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The spontaneous combustion of coal pile is very easy to cause fires, especially in the closed coal yards. Early detection and disposal of spontaneous-combustion-point (SCP) could avoid large losses. However, the traditional SCP detection methods are confronted with problem of hysteresis, and cannot effectively detect the small SCP in early stage. This paper proposes a novel detection method based on Yolov5s, by using “FPN+ PAN” in the Neck layer to enhance the SCP expressions. To improve the performance of the model in learning the characteristic of small SCP, the block labeling approach is applied in the labeling process. In addition, TensorRT is adopted to accelerate the forward inference of the model, and the deployment test is carried out on Jetson nano. Through comparative analysis, the proposed method outperforms yolov3s and yolov4s in detecting the SCP in very early stage (smoke or flame), and achieves 7 FPS speed in real-time video stream.

The Lipid Mass Spectrum Analysis Database (LMSAD) is an object-relational database encompassing MS data, structures and annotations of biologically relevant lipids. The initial version of this database contains about 80 records, representing mainly sphingolipids. LMSAD supports data searching by any combination of these data fields: LMSAD ID, systematic or common name, mass, formula and category data fields. Search results, in addition to annotations and structure, also include mass spectral data information, which will be shown by an applet tool named LipidMSViewer. LipidMSViewer provides some useful functions to analyze mass spectral data and identify unknown lipids obtained by experiments. The LMSAD is publicly available at http://lipid.zju.edu.cn/

Cardiovascular disease is the number one cause of death today in developed countries and is projected to remain so. Cardiac intervention is a minimally invasive solution to diagnose and treat the disease. Training of young cardiologists is playing an important role towards quality cardiac healthcare. The advance of interactive and graphics technologies has offered a new avenue to train young cardiologists using virtual patients which can tremendously reduce the cost and risk. This paper presents a novel interactive simulation for cardiac intervention. Two-body collision between the catheter and the heart wall is detected based on a boundary representation. GPU based algorithms are developed to enhance the performance of simulator for real-time and realistic cardiac intervention.