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A crucial aspect of contemporary image processing systems is image denoising. The anisotropic diffusion function is a feature of the partial differential equation employed for the purpose of noise reduction and the preservation of image characteristics such as edges. A new tangent sigmoid diffusion coefficient and a new adaptive threshold parameter have been proposed in this work, which leads to faster convergence. In comparison to traditional anisotropic diffusion model techniques, the proposed technique performs admirably. As evidenced by the results, which demonstrate that the new anisotropic diffusion technique is not only capable of efficiently removing noise, but also of maintaining content in the denoised image. The performance of the proposed method is evaluated using various metrics, including peak signal-to-noise ratio, convergence rate, structural similarity index, time complexity, and space complexity. When comparing the proposed approach to previous methods, it is evident that the proposed method outperforms in various aspects. These include a higher convergence rate (− 0.1278), a greater peak signal-to-noise ratio (37.9827 dB), a higher structural similarity index (0.97432), a lower time complexity (5.72 s), and a smaller space complexity (15.6 KB).

In this paper, we construct the NUS 1 S 2 A voters model of two political fanaticism figures which involves undecided and swing voters. We determine the equilibrium points and the threshold parameter of the voters model. We also perform a sensitivity analysis for the threshold number to determine the importance of model parameters. The results of the sensitivity analysis show that the rate of transfer from neutral voters to undecided and swing voters is not the most negative sensitive parameter of the model, even though an increase in its parameter will cause a decrease in voter interest in voting in the presidential elections.

In this article, we discuss the qualitative analysis and develop an optimal control mechanism to study the dynamics of the novel coronavirus disease (2019-nCoV) transmission using an epidemiological model. With the help of a suitable mathematical model, health officials often can take positive measures to control the infection. To develop the model, we assume two disease transmission sources (humans and reservoirs) keeping in view the characteristics of novel coronavirus transmission. We formulate the model to study the temporal dynamics and determine an optimal control mechanism to minimize the infected population and control the spreading of the novel coronavirus disease propagation. In addition, to understand the significance of each model parameter, we compute the threshold quantity and perform the sensitivity analysis of the basic reproductive number. Based on the temporal dynamics of the model and sensitivity analysis of the threshold parameter, we develop a control mechanism to identify the best control policy for eradicating the disease. We then conduct numerical experiments using large-scale numerical simulations to validate the theoretical findings.

The onset of nucleate boiling (ONB) and pressure drop during subcooled flow boiling in microchannels are key parameters for various applications, especially for the design of high-performance evaporators. However, available experimental data are limited, and existing correlations often show significant deviations. This work presents new ONB and pressure drop data obtained in a microchannel test section with an internal diameter of 1.1 mm, using R514a and R123 as working fluids. Experiments were conducted under high heat and mass flux conditions, ranging from 90 to 1808 kW/m 2 and 1500 to 7300 kg/m 2 s, respectively, with saturation temperatures between 40 and 55 ° C. Results show that both heat and mass flux influence the wall superheat required to initiate ONB. Comparisons with existing predictive methods reveal that traditional ONB correlations tend to underestimate the superheat, consistent with previous microchannel studies. A new ONB predictive method is proposed, incorporating effects of surface tension, viscosity, inertia, evaporation momentum, and turbulence. Additionally, a new microchannel threshold criteria based on ONB observations is introduced. The subcooled flow boiling pressure drop results indicate that the pressure drop increased with the increasing of mass flux, heat flux, and vapor quality, while predictive models could predict this data with mean absolute errors as low as 9.0%.

The spread of rumors in online social networks (OSNs) has caused a serious threat to the normal social order. In order to describe the rumor-spreading dynamics in OSNs during emergencies, a novel model with consideration of fans versus ordinary people is proposed in this paper. In contrast to previous studies, we consider the case that two rumors exist simultaneously. It is assumed that one is an entertainment rumor that fans care about, and the other is a common rumor. First, we derive the mean-field equations that describe the dynamics of this dual rumor propagation model and obtain the threshold parameter. Secondly, after finding the necessary and sufficient conditions for the existence of equilibriums, we examine the equilibrium’s local and global stability. Finally, simulations are used to explain how various parameters affect the process of spreading rumors.

PurposeThe present study aims to demonstrate the application of newly developed combinative distance-based assessment (CODAS) approach for grading and selection of Tossa jute fibres, which possesses some unique features uncommon to other variants of multi-criteria decision-making (MCDM) method.Design/methodology/approachThe CODAS method was used in this study to rank/grade ten candidate lots of Tossa fibres on the basis of six apposite jute fibre properties, namely, fibre defect, root content, fineness, strength, colour and density. These six fibre properties were considered as the six decision criteria, here, and they were assigned weights as determined previously by an earlier researcher using analytic hierarchy process. The grading of jute fibres was done based on a comprehensive single index known as the assessment scores (Hi), in descending order of magnitude.FindingsAmong the 10 Tossa jute lots, T2 was ranked 1 (top grade) because of the highest assessment score of 6.887, followed by T1 with Rank 2 (assessment score 1.830). Because of the least assessment score of −2.795, the candidate lot T4 was considered as the worst, and hence ranked 10. The overall ranking pattern given by the CODAS method was similar to the TOPSIS approach done by Ghosh and Das (2013). This study was supported by various sensitivity analyses to judge the efficacy of the present approach. No occurrence of rank reversal during the sensitivity analyses obviously corroborates the robustness and stability of the CODAS method.Practical implicationsJute pricing is fixed solely by the quality for which grading of fibre is prerequisite. The traditional “Hand and Eye” method or Bureau of Indian Standards (BIS) system for jute grading is basically subjective assessment and need domain expertise. MCDM is reported as the most viable solution which gives due importance to the fibre parameters while grading the fibres based on a single index. The present study demonstrates the maiden application of CODAS to address the fibre grading problems for jute industries. This approach can also be extended to solve other decision problems of textile industry, in general.Originality/valueCODAS is a recently developed exponent of MCDM. Uniqueness of the present study lies in the fact that this is the first ever application of CODAS in the domain of textile industry, in general, and jute industry, in particular. CODAS approach is very simple involving a few simple mathematical equations yet a potent tool of decision-making. This method possesses some features uncommon in other variants of MCDM. Moreover, the efficacy of CODAS method is investigated through various sensitivity analyses, which has been ignored in the earlier approaches.

In this contribution, we improve of the performance of the Rectified Linear Unit Memristor Like Activation Function with the implication to help training process of CNN without a lot of epochs by computing the best value of the flatness network parameter ( p ). In this regards the flatness network parameter threshold ( p ) has been investigated and a good performance of the activation function has been discovered at p  = 4.5 . We firstly used the MNIST and the CIFAR-10 datasets to trained and test the Alex-Net architecture model of convolutional neural network (CNN) and we showed better performances of Rectified Linear Unit Memristor-like Activation Function compared to those of the literature. We noticed that the performance of Alex-Net also improved and the better performance was recorded when p  = 4.5 with 99.50%, 99.25%, 98.81% for training, validation and testing accuracy respectively when using the MNIST. These results open the outcome to reduce the training time of neural networks when this activation function is used.

Ultrasonic additive manufacturing (UAM) is an advanced additive manufacturing technique that utilizes ultrasonic energy to rapidly joining thin metal tapes into solid parts in a layer accumulating manner. In this study, the effects of processing parameters on the bond properties of UAM samples were investigated via peel tests, linear weld density (LWD) measurements, microhardness tests and EBSD. The results reveal that, in terms of the overall tendency, the peeling strength and LWD increase with the increasing amplitude and normal force settings. However, a parameter threshold phenomenon and two different mechanisms that affect the bond properties were also observed. Furthermore, the microstructure evolution results show that the development of the interface is closely related to the applied parameters, which can also well explain the bond property variations and the parameter threshold phenomenon.

Cigarette smoking on college campuses has become a significant public health issue, which in turn led to an increasing focus on establishing programs to reduce its prevalence. In this paper, a compartmental model depicting the spread and cessation of the smoking habit on college campuses, obtained using theoretical principles often employed in mathematical epidemiology, is proposed and analysed. The existence and stability of the habitual smoking-free and habitual smoking-persistent equilibria, respectively, are explored in terms of a threshold parameter, hereby called the smokers generation number and denoted by Rc . A sensitivity analysis indicates that Rc is the most sensitive to the contact rate between habitual-smokers and occasional-smokers and to the rate of successfully quitting smoking. Numerical simulations of the proposed optimal control strategies reveal that the most effective approach to reduce the prevalence of cigarette smoking and possibly achieve a smoking-free campus should combine both control measures, namely allocating mandatory smoking rooms together with educating the public on the harmful effects of smoking and providing large scale guidance, counselling and support therapy to help students quit smoking.

We investigate a non-autonomous discrete-time SIRS epidemic model with nonlinear incidence rate and distributed delays combined with a nonlinear recovery rate taken into account the impact of health care resources. Two threshold parameters$ \\mathcal{R}_0, \\mathcal{R}_\\infty $are obtained so that the disease dies out when$ \\mathcal{R}_0 < 1 $ ; and the infective persists indefinitely when$ \\mathcal{R}_\\infty > 1 $ .