand years.

Increasing data heterogeneity, fragmentation and volume, coupled with complex connections among specialists in disaster response, mitigation, and recovery situations demand new approaches for information technology to support crisis management. Advances in GIServices show promise to support time-sensitive collaboration, analytical reasoning, problem solving and decision making for crisis management. Furthermore, as all crises have geospatial components, crisis management tools need to include geospatial data representation and support for geographic contextualization of location-specific decision-making throughout the crisis. This paper provides introduction and description of Mobile GIServices applied to crisis management activity. The goal of Mobile GIServices in this context is to support situational awareness, problem solving, and decision making using highly interactive, visual environments that integrate multiple data sources that include georeferencing.

The aim of this article is to describe the general methodology of early warning systems. The common disaster monitoring system was analyzed, and the construction system of disaster information management system was discussed particularly. This paper addresses traditional views of early warning systems and what is needed to turn them into efficient, people-centred systems.

The paper presents standard statistical procedures for determining expert assessments for the influence of magnesium alloy components on the controlled mechanical properties: tensile strength and relative elongation. There are obtained neural models describing mechanical characteristics from the amount of aluminum, manganese, nickel, iron and silicon directly related to the exploitation properties of the product. The applied approach makes it possible to determine of compositions ensuring relatively optimal values of the explored quality indicators.

The article is dedicated to an approach optimizing a task of statistical modeling of the mechanical properties of products in real production metallurgy design. The approach is designed for the benefit of producers-metallurgists aimed at providing panels of Ti – alloys of a specific set of eventual industrial properties. This is accomplished by a procedure of composition optimizing based on existing certificates of brands Ti – alloys. The article presents an approach using mathematical models of optimization problem following the implementation of approach the classical methodology capable of decision-making in the production practice.

The paper presents the application of modern computational approaches in determining the appropriate chemical composition of titanium alloys . Discusses three types of problems for the selection of the chemical composition of alloy at set requirements for mechanical properties . Tasks are solved in a different formulation of the target criteria . The proposed solutions are based on software model using approximation with artificial neural networks and genetic optimization algorithm.

An approach to the study of the relationship between the extreme values of the mechanical properties of titanium alloys . The approach is based on the approximated dependence of the mechanical properties of the chemical composition of the alloy of the ANN model . Approximating dependencies are included in the genetic optimization model based on optimization algorithm NSGA II, which was built by Pareto – fronts , modeling the relationship between the studied mechanical parameters.