Self-consistent analysis of sub-barrier fusion enhancement effect in Ca+Ca and Ni+Ni

The fusion dynamic mechanism of heavy ions at energies near the Coulomb barrier is complicated and still not very clear up to now. Accordingly, a self-consistent method based on the CCFULL calculations has been developed and applied for an ongoing study of the effect of the positive Q-value neutron transfer （PQNT） channels in this work. The typical experimental fusion data of Ca ＋ Ca and Ni ＋ Ni is analyzed within the unified calculation scheme. The PQNT effect in near-barrier fusion is further confirmed based on the self-consistent analysis and extracted quantitatively.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

Nuclear Fusion Within Extremely Dense Plasma Enhanced by Quantum Particle Waves

Quantum effects play an enhancement role in p-p chain reactions occurring within stars. Such an enhancement is quantified by a wave penetration factor that is proportional to the density of the participating fuel particles. This leads to an innovative theory for dense plasma, and its result shows good agreement with independent data derived from the solar energy output. An analysis of the first Z-pinch machine in mankind＇s history exhibiting neutron emission leads to a derived deuterium plasma beam density greater than that of water, with plasma velocities exceeding 10000 km/s. Fusion power could be achieved by the intersection of four such pinched plasma beams with powerful head-on collisions in their common focal region due to the beam and target enhanced reaction.

Fusion of the low-light-level visible and infrared images for night-vision context enhancement

For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details（edges）, the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.

Construction of the Eukaryotic Expression Vector with EGFP and hVE GF121 Gene and its Expression in Rat Mesenchymal Stem Cells

Objectives To construct a recombinant plasmid carrying enhanced green fluore- scent protein (EGFP) and human vascular endothelial growth factor (VEGF) 121 gene and detect its expre- ssion in rat mesenchymal stem cells (MSCs). Methods Human VEGF121 cDNA was amplified with polymerase chain reaction (PCR) from pCD/hVEGF121 and was inserted into the eukaryotic expression vector pEGFP- C1. After being identified with PCR, double enzyme digestion and DNA sequencing. The recombinant plasmid pEGFP/hVEGF121 was transferred into rat MSCs with lipofectamine. The expression of EGFP/VEGF121 fusion protein were detected with fluorescence microscope and immunocytochemical staining respectively. Results The recombinant plasmid was confirmed with PCR, double enzyme digestion and DNA sequencing. The fluoresce- nce microscope and immunocytochemical staining results showed that the EGFP and VEGF121 protein were expressed in MSCs 48 h after transfection. Conclusions The recombinant plasmid carrying EGFP and human VEGF was successfully constructed and expressed positively in rat MSCs. It offers a promise tool for further research on differentiation of MSCs and VEGF gene therapy for ischemial cardiovascular disease.