A comparative analysis was performed on poly(lactic acid)(PLA),poly(caprolactone)(PCL),basalt fiber(BF)composites produced using two distinct approaches:direct blending and masterbatching.The limitations of PLA-BF com...A comparative analysis was performed on poly(lactic acid)(PLA),poly(caprolactone)(PCL),basalt fiber(BF)composites produced using two distinct approaches:direct blending and masterbatching.The limitations of PLA-BF composites with regard to distribution and adhesion are well-documented,as are chemical treatment methods(addition of compatibilisers,surface treatments,silanization).This work aimed to study an industrially relevant potential solution of utilising a PCL-BF masterbatch,prepared as a 50/50 wt.%blend using planetary roller extrusion(PEX)to both improve the distribution and homogeneity of the fibers as well as provide a secondary adhesion site to facilitate improved mechanical properties of the final PLA-PCL-BF composite.The resultant materials were injection moulded to prepare ISO standard test specimens and tested on the basis of their physical properties via tensile testing,impact strength testing,flexural analysis,Fourier transforminfrared spectroscopy and water absorption capability.The results displayed that the incorporation of PCL and BF led to an increase in ductility of the composite materials,allowing for improvements in the inherent brittleness of virgin PLA.Major increases in the impact strength were achieved with the utilisation of a 25% PCL/BF masterbatch,allowing for a greater than 50%increase.As an overall observation,the use of a masterbatching process,opposed to direct blending of the constituent materials allows for a greater consistency of composite to be achieved at the expense of increased gains.展开更多
The microstructure and mechanical properties of ZK60 extruded alloy by rapid solidification(RS)and as-cast ingot processes were investigated using optical microscope,scanning electron microscope,X-ray diffraction,elec...The microstructure and mechanical properties of ZK60 extruded alloy by rapid solidification(RS)and as-cast ingot processes were investigated using optical microscope,scanning electron microscope,X-ray diffraction,electron back-scatter diffraction,and mechanical tests.The results show that the RS ZK60 extruded alloy exhibits relatively high tensile yield strength(TYS),compressive yield strength(CYS)and elongation of 300.8 MPa,303.6 MPa and 18.6%,respectively.The RS ZK60 extruded alloy with an ultra-fine grain size of 1.28μm not only has a weak texture with a maximum polar density of 3.3 but also addresses the tension-compression asymmetry with a CYS/TYS ratio of approximately 1.0.The calculation of the strengthening mechanism indicates that the improvement in the mechanical properties of the RS ZK60 extruded alloy is primarily attributed to grain refinement.展开更多
Additive Manufacturing,also known as 3D printing,has transformed conventional manufacturing by building objects layer by layer,with material extrusion or fused deposition modeling standing out as particularly popular....Additive Manufacturing,also known as 3D printing,has transformed conventional manufacturing by building objects layer by layer,with material extrusion or fused deposition modeling standing out as particularly popular.However,due to its manufacturing process and thermal nature,internal voids and pores are formed within the thermoplastic materials being fabricated,potentially leading to a decrease in mechanical properties.This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid(PLA)through micro-computed tomography(microCT),computational image analysis,and Charpy impact testing.The results for both tests were correlated to investigate the relationship between porosity and Charpy impact strength.PLA samples of 1 cm^(3)×1 cm^(3)×1 cm^(3) were 3D printed at printing temperatures of 180℃,200℃,220℃,and 240℃,and at printing speeds of 50,80,and 110 mm/s,while porosity was measured frommicroCT-reconstructed data.Additionally,impact strength was assessed using a notched Charpy impact tester following ASTMD6610-18.In general,results show that higher printing temperatures and lower printing speeds reduced pore size by improving material flow and fusion,while also increasing impact strength due to better thermal bonding and interlayer adhesion.A maximum 36.8% reduction in mean pore size and a 114% improvement in impact strength were observed at 110 mm/s and 220℃.Conversely,increasing printing speed led to lowerCharpy impact strength.Optimal impact behavior andminimal voids were observed at a printing temperature of 220℃ and a printing speed of 50 mm/s.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
To investigate the effects of extrusion temperature on the microstructure and mechanical properties of WE43 magnesium alloy,extrusion experiments were conducted under 330,380,430,and 450℃,and the extrusion ratio was ...To investigate the effects of extrusion temperature on the microstructure and mechanical properties of WE43 magnesium alloy,extrusion experiments were conducted under 330,380,430,and 450℃,and the extrusion ratio was 16.The experimental results indicate that,at a low temperature of 330℃,the alloy precipitates a large amount of second phases rich in Zr elements.Moreover,the texture strength and kernel average misorientation value are the highest,with values of 27.77 and 0.71,respectively.The increase in extrusion temperature leads to a gradual decrease in texture strength and kernel average misorientation value.The strength of the alloy is the highest at an extrusion temperature of 330℃.Its tensile yield stress is 254.7 MPa and ultimate tensile strength is 302.7 MPa,respectively.As the extrusion temperature increases,the strength of the alloy gradually decreases.At an extrusion temperature of 450℃,the tensile yield stress is 181.3 MPa and ultimate tensile strength is 265.7 MPa,respectively.The elongation first increases and then decreases,with an elongation of 20.9%at an extrusion temperature of 330℃.At an extrusion temperature of 430℃,the elongation reaches its maximum value,which is 23.6%.At an extrusion temperature of 450℃,the elongation reaches its lowest value,which is 16.4%.展开更多
Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentrat...Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.展开更多
Most failures in component operation occur due to cyclic loads.Validation has been performed under quasistatic loads,but the fatigue life of components under dynamic loads should be predicted to prevent failures durin...Most failures in component operation occur due to cyclic loads.Validation has been performed under quasistatic loads,but the fatigue life of components under dynamic loads should be predicted to prevent failures during component service life.Fatigue is a damage accumulation process where loads degrade the material,depending on the characteristics and number of repetitions of the load.Studies on themechanical fatigue of 3D-printedOnyx are limited.In this paper,the strength of 3D-printed Onyx components under dynamic conditions(repetitive loads)is estimated.Fatigue life prediction is influenced bymanufacturing processes,material properties,and applied loads,which can cause scatter in the results due to the interplay of these factors.By utilizing synthetic parameters derived from mechanical properties,the accuracy of fatigue life predictions has been improved significantly,from 23.13%to 98.33%.Additive manufacturing is flexible,but this flexibility generates scatter in the mechanical properties of produced components.This work also proposes the use of synthetic data with a neural network to improve the fatigue life prediction of printedOnyx subjected to tension–tension loads.Experimental uniaxial loads were used to characterize themechanical behaviorofprinted specimens.The experimental datawereused to evaluate thenumerical predictionsobtainedthrough finite element analysis using commercial software and an artificial neural network.The results showed that the use of synthetic data helped improve fatigue life prediction.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
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
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
Through the innovative integration of semi-solid rheo-casting with extrusion shear process,the short-process fabrication of low-alloyed wrought Mg-2Zn-1Mn alloy is achieved in this study.Uniaxial tensile testing of lo...Through the innovative integration of semi-solid rheo-casting with extrusion shear process,the short-process fabrication of low-alloyed wrought Mg-2Zn-1Mn alloy is achieved in this study.Uniaxial tensile testing of low-temperature extrusion shear specimens(200℃)demonstrates the exceptional strength-ductility synergy,yield strength of 277 MPa,yield strength ratio of 0.95,and elongation of 24%.Microstructural observations reveal the mechanisms underlying its high strength-plasticity synergy at room temperature.This study investigates the effects of different temperature gradients on the microstructure by analyzing experiments conducted at three temperatures:300℃,250℃,and 200◦C.Ultimately,the formation mechanism of the bimodal microstructure obtained at 200℃ is elucidated.The distinctive crystallographic texture oriented at 34°relative to the loading axis direction effectively mitigates stress concentration by inducing the synergistic activation of multiple slip systems.Furthermore,the transition trends of different slip systems and texture evolution during tensile deformation are validated through Visco-Plastic Self-Consistent(VPSC)simulations and corroborated by microstructural analysis.With geometrically necessary dislocation(GND)density(4.28×10^(15)m^(-2))and pyramidal slip activation(~45%).This study has successfully broken through the bottleneck of strength-ductility trade-off in magnesium alloys,providing theoretical support for the development of high-reliability magnesium alloys.展开更多
Based on thermodynamic calculations and continuous rheological extrusion(CRE)technology,Al-Ti-V-B master alloys were designed and prepared.The morphology and the distribution of the refined phases in the master alloys...Based on thermodynamic calculations and continuous rheological extrusion(CRE)technology,Al-Ti-V-B master alloys were designed and prepared.The morphology and the distribution of the refined phases in the master alloys were analyzed by XRD,SEM,and TEM.The effects of master alloy addition and holding time on the microstructure and mechanical properties of A356 alloy were investigated.Under the optimum refiner addition of 0.3wt.%and the holding time of 20 min,the average grain size of the refined A356 alloy is 151.8±9.11μm,89.62%lower than that of original A356 alloy.The tensile strength and elongation of as-cast A356refined alloy are 196.11 MPa and 5.75%,respectively.After T6 treatment,the tensile strength and elongation of A356 refined alloy are 290.1 MPa and 3.09%,respectively.The fracture morphology is characterized by a predominance of along-crystal fracture with a small amount of through-crystal fracture,attributed to the refined grains.Finer grains promote crack path deflection and localized plastic deformation,enhancing energy dissipation and reducing the tendency for brittle fracture.This study provides a novel approach to improving the mechanical properties of A356 alloy through grain refinement using CRE Al-Ti-V-B master alloy.展开更多
To tackle the common issue of green defects in material extrusion(MEX)additive manufacturing(AM)cemented carbides,warm isostatic pressing(WIP)was introduced to eliminate defects of MEX WC-9Co cemented carbide greens,t...To tackle the common issue of green defects in material extrusion(MEX)additive manufacturing(AM)cemented carbides,warm isostatic pressing(WIP)was introduced to eliminate defects of MEX WC-9Co cemented carbide greens,thereby improving both microstructure uniformity and mechanical properties of sintered bodies.The results indicate that WIP reduces defects in MEX greens,thus decreasing the dimensions and numbers of defects,modifying shapes of pores within sintered bodies,while preserving surface quality and shape characteristics.Compared with WC-9Co prepared via MEX followed by debinding and sintering(DS),the hardness of WC-9Co prepared using MEX-WIP-DS does not change significantly,ranging HV_(30)1494-1508,the transverse rupture strength increases by up to 49.3%,reaching 2998-3514 MPa,and the fracture toughness remains high,ranging 14.8-17.0 MPa·m^(1/2).The mechanical properties surpass comparable cemented carbides fabricated through other AM methods and are comparable to those produced by powder metallurgy.The integration of green WIP into MEX-DS broadens the MEX processing window,and improves the overall mechanical properties of MEX AM WC-Co cemented carbides.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
The possibility of the electric-hydraulic chattering technology and its application in the cold extrusion were presented.The conventional and electric-hydraulic chattering assisted backward extrusion processes were pe...The possibility of the electric-hydraulic chattering technology and its application in the cold extrusion were presented.The conventional and electric-hydraulic chattering assisted backward extrusion processes were performed on 6061 aluminum alloy billets at room temperature.The experimental results showed that 5.65% reduction in the extrusion load was attained if the die and ejector were vibrated at a frequency of 100 Hz and amplitude of 0.013 mm in the longitudinal direction.The friction coefficient at the billet and tool system interface determined from the finite element analysis（FEA） decreased from 0.2 without chattering to 0.1 with application of electric-hydraulic chattering.The higher values of instantaneous velocity and direction change of material flow were achieved during the chattering assisted backward extrusion process.The strain distribution of the chattering assisted backward extrusion billet revealed lower maximum strain and smoother strain distribution in comparison with that produced by the conventional extrusion method.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
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
Prefabricated twinning represents an effective strategy for optimizing the microstructure of extruded forming components and facilitating changes in texture.The study examines the incorporation of[10-12]twins into an ...Prefabricated twinning represents an effective strategy for optimizing the microstructure of extruded forming components and facilitating changes in texture.The study examines the incorporation of[10-12]twins into an AZ31 magnesium alloy billet via cold pre-upsetting deformation before alternating forward extrusion(CUAFE).The experimental results indicate that the initial presence of[10-12]twins is advantageous for the development of[10-10]and[11-20]texture components during the extrusion process.In addition,different DRX mechanisms have different influences on the evolution of basal texture.The CDRX grains tend to preferentially select the[11-20]texture orientation,weakening the[10-10]texture and enhancing the[11-20]texture.However,most DDRX grains deviate significantly from the orientation of their surrounding original grain and do not have a preferred orientation.This is reflected in the mechanical properties of the CUAFE part.The tensile strength is 323.5 MPa,while the elongation is as high as 20.1%.展开更多
文摘A comparative analysis was performed on poly(lactic acid)(PLA),poly(caprolactone)(PCL),basalt fiber(BF)composites produced using two distinct approaches:direct blending and masterbatching.The limitations of PLA-BF composites with regard to distribution and adhesion are well-documented,as are chemical treatment methods(addition of compatibilisers,surface treatments,silanization).This work aimed to study an industrially relevant potential solution of utilising a PCL-BF masterbatch,prepared as a 50/50 wt.%blend using planetary roller extrusion(PEX)to both improve the distribution and homogeneity of the fibers as well as provide a secondary adhesion site to facilitate improved mechanical properties of the final PLA-PCL-BF composite.The resultant materials were injection moulded to prepare ISO standard test specimens and tested on the basis of their physical properties via tensile testing,impact strength testing,flexural analysis,Fourier transforminfrared spectroscopy and water absorption capability.The results displayed that the incorporation of PCL and BF led to an increase in ductility of the composite materials,allowing for improvements in the inherent brittleness of virgin PLA.Major increases in the impact strength were achieved with the utilisation of a 25% PCL/BF masterbatch,allowing for a greater than 50%increase.As an overall observation,the use of a masterbatching process,opposed to direct blending of the constituent materials allows for a greater consistency of composite to be achieved at the expense of increased gains.
基金supported by Sichuan LTWT Metal Materials Co.,Ltd.,Sichuan Province,China(No.21H1367)。
文摘The microstructure and mechanical properties of ZK60 extruded alloy by rapid solidification(RS)and as-cast ingot processes were investigated using optical microscope,scanning electron microscope,X-ray diffraction,electron back-scatter diffraction,and mechanical tests.The results show that the RS ZK60 extruded alloy exhibits relatively high tensile yield strength(TYS),compressive yield strength(CYS)and elongation of 300.8 MPa,303.6 MPa and 18.6%,respectively.The RS ZK60 extruded alloy with an ultra-fine grain size of 1.28μm not only has a weak texture with a maximum polar density of 3.3 but also addresses the tension-compression asymmetry with a CYS/TYS ratio of approximately 1.0.The calculation of the strengthening mechanism indicates that the improvement in the mechanical properties of the RS ZK60 extruded alloy is primarily attributed to grain refinement.
文摘Additive Manufacturing,also known as 3D printing,has transformed conventional manufacturing by building objects layer by layer,with material extrusion or fused deposition modeling standing out as particularly popular.However,due to its manufacturing process and thermal nature,internal voids and pores are formed within the thermoplastic materials being fabricated,potentially leading to a decrease in mechanical properties.This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid(PLA)through micro-computed tomography(microCT),computational image analysis,and Charpy impact testing.The results for both tests were correlated to investigate the relationship between porosity and Charpy impact strength.PLA samples of 1 cm^(3)×1 cm^(3)×1 cm^(3) were 3D printed at printing temperatures of 180℃,200℃,220℃,and 240℃,and at printing speeds of 50,80,and 110 mm/s,while porosity was measured frommicroCT-reconstructed data.Additionally,impact strength was assessed using a notched Charpy impact tester following ASTMD6610-18.In general,results show that higher printing temperatures and lower printing speeds reduced pore size by improving material flow and fusion,while also increasing impact strength due to better thermal bonding and interlayer adhesion.A maximum 36.8% reduction in mean pore size and a 114% improvement in impact strength were observed at 110 mm/s and 220℃.Conversely,increasing printing speed led to lowerCharpy impact strength.Optimal impact behavior andminimal voids were observed at a printing temperature of 220℃ and a printing speed of 50 mm/s.
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Funded by the Fundamental Research Program of Shanxi Province(No.202203021211191)。
文摘To investigate the effects of extrusion temperature on the microstructure and mechanical properties of WE43 magnesium alloy,extrusion experiments were conducted under 330,380,430,and 450℃,and the extrusion ratio was 16.The experimental results indicate that,at a low temperature of 330℃,the alloy precipitates a large amount of second phases rich in Zr elements.Moreover,the texture strength and kernel average misorientation value are the highest,with values of 27.77 and 0.71,respectively.The increase in extrusion temperature leads to a gradual decrease in texture strength and kernel average misorientation value.The strength of the alloy is the highest at an extrusion temperature of 330℃.Its tensile yield stress is 254.7 MPa and ultimate tensile strength is 302.7 MPa,respectively.As the extrusion temperature increases,the strength of the alloy gradually decreases.At an extrusion temperature of 450℃,the tensile yield stress is 181.3 MPa and ultimate tensile strength is 265.7 MPa,respectively.The elongation first increases and then decreases,with an elongation of 20.9%at an extrusion temperature of 330℃.At an extrusion temperature of 430℃,the elongation reaches its maximum value,which is 23.6%.At an extrusion temperature of 450℃,the elongation reaches its lowest value,which is 16.4%.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3710805).
文摘Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.
文摘Most failures in component operation occur due to cyclic loads.Validation has been performed under quasistatic loads,but the fatigue life of components under dynamic loads should be predicted to prevent failures during component service life.Fatigue is a damage accumulation process where loads degrade the material,depending on the characteristics and number of repetitions of the load.Studies on themechanical fatigue of 3D-printedOnyx are limited.In this paper,the strength of 3D-printed Onyx components under dynamic conditions(repetitive loads)is estimated.Fatigue life prediction is influenced bymanufacturing processes,material properties,and applied loads,which can cause scatter in the results due to the interplay of these factors.By utilizing synthetic parameters derived from mechanical properties,the accuracy of fatigue life predictions has been improved significantly,from 23.13%to 98.33%.Additive manufacturing is flexible,but this flexibility generates scatter in the mechanical properties of produced components.This work also proposes the use of synthetic data with a neural network to improve the fatigue life prediction of printedOnyx subjected to tension–tension loads.Experimental uniaxial loads were used to characterize themechanical behaviorofprinted specimens.The experimental datawereused to evaluate thenumerical predictionsobtainedthrough finite element analysis using commercial software and an artificial neural network.The results showed that the use of synthetic data helped improve fatigue life prediction.
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 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 financial support from Basic Research Projects of Higher Education Institutions of Liaoning Province(Key Research Projects)(No.JYTZD2023108)General Project of Liaoning Provincial Department of Education(Nos.LJKMZ20220462 and JYTMS20231199).
文摘Through the innovative integration of semi-solid rheo-casting with extrusion shear process,the short-process fabrication of low-alloyed wrought Mg-2Zn-1Mn alloy is achieved in this study.Uniaxial tensile testing of low-temperature extrusion shear specimens(200℃)demonstrates the exceptional strength-ductility synergy,yield strength of 277 MPa,yield strength ratio of 0.95,and elongation of 24%.Microstructural observations reveal the mechanisms underlying its high strength-plasticity synergy at room temperature.This study investigates the effects of different temperature gradients on the microstructure by analyzing experiments conducted at three temperatures:300℃,250℃,and 200◦C.Ultimately,the formation mechanism of the bimodal microstructure obtained at 200℃ is elucidated.The distinctive crystallographic texture oriented at 34°relative to the loading axis direction effectively mitigates stress concentration by inducing the synergistic activation of multiple slip systems.Furthermore,the transition trends of different slip systems and texture evolution during tensile deformation are validated through Visco-Plastic Self-Consistent(VPSC)simulations and corroborated by microstructural analysis.With geometrically necessary dislocation(GND)density(4.28×10^(15)m^(-2))and pyramidal slip activation(~45%).This study has successfully broken through the bottleneck of strength-ductility trade-off in magnesium alloys,providing theoretical support for the development of high-reliability magnesium alloys.
基金supported by the National Key Research and Development Program of China (Grant No.2022YFB3706801)the National Natural Science Foundation of China (Grant Nos.U2341253,52371019,U2241232)+2 种基金the Dalian High-level Talents Innovation Support Program (Grant No.2021RD06)the Applied Basic Research Program of Liaoning Province (Grant No.2022JH2/101300003)the Natural Science Foundation of Liaoning Province (Grant Nos.2022-BS-262,JYTMS20230031)。
文摘Based on thermodynamic calculations and continuous rheological extrusion(CRE)technology,Al-Ti-V-B master alloys were designed and prepared.The morphology and the distribution of the refined phases in the master alloys were analyzed by XRD,SEM,and TEM.The effects of master alloy addition and holding time on the microstructure and mechanical properties of A356 alloy were investigated.Under the optimum refiner addition of 0.3wt.%and the holding time of 20 min,the average grain size of the refined A356 alloy is 151.8±9.11μm,89.62%lower than that of original A356 alloy.The tensile strength and elongation of as-cast A356refined alloy are 196.11 MPa and 5.75%,respectively.After T6 treatment,the tensile strength and elongation of A356 refined alloy are 290.1 MPa and 3.09%,respectively.The fracture morphology is characterized by a predominance of along-crystal fracture with a small amount of through-crystal fracture,attributed to the refined grains.Finer grains promote crack path deflection and localized plastic deformation,enhancing energy dissipation and reducing the tendency for brittle fracture.This study provides a novel approach to improving the mechanical properties of A356 alloy through grain refinement using CRE Al-Ti-V-B master alloy.
基金supported by the Key Project of Chinese Academy of Engineering(No.2019-XZ-11)the General Project of Chinese Academy of Engineering(No.2023-XY-18)+1 种基金the Open Fund of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials of China(No.HKDNM201907)the Independent Project of State Key Laboratory of Powder Metallurgy,China。
文摘To tackle the common issue of green defects in material extrusion(MEX)additive manufacturing(AM)cemented carbides,warm isostatic pressing(WIP)was introduced to eliminate defects of MEX WC-9Co cemented carbide greens,thereby improving both microstructure uniformity and mechanical properties of sintered bodies.The results indicate that WIP reduces defects in MEX greens,thus decreasing the dimensions and numbers of defects,modifying shapes of pores within sintered bodies,while preserving surface quality and shape characteristics.Compared with WC-9Co prepared via MEX followed by debinding and sintering(DS),the hardness of WC-9Co prepared using MEX-WIP-DS does not change significantly,ranging HV_(30)1494-1508,the transverse rupture strength increases by up to 49.3%,reaching 2998-3514 MPa,and the fracture toughness remains high,ranging 14.8-17.0 MPa·m^(1/2).The mechanical properties surpass comparable cemented carbides fabricated through other AM methods and are comparable to those produced by powder metallurgy.The integration of green WIP into MEX-DS broadens the MEX processing window,and improves the overall mechanical properties of MEX AM WC-Co cemented carbides.
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Project(51275475)supported by the National Natural Science Foundation of ChinaProject(2014BY001)supported by the Department of Education in Zhejiang Province,ChinaProject(2014EP0110)supported by the Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology,Ministry of Education and Zhejiang Province,China
文摘The possibility of the electric-hydraulic chattering technology and its application in the cold extrusion were presented.The conventional and electric-hydraulic chattering assisted backward extrusion processes were performed on 6061 aluminum alloy billets at room temperature.The experimental results showed that 5.65% reduction in the extrusion load was attained if the die and ejector were vibrated at a frequency of 100 Hz and amplitude of 0.013 mm in the longitudinal direction.The friction coefficient at the billet and tool system interface determined from the finite element analysis（FEA） decreased from 0.2 without chattering to 0.1 with application of electric-hydraulic chattering.The higher values of instantaneous velocity and direction change of material flow were achieved during the chattering assisted backward extrusion process.The strain distribution of the chattering assisted backward extrusion billet revealed lower maximum strain and smoother strain distribution in comparison with that produced by the conventional extrusion method.
基金supported by the National Natural Science Foundation of China Nos.82370983,81671015(X.W.),82230030(Y.L.),82101043(S.C.)and 82370922(Y.F.)Beijing International Science and Technology Cooperation Project No.Z221100002722003(Y.L.)+4 种基金Beijing Natural Science Foundation Nos.L234017,JL23002(Y.L.),No.7242282(S.C.)and 7232217(Y.G.)Clinical Medicine Plus X-Young Scholars Project of Peking University No.PKU2024LCXQ039(Y.L.)National Program for Multidisciplinary Cooperative Treatment on Major Diseases No.PKUSSNMP-202013(X.W.)Hygiene and Health Development Scientific Research Fostering Plan of Haidian District Beijing No.HP2023-12-509001(J.Z.)Young Clinical Research Fund of the Chinese Stomatological Association No.CSA-02022-03(J.Z.).
文摘Temporomandibular joint(TMJ)disc displacement is one of the most significant subtypes of temporomandibular joint disorders,but its etiology and mechanism are poorly understood.In this study,we elucidated the mechanisms by which destruction of inflamed collagen fibrils induces alterations in the mechanical properties and positioning of the TMJ disc.By constructing a rat model of TMJ arthritis,we observed anteriorly dislocated TMJ discs with aggravated deformity in vivo from five weeks to six months after a local injection of Freund’s complete adjuvant.By mimicking inflammatory conditions with interleukin-1 beta in vitro,we observed enhanced expression of collagen-synthesis markers in primary TMJ disc cells cultured in a conventional two-dimensional environment.In contrast,three-dimensional(3D)-cultivated disc cell sheets demonstrated the disordered assembly of inflamed collagen fibrils,inappropriate arrangement,and decreased Young’s modulus.Mechanistically,inflammation-related activation of the nuclear factor kappa-B(NF-κB)pathway occurs during the progression of TMJ arthritis.NF-κB inhibition reduced the collagen fibril destruction in the inflamed disc cell sheets in vitro,and early NF-κB blockade alleviated collagen degeneration and dislocation of the TMJ discs in vivo.Therefore,the NF-κB pathway participates in the collagen remodeling in inflamed TMJ discs,offering a potential therapeutic target for disc displacement.
文摘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 National Natural Science Foundation of China(No.52475341).
文摘Prefabricated twinning represents an effective strategy for optimizing the microstructure of extruded forming components and facilitating changes in texture.The study examines the incorporation of[10-12]twins into an AZ31 magnesium alloy billet via cold pre-upsetting deformation before alternating forward extrusion(CUAFE).The experimental results indicate that the initial presence of[10-12]twins is advantageous for the development of[10-10]and[11-20]texture components during the extrusion process.In addition,different DRX mechanisms have different influences on the evolution of basal texture.The CDRX grains tend to preferentially select the[11-20]texture orientation,weakening the[10-10]texture and enhancing the[11-20]texture.However,most DDRX grains deviate significantly from the orientation of their surrounding original grain and do not have a preferred orientation.This is reflected in the mechanical properties of the CUAFE part.The tensile strength is 323.5 MPa,while the elongation is as high as 20.1%.
