[Dynamic and Static Complementary Balance Theory-Based Functional Exercise Combined With Stepwise Rehabilitation Training Improves Postoperative Hip Function Recovery in Patients Undergoing Total Hip Replacement]. / åŠ¨é™äº’è¡¥å¹³è¡¡è®ºåŠŸèƒ½é”»ç‚¼ç»“åˆé˜¶æ¢¯æ€§åº·å¤è®­ç»ƒåˆ©äºŽå ¨é«‹å ³èŠ‚ç½®æ¢è€ æœ¯åŽé«‹å ³èŠ‚åŠŸèƒ½æ¢å¤.

Objective: To analyze the application value of dynamic and static complementary balance theory-based functional exercise combined with stepwise rehabilitation training in postoperative hip function recovery in patients undergoing total hip replacement (THR). Methods: The clinical data of 118 inpatients undergoing THR between June 2022 and June 2023 were retrospectively collected. Among the patients, 57 were given perioperative stepwise rehabilitation training (the control group), and 61 were given a combination of functional exercise based on static and dynamic complementary balance theory and stepwise rehabilitation training during the perioperative period (the combination group). The postoperative recovery status and the incidence of postoperative complications in the two groups were recorded. During postoperative recovery, the patients were assessed for pain with the visual analogue scale (VAS) and for self-efficacy with the Self-Efficacy for Rehabilitation Outcome Scale (SER). Hip function was measured and compared between the two groups at 2 weeks, 4 weeks and 8 weeks after surgery. Results: With regard to the primary outcome indicator, the postoperative length-of-stay was (7.63±1.36) d in the combination group, which was shorter than the (8.22±1.48) d in the control group, and the difference was statistically significant (t=2.257, P=0.026). As for the secondary outcome indicators, no statistically significant difference was observed in the incidence of postoperative complications between the two groups (4.92% vs. 14.04%) (P>0.05). The perioperative VAS scores in the two groups showed an interaction effect according to the repeated measurement variance test (P<0.05). The postoperative VAS scores in both groups declined significantly compared to the preoperative scores (P<0.05), and the VAS score decreased gradually over time after surgery (P<0.05). The VAS scores in the combination group at 7 and 14 days after surgery were lower than those in the control group (P<0.05). At 14 days after surgery, the scores for the coping and the task dimensions and the total score of SER increased in both groups compared with those at 3 days after surgery (P<0.05), with the combination group showing higher scores than the control group did (P<0.05). After surgery, the scores for the dimensions of deformity, pain, function, and joint mobility and the total score of the Harris Hip Scale revealed interaction effects in the two groups according to the repeated measurement variance test (P<0.05), with these scores increasing gradually over time after surgery (P<0.05), and the scores in the combination group were higher at 4 and 8 weeks after surgery compared to those in the control group (P<0.05). Conclusion: The combination of functional exercise based on dynamic and static complementary balance theory and stepwise rehabilitation training shows an advantage over stepwise rehabilitation training alone in enhancing the self-efficacy of THR patients, relieving the postoperative pain, shortening the length-of-stay, and improving the hip function in patients.

A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.