Study on the Toxicity of Stanching Satin by Internal Embedding in Rats

The toxicity of the new hemostatic material S-100 absorbable stanching satin was studied by embedding it in the body of rats. The staching satin embedded in the abdominal cavity of rats could be absorbed rapidly without any macroscopic residue or organic abnormality in one week. After being embedded outside the dura mater of rats for a week, some stanching satin was found to remain in the form of unadherent small soft balls which disappeared in one more week. In the pathological examination no residue in cells was found in the major organs, and no degeneration, necrosis, hemorrhage or thrombosis in these organs and the vessels was observed. All the biochemical parameters in blood and routine urine examination were in the normal ranges. The results of the three behavior function tests were negative. It showed S-100 stanching satin, embedded in the body of rats for 12 weeks, had neither general toxicity nor adverse effect on the higher nervous activity, general behavior and equilibrium function of rats.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.