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
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
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
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 order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisti...In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.展开更多
In recent years,notable progress has been achieved in both the hardware and algorithms of structured illumination microscopy(SIM).Nevertheless,the advancement of three-dimensional structured illumination microscopy(3D...In recent years,notable progress has been achieved in both the hardware and algorithms of structured illumination microscopy(SIM).Nevertheless,the advancement of three-dimensional structured illumination microscopy(3DSIM)has been impeded by challenges arising from the speed and intricacy of polarization modulation.We introduce a high-speed modulation 3DSIM system,leveraging the polarizationmaintaining and modulation capabilities of a digital micromirror device(DMD)in conjunction with an electrooptic modulator.The DMD-3DSIM system yields a twofold enhancement in both lateral(133 nm)and axial(300 nm)resolution compared to wide-field imaging and can acquire a data set comprising 29 sections of 1024 pixels×1024 pixels,with 15 ms exposure time and 6.75 s per volume.The versatility of the DMD-3DSIM approach was exemplified through the imaging of various specimens,including fluorescent beads,nuclear pores,microtubules,actin filaments,and mitochondria within cells,as well as plant and animal tissues.Notably,polarized 3DSIM elucidated the orientation of actin filaments.Furthermore,the implementation of diverse deconvolution algorithms further enhances 3D resolution.The DMD-based 3DSIM system presents a rapid and reliable methodology for investigating biomedical phenomena,boasting capabilities encompassing 3D superresolution,fast temporal resolution,and polarization imaging.展开更多
Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean...Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.展开更多
The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment ...The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.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
Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strate...Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.展开更多
Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferom...Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.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
The structural,relative stability,and electronic properties of two-dimensional AsP_(2)X_(6)(X=S,Se)were predicted and studied using the particle-swarm optimization method and first principles calculations.We proposed ...The structural,relative stability,and electronic properties of two-dimensional AsP_(2)X_(6)(X=S,Se)were predicted and studied using the particle-swarm optimization method and first principles calculations.We proposed two low energy structures with P312 and P-31m phases,both of which the structures are hexagonal in shape and show non-centrosymmetry for the P312 phase and centrosymmetry for the P-31m phase.According to our results,two structural phases are found to be stable thermally and dynamically.The P312 phase of AsP_(2)X_(6)(X=S,Se)are indirect semiconductors with band gaps of 2.44 eV(AsP2S6)and 2.18 eV(AsP2Se6)at the HSE06 level,and their absorption coefficients are predicted to reach the order of 10^(5)cm^(-1)from visible light to ultraviolet region,but the main absorption is manly in the ultraviolet region.The P-31m phase of AsP_(2)X_(6)(X=S,Se)exhibits metal character with the Fermi surface mainly occupied by the p orbital of S/Se.Remarkably,estimated by first principles calculations,the P-31m AsP2S6 is found to be an intrinsic phonon-mediated superconductor with a relatively high critical superconducting temperature of about 13.4 K,and the P-31m AsP2Se6 only has a superconducting temperature of 1.4 K,which suggest that the P-31m AsP2S6 may be a good candidate for a nanoscale superconductor.展开更多
Microbial corrosion of hydraulic concrete structures(HCSs)has received increasing research concerns.However,knowledge on the morphology of attached biofilms,as well as the community structures and functions cultivated...Microbial corrosion of hydraulic concrete structures(HCSs)has received increasing research concerns.However,knowledge on the morphology of attached biofilms,as well as the community structures and functions cultivated under variable nutrient levels is lacking.Here,biofilm colonization patterns and community structures responding to variable levels of ammonia and sulfate were explored.From field sampling,NH_(4)^(+)-N was proven key factor governing community structure in attached biofilms,verifying the reliability of selecting target nutrient species in batch experiments.Biofilms exhibited significant compositional differences in field sampling and incubation experiments.As the nutrient increased in batch experiments,the growth of biofilms gradually slowed down and uneven distribution was detected.The proportions of proteins and β-d-glucose polysaccharides in biofilms experienced a decrease in response to elevated levels of nutrients.With the increased of nutrients,themass losses of concretes exhibited an increase,reaching a highest value of 2.37%in the presence of 20 mg/L of ammonia.Microbial communities underwent a significant transition in structure and metabolic functions to ammonia gradient.The highest activity of nitrification was observed in biofilms colonized in the presence of 20 mg/L of ammonia.While the communities and their functions remained relativelymore stable responding to sulfate gradient.Our research provides novel insights into the structures of biofilms attached on HCSs and the metabolic functions in the presence of high level of nutrients,which is of significance for the operation and maintenance of hydraulic engineering structures.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 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.
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supported by the National Natural Science Foundation of China(Nos.51407134,52002196)Natural Science Foundation of Shandong Province(Nos.ZR2019YQ24,ZR2020QF084)+1 种基金Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Qingchuang Talents Induction Program of Shandong Higher Education Institution(Research and Innovation Team of Structural-Functional Polymer Composites)and Special Financial of Shandong Province(Structural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Talent Teams(No.37000022P990304116449)).
文摘In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.
基金supported by the National Key R&D Program of China (Award No.2022YFC3401100)the National Natural Science Foundation of China。
文摘In recent years,notable progress has been achieved in both the hardware and algorithms of structured illumination microscopy(SIM).Nevertheless,the advancement of three-dimensional structured illumination microscopy(3DSIM)has been impeded by challenges arising from the speed and intricacy of polarization modulation.We introduce a high-speed modulation 3DSIM system,leveraging the polarizationmaintaining and modulation capabilities of a digital micromirror device(DMD)in conjunction with an electrooptic modulator.The DMD-3DSIM system yields a twofold enhancement in both lateral(133 nm)and axial(300 nm)resolution compared to wide-field imaging and can acquire a data set comprising 29 sections of 1024 pixels×1024 pixels,with 15 ms exposure time and 6.75 s per volume.The versatility of the DMD-3DSIM approach was exemplified through the imaging of various specimens,including fluorescent beads,nuclear pores,microtubules,actin filaments,and mitochondria within cells,as well as plant and animal tissues.Notably,polarized 3DSIM elucidated the orientation of actin filaments.Furthermore,the implementation of diverse deconvolution algorithms further enhances 3D resolution.The DMD-based 3DSIM system presents a rapid and reliable methodology for investigating biomedical phenomena,boasting capabilities encompassing 3D superresolution,fast temporal resolution,and polarization imaging.
基金The National Key R&D Program of China under contract No.2021YFC3101603.
文摘Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions,grant number 2023QN082,awarded to Cheng ZhaoThe National Natural Science Foundation of China also provided funding,grant number 61902349,awarded to Cheng Zhao.
文摘The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.
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The National Natural Science Foundation of China(No.52278312)the National Key Research and Development Program of China(No.2022YFC3801202)the Fundamental Research Funds for the Central Universities.
文摘Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.
基金jointly supported by the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022GSP02)the National Natural Science Foundation of China(Grant Nos.42072320 and 42372264).
文摘Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.
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Funded by the National Natural Science Foundation of China(No.U1904612)the Natural Science Foundation of Henan Province(No.222300420506)。
文摘The structural,relative stability,and electronic properties of two-dimensional AsP_(2)X_(6)(X=S,Se)were predicted and studied using the particle-swarm optimization method and first principles calculations.We proposed two low energy structures with P312 and P-31m phases,both of which the structures are hexagonal in shape and show non-centrosymmetry for the P312 phase and centrosymmetry for the P-31m phase.According to our results,two structural phases are found to be stable thermally and dynamically.The P312 phase of AsP_(2)X_(6)(X=S,Se)are indirect semiconductors with band gaps of 2.44 eV(AsP2S6)and 2.18 eV(AsP2Se6)at the HSE06 level,and their absorption coefficients are predicted to reach the order of 10^(5)cm^(-1)from visible light to ultraviolet region,but the main absorption is manly in the ultraviolet region.The P-31m phase of AsP_(2)X_(6)(X=S,Se)exhibits metal character with the Fermi surface mainly occupied by the p orbital of S/Se.Remarkably,estimated by first principles calculations,the P-31m AsP2S6 is found to be an intrinsic phonon-mediated superconductor with a relatively high critical superconducting temperature of about 13.4 K,and the P-31m AsP2Se6 only has a superconducting temperature of 1.4 K,which suggest that the P-31m AsP2S6 may be a good candidate for a nanoscale superconductor.
基金supported by the National Key Research and Development Project of China(No.2021YFB2600200)the National Natural Science Foundation of China(Nos.52470185 and 52170159)the Open Research Fund of National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety,the Fund of National Key Laboratory of Water Disaster Prevention and Key Research and Development Program of Jiangsu Province(No.BE2022601).
文摘Microbial corrosion of hydraulic concrete structures(HCSs)has received increasing research concerns.However,knowledge on the morphology of attached biofilms,as well as the community structures and functions cultivated under variable nutrient levels is lacking.Here,biofilm colonization patterns and community structures responding to variable levels of ammonia and sulfate were explored.From field sampling,NH_(4)^(+)-N was proven key factor governing community structure in attached biofilms,verifying the reliability of selecting target nutrient species in batch experiments.Biofilms exhibited significant compositional differences in field sampling and incubation experiments.As the nutrient increased in batch experiments,the growth of biofilms gradually slowed down and uneven distribution was detected.The proportions of proteins and β-d-glucose polysaccharides in biofilms experienced a decrease in response to elevated levels of nutrients.With the increased of nutrients,themass losses of concretes exhibited an increase,reaching a highest value of 2.37%in the presence of 20 mg/L of ammonia.Microbial communities underwent a significant transition in structure and metabolic functions to ammonia gradient.The highest activity of nitrification was observed in biofilms colonized in the presence of 20 mg/L of ammonia.While the communities and their functions remained relativelymore stable responding to sulfate gradient.Our research provides novel insights into the structures of biofilms attached on HCSs and the metabolic functions in the presence of high level of nutrients,which is of significance for the operation and maintenance of hydraulic engineering structures.
