The full-length cDNA of LeTIR1 gene was isolated from tomato with EST-based in silico cloning followed by RACE amplification. LeTIR1 contained an open reading frame (ORF) 1872 bp long, encoding 624 amino acid residues. The predicted protein LeTIR1 had one F-box motif and eleven leucine-rich repeats (LRRs), all of which are highly conserved in TIR1 proteins of other plant species. Phylogenetic analysis showed that the LeTIR1 protein shared high similarity with other known TIR1 proteins. Both sequence and phylogenetic analysis suggested that LeTIR1 is a TIR1 homologue and encodes an F-box protein in tomato. Semi-quantitative RT-PCR indicated that LeTIR1 was expressed constitutively in all organs tested, with higher expression in stem than root, leaf, flower and fruit. Its expression level was positively correlated with the auxin distribution in stem or axillary shoot, and was induced by spraying exogenous IAA.

In order to improve the utilization rate of coal resources, it is necessary to classify coal and gangue, but the classification of coal is particularly important. Nevertheless, the current coal and gangue sorting technology mainly focus on the identification of coal and gangue, and no in-depth research has been carried out on the identification of coal species. Accordingly, in order to preliminary screen coal types, this paper proposed a method to predict the coal metamorphic degree while identifying coal and gangue based on Energy Dispersive X-Ray Diffraction (EDXRD) principle with 1/3 coking coal, gas coal, and gangue from Huainan mine, China as the research object. Differences in the phase composition of 1/3 coking coal, gas coal, and gangue were analyzed by combining the EDXRD patterns with the Angle Dispersive X-Ray Diffraction (ADXRD) patterns. The calculation method for characterizing the metamorphism degree of coal by EDXRD patterns was investigated, and then a PSO-SVM model for the classification of coal and gangue and the prediction of coal metamorphism degree was developed. Based on the results, it is shown that by embedding the calculation method of coal metamorphism degree into the coal and gangue identification model, the PSO-SVM model can identify coal and gangue and also output the metamorphism degree of coal, which in turn achieves the purpose of preliminary screening of coal types. As such, the method provides a new way of thinking and theoretical reference for coal and gangue identification.

The rapid and accurate detection and identification of coal gangue is one of the premises and key technologies of the intelligent separation of coal gangue, which is of considerable importance for the separation of coal gangue. Focusing on the problems in the current deep learning algorithms for the detection and recognition of coal gangue, such as large model memory and slow detection speed, a rapid detection method for lightweight coal gangue is proposed. YOLOv3 is taken as the basic structure and improved. The MobileNetv2 lightweight feature extraction network is selected to replace Darknet53 as the main network of the detection algorithm to improve the detection speed. Spatial pyramid pooling (SPP) is added after the backbone network to convert different feature maps into fixed feature maps in order to improve the positioning accuracy and detection capability of the algorithm, thereby obtaining the lightweight network MS-YOLOV3. The experimental equipment was set up and multi-condition coal and gangue datasets were constructed. The model was trained and the identification and positioning results of the model were tested under different sizes, illumination intensities and various working conditions, and compared with other algorithms. Experimental results show that the proposed algorithm can detect the coal gangue quickly and accurately, with an mAP of 99.08%, a speed of 139 fps and a memory occupation of only 9.2 M. In addition, the algorithm can effectively detect mutually stacking coal and gangue of different quantities and sizes under different lights with high confidence and with a certain degree of environmental robustness and practicability. Compared with the YOLOv3, the performance of the proposed algorithm is significantly improved. Under the premise that the accuracy is unchanged, the FPS increases by 127.9% and the memory decreases by 96.2%. Therefore, the MS-YOLOv3 algorithm has the advantages of small memory, high accuracy and fast speed, which can provide online technical support for the detection and identification of coal and gangue.

In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.