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
[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
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
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
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
In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical...In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).展开更多
Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate t...Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.展开更多
A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining...A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.展开更多
According to the chloride corrosion environment,service life prediction model of concrete structure of sea-crossing bridge was built using modified Fick's second law and the whole probability calculation method,wh...According to the chloride corrosion environment,service life prediction model of concrete structure of sea-crossing bridge was built using modified Fick's second law and the whole probability calculation method,which was suitable for China. Furthermore,a visual service life prediction program of concrete structure was developed by optimized Monte Carlo method. Meanwhile,Life 365 program was compared,indicating reliability of the prediction program. Finally,the validity of prediction model was verified in JinTang Bridge of Zhoushan Island Mainland Linkage Project.展开更多
Tannases produced by filamentous fungi are in a family of important hydrolases of gallotannins and have broad industry applications.But until now,the 3-D structures of fungi tannases have not been reported.The protein...Tannases produced by filamentous fungi are in a family of important hydrolases of gallotannins and have broad industry applications.But until now,the 3-D structures of fungi tannases have not been reported.The protein sequence deduced from the cDNA sequence obtained using RT-PCR amplification was identified as tannase through sequence alignment and phylogenetic analysis.Structure models based on the tannase sequence were collected using I-TASSER,and the model with the best match to the surface charge density-pH titration profile was selected as the final structure for tannase from Aspergillusniger N5-5.This work provides an effective method for protein structure research.The structure constructed in this work should be very important to understand the enzyme bioactivities and further developments of fungi tannases.展开更多
To improve understanding of essential aspects that influence forecasting of tropical cyclones(TCs),the National Key Research and Development Program,Ministry of Science and Technology of the People's Republic of C...To improve understanding of essential aspects that influence forecasting of tropical cyclones(TCs),the National Key Research and Development Program,Ministry of Science and Technology of the People's Republic of China conducted a five-year project titled“Key Dynamic and Thermodynamic Processes and Prediction for the Evolution of Typhoon Intensity and Structure”(KPPT).Through this project,new understandings of TC intensification,including outer rainbanddriven secondary eyewall formation and the roles of boundary layer dynamics and vertical wind shear,and improvements to TC data assimilation with integrated algorithms and adaptive localizations are achieved.To promote a breakthrough in TC intensity and structure forecasting,a new paradigm for TC evolution dynamics(i.e.,the correlations,interactions,and error propagation among the triangle of TC track,intensity,and structure)is proposed;and an era of dynamic-constrained,big-data driven,and strongly coupled data assimilation at the subkilometer scale and seamless prediction is expected.展开更多
Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clus...Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.展开更多
Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some st...Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three- dimensional （3D） structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.展开更多
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 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.
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 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.
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.
基金Mainly presented at the 6-th international meeting of acoustics in Aug. 2003, and The 1999 SPE Asia Pacific Oil and GasConference and Exhibition held in Jakarta, Indonesia, 20-22 April 1999, SPE 54274.
文摘In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).
基金supported by the National Natural Science Foundation of China(Nos.61671288,91530321,61603161)Science and Technology Commission of Shanghai Municipality(Nos.16JC1404300,17JC1403500,16ZR1448700)
文摘Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.
基金This work is supported by the National Natural Science Foundation of China (No.60421002) Priority supported financially by the New Century 151 Talent Project of Zhejiang Province.
文摘A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.
文摘According to the chloride corrosion environment,service life prediction model of concrete structure of sea-crossing bridge was built using modified Fick's second law and the whole probability calculation method,which was suitable for China. Furthermore,a visual service life prediction program of concrete structure was developed by optimized Monte Carlo method. Meanwhile,Life 365 program was compared,indicating reliability of the prediction program. Finally,the validity of prediction model was verified in JinTang Bridge of Zhoushan Island Mainland Linkage Project.
基金the National Natural Science Foundation of China (No. 21374117)the 100 Talents Program of Chinese Academy of Sciences for financial support
文摘Tannases produced by filamentous fungi are in a family of important hydrolases of gallotannins and have broad industry applications.But until now,the 3-D structures of fungi tannases have not been reported.The protein sequence deduced from the cDNA sequence obtained using RT-PCR amplification was identified as tannase through sequence alignment and phylogenetic analysis.Structure models based on the tannase sequence were collected using I-TASSER,and the model with the best match to the surface charge density-pH titration profile was selected as the final structure for tannase from Aspergillusniger N5-5.This work provides an effective method for protein structure research.The structure constructed in this work should be very important to understand the enzyme bioactivities and further developments of fungi tannases.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC1501600 and 2017YFC1501601)。
文摘To improve understanding of essential aspects that influence forecasting of tropical cyclones(TCs),the National Key Research and Development Program,Ministry of Science and Technology of the People's Republic of China conducted a five-year project titled“Key Dynamic and Thermodynamic Processes and Prediction for the Evolution of Typhoon Intensity and Structure”(KPPT).Through this project,new understandings of TC intensification,including outer rainbanddriven secondary eyewall formation and the roles of boundary layer dynamics and vertical wind shear,and improvements to TC data assimilation with integrated algorithms and adaptive localizations are achieved.To promote a breakthrough in TC intensity and structure forecasting,a new paradigm for TC evolution dynamics(i.e.,the correlations,interactions,and error propagation among the triangle of TC track,intensity,and structure)is proposed;and an era of dynamic-constrained,big-data driven,and strongly coupled data assimilation at the subkilometer scale and seamless prediction is expected.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1804121 and 11304167)
文摘Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.
基金supported by the National Natural Science Foundation of China(Grant Nos.11074191,11175132,and 11374234)the National Basic Research Programof China(Grant No.2011CB933600)the Program for New Century Excellent Talents of China(Grant No.NCET 08-0408)
文摘Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three- dimensional （3D） structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.
