The paper is a continuation of the publication under the title “Acoustic diagnostics applications in the study of technical condition of combustion engine” and concerns the detailed description of decision support system for identifying technical condition (type of failure) of specified combustion engine. The input data were measured sound pressure levels of specific faults in comparison to the noise generated by undamaged motor. In the article, the whole procedure of decision method based on game graphs is described, as well as the interface of the program for direct usage.

In this paper, the author compares the of characteristics of subsystems obtained by the approximate and exact method in order to answer to the question - if the approximate method can be used to nominate the characteristics of mechatronic systems. Frequency - modal analysis has been presented for a mechanical system, i.e. transverse-vibrating clamped-free beam. Consequently, the model of the beam was presented in a five-vertex hypergraph. This model, in the case of approximate frequency-modal analysis, can be imitated in a three-vertex hypergraph. Such formulation could be the introduction to synthesis of transverse-vibrating complex beam systems with constant cross-section.

In this paper we propose right-angled Artin groups as a platform for secret sharing schemes based on the efficiency (linear time) of the word problem. Inspired by previous work of Grigoriev-Shpilrain in the context of graphs, we define two new problems: Subgroup Isomorphism Problem and Group Homomorphism Problem. Based on them, we also propose two new authentication schemes. For right-angled Artin groups, the Group Homomorphism and Graph Homomorphism problems are equivalent, and the later is known to be NP-complete. In the case of the Subgroup Isomorphism problem, we bring some results due to Bridson who shows there are right-angled Artin groups in which this problem is unsolvable.

A gigantic amounts of data and information on molecules that constitute the very complex cell machinery have been collected, classified and stored in data banks. Although we posses enormous amount of knowledge about the properties and functions of thousands of molecular entities, we are still far from understanding how they do work in a living cell. It is clear now that these molecules (genes, proteins) are not autonomous, that there is no direct linear relation between genotype and phenotype, and that the majority of functions are carried and executed by concerted molecular activity, and that the majority of diseases are multifactorial. A basic property of the matter in a living cell (both normal and pathologic) is an interaction between variety of macromolecules, mainly proteins, genes (DNA) etc. In a process of self-organization they are able to form an active molecular biologic system – a complex, labile and dynamic network which integrity is secured by non-covalent bounds. In this essay some basic properties of network structure and the universal rules that govern them are described. Network or system biology is promising new research approach in biology and medicine.

The results presented here are twofold. First, a heuristic algorithm is proposed which, through removing some unnecessary arcs from a digraph, tends to reduce it into an adjoint and thus simplifies the search for a Hamiltonian cycle. Second, a heuristic algorithm for DNA sequence assembly is proposed, which uses a graph model of the problem instance, and incorporates two independent procedures of reducing the set of arcs - one of them being the former algorithm. Finally, results of tests of the assembly algorithm on parts of chromosome arm 2R of Drosophila melanogaster are presented.

The paper aims at the higher reactive power management complexity caused by the access of distributed power, and the problem such as large data exchange capacity, low accuracy of reactive power distribution, a slow convergence rate, and so on, may appear when the controlled objects are large. This paper proposes a reactive power and voltage control management strategy based on virtual reactance cloud control. The coupling between active power and reactive power in the system is effectively eliminated through the virtual reactance. At the same time, huge amounts of data are treated to parallel processing by using the cloud computing model parallel distributed processing, realize the uncertainty transformation between qualitative concept and quantitative value. The power distribution matrix is formed according to graph theory, and the accurate allocation of reactive power is realized by applying the cloud control model. Finally, the validity and rationality of this method are verified by testing a practical node system through simulation.

We propose an approach to indirectly learn the Web Ontology Language OWL 2 property characteristics as an explanation for a deep recurrent neural network (RNN). The input is a knowledge graph represented in Resource Description Framework (RDF) and the output are scored axioms representing the characteristics. The proposed method is capable of learning all the characteristics included in OWL 2: functional, inverse functional, reflexive and irreflexive, symmetric and asymmetric, transitive. We report and discuss experimental evaluation on DBpedia 2016-10, showing that the proposed approach has advantages over a simple counting baseline.