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To enhance the field applicability, based on enthalpy differences black box method was proposed to estimate the cooling load on mining face in this paper.The cooling load of mining face is composed of basic and increased cooling load. The former is considered with enthalpy difference between airflow in hottest period of summer and air flow under the cooling control at the target temperature, while the latter comes from the increased heat dissipation relative heat source after the implementation of cooling measures.Then taken above estimated results as a reference, theoretical analysis (i-d chart) and numerical simulation (Fluent software) were conducted on heat and moisture treatment process and distribution characteristics of air in tunnel after mixture cooling modes.The results show that the mixed air cooling process can be divided into three parts:cooling, mixing and warming, in which the most important is cooling process. Cooling process mainly depends on system refrigeration capacity and heat exchange equipment performance, which has great influence on the following two processes.During the mixing, air temperature (18℃~20℃ to 24℃~25℃,30℃~31℃ to 25℃~26℃) and wind speed (15~17 m/s to 1~2 m/s) change dramatically, showing a negative correlations between them.In addition, the length of mixing process should be controlled in less than 10 m (3~6 m) tunnel.At last, the warming stage should be reduced to release mining face heat and moisture source at the present stage, along with the system and parameter optimization, mine cooling should be made a scientific decision according to the concrete situation and diminish mine heat hazard during coal mining.

This study aimed to investigate the characteristics of different varieties of camellia oils and their diacylglycerol (DAG)-enriched derivatives in terms of triacylglycerol (TAG) species, bioactive components, volatile compounds, and antioxidant activity. Six types of camellia oils, including C. oleifera (C.O), C. semiserrata (C.S), C. gauchowensis (C.G), along with commercially refined C. oleifera oil (C-C.O) and its DAG-enriched counterparts (at 40% and 80% enrichment), were analyzed and compared. Unique patterns of TAG profiles, fatty acid distributions on different glycerol backbones, tocopherol, squalene, total polyphenols, and volatile compounds were observed, suggesting that these characteristics can be utilized as a criterion to differentiate them. DAG-enriched oils exhibited increased levels of unsaturated fatty acids (UFAs) compared to C-C.O, albeit with decreased contents of tocopherol, squalene, and total polyphenols. Moreover, diverse volatile compounds were identified across all types of camellia oils, among which the DAG-enriched oils had distinct distribution characteristics compared with their crude oils, indicating the influence of the enrichment process on volatile compounds. Furthermore, DAG-enriched oils demonstrated reduced antioxidant activity abilities compared to their counterparts, with the highest activity observed in C.O, followed by C.G. Additionally, strong correlations were observed between antioxidant activity and tocopherol, as well as squalene content. [ABSTRACT FROM AUTHOR]

Feeding is a critical process in aquaculture, as it has a direct impact on the quantity and quality of fish. With advances in convolutional neural network (CNN) and vision transformer (ViT), intelligent feeding has been widely adopted in aquaculture, as the real-time monitoring of fish behavior can lead to better feeding decisions. However, existing models still have the problem of insufficient accuracy in the fish behavior-recognition task. In this study, the largemouth bass (Micropterus salmoides) was selected as the research subject, and three categories (weakly, moderately, and strongly hungry) were defined. We applied the deformable attention to the vision transformer (DeformAtt-ViT) to identify the fish hunger degree. The deformable attention module was extremely powerful in feature extraction because it improved the fixed geometric structure of the receptive fields with data-dependent sparse attention, thereby guiding the model to focus on more important regions. In the experiment, the proposed DeformAtt-ViT was compared with the state-of-the-art transformers. Among them, DeformAtt-ViT achieved optimal performance in terms of accuracy, F1-score, recall, and precision at 95.50%, 94.13%, 95.87%, and 92.45%, respectively. Moreover, a comparative evaluation between DeformAtt-ViT and CNNs was conducted, and DeformAtt-ViT still dominated the others. We further visualized the important pixels that contributed the most to the classification result, enabling the interpretability of the model. As a prerequisite for determining the feed time, the proposed DeformAtt-ViT could identify the aggregation level of the fish and then trigger the feeding machine to be turned on. Also, the feeding machine will stop working when the aggregation disappears. Conclusively, this study was of great significance, as it explored the field of intelligent feeding in aquaculture, enabling precise feeding at a proper time. [ABSTRACT FROM AUTHOR]

Given the challenge of pear leaf disease recognition caused by uneven illumination, overlapping leaves, and other green plants in the background, a two‐stage strategy‐based framework for pear leaf segmentation and disease classification is proposed. Initially, a double branch polymerization net fusing features of the low‐level feature branch and semantic branch is constructed to extract the target diseased pear leaf and eliminate background interference. Then an improved lightweight neural network (Inverted‐Inception Efficient‐Excitation‐and‐Filtering‐Bottleneck MobileNet‐v2, I2EMv2) is used to capture multi‐scale lesion information and ainhibit invalid feature channels and filter redundant features for the final classification. The experimental results show that the proposed framework can accurately extract the pear leaf region with complete boundary from the complex background, maximize the retention of lesion information, and achieve high‐precision pear leaf disease identification. The mean absolute error, F‐measure, and intersection over union of leaf segmentation are 0.027, 0.947, and 0.880, respectively, and the average recognition accuracy of leaf disease is 92.05%. Compared with others, the method proposed in this paper has superior performance on segmentation and classification, which provides a reference for pear leaf disease classification under complex background. [ABSTRACT FROM AUTHOR]

The electrochemical CO2 reduction reaction (CO2RR) is a promising approach for mitigating the greenhouse effect arising from anthropogenic CO2 emission. Nonetheless, poor product selectivity associated with electrochemical catalysts is the main technical problem for the application of CO2RR technology. The catalytic performance of nano-catalysts is strongly dependent on their microstructural features. Anomalous small-angle X-ray scattering (ASAXS) is one of the most effective techniques for studying nanostructural change in an operando way, especially for complex systems and mixed-element catalyst situations. Furthermore, based on the research results of ASAXS, appropriate catalyst components and nanostructures can be designed to achieve stable catalytic performance of the catalyst, promote catalytic reaction rate, or improve catalytic reaction selectivity. In this paper, the basic concept, principle, and applications in different systems of ASAXS are reviewed thoroughly. Finally, the development prospect of ASAXS in the field of electrocatalysis is prospected. It is hoped that this review will further promote ASAXS technology to play a more far-reaching impact in the field of electrocatalytic CO2RR. [ABSTRACT FROM AUTHOR]