On global stability of a nonresident computer virus model

In this paper, we establish new sufficient conditions for the infected equilibrium of a nonresident computer virus model to be globally asymptotically stable. Our results extend two kind of known results in recent literature.     

An immersed boundary method wall model for high‐Reynolds‐number channel flow over complex topography

High‐Reynolds‐number channel flows regularly encounter topographies composed of multiple length scales and that protrude into the boundary layer. Physically, the presence of immersed obstacles leads to increased velocity gradients, turbulence production, and manifestation of wakes. Considerable challenges are associated with numerically describing the presence of obstacles in channel flows. Common approaches include generation of a computational mesh that is uniquely designed for the flow and obstacle, the immersed boundary method, and terrain‐following coordinates. There are challenges and limitations associated with each of these techniques. Specification of boundary conditions representing the perimeter of solid obstacles is a primary challenge of the immersed boundary method. In this document, a simplistic canopy stress‐like wall model is used to impose boundary conditions. The model isolates aerodynamically relevant local frontal areas through evaluation of the gradient of the topographic height field. The gradient of the height field describes both the surface‐normal direction and the frontal area, making it ideal for detecting areas on which the flow impinges. The model is tested in numerical simulations of turbulent half‐channel flow over topographies with different obstacles affixed–right prisms, rectangular prisms, ellipsoidal mounds, and sinusoids. In all cases, the performance is strong relative to datasets presented in the literature. Results are finally presented for numerical simulation of flow over complex synthetic fractal‐like topography and a synthetic city. These results show interesting trends in how the turbulent multiscale flow field responds to multiscale topography. Copyright © 2012 John Wiley & Sons, Ltd.     

Efficient computation of operator‐type response sensitivities for uncertainty quantification and predictive modeling: illustrative application to a spent nuclear fuel dissolver model

This work honors the 75th birthday of Professor Ionel Michael Navon by presenting original results highlighting the computational efficiency of the adjoint sensitivity analysis methodology for function‐valued operator responses by means of an illustrative paradigm dissolver model. The dissolver model analyzed in this work has been selected because of its applicability to material separations and its potential role in diversion activities associated with proliferation and international safeguards. This dissolver model comprises eight active compartments in which the 16 time‐dependent nonlinear differential equations modeling the physical and chemical processes comprise 619 scalar and time‐dependent model parameters, related to the model's equation of state and inflow conditions. The most important response for the dissolver model is the time‐dependent nitric acid in the compartment furthest away from the inlet, where measurements are available at 307 time instances over the transient's duration of 10.5 h. The sensitivities to all model parameters of the acid concentrations at each of these instances in time are computed efficiently by applying the adjoint sensitivity analysis methodology for operator‐valued responses. The uncertainties in the model parameters are propagated using the above‐mentioned sensitivities to compute the uncertainties in the computed responses. A predictive modeling formalism is subsequently used to combine the computational results with the experimental information measured in the compartment furthest from the inlet and then predict optimal values and uncertainties throughout the dissolver. This predictive modeling methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution for the a priori known mean values and uncertainties characterizing the model parameters and the computed and experimentally measured model responses. This approximate a priori distribution is subsequently combined using Bayes' theorem with the “likelihood” provided by the multi‐physics computational models. Finally, the posterior distribution is evaluated using the saddle‐point method to obtain analytical expressions for the optimally predicted values for the parameters and responses of both multi‐physics models, along with corresponding reduced uncertainties. This work shows that even though the experimental data pertains solely to the compartment furthest from the inlet (where the data were measured), the predictive modeling procedure used herein actually improves the predictions and reduces the predicted uncertainties for the entire dissolver, including the compartment furthest from the measurements, because this predictive modeling methodology combines and transmits information simultaneously over the entire phase‐space, comprising all time steps and spatial locations. Copyright © 2016 John Wiley & Sons, Ltd.     

Nonexistence of global solutions to new ordinary differential inequality and applications to nonlineardispersive equations

A new ordinary differential inequality without global solutions is proposed. Comparison with similar differential inequalities in the well‐known concavity method is performed. As an application, finite time blow up of the solutions to nonlinear Klein–Gordon equation and generalized Boussinesq equation is proven. The initial energy is arbitrary high positive. The structural conditions on the initial data generalize the assumptions used in the literature for the time being. Copyright © 2015 John Wiley & Sons, Ltd.     

Origin and Prediction of Highly Specific Bond Cleavage Sites in the Thermal Activation of Intact Protein Ions

Predicting the fragmentation patterns of proteins would be beneficial for the reliable identification of intact proteins by mass spectrometry. However, the ability to accurately make such predictions remains elusive. An approach to predict the specific cleavage sites in whole proteins resulting from collision-induced dissociation by use of an improved electrostatic model for calculating the proton configurations of highly-charged protein ions is reported. Using ubiquitin, cytochrome c, lysozyme and β-lactoglobulin as prototypical proteins, this approach can be used to predict the fragmentation patterns of intact proteins. For sufficiently highly charged proteins, specific cleavages occur near the first low-basicity amino acid residues that are protonated with increasing charge state. Hybrid QM/QM′ (QM=quantum mechanics) and molecular dynamics (MD) simulations and energy-resolved collision-induced dissociation measurements indicated that the barrier to the specific dissociation of the protonated amide backbone bond is significantly lower than competitive charge remote fragmentation. Unlike highly charged peptides, the protons at low-basicity sites in highly charged protein ions can be confined to a limited sequence of low-basicity amino acid residues by electrostatic repulsion, which results in highly specific fragmentation near the site of protonation. This research suggests that the optimal charge states to form specific sequence ions of intact proteins in higher abundances than the use of less specific ion dissociation methods can be predicted a priori.     

Ideal Free Distribution in Two Patches

In this paper, the formats of Julia sets for a class of nonlinear complex dynamic systems with variable coefficients were studied under certain conditions. For the complex dynamic systems in piecewise cases, we proposed some methods to control the forms of their Julia sets and stable domains analytically. What’s more, we illustrated that our methods worked well by computational simulations. Our work provides a better understanding about how to control the Julia sets of certain complex dynamic systems.