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
The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the ini...The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.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
The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbul...The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbulence model,coupling velocity,temperature,and phase field was established to study the effect of the ladle shroud immersion depth on the slag eye formation,slag entrainment,slag dragging,air dragging,and flow characteristics during the ladle change-over process of a two-strand tundish.The results showed that reducing the immersion depth decreases the high-velocity region area under the slag layer in the quasi-steady process.During the emptying stage,as the molten bath level gradually decreases,the outlet temperature exhibits a trend of initially decreasing and subsequently increasing across all three shroud immersion depths.However,under a 210 mm shroud immersion depth,molten slag and air are dragged into the shroud,forming slag droplets and causing significant fluctuations,with a maximum scalar velocity of 0.0764 m/s at the monitoring point.In the filling stage,air and molten slag are dragged into the molten bath,forming bubbles and slag droplets at an immersion depth of 210 mm.Bubbles are observed within the molten slag layer,which can readily cause an emulsification phenomenon,making it easier to be dragged as slag droplets.Additionally,the slag eye area measured under 210 mm immersion depth at 45 s is 0.303 m^(2),while the maximum scalar velocity of 2.4259 m/s is detected at 12 s.At an immersion depth of 360 mm,the average area of the slag eye is minimized to 0.06268 m2,with corresponding variances of 0.006753,representing the optimal immersion depth.展开更多
Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradien...Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradients can regulate the spatial distribu-tion and network complexity of the community structure.To explore the variations in soil microbial community structures and their as-sembly mechanisms across different elevations of the Changbai Mountains,as well as their responses to environmental factors,we col-lected microbial samples along an elevational gradient(seven elevations containing four vegetation zones)on the western slope of the Changbai Mountains using the method of metagenomic sequencing.The results showed a significant difference(P<0.05)for the Chao1 index across different elevations,but no significant difference was observed for the Shannon and Simpson indices.With increasing elev-ation,the number of nodes and links in the microbial network gradually decreased.Acidobacteria were highly connected to many nodes.The microbial communities indicated a significant distance-decay relationship(P<0.001)and were affected more by stochastic pro-cesses along the elevation gradient.The results of the Structural Equation Model(SEM)showed that elevation had direct significant ef-fect on carbon(C,P<0.01),nitrogen(N,P<0.01),and phosphorus(P,P<0.05)and weak negative effect on their ecological stoi-chiometry.Elevation was one of the major variables contributing to microbial network topology.The contribution of C and N to micro-bial network complexity was higher than that of P.Our study provides valuable insights into the responses of soil microbial communit-ies to elevation variations.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
Not many bathymetric maps are available for many lakes and reservoirs in developing countries. Usually the bathymetric mapping requires investment in expensive equipment and fieldwork, both of which are not accessible...Not many bathymetric maps are available for many lakes and reservoirs in developing countries. Usually the bathymetric mapping requires investment in expensive equipment and fieldwork, both of which are not accessible in these countries. This work demonstrates the ability to develop bathymetric map of Mosul Lake by using a digital elevation model (DEM). The depths model of the lake was designed through the use of three main stages;a coastline extraction, dataset interpolation and a triangular irregular network model. The normalized difference water index (NDWI) was used for automatic delineation of the lake coastline from satellite images. The ordinary kriging interpolation with a stable model was used to interpolate the water depths dataset. Finally a triangulated irregular network (TIN) model was used to visualize the resulting interpolation model. Calculated values of area and volume of a TIN model during 2011 were compared with values of supposed initial operation of the reservoir. The differences of water volume storage between these stages at 321 m water level was about 0.81 × 109 m3, where the lake lost around 10% of storage value. Also the results of depths lake model show that the change in water storage between March and July 2011 was about 3.08 × 109 m3.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
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
对于复杂天气场景图像模糊、低对比度和颜色失真所导致的深度信息预测不准的问题,以往的研究均以标准场景的深度图作为先验信息来对该类场景进行深度估计。然而,这一方式存在先验信息精度较低等问题。对此,提出一个基于多尺度注意力机...对于复杂天气场景图像模糊、低对比度和颜色失真所导致的深度信息预测不准的问题,以往的研究均以标准场景的深度图作为先验信息来对该类场景进行深度估计。然而,这一方式存在先验信息精度较低等问题。对此,提出一个基于多尺度注意力机制的单目深度估计模型TalentDepth,以实现对复杂天气场景的预测。首先,在编码器中融合多尺度注意力机制,在减少计算成本的同时,保留每个通道的信息,提高特征提取的效率和能力。其次,针对图像深度不清晰的问题,基于几何一致性,提出深度区域细化(Depth Region Refinement,DSR)模块,过滤不准确的像素点,以提高深度信息的可靠性。最后,输入图像翻译模型所生成的复杂样本,并计算相应原始图像上的标准损失来指导模型的自监督训练。在NuScence,KITTI和KITTI-C这3个数据集上,相比于基线模型,所提模型对误差和精度均有优化。展开更多
Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(...Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.展开更多
Satellites in LEO (Low Earth Orbits) are closest to the Earth’s surface, having the smallest coverage area compared to other orbits, depending on altitude and elevation angle, and providing relatively too short visib...Satellites in LEO (Low Earth Orbits) are closest to the Earth’s surface, having the smallest coverage area compared to other orbits, depending on altitude and elevation angle, and providing relatively too short visibility and communication duration, in range of (2 - 15) minutes. Communication duration represents the key performance indicator for LEO satellite communication systems. For longer communication sessions, more satellites must be involved, and the signals must be handed over from one satellite to the next to provide uninterrupted real-time services to the appropriate user or ground station. This leads to the concept and structure of the satellites organized in the constellation. Communication window (visibility window) depends on the designed horizon plane width determined by licensed elevation angle. For the appropriate calculations, a satellite from the Starlink constellation at altitude of 550 km is considered, observed under licensed designed elevations of 40˚ and 25˚. Calculations under two designed elevation levels confirmed the wider horizon and consequently longer communication under the lower elevation.展开更多
BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular ...BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.展开更多
Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a signi...Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a significant challenge faced in the field of computer vision.However,existing monocular video depth estimation models tend to produce blurred or inaccurate depth information in regions with object edges and low texture.To address this issue,we propose a monocular depth estimation model architecture guided by semantic segmentation masks,which introduces semantic information into the model to correct the ambiguous depth regions.We have evaluated the proposed method,and experimental results show that our method improves the accuracy of edge depth,demonstrating the effectiveness of our approach.展开更多
Allium stracheyi(Baker)is widely utilized as a culinary herb and is typically encountered in the higher elevations of the Himalayas.Consequently,it is of great significance to compare the ecological adaptability of th...Allium stracheyi(Baker)is widely utilized as a culinary herb and is typically encountered in the higher elevations of the Himalayas.Consequently,it is of great significance to compare the ecological adaptability of this indigenous species to alternative habitats and its introduction into new environments.This research aims to investigate and gain a comprehensive understanding of A.stracheyi,also known as faran,in Uttarakhand region.We aim to examine how this plant adapts morphologically,physiologically,biochemically,and anatomically to varying elevations,specifically at 550,2200,2460,and 3400 m above mean sea level(m AMSL).This plant demonstrated remarkable morphophysiological adjustments across various aspects of its development,encompassing modified growth patterns,alterations in leaf dimensions,leaf count,etc..Moreover,biochemical adaptations have been identified as pivotal in bolstering the plant resilience to the stress associated with higher elevation.Enzymes like superoxide dismutase(SOD)and peroxidase(POD)exhibited significant responsiveness to elevational variations,contributing to the plant's ability to confront the challenges posed by high-elevational conditions.In terms of anatomy,the plant manifested alterations in its leaf and vascular tissues along the elevational gradient.These modifications involve an increased density of stomata and a greater count of vascular bundles,optimizing gas exchange and adaptation to water stress in frequently encountered harsh environmental conditions at higher elevations.Understanding the adaptive mechanisms employed by A.stracheyi provides valuable insights,especially in forecasting how A.stracheyi might respond to global climate change,particularly in regions affected by habitat fragmentation.展开更多
Forests exert significant biogeophysical cooling effects(CE)through processes such as increased evapotranspiration,reduced albedo,and enhanced surface roughness.However,little is known about the extent to which elevat...Forests exert significant biogeophysical cooling effects(CE)through processes such as increased evapotranspiration,reduced albedo,and enhanced surface roughness.However,little is known about the extent to which elevation-induced temperature differences bias the observed CE and how this bias interacts with the underlying biogeophysical mechanisms.In this study,we integrated multisensor remote sensing products and Shuttle Radar Topography Mission(SRTM)elevation data on the Google Earth Engine(GEE)platform,and applied a spatial-temporal window regression approach to quantify and correct the sensitivity of land surface temperature(LST)to elevation for forest pixels across China from 2001 to 2022.First,we found that forest LST exhibited a significant negative relationship with elevation,leading to systematic CE overestimation by 0.61 K during the day and 0.60 K at night compared with altitudecorrected CE values.Second,after correction,the CE showed clear spatial heterogeneity,with stronger daytime cooling in tropical(-0.54 K)and temperate forests(-0.24 K),and warming in cold(+0.11 K)and arid regions(+0.53 K),while most regions experienced nighttime warming.Among forest types,evergreen needleleaf forests(ENF)exhibited the strongest daytime cooling(-0.36 K),whereas deciduous broadleaf(DBF)and open shrublands(OS)tended to warm.Third,mechanism analysis revealed that elevation correction strengthened the correlations of CE with leaf area index(LAI)and evapotranspiration,while maintaining a significant negative correlation with albedo,indicating that both radiative and non-radiative processes jointly shape the unbiased CE.These findings provide a more accurate quantification of forest CE by eliminating elevation-induced bias,which providing a more accurate assessment of the climate mitigation potential of forests,which is crucial for developing more effective forest management and ecological restoration strategies.展开更多
自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影...自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影响深度图的精度。针对上述问题,提出一种基于拉普拉斯金字塔的自监督单目深度估计方法(Self-supervised Monocular Depth Estimation Based on the Laplace Pyramid,LpDepth)。此方法的核心思想是:首先,使用拉普拉斯残差图丰富编码特征,以弥补在下采样过程中丢失的特征信息;其次,在下采样过程中使用最大池化层突显和放大特征信息,使编码器在特征提取过程中更容易地提取到训练模型所需要的特征信息;最后,使用残差模块解决过拟合问题,提高解码器对特征的利用效率。在KITTI和Make3D等数据集上对所提方法进行了测试,同时将其与现有经典方法进行了比较。实验结果证明了所提方法的有效性。展开更多
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National Key Research and Development Program of China (2022YFC3004004)National Natural Science Foundation of China (42075155,12241104)National Natural Science Foundation of China Joint Fund (U2342213)。
文摘The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.
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supported by the National Natural Science Foundation of China(Nos.52422408 and 52171031)the Liaoning Xingliao Talents-Top-notch Young Talents Project(No.XLYC2203064)+1 种基金the Excellent Youth Fund of Liaoning Natural Science Foundation(No.2023JH3/10200001)the Fundamental Research Funds for the Central Universities(No.N2425004).
文摘The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbulence model,coupling velocity,temperature,and phase field was established to study the effect of the ladle shroud immersion depth on the slag eye formation,slag entrainment,slag dragging,air dragging,and flow characteristics during the ladle change-over process of a two-strand tundish.The results showed that reducing the immersion depth decreases the high-velocity region area under the slag layer in the quasi-steady process.During the emptying stage,as the molten bath level gradually decreases,the outlet temperature exhibits a trend of initially decreasing and subsequently increasing across all three shroud immersion depths.However,under a 210 mm shroud immersion depth,molten slag and air are dragged into the shroud,forming slag droplets and causing significant fluctuations,with a maximum scalar velocity of 0.0764 m/s at the monitoring point.In the filling stage,air and molten slag are dragged into the molten bath,forming bubbles and slag droplets at an immersion depth of 210 mm.Bubbles are observed within the molten slag layer,which can readily cause an emulsification phenomenon,making it easier to be dragged as slag droplets.Additionally,the slag eye area measured under 210 mm immersion depth at 45 s is 0.303 m^(2),while the maximum scalar velocity of 2.4259 m/s is detected at 12 s.At an immersion depth of 360 mm,the average area of the slag eye is minimized to 0.06268 m2,with corresponding variances of 0.006753,representing the optimal immersion depth.
基金Under the auspices of the National Natural Science Foundation of China(No.42430511,U20A2083)the National Key Research and Development Program of China(No.2022YFF1300900)the Science and Technology Development Program of Jilin Province(No.20210509037RQ,20230101348JC)。
文摘Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradients can regulate the spatial distribu-tion and network complexity of the community structure.To explore the variations in soil microbial community structures and their as-sembly mechanisms across different elevations of the Changbai Mountains,as well as their responses to environmental factors,we col-lected microbial samples along an elevational gradient(seven elevations containing four vegetation zones)on the western slope of the Changbai Mountains using the method of metagenomic sequencing.The results showed a significant difference(P<0.05)for the Chao1 index across different elevations,but no significant difference was observed for the Shannon and Simpson indices.With increasing elev-ation,the number of nodes and links in the microbial network gradually decreased.Acidobacteria were highly connected to many nodes.The microbial communities indicated a significant distance-decay relationship(P<0.001)and were affected more by stochastic pro-cesses along the elevation gradient.The results of the Structural Equation Model(SEM)showed that elevation had direct significant ef-fect on carbon(C,P<0.01),nitrogen(N,P<0.01),and phosphorus(P,P<0.05)and weak negative effect on their ecological stoi-chiometry.Elevation was one of the major variables contributing to microbial network topology.The contribution of C and N to micro-bial network complexity was higher than that of P.Our study provides valuable insights into the responses of soil microbial communit-ies to elevation variations.
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Not many bathymetric maps are available for many lakes and reservoirs in developing countries. Usually the bathymetric mapping requires investment in expensive equipment and fieldwork, both of which are not accessible in these countries. This work demonstrates the ability to develop bathymetric map of Mosul Lake by using a digital elevation model (DEM). The depths model of the lake was designed through the use of three main stages;a coastline extraction, dataset interpolation and a triangular irregular network model. The normalized difference water index (NDWI) was used for automatic delineation of the lake coastline from satellite images. The ordinary kriging interpolation with a stable model was used to interpolate the water depths dataset. Finally a triangulated irregular network (TIN) model was used to visualize the resulting interpolation model. Calculated values of area and volume of a TIN model during 2011 were compared with values of supposed initial operation of the reservoir. The differences of water volume storage between these stages at 321 m water level was about 0.81 × 109 m3, where the lake lost around 10% of storage value. Also the results of depths lake model show that the change in water storage between March and July 2011 was about 3.08 × 109 m3.
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.
文摘对于复杂天气场景图像模糊、低对比度和颜色失真所导致的深度信息预测不准的问题,以往的研究均以标准场景的深度图作为先验信息来对该类场景进行深度估计。然而,这一方式存在先验信息精度较低等问题。对此,提出一个基于多尺度注意力机制的单目深度估计模型TalentDepth,以实现对复杂天气场景的预测。首先,在编码器中融合多尺度注意力机制,在减少计算成本的同时,保留每个通道的信息,提高特征提取的效率和能力。其次,针对图像深度不清晰的问题,基于几何一致性,提出深度区域细化(Depth Region Refinement,DSR)模块,过滤不准确的像素点,以提高深度信息的可靠性。最后,输入图像翻译模型所生成的复杂样本,并计算相应原始图像上的标准损失来指导模型的自监督训练。在NuScence,KITTI和KITTI-C这3个数据集上,相比于基线模型,所提模型对误差和精度均有优化。
基金Supported by the National Key R&D Program of China (No.2023YFC3008100)the National Natural Science Foundation of China (No.U23A2033)
文摘Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.
文摘Satellites in LEO (Low Earth Orbits) are closest to the Earth’s surface, having the smallest coverage area compared to other orbits, depending on altitude and elevation angle, and providing relatively too short visibility and communication duration, in range of (2 - 15) minutes. Communication duration represents the key performance indicator for LEO satellite communication systems. For longer communication sessions, more satellites must be involved, and the signals must be handed over from one satellite to the next to provide uninterrupted real-time services to the appropriate user or ground station. This leads to the concept and structure of the satellites organized in the constellation. Communication window (visibility window) depends on the designed horizon plane width determined by licensed elevation angle. For the appropriate calculations, a satellite from the Starlink constellation at altitude of 550 km is considered, observed under licensed designed elevations of 40˚ and 25˚. Calculations under two designed elevation levels confirmed the wider horizon and consequently longer communication under the lower elevation.
文摘BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.
文摘Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a significant challenge faced in the field of computer vision.However,existing monocular video depth estimation models tend to produce blurred or inaccurate depth information in regions with object edges and low texture.To address this issue,we propose a monocular depth estimation model architecture guided by semantic segmentation masks,which introduces semantic information into the model to correct the ambiguous depth regions.We have evaluated the proposed method,and experimental results show that our method improves the accuracy of edge depth,demonstrating the effectiveness of our approach.
基金supported by Uttarakhand Council for Biotechnology(grant number UCB/R&D PROJECT/2022/20 dated 06.05.2022).
文摘Allium stracheyi(Baker)is widely utilized as a culinary herb and is typically encountered in the higher elevations of the Himalayas.Consequently,it is of great significance to compare the ecological adaptability of this indigenous species to alternative habitats and its introduction into new environments.This research aims to investigate and gain a comprehensive understanding of A.stracheyi,also known as faran,in Uttarakhand region.We aim to examine how this plant adapts morphologically,physiologically,biochemically,and anatomically to varying elevations,specifically at 550,2200,2460,and 3400 m above mean sea level(m AMSL).This plant demonstrated remarkable morphophysiological adjustments across various aspects of its development,encompassing modified growth patterns,alterations in leaf dimensions,leaf count,etc..Moreover,biochemical adaptations have been identified as pivotal in bolstering the plant resilience to the stress associated with higher elevation.Enzymes like superoxide dismutase(SOD)and peroxidase(POD)exhibited significant responsiveness to elevational variations,contributing to the plant's ability to confront the challenges posed by high-elevational conditions.In terms of anatomy,the plant manifested alterations in its leaf and vascular tissues along the elevational gradient.These modifications involve an increased density of stomata and a greater count of vascular bundles,optimizing gas exchange and adaptation to water stress in frequently encountered harsh environmental conditions at higher elevations.Understanding the adaptive mechanisms employed by A.stracheyi provides valuable insights,especially in forecasting how A.stracheyi might respond to global climate change,particularly in regions affected by habitat fragmentation.
基金Under the auspices of National Social Sciences Foundation of China(No.21BJY114)。
文摘Forests exert significant biogeophysical cooling effects(CE)through processes such as increased evapotranspiration,reduced albedo,and enhanced surface roughness.However,little is known about the extent to which elevation-induced temperature differences bias the observed CE and how this bias interacts with the underlying biogeophysical mechanisms.In this study,we integrated multisensor remote sensing products and Shuttle Radar Topography Mission(SRTM)elevation data on the Google Earth Engine(GEE)platform,and applied a spatial-temporal window regression approach to quantify and correct the sensitivity of land surface temperature(LST)to elevation for forest pixels across China from 2001 to 2022.First,we found that forest LST exhibited a significant negative relationship with elevation,leading to systematic CE overestimation by 0.61 K during the day and 0.60 K at night compared with altitudecorrected CE values.Second,after correction,the CE showed clear spatial heterogeneity,with stronger daytime cooling in tropical(-0.54 K)and temperate forests(-0.24 K),and warming in cold(+0.11 K)and arid regions(+0.53 K),while most regions experienced nighttime warming.Among forest types,evergreen needleleaf forests(ENF)exhibited the strongest daytime cooling(-0.36 K),whereas deciduous broadleaf(DBF)and open shrublands(OS)tended to warm.Third,mechanism analysis revealed that elevation correction strengthened the correlations of CE with leaf area index(LAI)and evapotranspiration,while maintaining a significant negative correlation with albedo,indicating that both radiative and non-radiative processes jointly shape the unbiased CE.These findings provide a more accurate quantification of forest CE by eliminating elevation-induced bias,which providing a more accurate assessment of the climate mitigation potential of forests,which is crucial for developing more effective forest management and ecological restoration strategies.
文摘自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影响深度图的精度。针对上述问题,提出一种基于拉普拉斯金字塔的自监督单目深度估计方法(Self-supervised Monocular Depth Estimation Based on the Laplace Pyramid,LpDepth)。此方法的核心思想是:首先,使用拉普拉斯残差图丰富编码特征,以弥补在下采样过程中丢失的特征信息;其次,在下采样过程中使用最大池化层突显和放大特征信息,使编码器在特征提取过程中更容易地提取到训练模型所需要的特征信息;最后,使用残差模块解决过拟合问题,提高解码器对特征的利用效率。在KITTI和Make3D等数据集上对所提方法进行了测试,同时将其与现有经典方法进行了比较。实验结果证明了所提方法的有效性。
