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2.12 Definitions and Clarification
1.9. Applications
Table 1.7: A Train concerning its Stations and its Connections as a Fuzzy Graph in a Model.
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Stations of T s1 s2 s3 s4 s5 s6 s7 s8 s9, s10
Values 0.1 0.8 0.7 0.8 0.1 0.3 0.6 0.5 0.2 Connections of T s1s2 s2s3 s3s4 s4s5 s5s6 s6s7 s7s8 s8s9 s9s10
Values 0.1 0.6 0.4 0.1 0.1 0.2 0.4 0.2 0.1
couple of stations in every fuzzy(neutrosophic) graph excerpt from any trains, simultaneously.
Covid-19 and Identifying Infected People
Dark network is description for infected people who are anonymous in the matter of Covid-19. Virus and its anonymously style to transmit the virus from one person to another person, could make a dark network involving people. Consider everyone as network titled fuzzy(neutrosophic). It means that the person and his networks containing his connections make two models, fixed-edge fuzzy(neutrosophic) and fixed-vertex strong fuzzy(neutrosophic). Now, I have a family of people which everyone is a model in the terms of Covid-19.
Step 1. (Definition) Covid-19 is well-known disease which like every disease has general parameters. Parameters are intensity of symptom, decreasing impacts, relatively treatments, complete treatments and et cetera. But Covid-19 has specific ways which they transmit this disease. It’s coming up with finding impressive networks of people to identify infected people. People and their connections are important cases to develop this notion.
Step 2. (Issue) A person has been infected and I try to find the connections and the people which transmit this disease.
Step 3. (Model) A person and his connections are a network which are a fuzzy model. Two numbers are assigned to a person and his connections. To do this, I need to identify a couple of people which are given in a network of this person. I proposed two fuzzy models. Firstly, as Figure (1.11), a fuzzy graph containing the people who connect to this person, is proposed in Table (1.8). Secondly, as Figure (1.11), a fuzzy model including person with his two selective connections and other people with two selective connections of them, is posed in Table (1.9). The attributes are like the iterations of connections, the intensity of infected people, serious symptom, locations of people and et cetera, are used to have couple of people who are selected. Capable for being infected and infected people are used to make these models.
Step 4. (Solution) By Corollary (3.30.10), a person i1 and his partner i2 identify every given couple of partners which are in Figure (1.11) as T . To get beyond this result, if a person i1 and the partner i2 aren’t antipodal vertices in every fuzzy cycles are contained in a family of person’s networks, then by Corollary (3.30.11), a person i1 and the partner i2 identify every given couple of partners in every fuzzy cycles, simultaneously. By Corollary
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