Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering

In this paper, we propose a novel algorithm for medical image segmentation, which combines the density peaks clustering (DPC) with the fruit fly optimization algorithm, and it has the following advantages. Firstly, it avoids the problem of DPC that needs to artificially select parameters (such as the number of clusters) in its decision graph and thus can automatically determine their values. Secondly, our algorithm uses random step size, instead of the fixed step size as in the fruit fly optimization algorithm, which helps avoid falling into local optima. Thirdly, our algorithm selects the cut-off distance and the cluster centers using the image entropy value and can better capture the structures of the image. Experiments on benchmark dataset and proprietary dataset show that our algorithm can adaptively segment medical images with faster convergence and better robustness

Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing

Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, there are still deficiencies with these approaches, such as obvious artificial recovery traces and the distortion of image processing results. This paper proposes a novel enhanced CycleGAN network with an adaptive dark channel prior for unpaired single-image dehazing. First, a Wave-Vit semantic segmentation model is utilized to achieve the adaption of the dark channel prior (DCP) to accurately recover the transmittance and atmospheric light. Then, the scattering coefficient derived from both physical calculations and random sampling means is utilized to optimize the rehazing process. Bridged by the atmospheric scattering model, the dehazing/rehazing cycle branches are successfully combined to form an enhanced CycleGAN framework. Finally, experiments are conducted on reference/no-reference datasets. The proposed model achieved an SSIM of 94.9% and a PSNR of 26.95 on the SOTS-outdoor dataset and obtained an SSIM of 84.71% and a PSNR of 22.72 on the O-HAZE dataset. The proposed model significantly outperforms typical existing algorithms in both objective quantitative evaluation and subjective visual effect

Electrodeposition and Characterization of CuTe and Cu2Te Thin Films

An electrodeposition method for fabrication of CuTe and Cu2Te thin films is presented. The films’ growth is based on the epitaxial electrodeposition of Cu and Te alternately with different electrochemical parameter, respectively. The deposited thin films were characterized by X-ray diffraction (XRD), field emission scanning electronic microscopy (FE-SEM) with an energy dispersive X-ray (EDX) analyzer, and FTIR studies. The results suggest that the epitaxial electrodeposition is an ideal method for deposition of compound semiconductor films for photoelectric applications

Percolation Theory in Solid Oxide Fuel Cell Composite Electrodes with a Mixed Electronic and Ionic Conductor

Percolation theory is generalized to predict the effective properties of specific solid oxide fuel cell composite electrodes, which consist of a pure ion conducting material (e.g., YSZ or GDC) and a mixed electron and ion conducting material (e.g., LSCF, LSCM or CeO2). The investigated properties include the probabilities of an LSCF particle belonging to the electron and ion conducting paths, percolated three-phase-boundary electrochemical reaction sites, which are based on different assumptions, the exposed LSCF surface electrochemical reaction sites and the revised expressions for the inter-particle ionic conductivities among LSCF and YSZ materials. The effects of the microstructure parameters, such as the volume fraction of the LSCF material, the particle size distributions of both the LSCF and YSZ materials (i.e., the mean particle radii and the non-dimensional standard deviations, which represent the particle size distributions) and the porosity are studied. Finally, all of the calculated results are presented in non-dimensional forms to provide generality for practical application. Based on these results, the relevant properties can be easily evaluated, and the microstructure parameters and intrinsic properties of each material are specified

P-21 Activated Kinases in Liver Disorders

The p21 Activated Kinases (PAKs) are serine threonine kinases and play important roles in many biological processes, including cell growth, survival, cytoskeletal organization, migration, and morphology. Recently, PAKs have emerged in the process of liver disorders, including liver cancer, hepatic ischemia-reperfusion injury, hepatitis, and liver fibrosis, owing to their effects in multiple signaling pathways in various cell types. Activation of PAKs promotes liver cancer growth and metastasis and contributes to the resistance of liver cancer to radiotherapy and chemotherapy, leading to poor survival of patients. PAKs also play important roles in the development and progression of hepatitis and other pathological processes of the liver such as fibrosis and ischemia-reperfusion injury. In this review, we have summarized the currently available studies about the role of PAKs in liver disorders and the mechanisms involved, and further explored the potential therapeutic application of PAK inhibitors in liver disorders, with the aim to provide a comprehensive overview on current progress and perspectives of PAKs in liver disorders