The utilization of iron coke provides a green pathway for low-carbon ironmaking.To uncover the influence mechanism of iron ore on the behavior and kinetics of iron coke gasification,the effect of iron ore on the micro...The utilization of iron coke provides a green pathway for low-carbon ironmaking.To uncover the influence mechanism of iron ore on the behavior and kinetics of iron coke gasification,the effect of iron ore on the microstructure of iron coke was investigated.Furthermore,a comparative study of the gasification reactions between iron coke and coke was conducted through non-isothermal thermogravimetric method.The findings indicate that compared to coke,iron coke exhibits an augmentation in micropores and specific surface area,and the micropores further extend and interconnect.This provides more adsorption sites for CO_(2) molecules during the gasification process,resulting in a reduction in the initial gasification temperature of iron coke.Accelerating the heating rate in non-isothermal gasification can enhance the reactivity of iron coke.The metallic iron reduced from iron ore is embedded in the carbon matrix,reducing the orderliness of the carbon structure,which is primarily responsible for the heightened reactivity of the carbon atoms.The kinetic study indicates that the random pore model can effectively represent the gasification process of iron coke due to its rich pore structure.Moreover,as the proportion of iron ore increases,the activation energy for the carbon gasification gradually decreases,from 246.2 kJ/mol for coke to 192.5 kJ/mol for iron coke 15wt%.展开更多
In the structural reliability analysis,the first-order reliability method(FORM)often yields significant errors when addressing nonlinear problems.Although the second-order reliability method(SORM)can provide higher ac...In the structural reliability analysis,the first-order reliability method(FORM)often yields significant errors when addressing nonlinear problems.Although the second-order reliability method(SORM)can provide higher accuracy,the additional computation of the Hessian matrix leads to lower computational efficiency.Additionally,when the dimensionality of the random variables is high,the approximation formula of SORM can result in larger errors.To address these issues,a structural reliability analysis method based on Kriging and spherical cap area integral is proposed.Firstly,this method integrates FORM with the quasi-Newton algorithm Broyden-Fletcher-Goldfarb-Shanno(BFGS),trains the Kriging model by using sample points from the algorithm’s iteration process,and combines the Kriging model with gradient information to approximate the Hessian matrix.Then,the failure surface is approximated as a rotating paraboloid,utilizing the spherical cap to replace the complex surface.For the n-dimensional case,the hyperspherical cap area expression is combined with the integral method to calculate the failure probability.Finally,the method is validated through three examples,demonstrating improved computational accuracy and efficiency compared to traditional methods.展开更多
Design a precision electroplating mechanical structure for automobiles based on finite element analysis method and analyze its mechanical properties.Taking the automobile steering knuckle as the research object,ABAQUS...Design a precision electroplating mechanical structure for automobiles based on finite element analysis method and analyze its mechanical properties.Taking the automobile steering knuckle as the research object,ABAQUS parametric modeling technology is used to construct its three-dimensional geometric model,and geometric simplification is carried out.Two surface treatment processes,HK-35 zinc nickel alloy electroplating and pure zinc electroplating,were designed,and the influence of different coatings on the mechanical properties of steering knuckles was compared and analyzed through numerical simulation.At the same time,standard specimens were prepared for salt spray corrosion testing and scratch method combined strength testing to verify the numerical simulation results.The results showed that under emergency braking and composite working conditions,the peak Von Mises stress of the zinc nickel alloy coating was 119.85 MPa,which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Its equivalent strain value was 652×10^(-6),which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Experimental data confirms that zinc nickel alloy coatings exhibit significant advantages in stress distribution uniformity,strain performance,and load-bearing capacity in high stress zones.The salt spray corrosion test further indicates that the coating has superior corrosion resistance and coating substrate interface bonding strength,which can significantly improve the mechanical stability and long-term reliability of automotive precision electroplating mechanical structures.展开更多
Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NP...Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.展开更多
Double-shaft-driven needle punching machine is a specialized equipment designed for processing C/C crucible preforms.Its main needle punching module is operated by two sets of reciprocating crank-slider mechanisms.The...Double-shaft-driven needle punching machine is a specialized equipment designed for processing C/C crucible preforms.Its main needle punching module is operated by two sets of reciprocating crank-slider mechanisms.The intense vibration during needle punching not only generates huge noise,but also substantially reduces the quality of the preform.It is imperative to perform a dynamic analysis and optimization of the entire needle punching machine.In this paper,the three-dimensional(3D)model of the entire double-shaft-driven needle punching machine for C/C crucible preforms is established.Based on the modal analysis theory,the modal characteristics of the needle punching machine under various operating conditions are analyzed and its natural frequencies and vibration modes are determined.The harmonic response analysis is then employed to obtain the amplitude of the needle plate at different frequencies,and the structural weak points of the needle punching machine are identified and improved.The feasibility of the optimized scheme is subsequently reevaluated and verified.The results indicate that the first six natural frequencies of the machine increase,and the maximum amplitude of the needle plate decreases by 70.3%.The enhanced dynamic characteristics of the machine significantly improve its performance,enabling more efficient needle punching of C/C crucible preforms.展开更多
With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people&#...With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people's sight,and the lower limb exoskeleton robot withrehabilitation training is also favored by more and more people.In this paper,the structural designand analysis of the lower limb exoskeleton robot are carried out in view of the patients'expectation ofnormal walking.First,gait analysis and structural design of lower limb exoskeleton robot.Based onthe analysis of the walking gait of normal people,the freedom of the three key joints of the lower limbexoskeleton robot hip joint,knee joint and ankle joint is determined.at the same time,according tothe structuralcharacteristics of each joint,the three key joints are modeled respectively,and theoverall model assembly of the lower limb exoskeleton robot is completed.Secondly,the kinematicsanalysis of the lower limb exoskeleton robot was carried out to obtain the relationship between thelinear displacement,linear speed and acceleration of each joint,so as to ensure the coordination ofthe model with the human lower limb movement.Thirdly,the static analysis of typical gait of hipjoint,knee joint and ankle joint is carried out to verify the safety of the design model under thepremise of ensuring the structural strength requirements.Finally,the parts of the model were 3Dprinted,and the rationality of the design was further verified in the process of assembling the model.展开更多
This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-...This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.展开更多
In this paper,the failure caused by HRAM loads which were generated by high-speed projectile penetration,and protection technology of the fluid-filled structure were explored.A bubble was preset on the projectile traj...In this paper,the failure caused by HRAM loads which were generated by high-speed projectile penetration,and protection technology of the fluid-filled structure were explored.A bubble was preset on the projectile trajectory in a fluid-filled structure.Based on the reflection and transmission phenomena of pressure waves at the gas-liquid interface and the compressibility characteristics of gases,a numerical analysis was conducted on the influence of preset bubble on projectile penetration and structural failure characteristics.The results indicate that the secondary water-entry impact phenomenon occurs when a preset bubble exists on the projectile trajectory,leading to the secondary water entry impact loads.The rarefaction waves reflected on the surface of the preset bubble cause the attenuation ratio of the initial impact pressure peak to reach 68.8%and the total specific impulse attenuation ratio to reach 48.6%.Furthermore,the larger the bubble,the faster the projectile,and the more obvious the attenuation effect.Moreover,due to the compressibility of the bubble,the global deformation attenuation ratio of the front and rear walls can reach over 80%.However,the larger the bubble size,the faster the projectile velocity,the smaller the local deformation attenuation effect of the rear wall,and the more severe the failure at the perforation of the rear wall.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee...Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.展开更多
The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus...The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus(SCN)integrates environmental signals to maintain complex and robust circadian rhythms.Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function.Synchrony involves coordinated neuronal firing for robust rhythms,while complexity reflects diverse activity patterns and interactions,indicating adaptability.Interestingly,the SCN retains circadian rhythms in vitro,demonstrating intrinsic rhythmicity.This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels,based on Bagrov et al.’s approach.By examining structural complexity and local complexities across scales,we aim to understand how tetrodotoxin,a neurotoxin that inhibits action potentials,affects SCN neurons.Our method captures critical scales in neuronal interactions that traditional methods may overlook.Validation with the Goodwin model confirms the reliability of our observations.By integrating experimental data with theoretical models,this study provides new insights into the effects of tetrodotoxin(TTX)on neuronal complexities,contributing to the understanding of circadian rhythms.展开更多
Soot is a flocculent carbon nanoparticle that results the imperfect combustion of fossil fuel,and numerous studies are dedicated to the reduction of soot production to alleviate the associated environmental problems.H...Soot is a flocculent carbon nanoparticle that results the imperfect combustion of fossil fuel,and numerous studies are dedicated to the reduction of soot production to alleviate the associated environmental problems.However,soot as a functional material is also widely used in energy storage and superhydrophobic materials.As a partial oxidation technology,the entrained flow coal gasification process will produce part of the soot.It is important to separate soot from the coal gasification fine slag(CGFS)and understand its structural characteristics for soot utilization.For this purpose,two industrial typical pulverized coal gasification fine slag(PCGFS)and coal-water slurry gasification fine slag(WCGFS)were selected for this study.The results showed that both fine slags were rich in soot,and the dry ash free mass fraction of soot in PCGFS and WCGFS was 6.24%and 2.91%,respectively,and the soot of PCGFS had a hollow carbon nanosphere morphology,while the soot of WCGFS showed a flocculent irregular morphology.The average fringe length,fringe tortuosity,and fringe spacing of the soot were 0.84 nm,1.21,and 0.45 nm,respectively.Compared to the WCGFS,the soot particles of PCGFS have less continuity of molecular bonds within the lattice,the larger the defects within the lattice,the fewer isolated lattice carbon layers there are.This study provides important theoretical support for understanding the structural characteristics and next applications of soot in the entrained flow coal gasification fine slag.展开更多
Blades are important parts of rotating machinery such as marine gas turbines and wind turbines,which are exposed to harsh environments during mechanical operations,including centrifugal loads,aerodynamic forces,or hig...Blades are important parts of rotating machinery such as marine gas turbines and wind turbines,which are exposed to harsh environments during mechanical operations,including centrifugal loads,aerodynamic forces,or high temperatures.These demanding working conditions considerably influence the dynamic performance of blades.Therefore,because of the challenges posed by blades in complex working environments,in-depth research and optimization are necessary to ensure that blades can operate safely and efficiently,thus guaranteeing the reliability and performance of mechanical systems.Focusing on the vibration analysis of blades in rotating machinery,this paper conducts a comprehensive literature review on the research advancements in vibration modeling and structural optimization of blades under complex operational conditions.First,the paper outlines the development of several modeling theories for rotating blades,including one-dimensional beam theory,two-dimensional plate-shell theory,and three-dimensional solid theory.Second,the research progress in the vibrational analysis of blades under aerodynamic loads,thermal environments,and crack factors is separately discussed.Finally,the developments in rotating blade structural optimization are presented from material optimization and shape optimization perspectives.The methodology and theory of analyzing and optimizing blade vibration characteristics under multifactorial operating conditions are comprehensively outlined,aiming to assist future researchers in proposing more effective and practical approaches for the vibration analysis and optimization of blades.展开更多
BACKGROUND Radiotherapy(RT)is a cornerstone of cancer treatment.Compared with conven-tional high-dose radiation,low-dose radiation(LDR)causes less damage to normal tissues while potentially modulating immune responses...BACKGROUND Radiotherapy(RT)is a cornerstone of cancer treatment.Compared with conven-tional high-dose radiation,low-dose radiation(LDR)causes less damage to normal tissues while potentially modulating immune responses and inhibiting tumor growth.LDR stimulates both innate and adaptive immunity,enhancing the activity of natural killer cells,dendritic cells,and T cells.However,the me-chanisms underlying the effects of LDR on the immune system remain unclear.AIM To explore the history,research hotspots,and emerging trends in immune response to LDR literature over the past two decades.METHODS Publications on immune responses to LDR were retrieved from the Web of Science Core Collection.Bibliometric tools,including CiteSpace and HistCite,were used to identify historical features,active topics,and emerging trends in this field.RESULTS Analysis of 1244 publications over the past two decades revealed a significant surge in research on immune responses to LDR,particularly in the last decade.Key journals such as INR J Radiat Biol,Cancers,and Radiat Res published pivotal studies.Citation networks identified key studies by authors like Twyman-Saint Victor C(2015)and Vanpouille-Box C(2017).Keyword analysis revealed hotspots such as ipilimumab,stereotactic body RT,and targeted therapy,possibly identifying future research directions.Temporal variations in keyword clusters and alluvial flow maps illustrate the evolution of research themes over time.CONCLUSION This bibliometric analysis provides valuable insights into the evolution of studies on responses to LDR,highlights research trends,and identifies emerging areas for further investigation.展开更多
Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee th...Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.展开更多
Fractional differential equations(FDEs)provide a powerful tool for modeling systems with memory and non-local effects,but understanding their underlying structure remains a significant challenge.While numerous numeric...Fractional differential equations(FDEs)provide a powerful tool for modeling systems with memory and non-local effects,but understanding their underlying structure remains a significant challenge.While numerous numerical and semi-analytical methods exist to find solutions,new approaches are needed to analyze the intrinsic properties of the FDEs themselves.This paper introduces a novel computational framework for the structural analysis of FDEs involving iterated Caputo derivatives.The methodology is based on a transformation that recasts the original FDE into an equivalent higher-order form,represented as the sum of a closed-form,integer-order component G(y)and a residual fractional power seriesΨ(x).This transformed FDE is subsequently reduced to a first-order ordinary differential equation(ODE).The primary novelty of the proposed methodology lies in treating the structure of the integer-order component G(y)not as fixed,but as a parameterizable polynomial whose coefficients can be determined via global optimization.Using particle swarm optimization,the framework identifies an optimal ODE architecture by minimizing a dual objective that balances solution accuracy against a high-fidelity reference and the magnitude of the truncated residual series.The effectiveness of the approach is demonstrated on both a linear FDE and a nonlinear fractional Riccati equation.Results demonstrate that the framework successfully identifies an optimal,low-degree polynomial ODE architecture that is not necessarily identical to the forcing function of the original FDE.This work provides a new tool for analyzing the underlying structure of FDEs and gaining deeper insights into the interplay between local and non-local dynamics in fractional systems.展开更多
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,an...Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.展开更多
China's crop structure has undergone significant changes in the last two decades since 2000,with an increase in the share of cereals,vegetables,and fruit,squeezing out other crops.As a result,land productivity,nut...China's crop structure has undergone significant changes in the last two decades since 2000,with an increase in the share of cereals,vegetables,and fruit,squeezing out other crops.As a result,land productivity,nutrient supply,and carbon emissions have changed.How to reallocate limited farmland among crops to achieve the multiple goals of agrifood systems becomes an important issue.This study explores the sources of land productivity and nutrition supply growth and carbon emissions reduction,and identifies the multiple roles of crop structural change from 2003 to 2020 based on a decomposition analysis.The results reveal that the growth within crops is still the primary driver in land productivity and nutrition supply and the reduction in carbon emissions.However,structural change also plays various roles at different periods.From 2003 to 2010,crop structural change increased the total calorie supply but lowered land productivity and contributed at least 70%of the total growth of carbon emissions.The crop structure was relatively stable,and their effects were modest from 2010 to 2015.From 2015 to 2020,the crop structural change began to play a greater role and generate synergistic effects in improving land productivity,micronutrient supply,and reducing carbon emissions,contributing to approximately a quarter of the growth of land productivity and 30%of total carbon emissions reduction.These results suggest that strategies for crop structural change should comprehensively consider its multiple impacts,aiming to achieve co-benefits while minimizing trade-offs.展开更多
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
基金financially supported by the National Science Foundation of China(Nos.51974212 and 52274316)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202116)+1 种基金the Science and Technology Major Project of Wuhan(No.2023020302020572)the Foundation of Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education(No.FMRUlab23-04)。
文摘The utilization of iron coke provides a green pathway for low-carbon ironmaking.To uncover the influence mechanism of iron ore on the behavior and kinetics of iron coke gasification,the effect of iron ore on the microstructure of iron coke was investigated.Furthermore,a comparative study of the gasification reactions between iron coke and coke was conducted through non-isothermal thermogravimetric method.The findings indicate that compared to coke,iron coke exhibits an augmentation in micropores and specific surface area,and the micropores further extend and interconnect.This provides more adsorption sites for CO_(2) molecules during the gasification process,resulting in a reduction in the initial gasification temperature of iron coke.Accelerating the heating rate in non-isothermal gasification can enhance the reactivity of iron coke.The metallic iron reduced from iron ore is embedded in the carbon matrix,reducing the orderliness of the carbon structure,which is primarily responsible for the heightened reactivity of the carbon atoms.The kinetic study indicates that the random pore model can effectively represent the gasification process of iron coke due to its rich pore structure.Moreover,as the proportion of iron ore increases,the activation energy for the carbon gasification gradually decreases,from 246.2 kJ/mol for coke to 192.5 kJ/mol for iron coke 15wt%.
基金National Natural Science Foundation of China(No.52375236)Fundamental Research Funds for the Central Universities,China(No.23D110316)。
文摘In the structural reliability analysis,the first-order reliability method(FORM)often yields significant errors when addressing nonlinear problems.Although the second-order reliability method(SORM)can provide higher accuracy,the additional computation of the Hessian matrix leads to lower computational efficiency.Additionally,when the dimensionality of the random variables is high,the approximation formula of SORM can result in larger errors.To address these issues,a structural reliability analysis method based on Kriging and spherical cap area integral is proposed.Firstly,this method integrates FORM with the quasi-Newton algorithm Broyden-Fletcher-Goldfarb-Shanno(BFGS),trains the Kriging model by using sample points from the algorithm’s iteration process,and combines the Kriging model with gradient information to approximate the Hessian matrix.Then,the failure surface is approximated as a rotating paraboloid,utilizing the spherical cap to replace the complex surface.For the n-dimensional case,the hyperspherical cap area expression is combined with the integral method to calculate the failure probability.Finally,the method is validated through three examples,demonstrating improved computational accuracy and efficiency compared to traditional methods.
文摘Design a precision electroplating mechanical structure for automobiles based on finite element analysis method and analyze its mechanical properties.Taking the automobile steering knuckle as the research object,ABAQUS parametric modeling technology is used to construct its three-dimensional geometric model,and geometric simplification is carried out.Two surface treatment processes,HK-35 zinc nickel alloy electroplating and pure zinc electroplating,were designed,and the influence of different coatings on the mechanical properties of steering knuckles was compared and analyzed through numerical simulation.At the same time,standard specimens were prepared for salt spray corrosion testing and scratch method combined strength testing to verify the numerical simulation results.The results showed that under emergency braking and composite working conditions,the peak Von Mises stress of the zinc nickel alloy coating was 119.85 MPa,which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Its equivalent strain value was 652×10^(-6),which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Experimental data confirms that zinc nickel alloy coatings exhibit significant advantages in stress distribution uniformity,strain performance,and load-bearing capacity in high stress zones.The salt spray corrosion test further indicates that the coating has superior corrosion resistance and coating substrate interface bonding strength,which can significantly improve the mechanical stability and long-term reliability of automotive precision electroplating mechanical structures.
基金National Natural Science Foundation of China under Grant Nos.52208191 and 51908397Shanxi Province Science Foundation for Youths under Grant No.201901D211025China Postdoctoral Science Foundation under Grant No.2020M670695。
文摘Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.
基金Open Project of Shanghai Key Laboratory of Lightweight Composite,China(No.2232021A4-04)。
文摘Double-shaft-driven needle punching machine is a specialized equipment designed for processing C/C crucible preforms.Its main needle punching module is operated by two sets of reciprocating crank-slider mechanisms.The intense vibration during needle punching not only generates huge noise,but also substantially reduces the quality of the preform.It is imperative to perform a dynamic analysis and optimization of the entire needle punching machine.In this paper,the three-dimensional(3D)model of the entire double-shaft-driven needle punching machine for C/C crucible preforms is established.Based on the modal analysis theory,the modal characteristics of the needle punching machine under various operating conditions are analyzed and its natural frequencies and vibration modes are determined.The harmonic response analysis is then employed to obtain the amplitude of the needle plate at different frequencies,and the structural weak points of the needle punching machine are identified and improved.The feasibility of the optimized scheme is subsequently reevaluated and verified.The results indicate that the first six natural frequencies of the machine increase,and the maximum amplitude of the needle plate decreases by 70.3%.The enhanced dynamic characteristics of the machine significantly improve its performance,enabling more efficient needle punching of C/C crucible preforms.
基金College Student Innovation andEntrepreneurship Project(Grant No.:S202414435026)ingkou Institute of Technology campus level research project——Development of food additive supercritical extraction equipment and fluid transmission systemresearch(Grant No.HX202427).
文摘With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people's sight,and the lower limb exoskeleton robot withrehabilitation training is also favored by more and more people.In this paper,the structural designand analysis of the lower limb exoskeleton robot are carried out in view of the patients'expectation ofnormal walking.First,gait analysis and structural design of lower limb exoskeleton robot.Based onthe analysis of the walking gait of normal people,the freedom of the three key joints of the lower limbexoskeleton robot hip joint,knee joint and ankle joint is determined.at the same time,according tothe structuralcharacteristics of each joint,the three key joints are modeled respectively,and theoverall model assembly of the lower limb exoskeleton robot is completed.Secondly,the kinematicsanalysis of the lower limb exoskeleton robot was carried out to obtain the relationship between thelinear displacement,linear speed and acceleration of each joint,so as to ensure the coordination ofthe model with the human lower limb movement.Thirdly,the static analysis of typical gait of hipjoint,knee joint and ankle joint is carried out to verify the safety of the design model under thepremise of ensuring the structural strength requirements.Finally,the parts of the model were 3Dprinted,and the rationality of the design was further verified in the process of assembling the model.
基金support from the National Natural Science Foundation of China(Grant Nos.52174123&52274222).
文摘This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.
文摘In this paper,the failure caused by HRAM loads which were generated by high-speed projectile penetration,and protection technology of the fluid-filled structure were explored.A bubble was preset on the projectile trajectory in a fluid-filled structure.Based on the reflection and transmission phenomena of pressure waves at the gas-liquid interface and the compressibility characteristics of gases,a numerical analysis was conducted on the influence of preset bubble on projectile penetration and structural failure characteristics.The results indicate that the secondary water-entry impact phenomenon occurs when a preset bubble exists on the projectile trajectory,leading to the secondary water entry impact loads.The rarefaction waves reflected on the surface of the preset bubble cause the attenuation ratio of the initial impact pressure peak to reach 68.8%and the total specific impulse attenuation ratio to reach 48.6%.Furthermore,the larger the bubble,the faster the projectile,and the more obvious the attenuation effect.Moreover,due to the compressibility of the bubble,the global deformation attenuation ratio of the front and rear walls can reach over 80%.However,the larger the bubble size,the faster the projectile velocity,the smaller the local deformation attenuation effect of the rear wall,and the more severe the failure at the perforation of the rear wall.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金funded by the National Key Research and Development Program(Grant No.2022YFB3706904).
文摘Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179,11875042,and 12150410309)the Natural Science Foundation of Shanghai(Grant No.21ZR1443900).
文摘The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus(SCN)integrates environmental signals to maintain complex and robust circadian rhythms.Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function.Synchrony involves coordinated neuronal firing for robust rhythms,while complexity reflects diverse activity patterns and interactions,indicating adaptability.Interestingly,the SCN retains circadian rhythms in vitro,demonstrating intrinsic rhythmicity.This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels,based on Bagrov et al.’s approach.By examining structural complexity and local complexities across scales,we aim to understand how tetrodotoxin,a neurotoxin that inhibits action potentials,affects SCN neurons.Our method captures critical scales in neuronal interactions that traditional methods may overlook.Validation with the Goodwin model confirms the reliability of our observations.By integrating experimental data with theoretical models,this study provides new insights into the effects of tetrodotoxin(TTX)on neuronal complexities,contributing to the understanding of circadian rhythms.
基金supported by the National Natural Science Foundation of China(22168032,21968024)the National Key Research and Development Program of China(2023YFC3904302).
文摘Soot is a flocculent carbon nanoparticle that results the imperfect combustion of fossil fuel,and numerous studies are dedicated to the reduction of soot production to alleviate the associated environmental problems.However,soot as a functional material is also widely used in energy storage and superhydrophobic materials.As a partial oxidation technology,the entrained flow coal gasification process will produce part of the soot.It is important to separate soot from the coal gasification fine slag(CGFS)and understand its structural characteristics for soot utilization.For this purpose,two industrial typical pulverized coal gasification fine slag(PCGFS)and coal-water slurry gasification fine slag(WCGFS)were selected for this study.The results showed that both fine slags were rich in soot,and the dry ash free mass fraction of soot in PCGFS and WCGFS was 6.24%and 2.91%,respectively,and the soot of PCGFS had a hollow carbon nanosphere morphology,while the soot of WCGFS showed a flocculent irregular morphology.The average fringe length,fringe tortuosity,and fringe spacing of the soot were 0.84 nm,1.21,and 0.45 nm,respectively.Compared to the WCGFS,the soot particles of PCGFS have less continuity of molecular bonds within the lattice,the larger the defects within the lattice,the fewer isolated lattice carbon layers there are.This study provides important theoretical support for understanding the structural characteristics and next applications of soot in the entrained flow coal gasification fine slag.
基金Supported by the National Natural Science Foundation of China under Grant No.52271309Natural Science Foundation of Heilongjiang Province of China under Grant No.YQ2022E104.
文摘Blades are important parts of rotating machinery such as marine gas turbines and wind turbines,which are exposed to harsh environments during mechanical operations,including centrifugal loads,aerodynamic forces,or high temperatures.These demanding working conditions considerably influence the dynamic performance of blades.Therefore,because of the challenges posed by blades in complex working environments,in-depth research and optimization are necessary to ensure that blades can operate safely and efficiently,thus guaranteeing the reliability and performance of mechanical systems.Focusing on the vibration analysis of blades in rotating machinery,this paper conducts a comprehensive literature review on the research advancements in vibration modeling and structural optimization of blades under complex operational conditions.First,the paper outlines the development of several modeling theories for rotating blades,including one-dimensional beam theory,two-dimensional plate-shell theory,and three-dimensional solid theory.Second,the research progress in the vibrational analysis of blades under aerodynamic loads,thermal environments,and crack factors is separately discussed.Finally,the developments in rotating blade structural optimization are presented from material optimization and shape optimization perspectives.The methodology and theory of analyzing and optimizing blade vibration characteristics under multifactorial operating conditions are comprehensively outlined,aiming to assist future researchers in proposing more effective and practical approaches for the vibration analysis and optimization of blades.
文摘BACKGROUND Radiotherapy(RT)is a cornerstone of cancer treatment.Compared with conven-tional high-dose radiation,low-dose radiation(LDR)causes less damage to normal tissues while potentially modulating immune responses and inhibiting tumor growth.LDR stimulates both innate and adaptive immunity,enhancing the activity of natural killer cells,dendritic cells,and T cells.However,the me-chanisms underlying the effects of LDR on the immune system remain unclear.AIM To explore the history,research hotspots,and emerging trends in immune response to LDR literature over the past two decades.METHODS Publications on immune responses to LDR were retrieved from the Web of Science Core Collection.Bibliometric tools,including CiteSpace and HistCite,were used to identify historical features,active topics,and emerging trends in this field.RESULTS Analysis of 1244 publications over the past two decades revealed a significant surge in research on immune responses to LDR,particularly in the last decade.Key journals such as INR J Radiat Biol,Cancers,and Radiat Res published pivotal studies.Citation networks identified key studies by authors like Twyman-Saint Victor C(2015)and Vanpouille-Box C(2017).Keyword analysis revealed hotspots such as ipilimumab,stereotactic body RT,and targeted therapy,possibly identifying future research directions.Temporal variations in keyword clusters and alluvial flow maps illustrate the evolution of research themes over time.CONCLUSION This bibliometric analysis provides valuable insights into the evolution of studies on responses to LDR,highlights research trends,and identifies emerging areas for further investigation.
基金National Key R&D Program of China under Grant Nos.2018YFC1504504 and 2018YFC0809404。
文摘Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.
基金Research Council of Lithuania(LMTLT),agreement No.S-PD-24-120Research Council of Lithuania(LMTLT),agreement No.S-PD-24-120funded by the Research Council of Lithuania.
文摘Fractional differential equations(FDEs)provide a powerful tool for modeling systems with memory and non-local effects,but understanding their underlying structure remains a significant challenge.While numerous numerical and semi-analytical methods exist to find solutions,new approaches are needed to analyze the intrinsic properties of the FDEs themselves.This paper introduces a novel computational framework for the structural analysis of FDEs involving iterated Caputo derivatives.The methodology is based on a transformation that recasts the original FDE into an equivalent higher-order form,represented as the sum of a closed-form,integer-order component G(y)and a residual fractional power seriesΨ(x).This transformed FDE is subsequently reduced to a first-order ordinary differential equation(ODE).The primary novelty of the proposed methodology lies in treating the structure of the integer-order component G(y)not as fixed,but as a parameterizable polynomial whose coefficients can be determined via global optimization.Using particle swarm optimization,the framework identifies an optimal ODE architecture by minimizing a dual objective that balances solution accuracy against a high-fidelity reference and the magnitude of the truncated residual series.The effectiveness of the approach is demonstrated on both a linear FDE and a nonlinear fractional Riccati equation.Results demonstrate that the framework successfully identifies an optimal,low-degree polynomial ODE architecture that is not necessarily identical to the forcing function of the original FDE.This work provides a new tool for analyzing the underlying structure of FDEs and gaining deeper insights into the interplay between local and non-local dynamics in fractional systems.
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
文摘Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.
基金This work was supported by the National Natural Science Foundation of China(72061147002 and 72373143)the National Social Science Fund of China(22&ZD085).
文摘China's crop structure has undergone significant changes in the last two decades since 2000,with an increase in the share of cereals,vegetables,and fruit,squeezing out other crops.As a result,land productivity,nutrient supply,and carbon emissions have changed.How to reallocate limited farmland among crops to achieve the multiple goals of agrifood systems becomes an important issue.This study explores the sources of land productivity and nutrition supply growth and carbon emissions reduction,and identifies the multiple roles of crop structural change from 2003 to 2020 based on a decomposition analysis.The results reveal that the growth within crops is still the primary driver in land productivity and nutrition supply and the reduction in carbon emissions.However,structural change also plays various roles at different periods.From 2003 to 2010,crop structural change increased the total calorie supply but lowered land productivity and contributed at least 70%of the total growth of carbon emissions.The crop structure was relatively stable,and their effects were modest from 2010 to 2015.From 2015 to 2020,the crop structural change began to play a greater role and generate synergistic effects in improving land productivity,micronutrient supply,and reducing carbon emissions,contributing to approximately a quarter of the growth of land productivity and 30%of total carbon emissions reduction.These results suggest that strategies for crop structural change should comprehensively consider its multiple impacts,aiming to achieve co-benefits while minimizing trade-offs.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
