Nano-scale Reinforcements and Properties of Al-Si-Cu Alloy Processed by High-Pressure Torsion

To improve the comprehensive mechanical properties of Al-Si-Cu alloy,it was treated by a high-pressure torsion process,and the effect of the deformation degree on the microstructure and properties of the Al-Si-Cu alloy was studied.The results show that the reinforcements(β-Si andθ-CuAl_(2)phases)of the Al-Si-Cu alloy are dispersed in theα-Al matrix phase with finer phase size after the treatment.The processed samples exhibit grain sizes in the submicron or even nanometer range,which effectively improves the mechanical properties of the material.The hardness and strength of the deformed alloy are both significantly raised to 268 HV and 390.04 MPa by 10 turns HPT process,and the fracture morphology shows that the material gradually transits from brittle to plastic before and after deformation.The elements interdiffusion at the interface between the phases has also been effectively enhanced.In addition,it is found that the severe plastic deformation at room temperature induces a ternary eutectic reaction,resulting in the formation of ternary Al+Si+CuAl_(2)eutectic.

Magnesium composites with hybrid nano-reinforcements:3D simulation of dynamic tensile response at elevated temperatures

3D numerical simulations of dynamical tensile response of hybrid carbon nanotube(CNT)and SiC nanoparticle reinforced AZ91D magnesium(Mg)based composites considering interface cohesion over a temperature range from 25 to 300℃ were carried out using a 3D representative volume element(RVE)approach.The simulation predictions were compared with the experimental results.It is clearly shown that the overall dynamic tensile properties of the nanocomposites at different temperatures are improved when the total volume fraction and volume fraction ratio of hybrid CNTs to SiC nanoparticles increase.The overall maximum hybrid effect is achieved when the hybrid volume fraction ratio of CNTs to SiC nanoparticles is in the range from 7:3 to 8:2 under the condition of total volume fraction of 1.0%.The composites present positive strain rate hardening and temperature softening effects under dynamic loading at high temperatures.The simulation results are in good agreement with the experimental data.

Effect of TiO_2 and ZrO_2 reinforcements on properties of Al_2O_3 coatings fabricated by thermal flame spraying

The alumina composite coatings reinforced with 25% ZrO2 （denoted as AZ-25） and 3% TiO2 （denoted as AT-3） were deposited on low carbon steel using a thermal flame spraying. The microstructure, phase composition, microhardness and tribological properties of the coatings were investigated. The XRD results of the coatings reinforced by TiO2 （AT-3） revealed the presence of α-Al2O3 phase as matrix and new metastable phases of α-Al2O3 and α-Al2O3. However, the coatings reinforced by ZrO2 （AZ-25） consist of α-Al2O3 as matrix, q-ZrO2 and m-ZrO2. In most studied conditions, the AT-3 coating displays a better tribological performance, i.e., lower coefficient of frictions and wear rates, than the AZ-25 coating. It was also found that the microhardness of the coatings was decreased with the reinforcement of ZrO2 and increased with TiO2.

Dynamic Compression Behavior of Ultra-high Performance Cement-based Composite with Hybrid Steel Fiber Reinforcements

Ultra-high performance cement-based composites (UHPCC) is promising in construction of concrete structures that suffer impact and explosive loads.In this study,a reference UHPCC mixture with no fiber reinforcement and four mixtures with a single type of fiber reinforcement or hybrid fiber reinforcements of straight smooth and end hook type of steel fibers were prepared.Split Hopkinson pressure bar (SHPB) was performed to investigate the dynamic compression behavior of UHPCC and X-CT test and 3D reconstruction technology were used to indicate the failure process of UHPCC under impact loading.Results show that UHPCC with 1% straight smooth fiber and 2% end hook fiber reinforcements demonstrated the best static and dynamic mechanical properties.When the hybrid steel fiber reinforcements are added in the concrete,it may need more impact energy to break the matrix and to pull out the fiber reinforcements,thus,the mixture with hybrid steel fiber reinforcements demonstrates excellent dynamic compressive performance.

Nonlinear behavior of concrete beams with hybrid FRP and stainless steel reinforcements

The full-range behavior of partially bonded, together with partially prestressed concrete beams containing fiber reinforced polymer （FRP） tendons and stainless steel reinforcing bars was simulated using a simplified theoretical model. The model assumes that a section in the beam has a trilinear moment--curvature relationship characterized by three particular points, initial cracking of concrete, yielding of non-prestressed steel, and crushing of concrete or rupturing of prestressing tendons. Predictions from the model were compared with the limited available test data, and a reasonable agreement was obtained. A detailed parametric study of the behavior of the prestressed concrete beams with hybrid FRP and stainless steel reinforcements was conducted. It can be concluded that the deformability of the beam can be enhanced by increasing the ultimate compressive strain of concrete, unhonded length of tendon, percentage of compressive reinforcement and partial prestress ratio, and decreasing the effective prestress in tendons, and increasing in ultimate compressive strain of concrete is the most efficient one. The deformability of the beam is almost directly proportional to the concrete ultimate strain provided the failure mode is concrete crushing, even though the concrete ultimate strain has less influence on the load-carrying capacity.

Quantitative analysis of magnetic anomaly of reinforcements in bored in-situ concrete piles

We quantitatively study magnetic anomalies of reinforcement rods in bored insitu concrete piles for the first time and summarized their magnetic anomaly character. Key factors such as measuring borehole orientation, borehole-reinforcement distance, and multiple-section reinforcement rods are discussed which contributes valid and quantitative reference for using the magnetic method to detect reinforcement rods. Through tests with model piles, we confirm the accuracy of theoretical computations and then utilize the law discovered in theoretical computations to explain the characteristics of the actual testing curves. The results show that the Za curves of the reinforcement rod reflect important factors regarding the reinforcement rods, such as rod length, change of reinforcement ratio, length of overlap, and etc. This research perfects the magnetic method for detecting reinforcement rods in bored in-situ concrete piles and the method has great importance for preventing building contractor fraud.

Competitive precipitation behavior of hybrid reinforcements in copper matrix composites fabricated by powder metallurgy

Copper matrix composites reinforced by in situ-formed hybrid titanium boride whiskers(TiB_(w))and titanium diboride particles(TiB_(2p))were fabricated by powder metallurgy.Microstructural observations showed competitive precipitation behavior between TiB_(w) and TiB_(2p),where the relative contents of the two reinforcements varied with sintering temperature.Based on thermodynamic and kinetic assessments,the precipitation mechanisms of the hybrid reinforcements were discussed,and the formation of both TiB_(w) and TiB_(2p) from the local melting zone was thermodynamically favored.The precipitation kinetics were mainly controlled by a solid-state diffusion of B atoms.By forming a compact compound layer,in situ reactions were divided into two stages,where Zener growth and Dybkov growth prevailed,respectively.Accordingly,the competitive precipitation behavior was attributed to the transition of the growth model during the reaction process.

Study of the Protective Capacity of Cements Regarding Corrosion of Reinforcements under Joint Action of CO2 and Chloride Ions

To examine the protection against reinforcement corrosion due to the combined action of CO2 and chlorides, experimental results of the evaluation of a study with three types of cement are presented. The study was performed observing the behavior of reinforcements which were put in samples submitted to accelerated carbonatation tests and accelerated tests under the effect of chlorides. For the evaluation, intensity corrosion measurements were used using the Pr （polarization resistance） technique, employing these measures as a deterioration indicator. Three types of cement available in the national market were used. The obtained results enabled the classification of the used cements, comparing their profile behaviors in the conditions of the proposed tests.

A review of numerical analyses and experimental characterization methods for forming of textile reinforcements

The forming of textile reinforcements is an important stage in the manufacturing of textile composite parts with Liquid Composite Molding process.Fiber orientations and part geometry obtained from this stage have significant impact on the subsequent resin injection and final mechanical properties of composite part.Numerical simulation of textile reinforcement forming is in strong demand as it can greatly reduce the time and cost in the determination of the optimized processing parameters,which is the foundation of the low-cost application of composite materials.This review presents the state of the art of forming modeling methods for textile reinforcement and the corresponding experimental characterization methods developed in this field.The microscopic,mesoscopic and macroscopic models are discussed.Studies concerning the simulation of wrinkling are also presented since it is the most common defect occurred in the textile reinforcement forming.Finally,challenges and recommendations on the future research directions for textile reinforcement modeling and experimental characterization are provided.

Energy absorption potential of concrete floors containing secondary （shrinkage and temperature） reinforcements

This paper experimentally investigates the namely, normal density concrete and structural low-density energy absorption potential of two types of concrete floors, concrete, containing secondary （shrinkage and temperature） reinforcements. The test program considered the following secondary reinforcements： 1） traditional welded-wire steel mesh, 2） steel fiber and 3） poly composite fiber. To estimate the extent to which crushing of floor slab materials would help absorb energy, a series of concrete penetration tests employing patch loading was undertaken on scaled down model slabs. Each concrete-secondary reinforcement combination considered slabs of 50 mm in depth with square plan dimensions ranging from 50 to 500 mm, resulting in a total of 30 test specimens. The first part of the paper discusses the test specimens, the test setup, and the test procedure. The second part of the paper presents the experimental results and establishes the energy absorption of different concrete- secondary reinforcement combinations. Sieve analysis results of the crushed specimens were used to derive a ＂work index＂ value that relates the pulverized particle size distributions to energy inputs. The work index values of concrete-secondary reinforcement systems can be used to assess the energy dissipation potential associated with such floor slabs in buildings undergoing progressive collapse. The results indicate that floors with secondary reinforcements could play an important role in helping arrest global progressive collapse.

Passive Control Reinforced Concrete Frame Mechanism with High Strength Reinforcements and Its Potential Benefits Against Earthquakes

Severe earthquakes continue to cause major catastrophes. Many devices in active, hybrid, and semi-active structural control systems which are used as controllable force devices are costly to build and maintain. The passive control reinforced concrete frame （PCRCF） reinforced with high strength steel only in the columns presented here provides structural systems more resistance to lateral earthquake loadings at comparatively lower cost. The effectiveness is demonstrated by a nonlinear static analysis using fiber model for a single story single bay frame. The study shows that the use of high performance steel in columns prevents formation of plastic hinges at the critical column base sections and failures are always initiated by reinforcement yielding at the beam ends. Furthermore, after experiencing severe lateral drift, the passive control design has small residual displacements compared to ordinary reinforced concrete frames. PCRCF rehabilitation and strengthening can be achieved more easily as compared with ordinary reinforced concrete frame.

Effect of axial load and transverse reinforcements on the seismic performance of reinforced concrete columns

The aim of this research is to assess the seismic performance of reinforced concrete columns under different axial load and transverse reinforcement ratios. These two parameters are very important as for the ductility, strength, stiffness, and energy dissipation capacity for a given reinforced concrete column. Effects of variable axial load ratio and transverse reinforcement ratio on the seismic performance of reinforced concrete columns are thoroughly analyzed. The finite element computer program Seismo-Structure was used to perform the analysis of series of reinforced concrete columns tested by the second author and other researchers. In order to reflect the reality and grasp the actual behavior of the specimens, special attention was paid to select the models for concrete, confined concrete, and steel components. Good agreements were obtained between the experimental and the analytical results either for the lateral force-drift relationships or for the damage progress prediction at different stages of the loading.

Numerical model for prediction of corrosion of steel reinforcements in reinforced concrete structures

Reinforced concrete structures experience alteration and degradation during their lifetime due to the corrosion of the steel reinforcements.Prevention of the steel corrosion is indispensable to avoid structural degradation.In this paper,a preventive numerical approach of corrosion of steel reinforcements is presented.An in-house program which is part of a developed software called REHA is used in the present work.The corrosion initiation time due to carbonatation and penetration of chloride ions is studied.The model is applied on a case study which concerns a reinforced concrete T-beam of a bridge.The results revealed that the penetration of chloride ions represents the unfavorable case,which leads to rapid corrosion of the steel reinforcement,and the environmental conditions do not have high influence on the crack opening width in the service and spalling phases.The objective of the present model is to act as a decision aid for including the problem of corrosion cracking of steel reinforcements when planning strategies for rehabilitation and maintenance of existing structures or when dimensioning elements of new structures.

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.