Despite its benign histopathology, the treatment of craniopharyngioma remains one of the most formidable challenges faced by skull base surgeons. The technical challenges of tackling these complex central skull base lesions are paralleled by clinical challenges related to their unique tumor biology and the often-complex decision making required. In this article, we critically appraise the most recent literature to explore the challenges and controversies surrounding the management of these lesions. The role of curative resections and the shift in the surgical paradigm toward the multidisciplinary goal-directed management approach are discussed.Surgery is the main treatment option for the management of craniopharyngiomas. Transcranial microsurgical approaches, such as pterional and subfrontal approaches, have constituted the classic operative strategy for resection of these tumors. However, the development of endoscopic endonasal approaches has revolutionized the treatment of craniopharyngiomas in the last 15 years, and endoscopic resection is favored for most craniopharyngiomas. In this article, we discuss our experience with the management of craniopharyngiomas and review the current results of the surgical treatment of those tumors, including discussion of goals of surgery, complications, recurrences, and the role of adjuvant treatment.This article presents the results of a study of the sieve mill of a grain cleaning machine with a drive based on a linear asynchronous motor instead of a classic mechanical drive. The purpose of this work is to describe the structural and technological parameters of a sieve mill with a linear asynchronous drive to implement a mathematical model of the technological process of a grain cleaning machine work. A kinematic study of the flat hinged mechanism of the sieve mill of a grain cleaning machine was carried out, for which all geometric dimensions are known and the laws of motion of the leading link - the electric drive of the sieve mill based on a linear asynchronous motor are determined. As a result, the following were determined kinematic modes k P > k B > k H of sieve mill vibrations under various technological conditions; laws of motion of all parts of the mechanism of the sieve mill, movement, speed (0.34 ... 0.36 m/s) and acceleration (5.8 ... 6.9 m/s2) of the driven links; a mathematical model of the kinematic scheme of a sieve mill of a grain cleaning machine with a drive from a linear induction motor has been developed. The use of a linear induction motor compared to existing (classical) drive designs as a drive of a sieve mill in a grain-cleaning machine significantly reduces the metal consumption of the structure (drive shafts, transmission mechanisms, connecting rods, bearings are excluded from the structure), and energy consumption is also reduced due to pulse drive operation; makes it possible in a wide range of technological parameters regulation for various crops, including various physical and mechanical parameters of the crop being cleaned.A periodically forced Filippov forest-pest model incorporating threshold policy control and integrated pest management is proposed. It is very natural and reasonable to introduce Filippov non-smooth system into the ecosystem since there were many disadvantageous factors in pest control at fixed time and the threshold control according to state variable showed rewarding characteristics. The main aim of this paper is to quest the association between pests dynamics and system parameters especially the economical threshold ET, the amplitude and frequency of periodic forcing term. From the view of pest control, if the maximum amplitude of the sliding periodic solution does not exceed economic injury level(EIL), the sliding periodic solution is a desired result for pest control. The Filippov forest-pest model exhibits the rich dynamic behaviors including multiple attractors coexistence, period-adding bifurcation, quasi-periodic feature and chaos. At certain frequency of periodic forcing, the varying system initial densities trigger the system state switch between different attractors with diverse amplitudes and periods. 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. 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. https://www.selleckchem.com/products/LY2784544.html 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.In this paper we develop a compartmental epidemic model to study the transmission dynamics of the COVID-19 epidemic outbreak, with Mexico as a practical example. In particular, we evaluate the theoretical impact of plausible control interventions such as home quarantine, social distancing, cautious behavior and other self-imposed measures. We also investigate the impact of environmental cleaning and disinfection, and government-imposed isolation of infected individuals. We use a Bayesian approach and officially published data to estimate some of the model parameters, including the basic reproduction number. Our findings suggest that social distancing and quarantine are the winning strategies to reduce the impact of the outbreak. Environmental cleaning can also be relevant, but its cost and effort required to bring the maximum of the outbreak under control indicate that its cost-efficacy is low.