Evolution-Operator-Based Single-Step Method for Image Processing

This work proposes an evolution-operator-based single-time-step method for image and signal processing. The key component of the proposed method is a local spectral evolution kernel (LSEK) that analytically integrates a class of evolution partial differential equations (PDEs). From the point of view PDEs, the LSEK provides the analytical solution in a single time step, and is of spectral accuracy, free of instability constraint. From the point of image/signal processing, the LSEK gives rise to a family of lowpass filters. These filters contain controllable time delay and amplitude scaling. The new evolution operator-based method is constructed by pointwise adaptation of anisotropy to the coefficients of the LSEK. The Perona-Malik-type of anisotropic diffusion schemes is incorporated in the LSEK for image denoising. A forward-backward diffusion process is adopted to the LSEK for image deblurring or sharpening. A coupled PDE system is modified for image edge detection. The resulting image edge is utilized for image enhancement. Extensive computer experiments are carried out to demonstrate the performance of the proposed method. The major advantages of the proposed method are its single-step solution and readiness for multidimensional data analysis

A smooth response surface algorithm for constructing gene regulatory network

A smooth response surface algorithm is developed as an elaborate data mining technique for analyzing gene expression data and constructing gene regulatory network. A three-dimensional smooth response surface is generated to capture the biological relationship between the target and activator-repressor. This new technique is applied to functionally describe triplets of activators, repressors and targets, and their regulations in gene expression data. A diagnostic strategy is built into the algorithm to evaluate the scores of the triplets so that those with low scores are kept and a regulatory network is constructed based on this information and existing biological knowledge. The predictions based on the identified triplets in two yeast gene expression data sets agree with some experimental data in the literature. It provides a novel model with attractive mathematical and statistical features that make the algorithm valuable for mining expression or concentration information, assist in determining the function of uncharacterized proteins, and can lead to a better understanding of coherent pathways

Physically-Based Modeling and Characterization of Hot Flow Behavior in an Interphase-Precipitated Ti-Mo Microalloyed Steel

In this contribution, a series of hot compression tests was conducted on a typical interphase-precipitated Ti-Mo steel at relatively higher strain rates of 0.1~10 s−1 and temperatures of 900~1150 °C using a Gleeble-2000 thermo-mechanical simulator. A combination of Bergstrom and Kolmogorov–Johnson–Mehl–Avrami models was first used to accurately predict the whole flow behaviors of Ti-Mo steel involving dynamic recrystallization, under various hot deformation conditions. By comparing the characteristic stresses and material parameters, especially at the higher strain rates studied, the dependence of hot flow behavior on strain rate and deformation temperature was further clarified. The hardening parameter U and peak density ρp exhibited an approximately positive linear relationship with the Zener–Hollomon (Z) parameter, while the softening parameter Ω dropped with increasing Z value. The Avrami exponent nA varied between 1.2 and 2.1 with lnZ, implying two diverse nucleation mechanisms of dynamic recrystallization. The experimental verification was performed as well based on the microstructural evolution and mechanism analysis upon straining. The proposed constitutive models may provide a powerful tool for optimizing the hot working processes of high performance Ti-Mo microalloyed steels with interphase precipitation

Theoretical Study on the Aggregation and Adsorption Behaviors of Anticancer Drug Molecules on Graphene/Graphene Oxide Surface

Graphene and its derivatives are frequently used in cancer therapy, and there has been widespread interest in improving the therapeutic efficiency of targeted drugs. In this paper, the geometrical structure and electronic effects of anastrozole(Anas), camptothecin(CPT), gefitinib (Gefi), and resveratrol (Res) on graphene and graphene oxide(GO) were investigated by density functional theory (DFT) calculations and molecular dynamics (MD) simulation. Meanwhile, we explored and compared the adsorption process between graphene/GO and four drug molecules, as well as the adsorption sites between carriers and payloads. In addition, we calculated the interaction forces between four drug molecules and graphene. We believe that this work will contribute to deepening the understanding of the loading behaviors of anticancer drugs onto nanomaterials and their interaction