Exploration of the Inheritance and Innovation Carrier of National Culture in the Teaching Model of Art Design Major in Higher Vocational Colleges:Integrating Lijiang Baisha Murals into Higher Vocational Art Design Curriculum System

This study aims to further promote the inheritance and innovative development of intangible cultural heritage in Yunnan Province,promote the protection,inheritance,integration,and innovation of ethnic culture in Lijiang,strengthen the protection and inheritance of Baisha murals in Lijiang,and change the teaching of art design majors in vocational colleges in Yunnan.Given the lack of traditional Chinese culture and local ethnic culture,this article focuses on the teaching of art design majors in Yunnan vocational colleges.It explores the construction model of the cultural inheritance and innovation carrier of Lijiang Baisha murals to meet the spiritual and cultural needs of the local people,and efforts will be made to promote the high-quality development of the Baisha ethnic area in Lijiang.

Teaching Reform and Practice of Embedded System Design Based on Outcome-Based Education

Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

Stabilization Controller of An Extended Chained Nonholonomic System With Disturbance:An FAS Approach

This study examines the stabilization issue of extended chained nonholonomic systems(ECNSs)with external disturbance.Unlike the existing approaches,we transform the considered system into a fully actuated system(FAS)model,simplifying the stabilizing controller design.We implement a separate controller design and propose exponential stabilization controller and finite-time stabilization controller under finite-time disturbance observer(FTDO)for the two system inputs.In addition,we discuss the specifics of global stabilization control design.Our approach demonstrates that two system states exponentially or asymptotically converge to zero under the provided switching stabilization control strategy,while all other system states converge to zero within a finite time.

Deep Insight of Design,Mechanism,and Cancer Theranostic Strategy of Nanozymes

Since the discovery of enzyme-like activity of Fe3O4 nanoparticles in 2007,nanozymes are becoming the promising substitutes for natural enzymes due to their advantages of high catalytic activity,low cost,mild reaction conditions,good stability,and suitable for large-scale production.Recently,with the cross fusion of nanomedicine and nanocatalysis,nanozyme-based theranostic strategies attract great attention,since the enzymatic reactions can be triggered in the tumor microenvironment to achieve good curative effect with substrate specificity and low side effects.Thus,various nanozymes have been developed and used for tumor therapy.In this review,more than 270 research articles are discussed systematically to present progress in the past five years.First,the discovery and development of nanozymes are summarized.Second,classification and catalytic mechanism of nanozymes are discussed.Third,activity prediction and rational design of nanozymes are focused by highlighting the methods of density functional theory,machine learning,biomimetic and chemical design.Then,synergistic theranostic strategy of nanozymes are introduced.Finally,current challenges and future prospects of nanozymes used for tumor theranostic are outlined,including selectivity,biosafety,repeatability and stability,in-depth catalytic mechanism,predicting and evaluating activities.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Experimental research on the launching system of auxiliary charge with filter cartridge structure

A launching system with a filter cartridge structure was proposed to improve the muzzle velocity of the projectile.The combustion chamber of the launching system is divided into two fixed chambers,one is located in the breech chamber,and the other is arranged in the barrel.The breech chamber charge was ignited first,and the charges in the auxiliary chambers were ignited by the high-temperature,highpressure combustible gas trailing the projectile.In this way,the combustible gas in the auxiliary chambers could compensate for the pressure drop caused by the movement of the projectile.The proposed device features the advantage of launching a projectile with high muzzle velocity without exceeding the maximum pressure in the chamber.In order to obtain some internal ballistic characteristics of the launch system,some critical structure,such as the length of the filter cartridge auxiliary charge,the combustion degree of the propellant in the chamber,and the length of the barrel,are discussed.The experimental results show that with the increased auxiliary charge length,a pressure plateau or even a secondary peak pressure can be formed,which is less than the peak pressure.The projectile velocity increased by 23.57%,14.64%,and 7.65%when the diaphragm thickness was 0 mm,1 mm,and2 mm,respectively.The muzzle velocity of the projectile can be increased by 13.42%by increasing the length of the barrel.Under the same charge condition,with the increase of barrel length,the energy utilization rate of propellant increases by 28.64%.

Review on the design of high-strength and hydrogen-embrittlement-resistant steels

Given the carbon peak and carbon neutrality era,there is an urgent need to develop high-strength steel with remarkable hydrogen embrittlement resistance.This is crucial in enhancing toughness and ensuring the utilization of hydrogen in emerging iron and steel materials.Simultaneously,the pursuit of enhanced metallic materials presents a cross-disciplinary scientific and engineering challenge.Developing high-strength,toughened steel with both enhanced strength and hydrogen embrittlement(HE)resistance holds significant theoretical and practical implications.This ensures secure hydrogen utilization and further carbon neutrality objectives within the iron and steel sector.Based on the design principles of high-strength steel HE resistance,this review provides a comprehensive overview of research on designing surface HE resistance and employing nanosized precipitates as intragranular hydrogen traps.It also proposes feasible recommendations and prospects for designing high-strength steel with enhanced HE resistance.

Effect of bipolar-plates design on corrosion,mass and heat transfer in proton-exchange membrane fuel cells and water electrolyzers:A review

Attaining a decarbonized and sustainable energy system,which is the core solution to global energy issues,is accessible through the development of hydrogen energy.Proton-exchange membrane water electrolyzers(PEMWEs)are promising devices for hydrogen production,given their high efficiency,rapid responsiveness,and compactness.Bipolar plates account for a relatively high percentage of the total cost and weight compared with other components of PEMWEs.Thus,optimization of their design may accelerate the promotion of PEMWEs.This paper reviews the advances in materials and flow-field design for bipolar plates.First,the working conditions of proton-exchange membrane fuel cells(PEMFCs)and PEMWEs are compared,including reaction direction,operating temperature,pressure,input/output,and potential.Then,the current research status of bipolar-plate substrates and surface coatings is summarized,and some typical channel-rib flow fields and porous flow fields are presented.Furthermore,the effects of materials on mass and heat transfer and the possibility of reducing corrosion by improving the flow field structure are explored.Finally,this review discusses the potential directions of the development of bipolar-plate design,including material fabrication,flow-field geometry optimization using threedimensional printing,and surface-coating composition optimization based on computational materials science.

To improve robustness of mechanical properties of semi-solid cast A356 alloy using taguchi design method

Mechanical properties of semi-solid casting are dependent on multiple processing parameters,and improper processing parameters will not only reduce mean data but also increase variations.The present study investigated the impact of parameters in slurry preparation and heat treatment on the yield strength and ductility of T6 heat-treated A356 Al-Si alloy using rapid slurry forming(RSF)semi-solid casting.The focus was primarily on the robustness of mechanical properties based on Taguchi design method.By analyzing signal-to-noise ratio and minimum value calculated from-3S,the optimum slurry preparation parameters and heat treatment parameters were determined to be no quench,enthalpy exchange material(EEM)temperature of 140℃,EEM-to-melt ratio of 6mass%,stirring time of 18 s,solution heat treated at 520℃ for 2 h,and ageing heat treated at 190℃ for 6 h.In a small batch validation,the-3S yield strength and-3S elongation reach 256.1 MPa and 5.03% respectively,showing a satisfactory robustness.The hardness and microstructure of heat-treated samples with the best and worst properties were characterized to gain insight into the underlying mechanisms affecting the mean value and variations of mechanical properties.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Rationally designing electrolyte additives for highly improving cyclability of LiNi_(0.5)Mn_(1.5)O_(4)/Graphite cells

High voltage is necessary for high energy lithium-ion batteries but difficult to achieve because of the highly deteriorated cyclability of the batteries.A novel strategy is developed to extend cyclability of a high voltage lithium-ion battery,LiNi_(0.5)Mn_(1.5)O_(4)/Graphite(LNMO/Graphite)cell,which emphasizes a rational design of an electrolyte additive that can effectively construct protective interphases on anode and cathode and highly eliminate the effect of hydrogen fluoride(HF).5-Trifluoromethylpyridine-trime thyl lithium borate(LTFMP-TMB),is synthesized,featuring with multi-functionalities.Its anion TFMPTMB-tends to be enriched on cathode and can be preferentially oxidized yielding TMB and radical TFMP-.Both TMB and radical TFMP can combine HF and thus eliminate the detrimental effect of HF on cathode,while the TMB dragged on cathode thus takes a preferential oxidation and constructs a protective cathode interphase.On the other hand,LTFMP-TMB is preferentially reduced on anode and constructs a protective anode interphase.Consequently,a small amount of LTFMP-TMB(0.2%)in 1.0 M LiPF6in EC/DEC/EMC(3/2/5,wt%)results in a highly improved cyclability of LNMO/Graphite cell,with the capacity retention enhanced from 52%to 80%after 150 cycles at 0.5 C between 3.5 and 4.8 V.The as-developed strategy provides a model of designing electrolyte additives for improving cyclability of high voltage batteries.

Resistive switching behavior and mechanism of HfO_(x) films with large on/off ratio by structure design

Different bilayer structures of HfO_(x)/Ti(TiO_(x)) are designed for hafnium-based memory to investigate the switching characteristics. The chemical states in the films and near the interface are characterized by x-ray photoelectron spectroscopy,and the oxygen vacancies are analyzed. Highly improved on/off ratio(~104) and much uniform switching parameters are observed for bilayer structures compared to single layer HfO_(x) sample, which can be attributed to the modulation of oxygen vacancies at the interface and better control of the growth of filaments. Furthermore, the reliability of the prepared samples is investigated. The carrier conduction behaviors of HfO_(x)-based samples can be attributed to the trapping and de-trapping process of oxygen vacancies and a filamentary model is proposed. In addition, the rupture of filaments during the reset process for the bilayer structures occur at the weak points near the interface by the recovery of oxygen vacancies accompanied by the variation of barrier height. The re-formation of fixed filaments due to the residual filaments as lightning rods results in the better switching performance of the bilayer structure.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

The design and engineering strategies of metal tellurides for advanced metal-ion batteries

Owning various crystal structures and high theoretical capacity,metal tellurides are emerging as promising electrode materials for high-performance metal-ion batteries(MBs).Since metal telluride-based MBs are quite new,fundamental issues raise regarding the energy storage mechanism and other aspects affecting electrochemical performance.Severe volume expansion,low intrinsic conductivity and slow ion diffusion kinetics jeopardize the performance of metal tellurides,so that rational design and engineering are crucial to circumvent these disadvantages.Herein,this review provides an in-depth discussion of recent investigations and progresses of metal tellurides,beginning with a critical discussion on the energy storage mechanisms of metal tellurides in various MBs.In the following,recent design and engineering strategies of metal tellurides,including morphology engineering,compositing,defect engineering and heterostructure construction,for high-performance MBs are summarized.The primary focus is to present a comprehensive understanding of the structural evolution based on the mechanism and corresponding effects of dimension control,composition,electron configuration and structural complexity on the electrochemical performance.In closing,outlooks and prospects for future development of metal tellurides are proposed.This work also highlights the promising directions of design and engineering strategies of metal tellurides with high performance and low cost.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Reliability-based life-cycle cost seismic design optimization of coastal bridge piers with nonuniform corrosion using different materials

Reinforcement corrosion is the main cause of performance deterioration of reinforced concrete(RC)structures.Limited research has been performed to investigate the life-cycle cost(LCC)of coastal bridge piers with nonuniform corrosion using different materials.In this study,a reliability-based design optimization(RBDO)procedure is improved for the design of coastal bridge piers using six groups of commonly used materials,i.e.,normal performance concrete(NPC)with black steel(BS)rebar,high strength steel(HSS)rebar,epoxy coated(EC)rebar,and stainless steel(SS)rebar(named NPC-BS,NPC-HSS,NPC-EC,and NPC-SS,respectively),NPC with BS with silane soakage on the pier surface(named NPC-Silane),and high-performance concrete(HPC)with BS rebar(named HPC-BS).First,the RBDO procedure is improved for the design optimization of coastal bridge piers,and a bridge is selected to illustrate the procedure.Then,reliability analysis of the pier designed with each group of materials is carried out to obtain the time-dependent reliability in terms of the ultimate and serviceability performances.Next,the repair time of the pier is predicted based on the time-dependent reliability indices.Finally,the time-dependent LCCs for the pier are obtained for the selection of the optimal design.

Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.

Multi-Scale Design and Optimization of Composite Material Structure for Heavy-Duty Truck Protection Device

In this paper,to present a lightweight-developed front underrun protection device(FUPD)for heavy-duty trucks,plain weave carbon fiber reinforced plastic(CFRP)is used instead of the original high-strength steel.First,the mechanical and structural properties of plain carbon fiber composite anti-collision beams are comparatively analyzed from a multi-scale perspective.For studying the design capability of carbon fiber composite materials,we investigate the effects of TC-33 carbon fiber diameter(D),fiber yarn width(W)and height(H),and fiber yarn density(N)on the front underrun protective beam of carbon fiber compositematerials.Based on the investigation,a material-structure matching strategy suitable for the front underrun protective beam of heavy-duty trucks is proposed.Next,the composite material structure is optimized by applying size optimization and stack sequence optimization methods to obtain the higher performance carbon fiber composite front underrun protection beam of commercial vehicles.The results show that the fiber yarn height(H)has the greatest influence on the protective beam,and theH1matching scheme for the front underrun protective beamwith a carbon fiber composite structure exhibits superior performance.The proposed method achieves a weight reduction of 55.21% while still meeting regulatory requirements,which demonstrates its remarkable weight reduction effect.