Arthroscopic anatomical reconstruction of lateral collateral ligaments with ligament advanced reinforcement system artificial ligament for chronic ankle instability

BACKGROUND Recently,the use of ligament advanced reinforcement system(LARS)artificial ligament,a new graft which has several unique advantages such as no donor-site morbidity,early recovery and no risk of disease transmission which has been a significant breakthrough for anatomical ligament reconstruction.Growing studies suggested that the special design of the LARS ligament with open fibers in its intra-articular part was believed to be more resistant to torsional fatigue and wearing.However,the safety and efficacy of LARS artificial ligament for ankle joint lateral collateral ankle ligament reconstruction has not been defined to date.AIM To evaluate the clinical results of all-arthroscopic anatomical reconstruction of ankle joint lateral collateral ligaments with the LARS artificial ligament for chronic ankle instability.METHODS Twenty-two patients with chronic lateral instability underwent anatomical reconstruction of the lateral collateral ligaments of ankle with LARS artificial ligament.The visual analogue score(VAS),American Orthopaedic Foot and Ankle Society score(AOFAS score)and Karlsson score were used to evaluate the clinical results before and after surgery.RESULTS A total of 22 patients(22 ankles)were followed up for a mean of 12 mo.All patients reported significant improvement compared to their preoperative status.The mean AOFAS score improved from 42.3±4.9 preoperatively to 90.4±6.7 postoperatively.The mean Karlsson score improved from 38.5±3.2 preoperatively to 90.1±7.8 postoperatively.The mean VAS score improved from 1.9±2.5 preoperatively to 0.8±1.7 postoperatively.CONCLUSION All-arthroscopic anatomical reconstruction of the lateral collateral ligaments with LARS artificial ligament achieved a satisfactory surgical outcome for chronic ankle instability.

Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning

Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling timeand promote intersection capacity. However, the existing RLTSC methods do not consider the driver’s response time requirement, sothe systems often face efficiency limitations and implementation difficulties.We propose the advance decision-making reinforcementlearning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment.First, the relationship between the intersection perception range and the signal control period is established and the trust region state(TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will bedisplayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automatedvehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speedbased on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcementlearning training;a standardized reward is proposed to enhance the performance of intersection control and prioritized experiencereplay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiencyshowed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.