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Based on the panel data of the province and municipality in 2010–2021, this paper measures the fishery eco-efficiency by using the SBM-DEA model and empirically analyses how China’s marine ranch construction improves the fishery eco-efficiency by using the double-difference fixed-effect model. The results show that, firstly, China’s marine ranch construction can improve the fishery eco-efficiency. Secondly, China’s marine ranch construction promotes fisheries eco-efficiency by improving the input efficiency of labor, fish species, fishing vessel, fishery technology extension, aquaculture area and by reducing pollutant outputs. Thirdly, the fishery eco-efficiency of the marine ranch construction region has increased annually since 2015, and technological changes, scale efficiency changes and technical efficiency changes can all contribute to the fishery eco-efficiency improvement, and increase in order. Based on the results of the empirical analyses, this paper proposes policy recommendations to improve the fishery eco-efficiency in the conjunction with the construction of marine ranch in China.

The economic stability and sustainable development of ethnic minority autonomous regions is an important guarantee for promoting China’s economic growth and national harmony. Based on the Solow model, the economic growth factors of Enshi Tujia and Miao Autonomous Prefecture in Hubei Province (hereinafter referred to as “Enshi Prefecture of Hubei Province”) were measured and analyzed and then extended to the economic development of the entire ethnic regions in China. Through domestic and foreign comparisons, under the premise of correct understanding and analysis of the basic laws and characteristics of economic growth in ethnic areas, this paper gives appropriate economic development suggestions to promote the rapid and stable economic development of ethnic areas in China.

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS2Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network's capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively. [ABSTRACT FROM AUTHOR]

Background: Procedural sedation is essential for optimizing upper gastrointestinal endoscopy, particularly in high-risk patients with multiple underlying diseases. Respiratory and circulatory complications present significant challenges for procedural sedation in this population. This non-inferiority randomized controlled trial aims to investigate the safety and comfort of remimazolam compared to propofol for procedural sedation during upper gastrointestinal endoscopy in high-risk patients. Methods: A total of 576 high-risk patients scheduled to undergo upper gastrointestinal endoscopy are planned to be enrolled in this study and randomly allocated to either the remimazolam or propofol group. The primary outcome measure is a composite endpoint, which includes (1) achieving a Modified Observer's Alertness/Sedation scale (MOAA/S) score ≤ 3 before endoscope insertion, (2) successful completion of the endoscopic procedure, (3) the absence of significant respiratory instability during the endoscopy and treatment, and (4) the absence of significant circulatory instability during the examination. The noninferiority margin was 10%. Any adverse events (AEs) that occur will be reported. Discussion: This trial aims to determine whether remimazolam is non-inferior to propofol for procedural sedation during upper gastrointestinal endoscopy in high-risk patients, regarding success rate, complication incidence, patient comfort, and satisfaction. Trial registration {2a and 2b}: Chinese Clinical Trial Registry ClinicalTrials.gov ChiCTR2200066527. Registered on 7 December 2022. [ABSTRACT FROM AUTHOR]

The moisture content of cigar leaves during drying is an important indicator for controlling the management of drying rooms. At present, the determination of cigar leaf moisture content is mainly dependent on traditional destructive detection methods, which are inefficient and damaging to plants. In this study, a Convolution Neural Network method consisting of digital images for monitoring the moisture content of cigar leaves during the drying process was proposed. In this study, the Convolution Neural Network model was trained to learn the relationship between the images and the corresponding moisture content using the extracted colour, shape, and texture features as input factors. In order to compare the Convolution Neural Network estimation results, a widely used traditional machine learning algorithm was applied. The results demonstrated that the estimated value of Convolution Neural Network agreed with the predicted value; the R² was 0.9044, and the average accuracy was 87.34%. These results were better than those produced by traditional machine learning methods. The generalization test of the proposed method was conducted using varieties of cigar leaves in other drying rooms. The results showed that Convolution Neural Network is a viable method for an accurate estimation of the moisture content, the R² was 0.8673 and the average accuracy was 86.81%. The Convolution Neural Network established by the features extracted from digital images could accurately estimate the moisture content of cigar leaves during drying and was therefore shown to be an effective monitoring tool. [ABSTRACT FROM AUTHOR]