The transmission of infectious disease in epidemiological models usually is based on the assumption that population within random-mixing. Although medical developments can reduce the consequences of the spread of infectious diseases, prevention of plague remains a major toehold. After a model is formulated containing the main fitur the development and transmission of infectious disease, onward to the model can be used to predict, making eradication strategies, control or prevent the spread. Modeling the spread of the disease has the potential to improve the quality of human life. The social life of humans far more complex exceed a diverse population. The transmission dynamics of infectious diseases is sensitive to patterns the interaction between the individual vulnerable (susceptible) and contracted (infectious). Human social contact very heterogeneous group. To predict the impact of this pattern against the transmission of infectious diseases, the use of epidemiological random network model, where the nodes serves individuals exposed, contracting or cured and connectedness presents contact transmission. Type the model spread (epidemic) that examined the model type is exposed, tetular exposed, and cured, or better known as a type of SIRS.
In this research, the (2 + 1)-dimensional (D) variable-coefficient (VC) Caudrey–Dodd–Gibbon–Kotera–Sawada model used in soliton hypothesis and implemented by operating the Hirota bilinear scheme is studied. A few modern exact analytical outcomes containing interaction between a lump-two kink soliton, interaction between two-lump, the interaction between two-lump soliton, lump-periodic, and lump-three kink outcomes for the (2 + 1)-D VC Caudrey–Dodd–Gibbon–Kotera–Sawada equation by Maple Symbolic packages are obtained. By employing Hirota’s bilinear technique, the extended soliton solutions according to bilinear frame equation are received. For this model, the contemplated model can be got by multi-D binary Bell polynomials (bBPs). In addition, the analytical analysis of the high-order soliton outcomes to present the discipline of outcomes. The effect of the free parameters on the behavior of acquired figures of a few obtained solutions for the nonlinear rational exact cases was also discussed. The above technique could also be employed to get exact solutions for other nonlinear models in physics, applied mathematics, and engineering.
Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.
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