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In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree. [ABSTRACT FROM AUTHOR]

Featured Application: The findings of this study can serve as a valuable reference for the cross-regional integration and optimal utilization of energy resources. Simultaneously, they also contribute to enhancing the accuracy of global solar radiation forecasting by integrating meteorological data and air quality conditions. The majority of the energy required for human survival is derived either directly or indirectly from solar radiation, thus it is important to investigate the periodic fluctuations in global solar radiation over time. In this study, six cities—Harbin, Shenyang, Beijing, Shanghai, Wuhan, and Guangzhou—located in the utilizable areas of solar energy in China, were selected, and the periodicity of the daily global solar radiation of these six cities with time were investigated by means of wavelet power spectrum analysis. Furthermore, Harbin, which has the lowest monthly average of global solar radiation in the cold of winter, was selected to explore the temporal evolution relationship between global solar radiation and other meteorological factors, and air quality factors by wavelet coherence analysis. Among the meteorological factors, the correlation between global solar radiation and sunshine duration exhibits the highest level of consistency. Global solar radiation demonstrates a positive association with atmospheric temperature/wind speed/precipitation over the annual cycle. Conversely, it displays a negative correlation with atmospheric pressure during this time frame. Additionally, on shorter timescales, global solar radiation is negatively correlated with air humidity and precipitation. Among air quality factors, the relationship between global solar radiation and CO/NO2/O3/PM2.5/PM10/SO2 primarily manifests in the broader annual cycle time scale. Within this context, global solar radiation shows a positive correlation with O3, while displaying negative associations with the other five air quality indicators. These findings contribute to urban environmental planning and the effective management and utilization of solar radiation, thereby providing valuable insights to guide the future development of cross-regional comprehensive energy utilization projects under diverse climatic and geographical conditions. Additionally, they serve as a reference for subsequent research aimed at enhancing the accuracy of global solar radiation prediction models. [ABSTRACT FROM AUTHOR]

Under the complex roof condition, the 103-06 lower working face of Baodian Coal Mine has the risk of rock burst, which is prejudicial to the normal production. Aiming at the impact problem of the 103-06 lower working face, the influence of natural factors and technical factors on the rock burst is analyzed at first, and it is concluded that the re-movement of roof and the overburden rock in the upper goaf are the main inducing factors. Then, PFC is used to simulate the specific impact mechanism of overburden strata migration on rock burst. The simulation results show that after the mining of upper and lower coal seams are completed, the interlayer rock mass is damaged to a high degree, and a large amount of accumulated elastic energy can be released. However, the mid-sandstone (17.21 m) and siltstone (11.45 m) in the upper coal seam have not been collapsed, and the re-movement of the strata in the upper goaf has laid a safety hazard for the next extraction. Finally, based on the impact-induced mechanism of the working face lower 103-06, the corresponding risk prevention and control system is established.