Objective: The therapeutic effects of electroacupuncture (EA) on osteoarthritis (OA) are well documented; however, the precise mechanisms of action have not yet been fully elucidated. The present study aimed to investigate the effect of EA on cartilage in an experimental animal model of OA induced by anterior cruciate ligament transection (ACLT) and to examine for concomitant changes in the expression of mitogen-activated protein kinases (MAPKs) in the articular cartilage. Methods: Thirty-three-month-old male Sprague Dawley rats were randomly divided into the following three groups (n=10 each): sham operated group (Control group), ACLT without treatment (ACLT group), and ACLT with EA treatment (ACLT+EA group). One week after ACLT, rats in the ACLT+EA group received 12 weeks of EA treatment. Histological analysis and quantitative real-time PCR were used to investigate the effects of EA on cartilage morphology (quantified using modified Mankin scores) and expression of MAPKs (p38, c-Jun N-terminal kinase (c-Jun), and extracellular signal-regulated kinase (ERK)1), respectively. Results: ACLT produced coarse cartilage surfaces, fibrous degeneration, and fissuring, all of which were suppressed by EA treatment. Although Mankin scores in the ACLT+EA group were significantly higher compared to the Control group (p<0.01), they were significantly lower than the (untreated) ACLT group (p<0.01). The increase in mRNA expression of p38, c-Jun, ERK1, and matrix metalloproteinase (MMP)-13 observed in cartilage after ACLT was significantly inhibited by EA. Conclusions: EA appears to prevent the degeneration of articular cartilage, at least partly through regulation of MMP-13 and inhibition of MAPKs in the cartilage of rats with ACLT-induced OA.

The directivity of the quasi-static component (QSC) is quantitatively investigated for evaluating the orientation of a micro-crack buried in a thin solid plate using the numerical simulation method. Based on the bilinear stress–strain constitutive model, a three-dimensional (3D) finite element model (FEM) is built for investigating the nonlinear interaction between primary Lamb waves and the micro-crack. When the primary Lamb waves at A0 mode impinge on the micro-crack, under the modulation of the contact acoustic nonlinearity (CAN), the micro-crack itself will induce QSC. The amplitude of the QSC generated can be used for directly charactering the micro-crack orientation. The finite element simulation results show that the directivity of the QSC radiated by the micro-crack is closely related to the orientation of the micro-crack, allowing for the characterization of micro-crack orientation without the need for baseline signals. The results indicate that the directionality of the QSC can be used for characterizing the orientation of the micro-crack. The amplitude of the QSC is affected by the contact area between two surfaces of the micro-crack. It is demonstrated that the proposed method is a feasible means for the characterization of micro-crack orientation.

The advancement of mobile robot technology has made path planning a necessary condition for autonomous navigation, but traditional algorithms have issues with efficiency and reliability in dynamic and unstructured environments. This study proposes a Dynamic Hybrid A* (DHA*)–Adaptive Dynamic Window Approach (ADA-DWA) fusion algorithm for efficient and reliable path planning in dynamic unstructured environments. This paper improves the A* algorithm by introducing a dynamic hybrid heuristic function, optimizing the selection of key nodes, and enhancing the neighborhood search strategy, and collaboratively optimizes the search efficiency and path smoothness through curvature optimization. On this basis, the local planning layer introduces a self-adjusting weight-adaptive system in the DWA framework to dynamically optimize the speed, sampling distribution, and trajectory evaluation metrics, achieving a balance between obstacle avoidance and environmental adaptability. The proposed fusion algorithm’s comprehensive advantages over traditional methods in key operational indicators, including path optimality, computational efficiency, and obstacle avoidance capability, have been widely verified through numerical simulations and physical platforms. This method successfully resolves the inherent trade-off between efficiency and reliability in complex robot navigation scenarios, providing enhanced operational robustness for practical applications ranging from industrial logistics to field robots.