In-situ Si particle-reinforced joints of hypereutectic Al−60Si alloys by ultrasonic-assisted soldering

To improve the wettability of hypereutectic Al−60Si alloy and enhance the mechanical properties of the joints,Al−60Si alloy was joined by ultrasonic soldering with Sn-9Zn solder,and a sound joint with in-situ Si particle reinforcement was obtained.The oxide film of Al−60Si alloy at the interface was identified by transmission electron microscopy(TEM)analysis as amorphous Al_(2)O_(3).The oxide of Si particles in the base metal was also alumina.The oxide film of Al−60Si alloy was observed to be removed by ultrasonic vibration instead of holding treatment.Si particle-reinforced joints(35.7 vol.%)were obtained by increasing the ultrasonication time.The maximum shear strength peaked at 99.5 MPa for soldering at 330℃with an ultrasonic vibration time of 50 s.A model of forming of Si particles reinforced joint under the ultrasound was proposed,and ultrasonic vibration was considered to promote the dissolution of Al and migration of Si particles.

Performance Evaluation of Damaged T-Beam Bridges with External Prestressing Reinforcement Based on Natural Frequencies

As an evaluation index,the natural frequency has the advantages of easy acquisition and quantitative evaluation.In this paper,the natural frequency is used to evaluate the performance of external cable reinforced bridges.Numerical examples show that compared with the natural frequencies of first-order modes,the natural frequencies of higher-order modes are more sensitive and can reflect the damage situation and external cable reinforcement effect of T-beam bridges.For damaged bridges,as the damage to the T-beam increases,the natural frequency value of the bridge gradually decreases.When the degree of local damage to the beam reaches 60%,the amplitude of natural frequency change exceeds 10%for the first time.The natural frequencies of the firstorder vibration mode and higher-order vibration mode can be selected as indexes for different degrees of the damaged T-beam bridges.For damaged bridges reinforced with external cables,the traditional natural frequency of the first-order vibration mode cannot be used as the index,which is insensitive to changes in prestress of the external cable.Some natural frequencies of higher-order vibration modes can be selected as indexes,which can reflect the reinforcement effect of externally prestressed damaged T-beam bridges,and its numerical value increases with the increase of external prestressed cable force.

Model tests and numerical analysis of emergency treatment of cohesionless soil landslide with quick-setting polyurethane

Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.

A Damage Control Model for Reinforced Concrete Pier Columns Based on Pre-Damage Tests under Cyclic Reverse Loading

To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK(Ibarra Medina Krawinkler)recovery model,the energy dissipation capacity coefficient of the pier columns were able to be determined.Furthermore,utilizing the calibrated damage model parameters,the damage index for each specimen were calculated.Based on the obtained damage levels,three distinct pre-damage conditions were designed for the pier columns:minor damage,moderate damage,and severe damage.The study then predicted the variations in hysteresis curves and damage indices under cyclic loading conditions.The experimental findings reveal that the displacement at the top of the pier columns can serve as a reliable indicator for controlling the damage level of pier columns post-loading.Moreover,the calibrated damage index model exhibits proficiency in accurately predicting the damage level of RC pier columns under cyclic loading.

Optimized reinforcement of granite residual soil using a cement and alkaline solution: A coupling effect

Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to reinforce GRS. The effects of cement content and SiO_(2)/Na2O ratio of the alkaline solution on the static and dynamic strengths of GRS were discussed. Microscopically, the reinforcement mechanism and coupling effect were examined using X-ray diffraction (XRD), micro-computed tomography (micro-CT), and scanning electron microscopy (SEM). The results indicated that the addition of 2% cement and an alkaline solution with an SiO_(2)/Na2O ratio of 0.5 led to the densest matrix, lowest porosity, and highest static compressive strength, which was 4994 kPa with a dynamic impact resistance of 75.4 kN after adding glass fiber. The compressive strength and dynamic impact resistance were a result of the coupling effect of cement hydration, a pozzolanic reaction of clay minerals in the GRS, and the alkali activation of clay minerals. Excessive cement addition or an excessively high SiO_(2)/Na2O ratio in the alkaline solution can have negative effects, such as the destruction of C-(A)-S-H gels by the alkaline solution and hindering the production of N-A-S-H gels. This can result in damage to the matrix of reinforced GRS, leading to a decrease in both static and dynamic strengths. This study suggests that further research is required to gain a more precise understanding of the effects of this mixture in terms of reducing our carbon footprint and optimizing its properties. The findings indicate that cement and alkaline solution are appropriate for GRS and that the reinforced GRS can be used for high-strength foundation and embankment construction. The study provides an analysis of strategies for mitigating and managing GRS slope failures, as well as enhancing roadbed performance.

Combining deep reinforcement learning with heuristics to solve the traveling salesman problem

Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.

Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

An extended discontinuous deformation analysis for simulation of grouting reinforcement in a water-rich fractured rock tunnel

Grouting has been the most effective approach to mitigate water inrush disasters in underground engineering due to its ability to plug groundwater and enhance rock strength.Nevertheless,there is a lack of potent numerical tools for assessing the grouting effectiveness in water-rich fractured strata.In this study,the hydro-mechanical coupled discontinuous deformation analysis(HM-DDA)is inaugurally extended to simulate the grouting process in a water-rich discrete fracture network(DFN),including the slurry migration,fracture dilation,water plugging in a seepage field,and joint reinforcement after coagulation.To validate the capabilities of the developed method,several numerical examples are conducted incorporating the Newtonian fluid and Bingham slurry.The simulation results closely align with the analytical solutions.Additionally,a set of compression tests is conducted on the fresh and grouted rock specimens to verify the reinforcement method and calibrate the rational properties of reinforced joints.An engineering-scale model based on a real water inrush case of the Yonglian tunnel in a water-rich fractured zone has been established.The model demonstrates the effectiveness of grouting reinforcement in mitigating water inrush disaster.The results indicate that increased grouting pressure greatly affects the regulation of water outflow from the tunnel face and the prevention of rock detachment face after excavation.

基于端到端深度强化学习求解有能力约束的车辆路径问题

有能力约束的车辆路径问题(CVRP)是现阶段供应链应用最常见的问题模型,现多采用启发式算法求解。但随着问题规模增大,启发式算法求解速度慢且无法保证解的质量。提出端到端深度强化学习(DRL)网络框架对CVRP进行研究。首先利用边聚合图注意力网络编码器(EGATE)对车辆路径规划问题的图表示进行特征嵌入编码;然后设计多头注意力解码器(MAD)进行解码,并提出多解码策略以增加解的空间多样性;接着利用带回滚基线的基线REINFORCE算法对端到端网络模型进行训练,基线可自适应性更新以提升模型训练效果,并利用奖励函数归一化和Adam优化器对算法进行优化。最后通过对不同规模问题的实验以及与其他算法进行对比,验证了所提出端到端DRL框架的可行性与有效性,经过训练的模型在CVRPLIB公共数据集上的平均求解时间仅需0.189 s即可得到较优解。

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

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.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

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.

Shear behavior of single-joint bolted sandstone subjected to dryewet cycles:Experimental and analytical approaches

A series of direct shear tests under constant normal loading conditions were carried out on specimens of bolted sandstone single-joint treated with different numbers of dryewet cycles.The experimental results show that the peak shear strength and shear stiffness of bolted sandstone joints were significantly reduced after 12 dryewet cycles.The decrease in the shear strength of rough joints is more significant than that of flat joints.Due to the decrease in the strength of the surrounding rock,the deformation characteristics of the bolts are significantly affected by the number of dryewet cycles performed.With an increase in the number of dryewet cycles,the plastic hinge length of the bolt gradually increases,resulting in an increase in the corresponding shear displacement when the bolt breaks.Compared with the tensileeshear failure mode of the bolts in flat joints,the tensileebending failure mode arises for bolts in rough joints.A shear curve model describing the whole process of bolted rock joints is established based on the deterioration of rock mechanical parameters caused by dry‒wet cycles.The model proposed considers the change in the friction angle of the joint surface with the shear displacement,which is applied to the derivation of the model by introducing the dynamic evolutionary friction angle parameter.The reasonably good agreement between a predicted curve and the corresponding experimental curve indicates that this method can effectively predict the shear strength of a bolted rock joint involving rough joint under dryewet cycling conditions.

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.

A Comparative Study on the Post-Buckling Behavior of Reinforced Thermoplastic Pipes(RTPs)Under External Pressure Considering Progressive Failure

The collapse pressure is a key parameter when RTPs are applied in harsh deep-water environments.To investigate the collapse of RTPs,numerical simulations and hydrostatic pressure tests are conducted.For the numerical simulations,the eigenvalue analysis and Riks analysis are combined,in which the Hashin failure criterion and fracture energy stiffness degradation model are used to simulate the progressive failure of composites,and the“infinite”boundary conditions are applied to eliminate the boundary effects.As for the hydrostatic pressure tests,RTP specimens were placed in a hydrostatic chamber after filled with water.It has been observed that the cross-section of the middle part collapses when it reaches the maximum pressure.The collapse pressure obtained from the numerical simulations agrees well with that in the experiment.Meanwhile,the applicability of NASA SP-8007 formula on the collapse pressure prediction was also discussed.It has a relatively greater difference because of the ignorance of the progressive failure of composites.For the parametric study,it is found that RTPs have much higher first-ply-failure pressure when the winding angles are between 50°and 70°.Besides,the effect of debonding and initial ovality,and the contribution of the liner and coating are also discussed.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

Strong ground motion characteristics observed in the February 6,2023 M_(W)7.7 Türkiye earthquake

Türkiye is located in a seismically active region,where the Anatolian,African,and Arabian tectonic plates converge.High seismic hazards cause the region to be struck repeatedly by major earthquakes.On February 06,2023,a devastating M_(W)7.7 earthquake struck Türkiye at 01:17 am local time(01:17 UTC).In this regard,near and far-field ground motion data within the distance of 120 km are compiled and later characterized to identify the key ground motion intensity measures.Additionally,the vertical components of ground motions were examined to capture the complete three-dimensional nature of the seismic event.Moreover,the effect of Pulse-Like(PL)and Non-Pulse-Like(NPL)ground motion on a representative RC frame structure built as per the Türkiye code was investigated.The results indicate that PL behavior was observed in both horizontal and vertical components of ground motions and PL behavior were noted both near the epicenter and at higher distances from the epicenter.Moreover,the ratio of the peak vertical acceleration to peak horizontal acceleration at certain stations was found to be close to 1.Finally,the non-linear time history analysis of the representative reinforced concrete frame structure for ground motions recorded at stations located equidistant from the epicenter,indicated that PL ground motions led to more significant damage compared to NPL ground motions.

Dynamic and electrical responses of a curved sandwich beam with glass reinforced laminate layers and a pliable core in the presence of a piezoelectric layer under low-velocity impact

The dynamic responses and generated voltage in a curved sandwich beam with glass reinforced laminate(GRL)layers and a pliable core in the presence of a piezoelectric layer under low-velocity impact(LVI)are investigated.The current study aims to carry out a dynamic analysis on the sandwich beam when the impactor hits the top face sheet with an initial velocity.For the layer analysis,the high-order shear deformation theory(HSDT)and Frostig's second model for the displacement fields of the core layer are used.The classical non-adhesive elastic contact theory and Hunter's principle are used to calculate the dynamic responses in terms of time.In order to validate the analytical method,the outcomes of the current investigation are compared with those gained by the experimental tests carried out by other researchers for a rectangular composite plate subject to the LVI.Finite element(FE)simulations are conducted by means of the ABAQUS software.The effects of the parameters such as foam modulus,layer material,fiber angle,impactor mass,and its velocity on the generated voltage are reviewed.