In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understandin...In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.展开更多
It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and externa...It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.展开更多
RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performa...RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.展开更多
Crystal structure prediction aims to predict stable and easily experimentally synthesized materials,which accelerates the discovery of new materials.It is worth noting that the stability of materials is the basis for ...Crystal structure prediction aims to predict stable and easily experimentally synthesized materials,which accelerates the discovery of new materials.It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials.Among which,the thermodynamic and molecular dynamics stability is especially important.Therefore,this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators.Specifically,we use graph neural network models to predict the formation energy of crystals,and employ empirical formulas to calculate the Lennard-Jones potential.Then,we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero,in order to ensure the thermodynamic stability and dynamics stability of materials.In addition,considering the impact of the bonding situation between atoms in the crystal on the structural stability,this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials.Finally,the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction,but also ensure the stability of crystal materials.展开更多
The objective of the present paper is to develop nonlinear finite element method models for predicting the weld-induced initial deflection and residual stress of plating in steel stiffened-plate structures. For this p...The objective of the present paper is to develop nonlinear finite element method models for predicting the weld-induced initial deflection and residual stress of plating in steel stiffened-plate structures. For this purpose, three-dimensional thermo-elastic-plastic finite element method computations are performed with varying plate thickness and weld bead length (leg length) in welded plate panels, the latter being associated with weld heat input. The finite element models are verified by a comparison with experimental database which was obtained by the authors in separate studies with full scale measurements. It is concluded that the nonlinear finite element method models developed in the present paper are very accurate in terms of predicting the weld-induced initial imperfections of steel stiffened plate structures. Details of the numerical computations together with test database are documented.展开更多
[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm su...[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.展开更多
[Objective] This study aimed to predict the structure of protein OmpH from Pasteurella multocida C47-8 （PmC47-8） strain of yak. [Method] Online BLAST, signal peptide prediction, secondary structure prediction and pr...[Objective] This study aimed to predict the structure of protein OmpH from Pasteurella multocida C47-8 （PmC47-8） strain of yak. [Method] Online BLAST, signal peptide prediction, secondary structure prediction and protein characteristics of sequencing result of gene OmpH from PmC47-8 strain were analyzed. [Result] The similarities of gene OmpH from PmC47-8 with the published 81 OmpH genes were between 84% and 99%; a signal peptide was found with the cleavage sites between 20 and 21 in the polypeptide; secondary structure prediction showed that folding structure accounted for 49.8% and loop structure for 50.2%; it predicted that there were 7 O-glycosylation sites in OmpH protein with the amino acid residual sites of 2, 45, 48, 330, 716, 721, 723, respectively, and 2 N-glycosylation sites with the amino acid residual sites of 15 and 35. [Conclusion] This study lays the foundation for the study on the immunity of OmpH gene from yak.展开更多
The structure and the acoustic medium of a passenger vehicle are modeled using the finite element method(FEM), and the interior noise is studied the help of the modal synthesis method (MSM). Sound pressure level (Lp) ...The structure and the acoustic medium of a passenger vehicle are modeled using the finite element method(FEM), and the interior noise is studied the help of the modal synthesis method (MSM). Sound pressure level (Lp) of the noise is calculated in several conditions of the models, and has good agreements with its test results. The MSM am be consequently used for predicting the vehicle interior noise in dssign stage so that the structure may be optimized for the Purpose of the most reduction of noise.展开更多
This study presents a predictive model for condensed-phase heats of formation of metal-containing energetic complexes(MCECs)and energetic metal-organic frameworks(EMOFs),leveraging a dataset of 148 compounds.Using ele...This study presents a predictive model for condensed-phase heats of formation of metal-containing energetic complexes(MCECs)and energetic metal-organic frameworks(EMOFs),leveraging a dataset of 148 compounds.Using elemental composition,triazole rings,and metal presence,the model achieves high accuracy(R^(2)>0.94,mean absolute error(MAE)≈390 kJ/mol)for screening high-energy materials.It outperforms prior methods,particularly for polycyclic systems,offering a practical tool for safer design and risk assessment in defense and industrial applications.展开更多
This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700℃by integrating experimental testing and advanced numerical modeling.A tempe...This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700℃by integrating experimental testing and advanced numerical modeling.A temperature-indexed Concrete Damage Plasticity(CDP)framework incorporating bond–slip effects was developed in Abaqus to capture both global stress–strain responses and localized damage evolution.Uniaxial compression tests on thermally exposed cylinders provided residual strength data and failure observations for model calibration and validation.Results demonstrated a distinct two-stage degradation regime:moderate stiffness and strength reduction up to~400℃,followed by sharp deterioration beyond 500℃–600℃,with residual capacity at 700℃reduced to~20%–25%of the ambient value.Strain–damage analyses revealed the formation of a peripheral tensile strain band,which thickened and propagated inward with increasing temperature,governing crack initiation and cover spalling.Supplemental analyses highlighted that transverse reinforcement improved ductility and damage distribution at moderate temperatures(~300℃),but bond deterioration and steel softening beyond~600℃substantially diminished confinement effectiveness.The proposed CDP model accurately reproduced experimental stress–strain curves(R^(2)≈0.94–0.98 up to 600℃;≈0.90 at 700℃),with peak stress errors within 7%–10%and energy absorption captured within~12%.These findings confirm the robustness of the temperature-indexed CDP framework for simulating fire-damaged reinforced concrete and provide practical guidelines for post-fire assessment,spalling detection,and fire-resilient design of structural members.展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for ...Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.展开更多
This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a no...This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its tlight trajectory, and the speed of the interceptor is required to be lower than that of the target, A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method.展开更多
AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a...AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a folded protein,and out pops a tertiary structure nearly as good as one from an experiment-based structure.展开更多
Material performance of LY12CZ aluminum is greatly degraded because of corrosion and corrosion fatigue, which severely affect the integrity and safety of aircraft structure, especially those of lbe navy aircraft struc...Material performance of LY12CZ aluminum is greatly degraded because of corrosion and corrosion fatigue, which severely affect the integrity and safety of aircraft structure, especially those of lbe navy aircraft structure. The corrosion and corrosion fatigue failure process of aircraft structure are directly concerned with many factors, such as load, material characteristics, corrosive environment and so on. The damage mechanism is very complicated, and there are both randomness and fuzziness in the failure process. With consideration of the limitation of those conventional probabilistic approaches for prediction of corrosion fatigue life of aircraft structure at present, and based on the operational load spectrum obtained through investigating service status of the aircraft in naval aviation force, a fuzzy reliability approach is proposed, which is more reasonable and closer to the fact. The effects of the pit aspect ratio, the crack aspect ratio and all fuzzy factors on corrosion fatigue life of aircraft structure are discussed. The results demonstrate that the approach can be applied to predict the corrosion fatigue life of aircraft structure.展开更多
Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not ex...Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the network.The cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct edges.If a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some extent.Furthermore,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be connected.Therefore,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural information.Secondly,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure information.Then,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is developed.Finally,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.展开更多
Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods ...Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.展开更多
Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregula...Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods.To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures,a velocity-free MS/AE source location method is developed in this paper.It avoids manual repetitive training by using equidistant grid points to search the path,which introduces A*search algorithm and uses grid points to accommodate complex structures with irregular holes.It also takes advantage of the velocity-free source location method.To verify the validity of the proposed method,lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm10 cm10 cm.It was cut out into a cylindrical empty space with a size of/6cm10 cm.Based on the arrivals,the classical Geiger method and the proposed method are used to locate lead-breaking sources.Results show that the locating error of the proposed method is 1.20 cm,which is less than 2.02 cm of the Geiger method.Hence,the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.展开更多
Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent he...Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction.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
基金support of the Fundamental Research Funds for the Central Universities(No.E2ET0411X2).
文摘In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB2101003National Natural Science Foundation of China,Grant/Award Numbers:51991395,U1806226,51778033,51822802,71901011,U1811463,51991391Science and Technology Major Project of Beijing,Grant/Award Number:Z191100002519012。
文摘It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
基金supported by grants from the National Science Foundation of China(Grant Nos.12375038 and 12075171 to ZJT,and 12205223 to YLT).
文摘RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.
基金supported by the Nature Science Foundation of China(Nos.61671362 and 62071366)。
文摘Crystal structure prediction aims to predict stable and easily experimentally synthesized materials,which accelerates the discovery of new materials.It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials.Among which,the thermodynamic and molecular dynamics stability is especially important.Therefore,this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators.Specifically,we use graph neural network models to predict the formation energy of crystals,and employ empirical formulas to calculate the Lennard-Jones potential.Then,we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero,in order to ensure the thermodynamic stability and dynamics stability of materials.In addition,considering the impact of the bonding situation between atoms in the crystal on the structural stability,this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials.Finally,the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction,but also ensure the stability of crystal materials.
文摘The objective of the present paper is to develop nonlinear finite element method models for predicting the weld-induced initial deflection and residual stress of plating in steel stiffened-plate structures. For this purpose, three-dimensional thermo-elastic-plastic finite element method computations are performed with varying plate thickness and weld bead length (leg length) in welded plate panels, the latter being associated with weld heat input. The finite element models are verified by a comparison with experimental database which was obtained by the authors in separate studies with full scale measurements. It is concluded that the nonlinear finite element method models developed in the present paper are very accurate in terms of predicting the weld-induced initial imperfections of steel stiffened plate structures. Details of the numerical computations together with test database are documented.
基金Supported by the Science Foundation of Hengyang Normal University of China(09A36)~~
文摘[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.
基金Supported by the Project for High-level Talents of Qinghai University (2008-QGC-7)~~
文摘[Objective] This study aimed to predict the structure of protein OmpH from Pasteurella multocida C47-8 （PmC47-8） strain of yak. [Method] Online BLAST, signal peptide prediction, secondary structure prediction and protein characteristics of sequencing result of gene OmpH from PmC47-8 strain were analyzed. [Result] The similarities of gene OmpH from PmC47-8 with the published 81 OmpH genes were between 84% and 99%; a signal peptide was found with the cleavage sites between 20 and 21 in the polypeptide; secondary structure prediction showed that folding structure accounted for 49.8% and loop structure for 50.2%; it predicted that there were 7 O-glycosylation sites in OmpH protein with the amino acid residual sites of 2, 45, 48, 330, 716, 721, 723, respectively, and 2 N-glycosylation sites with the amino acid residual sites of 15 and 35. [Conclusion] This study lays the foundation for the study on the immunity of OmpH gene from yak.
文摘The structure and the acoustic medium of a passenger vehicle are modeled using the finite element method(FEM), and the interior noise is studied the help of the modal synthesis method (MSM). Sound pressure level (Lp) of the noise is calculated in several conditions of the models, and has good agreements with its test results. The MSM am be consequently used for predicting the vehicle interior noise in dssign stage so that the structure may be optimized for the Purpose of the most reduction of noise.
文摘This study presents a predictive model for condensed-phase heats of formation of metal-containing energetic complexes(MCECs)and energetic metal-organic frameworks(EMOFs),leveraging a dataset of 148 compounds.Using elemental composition,triazole rings,and metal presence,the model achieves high accuracy(R^(2)>0.94,mean absolute error(MAE)≈390 kJ/mol)for screening high-energy materials.It outperforms prior methods,particularly for polycyclic systems,offering a practical tool for safer design and risk assessment in defense and industrial applications.
文摘This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700℃by integrating experimental testing and advanced numerical modeling.A temperature-indexed Concrete Damage Plasticity(CDP)framework incorporating bond–slip effects was developed in Abaqus to capture both global stress–strain responses and localized damage evolution.Uniaxial compression tests on thermally exposed cylinders provided residual strength data and failure observations for model calibration and validation.Results demonstrated a distinct two-stage degradation regime:moderate stiffness and strength reduction up to~400℃,followed by sharp deterioration beyond 500℃–600℃,with residual capacity at 700℃reduced to~20%–25%of the ambient value.Strain–damage analyses revealed the formation of a peripheral tensile strain band,which thickened and propagated inward with increasing temperature,governing crack initiation and cover spalling.Supplemental analyses highlighted that transverse reinforcement improved ductility and damage distribution at moderate temperatures(~300℃),but bond deterioration and steel softening beyond~600℃substantially diminished confinement effectiveness.The proposed CDP model accurately reproduced experimental stress–strain curves(R^(2)≈0.94–0.98 up to 600℃;≈0.90 at 700℃),with peak stress errors within 7%–10%and energy absorption captured within~12%.These findings confirm the robustness of the temperature-indexed CDP framework for simulating fire-damaged reinforced concrete and provide practical guidelines for post-fire assessment,spalling detection,and fire-resilient design of structural members.
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
基金the support of Research Program of Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010the Sichuan Science and Technology Program under Grant 2024NSFSC1984 and Grant 2024NSFSC1990。
文摘Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.
文摘This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its tlight trajectory, and the speed of the interceptor is required to be lower than that of the target, A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method.
基金supported by the U.S.National Natural Science Foundation(CHE-2203505 and MCB-2335137).
文摘AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a folded protein,and out pops a tertiary structure nearly as good as one from an experiment-based structure.
文摘Material performance of LY12CZ aluminum is greatly degraded because of corrosion and corrosion fatigue, which severely affect the integrity and safety of aircraft structure, especially those of lbe navy aircraft structure. The corrosion and corrosion fatigue failure process of aircraft structure are directly concerned with many factors, such as load, material characteristics, corrosive environment and so on. The damage mechanism is very complicated, and there are both randomness and fuzziness in the failure process. With consideration of the limitation of those conventional probabilistic approaches for prediction of corrosion fatigue life of aircraft structure at present, and based on the operational load spectrum obtained through investigating service status of the aircraft in naval aviation force, a fuzzy reliability approach is proposed, which is more reasonable and closer to the fact. The effects of the pit aspect ratio, the crack aspect ratio and all fuzzy factors on corrosion fatigue life of aircraft structure are discussed. The results demonstrate that the approach can be applied to predict the corrosion fatigue life of aircraft structure.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFC1523300Construction of Innovation Platform Program of Qinghai Province of China,Grant/Award Number:2022-ZJ-T02。
文摘Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the network.The cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct edges.If a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some extent.Furthermore,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be connected.Therefore,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural information.Secondly,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure information.Then,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is developed.Finally,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11534003 and 11604117)the National Key Research and Development Program of China(Grant No.2016YFB0201201)+1 种基金the Program for JLU Science and Technology Innovative Research Team(JLUSTIRT)of Chinathe Science Challenge Project of China(Grant No.TZ2016001)
文摘Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.
基金The authors wish to acknowledge financial support from the National Natural Science Foundation of China(51822407 and 51774327)Natural Science Foundation of Hunan Province in China(2018JJ1037)Innovation Driven project of Central South University(2020CX014).
文摘Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods.To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures,a velocity-free MS/AE source location method is developed in this paper.It avoids manual repetitive training by using equidistant grid points to search the path,which introduces A*search algorithm and uses grid points to accommodate complex structures with irregular holes.It also takes advantage of the velocity-free source location method.To verify the validity of the proposed method,lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm10 cm10 cm.It was cut out into a cylindrical empty space with a size of/6cm10 cm.Based on the arrivals,the classical Geiger method and the proposed method are used to locate lead-breaking sources.Results show that the locating error of the proposed method is 1.20 cm,which is less than 2.02 cm of the Geiger method.Hence,the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY23E040001)Fundamental Research Funding Project of Zhejiang Province,China(Project Category A,Grant No.2022YW06)National Key R&D Program of China(Grant No.2023YFF0614902).
文摘Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction.
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.
