UAEC combined with vacuum aspiration is technically feasible and may help avoid laparotomy in women with cesarean scar pregnancy.

For the problem of large noise interference in gearbox fault diagnosis and inaccurate time-frequency resolution of analysis signals, a method for analyzing acoustic emission signals using Hilbert-Huang transform method was proposed. Firstly, use the acoustic emission method to collect the fault signal of the gearbox, which reduces the interference of environmental noise. Then perform domain of time analysis on the single, and the time-frequency analysis was performed by short-time Fourier transform, wavelet transform and Hilbert-Huang transform. The results show that the Hilbert-Huang transform method is the best to analyze the acoustic emission signal, and the frequency of the fault can be analyzed accurately, and the time-frequency aggregation is accurate, which verifies the superiority of the method in gear fault diagnosis.

As one of the four most important woody oil-tree in the world, Camellia oleifera has significant economic value. Rapid and accurate acquisition of C. oleifera tree-crown information is essential for enhancing the effectiveness of C. oleifera tree management and accurately predicting fruit yield. This study is the first of its kind to explore training the ResU-Net model with UAV (unmanned aerial vehicle) images containing elevation information for automatically detecting tree crowns and estimating crown width (CW) and crown projection area (CPA) to rapidly extract tree-crown information. A Phantom 4 RTK UAV was utilized to acquire high-resolution images of the research site. Using UAV imagery, the tree crown was manually delineated. ResU-Net model’s training dataset was compiled using six distinct band combinations of UAV imagery containing elevation information [RGB (red, green, and blue), RGB-CHM (canopy height model), RGB-DSM (digital surface model), EXG (excess green index), EXG-CHM, and EXG-DSM]. As a test set, images with UAV-based CW and CPA reference values were used to assess model performance. With the RGB-CHM combination, ResU-Net achieved superior performance. Individual tree-crown detection was remarkably accurate (Precision = 88.73%, Recall = 80.43%, and F1score = 84.68%). The estimated CW (R2 = 0.9271, RMSE = 0.1282 m, rRMSE = 6.47%) and CPA (R2 = 0.9498, RMSE = 0.2675 m2, rRMSE = 9.39%) values were highly correlated with the UAV-based reference values. The results demonstrate that the input image containing a CHM achieves more accurate crown delineation than an image containing a DSM. The accuracy and efficacy of ResU-Net in extracting C. oleifera tree-crown information have great potential for application in non-wood forests precision management.

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