Solving Nonlinear Wave Equation Based on Topology

A method of solving nonlinear wave equation based on topology is proposed. Firstly, the characteristics of stochastic graph and Scaleless network are compared, and their topological characteristics are analyzed. Because of the existence of a few axis nodes, Scaleless networks have higher average aggregation than those with the same number of airport nodes and connected stochastic graphs. According to the topological structure of nonlinear wave equation, the first-order integral method is used to solve the nonlinear wave equation. According to the first integration, the threshold range is set, and the solution flow is designed in line with the division theorem. The topology of the network is analyzed according to the node degree, aggregation coefficient and reciprocity of the network, so as to verify and analyze. The experimental results show that the application of this method is 98%, which is still effective for the hyperbolic development equation of the same type.

Resulting effects in the evaluation of innovative technology implementation by hotel organisations

The article describes the problem of the reflection of resulting effects in the innovative activity of hotel organisations. Despite the special attention and interest in the issue under discussion, a unified approach to the category of “resulting effects” of innovative technology application regarding hotel organisations has not been developed in the research community so far that would take into account the specific characteristics of their activities. A number of existing definitions limit innovative activity to a set of actions for the development and implementation of technologically new services without considering the effects of carrying out innovative processes, the ability to maintain a certain level of innovative activity and availability of a resource complex. The content of resulting effects is described based on the group of indicators. The authors build the stochastic graph of indicators of resulting effects in the innovative processes of hotel organisations. The proposed technique was tested in ten hotel organisations of the resort city of Sochi.

A Stochastic Graphs Semantics for Conditionals

We define a semantics for conditionals in terms of stochastic graphs which gives a straightforward and simple method of evaluating the probabilities of conditionals. It seems to be a good and useful method in the cases already discussed in the literature, and it can easily be extended to cover more complex situations. In particular, it allows us to describe several possible interpretations of the conditional (the global and the local interpretation, and generalizations of them) and to formalize some intuitively valid but formally incorrect considerations concerning the probabilities of conditionals under these two interpretations. It also yields a powerful method of handling more complex issues (such as nested conditionals). The stochastic graph semantics provides a satisfactory answer to Lewis’s arguments against the PC = CP principle, and defends important intuitions which connect the notion of probability of a conditional with the (standard) notion of conditional probability. It also illustrates the general problem of finding formal explications of philosophically important notions and applying mathematical methods in analyzing philosophical issues.