• Bryan Merrill posted an update 6 months, 3 weeks ago

    Besides, parameters sensitivity analysis shows that the pest could be controlled at a certain level by choosing suitable parameters.In this paper, we investigate the relationship between the air pollution and tuberculosis cases and its prediction in Jiangsu, China by using the time-series analysis method, and find that the seasonal ARIMA(1, 1, 0)×(0, 1, 1)12 model is the preferred model for predicting the TB cases in Jiangsu, China. Furthermore, we evaluate the relationship between AQI, PM2.5, PM10 and the number of TB cases, and find that the prediction accuracy of the ARIMA model is improved by adding monthly PM2.5 with 0-month lag as an external variable, i.e., ARIMA(1, 1, 0)×(0, 1, 1)12+PM2.5. The results show that ARIMAX model can be a useful tool for predicting TB cases in Jiangsu, China, and it can provide a scientific basis for the prevention and treatment of TB.Secret image sharing (SIS) is an important research direction in information hiding and data security transmission. Since the generated shadow images (shares) are always noise-like, it is difficult to distinguish the fake share from the unauthorized participant before recovery. Even more serious is that an attacker with a fake share can easily collect shares of other honest participants. As a result, it is significant to verify the shares, before being taken out for recovery. Based on two mainstream methods of SIS, such as polynomial-based SIS and visual secret sharing(VSS), this paper proposed a novel compressed SIS with the ability of shadow image verification. Considering that the randomness of the sharing phase of polynomial-based SIS can be utilized, one out of shares of (2, 2)-threshold random-grid VSS is embedded into all shares of polynomial-based SIS by a XOR operation as the verification information, while the other binary share is private for verification. Before recovery, each participant must extract the binary share from the grayscale share to perform XOR operation with the private share, and the original binary image can be recovered only with the true share. The proposed scheme also has the characteristics of shadow image verification, pixel compression, loss tolerance and lossless recovery. Through experiments and comparative analysis of related research results, the effectiveness and advantages of the method are verified.The complexity of oncolytic virotherapy arises from many factors. In this study, we incorporate environmental noise and stochastic effects to our basic deterministic model and propose a stochastic model for viral therapy in terms of Ito stochastic differential equations. We conduct a detailed analysis of the model using boundary methods. We find two combined parameters, one describes possibilities of eradicating tumors and one is an increasing function of the viral burst size, which serve as thresholds to classify asymptotical dynamics of the model solution paths. We show there are three ergodic invariant probability measures which correspond to equilibrium states of the deterministic model, and extra possibility to eradicate tumor due to strong variance of tumor growth rate and medium viral burst size. Numerical analysis demonstrates several typical solution paths with biological explanations. In addition, we provide some medical interpretations and implications.The detection of neural spikes plays an important role in studying and processing extracellular recording signals, which promises to be able to extract the necessary spike data for all subsequent analyses. The existing algorithms for spike detection have achieved great progress but there still remains much room for improvement in terms of the robustness to noise and the flexibility in the spike shape. To address this issue, this paper presents a novel method for spike detection based on the theory of sparse representation. By analyzing the characteristics of extracellular neural recordings, a targetdriven sparse representation framework is firstly constructed, with which the neural spike signals can be effectively separated from background noise. In addition, considering the fact that the spikes emitted by different neurons have different shapes, we then learn a universal dictionary to give a sparse representation of various spike signals. Finally, the information (location and number) of spikes in the recorded signal are achieved by comprehensively analyzing the sparse features. Experimental results demonstrate that the proposed method outperforms the existing methods in the spike detection problem.This paper formulates and analyzes a modified Previte-Hoffman food web with mixed functional responses. We investigate the existence, uniqueness, positivity and boundedness of the proposed model’s solutions. The asymptotic local and global stability of the steady states are discussed. Cenicriviroc Analytical study of the proposed model reveals that it can undergo supercritical Hopf bifurcation. Furthermore, analysis of Turing instability in spatiotemporal version of the model is carried out where regions of pattern creation in parameters space are obtained. Using detailed numerical simulations for the diffusive and non-diffusive cases, the theoretical findings are verified for distinct sets of parameters.In this paper, we consider a cholera infection model with vaccination and multiple transmission pathways. Dynamical properties of the model are analyzed in detail. It is shown that the disease-free equilibrium is globally asymptotically stable if the basic reproduction number is less than unity; the endemic equilibrium exists and is globally asymptotically stable if the basic reproduction number is greater than unity. In addition, the model is successfully used to fit the real disease situation of cholera outbreak in Somalia. We consider an optimal control problem of cholera transmission with vaccination, quarantine, treatment and sanitation control strategies, and use Pontryagin’s minimum principle to determine the optimal control level. The optimal control problem is solved numerically.Many diseases, such as HIV, are heterogeneous for risk. In this paper, we study an infectious-disease model for a population with demography, mass-action incidence, an arbitrary number of risk classes, and separable mixing. We complement our general analyses with two specific examples. In the first example, the mean of the components of the transmission coefficients decreases as we add more risk classes. In the second example, the mean stays constant but the variance decreases. For each example, we determine the disease-free equilibrium, the basic reproduction number, and the endemic equilibrium. We also characterize the spectrum of eigenvalues that determine the stability of the endemic equilibrium. For both examples, the basic reproduction number decreases as we add more risk classes. The endemic equilibrium, when present, is asymptotically stable. Our analyses suggest that risk structure must be modeled correctly, since different risk structures, with similar mean properties, can produce different dynamics.

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