MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster R-CNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses. Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. .

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To address the gas flow pattern and pressure drop characteristics for small longcylinder cyclones (SLCCs) in the high operating flow rate range, experimental investigation and computational fluid dynamics (CFD)-based simulation were performed. The pressure drop coefficient depends insignificantly on the Reynolds number at high flow rates. The tangential and axial velocities present the Rankine vortex and the roughly inverted V-shaped distribution, respectively, similar to those in typical cyclones. The CFD simulation approximated well the experimental data of pressure drop. The pressure drop caused by vortex loss, turbulent energy loss, and resistance loss accounted for 72.5 % of the total pressure drop. The Stairmand model was found to be relatively accurate among the classical pressure drop models for the proposed cyclone. The results may help in the design and applications of cyclone separators and reactors.

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