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Objectives: To evaluate the effect of exercise intervention on disability, pain, and kinesiophobia in a retired athlete with old patella fracture.Methods: A 34-year-old retired football player with old patella fracture conducted the exercise intervention for 12 weeks, 1 h each time, three times a week. the retired football player completed the Lysholm Knee Score (LKS), Visual Analog Scale (VAS), and the Tampa Scale for Kinesiophobia (TSK) were measured at pre-intervention, mid-intervention, and post-intervention.Results: Based on the functional training perspective, the retired athlete was subjected to two stages of exercise intervention for a total of 12 weeks. The patient's LKS score increased from 76 to 95, and the pain level of various physical states was relieved. When walking, the VAS score was reduced from 3 to 1, and when running, the VAS score was reduced from 5 to 2. Jumping VAS score for actions was reduced from 6 to 3, and the VAS score for of daily life activities was reduced from 3 points to 2. The patient's TSK score from 50 to 37.Conclusion: A 12-week exercise intervention could improve knee joint function, relieve pain and relieve kinesiophobia.

Job shop scheduling problem (JSSP) is essential in the production, which can significantly improve production efficiency. Dynamic events such as machine breakdown and job rework frequently occur in smart manufacturing, making the dynamic job shop scheduling problem (DJSSP) methods urgently needed. Existing rule-based and meta-heuristic methods cannot cope with dynamic events in DJSSPs of different sizes in real time. This paper proposes an end-to-end transformer-based deep learning method named spatial pyramid pooling-based transformer (SPP-Transformer), which shows strong generalizability and can be applied to different-sized DJSSPs. The feature extraction module extracts the production environment features that are further compressed into fixed-length vectors by the feature compression module. Then, the action selection module selects the simple priority rule in real time. The experimental results show that the makespan of SPP-Transformer is 11.67% smaller than the average makespan of dispatching rules, meta-heuristic methods, and RL methods, proving that SPP-Transformer realizes effective dynamic scheduling without training different models for different DJSSPs. To the best of our knowledge, SPP-Transformer is the first application of an end-to-end transformer in DJSSP, which not only improves the productivity of industrial scheduling but also provides a paradigm for future research on deep learning in DJSSP.

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