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The internal airlift loop reactor with ceramic honeycomb supports (IAL-CHS) was applied for biodegradation of 2,4-dichlorophenol (2,4-DCP) and phenol. A strain of DCP-degrading bacteria isolated from activated sludge, Achromobacter sp., was rapidly immobilized onto the ceramic honeycomb supports. The immobilized cells effectively biodegraded 2,4-DCP alone and together with phenol in batch and continuous-flow experiments. For example, 2,4-DCP was biodegraded from an influent concentration of 50 mg/L to less than 1 mg/L with a 6-h hydraulic retention time (HRT) in continuous flow tests. The immobilized biomass grew and accumulated through 2,4-DCP biodegradation, and the rate of degradation increased accordingly.

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Using the sports injury monitoring system to detect injury symptoms in time and take effective treatment measures in time can reduce the damage caused by sports injuries to athletes. However, many current detection methods lack the support of advanced technologies and algorithms, resulting in poor performance in sports injury detection. Based on this, a mobile intelligent medical system is designed in this paper, and an athlete injury detection method based on CNN and sensors is proposed. The method includes three parts: motion region acquisition, motion injury feature extraction, and motion injury detection. In addition, for emergency treatment, this paper proposes a variety of CNN-based image data analysis methods to ensure the accuracy of the processing process. The experimental results show that the athlete injury detection method based on the convolutional neural network improves the detection accuracy by 6.73% compared with the traditional method, which also provides an important reference for the future application of ML in medical treatment. The research confirms that the construction and analysis of mobile intelligent medical system can effectively improve the accuracy of sports injury detection.