As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of ai...As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of aircraft structures. The assessment of electromagnetic SE for Slotted Composite Structures(SCSs) is particularly challenging due to their complex geometries and there remains a lack of suitable models for accurately predicting the SE performance of these intricate configurations. To address this issue, this paper introduces SCS-Net, a Deep Neural Network (DNN) method designed to accurately predict the SE of SCS. This method considers the impacts of various structural parameters, material properties and incident wave parameters on the SE of SCSs. In order to better model the SCS, an improved Nicolson-Ross-Weir (NRW) method is introduced in this paper to provide an equivalent flat structure for the SCS and to calculate the electromagnetic parameters of the equivalent structure. Additionally, the prediction of SE via DNNs is limited by insufficient test data, which hinders support for large-sample training. To address the issue of limited measured data, this paper develops a Measurement-Computation Fusion (MCF) dataset construction method. The predictions based on the simulation results show that the proposed method maintains an error of less than 0.07 dB within the 8–10 GHz frequency range. Furthermore, a new loss function based on the weighted L1-norm is established to improve the prediction accuracy for these parameters. Compared with traditional loss functions, the new loss function reduces the maximum prediction error for equivalent electromagnetic parameters by 47%. This method significantly improves the prediction accuracy of SCS-Net for measured data, with a maximum improvement of 23.88%. These findings demonstrate that the proposed method enables precise SE prediction and design for composite structures while reducing the number of test samples needed.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an...Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
Ribonucleic acid(RNA)structures and dynamics play a crucial role in elucidating RNA functions and facilitating the design of drugs targeting RNA and RNA-protein complexes.However,obtaining RNA structures using convent...Ribonucleic acid(RNA)structures and dynamics play a crucial role in elucidating RNA functions and facilitating the design of drugs targeting RNA and RNA-protein complexes.However,obtaining RNA structures using conventional biophysical techniques,such as Xray crystallography and solution nuclear magnetic resonance(NMR),presents challenges due to the inherent flexibility and susceptibility to degradation of RNA.In recent years,solid-state NMR(SSNMR)has rapidly emerged as a promising alternative technique for characterizing RNA structure and dynamics.SSNMR has several distinct advantages,including flexibility in sample states,the ability to capture dynamic features of RNA in solid form,and suitability to character RNAs in various sizes.Recent decade witnessed the growth of ^(1)H-detected SSNMR methods on RNA,which targeted elucidating RNA topology and base pair dynamics in solid state.They have been applied to determine the topology of RNA segment in human immunodeficiency virus(HIV)genome and the base pair dynamics of riboswitch RNA.These advancements have expanded the utility of SSNMR techniques within the RNA research field.This review provides a comprehensive discussion of recent progress in ^(1)H-detected SSNMR investigations into RNA structure and dynamics.We focus on the established ^(1)H-detected SSNMR methods,sample preparation protocols,and the implementation of rapid data acquisition approaches.展开更多
This paper employs the Direct Finite Element Squared(DFE2)method to develop Sparse Polynomial Chaos Expansions(SPCE)models for analyzing the electromechanical properties of multiscale piezoelectric structures.By incor...This paper employs the Direct Finite Element Squared(DFE2)method to develop Sparse Polynomial Chaos Expansions(SPCE)models for analyzing the electromechanical properties of multiscale piezoelectric structures.By incorporating variations in piezoelectric and elastic constants,the DFE2 method is utilized to simulate the statistical characteristics—such as expected values and standard deviations—of electromechanical properties,including Mises stress,maximum in-plane principal strain,electric potential gradient,and electric potential,under varying parameters.This approach achieves a balance between computational efficiency and accuracy.Different SPCE models are used to investigate the influence of piezoelectric and elastic constants on multiscale piezoelectric materials.Additionally,the multiscale parameterization study investigates how microscale material properties affect the macroscopic response of these structures and materials.展开更多
Reticular structures are the basis of major infrastructure projects,including bridges,electrical pylons and airports.However,inspecting and maintaining these structures is both expensive and hazardous,traditionally re...Reticular structures are the basis of major infrastructure projects,including bridges,electrical pylons and airports.However,inspecting and maintaining these structures is both expensive and hazardous,traditionally requiring human involvement.While some research has been conducted in this field of study,most efforts focus on faults identification through images or the design of robotic platforms,often neglecting the autonomous navigation of robots through the structure.This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures,thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments.The paper proposes multiple approaches for the binary segmentation between navigable surfaces and background from 3D point clouds captured from metallic trusses.Approaches can be classified into two paradigms:analytical algorithms and deep learning methods.Within the analytical approach,an ad hoc algorithm is developed for segmenting the structures,leveraging different techniques to evaluate the eigendecomposition of planar patches within the point cloud.In parallel,widely used and advanced deep learning models,including PointNet,PointNet++,MinkUNet34C,and PointTransformerV3,are trained and evaluated for the same task.A comparative analysis of these paradigms reveals some key insights.The analytical algorithm demonstrates easier parameter adjustment and comparable performance to that of the deep learning models,despite the latter’s higher computational demands.Nevertheless,the deep learning models stand out in segmentation accuracy,with PointTransformerV3 achieving impressive results,such as a Mean Intersection Over Union(mIoU)of approximately 97%.This study highlights the potential of analytical and deep learning approaches to improve the autonomous navigation of climbing robots in complex truss structures.The findings underscore the trade-offs between computational efficiency and segmentation performance,offering valuable insights for future research and practical applications in autonomous infrastructure maintenance and inspection.展开更多
The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and e...The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and efficacy of drugs.Unlike the inherent molecular structures of active pharmaceutical ingredients(APIs)and excipients,the microstructures of pharmaceutical preparations are developed during the formulation process,presenting unique analytical challenges.In this review,we primarily focus on presenting the research methods used to elucidate the microstructures of pharmaceutical preparations,including X-ray imaging(XRI),scanning electron microscopy(SEM),atomic force microscopy(AFM),Raman spectroscopy,infrared(IR)spectroscopy,and rheometer technology.Subsequently,we highlight the applications,advantages,and limitations of these methods.Finally,we discuss the current challenges and future perspectives in this field.This review aims to provide a comprehensive reference for understanding the microstructures of pharmaceutical preparations,offering new insights and potential advancements in their development.展开更多
This paper proposes a new step-by-step Chebyshev space-time spectral method to analyze the force vibration of functionally graded material structures.Although traditional space-time spectral methods can reduce the acc...This paper proposes a new step-by-step Chebyshev space-time spectral method to analyze the force vibration of functionally graded material structures.Although traditional space-time spectral methods can reduce the accuracy mismatch between tem-poral low-order finite difference and spatial high-order discre tization,the ir time collocation points must increase dramatically to solve highly oscillatory solutions of structural vibration,which results in a surge in computing time and a decrease in accuracy.To address this problem,we introduced the step-by-step idea in the space-time spectral method.The Chebyshev polynomials and Lagrange's equation were applied to derive discrete spatial goverming equations,and a matrix projection method was used to map the calculation results of prev ious steps as the initial conditions of the subsequent steps.A series of numerical experiments were carried out.The results of the proposed method were compared with those obtained by traditional space-time spectral methods,which showed that higher accuracy could be achieved in a shorter computation time than the latter in highly oscillatory cases.展开更多
This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure(CFRCS).The proposed method mainly...This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure(CFRCS).The proposed method mainly includes three steps:(1)a ResUNet-involved generative and adversarial network(ResUNet-GAN)is developed to establish the end-to-end mapping from structural design parameters to fiber-reinforced composite optimized structure,and a fiber orientation chromatogram is presented to represent continuous fiber angles;(2)to avoid the local optimum problem,the independent continuous mapping method(ICM method)considering the improved principal stress orientation interpolated continuous fiber angle optimization(PSO-CFAO)strategy is utilized to construct CFRCS topology optimization dataset;(3)the well-trained ResUNet-GAN is deployed to design the optimal structural material distribution together with the corresponding continuous fiber orientations.Numerical simulations for benchmark structure verify that the proposed method greatly improves the design efficiency of CFRCS along with high design accuracy.Furthermore,the CFRCS topology configuration designed by ResUNet-GAN is fabricated by additive manufacturing.Compression experiments of the specimens show that both the stiffness structure and peak load of the CFRCS topology configuration designed by the proposed method have significantly enhanced.The proposed deep learning-based topology optimization method will provide great flexibility in CFRCS for engineering applications.展开更多
In this research,we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method(CDM-FEM)with the Particle Swarm Optimization(PSO)-based technique,to predict the Medium-Low-Cycle...In this research,we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method(CDM-FEM)with the Particle Swarm Optimization(PSO)-based technique,to predict the Medium-Low-Cycle Fatigue(MLCF)life of perforated structures.First,fatigue tests are carried out on three center-perforated structures,aiming to assess their fatigue life under various strengthening conditions.These tests reveal significant variations in fatigue life,accompanied by an examination of crack initiation through the analysis of fatigue fracture surfaces.Second,an innovative fatigue life prediction methodology is applied to perforated structures,which not only forecasts the initiation of fatigue cracks but also traces the progression of damage within these structures.It leverages an elastoplastic constitutive model integrated with damage and a damage evolution model under cyclic loads.The accuracy of this approach is validated by comparison with test results,falling within the three times error band.Finally,we explore the impact of various strengthening techniques,including cross-sectional reinforcement and cold expansion,on the fatigue life and damage evolution of these structures.This is achieved through an in-depth comparative analysis of both experimental data and computational predictions,which provides valuable insights into the behavior of perforated structures under fatigue conditions in practical applications.展开更多
Magneto-electro-elastic(MEE)materials are widely utilized across various fields due to their multi-field coupling effects.Consequently,investigating the coupling behavior of MEE composite materials is of significant i...Magneto-electro-elastic(MEE)materials are widely utilized across various fields due to their multi-field coupling effects.Consequently,investigating the coupling behavior of MEE composite materials is of significant importance.The traditional finite element method(FEM)remains one of the primary approaches for addressing such issues.However,the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision,which inevitably leads to a reduction in computational efficiency.To enhance the computational accuracy and efficiency of the FEM for heterogeneous multi-field coupling problems,this study presents the coupling magneto-electro-elastic multiscale finite element method(CM-MsFEM)for heterogeneous MEE structures.Unlike the conventional multiscale FEM(MsFEM),the proposed algorithm simultaneously constructs displacement,electric,and magnetic potential multiscale basis functions to address the heterogeneity of the corresponding parameters.The macroscale formulation of CM-MsFEM was derived,and the macroscale/microscale responses of the problems were obtained through up/downscaling calculations.Evaluation using numerical examples analyzing the transient behavior of heterogeneous MEE structures demonstrated that the proposed method outperforms traditional FEM in terms of both accuracy and computational efficiency,making it an appropriate choice for numerically modeling the dynamics of heterogeneous MEE structures.展开更多
Aiming at addressing the issues of unclear dynamic response mechanisms and insufficient quantification of temperature coupling effects in building structures under long-duration blast loads,this study investigates typ...Aiming at addressing the issues of unclear dynamic response mechanisms and insufficient quantification of temperature coupling effects in building structures under long-duration blast loads,this study investigates typical composite beam-slab structures through integrated blast shock tube experiments and multiscale numerical simulations using Voronoi-coupled Finite-Discrete Element Method(VoroFDEM).The research systematically reveals the dynamic response mechanisms and damage evolution patterns of composite beam-slab structures subjected to prolonged blast loading.An environmenttemperature-coupled P-I curve damage assessment system is established,and a rapid evaluation method based on image crack characteristics is proposed,achieving innovative transition from traditional mechanical indicators to intelligent recognition paradigms.Results demonstrate that composite beam-slab structures exhibit three-phase failure modes:elastic vibration,plastic hinge formation,and global collapse.Numerical simulations identify the brittle-to-ductile transition temperature threshold at-10℃,and establish a temperature-dependent piecewise function-based P-I curve prediction model,whose overpressure asymptote displays nonlinear temperature sensitivity within-50-30℃.A novel dual-mode evaluation methodology integrating Voro-FDEM numerical simulations with image-based damage feature recognition is developed,enabling quantitative mapping between crack area and destruction levels.These findings provide theoretical foundations and technical pathways for rapid blast damage assessment and protective engineering design.展开更多
A novel rheocasting process, self-inoculation method （SIM）, was developed for the microstructure control of semisolid wrought Mg alloy. This process involves mixing between liquid alloy and particles of solid alloy ...A novel rheocasting process, self-inoculation method （SIM）, was developed for the microstructure control of semisolid wrought Mg alloy. This process involves mixing between liquid alloy and particles of solid alloy （self-inoculants）, subsequently pouring the mixed melt into a special designed multi-stream fluid director. The primary phase with dendritic morphology in the conventionally cast AZ31 alloy has readily transformed into near spherical one in the slurry produced by SIM from melt treatment temperature between 690 ℃ and 710 ℃ and self-inoculants addition of 3%-7%. Achievement of the non-dendritic microstructure at the higher melt treatment temperature requires more self-inoculants addition or decreases in the slope angle of fluid director. Primary phase in the slurry thus produced has attained an ideally globular morphology after isothermal holding at 620 ℃ for 30 s. The increasing holding time leads to decrease of shape factor but the coarsening of particle size. The spheroidization and coarsening evolution process of solid particles during the isothermal holding were analyzed by Lifshitz-Slyozov-Wagner （LSW） theory.展开更多
In order to evaluate the carbonation life of newly-built concrete structures,two kinds of nondestructive methods are adopted to test the thickness of the concrete cover and the ultrasonic velocity of two newly-built t...In order to evaluate the carbonation life of newly-built concrete structures,two kinds of nondestructive methods are adopted to test the thickness of the concrete cover and the ultrasonic velocity of two newly-built tunnel structures.Simultaneously a probabilistic method is proposed based on the relationship between the accelerated carbonation rate and the ultrasonic velocity.This proposed method is applied to evaluate the carbonation related lives of two newly-built tunnels and the results indicate that even under nearly the same environment and CO2 combining conditions,there exits a big difference in the probabilistic carbonation lives between the two tunnels;i.e.,the probabilistic lives of Tunnel A and Tunnel B are 94.0% and 82.3% and the corresponding maximum discrepancies are 11.6% and 27.0%,respectively.Thus,it can be concluded that the scattered quality of the concrete cover is attributed to the differences in construction technique,which eventually leads to the diversity in the evaluated probabilistic carbonation lives of the two tunnels.展开更多
In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, an...In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.展开更多
A new method for separating complex touching equiaxed and lamellar alpha phases in the optical micrograph of titanium alloy was proposed for quantitative characterization. This new method involves three steps. First, ...A new method for separating complex touching equiaxed and lamellar alpha phases in the optical micrograph of titanium alloy was proposed for quantitative characterization. This new method involves three steps. First, concave points of the microstructural feature are identified with a threshold of the concaveness of the comer points which are extracted from the binarized image. Secondly, concave points pairs are selected from the concave points group established by means of marker circle or distance. Third, whether a candidate separation line which connects two concave points within a pair can be accepted or not is determined by the proposed four rules. The obtained results show that this method is effective on separating complex touching microstructural features.展开更多
A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A norm...A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A normal relative function about the evaluation indices of each element is developed to calculate the relative degree, and for each element there are no sub-level elements. When evaluating the elements in the sub-item level or the index level of the model, the weights of elements pertain to one adopted element, taking into account their degrees of deterioration. Since the relative degrees and structure evaluation scales on the damage conditions are applied to characterize the superstructure of damaged prestressed concrete girder bridges, this method can evaluate the prestressed structure in detail, and the evaluation results agree with the Code for Maintenance of Highway Bridges and Culvers （JTG Hll--2004 ）. Finally, a bridge in Jilin province is taken as an example, using the method developed to evaluate its damage conditions, which gives an effective way for bridge engineering.展开更多
Nanosphere-like Li2FeSiO4/C was synthesized via a solution method using sucrose as carbon sources under a mild condition of time-saving and energy-saving, followed by sintering at high temperatures for crystallization...Nanosphere-like Li2FeSiO4/C was synthesized via a solution method using sucrose as carbon sources under a mild condition of time-saving and energy-saving, followed by sintering at high temperatures for crystallization. The amount of carbon in the composite is less than 10% （mass fraction）, and the X-ray diffraction result confirms that the sample is of pure single phase indexed with the orthorhombic Pmn21 space group. The particle size of the Li2FeSiO4/C synthesized at 700 °C for 9 h is very fine and spherical-like with a size of 200 nm. The electrochemical performance of this material, including reversible capacity, cycle number, and charge-discharge characteristics, were tested. The cell of this sample can deliver a discharge capacity of 166 mA-h/g at C/20 rate in the first three cycles. After 30 cycles, the capacity decreases to 158 mA-h/g, and the capacity retention is up to 95%. The results show that this method can prepare nanosphere-like Li2FeSiO4/C composite with good electrochemical performance.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62101020 and 62141405)the Special Scientific Research Project of Civil Aircraft,China(No.MJZ5-2N22).
文摘As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of aircraft structures. The assessment of electromagnetic SE for Slotted Composite Structures(SCSs) is particularly challenging due to their complex geometries and there remains a lack of suitable models for accurately predicting the SE performance of these intricate configurations. To address this issue, this paper introduces SCS-Net, a Deep Neural Network (DNN) method designed to accurately predict the SE of SCS. This method considers the impacts of various structural parameters, material properties and incident wave parameters on the SE of SCSs. In order to better model the SCS, an improved Nicolson-Ross-Weir (NRW) method is introduced in this paper to provide an equivalent flat structure for the SCS and to calculate the electromagnetic parameters of the equivalent structure. Additionally, the prediction of SE via DNNs is limited by insufficient test data, which hinders support for large-sample training. To address the issue of limited measured data, this paper develops a Measurement-Computation Fusion (MCF) dataset construction method. The predictions based on the simulation results show that the proposed method maintains an error of less than 0.07 dB within the 8–10 GHz frequency range. Furthermore, a new loss function based on the weighted L1-norm is established to improve the prediction accuracy for these parameters. Compared with traditional loss functions, the new loss function reduces the maximum prediction error for equivalent electromagnetic parameters by 47%. This method significantly improves the prediction accuracy of SCS-Net for measured data, with a maximum improvement of 23.88%. These findings demonstrate that the proposed method enables precise SE prediction and design for composite structures while reducing the number of test samples needed.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金Supported by the National Natural Science Foundation of China (Grant Nos.52088102 and 51879287)National Key Research and Development Program of China (Grant No.2022YFB2602301)。
文摘Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
基金supported by the National Natural Science Foundation of China(grant number:22274050)the Shanghai Science and Technology Commission(contract number:23J21900300)the Fundamental Research Funds for the Central Universities.
文摘Ribonucleic acid(RNA)structures and dynamics play a crucial role in elucidating RNA functions and facilitating the design of drugs targeting RNA and RNA-protein complexes.However,obtaining RNA structures using conventional biophysical techniques,such as Xray crystallography and solution nuclear magnetic resonance(NMR),presents challenges due to the inherent flexibility and susceptibility to degradation of RNA.In recent years,solid-state NMR(SSNMR)has rapidly emerged as a promising alternative technique for characterizing RNA structure and dynamics.SSNMR has several distinct advantages,including flexibility in sample states,the ability to capture dynamic features of RNA in solid form,and suitability to character RNAs in various sizes.Recent decade witnessed the growth of ^(1)H-detected SSNMR methods on RNA,which targeted elucidating RNA topology and base pair dynamics in solid state.They have been applied to determine the topology of RNA segment in human immunodeficiency virus(HIV)genome and the base pair dynamics of riboswitch RNA.These advancements have expanded the utility of SSNMR techniques within the RNA research field.This review provides a comprehensive discussion of recent progress in ^(1)H-detected SSNMR investigations into RNA structure and dynamics.We focus on the established ^(1)H-detected SSNMR methods,sample preparation protocols,and the implementation of rapid data acquisition approaches.
基金supported by the Zhumadian 2023 Major Science and Technology Special Project(Grant No.ZMDSZDZX2023002)the Postgraduate Education Reform and Quality Improvement Project of Henan Province(Grant No.YJS2023JD52).
文摘This paper employs the Direct Finite Element Squared(DFE2)method to develop Sparse Polynomial Chaos Expansions(SPCE)models for analyzing the electromechanical properties of multiscale piezoelectric structures.By incorporating variations in piezoelectric and elastic constants,the DFE2 method is utilized to simulate the statistical characteristics—such as expected values and standard deviations—of electromechanical properties,including Mises stress,maximum in-plane principal strain,electric potential gradient,and electric potential,under varying parameters.This approach achieves a balance between computational efficiency and accuracy.Different SPCE models are used to investigate the influence of piezoelectric and elastic constants on multiscale piezoelectric materials.Additionally,the multiscale parameterization study investigates how microscale material properties affect the macroscopic response of these structures and materials.
基金funded by the spanish Ministry of Science,Innovation and Universities as part of the project PID2020-116418RB-I00 funded by MCIN/AEI/10.13039/501100011033.
文摘Reticular structures are the basis of major infrastructure projects,including bridges,electrical pylons and airports.However,inspecting and maintaining these structures is both expensive and hazardous,traditionally requiring human involvement.While some research has been conducted in this field of study,most efforts focus on faults identification through images or the design of robotic platforms,often neglecting the autonomous navigation of robots through the structure.This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures,thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments.The paper proposes multiple approaches for the binary segmentation between navigable surfaces and background from 3D point clouds captured from metallic trusses.Approaches can be classified into two paradigms:analytical algorithms and deep learning methods.Within the analytical approach,an ad hoc algorithm is developed for segmenting the structures,leveraging different techniques to evaluate the eigendecomposition of planar patches within the point cloud.In parallel,widely used and advanced deep learning models,including PointNet,PointNet++,MinkUNet34C,and PointTransformerV3,are trained and evaluated for the same task.A comparative analysis of these paradigms reveals some key insights.The analytical algorithm demonstrates easier parameter adjustment and comparable performance to that of the deep learning models,despite the latter’s higher computational demands.Nevertheless,the deep learning models stand out in segmentation accuracy,with PointTransformerV3 achieving impressive results,such as a Mean Intersection Over Union(mIoU)of approximately 97%.This study highlights the potential of analytical and deep learning approaches to improve the autonomous navigation of climbing robots in complex truss structures.The findings underscore the trade-offs between computational efficiency and segmentation performance,offering valuable insights for future research and practical applications in autonomous infrastructure maintenance and inspection.
文摘The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and efficacy of drugs.Unlike the inherent molecular structures of active pharmaceutical ingredients(APIs)and excipients,the microstructures of pharmaceutical preparations are developed during the formulation process,presenting unique analytical challenges.In this review,we primarily focus on presenting the research methods used to elucidate the microstructures of pharmaceutical preparations,including X-ray imaging(XRI),scanning electron microscopy(SEM),atomic force microscopy(AFM),Raman spectroscopy,infrared(IR)spectroscopy,and rheometer technology.Subsequently,we highlight the applications,advantages,and limitations of these methods.Finally,we discuss the current challenges and future perspectives in this field.This review aims to provide a comprehensive reference for understanding the microstructures of pharmaceutical preparations,offering new insights and potential advancements in their development.
基金supported by the Advance Research Project of Civil Aerospace Technology(Grant No.D020304)National Nat-ural Science Foundation of China(Grant Nos.52205257 and U22B2083).
文摘This paper proposes a new step-by-step Chebyshev space-time spectral method to analyze the force vibration of functionally graded material structures.Although traditional space-time spectral methods can reduce the accuracy mismatch between tem-poral low-order finite difference and spatial high-order discre tization,the ir time collocation points must increase dramatically to solve highly oscillatory solutions of structural vibration,which results in a surge in computing time and a decrease in accuracy.To address this problem,we introduced the step-by-step idea in the space-time spectral method.The Chebyshev polynomials and Lagrange's equation were applied to derive discrete spatial goverming equations,and a matrix projection method was used to map the calculation results of prev ious steps as the initial conditions of the subsequent steps.A series of numerical experiments were carried out.The results of the proposed method were compared with those obtained by traditional space-time spectral methods,which showed that higher accuracy could be achieved in a shorter computation time than the latter in highly oscillatory cases.
基金supported by the National Natural Science Foundation of China(Grant No.11872080)Beijing Natural Science Foundation(Grant No.3192005).
文摘This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure(CFRCS).The proposed method mainly includes three steps:(1)a ResUNet-involved generative and adversarial network(ResUNet-GAN)is developed to establish the end-to-end mapping from structural design parameters to fiber-reinforced composite optimized structure,and a fiber orientation chromatogram is presented to represent continuous fiber angles;(2)to avoid the local optimum problem,the independent continuous mapping method(ICM method)considering the improved principal stress orientation interpolated continuous fiber angle optimization(PSO-CFAO)strategy is utilized to construct CFRCS topology optimization dataset;(3)the well-trained ResUNet-GAN is deployed to design the optimal structural material distribution together with the corresponding continuous fiber orientations.Numerical simulations for benchmark structure verify that the proposed method greatly improves the design efficiency of CFRCS along with high design accuracy.Furthermore,the CFRCS topology configuration designed by ResUNet-GAN is fabricated by additive manufacturing.Compression experiments of the specimens show that both the stiffness structure and peak load of the CFRCS topology configuration designed by the proposed method have significantly enhanced.The proposed deep learning-based topology optimization method will provide great flexibility in CFRCS for engineering applications.
基金support from the National Natural Science Foundation of China(No.12472072)the Fundamental Research Funds for the Central Universities,China.
文摘In this research,we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method(CDM-FEM)with the Particle Swarm Optimization(PSO)-based technique,to predict the Medium-Low-Cycle Fatigue(MLCF)life of perforated structures.First,fatigue tests are carried out on three center-perforated structures,aiming to assess their fatigue life under various strengthening conditions.These tests reveal significant variations in fatigue life,accompanied by an examination of crack initiation through the analysis of fatigue fracture surfaces.Second,an innovative fatigue life prediction methodology is applied to perforated structures,which not only forecasts the initiation of fatigue cracks but also traces the progression of damage within these structures.It leverages an elastoplastic constitutive model integrated with damage and a damage evolution model under cyclic loads.The accuracy of this approach is validated by comparison with test results,falling within the three times error band.Finally,we explore the impact of various strengthening techniques,including cross-sectional reinforcement and cold expansion,on the fatigue life and damage evolution of these structures.This is achieved through an in-depth comparative analysis of both experimental data and computational predictions,which provides valuable insights into the behavior of perforated structures under fatigue conditions in practical applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.42102346,42172301).
文摘Magneto-electro-elastic(MEE)materials are widely utilized across various fields due to their multi-field coupling effects.Consequently,investigating the coupling behavior of MEE composite materials is of significant importance.The traditional finite element method(FEM)remains one of the primary approaches for addressing such issues.However,the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision,which inevitably leads to a reduction in computational efficiency.To enhance the computational accuracy and efficiency of the FEM for heterogeneous multi-field coupling problems,this study presents the coupling magneto-electro-elastic multiscale finite element method(CM-MsFEM)for heterogeneous MEE structures.Unlike the conventional multiscale FEM(MsFEM),the proposed algorithm simultaneously constructs displacement,electric,and magnetic potential multiscale basis functions to address the heterogeneity of the corresponding parameters.The macroscale formulation of CM-MsFEM was derived,and the macroscale/microscale responses of the problems were obtained through up/downscaling calculations.Evaluation using numerical examples analyzing the transient behavior of heterogeneous MEE structures demonstrated that the proposed method outperforms traditional FEM in terms of both accuracy and computational efficiency,making it an appropriate choice for numerically modeling the dynamics of heterogeneous MEE structures.
基金supported by Open Research Fund of State Key Laboratory of Target Vulnerability Assessment,Defense Engineering Institute,AMS,PLA(Grant No.YSX2024KFPG002)。
文摘Aiming at addressing the issues of unclear dynamic response mechanisms and insufficient quantification of temperature coupling effects in building structures under long-duration blast loads,this study investigates typical composite beam-slab structures through integrated blast shock tube experiments and multiscale numerical simulations using Voronoi-coupled Finite-Discrete Element Method(VoroFDEM).The research systematically reveals the dynamic response mechanisms and damage evolution patterns of composite beam-slab structures subjected to prolonged blast loading.An environmenttemperature-coupled P-I curve damage assessment system is established,and a rapid evaluation method based on image crack characteristics is proposed,achieving innovative transition from traditional mechanical indicators to intelligent recognition paradigms.Results demonstrate that composite beam-slab structures exhibit three-phase failure modes:elastic vibration,plastic hinge formation,and global collapse.Numerical simulations identify the brittle-to-ductile transition temperature threshold at-10℃,and establish a temperature-dependent piecewise function-based P-I curve prediction model,whose overpressure asymptote displays nonlinear temperature sensitivity within-50-30℃.A novel dual-mode evaluation methodology integrating Voro-FDEM numerical simulations with image-based damage feature recognition is developed,enabling quantitative mapping between crack area and destruction levels.These findings provide theoretical foundations and technical pathways for rapid blast damage assessment and protective engineering design.
基金Project (2007CB613700) supported by the National Basic Research Program of ChinaProject (50964010) supported by the National Natural Science Foundation of China
文摘A novel rheocasting process, self-inoculation method （SIM）, was developed for the microstructure control of semisolid wrought Mg alloy. This process involves mixing between liquid alloy and particles of solid alloy （self-inoculants）, subsequently pouring the mixed melt into a special designed multi-stream fluid director. The primary phase with dendritic morphology in the conventionally cast AZ31 alloy has readily transformed into near spherical one in the slurry produced by SIM from melt treatment temperature between 690 ℃ and 710 ℃ and self-inoculants addition of 3%-7%. Achievement of the non-dendritic microstructure at the higher melt treatment temperature requires more self-inoculants addition or decreases in the slope angle of fluid director. Primary phase in the slurry thus produced has attained an ideally globular morphology after isothermal holding at 620 ℃ for 30 s. The increasing holding time leads to decrease of shape factor but the coarsening of particle size. The spheroidization and coarsening evolution process of solid particles during the isothermal holding were analyzed by Lifshitz-Slyozov-Wagner （LSW） theory.
基金Key Construction Project of Nanjing Yangtze River Tunnel(No.7612005822)the National Basic Research Program of China(973Program)(No.2009CB623203).
文摘In order to evaluate the carbonation life of newly-built concrete structures,two kinds of nondestructive methods are adopted to test the thickness of the concrete cover and the ultrasonic velocity of two newly-built tunnel structures.Simultaneously a probabilistic method is proposed based on the relationship between the accelerated carbonation rate and the ultrasonic velocity.This proposed method is applied to evaluate the carbonation related lives of two newly-built tunnels and the results indicate that even under nearly the same environment and CO2 combining conditions,there exits a big difference in the probabilistic carbonation lives between the two tunnels;i.e.,the probabilistic lives of Tunnel A and Tunnel B are 94.0% and 82.3% and the corresponding maximum discrepancies are 11.6% and 27.0%,respectively.Thus,it can be concluded that the scattered quality of the concrete cover is attributed to the differences in construction technique,which eventually leads to the diversity in the evaluated probabilistic carbonation lives of the two tunnels.
文摘In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.
基金Projects(50935007,51175428) supported by the National Natural Science Foundation of ChinaProject(2010CB731701) supported by the National Basic Research Program of ChinaProject(B08040) supported by the Program of Introducing Talents of Discipline to Universities,China
文摘A new method for separating complex touching equiaxed and lamellar alpha phases in the optical micrograph of titanium alloy was proposed for quantitative characterization. This new method involves three steps. First, concave points of the microstructural feature are identified with a threshold of the concaveness of the comer points which are extracted from the binarized image. Secondly, concave points pairs are selected from the concave points group established by means of marker circle or distance. Third, whether a candidate separation line which connects two concave points within a pair can be accepted or not is determined by the proposed four rules. The obtained results show that this method is effective on separating complex touching microstructural features.
文摘A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A normal relative function about the evaluation indices of each element is developed to calculate the relative degree, and for each element there are no sub-level elements. When evaluating the elements in the sub-item level or the index level of the model, the weights of elements pertain to one adopted element, taking into account their degrees of deterioration. Since the relative degrees and structure evaluation scales on the damage conditions are applied to characterize the superstructure of damaged prestressed concrete girder bridges, this method can evaluate the prestressed structure in detail, and the evaluation results agree with the Code for Maintenance of Highway Bridges and Culvers （JTG Hll--2004 ）. Finally, a bridge in Jilin province is taken as an example, using the method developed to evaluate its damage conditions, which gives an effective way for bridge engineering.
基金Project supported by Ministry of Education Key Laboratory of Synthetic and Natural Functional Molecular Chemistry, China Project (2010JK765) supported by the Education Department of Shaanxi Province, China
文摘Nanosphere-like Li2FeSiO4/C was synthesized via a solution method using sucrose as carbon sources under a mild condition of time-saving and energy-saving, followed by sintering at high temperatures for crystallization. The amount of carbon in the composite is less than 10% （mass fraction）, and the X-ray diffraction result confirms that the sample is of pure single phase indexed with the orthorhombic Pmn21 space group. The particle size of the Li2FeSiO4/C synthesized at 700 °C for 9 h is very fine and spherical-like with a size of 200 nm. The electrochemical performance of this material, including reversible capacity, cycle number, and charge-discharge characteristics, were tested. The cell of this sample can deliver a discharge capacity of 166 mA-h/g at C/20 rate in the first three cycles. After 30 cycles, the capacity decreases to 158 mA-h/g, and the capacity retention is up to 95%. The results show that this method can prepare nanosphere-like Li2FeSiO4/C composite with good electrochemical performance.
