• No results found

Equation (58) in this adjusted format for a hydrogen atom becomes

5. Conclusions

The paraconsistent model of the atom proposed in this work is based on the foundations of the PAL and combines the concepts of the entropy of information theory with quantum mechanics. The proposed paraconsistent model of the atom, which applies the interlaced PqL bilattice, shows a convincing geometric aspect for the atomic particle. Thus, well-adjusted fundamentals for the phenomena of quantum physics could be demonstrated. Despite the impossibility of covering all quantum phenomena in this work, the equations obtained by the analyses provide the characteristics of symmetry, recurrence, and superposition of states. The entanglement concept can be expressed in the form of the division obtained through the distance factor dF

used in the equations of the degrees of certainty. The quantum phenomenon of the superposition of states is explicitly expressed through the equations and the representation in which two observers are active. This procedure, which was conducted through the simultaneous use of equations and the analysis of an observer in the vector of the base X and an observer in the vector of the base Y, is similar to the quantum theory. The model presented in this work is innovative and opens a field of in-depth investigations of different conditions and the effects originating from interpretations of the model under diverse conditions and dimensions. All the equations feature good computability and ensure that all the procedures can be presented in matrix and algorithmic forms. Notably, in the presented paraconsistent model of the atom, no equation results in a defined value. In general, all the equations used in the simulations are probabilistic functions that result in indefinite trajectories of paraquantum logical states. Even the direction and orientation of the orbital trajectories shown in the result graphics are indefinite because they are always opposite to one another. In the simulations, the paraconsistent model of the atom was represented by algorithms I and II that allowed showing the probabilistic trajectories of the paraquantum logical states that can be related to the probable orbits of the elementary particles. With the simulation applying the PqL equations, the energy functions, represented by the degrees of certainty and contradiction, therefore values generated in the quantum universe - Interlaced PqL Bilattice -, can to express the probabilities to be measured in the real world. These algorithms and equations have high significance for the beginning of new studies in computational applications of PqL. In its application to Raman spectroscopy data, the paraconsistent model showed versatility and a good representation of the energy variations at the atomic level of hydrogen. These results enable us to further explore the use of the proposed model with the presented concepts, such as the adjustment of the energy values in the lower layers of the hydrogen atom and for other atomic models. This work demonstrates that the use of the Shannon entropy in a paraconsistent model is an excellent method for modeling quantum systems. In the future, new simulation procedures will expand the application possibilities of the paraconsistent model of the atom in other areas, including computation, quantum logic gates, quantum systems for signal recognition, and quantum cryptography.

Supplementary Materials: The data that support the plots within this paper and other findings of this study are available in additional material and from the corresponding author.

Author Contributions: J.I. carried out the experiments, did the analysis of the results, wrote the paper and prepared the illustrations.

Acknowledgments:

The author acknowledges ISESC - Instituto Superior de Educação Santa Cecilia for the financial support for this research.

The author would like to acknowledge L. Silveira Jr. for Raman spectroscopy data obtained by FAPESP (São Paulo Research Foundation) process no. 2009/01788-5.

Conflicts of Interest: The author declares no competing interests.

References

[1] Kragh, H.; Rigden, J. S. Niels Bohr and the Quantum Atom: The Bohr Model of Atomic Structure 1913- 1925. American Journal of Physics 81(3):237-238, 2013. DOI: 10.1119/1.4771884

[2] Heilbron, J. L.; Kuhn, T. S. The genesis of the Bohr atom. Historical Studies in the Physical Sciences 1, 211-290, 1969. DOI: 10.2307/27757291

[3] Auletta, G. Foundations and Interpretation of Quantum Mechanics, World Scientific, 2001.

[4] Gribbin, J. Erwin Schrodinger and the Quantum Revolution. Ed. Random House ISBN1446465713, 9781446465714, 2012.

[5] Bohm, D. A. Suggested Interpretation of the Quantum Theory in Terms of ”Hidden” Variables, Phys. Rev. 85, 166-193, 1952.

[6] Feynmann, R. P. The Feynman Lectures on Physics, Volume 3, Addison Wesley, 1963. [7] Feynmann, R. P. Simulation physics with Computers, Journ. Th. Phys. 21, 467, 1982. [8] Dirac, P. A. M. The Principles of Quantum Mechanics, 16 Oxford University Press, 1958.

[9] Born, M. The statistical interpretation of quantum mechanics - Nobel Lecture, December 11, 1954. [10] Nielsen, M. A.; Chuang, I. L. Quantum Computation and Quantum Information. Cambridge University

Press, 2000.

[11] von Neumann, J. Applications of the ergodic hypothesis. Proceedings of the National Academy of Sciences 18, 263-266. Reprinted in (Taub 1961a) 13. 1932.

[12] Engesser, K.; Gabbay, D. M.; Lehmann, D. A. New Approach to Quantum Logic (Studies in Logic book 8). 200p. ISBN-13: 978-1904987536. College Publications, 2007.

[13] Aspray, W. The mathematical reception of the modern computer: John von Neumann and the institute for advanced study computer. In Studies in the History of Mathematics, E. R. Phillips, Ed., v. 26. MAA, Washington DC, pp. 166-194, 1987.

[14] Bruckmann, G.; Weber, W. Eds. Contributions to the von Neumann Growth Model (New York), Springer Verlag, 1971.

[15] Da Costa, N. C.; de Ronde, A. C. Found Phys 43: 845, 2013, DOI: 10.1007/s10701-013-9721-9.

[16] Bueno-Soler, J.; Carnielli W. Paraconsistent Probabilities: Consistency, Contradictions and Bayes’ Theorem. Entropy18(9), 325, 2016 ; https://doi.org/10.3390/e18090325

[17] Da Costa, N. C. A. On the theory of inconsistent formal systems. NotreDame Journal Form Log, 15(4), 497–510. 1974. DOI: http://dx.doi.org/10.1305/sdjfl/1093891487

[18] Abe, J. M.; Nakamatsu, K.; Akama, S.; Da Silva Filho, J. I. The Importance of Paraconsistency and Paracompleteness in Intelligent Systems. In: Czarnowski I., Howlett R., Jain L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, 73. Springer, Cham, (2018). [19] Da Costa, N. C. A.; Marconi, D. An overview of Paraconsistent logic in the 80’s., The Journal of Non-

Classical Logic, 6, 5-31, 1989.

[20] Abe, J. M.; Akama, S.; Nakamatsu, K. Introduction to Annotated Logics. Springer, Heidelberg, 2016 [21] Blair, H. A.; Subrahmanian, V. S. Paraconsistent Logic Programming, Theoretical Computer Science 68,

135-154, 1989.

[22] Belnap, N. A. Useful four-valued logic. In J. M. Dunn & G. Epstein (orgs.), Modern uses of multiple- valued logic (p. 8–37). Dordrecht: Reidel. 1977b.

[23] Belnap, N. How a computer should think. In G. Ryle (ed.), Contemporary Aspects of Philosophy (pp. 30-55). Stocks_eld: Oriel Press. 1977a.

[24] Subrahmanian, V. S. On the semantics of quantitative logic programs. Proc. 4th. IEEE Symposium on Logic Programming, Computer Society Press, Washington D.C, 1987.

[25] Da Silva Filho,J. I. Interpretation methods of Paraconsistent Annotated logic with annotation of two values - LPA2v with construction of algorithm and implementation of Electronic Circuits. Doctoral. Thesis (in portuguese), Polytechnic School of the University of São Paulo, POLI / USP São Paulo, Brazil, p. 115, 1999.

[26] Da Silva Filho J. I.; Lambert-Torres, G.; Abe J. M. Uncertainty Treatment Using Paraconsistent Logic - Introducing Paraconsistent Artificial Neural Networks. IOS Press, 328 Volume 211- Frontiers in Artificial Intelligence and Applications, Amsterdam, Netherlands, 2010.

[27] Garcia, D. V.; Da Silva Filho, J. I.; Silveira Jr, L. et al., Analysis of Raman spectroscopy data with algorithms based on paraconsistent logic for characterization of skin cancer lesions. Vibrational Spectroscopy, 103, 2019. DOI: 10.1016/j.vibspec.2019.102929

[28] Da Silva Filho, J. I.; Nunes, V. C.; Garcia, D. V.; Mario, M.C.; Giordano, F.; Abe, J. M.; Pacheco, M. T. T.; Silveira Jr., L. Paraconsistent analysis network applied in the treatment of Raman spectroscopy data to support medical diagnosis of skin cancer. Med. Biol. Eng. Comput. 54, 1-15, 2016). DOI:10.1007/s11517-016-1471-3

[29] Coelho, M. S.; Da Silva Filho, J. I.; Côrtes, H. M. et al. Hybrid PI controller constructed with paraconsistent annotated logic. Control Engineering Practice, 84, 112-124, 2019. DOI: 10.1016/j.conengprac.2018.11.007

[30] Misseno Da Cruz, C.; Rocco, A.; Mario, M.C.; Garcia, D.V.; Lambert-Torres, G.; Abe, J.M.; Torres, C.R.; Da Silva Filho, J.I. Application of Paraconsistent Artificial Neural Network in Statistical Process Control acting on voltage level monitoring in Electrical Power Systems. 18th International Conference on Intelligent System Application to Power Systems, ISAP 201510 November 2015, Porto; Portugal; 11 September 2015 through 17 September 2015; Category number CFP15755-USB; Code 118601, 2015. [31] Da Silva Filho, J. I. A Probabilistic Paraconsistent Logical Model for Non-Relativistic Quantum

Mechanics Using Interlaced Bilattices with Conflation and Bernoulli Distribution. Journal of Quantum Information Science, 7, 89-124. 2017. DOI: 10.4236/jqis.2017.73009

[32] Da Silva Filho, J. I. Undulatory Theory with Paraconsistent Logic (Part I): Quantum Logical Model with Two Wave Functions. Journal of Quantum Information Science, 6, 143-180, 2016. DOI: 10.4236/jqis.2016.63012

[33] Da Silva Filho, J. I. Undulatory Theory with Paraconsistent Logic (Part II): Schrödinger Equation and Probability Representation. Journal of Quantum Information Science, 6, 181-213, 2016. DOI: 10.4236/jqis.2016.63013

[34] Ginsberg, M. L. Multivalued logics: A uniform approach to inference in artificial intelligence, Computational Intelligence 4, 265-316, 1988.

[35] Shannon, C. E. Bell Syst. Tech. J. 27 1948 379{423 and 623{656, DOI:10.1002/j.1538- 7305.1948.tb01338.x.

[36] Shannon, C. E.; Weaver W. The Mathematical Theory of Communication, Illini Books, Illinois, 1949.

[37] Tempesta, P. Beyond the Shannon–Khinchin formulation: The composability axiom and the universal- group entropy. Annals of Physics, 365, 180-197, 2016. DOI: 10.1016/j.aop.2015.08.013

[38] Vedral, V. The role of relative entropy in quantum information theory. Rev. Mod. Phys. 74, 197, 2002.

[39] Nielsen, M. A., Chuang, I. L. Quantum Computation and Quantum Information. Massachusetts Institute of Technology, 702 pages – 2010,ISBN: 9781107002173

[40] Rydberg, J.R. Den Kungliga Svenska Vetenskapsakadem-iens Handlingar 23 (11), 1889.

[41] Nedelman, J. and Wallenius, T. Bernoulli Trials, Poison Trials, Surprising Variances, and Jensens’s Inequality. The American Statistician, 40, 286-289. 1986.

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