查看更多>>摘要：Recent outbreaks of novel infectious diseases (e.g., COVID-19, H2N3) have highlighted the threat of pathogen transmission, and vaccination offers a necessary tool to relieve illness. However, vaccine effi-cacy is one of the barriers to eradicating the epidemic. Intuitively, vaccine efficacy is closely related to age structures, and the distribution of vaccine efficacy usually obeys a Gaussian distribution, such as with H3N2 and influenza A and B. Based on this fact, in this paper, we study the effect of vaccine efficacy on disease spread by considering different age structures and extending the traditional susceptible-infected-recovery/vaccinator(SIR/V) model with two stages to three stages, which includes the decision-making stage, epidemic stage, and birth-death stage. Extensive numerical simulations show that our model gen-erates a higher vaccination level compared with the case of complete vaccine efficacy because the vacci-nated individuals in our model can form small and numerous clusters slower than that of complete vac-cine efficacy. In addition, priority vaccination for the elderly is conducive to halting the epidemic when facing population ageing. Our work is expected to provide valuable information for decision-making and the design of more effective disease control strategies. (c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Dempster-Shafer evidence theory is an extension of classical probability theory in the evidential environment. Evidential environment is an environment in which Dempster-Shafer evidence theory is used. The decomposable entropy for the Dempster-Shafer evidence theory can efficiently decompose the Shannon entropy for the Dempster-Shafer evidence theory, and has high theoretical and application value. This article proposes the decomposable Deng entropy, which is an extension of the decomposable entropy for the Dempster-Shafer evidence theory. The decomposable Deng entropy can effectively decompose the Deng entropy. When the cardinalities of all focal elements of a mass function are 1, then the decomposable Deng entropy will collapse to the decomposable entropy for the Dempster-Shafer evidence theory. Many calculation examples are used to verify the performance of the proposed model in decomposing Deng entropy. Experimental results show that the proposed model can efficiently decompose the Deng entropy.(c) 2022 Elsevier Ltd. All rights reserved.
She, Zi-HangRahman, Mati UrQu, Hai-DongLiu, Xuan...
11页
查看更多>>摘要：In this article, neural network method (NNM) is presented to solve the spatiotemporal variable-order fractional advection-diffusion equation with a nonlinear source term. The network is established by using shifted Legendre orthogonal polynomials with adjustable coefficients. According to the properties of variable fractional derivative, the loss function of neural network is deduced theoretically. Assume that the source function satisfies the Lipschitz hypothesis, the reasonable range for learning rate is discussed in details. Then neural networks are trained repeatedly on the training set to reduce the loss functions for two numerical examples. Numerical results show that the neural network method is better than the finite difference method in solving some nonlinear variable fractional order problems. Finally, several graphs and some numerical analysis are given to confirm our conclusions.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Tungiasis, a neglected seasonal disease, leads to long-term injury and life threats to humans in developing countries. In this paper, we investigate the optimal control of tungiasis disease with an uncertain param-eter, where both epidemic prevention and economic concerns are considered. Based on the life cycle of jiggers and propagation process of the disease, a human-jigger parasite model with control schemes for humans and jiggers is established. The effect of controls on the control reproduction number is discussed. A robust bi-objective optimal control problem is proposed, in which the accumulated number of infected humans and control cost, and their sensitivities to the uncertain parameter are all in the vector objective. Since the objective vector contains non-standard sensitivity terms, it is difficult to solve this problem using conventional optimization techniques. By introducing an auxiliary initial value system, we trans -form this problem into a standard form. Furthermore, a numerical method which combines a modified normal boundary intersection scheme with the interior point scheme is constructed. Finally, numerical simulations for different seasons are carried out using actual data of humans and jiggers in Nigeria. From all results, we conclude that enhancing the jigger adulticiding effort can significantly reduce the control reproduction number; the intervention control measures should be carried out in time, especially in the dry season; the obtained Pareto points can provide decision-makers with a trade-off between the two goals and choose an appropriate policy to implement.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：In the present paper, we investigate both the finite-time and fixed-time synchronization of retarded shunting inhibitory cellular neural networks. By constructing suitable Lyapunov functions and feedback control schemes we derive several sufficient conditions to guarantee finite-time and fixed-time synchronization of such networks. Finally, to illustrate the effectiveness of our theoretical results we consider examples with numerical simulations.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：During the COVID-19 pandemic, predicting case spikes at the local level is important for a precise, targeted public health response and is generally done with compartmental models. The performance of compartmental models is highly dependent on the accuracy of their assumptions about disease dynamics within a population; thus, such models are susceptible to human error, unexpected events, or unknown characteristics of a novel infectious agent like COVID-19. We present a relatively non-parametric random forest model that forecasts the number of COVID-19 cases at the U.S. county level. Its most prioritized training features are derived from easily accessible, standard epidemiological data (i.e., regional test positivity rate) and the effective reproduction number ( R t ) from compartmental models. A novel input training feature is case projections generated by aligning estimated effective reproduction number (pre-computed by COVIDActNow.org) with real time testing data until maximally correlated, helping our model fit better to the epidemic's trajectory as ascertained by traditional models. Poor reliability of R t is partially mitigated with dynamic population mobility and prevalence and mortality of non-COVID-19 diseases to gauge population disease susceptibility. The model was used to generate forecasts for 1, 2, 3, and 4 weeks into the future for each reference week within 11/01/2020 - 01/10/2021 for 3068 counties. Over this time period, it maintained a mean absolute error (MAE) of less than 300 weekly cases/10 0,0 0 0 and consistently outperformed or performed comparably with gold-standard compartmental models. Furthermore, it holds great potential in ensemble modeling due to its potential for a more expansive training feature set while maintaining good performance and limited resource utilization. (c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：In self-organized criticality (SOC) models, as well as in standard phase transitions, criticality is only present for vanishing external fields h -> 0 . Considering that this is rarely the case for natural systems, such a restriction poses a challenge to the explanatory power of these models. Besides that, in models of dissipative systems like earthquakes, forest fires, and neuronal networks, there is no true critical behavior, as expressed in clean power laws obeying finite-size scaling, but a scenario called "dirty" criticality or self-organized quasi-criticality (SOqC). Here, we propose simple homeostatic mechanisms which promote self-organization of coupling strengths, gains, and firing thresholds in neuronal networks. We show that with an adequate separation of the timescales for the coupling strength and firing threshold dynamics, near criticality (SOqC) can be reached and sustained even in the presence of significant external input. The firing thresholds adapt to and cancel the inputs ( h decreases towards zero). Similar mechanisms can be proposed for the couplings and local thresholds in spin systems and cellular automata, which could lead to applications in earthquake, forest fire, stellar flare, voting, and epidemic modeling.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Background: Visual analysis to identify inter-ictal activity in scalp EEG to support the diagnosis of epilepsy is a challenging task, which is embarked on by an experienced neurologist. Inter-Ictal state is a phase between convolutions (seizures) that are a feature of epilepsy disorder. The objective of this work is to automate the process of identification of inter-ictal activity and to distinguish it from the activity of a controlled patient with and without presence of artifacts Methods: In this work, we have used two-second scalp EEG data. The novel data is collected from Max Super Speciality Hospital, Saket, New Delhi. Expert neurologists mark the data according to the exclusion and inclusion criterion presented and approved by the scientific and ethical committee. Under our archi-tecture, we have first divided the EEG data collected from the patients into two-second segments. The two-second EEG signal is converted to scalograms used as input to fourteen layer novel Residual neu-ral network architecture. For comparison we have created fourteen layer convolution neural network and sixteen layer model where CNN and LSTM models are stacked. For this work we have worked on two cases, the first group is a comparison between intect-ictal and controlled, while the second group is a classification between inte-ictal vs (different artifacts and controlled). Results: We have evaluated our model based on six parameters Accuracy, Sensitivity, Specificity, Precision, Recall, and AUC. Under this architecture, we have divided the complete data set into two parts 80% of data is training data on which k-fold validation is being applied. The value of k is set to 10. The rest, 20%, is used as testing data on which the performance of the model is evaluated. The developed model (RNN) has provided outstanding results in identifying the inter-ictal activity, detecting test dataset with 97.98% accuracy, and has achieved an AUC value of .9974 without the presence of artifacts accuracy of 91.42% and AUC value of 0.9698, has been acheived.Conclusion: Residual neural network in its two-dimensional implementation with fourteen layers has outperformed the two other models developed on similar lines. This research suggests that the proposed architecture has the potential to be utilized in the real-time clinical setup.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：This paper considers pricing of European-style vulnerable options under the Heston stochastic volatility and stochastic interest rate model in which the mean-reversion levels of both variance and interest rate processes are modulated by a continuous-time Markov process with a finite state space. An analytical pricing formula is derived by using the Esscher transform, joint characteristic function and multivariate Fourier transform technique, where the closed-form solution of the characteristic function is obtained by the law of iterated expectation. Then we provide the efficient approximation to calculate the analytical pricing formula of option using the FFT approach and examine the accuracy of the approximation by Monte Carlo simulation. Finally, the sensitivity analysis of different parameters in the proposed model on the vulnerable call option price and its Delta value are provided, and the difference between the proposed model and the Heston and stochastic interest rate model with non-Markov regime-switching are presented by some numerical experiments, which shows the influence of introducing regime-switching into Heston model with stochastic interest rate.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：As the climate problem continues to worsen, carbon trading markets for energy conservation and emission reduction have been established in many countries. Accurate forecasting of carbon trading prices is not only a realistic problem, but also brings huge challenges to relevant researches. In this study, a novel predicting model is proposed to predict carbon price. And this model combines the advantages of the improved variational mode decomposition (IVMD) algorithm, multiscale entropy (MSE) algorithm, and the extreme learning machine (ELM) model improved by the intelligent optimization algorithm. Firstly, center frequency (CF) and mutual information (MI) entropy are utilized to jointly determine the number of decomposition layers of the variational mode decomposition (VMD), and avoid the problem of excessive decomposition. Subsequently, the complexity of each intrinsic mode function (IMF) from the improved variational mode decomposition is calculated by multiscale entropy, and intrinsic mode functions are recombined to reduce the complexity of subsequent modeling. At the last, the extreme learning machine optimized by the sparrow search algorithm (SSA) is adopted to model and predict the different sequence combinations. The performance indicators of the proposed model are significantly lower than others. For example, the root mean square error (RMSE) of the proposed model is 0.6653 in Hubei market, 0.9719 in Guangdong market and 1.2819 in Shanghai market. Additionally, the optimized extreme learning machine model is more suitable for the prediction of time series, which also provides an effective forecasting tool for related researchers. (C) 2021 Elsevier Ltd. All rights reserved.
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