Modification in Two-Connected Graph with Gallai’s Property in 2-Dimensional and 3-Dimensional Graph Containing 19 Vertices
Rabnawaz Mallah,
Inayatullah Soomro,
Sarang Latif,
Dost Muhammad,
Altaf Hussain
Issue:
Volume 8, Issue 1, March 2023
Pages:
1-5
Received:
24 November 2022
Accepted:
20 January 2023
Published:
9 February 2023
Abstract: The graph theory plays an important role in the network analysis, social networking as well as in many engineering fields such as electrical circuits, artificial intelligence, architecture, making the design or pattern of roads, buildings, shopping mall and etc. Due to this wide range application human enjoying her life with peacefully, Graph theory creates a way for human being to connect among themselves by social network. All above applications based on graph or molecule which may be the planer, non-planer and Peterson graph or etc. Peterson graph is the most important and reasonable example of Hypo-Hamiltonian graph. In the earlier, it was found as a hypo-traceable graph (graph which has not Hamiltonian graph. Naeem et al has worked on “A Two-Connected Graph with Gallai’s Property” In his research paper he has applied the property and has found the longest path and cycle in the graph. In this research paper we will develop the 3-dimensional graph of computational molecule contains 19 vertices and will split it into three different planes (xy, xz and yz-plane), and will find the longest path, longest cycle the molecule. The designed graphs can be useful in various fields of science and technology including computational geometry, networking, theoretical computer science and circuit designing.
Abstract: The graph theory plays an important role in the network analysis, social networking as well as in many engineering fields such as electrical circuits, artificial intelligence, architecture, making the design or pattern of roads, buildings, shopping mall and etc. Due to this wide range application human enjoying her life with peacefully, Graph theor...
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Volatile Network as a Simple Memory Model
Issue:
Volume 8, Issue 1, March 2023
Pages:
6-16
Received:
10 February 2023
Accepted:
27 February 2023
Published:
22 May 2023
Abstract: Technical information systems, from PCs to supercomputers, are characterized over time by ever-increasing storage capacities, while biological systems are permanently characterized by their trainable memory abilities. Although both systems are not comparable with each other, because they are based on different phenomena, the existing efficiency of biological systems offers a constant borrowing for the further development of technical systems. For this purpose, it is necessary to develop technical equivalence models. The following considerations aim to reproduce the factually limitless abilities of biological systems to store memory content as a result of the plasticity of neuronal populations. The difference between technical and biological systems becomes particularly clear under this aspect: while the development of technical systems aims to permanently increase the existing storage capacity, biological systems are based on independently separating relevant from irrelevant information and, moreover, permanently reorienting existing memory structures, called plasticity. Accordingly, the transmitter flow between the neurons constantly changes in direction and intensity. A network with a transient topology that is marginally able to model a memory-capable neuronal population characterized by a permanent loss of neuronal contact points is proposed for discussion. Such a loss permanently changes the direction and intensity of the transmitter flow between the neurons. Another focus of the topic is the question of how different stimuli, meaning optical, acoustic, tactile, etc., can become one and the same memory description of a neuron population. Here it is assumed that a pre-processing takes place in the biological system in the form of a functional transformation, the result of which is a neutral basis for representing the information. Although such an assumption seems to be highly speculative, a discussion of it would contribute to answering the question of which physiological mechanisms have to be taken into account to explain memory phenomena, reproduced in a model.
Abstract: Technical information systems, from PCs to supercomputers, are characterized over time by ever-increasing storage capacities, while biological systems are permanently characterized by their trainable memory abilities. Although both systems are not comparable with each other, because they are based on different phenomena, the existing efficiency of ...
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