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.

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.

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.

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.

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.

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.

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.

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.

Development and application of novel high‐efficiency composite ultrafine cement grouts for roadway in fractured surrounding rocks

The fractured surrounding rocks of roadways pose major challenges to safe mining.Grouting has often been used to reinforce the surrounding rocks to mitigate the safety risks associated with fractured rocks.The aim of this study is to develop highly efficient composite ultrafine cement(CUC)grouts to reinforce the roadway in fractured surrounding rocks.The materials used are ultrafine cement(UC),ultrafine fly ash(UF),ultrafine slag(US),and additives(superplasticizer[SUP],aluminate ultrafine expansion agent[AUA],gypsum,and retarder).The fluidity,bleeding,shrinkage,setting time,chemical composition,microstructure,degree of hydration,and mechanical property of grouting materials were evaluated in this study.Also,a suitable and effective CUC grout mixture was used to reinforce the roadway in the fractured surrounding rock.The results have shown that the addition of UF and US reduces the plastic viscosity of CUC,and the best fluidity can be obtained by adding 40%UF and 10%US.Since UC and UF particles are small,the pozzolanic effect of UF promotes the hydration reaction,which is conductive to the stability of CUC grouts.In addition,fine particles of UC,UF,and US can effectively fill the pores,while the volumetric expansion of AUA and gypsum decreases the pores and thus affects the microstructure of the solidified grout.The compressive test results have shown that the addition of specific amounts of UF and US can ameliorate the mechanical properties of CUC grouts.Finally,the CUC22‐8 grout was used to reinforce the No.20322 belt roadway.The results of numerical simulation and field monitoring have indicated that grouting can efficaciously reinforce the surrounding rock of the roadway.In this research,high‐performance CUC grouts were developed for surrounding rock reinforcement of underground engineering by utilizing UC and some additives.

Microstructural characterization,tribological and corrosion behavior of AA7075-TiC composites

Aluminum alloys are the potential materials in the automobile and aerospace sectors due to their lower density,easy forming and excellent corrosion resistance.The demand of high strength-to-weight ratio materials in structural applications needs the engineering industries to seek aluminum alloy with new versions of hard and brittle ceramic particles.The microstructure,hardness,wear and corrosion behaviors of AA7075 composites with 2.5wt.%and 5wt.%TiC particles were studied.Microscopic analysis is evident that the transformation of the strong dendritic morphology to non-dendritic morphology on the incorporation of TiC into AA7075.Furthermore,the precipitation of the second-phase compounds such as Al_(2)CuMg,Al_(2)Cu andFe-rich Al_6(Cu,Fe)/Al_(7)Cu_(2)Fe)is promoted by TiC particles at inter-and intra-dendritic regions.Accordingly,the hardness of composites is improved by grain boundary strengthening and particulate strengthening mechanisms.Both coefficient of friction and wear rate have an inverse relation with TiC concentration.The base alloy without TiC shows adhesive-type wear-induced deformation due to the formation of an oxide film,while composite samples exhibit a mechanically mixed layer and abrasive-type wear behavior.Composite samples shows a higher corrosion rate due to the presence of numerous precipitates which promote pitting corrosion.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

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.