Arthroscopic Reconstruction of the Posterior Cruciate Ligament with a Ligament-advanced Reinforcement System and Hamstring Tendon Autograft:A Retrospective Study

Objective:Both ligament-advanced reinforcement system(LARS)and hamstring tendon autograft can serve as grafts for posterior cruciate ligament(PCL)reconstruction.However,few studies have compared the effectiveness of these two approaches.This study therefore aimed to compare the clinical efficacy of arthroscopic reconstruction of the PCL using either the LARS or hamstring tendon autograft.Methods:A total of 36 patients who underwent PCL reconstruction were retrospectively analyzed.Within this cohort,15 patients received a reconstruction using the LARS(LARS group)and 21 using the hamstring tendon autograft(HT group).Results:The pre-and post-operative subjective scores and knee stability were evaluated and the patients were followed up for a period of 2 to 10.5 years(4.11±2.0 years on average).The last follow-up showed that functional scores and knee stability were significantly improved in both groups(P<0.05).Six months after operation,Lysholm scores and IKDC subjective scores were higher in the LARS group than in the HT group(P<0.05).Nonetheless,the last follow-up showed no significant differences in the functional scores or the posterior drawer test between the two groups(P>0.05).In the LARS and HT groups,12 and 9 patients,respectively exhibited KT1000 values<3 mm,with the difference being statistically significant(P<0.05).In the HT group,the diameter of the four-strand hamstring tendon was positively correlated with height(P<0.05),which was 7.37±0.52 mm in males and 6.50±0.77 mm in females(P<0.05).Conclusion:Both LARS and hamstring tendon approaches achieved good efficacy for PCL reconstruction,but patients in the LARS group exhibited faster functional recovery and better knee stability in the long term.LARS is especially suitable for those who hope to resume activities as early as possible.

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.

Use of Ligament Advanced Reinforcement System tube in stabilization of proximal humeral endoprostheses

AIM:To review outcomes following usage of the Ligament Advanced Reinforcement System(LARS?)in shoulder tumors.METHODS:Medical records of nineteen patients(19 shoulders)that underwent tumor excisional procedure and reconstruction with the LARS synthetic fabric,were retrospectively reviewed.RESULTS:Patients’median age was 58 years old,while the median length of resection was 110 mm(range 60-210 mm).Compared to immediate post-operative radiographs,the prosthesis mean end-point position migrated superiorly at a mean follow up period of 26 mo(P=0.002).No statistical significant correlations between the prosthesis head size(P=0.87);the implant stem body length(P=0.949);and the length of resection(P=0.125)with the position of the head,were found at last follow up.Two cases of radiological dislocation were noted but only one was clinically symptomatic.A minor superficial wound dehiscence,healed without surgery,occurred.There was no evidence of aseptic loosening either,and no prosthetic failure.CONCLUSION:LARS?use ensured stability of the shoulder following endoprosthetic reconstruction in most patients.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

A new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning

In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods.To achieve optimal control,RL algorithm based on critic–actor architecture is considered for the nonlinear system.Due to the significant security risks of network transmission,the system is vulnerable to deception attacks,which can make all the system state unavailable.By using the attacked states to design coordinate transformation,the harm brought by unknown deception attacks has been overcome.The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded.Finally,the simulation experiment is shown to prove the effectiveness of the strategy.

Joint Flexible Duplexing and Power Allocation with Deep Reinforcement Learning in Cell-Free Massive MIMO System

Network-assisted full duplex(NAFD)cellfree(CF)massive MIMO has drawn increasing attention in 6G evolvement.In this paper,we build an NAFD CF system in which the users and access points(APs)can flexibly select their duplex modes to increase the link spectral efficiency.Then we formulate a joint flexible duplexing and power allocation problem to balance the user fairness and system spectral efficiency.We further transform the problem into a probability optimization to accommodate the shortterm communications.In contrast with the instant performance optimization,the probability optimization belongs to a sequential decision making problem,and thus we reformulate it as a Markov Decision Process(MDP).We utilizes deep reinforcement learning(DRL)algorithm to search the solution from a large state-action space,and propose an asynchronous advantage actor-critic(A3C)-based scheme to reduce the chance of converging to the suboptimal policy.Simulation results demonstrate that the A3C-based scheme is superior to the baseline schemes in term of the complexity,accumulated log spectral efficiency,and stability.

Deep Reinforcement Learning Based Power Minimization for RIS-Assisted MISO-OFDM Systems

In this paper,we investigate the downlink orthogonal frequency division multiplexing(OFDM)transmission system assisted by reconfigurable intelligent surfaces(RISs).Considering multiple antennas at the base station(BS)and multiple single-antenna users,the joint optimization of precoder at the BS and the phase shift design at the RIS is studied to minimize the transmit power under the constraint of the certain quality-of-service.A deep reinforcement learning(DRL)based algorithm is proposed,in which maximum ratio transmission(MRT)precoding is utilized at the BS and the twin delayed deep deterministic policy gradient(TD3)method is utilized for RIS phase shift optimization.Numerical results demonstrate that the proposed DRL based algorithm can achieve a transmit power almost the same with the lower bound achieved by manifold optimization(MO)algorithm while has much less computation delay.

Discrete Phase Shifts Control and Beam Selection in RIS-Aided MISO System via Deep Reinforcement Learning

Reconfigurable intelligent surface(RIS)for wireless networks have drawn lots of attention in both academic and industry communities.RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction,thus it provides supplementary links for wireless networks.Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts,but phase shifts of RIS are discrete in practical hardware.Thus we focus on the actual discrete phase shifts on RIS in this paper.Using the advanced deep reinforcement learning(DRL),we jointly optimize the transmit beamforming matrix from the discrete Fourier transform(DFT)codebook at the base station(BS)and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio(SINR).Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts,the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network.Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.

Reinforcement learning-based scheduling of multi-battery energy storage system

In this paper, a reinforcement learning-based multibattery energy storage system(MBESS) scheduling policy is proposed to minimize the consumers ’ electricity cost. The MBESS scheduling problem is modeled as a Markov decision process(MDP) with unknown transition probability. However, the optimal value function is time-dependent and difficult to obtain because of the periodicity of the electricity price and residential load. Therefore, a series of time-independent action-value functions are proposed to describe every period of a day. To approximate every action-value function, a corresponding critic network is established, which is cascaded with other critic networks according to the time sequence. Then, the continuous management strategy is obtained from the related action network. Moreover, a two-stage learning protocol including offline and online learning stages is provided for detailed implementation in real-time battery management. Numerical experimental examples are given to demonstrate the effectiveness of the developed algorithm.

Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

A Deep Reinforcement Learning-Based Power Control Scheme for the 5G Wireless Systems

In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.

A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

Stability behavior of the Lanxi ancient flood control levee after reinforcement with upside-down hanging wells and grouting curtain

The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.