The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ...The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.展开更多
Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accur...Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments.展开更多
Soil desiccation cracking is a prevalent natural phenomenon that poses significant geotechnical and geoenvironmental challenges.Cracks typically initiate at surface defects such as air bubbles,large aggregates,tiny pi...Soil desiccation cracking is a prevalent natural phenomenon that poses significant geotechnical and geoenvironmental challenges.Cracks typically initiate at surface defects such as air bubbles,large aggregates,tiny pits,or uneven surfaces,where localized stress concentrations are readily induced.This study conducted a series of laboratory desiccation tests on slurry samples to investigate the initiation and propagation of desiccation cracks in the presence of varying types and quantities of surface defects.Digital image correlation(DIC)technology was employed to monitor the strain and displacement fields on the soil surface during the desiccation process.The results reveal that strain and displacement data derived from DIC can precisely predict the initiation sites and propagation directions of desiccation cracks.In samples with internal defects,cracks predominantly propagate through the defect,whereas external defects tend to initiate cracks along their edges.In samples with multiple defects,Y-shaped crack patterns generally form initially,followed by T-shaped and straight cracks,driven by the evolving stress field.The dynamic interplay between crack formation and tensile stress redistribution governs the initiation and propagation of desiccation cracks.展开更多
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
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
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
Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.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
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
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
As metropolitan areas expand spatially,they encounter constraints imposed by the fixed daily time budget.Rail transit enhances transport efficiency,reduces costs,and facilitates the formation of a“transit economic fi...As metropolitan areas expand spatially,they encounter constraints imposed by the fixed daily time budget.Rail transit enhances transport efficiency,reduces costs,and facilitates the formation of a“transit economic field”centered on rail networks,thereby alleviating such temporal-spatial pressures.This paper adopts an integrated temporal-spatial analytical framework.Following a conceptual clarification of the transit economic field,it dissects the mechanisms through which rail transit improves mobility and examines how this field influences urban spatial patterns,temporal dynamics,and their interrelationships.It constructs a theoretical framework to explain the co-development of transit economic fields and cities,supplemented by empirical case studies.The key findings are as follows:Firstly,the transit economic field represents a high-density development model that expands both horizontally and vertically around rail networks.It mitigates temporal-spatial conflicts.Secondly,with rail networks as the core,the field integrates diverse spatial functions,facilitating the establishment of economic connections and stabilizing temporal-spatial relationships.Thirdly,the transit economic field contributes to the preservation of urban natural ecosystems and enhances urban livability.Overall,this research can provide insights for promoting rail transit-oriented development transitions in large cities and urban agglomerations.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
Accurately revealing the spatial distribution law of seismic displacement is signifcance for revealing the mechanism of dynamic load induced rockburst and guiding the dynamic support of roadway.This paper established ...Accurately revealing the spatial distribution law of seismic displacement is signifcance for revealing the mechanism of dynamic load induced rockburst and guiding the dynamic support of roadway.This paper established the function of seismic displacement based on radiation energy,and compared the infuences of fracture type,radiation energy,shear strength,fracture velocity and medium density on the seismic displacement.The results showed that the displacement amplitudes of surrounding rock caused by P-wave,SH-wave and SV-wave increased with the rising of radiation energy,and the rate of displacement amplitude also accelerated.The displacement amplitudes of seismic wave associated with tensile fractures are signifcantly higher than that with shear fractures.The spatial displacement amplitude of S-wave was signifcantly higher than that of P-wave by one order of magnitude.The peak value of P-wave displacement of shear fracture was concentrated in two planes at 45°angle to the fracture surface.For SH-wave and SV-wave components,peak values were mainly observed on the fracture surface and its orthogonal plane.The P-wave displacement on the orthogonal plane to the fracture movement was zero,the displacement feld of SV-wave was distributed in four quadrants,and the displacement feld of SH-wave was symmetrical.The higher the value of medium attribute,the more signifcant the damage efect of coal-rock seismic wave weakening,and the infuence on the S-wave is greater than that of the P-wave.The displacement amplitude caused by seismic wave gradually increased with the rising of fracture velocity of coal-rock mass.The peak value of P-wave displacement increased linearly,and the peak value of S-wave displacement was nonlinear.The research results laid a theoretical foundation for dynamic support design for roadways.展开更多
This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods,such as the need for extensive historical datasets for training,t...This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods,such as the need for extensive historical datasets for training,the reliance on manual feature selection,and the difficulty in effectively utilizing landslide historical data.We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance.The proposed methodology follows a three-stage framework:(1)Empirical Mode Decomposition(EMD)effectively segregates cumulative displacement and feature factors;(2)We have developed a Double Exponential Smoothing(DES)ensemble optimized through a Non-dominated Sorting Genetic Algorithm-II(NSGA-II)to enhance trend prediction;while employing a Bidirectional Long Short-Term Memory-Radial Basis Function(BiLSTM-RBF)network enhanced by a hybrid attention mechanism,which facilitates a global-local synergistic approach to hierarchical feature extraction,thereby improving the prediction of periodic displacements;(3)A bidirectional adaptive feature extraction mechanism aligns attention weights with BiLSTM propagation paths through spatial mapping,complemented by an innovative loss function incorporating Prediction Interval(PI)width optimization.In the comparative experiments of the Baishuihe landslide:the RMSE,MAE,and R^(2) indexes of monitoring point ZG118 are improved by 19.8%,35.2%,and 3.2%compared with the optimal baseline model(RBF-MIC);in the monitoring point ZG93,where the amount of data is less,the three indexes are even more improved by 52.1%,32.3%,and 21.8%compared with the optimal baseline model(GRU-None).These results substantiate the model’s capacity to overcome dual constraints of data paucity and feature engineering limitations in geohazard prediction.展开更多
Irradiation experiments on p-Ga N gate high-electron-mobility transistors(HEMTs) were conducted using neutrons at Back-streaming White Neutron(Back-n) facility at the China Spallation Neutron Source(CSNS).Two groups o...Irradiation experiments on p-Ga N gate high-electron-mobility transistors(HEMTs) were conducted using neutrons at Back-streaming White Neutron(Back-n) facility at the China Spallation Neutron Source(CSNS).Two groups of devices were float-biased,while one group was ON-biased.Post-irradiation analysis revealed that the electrical performance of the devices exhibited progressive degradation with increasing Back-n fluence,with the ON-biased group demonstrating the most pronounced deterioration.This degradation was primarily characterized by a negative shift in the threshold voltage,a significant increase in reverse gate leakage current,and a slight reduction in forward gate leakage.Further analysis of the gate leakage current and capacitance-voltage characteristics indicated an elevated concentration of two-dimensional electron gas(2DEG),attributed to donor-type defects introduced within the barrier layer by Back-n irradiation.These defects act as hole traps,converting into fixed positive charges that deepen the quantum-well conduction band,thereby enhancing the 2DEG density.Additionally,through the trap-assisted tunneling mechanism,these defects serve as tunneling centers,increasing the probability of electron tunneling and consequently elevating the reverse gate leakage current.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
Upon encountering external challenges,immune cell recognition of response to pathogens constitutes a pivotal physiological process.Here,we designed and engineering an artificial immune signal transduction system utili...Upon encountering external challenges,immune cell recognition of response to pathogens constitutes a pivotal physiological process.Here,we designed and engineering an artificial immune signal transduction system utilizing DNA strands and liposomes to simulate antigen signals presentation,i.e.,the uptake and processing of antigens by antigen-presenting cells(APCs).Through controlled DNA strand displacement reactions,we engineered artificial antigen-presenting cells(mAPCs)that display antigen signals on their surface and mimic phagocytosis.To further simulate antigen presentation,we constructed mimic naïve T cells(mTCs).Then,deoxyribonucleic acid(DNA)ion channels across mTCs membranes,simulating Tcell receptors,were opened by DNA strands on mAPCs mimicking the major histocompatibility complex(MHC),i.e.,MHC molecules that present peptides to the T-cell receptor(TCR)on mTCs(recognition).This allowed Ca^(2+)ions to enter mTCs,increasing calcein fluorescence as activated mTC response indicator.The DNA strands on the surface of A-mAPCs and the Ca^(2+)ions in the solution together act like costimulatory molecules on APCs to trigger responses of mTCs.This simulation of immune signal transduction provides a significant reference value for the construction of bioinspired signal transduction systems and the design of more realistic artificial biological systems.展开更多
基金funded by the National Natural Science Foundation of China(NSFC)(No.62066024)funded by Basic Scientific Research Projects of Higher Education Institutions in Liaoning Province(LJ212411632063)the National Undergraduate Training Program for Innovation and Entrepreneurship(S202511632045).
文摘The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.
基金supported by the National Natural Science Foundation of China(No.52408533)the Natural Science Foundation of Shandong Province(No.ZR2024QE408)+3 种基金the University of Jinan Disciplinary Cross-Convergence Construction Project 2023(XKJC202310)the Municipal and School Integration Development Strategic Project of Jinan City(JNSX2023023)Natural Science Foundation of Tianjin(24JCQNJC00870)Doctoral Fund Support Project of University of Jinan(XRC2563).
文摘Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.42525201,42230710,42407521).
文摘Soil desiccation cracking is a prevalent natural phenomenon that poses significant geotechnical and geoenvironmental challenges.Cracks typically initiate at surface defects such as air bubbles,large aggregates,tiny pits,or uneven surfaces,where localized stress concentrations are readily induced.This study conducted a series of laboratory desiccation tests on slurry samples to investigate the initiation and propagation of desiccation cracks in the presence of varying types and quantities of surface defects.Digital image correlation(DIC)technology was employed to monitor the strain and displacement fields on the soil surface during the desiccation process.The results reveal that strain and displacement data derived from DIC can precisely predict the initiation sites and propagation directions of desiccation cracks.In samples with internal defects,cracks predominantly propagate through the defect,whereas external defects tend to initiate cracks along their edges.In samples with multiple defects,Y-shaped crack patterns generally form initially,followed by T-shaped and straight cracks,driven by the evolving stress field.The dynamic interplay between crack formation and tensile stress redistribution governs the initiation and propagation of desiccation cracks.
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The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
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.
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.
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.
基金Hubei Social Science Foundation Project“Research on the Relationship Between Rail Transit and Intensive and Sustainable Development of Large Cities”(2020052)。
文摘As metropolitan areas expand spatially,they encounter constraints imposed by the fixed daily time budget.Rail transit enhances transport efficiency,reduces costs,and facilitates the formation of a“transit economic field”centered on rail networks,thereby alleviating such temporal-spatial pressures.This paper adopts an integrated temporal-spatial analytical framework.Following a conceptual clarification of the transit economic field,it dissects the mechanisms through which rail transit improves mobility and examines how this field influences urban spatial patterns,temporal dynamics,and their interrelationships.It constructs a theoretical framework to explain the co-development of transit economic fields and cities,supplemented by empirical case studies.The key findings are as follows:Firstly,the transit economic field represents a high-density development model that expands both horizontally and vertically around rail networks.It mitigates temporal-spatial conflicts.Secondly,with rail networks as the core,the field integrates diverse spatial functions,facilitating the establishment of economic connections and stabilizing temporal-spatial relationships.Thirdly,the transit economic field contributes to the preservation of urban natural ecosystems and enhances urban livability.Overall,this research can provide insights for promoting rail transit-oriented development transitions in large cities and urban agglomerations.
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funded by the National Natural Science Foundation of China(52204197)the National Natural Science Foundation of China(52104175)+1 种基金the China Postdoctoral Science Foundation(2025T180498,2022M712935)the Open Subjects of Xinjiang Key Laboratory of Green Mining of Coal Resources,Ministry of Education,China(KLXGY-KB2425).
文摘Accurately revealing the spatial distribution law of seismic displacement is signifcance for revealing the mechanism of dynamic load induced rockburst and guiding the dynamic support of roadway.This paper established the function of seismic displacement based on radiation energy,and compared the infuences of fracture type,radiation energy,shear strength,fracture velocity and medium density on the seismic displacement.The results showed that the displacement amplitudes of surrounding rock caused by P-wave,SH-wave and SV-wave increased with the rising of radiation energy,and the rate of displacement amplitude also accelerated.The displacement amplitudes of seismic wave associated with tensile fractures are signifcantly higher than that with shear fractures.The spatial displacement amplitude of S-wave was signifcantly higher than that of P-wave by one order of magnitude.The peak value of P-wave displacement of shear fracture was concentrated in two planes at 45°angle to the fracture surface.For SH-wave and SV-wave components,peak values were mainly observed on the fracture surface and its orthogonal plane.The P-wave displacement on the orthogonal plane to the fracture movement was zero,the displacement feld of SV-wave was distributed in four quadrants,and the displacement feld of SH-wave was symmetrical.The higher the value of medium attribute,the more signifcant the damage efect of coal-rock seismic wave weakening,and the infuence on the S-wave is greater than that of the P-wave.The displacement amplitude caused by seismic wave gradually increased with the rising of fracture velocity of coal-rock mass.The peak value of P-wave displacement increased linearly,and the peak value of S-wave displacement was nonlinear.The research results laid a theoretical foundation for dynamic support design for roadways.
基金supported in part by the Guizhou Province Science Technology Support Plan([2024]General 007,[2022]General 264,[2023]General 096,[2023]General 412,and[2023]General 409)in part by the National Natural Science Foundation of China(Grant No.61861007)+2 种基金in part by the Guizhou Province Science and Technology Planning Project(ZK[2021]General 303)in part by the Project of GUIYANG HYDROPOWER INVESTIGATION DESIGN&RESEARCH INSTITUTE CHECC(YJ2022-12)in part by the Science and Technology Project of Power Construction Corporation of China,Ltd.(DJ-ZDXM-2022-44).
文摘This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods,such as the need for extensive historical datasets for training,the reliance on manual feature selection,and the difficulty in effectively utilizing landslide historical data.We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance.The proposed methodology follows a three-stage framework:(1)Empirical Mode Decomposition(EMD)effectively segregates cumulative displacement and feature factors;(2)We have developed a Double Exponential Smoothing(DES)ensemble optimized through a Non-dominated Sorting Genetic Algorithm-II(NSGA-II)to enhance trend prediction;while employing a Bidirectional Long Short-Term Memory-Radial Basis Function(BiLSTM-RBF)network enhanced by a hybrid attention mechanism,which facilitates a global-local synergistic approach to hierarchical feature extraction,thereby improving the prediction of periodic displacements;(3)A bidirectional adaptive feature extraction mechanism aligns attention weights with BiLSTM propagation paths through spatial mapping,complemented by an innovative loss function incorporating Prediction Interval(PI)width optimization.In the comparative experiments of the Baishuihe landslide:the RMSE,MAE,and R^(2) indexes of monitoring point ZG118 are improved by 19.8%,35.2%,and 3.2%compared with the optimal baseline model(RBF-MIC);in the monitoring point ZG93,where the amount of data is less,the three indexes are even more improved by 52.1%,32.3%,and 21.8%compared with the optimal baseline model(GRU-None).These results substantiate the model’s capacity to overcome dual constraints of data paucity and feature engineering limitations in geohazard prediction.
基金supported by the National Natural Science Foundation of China (Grant Nos.12120101005,U2030104,12175174,11975174,and 12105229)State Key Laboratory Foundation of Laser Interaction with Matter (Grant Nos.SKLLIM1807 and SKLLIM2106)+1 种基金the Postdoctoral Fellowship Program of CPSF (Grant No.GZC20241372)National Key Laboratory of Intense Pulsed Radiation Simulation and Effect (Grant No.NKLIPR2419)。
文摘Irradiation experiments on p-Ga N gate high-electron-mobility transistors(HEMTs) were conducted using neutrons at Back-streaming White Neutron(Back-n) facility at the China Spallation Neutron Source(CSNS).Two groups of devices were float-biased,while one group was ON-biased.Post-irradiation analysis revealed that the electrical performance of the devices exhibited progressive degradation with increasing Back-n fluence,with the ON-biased group demonstrating the most pronounced deterioration.This degradation was primarily characterized by a negative shift in the threshold voltage,a significant increase in reverse gate leakage current,and a slight reduction in forward gate leakage.Further analysis of the gate leakage current and capacitance-voltage characteristics indicated an elevated concentration of two-dimensional electron gas(2DEG),attributed to donor-type defects introduced within the barrier layer by Back-n irradiation.These defects act as hole traps,converting into fixed positive charges that deepen the quantum-well conduction band,thereby enhancing the 2DEG density.Additionally,through the trap-assisted tunneling mechanism,these defects serve as tunneling centers,increasing the probability of electron tunneling and consequently elevating the reverse gate leakage current.
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 Natural Science Foundation of China(No.82002241)National Key Research and Development Program of China(No.2020YFA0909000)“Clinic Plus”Outstanding Project(No.2024ZY012)from Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine。
文摘Upon encountering external challenges,immune cell recognition of response to pathogens constitutes a pivotal physiological process.Here,we designed and engineering an artificial immune signal transduction system utilizing DNA strands and liposomes to simulate antigen signals presentation,i.e.,the uptake and processing of antigens by antigen-presenting cells(APCs).Through controlled DNA strand displacement reactions,we engineered artificial antigen-presenting cells(mAPCs)that display antigen signals on their surface and mimic phagocytosis.To further simulate antigen presentation,we constructed mimic naïve T cells(mTCs).Then,deoxyribonucleic acid(DNA)ion channels across mTCs membranes,simulating Tcell receptors,were opened by DNA strands on mAPCs mimicking the major histocompatibility complex(MHC),i.e.,MHC molecules that present peptides to the T-cell receptor(TCR)on mTCs(recognition).This allowed Ca^(2+)ions to enter mTCs,increasing calcein fluorescence as activated mTC response indicator.The DNA strands on the surface of A-mAPCs and the Ca^(2+)ions in the solution together act like costimulatory molecules on APCs to trigger responses of mTCs.This simulation of immune signal transduction provides a significant reference value for the construction of bioinspired signal transduction systems and the design of more realistic artificial biological systems.
