TEM observation and analysis have been conducted on bainitie transformation with and without the influence of externally applied tensile sress for alloyed steel 35MV7. Recrystallizafion was found to occur within the b...TEM observation and analysis have been conducted on bainitie transformation with and without the influence of externally applied tensile sress for alloyed steel 35MV7. Recrystallizafion was found to occur within the bainitic structures transformed at 450 ℃ in cases of both with and without the application of external stress, and coupling between the reconstructive and displacive mechanisms is expected, due to the relatively high holding temperature and high dislocation density yielded with the displacive mechanism. RecrystaUization was not observed within the bainitic structures transformed at lower temperature of 350 ℃, both with and without the application of stress; However, for the stressed specimen, the structure with very fine subgrains was found to be preserved and not reconstructed thermically, due to the low temperature and short holding time.展开更多
As a controllable power generation method requiring no energy storage,Ocean Thermal Energy Conversion(OTEC)technology demonstrates characteristics of abundant reserves,low pollution,and round-the-clock stable operatio...As a controllable power generation method requiring no energy storage,Ocean Thermal Energy Conversion(OTEC)technology demonstrates characteristics of abundant reserves,low pollution,and round-the-clock stable operation.The free-standing cold-water pipe(CWP)in the system withstands various complex loads during operation,posing potential failure risks.To reveal the deformation and stress mechanisms of OTEC CWPs,this study first analyzes wave particle velocity and acceleration to determine wave loads at different water depths.Based on the Euler-Bernoulli beam model,a quasi-static load calculation model for OTEC CWPs was established.The governing equations were discretized using the finite difference method,and matrix equations were solved to analyze bending deformation,bending moments,and surface stresses at discrete points along the pipe.Results indicate that water depths within 50 m represent a critical zone where wave particle velocity,acceleration,and wave loads exhibit significant variations in harmonic patterns,while beyond 50 m depth wave loads decrease linearly.Ocean currents and surface wind-driven currents substantially influence the CWP’s lateral displacement.Considering the effect of clump weights,the maximum lateral displacement occurs at 600–800 m below sea level.Utilizing large-wall-thickness high-strength pipes at the top section significantly enhances the structural safety of the CWP system.展开更多
A novel vibration isolation system designed for superior performance in low-frequency environments is proposed in this work.The isolator is based on a unique hexagonal arrangement of linear springs,allowing for an adj...A novel vibration isolation system designed for superior performance in low-frequency environments is proposed in this work.The isolator is based on a unique hexagonal arrangement of linear springs,allowing for an adjustable geometric configuration via the initial inclination angle.Based on the principle of Lagrangian mechanics,the equation of motion governing the structural dynamics is rigorously derived.The system is modeled as a strongly nonlinear single-degree-of-freedom dynamical system,loaded with a normalized payload and subject to harmonic base excitation.To analyze the steady-state response,the harmonic balance method is employed,providing accurate predictions of the payload's vibration amplitude and displacement transmissibility as functions of both the base excitation amplitude and frequency.The analysis reveals a direct relationship between the isolator's geometric and stiffness parameters and its load-bearing capacity,leading to the identification of three distinct operational regimes.Depending on the unloaded initial inclination angle,the equivalent stiffness ratio,and the payload design configuration,the system can exhibit one of three vibration isolation modes:(i)the quasizero stiffness(QZS)isolation mode,(ii)the zero linear stiffness with controllable nonlinear stiffness,and(iii)the full-band perfect zero stiffness.The vibration isolation performance of the proposed structure is thoroughly discussed for all three oscillation modes in terms of frequency response curves,displacement transmissibility,and time-domain responses.The key novel finding is that this structure can operate as a full-band,high-performance vibration isolator when the initial inclination angle is designed to be a right angle,enabling full isolation of the maximum possible payload.Moreover,the analytical results and numerical simulations demonstrate that the isolator's displacement transmissibility T with the unit dB tends to-∞as the air-damping coefficient approaches zero,enabling ideal vibration isolation across the entire excitation frequency range.These analytical insights are validated through comprehensive numerical simulations,which show excellent agreement with the theoretical predictions.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
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
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
To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing ...To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK(Ibarra Medina Krawinkler)recovery model,the energy dissipation capacity coefficient of the pier columns were able to be determined.Furthermore,utilizing the calibrated damage model parameters,the damage index for each specimen were calculated.Based on the obtained damage levels,three distinct pre-damage conditions were designed for the pier columns:minor damage,moderate damage,and severe damage.The study then predicted the variations in hysteresis curves and damage indices under cyclic loading conditions.The experimental findings reveal that the displacement at the top of the pier columns can serve as a reliable indicator for controlling the damage level of pier columns post-loading.Moreover,the calibrated damage index model exhibits proficiency in accurately predicting the damage level of RC pier columns under cyclic loading.展开更多
Bedding parallel stepped rock slopes exist widely in nature and are used in slope engineering.They are characterized by complex topography and geological structure and are vulnerable to shattering under strong earthqu...Bedding parallel stepped rock slopes exist widely in nature and are used in slope engineering.They are characterized by complex topography and geological structure and are vulnerable to shattering under strong earthquakes.However,no previous studies have assessed the mechanisms underlying seismic failure in rock slopes.In this study,large-scale shaking table tests and numerical simulations were conducted to delineate the seismic failure mechanism in terms of acceleration,displacement,and earth pressure responses combined with shattering failure phenomena.The results reveal that acceleration response mutations usually occur within weak interlayers owing to their inferior performance,and these mutations may transform into potential sliding surfaces,thereby intensifying the nonlinear seismic response characteristics.Cumulative permanent displacements at the internal corners of the berms can induce quasi-rigid displacements at the external corners,leading to greater permanent displacements at the internal corners.Therefore,the internal corners are identified as the most susceptible parts of the slope.In addition,the concept of baseline offset was utilized to explain the mechanism of earth pressure responses,and the result indicates that residual earth pressures at the internal corners play a dominant role in causing deformation or shattering damage.Four evolutionary deformation phases characterize the processes of seismic responses and shattering failure of the bedding parallel stepped rock slope,i.e.the formation of tensile cracks at the internal corners of the berm,expansion of tensile cracks and bedding surface dislocation,development of vertical tensile cracks at the rear edge,and rock mass slipping leading to slope instability.Overall,this study provides a scientific basis for the seismic design of engineering slopes and offers valuable insights for further studies on preventing seismic disasters in bedding parallel stepped rock slopes.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
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
The girder end restraint devices such as bearings and dampers on long span suspension bridge will deteriorate over time.However,it is difficult to achieve the quantitative assessment of the performance of the restrain...The girder end restraint devices such as bearings and dampers on long span suspension bridge will deteriorate over time.However,it is difficult to achieve the quantitative assessment of the performance of the restraint device through existing detection methods in actual inspections,making it difficult to obtain the impact of changes in the performance of the restraint device on the bridge structure.In this paper,a random vehicle load model is firstly established based on the WIM data of Jiangyin Bridge,and the displacement of girder end under the actual traffic flow is simulated by using finite element dynamic time history analysis.On this basis,according to the performance test data of the bearings and dampers,the performance degradation laws of the above two restraint devices are summarized,and the performance degradation process of the two restraint devices and the effects of different restraint parameters on the bridge structure are simulated.The results show that the performance degradation of the damper will significantly reduce the damping force at low speed,resulting in a significant increase in the cumulative displacement of the girder end;in the presence of longitudinal dampers,the increase in the friction coefficient caused by the deterioration of the bearing sliding plate has little effect on the cumulative displacement,but excessive wear of the bearing sliding plate adversely affects the structural dynamic performance.展开更多
Carbon dioxide enhanced oil recovery(CO_(2)-EOR)technology is used for oil production and CO_(2) storage in reservoirs.Methods are being constantly developed to optimize oil recovery and CO_(2) storage during the CO_(...Carbon dioxide enhanced oil recovery(CO_(2)-EOR)technology is used for oil production and CO_(2) storage in reservoirs.Methods are being constantly developed to optimize oil recovery and CO_(2) storage during the CO_(2) displacement process,especially for low-permeability reservoirs under varying geological conditions.In this study,long-core experiments and trans-scale numerical simulations are employed to examine the characteristics of oil production and CO_(2) storage.Optimal production parameters for the target reservoir are also proposed.The results indicate that maintaining the pressure at 1.04 to 1.10 times the minimum miscible pressure(MMP)and increasing the injection rate can enhance oil production in the early stage of reservoir development.In contrast,reducing the injection rate at the later stages prevents CO_(2) channeling,thus improving oil recovery and CO_(2) storage efficiency.A solution-doubling factor is introduced to modify the calculation method for CO_(2) storage,increasing its accuracy to approximately 90%.Before CO_(2) breakthrough,prioritizing oil production is recommended to maximize the economic benefits of this process.In the middle stage of CO_(2) displacement,decreasing the injection rate optimizes the coordination between oil displacement and CO_(2) storage.Further,in the late stage,reduced pressure and injection rates are required as the focus shifts to CO_(2) storage.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
Two international standards,ISO 18501:2025,Performance rating of positive displacement refrigerant compressor,and ISO 18483:2025,Performance rating of centrifugal refrigerant compressor,were released at an event held ...Two international standards,ISO 18501:2025,Performance rating of positive displacement refrigerant compressor,and ISO 18483:2025,Performance rating of centrifugal refrigerant compressor,were released at an event held by GREE and Hefei General Machinery Research Institute Co.,Ltd.in Zhuhai,South China’s Guangdong province on June 12.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
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
The field of diffusion micro structural magnetic resonance(MR)aims to probe timedependent diffusion,i.e.,an ensemble-averaged mean-squared displacement that is not linear in time.This time-dependence contains rich inf...The field of diffusion micro structural magnetic resonance(MR)aims to probe timedependent diffusion,i.e.,an ensemble-averaged mean-squared displacement that is not linear in time.This time-dependence contains rich information about the surrounding microenvironment.MR methods to measure time-dependent diffusion quantitatively,however,require either non-standard pulse sequences,such as oscillating gradients,or make non-physical assumptions,such as infinitely narrow gradient pulses.Here,we argue that standard spin echo and stimulated echo MR sequences can be used to probe directly.In particular,we propose a framework in which the log-signal ratio obtained from a pair of measurements with different inter-pulse spacingΔis proportional to the MSD between these twoΔvalues along the gradient direction x:-.The framework is quantitative for short,finite-duration gradient pulses and under the Gaussian phase approximation(GPA).To validate the framework,we consider onedimensional diffusion between impermeable,parallel planes,as well as periodicallyspaced,permeable planes.Excellent agreement is obtained between the estimation and the ground truth in the regime where the GPA is expected to hold.Importantly,the GPA can be made to hold for any underlying microstructure,making the proposed framework widely applicable.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
Shape memory alloy(SMA)bars are currently preferred over elastomeric seismic isolators due to the elimination of degradation within effective damping and stiffness factors during the cyclic response of an isolation sy...Shape memory alloy(SMA)bars are currently preferred over elastomeric seismic isolators due to the elimination of degradation within effective damping and stiffness factors during the cyclic response of an isolation system.These bars could also be used to prevent the functionality of the isolator units from failing due to large deformations.This study aims to investigate the performance of a high damping rubber bearing(HDRB)isolator that is combined with two different types of SMA bars,i.e.,Nickel-Titanium(Ni-Ti)and Copper-Aluminum-Beryllium(Cu-Al-Be),subjected to near-fault ground motions that are characterized with forward directivity and fling step effects.To achieve this objective,a self-centering material with flag-shape,force-deformation hysteresis was utilized to simulate the behavior of SMA bars in OpenSees.A single degree of freedom(SDOF)system representing an isolated one-story shear building was developed to conduct nonlinear analysis under selected ground motions.The SMA bars were introduced as an X-shape within the model and were connected diagonally to the top and bottom of the isolator.Results showed that the HDRB system’s hysteretic response under near-fault ground accelerations experiences significant degradation,especially when near-fault motions involve the fling step effect.It was demonstrated that SMA bars effectively reduce large displacement observed in HDRB systems under near-fault earthquakes.Comparing the results of the base-isolated HDRB and SMA-HDRB subjected to selected ground motions demonstrated that maximum displacement was found to be significantly reduced by an average of 79%when SMA bars were used.Incorporating SMA bars with a larger diameter significantly improves the efficiency of SMA HDRB systems,and a reduction in maximum displacements is more pronounced for fling step,near-fault ground motions.展开更多
The compound system of polyacrylamide hydrogels and surfactant solutions are used for enhanced oil recovery(EOR).The polyacrylamide hydrogels are injected into block high-permeability zones firstly,followed by a low-c...The compound system of polyacrylamide hydrogels and surfactant solutions are used for enhanced oil recovery(EOR).The polyacrylamide hydrogels are injected into block high-permeability zones firstly,followed by a low-cost sacrificial agent,then an oil-displacing surfactant,and finally an aqueous polymer solution containing diethanolamine,to enhance oil production.The hydrogels are selected through oscillatory rheometry,while the surfactant is optimized after optical imaging analysis.The EOR performance of the compound system is evaluated through core flooding experiments and reservoir numerical simulation.Specifically,the properly cross-linked polyacrylamide hydrogel can be selected using its elastic modulus as a quantitative parameter while accounting for pore structure.The sacrificial agent is used to block active adsorption sites in the rock matrix before mobilizing more crude oil with a nonionic-anionic surfactant system.The addition of the mild organic alkali(diethanolamine)into the polymer slug reduces surfactant adsorption and improves sweep efficiency,thereby enhancing the oil-washing effect.Flooding experimental results show that the sequential injection of hydrogel and surfactant compositions prolongs the period of increasing pressure gradient during subsequent waterflooding and significantly boosts oil production,achieving a 21-percentage-point increase in oil displacement efficiency.Numerical simulation for the target reservoir in the West Siberian oil province confirms the effectiveness,projecting a maximum cumulative oil increase of 6851 t over three years.展开更多
文摘TEM observation and analysis have been conducted on bainitie transformation with and without the influence of externally applied tensile sress for alloyed steel 35MV7. Recrystallizafion was found to occur within the bainitic structures transformed at 450 ℃ in cases of both with and without the application of external stress, and coupling between the reconstructive and displacive mechanisms is expected, due to the relatively high holding temperature and high dislocation density yielded with the displacive mechanism. RecrystaUization was not observed within the bainitic structures transformed at lower temperature of 350 ℃, both with and without the application of stress; However, for the stressed specimen, the structure with very fine subgrains was found to be preserved and not reconstructed thermically, due to the low temperature and short holding time.
基金funded by Nansha District Science and Technology Project(Grant Number.2024ZD008)funded by China Geological Survey(Grant number:No.DD20230066,DD20242659).
文摘As a controllable power generation method requiring no energy storage,Ocean Thermal Energy Conversion(OTEC)technology demonstrates characteristics of abundant reserves,low pollution,and round-the-clock stable operation.The free-standing cold-water pipe(CWP)in the system withstands various complex loads during operation,posing potential failure risks.To reveal the deformation and stress mechanisms of OTEC CWPs,this study first analyzes wave particle velocity and acceleration to determine wave loads at different water depths.Based on the Euler-Bernoulli beam model,a quasi-static load calculation model for OTEC CWPs was established.The governing equations were discretized using the finite difference method,and matrix equations were solved to analyze bending deformation,bending moments,and surface stresses at discrete points along the pipe.Results indicate that water depths within 50 m represent a critical zone where wave particle velocity,acceleration,and wave loads exhibit significant variations in harmonic patterns,while beyond 50 m depth wave loads decrease linearly.Ocean currents and surface wind-driven currents substantially influence the CWP’s lateral displacement.Considering the effect of clump weights,the maximum lateral displacement occurs at 600–800 m below sea level.Utilizing large-wall-thickness high-strength pipes at the top section significantly enhances the structural safety of the CWP system.
基金Project supported by the National Key R&D Program of China(No.2023YFE0125900)。
文摘A novel vibration isolation system designed for superior performance in low-frequency environments is proposed in this work.The isolator is based on a unique hexagonal arrangement of linear springs,allowing for an adjustable geometric configuration via the initial inclination angle.Based on the principle of Lagrangian mechanics,the equation of motion governing the structural dynamics is rigorously derived.The system is modeled as a strongly nonlinear single-degree-of-freedom dynamical system,loaded with a normalized payload and subject to harmonic base excitation.To analyze the steady-state response,the harmonic balance method is employed,providing accurate predictions of the payload's vibration amplitude and displacement transmissibility as functions of both the base excitation amplitude and frequency.The analysis reveals a direct relationship between the isolator's geometric and stiffness parameters and its load-bearing capacity,leading to the identification of three distinct operational regimes.Depending on the unloaded initial inclination angle,the equivalent stiffness ratio,and the payload design configuration,the system can exhibit one of three vibration isolation modes:(i)the quasizero stiffness(QZS)isolation mode,(ii)the zero linear stiffness with controllable nonlinear stiffness,and(iii)the full-band perfect zero stiffness.The vibration isolation performance of the proposed structure is thoroughly discussed for all three oscillation modes in terms of frequency response curves,displacement transmissibility,and time-domain responses.The key novel finding is that this structure can operate as a full-band,high-performance vibration isolator when the initial inclination angle is designed to be a right angle,enabling full isolation of the maximum possible payload.Moreover,the analytical results and numerical simulations demonstrate that the isolator's displacement transmissibility T with the unit dB tends to-∞as the air-damping coefficient approaches zero,enabling ideal vibration isolation across the entire excitation frequency range.These analytical insights are validated through comprehensive numerical simulations,which show excellent agreement with the theoretical predictions.
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.
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 National Natural Science Foundation of China(Project No.51878156)EPC Innovation Consulting Project for Longkou Nanshan LNG Phase I Receiving Terminal(Z2000LGENT0399).
文摘To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK(Ibarra Medina Krawinkler)recovery model,the energy dissipation capacity coefficient of the pier columns were able to be determined.Furthermore,utilizing the calibrated damage model parameters,the damage index for each specimen were calculated.Based on the obtained damage levels,three distinct pre-damage conditions were designed for the pier columns:minor damage,moderate damage,and severe damage.The study then predicted the variations in hysteresis curves and damage indices under cyclic loading conditions.The experimental findings reveal that the displacement at the top of the pier columns can serve as a reliable indicator for controlling the damage level of pier columns post-loading.Moreover,the calibrated damage index model exhibits proficiency in accurately predicting the damage level of RC pier columns under cyclic loading.
基金supported by the National Natural Science Foundation of China (Grant No.52108361)the Sichuan Science and Technology Program of China (Grant No.2023YFS0436)the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (Grant No.SKLGP2022Z015).
文摘Bedding parallel stepped rock slopes exist widely in nature and are used in slope engineering.They are characterized by complex topography and geological structure and are vulnerable to shattering under strong earthquakes.However,no previous studies have assessed the mechanisms underlying seismic failure in rock slopes.In this study,large-scale shaking table tests and numerical simulations were conducted to delineate the seismic failure mechanism in terms of acceleration,displacement,and earth pressure responses combined with shattering failure phenomena.The results reveal that acceleration response mutations usually occur within weak interlayers owing to their inferior performance,and these mutations may transform into potential sliding surfaces,thereby intensifying the nonlinear seismic response characteristics.Cumulative permanent displacements at the internal corners of the berms can induce quasi-rigid displacements at the external corners,leading to greater permanent displacements at the internal corners.Therefore,the internal corners are identified as the most susceptible parts of the slope.In addition,the concept of baseline offset was utilized to explain the mechanism of earth pressure responses,and the result indicates that residual earth pressures at the internal corners play a dominant role in causing deformation or shattering damage.Four evolutionary deformation phases characterize the processes of seismic responses and shattering failure of the bedding parallel stepped rock slope,i.e.the formation of tensile cracks at the internal corners of the berm,expansion of tensile cracks and bedding surface dislocation,development of vertical tensile cracks at the rear edge,and rock mass slipping leading to slope instability.Overall,this study provides a scientific basis for the seismic design of engineering slopes and offers valuable insights for further studies on preventing seismic disasters in bedding parallel stepped rock slopes.
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supported by the National Key Research and Development Program of China(No.2022YFB3706704)the Academician Special Science Research Project of CCCC(No.YSZX-03-2022-01-B).
文摘The girder end restraint devices such as bearings and dampers on long span suspension bridge will deteriorate over time.However,it is difficult to achieve the quantitative assessment of the performance of the restraint device through existing detection methods in actual inspections,making it difficult to obtain the impact of changes in the performance of the restraint device on the bridge structure.In this paper,a random vehicle load model is firstly established based on the WIM data of Jiangyin Bridge,and the displacement of girder end under the actual traffic flow is simulated by using finite element dynamic time history analysis.On this basis,according to the performance test data of the bearings and dampers,the performance degradation laws of the above two restraint devices are summarized,and the performance degradation process of the two restraint devices and the effects of different restraint parameters on the bridge structure are simulated.The results show that the performance degradation of the damper will significantly reduce the damping force at low speed,resulting in a significant increase in the cumulative displacement of the girder end;in the presence of longitudinal dampers,the increase in the friction coefficient caused by the deterioration of the bearing sliding plate has little effect on the cumulative displacement,but excessive wear of the bearing sliding plate adversely affects the structural dynamic performance.
基金funded by the National Science and Technology Major Project for the Exploration and Development of New Types of Oil and Gas(No.2024ZD14066)the National Natural Science Foundation of China(No.52274053)+1 种基金the Natural Science Foundation of Beijing Municipality(No.3173044)the Xinjiang Conglomerate Reservoir Laboratory Development Foundation Project(No.2020D04045)。
文摘Carbon dioxide enhanced oil recovery(CO_(2)-EOR)technology is used for oil production and CO_(2) storage in reservoirs.Methods are being constantly developed to optimize oil recovery and CO_(2) storage during the CO_(2) displacement process,especially for low-permeability reservoirs under varying geological conditions.In this study,long-core experiments and trans-scale numerical simulations are employed to examine the characteristics of oil production and CO_(2) storage.Optimal production parameters for the target reservoir are also proposed.The results indicate that maintaining the pressure at 1.04 to 1.10 times the minimum miscible pressure(MMP)and increasing the injection rate can enhance oil production in the early stage of reservoir development.In contrast,reducing the injection rate at the later stages prevents CO_(2) channeling,thus improving oil recovery and CO_(2) storage efficiency.A solution-doubling factor is introduced to modify the calculation method for CO_(2) storage,increasing its accuracy to approximately 90%.Before CO_(2) breakthrough,prioritizing oil production is recommended to maximize the economic benefits of this process.In the middle stage of CO_(2) displacement,decreasing the injection rate optimizes the coordination between oil displacement and CO_(2) storage.Further,in the late stage,reduced pressure and injection rates are required as the focus shifts to CO_(2) storage.
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Two international standards,ISO 18501:2025,Performance rating of positive displacement refrigerant compressor,and ISO 18483:2025,Performance rating of centrifugal refrigerant compressor,were released at an event held by GREE and Hefei General Machinery Research Institute Co.,Ltd.in Zhuhai,South China’s Guangdong province on June 12.
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supported by the intramural research program(IRP)of the Eunice Kennedy Shriver National Institute of Child Health and Human Development。
文摘The field of diffusion micro structural magnetic resonance(MR)aims to probe timedependent diffusion,i.e.,an ensemble-averaged mean-squared displacement that is not linear in time.This time-dependence contains rich information about the surrounding microenvironment.MR methods to measure time-dependent diffusion quantitatively,however,require either non-standard pulse sequences,such as oscillating gradients,or make non-physical assumptions,such as infinitely narrow gradient pulses.Here,we argue that standard spin echo and stimulated echo MR sequences can be used to probe directly.In particular,we propose a framework in which the log-signal ratio obtained from a pair of measurements with different inter-pulse spacingΔis proportional to the MSD between these twoΔvalues along the gradient direction x:-.The framework is quantitative for short,finite-duration gradient pulses and under the Gaussian phase approximation(GPA).To validate the framework,we consider onedimensional diffusion between impermeable,parallel planes,as well as periodicallyspaced,permeable planes.Excellent agreement is obtained between the estimation and the ground truth in the regime where the GPA is expected to hold.Importantly,the GPA can be made to hold for any underlying microstructure,making the proposed framework widely applicable.
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Open Access funding enabled and organized by CAUL and its Member Institutions。
文摘Shape memory alloy(SMA)bars are currently preferred over elastomeric seismic isolators due to the elimination of degradation within effective damping and stiffness factors during the cyclic response of an isolation system.These bars could also be used to prevent the functionality of the isolator units from failing due to large deformations.This study aims to investigate the performance of a high damping rubber bearing(HDRB)isolator that is combined with two different types of SMA bars,i.e.,Nickel-Titanium(Ni-Ti)and Copper-Aluminum-Beryllium(Cu-Al-Be),subjected to near-fault ground motions that are characterized with forward directivity and fling step effects.To achieve this objective,a self-centering material with flag-shape,force-deformation hysteresis was utilized to simulate the behavior of SMA bars in OpenSees.A single degree of freedom(SDOF)system representing an isolated one-story shear building was developed to conduct nonlinear analysis under selected ground motions.The SMA bars were introduced as an X-shape within the model and were connected diagonally to the top and bottom of the isolator.Results showed that the HDRB system’s hysteretic response under near-fault ground accelerations experiences significant degradation,especially when near-fault motions involve the fling step effect.It was demonstrated that SMA bars effectively reduce large displacement observed in HDRB systems under near-fault earthquakes.Comparing the results of the base-isolated HDRB and SMA-HDRB subjected to selected ground motions demonstrated that maximum displacement was found to be significantly reduced by an average of 79%when SMA bars were used.Incorporating SMA bars with a larger diameter significantly improves the efficiency of SMA HDRB systems,and a reduction in maximum displacements is more pronounced for fling step,near-fault ground motions.
文摘The compound system of polyacrylamide hydrogels and surfactant solutions are used for enhanced oil recovery(EOR).The polyacrylamide hydrogels are injected into block high-permeability zones firstly,followed by a low-cost sacrificial agent,then an oil-displacing surfactant,and finally an aqueous polymer solution containing diethanolamine,to enhance oil production.The hydrogels are selected through oscillatory rheometry,while the surfactant is optimized after optical imaging analysis.The EOR performance of the compound system is evaluated through core flooding experiments and reservoir numerical simulation.Specifically,the properly cross-linked polyacrylamide hydrogel can be selected using its elastic modulus as a quantitative parameter while accounting for pore structure.The sacrificial agent is used to block active adsorption sites in the rock matrix before mobilizing more crude oil with a nonionic-anionic surfactant system.The addition of the mild organic alkali(diethanolamine)into the polymer slug reduces surfactant adsorption and improves sweep efficiency,thereby enhancing the oil-washing effect.Flooding experimental results show that the sequential injection of hydrogel and surfactant compositions prolongs the period of increasing pressure gradient during subsequent waterflooding and significantly boosts oil production,achieving a 21-percentage-point increase in oil displacement efficiency.Numerical simulation for the target reservoir in the West Siberian oil province confirms the effectiveness,projecting a maximum cumulative oil increase of 6851 t over three years.
