This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shea...This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.展开更多
Objective:To explore the relationship between anesthetic depth and surgical stress response in minimally invasive cardiothoracic surgery.Methods:A total of 89 patients who underwent thoracoscopic minimally invasive ca...Objective:To explore the relationship between anesthetic depth and surgical stress response in minimally invasive cardiothoracic surgery.Methods:A total of 89 patients who underwent thoracoscopic minimally invasive cardiothoracic surgery in our hospital from June 2024 to December 2024 were selected as the research objects.They were divided into the light anesthesia group(n=45)and the deep anesthesia group(n=44).The vital signs at different intraoperative nodes and perioperative stress status of the two groups were compared.Results:Before lesion resection and after surgery,the mean arterial pressure and heart rate of the deep anesthesia group were lower than those of the light anesthesia group,with statistically significant differences.Conclusion:In thoracoscopic minimally invasive cardiothoracic surgery,deep anesthesia can effectively control the patient’s surgical stress response,but the postoperative awakening time is longer;patients under light anesthesia have a shorter awakening time,but the intraoperative stress response is obvious.展开更多
Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters...Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.展开更多
Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the pot...Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the potential of photoacoustic imaging(PAI)in monitoring MWA by examining ex vivo porcine liver tissues.In this study,a comprehensive analysis of photoacoustic signals was performed to compare the main lobe width(MLW)between ablated and normal regions in porcine liver tissue.Histological staining with succinate dehydrogenase(SDH)and shear wave elastography(SWE)were employed to validate the changes in tissue elasticity after ablation.The analysis demonstrated a notable reduction in the MLW of the average A-lines in ablated tissues compared to nonablated regions(p<0.01).This reduction,attributed to increased tissue density and enhanced elasticity,indicates accelerated sound propagation in thermally ablated areas,which then serves as a critical parameter for mapping tissue characteristics.The reconstruction of the MLW distribution successfully delineated the ablated regions,and was consistent with the results of SDH staining and SWE.In addition,MLW-based imaging exhibited higher spatial resolution compared to SWE.Incorporating MLW analysis into PAI may be a promising strategy to improve the accuracy and effectiveness of MWA monitoring in clinical settings.展开更多
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc...This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.展开更多
The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce...The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.展开更多
Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet ...Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.展开更多
Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observat...Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.展开更多
The effects of process parameters on the depth-to-width ratio （D/W） of flux-cored wire underwater wet welding with a certain type of homemade .flux-cored wire are studied. It is found that the welding .speed, wire f...The effects of process parameters on the depth-to-width ratio （D/W） of flux-cored wire underwater wet welding with a certain type of homemade .flux-cored wire are studied. It is found that the welding .speed, wire feeding speed and torch oscillating amplitude hare significant effects on the dopth-to-width ratio （D/W） of welds. The D/W ratio of welds increases significantly with the increase of welding speed without the oscillating of welding torch. It increased （from 0. 14 to 0. 26 ） with the increase of wire feeding speed while the torch oscillating. And it decreased linearly with the increase of torch oscillating amplitude. However, the influelwe of oscillating speed, wire extension and welding voltage on the D/W ratio of welds was not obvious.展开更多
Compound section is referred to a section the surface of which is made of several sub-sections with different flow characteristics. The difference in the hydraulic and geometry characteristics causes a complexity in f...Compound section is referred to a section the surface of which is made of several sub-sections with different flow characteristics. The difference in the hydraulic and geometry characteristics causes a complexity in flow hydraulic and creates an interaction between the main channel and floodplains, resulting in an apparent shear stress and a transverse momentum transfer. The amount of such a stress plays an important role in many river engineering measures [1]. Due to the flow complexity, the common approximate analytical methods are not enough to identify the flow profile. The FLOW3D Software with its great features in three-dimensional analysis of flow field is used as a tool to investigate the shear stress in a direct symmetrical compound rectangular channel. After the simulation of models, it is found that an increase in the relative width and relative depth parameters decreases the percentage of apparent shear stress and an increase in the relative roughness causes it to be increased [2].展开更多
In order to meet the requirement of structural inspection,the crack spacing and crack width at various heights in the tensile zone of six large depth reinforced concrete beams were measured under several loading level...In order to meet the requirement of structural inspection,the crack spacing and crack width at various heights in the tensile zone of six large depth reinforced concrete beams were measured under several loading levels of serviceability state.The effects of the depth of normal section beams on the crack spacing and crack width were analyzed,and the modified model is proposed for calculating the average crack spacing by thinking about the depth of normal section,the reinforcement arrangement and the effective reinforcement ratio.The relationships of crack widths at any position in the tensile zone and at the reinforcement level on the side surface of beam were studied.By theoretical and statistical analysis,a method is proposed to calculate the ratios of crack widths between any position and the reinforcement level on the side surface of large depth reinforced concrete beams.展开更多
Bed morphology is the result of a dynamic response to a complex meandering river system. It is an important factor for the further development of river. Based on the meandering river characterized by a large depth-to-...Bed morphology is the result of a dynamic response to a complex meandering river system. It is an important factor for the further development of river. Based on the meandering river characterized by a large depth-to-width ratio, a theoretical model is established with the coupling of Navier-Stokes （N-S）~ sediment transport, and bed deformation equations. The flow characteristics and bed response of river are obtained with the perturbation method. The research results show that, under the effect of two- dimensional flow disturbance, the bars and pools present the regular response. For a given sinuousness, the amplitude of the bed response can be used as a criterion to judge the bedform stability. The effects of the Reynolds number, disturbance wavenumber, sinuousness, and bed morphology gradient on the bed response development are described.展开更多
Overstress in the surrounding rock of the roadway is a key reason that causes failures of deep roadways. Destressing blasting is one of the promising techniques that could improve the supporting quality. If the depth ...Overstress in the surrounding rock of the roadway is a key reason that causes failures of deep roadways. Destressing blasting is one of the promising techniques that could improve the supporting quality. If the depth of the pressure relief blast hole is too shallow, the surrounding rock of the roadway will be broken or even collapsed. If the pressure relief blast hole is too deep, the pressure relief area will be located in the deep part of the surrounding rock of the roadway, which cannot achieve the purpose of releasing the stress in the shallow part of the surrounding rock and cause waste of the blast hole. The width or range of the pressure relief area should just fall in the high stress area of the surrounding rock of the roadway, so the pressure relief blast hole should have a reasonable depth. In order to quantitatively describe the relationship between borehole depth and the width of the stress relief zone, numerical simulations were carried out in ANSYS according to different borehole depths. The results show that the optimal destressing effect is achieved when borehole depth is 4 m. Peak stress of and is significantly reduced by 30.51% and 49.07% after blasting. Meanwhile, the high-stress area shifts about 4.8 m from the roadside to the depth of surrounding rock, thus a 3.8 m wide stress relief zone is formed around the roadside, thus, the aim of quantizing the effects of destress blasting is achieved.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
The active sensor often uses the convergence zone mode to detect a distant target in the deep ocean.However,convergence zones are regions with limited widths that only appear at some discrete distances.Thus,widening t...The active sensor often uses the convergence zone mode to detect a distant target in the deep ocean.However,convergence zones are regions with limited widths that only appear at some discrete distances.Thus,widening the width by adjusting the transmitting array depth facilitates target observation and detection.Traversal search is an effective method for determining the optimal depth,but the heavy computation burden resulting from the calculation of the transmission losses at all source depths impedes its application.To solve the problem,a fast method based on ray cluster theory is proposed.Due to the coherent sound field structure in the deep ocean,several ray clusters with different departure angles radiate from the source,where ray clusters with small departure angles reverse in the water and form a convergence zone.When the source is set to a depth that only the first ray cluster inverts in water,the maximum width of the convergence zone is obtained.Based on this,an optimal transmitting array depth selection method utilizing the reversion condition of the first ray cluster is formulated.Simulation results show that the active sensor can achieve a large convergence zone width with real-time performance using the proposed method.展开更多
Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level st...Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level stemming from the doped rare earth ion or intrinsic defects to the electronic structure of the host,and therefore thermoluminescence measurement becomes a radical technology in studying trap depth,which is one of the significant parameters that determine the properties of persistent luminescence and photostimulated luminescence.However,the results of trap depth obtained by different thermoluminescence methods are quite different so that they are not comparable.Herein,we analyzed different thermoluminescence methods,selected and improved the traditional peak position method of T_(m)/500 to be E=(-0.94Inβ+30.09)kT_(m).Only the experimental heating rate(β)is needed additionally,but the accuracy is improved greatly in most cases.This convenient and accurate method will accelerate the discovery of novel rare earth-doped materials.展开更多
Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their us...Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.展开更多
BACKGROUND Post-hepatectomy liver failure(PHLF)after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma(HCC).It is crucial to help clinicians identif...BACKGROUND Post-hepatectomy liver failure(PHLF)after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma(HCC).It is crucial to help clinicians identify potential high-risk PHLF patients as early as possible through preoperative evaluation.AIM To identify risk factors for PHLF and develop a prediction model.METHODS This study included 248 patients with HCC at The Second Affiliated Hospital of Air Force Medical University between January 2014 and December 2023;these patients were divided into a training group(n=164)and a validation group(n=84)via random sampling.The independent variables for the occurrence of PHLF were identified by univariate and multivariate analyses and visualized as nomograms.Ultimately,comparisons were made with traditional models via receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS In this study,portal vein width[odds ratio(OR)=1.603,95%CI:1.288-1.994,P≤0.001],the preoperative neutrophil-to-lymphocyte ratio(NLR)(OR=1.495,95%CI:1.126-1.984,P=0.005),and the albumin-bilirubin(ALBI)score(OR=8.868,95%CI:2.144-36.678,P=0.003)were independent risk factors for PHLF.A nomogram prediction model was developed using these factors.ROC and DCA analyses revealed that the predictive efficacy and clinical value of this model were better than those of traditional models.CONCLUSION A new Nomogram model for predicting PHLF in HCC patients was successfully established based on portal vein width,the NLR,and the ALBI score,which outperforms the traditional model.展开更多
BACKGROUND As red blood cell distribution width(RDW)and albumin have been shown to be independent predictors of mortality from various diseases,this study aimed to investigate the effect of the RDW to albumin ratio(RA...BACKGROUND As red blood cell distribution width(RDW)and albumin have been shown to be independent predictors of mortality from various diseases,this study aimed to investigate the effect of the RDW to albumin ratio(RA)as an independent predictor of the prognosis of patients admitted to the coronary care unit(CCU).AIM To use the RDW and albumin level to predict the prognosis of patients in the CCU.METHODS Data were obtained from the Medical Information Mart Intensive Care III database.The primary outcome was 365-day all-cause mortality,whereas the secondary outcomes were 30-and 90-day all-cause mortality,hospital length of stay(LOS),and CCU LOS.Cox proportional hazards regression model,propen-sity score matching,and receiver operating characteristic curve analyses were used.RESULTS The hazard ratio(95%confidence interval)of the upper tertile(RA>4.66)was 1.62(1.29 to 2.03)when compared with the reference(RA<3.84)in 365-day all-cause mortality.This trend persisted after adjusting for demographic and clinical variables in the propensity score-matching analysis.Similar trends were observed for the secondary outcomes of hospital and CCU LOS.Receiver operating characteristic curve analysis was performed by combining the RA and sequential organ failure assessment(SOFA)scores,and the C-statistic was higher than that of the SOFA scores(0.733 vs 0.702,P<0.001).CONCLUSION RA is an independent prognostic factor in patients admitted to the CCU.RA combined with the SOFA score can improve the predictive ability of the SOFA score.However,our results should be verified in future prospective studies.展开更多
基金jointly supported by the National Natural Science Foundation of China[grant numbers U2342202,42175005,and 42175016]the Qing Lan Project[grant number R2023Q06]。
文摘This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.
文摘Objective:To explore the relationship between anesthetic depth and surgical stress response in minimally invasive cardiothoracic surgery.Methods:A total of 89 patients who underwent thoracoscopic minimally invasive cardiothoracic surgery in our hospital from June 2024 to December 2024 were selected as the research objects.They were divided into the light anesthesia group(n=45)and the deep anesthesia group(n=44).The vital signs at different intraoperative nodes and perioperative stress status of the two groups were compared.Results:Before lesion resection and after surgery,the mean arterial pressure and heart rate of the deep anesthesia group were lower than those of the light anesthesia group,with statistically significant differences.Conclusion:In thoracoscopic minimally invasive cardiothoracic surgery,deep anesthesia can effectively control the patient’s surgical stress response,but the postoperative awakening time is longer;patients under light anesthesia have a shorter awakening time,but the intraoperative stress response is obvious.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274348 and 12004335)the National Key Research and Development Program of China(Grant No.2024YFC2813800)。
文摘Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.
基金supported by the National Natural Science Foundation of China(82427808,61875085)the Jiangsu Provincial University Natural Science Foundation(25KJB413004)+1 种基金the Nanjing Health Science and Technology Development Foundation(ZKX24043)the Fundamental Research Funds for the Central Universities(NJ2024029).
文摘Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the potential of photoacoustic imaging(PAI)in monitoring MWA by examining ex vivo porcine liver tissues.In this study,a comprehensive analysis of photoacoustic signals was performed to compare the main lobe width(MLW)between ablated and normal regions in porcine liver tissue.Histological staining with succinate dehydrogenase(SDH)and shear wave elastography(SWE)were employed to validate the changes in tissue elasticity after ablation.The analysis demonstrated a notable reduction in the MLW of the average A-lines in ablated tissues compared to nonablated regions(p<0.01).This reduction,attributed to increased tissue density and enhanced elasticity,indicates accelerated sound propagation in thermally ablated areas,which then serves as a critical parameter for mapping tissue characteristics.The reconstruction of the MLW distribution successfully delineated the ablated regions,and was consistent with the results of SDH staining and SWE.In addition,MLW-based imaging exhibited higher spatial resolution compared to SWE.Incorporating MLW analysis into PAI may be a promising strategy to improve the accuracy and effectiveness of MWA monitoring in clinical settings.
文摘This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.
文摘The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.
基金partially supported by the Natural Science Foundation of Shandong Province,China(No.ZR2023ME009)the National Natural Science Foundation of China(No.51909252)。
文摘Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.
基金Supported by the National Natural Science Foundation of China(Nos.T2261149752,41976163,42476172)。
文摘Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.
文摘The effects of process parameters on the depth-to-width ratio （D/W） of flux-cored wire underwater wet welding with a certain type of homemade .flux-cored wire are studied. It is found that the welding .speed, wire feeding speed and torch oscillating amplitude hare significant effects on the dopth-to-width ratio （D/W） of welds. The D/W ratio of welds increases significantly with the increase of welding speed without the oscillating of welding torch. It increased （from 0. 14 to 0. 26 ） with the increase of wire feeding speed while the torch oscillating. And it decreased linearly with the increase of torch oscillating amplitude. However, the influelwe of oscillating speed, wire extension and welding voltage on the D/W ratio of welds was not obvious.
文摘Compound section is referred to a section the surface of which is made of several sub-sections with different flow characteristics. The difference in the hydraulic and geometry characteristics causes a complexity in flow hydraulic and creates an interaction between the main channel and floodplains, resulting in an apparent shear stress and a transverse momentum transfer. The amount of such a stress plays an important role in many river engineering measures [1]. Due to the flow complexity, the common approximate analytical methods are not enough to identify the flow profile. The FLOW3D Software with its great features in three-dimensional analysis of flow field is used as a tool to investigate the shear stress in a direct symmetrical compound rectangular channel. After the simulation of models, it is found that an increase in the relative width and relative depth parameters decreases the percentage of apparent shear stress and an increase in the relative roughness causes it to be increased [2].
基金Sponsored by the Outstanding Youth Scientific Fund of Henan Province(Grant No.04120002300)Program for Innovation in University of Henan Province(Grant No.[2004]294)
文摘In order to meet the requirement of structural inspection,the crack spacing and crack width at various heights in the tensile zone of six large depth reinforced concrete beams were measured under several loading levels of serviceability state.The effects of the depth of normal section beams on the crack spacing and crack width were analyzed,and the modified model is proposed for calculating the average crack spacing by thinking about the depth of normal section,the reinforcement arrangement and the effective reinforcement ratio.The relationships of crack widths at any position in the tensile zone and at the reinforcement level on the side surface of beam were studied.By theoretical and statistical analysis,a method is proposed to calculate the ratios of crack widths between any position and the reinforcement level on the side surface of large depth reinforced concrete beams.
文摘Bed morphology is the result of a dynamic response to a complex meandering river system. It is an important factor for the further development of river. Based on the meandering river characterized by a large depth-to-width ratio, a theoretical model is established with the coupling of Navier-Stokes （N-S）~ sediment transport, and bed deformation equations. The flow characteristics and bed response of river are obtained with the perturbation method. The research results show that, under the effect of two- dimensional flow disturbance, the bars and pools present the regular response. For a given sinuousness, the amplitude of the bed response can be used as a criterion to judge the bedform stability. The effects of the Reynolds number, disturbance wavenumber, sinuousness, and bed morphology gradient on the bed response development are described.
文摘Overstress in the surrounding rock of the roadway is a key reason that causes failures of deep roadways. Destressing blasting is one of the promising techniques that could improve the supporting quality. If the depth of the pressure relief blast hole is too shallow, the surrounding rock of the roadway will be broken or even collapsed. If the pressure relief blast hole is too deep, the pressure relief area will be located in the deep part of the surrounding rock of the roadway, which cannot achieve the purpose of releasing the stress in the shallow part of the surrounding rock and cause waste of the blast hole. The width or range of the pressure relief area should just fall in the high stress area of the surrounding rock of the roadway, so the pressure relief blast hole should have a reasonable depth. In order to quantitatively describe the relationship between borehole depth and the width of the stress relief zone, numerical simulations were carried out in ANSYS according to different borehole depths. The results show that the optimal destressing effect is achieved when borehole depth is 4 m. Peak stress of and is significantly reduced by 30.51% and 49.07% after blasting. Meanwhile, the high-stress area shifts about 4.8 m from the roadside to the depth of surrounding rock, thus a 3.8 m wide stress relief zone is formed around the roadside, thus, the aim of quantizing the effects of destress blasting is achieved.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金supported by the National Key R&D Program of China(No.2021YFF0501200)the National Natural Science Foundation of China(No.11774374)。
文摘The active sensor often uses the convergence zone mode to detect a distant target in the deep ocean.However,convergence zones are regions with limited widths that only appear at some discrete distances.Thus,widening the width by adjusting the transmitting array depth facilitates target observation and detection.Traversal search is an effective method for determining the optimal depth,but the heavy computation burden resulting from the calculation of the transmission losses at all source depths impedes its application.To solve the problem,a fast method based on ray cluster theory is proposed.Due to the coherent sound field structure in the deep ocean,several ray clusters with different departure angles radiate from the source,where ray clusters with small departure angles reverse in the water and form a convergence zone.When the source is set to a depth that only the first ray cluster inverts in water,the maximum width of the convergence zone is obtained.Based on this,an optimal transmitting array depth selection method utilizing the reversion condition of the first ray cluster is formulated.Simulation results show that the active sensor can achieve a large convergence zone width with real-time performance using the proposed method.
基金Project supported by the National Natural Science Foundation of China(52372134,12274023)the Fundamental Re search Funds for the Central Universities(FRF-EYIT-23-04)。
文摘Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level stemming from the doped rare earth ion or intrinsic defects to the electronic structure of the host,and therefore thermoluminescence measurement becomes a radical technology in studying trap depth,which is one of the significant parameters that determine the properties of persistent luminescence and photostimulated luminescence.However,the results of trap depth obtained by different thermoluminescence methods are quite different so that they are not comparable.Herein,we analyzed different thermoluminescence methods,selected and improved the traditional peak position method of T_(m)/500 to be E=(-0.94Inβ+30.09)kT_(m).Only the experimental heating rate(β)is needed additionally,but the accuracy is improved greatly in most cases.This convenient and accurate method will accelerate the discovery of novel rare earth-doped materials.
基金financially supported by the POSCO-POSTECH-RIST Convergence Research Center program funded by POSCOthe National Research Foundation (NRF) grants (RS-2024-00462912, RS-2024-00416272, RS-2024-00337012, RS-2024-00408446) funded by the Ministry of Science and ICT (MSIT) of the Korean government+2 种基金the Korea Evaluation Institute of Industrial Technology (KEIT) grant (No. 1415185027/20019169, Alchemist project) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Korean governmentthe Soseon Science fellowship funded by Community Chest of Koreathe NRF PhD fellowship (RS-2023-00275565) funded by the Ministry of Education (MOE) of the Korean government。
文摘Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.
基金Supported by Shaanxi Provincial Social Development Fund,No.2024SF-YBXM-140.
文摘BACKGROUND Post-hepatectomy liver failure(PHLF)after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma(HCC).It is crucial to help clinicians identify potential high-risk PHLF patients as early as possible through preoperative evaluation.AIM To identify risk factors for PHLF and develop a prediction model.METHODS This study included 248 patients with HCC at The Second Affiliated Hospital of Air Force Medical University between January 2014 and December 2023;these patients were divided into a training group(n=164)and a validation group(n=84)via random sampling.The independent variables for the occurrence of PHLF were identified by univariate and multivariate analyses and visualized as nomograms.Ultimately,comparisons were made with traditional models via receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS In this study,portal vein width[odds ratio(OR)=1.603,95%CI:1.288-1.994,P≤0.001],the preoperative neutrophil-to-lymphocyte ratio(NLR)(OR=1.495,95%CI:1.126-1.984,P=0.005),and the albumin-bilirubin(ALBI)score(OR=8.868,95%CI:2.144-36.678,P=0.003)were independent risk factors for PHLF.A nomogram prediction model was developed using these factors.ROC and DCA analyses revealed that the predictive efficacy and clinical value of this model were better than those of traditional models.CONCLUSION A new Nomogram model for predicting PHLF in HCC patients was successfully established based on portal vein width,the NLR,and the ALBI score,which outperforms the traditional model.
文摘BACKGROUND As red blood cell distribution width(RDW)and albumin have been shown to be independent predictors of mortality from various diseases,this study aimed to investigate the effect of the RDW to albumin ratio(RA)as an independent predictor of the prognosis of patients admitted to the coronary care unit(CCU).AIM To use the RDW and albumin level to predict the prognosis of patients in the CCU.METHODS Data were obtained from the Medical Information Mart Intensive Care III database.The primary outcome was 365-day all-cause mortality,whereas the secondary outcomes were 30-and 90-day all-cause mortality,hospital length of stay(LOS),and CCU LOS.Cox proportional hazards regression model,propen-sity score matching,and receiver operating characteristic curve analyses were used.RESULTS The hazard ratio(95%confidence interval)of the upper tertile(RA>4.66)was 1.62(1.29 to 2.03)when compared with the reference(RA<3.84)in 365-day all-cause mortality.This trend persisted after adjusting for demographic and clinical variables in the propensity score-matching analysis.Similar trends were observed for the secondary outcomes of hospital and CCU LOS.Receiver operating characteristic curve analysis was performed by combining the RA and sequential organ failure assessment(SOFA)scores,and the C-statistic was higher than that of the SOFA scores(0.733 vs 0.702,P<0.001).CONCLUSION RA is an independent prognostic factor in patients admitted to the CCU.RA combined with the SOFA score can improve the predictive ability of the SOFA score.However,our results should be verified in future prospective studies.
