Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.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
Succinylcholine(SC)is a widely used depolarizing muscle relaxant,but improper use can lead to arrhythmias and,in severe cases,pose a life-threatening risk.Additionally,some criminals exploit SC for illicit activities....Succinylcholine(SC)is a widely used depolarizing muscle relaxant,but improper use can lead to arrhythmias and,in severe cases,pose a life-threatening risk.Additionally,some criminals exploit SC for illicit activities.Therefore,rapid SC detection is paramount for clinical practice and public safety.Currently,however,limited methods are available for the rapid detection of SC.A fluorescent indicator displacement assay sensor based on molecular recognition of an amide naphthotube was developed.This sensor enabled the rapid fluorescent detection of SC through competitive binding between SC and methylene blue with the amide naphthotube.The sensor exhibited exceptional sensitivity with a detection limit as low as 1.1μmol/L and a detection range of 1.1~60μmol/L,coupled with outstanding selectivity and robust stability.Furthermore,this sensor accurately determined SC levels in biological samples such as serum.In summary,this research provides a new solution for the rapid and accurate sensing of SC in complex matrices and offers new insights for the swift identification and detection of toxins.展开更多
Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps...Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.展开更多
Diamond is a promising semiconductor material for future space exploration,owing to its unique atomic and electronic structures.However,diamond materials and related devices still suffer from irradiation damage under ...Diamond is a promising semiconductor material for future space exploration,owing to its unique atomic and electronic structures.However,diamond materials and related devices still suffer from irradiation damage under space irradiation involving high-energy irradiating particles.The study of the generation and evolution of point defects can help understand the irradiation damage mechanisms in diamond.This study systematically investigated the defect dynamics of diamond in 162 crystallographic directions uniformly selected on a spherical surface using molecular dynamics simulations,with primary knock-on atom(PKA)energies up to 20 keV,and temperatures ranging from 300 K to 1800 K.The results reveal that the displacement threshold energy of diamond changes periodically with crystallographic directions,which is related to the shape of potential energy surface along that direction.Additionally,the number of residual defects correlates positively with PKA energy.However,temperature has dual competing effects:while it enhances the probability of atomic displacement,it simultaneously suppresses the probability of defect formation by accelerating defect recombination.The calculation of sparse radial distribution function indicates that the defect distribution shows a certain degree of similarity in the short-range region across different PKA energies.As the PKA energy increases,defect clusters tend to become larger in size and more numerous in quantity.This study systematically investigates the anisotropy of displacement threshold energy and elucidates the relationship between various irradiation conditions and the final states of irradiation-induced defects.展开更多
To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-dec...To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.展开更多
To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specif...To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R^(2) increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R^(2).Compared to ANN,with an MSE of 4.6371 and an R^(2) of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R^(2) of 13.23%,demonstrating a significant enhancement in predictive performance.展开更多
License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation,which lead to pixel displacements.This article introduces a Displacement Region Recognition M...License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation,which lead to pixel displacements.This article introduces a Displacement Region Recognition Method(DR2M)to address such a problem.This method operates on displaced features compared to the training input observed throughout definite time frames.The technique focuses on detecting features that remain relatively stable under haze,using a frame-based analysis to isolate edges minimally affected by visual noise.The edge detection failures are identified using a bilateral neural network through displaced feature training.The training converges bilaterally towards the minimum edges from the maximum region.Thus,the training input and detected edges are used to identify the displacement between observed image frames to extract and differentiate the license plate region from the other vehicle regions.The proposed method maps the similarity feature between the detected and identified vehicle regions.This aids in leveraging the plate recognition precision with a high F1 score.Thus,this technique achieves a 10.27%improvement in identification precision,a 10.57%increase in F1 score,and a 9.73%reduction in false positive rate compared to baseline methods under maximum displacement conditions caused by haze.The technique attains an identification precision of 95.68%,an F1 score of 94.68%,and a false positive rate of 4.32%,indicating robust performance under haze-affected settings.展开更多
This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-preci...This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement.A stable power supply module is designed to provide low-noise voltage to the entire circuit.An analog circuit system is constructed,including key circuits such as photoelectric sensors,I-V amplification,zero adjustment,fully differential amplification,and amplitude modulation filtering.To acquire and process signals,the PMAC Acc24E3 data acquisition card is selected,which realizes phase demodulation through reversible square wave counting,inverts displacement information,and a visual interface for the host computer is designed.Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0-10mm,with a maximum measurement error of less than 1.2μm,a maximum measurement speed of 6m/s,and a resolution better than 0.158μm.展开更多
Rock discontinuities such as joints widely exist in natural rock masses,and wave attenuation through rock masses is mainly caused by discontinuities.The displacement discontinuity model(DDM)has been widely used in the...Rock discontinuities such as joints widely exist in natural rock masses,and wave attenuation through rock masses is mainly caused by discontinuities.The displacement discontinuity model(DDM)has been widely used in theoretical and numerical analysis of wave propagation across rock discontinuity.However,the circumstance under which the DDM is applicable to predict wave propagation across rock discontinuity remains poorly understood.In this study,theoretical analysis and ultrasonic laboratory tests were carried out to examine the theoretical applicability of the DDM for wave propagation,where specimens with rough joints comprising regular rectangular asperities of different spacings and heights were prepared by 3D printing technology.It is found that the theoretical applicability of the DDM to predict wave propagation across rock discontinuity is determined by three joint parameters,i.e.the dimensionless asperity spacing(L),the dimensionless asperity height(H)and the groove density(D).Through theoretical analysis and laboratory tests,the conditions under which the DDM is applicable are derived as follows:and,.With increase in the groove density,the thresholds of the dimensionless asperity spacing and the dimensionless asperity height show a decreasing trend.In addition,the transmission coefficient in the frequency domain decreases with increasing groove density,dimensionless asperity spacing or dimensionless asperity height.The findings can facilitate our understanding of DDM for predicting wave propagation across rock discontinuity.展开更多
Collapses of seismic slopes demonstrate the characteristics of three-dimensional(3D)shapes.Conducting a 3D analysis of seismic slope stability is more complicated than doing a simplified two-dimensional(2D)analysis.Th...Collapses of seismic slopes demonstrate the characteristics of three-dimensional(3D)shapes.Conducting a 3D analysis of seismic slope stability is more complicated than doing a simplified two-dimensional(2D)analysis.The upper-bound solutions derived from limit analysis of seismic slopes using the pseudo-static method are used to generate an approximate solution for the factor of 3D safety through regression analysis.Such a solution can degenerate to a 2D result when the slope width tends to infinity.The approximation method also can be extended for determining the permanent displacements of 3D slopes under seismic loading.The method is non-iterative and relatively accurate through comparisons with analytical results.Involving stochastic ground motions could easily be used to assess the distribution of permanent displacement that is induced in 3D slopes.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmente...The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmented reality technology to achieve three-dimensional visualization,thereby enhancing the complete resection rate of tumor and the success rate of surgery.Based on the preoperative MRI data of the patients,a 3D virtual model is reconstructed and 3D printed.A brain biomimetic model is created using gel injection molding.By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables,the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery.An orthogonal experiment is conducted on the biomimetic model to establish a predictive model,and this model is incorporated into the augmented reality navigation system.To validate the predictive model,five participants wore HoloLens2 devices,overlaying the patient’s 3D virtual model onto the physical head model.Subsequently,the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models(actual coordinates and predicted coordinates,respectively).The difference between these coordinates represents the model’s prediction error.The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from−0.6787 mm to 0.2957 mm and−0.4314 mm to 0.2253 mm,respectively.The relative errors for each experimental group are within 10%,demonstrating a good fit of the model.This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations.It also provides a new approach for surgeons.By combining augmented reality visualization,it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.展开更多
The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)...The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.展开更多
Shaking table tests are widely used to evaluate seismic effects on railway structures,but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displace...Shaking table tests are widely used to evaluate seismic effects on railway structures,but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displacements,ambient noise interference,and limitations in displacement meter installation.In this paper,a novel method that integrates the Kanade-Lucas-Tomasi(KLT)feature tracker with an extended Kalman filter(EKF)is presented for measuring rail displacement during shaking table tests.The method employs KLT feature tracker and a random sample consensus algorithm to extract and track key feature points,while EKF optimally estimates dynamic states by accounting for system noise and observation errors.Shaking table test results demonstrate that the proposed method achieves an acceleration root mean square error of 0.300 m/s^(2)and a correlation with accelerometer data exceeding 99.7%,significantly outper-forming the original KLT approach.This innovative method provides a more efficient and reliable solution for measuring rail displacement under large nonlinear vibrations.展开更多
Temporomandibular joint(TMJ)disc displacement is one of the most significant subtypes of temporomandibular joint disorders,but its etiology and mechanism are poorly understood.In this study,we elucidated the mechanism...Temporomandibular joint(TMJ)disc displacement is one of the most significant subtypes of temporomandibular joint disorders,but its etiology and mechanism are poorly understood.In this study,we elucidated the mechanisms by which destruction of inflamed collagen fibrils induces alterations in the mechanical properties and positioning of the TMJ disc.By constructing a rat model of TMJ arthritis,we observed anteriorly dislocated TMJ discs with aggravated deformity in vivo from five weeks to six months after a local injection of Freund’s complete adjuvant.By mimicking inflammatory conditions with interleukin-1 beta in vitro,we observed enhanced expression of collagen-synthesis markers in primary TMJ disc cells cultured in a conventional two-dimensional environment.In contrast,three-dimensional(3D)-cultivated disc cell sheets demonstrated the disordered assembly of inflamed collagen fibrils,inappropriate arrangement,and decreased Young’s modulus.Mechanistically,inflammation-related activation of the nuclear factor kappa-B(NF-κB)pathway occurs during the progression of TMJ arthritis.NF-κB inhibition reduced the collagen fibril destruction in the inflamed disc cell sheets in vitro,and early NF-κB blockade alleviated collagen degeneration and dislocation of the TMJ discs in vivo.Therefore,the NF-κB pathway participates in the collagen remodeling in inflamed TMJ discs,offering a potential therapeutic target for disc displacement.展开更多
This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root dis...This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.展开更多
The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacem...The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System(GNSS)positioning.First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes.Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement,rainfall,groundwater table and soil moisture content and the graph structure.Last introduce the state-of-the-art graph deep learning GTS(Graph for Time Series)model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system.This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system.The proposed method performs better than SVM,XGBoost,LSTM and DCRNN models in terms of RMSE(1.35 mm),MAE(1.14 mm)and MAPE(0.25)evaluation metrics,which is provided to be effective in future landslide failure early warning.展开更多
Ferroelastic hybrid perovskite materials have been revealed the significance in the applications of switches,sensors,actuators,etc.However,it remains a challenge to design high-temperature ferroelastic to meet the req...Ferroelastic hybrid perovskite materials have been revealed the significance in the applications of switches,sensors,actuators,etc.However,it remains a challenge to design high-temperature ferroelastic to meet the requirements for the practical applications.Herein,we reported an one-dimensional organicinorganic hybrid perovskites(OIHP)(3-methylpyrazolium)CdCl_(3)(3-MBCC),which possesses a mmmF2/m ferroelastic phase transition at 263 K.Moreover,utilizing crystal engineering,we replace-CH_(3) with-NH_(2) and-H,which increases the intermolecular force between organic cations and inorganic frameworks.The phase transition temperature of(3-aminopyrazolium)CdCl_(3)(3-ABCC),and(pyrazolium)CdCl_(3)(BCC)increased by 73 K and 10 K,respectively.Particularly,BCC undergoes an unconventional inverse temperature symmetry breaking(ISTB)ferroelastic phase transition around 273 K.Differently,it transforms from a high symmetry low-temperature paraelastic phase(point group 2/m)to a low symmetry high-temperature ferroelastic phase(point group ī)originating from the rare mechanism of displacement of organic cations phase transition.It means that crystal BCC retains in ferroelastic phase above 273 K until melting point(446 K).Furthermore,characteristic ferroelastic domain patterns on crystal BCC are confirmed with polarized optical microscopy.Our study enriches the molecular mechanism of ferroelastics in the family of organic-inorganic hybrids and opens up a new avenue for exploring high-temperature ferroic materials.展开更多
A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating...A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.展开更多
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
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.
文摘Succinylcholine(SC)is a widely used depolarizing muscle relaxant,but improper use can lead to arrhythmias and,in severe cases,pose a life-threatening risk.Additionally,some criminals exploit SC for illicit activities.Therefore,rapid SC detection is paramount for clinical practice and public safety.Currently,however,limited methods are available for the rapid detection of SC.A fluorescent indicator displacement assay sensor based on molecular recognition of an amide naphthotube was developed.This sensor enabled the rapid fluorescent detection of SC through competitive binding between SC and methylene blue with the amide naphthotube.The sensor exhibited exceptional sensitivity with a detection limit as low as 1.1μmol/L and a detection range of 1.1~60μmol/L,coupled with outstanding selectivity and robust stability.Furthermore,this sensor accurately determined SC levels in biological samples such as serum.In summary,this research provides a new solution for the rapid and accurate sensing of SC in complex matrices and offers new insights for the swift identification and detection of toxins.
文摘Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.
基金supported by the Science and Technology Innovation Program of Hunan Province,China(Grant No.2021RC4026)the National Natural Science Foundation of China(Grant Nos.12204538,12104507,and 92365203)Hunan Provincial Science Fund for Distinguished Young Scholars(Grant No.2022JJ10060).
文摘Diamond is a promising semiconductor material for future space exploration,owing to its unique atomic and electronic structures.However,diamond materials and related devices still suffer from irradiation damage under space irradiation involving high-energy irradiating particles.The study of the generation and evolution of point defects can help understand the irradiation damage mechanisms in diamond.This study systematically investigated the defect dynamics of diamond in 162 crystallographic directions uniformly selected on a spherical surface using molecular dynamics simulations,with primary knock-on atom(PKA)energies up to 20 keV,and temperatures ranging from 300 K to 1800 K.The results reveal that the displacement threshold energy of diamond changes periodically with crystallographic directions,which is related to the shape of potential energy surface along that direction.Additionally,the number of residual defects correlates positively with PKA energy.However,temperature has dual competing effects:while it enhances the probability of atomic displacement,it simultaneously suppresses the probability of defect formation by accelerating defect recombination.The calculation of sparse radial distribution function indicates that the defect distribution shows a certain degree of similarity in the short-range region across different PKA energies.As the PKA energy increases,defect clusters tend to become larger in size and more numerous in quantity.This study systematically investigates the anisotropy of displacement threshold energy and elucidates the relationship between various irradiation conditions and the final states of irradiation-induced defects.
基金financially supported by the National Natural Science Foundation of China(Nos.42277149,41502299,41372306)the Research Planning of Sichuan Education Department,China(No.16ZB0105)+3 种基金the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Nos.SKLGP2016Z007,SKLGP2018Z017,SKLGP2020Z009)Chengdu University of Technology Young and Middle Aged Backbone Program(No.KYGG201720)Sichuan Provincial Science and Technology Department Program(No.19YYJC2087)China Scholarship Council。
文摘To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.
基金The National Key Research and Development Program of China grant No.2022YFB3706704 received by Yuan Renthe National Natural and Science Foundation of China grant No.52308150 received by Xiang Xu.
文摘To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R^(2) increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R^(2).Compared to ANN,with an MSE of 4.6371 and an R^(2) of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R^(2) of 13.23%,demonstrating a significant enhancement in predictive performance.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R848)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the project number“NBU-FFR-2025-2932-09”.
文摘License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation,which lead to pixel displacements.This article introduces a Displacement Region Recognition Method(DR2M)to address such a problem.This method operates on displaced features compared to the training input observed throughout definite time frames.The technique focuses on detecting features that remain relatively stable under haze,using a frame-based analysis to isolate edges minimally affected by visual noise.The edge detection failures are identified using a bilateral neural network through displaced feature training.The training converges bilaterally towards the minimum edges from the maximum region.Thus,the training input and detected edges are used to identify the displacement between observed image frames to extract and differentiate the license plate region from the other vehicle regions.The proposed method maps the similarity feature between the detected and identified vehicle regions.This aids in leveraging the plate recognition precision with a high F1 score.Thus,this technique achieves a 10.27%improvement in identification precision,a 10.57%increase in F1 score,and a 9.73%reduction in false positive rate compared to baseline methods under maximum displacement conditions caused by haze.The technique attains an identification precision of 95.68%,an F1 score of 94.68%,and a false positive rate of 4.32%,indicating robust performance under haze-affected settings.
文摘This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement.A stable power supply module is designed to provide low-noise voltage to the entire circuit.An analog circuit system is constructed,including key circuits such as photoelectric sensors,I-V amplification,zero adjustment,fully differential amplification,and amplitude modulation filtering.To acquire and process signals,the PMAC Acc24E3 data acquisition card is selected,which realizes phase demodulation through reversible square wave counting,inverts displacement information,and a visual interface for the host computer is designed.Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0-10mm,with a maximum measurement error of less than 1.2μm,a maximum measurement speed of 6m/s,and a resolution better than 0.158μm.
基金supported by the National Key R&D Program of China (No.2022YFC3004602)the National Natural Science Foundation of China (No.52325404)the Shenzhen Science and Technology Program (No.JCYJ20220818095605012).
文摘Rock discontinuities such as joints widely exist in natural rock masses,and wave attenuation through rock masses is mainly caused by discontinuities.The displacement discontinuity model(DDM)has been widely used in theoretical and numerical analysis of wave propagation across rock discontinuity.However,the circumstance under which the DDM is applicable to predict wave propagation across rock discontinuity remains poorly understood.In this study,theoretical analysis and ultrasonic laboratory tests were carried out to examine the theoretical applicability of the DDM for wave propagation,where specimens with rough joints comprising regular rectangular asperities of different spacings and heights were prepared by 3D printing technology.It is found that the theoretical applicability of the DDM to predict wave propagation across rock discontinuity is determined by three joint parameters,i.e.the dimensionless asperity spacing(L),the dimensionless asperity height(H)and the groove density(D).Through theoretical analysis and laboratory tests,the conditions under which the DDM is applicable are derived as follows:and,.With increase in the groove density,the thresholds of the dimensionless asperity spacing and the dimensionless asperity height show a decreasing trend.In addition,the transmission coefficient in the frequency domain decreases with increasing groove density,dimensionless asperity spacing or dimensionless asperity height.The findings can facilitate our understanding of DDM for predicting wave propagation across rock discontinuity.
基金National Natural Science Foundation of China under Grant No.52322808the Fundamental Research Funds for the Central Universities under Grant No.B220202013。
文摘Collapses of seismic slopes demonstrate the characteristics of three-dimensional(3D)shapes.Conducting a 3D analysis of seismic slope stability is more complicated than doing a simplified two-dimensional(2D)analysis.The upper-bound solutions derived from limit analysis of seismic slopes using the pseudo-static method are used to generate an approximate solution for the factor of 3D safety through regression analysis.Such a solution can degenerate to a 2D result when the slope width tends to infinity.The approximation method also can be extended for determining the permanent displacements of 3D slopes under seismic loading.The method is non-iterative and relatively accurate through comparisons with analytical results.Involving stochastic ground motions could easily be used to assess the distribution of permanent displacement that is induced in 3D slopes.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金the University of Shanghai for Science and Technology’s Medical Engineering Interdisciplinary Project(No.10-22-308-520)the Ministry of Education’s First Batch of Industry-Education Cooperation Collaborative Education Projects(No.202101042008)+1 种基金the Fundamental Research Funds for the Central Universities(No.YG2019QNA34)the Shanghai Municipal Health Commission for Youth Clinical Research Project(No.20194Y0134)。
文摘The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery.And the study will be integrated with augmented reality technology to achieve three-dimensional visualization,thereby enhancing the complete resection rate of tumor and the success rate of surgery.Based on the preoperative MRI data of the patients,a 3D virtual model is reconstructed and 3D printed.A brain biomimetic model is created using gel injection molding.By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables,the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery.An orthogonal experiment is conducted on the biomimetic model to establish a predictive model,and this model is incorporated into the augmented reality navigation system.To validate the predictive model,five participants wore HoloLens2 devices,overlaying the patient’s 3D virtual model onto the physical head model.Subsequently,the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models(actual coordinates and predicted coordinates,respectively).The difference between these coordinates represents the model’s prediction error.The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from−0.6787 mm to 0.2957 mm and−0.4314 mm to 0.2253 mm,respectively.The relative errors for each experimental group are within 10%,demonstrating a good fit of the model.This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations.It also provides a new approach for surgeons.By combining augmented reality visualization,it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.
基金The Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42407250)the Fund from Research Centre for Resources Engineering towards Carbon Neutrality(RCRE)of The Hong Kong Polytechnic University(Grant No.No.1-BBEM)the Fund from Natural Science Foundation of Jiangsu Province(Grant No.BK20241211)。
文摘The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.
基金The National Key Research and Development Program of China(No.2021YFB2600600,2021YFB2600601)the National Natural Science Foundation of China(No.52408456)+2 种基金China Postdoctoral Science Foundation(No.2022M720533)College Students’Innovative Entrepreneurial Training Plan Program(No.202410710009)Key Research and Development Program of Shaanxi,China(No.2024SF-YBXM-659).
文摘Shaking table tests are widely used to evaluate seismic effects on railway structures,but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displacements,ambient noise interference,and limitations in displacement meter installation.In this paper,a novel method that integrates the Kanade-Lucas-Tomasi(KLT)feature tracker with an extended Kalman filter(EKF)is presented for measuring rail displacement during shaking table tests.The method employs KLT feature tracker and a random sample consensus algorithm to extract and track key feature points,while EKF optimally estimates dynamic states by accounting for system noise and observation errors.Shaking table test results demonstrate that the proposed method achieves an acceleration root mean square error of 0.300 m/s^(2)and a correlation with accelerometer data exceeding 99.7%,significantly outper-forming the original KLT approach.This innovative method provides a more efficient and reliable solution for measuring rail displacement under large nonlinear vibrations.
基金supported by the National Natural Science Foundation of China Nos.82370983,81671015(X.W.),82230030(Y.L.),82101043(S.C.)and 82370922(Y.F.)Beijing International Science and Technology Cooperation Project No.Z221100002722003(Y.L.)+4 种基金Beijing Natural Science Foundation Nos.L234017,JL23002(Y.L.),No.7242282(S.C.)and 7232217(Y.G.)Clinical Medicine Plus X-Young Scholars Project of Peking University No.PKU2024LCXQ039(Y.L.)National Program for Multidisciplinary Cooperative Treatment on Major Diseases No.PKUSSNMP-202013(X.W.)Hygiene and Health Development Scientific Research Fostering Plan of Haidian District Beijing No.HP2023-12-509001(J.Z.)Young Clinical Research Fund of the Chinese Stomatological Association No.CSA-02022-03(J.Z.).
文摘Temporomandibular joint(TMJ)disc displacement is one of the most significant subtypes of temporomandibular joint disorders,but its etiology and mechanism are poorly understood.In this study,we elucidated the mechanisms by which destruction of inflamed collagen fibrils induces alterations in the mechanical properties and positioning of the TMJ disc.By constructing a rat model of TMJ arthritis,we observed anteriorly dislocated TMJ discs with aggravated deformity in vivo from five weeks to six months after a local injection of Freund’s complete adjuvant.By mimicking inflammatory conditions with interleukin-1 beta in vitro,we observed enhanced expression of collagen-synthesis markers in primary TMJ disc cells cultured in a conventional two-dimensional environment.In contrast,three-dimensional(3D)-cultivated disc cell sheets demonstrated the disordered assembly of inflamed collagen fibrils,inappropriate arrangement,and decreased Young’s modulus.Mechanistically,inflammation-related activation of the nuclear factor kappa-B(NF-κB)pathway occurs during the progression of TMJ arthritis.NF-κB inhibition reduced the collagen fibril destruction in the inflamed disc cell sheets in vitro,and early NF-κB blockade alleviated collagen degeneration and dislocation of the TMJ discs in vivo.Therefore,the NF-κB pathway participates in the collagen remodeling in inflamed TMJ discs,offering a potential therapeutic target for disc displacement.
文摘This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.
基金funded by the National Natural Science Foundation of China (Grant No.41902240).
文摘The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System(GNSS)positioning.First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes.Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement,rainfall,groundwater table and soil moisture content and the graph structure.Last introduce the state-of-the-art graph deep learning GTS(Graph for Time Series)model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system.This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system.The proposed method performs better than SVM,XGBoost,LSTM and DCRNN models in terms of RMSE(1.35 mm),MAE(1.14 mm)and MAPE(0.25)evaluation metrics,which is provided to be effective in future landslide failure early warning.
基金support from the National Natural Science Foundation of China(No.22175079)support from the National Natural Science Foundation of China(No.22205087)+2 种基金the Open Project Program of Jiangxi Provincial Key Laboratory of Functional Molecular Materials Chemistry,Jiangxi University of Science and Technology(No.20212BCD42018)National Natural Science Foundation of China(No.22275075)Natural Science Foundation of Jiangxi Province(Nos.20204BCJ22015 and 20202ACBL203001).
文摘Ferroelastic hybrid perovskite materials have been revealed the significance in the applications of switches,sensors,actuators,etc.However,it remains a challenge to design high-temperature ferroelastic to meet the requirements for the practical applications.Herein,we reported an one-dimensional organicinorganic hybrid perovskites(OIHP)(3-methylpyrazolium)CdCl_(3)(3-MBCC),which possesses a mmmF2/m ferroelastic phase transition at 263 K.Moreover,utilizing crystal engineering,we replace-CH_(3) with-NH_(2) and-H,which increases the intermolecular force between organic cations and inorganic frameworks.The phase transition temperature of(3-aminopyrazolium)CdCl_(3)(3-ABCC),and(pyrazolium)CdCl_(3)(BCC)increased by 73 K and 10 K,respectively.Particularly,BCC undergoes an unconventional inverse temperature symmetry breaking(ISTB)ferroelastic phase transition around 273 K.Differently,it transforms from a high symmetry low-temperature paraelastic phase(point group 2/m)to a low symmetry high-temperature ferroelastic phase(point group ī)originating from the rare mechanism of displacement of organic cations phase transition.It means that crystal BCC retains in ferroelastic phase above 273 K until melting point(446 K).Furthermore,characteristic ferroelastic domain patterns on crystal BCC are confirmed with polarized optical microscopy.Our study enriches the molecular mechanism of ferroelastics in the family of organic-inorganic hybrids and opens up a new avenue for exploring high-temperature ferroic materials.
基金Projects(52174092,51904290)supported by the National Natural Science Foundation,ChinaProject(BK20220157)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(232102321009)supported by Henan Province Science and Technology Key Project,ChinaProject(2022YCPY0202)supported by Fundamental Research Funds for the Central Universities,China。
文摘A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.
