Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of arti...With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.展开更多
The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly impro...The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards.Utilising the SKUA GoCAD software,we constructed detailed seismic fault models for the 2021 M_(S)6.4 Yangbi earthquake in Yunnan,China,using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow.Our analysis revealed a NW-striking main fault with a high-angle SW dip,accompanied by two branch faults.Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault,whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern.Additionally,a third ENE-striking short fault was identified NE of the main fault.In combination with the spatial distribution of pre-existing faults,our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW-and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone.The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones,through either cascade or conjugate rupture modes,can cause unexpected moderate-large earthquakes and severe disasters,necessitating attention in regions like southeast Xizang,which have complex fault systems.展开更多
This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)syste...This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.展开更多
This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are ob...This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are obtained.Pore structures are segmented by the U-shaped network(U-Net)neural network integrated with the Canny edge detection operator,ensuring accurate pore delineation and edge extraction.The trained U-Net achieves 98.55%accuracy.The 2D data are superimposed and processed into 3D point clouds,enabling reconstruction of the pore structure and aluminum skeleton.Analysis of pore 01 shows the cross-sectional area initially increases,and then decreases with milling depth,with a uniform point distribution of 40 per layer.The reconstructed model exhibits a porosity of 77.5%,with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%,confirming high fidelity.Equivalent sphere diameters decrease with size,averaging 1.95 mm.Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations,and the stresses in the reconstruction model concentrate on thin cell walls,leading to localized deformations.This method accurately restores the aluminum foam’s complex internal structure,improving reconstruction preci-sion and simulation reliability.The approach offers a cost-efficient,high-precision technique for optimizing material performance in engineering applications.展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep ...Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.展开更多
Highway planning requires geological surveys and stability analysis of the surrounding area.In the early stage of the survey,the modeling and stability analysis of the survey area can be carried out by using GIS softw...Highway planning requires geological surveys and stability analysis of the surrounding area.In the early stage of the survey,the modeling and stability analysis of the survey area can be carried out by using GIS software to intuitively understand the topography of the study area.The use of DEM to extract terrain factors can be used for simple stability analysis and the source data is easy to obtain,simple to operate,fast to analyze,and reliable analysis results.In this paper,taking the X104 road section in Ganxian County as an example,the ArcGIS platform is used to carry out 3D modeling visualization and stability analysis,and the stability evaluation map of the study area is obtained.展开更多
With the widespread application of 3D visualization in digital exhibition halls and virtual reality,achieving efficient rendering and high-fidelity presentation has become a key challenge.This study proposes a hybrid ...With the widespread application of 3D visualization in digital exhibition halls and virtual reality,achieving efficient rendering and high-fidelity presentation has become a key challenge.This study proposes a hybrid point cloud generation method that combines traditional sampling with 3D Gaussian splatting,aiming to address the issues of rendering delay and missing details in existing 3D displays.By improving the OBJ model parsing process and incorporating an adaptive area-weighted sampling algorithm,we achieve adaptive point cloud generation based on triangle density.Innovatively,we advance the ellipsoidal parameter estimation process of 3D Gaussian splatting to the point cloud generation stage.By establishing a mathematical relationship between the covariance matrix and local curvature,the generated point cloud naturally exhibits Gaussian distribution characteristics.Experimental results show that,compared to traditional methods,our approach reduces point cloud data by 38% while maintaining equivalent visual quality at a 4096×4096 texture resolution.By introducing mipmap texture optimization strategies and a GPU-accelerated rasterization pipeline,stable rendering at 60 frames per second is achieved in a WebGL environment.Additionally,we quantize and compress the spherical harmonic function parameters specific to 3D Gaussian splatting,reducing network transmission bandwidth to 52% of the original data.This study provides a new technical pathway for fields requiring high-precision display,such as the digitization of cultural heritage.展开更多
Given the severe toxicity and widespread presence of cadmium(Cd)in staple foods such as rice,accurate dietary exposure assessments are imperative for public health.In vitro bioavailability is commonly used to adjust d...Given the severe toxicity and widespread presence of cadmium(Cd)in staple foods such as rice,accurate dietary exposure assessments are imperative for public health.In vitro bioavailability is commonly used to adjust dietary exposure levels of risk factors;however,traditional planar Transwell models have limitations,such as cell dedifferentiation and lack of key intestinal components,necessitating a more physiologically relevant in vitro platform.This study introduces an innovative three-dimensional(3D)intestinal organoid model using a microfluidic chip to evaluate Cd bioavailability in food.Caco-2 cells were cultured on the chip to mimic small intestinal villi's 3D structure,mucus production,and absorption functions.The model's physiological relevance was thoroughly characterized,demonstrating the formation of a confluent epithelial monolayer with well-developed tight junctions(ZO-1),high microvilli density(F-actin),and significant mucus secretion(Alcian blue staining),closely resembling the physiological intestinal epithelium.Fluorescent particle tracking confirmed its ability to simulate intestinal transport and diffusion.The Cd bioavailability in rice measured by the 3D intestinal organoid model((9.07±0.21)%)was comparable to the mouse model((12.82±3.42)%)but significantly lower than the Caco-2 monolayer model((26.97±1.11)%).This 3D intestinal organoid model provides a novel and reliable strategy for in vitro assessment of heavy metal bioavailability in food,with important implications for food safety and risk assessment.展开更多
Accurate retrieval of casting 3D models is crucial for process reuse.Current methods primarily focus on shape similarity,neglecting process design features,which compromises reusability.In this study,a novel deep lear...Accurate retrieval of casting 3D models is crucial for process reuse.Current methods primarily focus on shape similarity,neglecting process design features,which compromises reusability.In this study,a novel deep learning retrieval method for process reuse was proposed,which integrates process design features into the retrieval of casting 3D models.This method leverages the comparative language-image pretraining(CLIP)model to extract shape features from the three views and sectional views of the casting model and combines them with process design features such as modulus,main wall thickness,symmetry,and length-to-height ratio to enhance process reusability.A database of 230 production casting models was established for model validation.Results indicate that incorporating process design features improves model accuracy by 6.09%,reaching 97.82%,and increases process similarity by 30.25%.The reusability of the process was further verified using the casting simulation software EasyCast.The results show that the process retrieved after integrating process design features produces the least shrinkage in the target model,demonstrating this method’s superior ability for process reuse.This approach does not require a large dataset for training and optimization,making it highly applicable to casting process design and related manufacturing processes.展开更多
Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as...Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask segmentation. Then, based on the obtained shape masks, a vector extraction network guided by heatmaps is designed to extract structural vectors by fusing the features from node heatmap and skeleton heatmap, respectively. Compared with the state-of-the-art methods, experiment results illustrate that the proposed semantic segmentation method can effectively eliminate the interference of many elements on drawings to segment the shape masks effectively, meanwhile, the model trained by the proposed vector extraction network can accurately extract the vectors such as nodes and line connections, avoiding redundant vector detection. The proposed method lays a solid foundation for automatic 3D model reconstruction and contributes to technological advancements in relevant fields.展开更多
Multistage fracturing technology has been used to enhance tight hydrocarbon resource recovery.Determining the proper well spacing and fracturing strategy is crucial for generating a complex fracture network that facil...Multistage fracturing technology has been used to enhance tight hydrocarbon resource recovery.Determining the proper well spacing and fracturing strategy is crucial for generating a complex fracture network that facilitates oil and gas flow in reservoirs.The stress-shadow effect that occurs between multiple wells significantly affects the development of fracture networks in reservoirs.However,the quantification of the stress-shadow effect and its influence on fracture networks has not been satisfactorily resolved because of the difficulties in detecting and identifying fracture propagation and reorientation in reservoirs.In this study,based on the geological information from the Shengli oilfield,we applied a hybrid finite element-discrete element method to analyze engineering-scale three-dimensional fracture propagation and reorientation by altering well spacings and fracturing strategies.The results indicate that the fracturing area generated by the synchronous fracturing scheme is much smaller than those generated by the sequential and alternative schemes.An alternative hydrofracturing scheme is optimal with respect to fracturing area.The stress-blind area was defined to quantify the mechanical disturbance between adjacent wells.Our study improves the understanding of the effect of fracturing schemes on fracture networks and the impact of independent factors contributing to stress-shadow effects.展开更多
The Kunene Complex(KC)represents a very large Mesoproterozoic igneous body,mainly composed of anorthosites and gabbroic rocks that extends from SW Angola to NW Namibia(outcropping 18,000 km^(2),NE-SW trend,and ca.350 ...The Kunene Complex(KC)represents a very large Mesoproterozoic igneous body,mainly composed of anorthosites and gabbroic rocks that extends from SW Angola to NW Namibia(outcropping 18,000 km^(2),NE-SW trend,and ca.350 km long and up to 50 km wide).Little is known about its structure at depth.Here,we use recently acquired aerogeophysical data to accurately determine its hidden extent and to unravel its morphology at depth.These data have been interpreted and modelled to investigate the unexposed KC boundaries,reconstructing the upper crustal structure(between 0 and 15 km depth)overlain by the thin sedimentary cover of the Kalahari Basin.The modelling reveals that the KC was emplaced in the upper crust and extends in depth up to ca.5 km,showing a lobular geometry and following a large NE-SW to NNE-SSW linear trend,presumably inherited from older Paleoproterozoic structures.The lateral continuation of the KC to the east(between 50 and 125 km)beneath the Kalahari Cenozoic sediments suggests an overall size three times the outcropping dimension(about 53,500 km^(2)).This affirmation clearly reinforces the economic potential of this massif,related to the prospecting of raw materials and certain types of economic mineralization(Fe-Ti oxides,metallic sulphides or platinum group minerals).Up to 11 lobes have been isolated with dimensions ranging from 135.5 to 37.3 km in length and 81.9 to 20.7 km in width according to remanent bodies revealed by TMI mapping.A total volume of 65,184 km3 was calculated only for the magnetically remanent bodies of the KC.A long-lasting complex contractional regime,where large strike-slip fault systems were involved,occurred in three kinematic pulses potentially related to a change of velocity or convergence angle acting on previous Paleoproterozoic inherited sutures.The coalescent magmatic pulses can be recognized by means of magnetic anomalies,age of the bodies as well as the lineations inferred in this work:(i)Emplacement of the eastern mafic bodies and granites in a stage of significant lateral extension in a transtensional context between 1500 Ma and 1420 Ma;(ii)Migration of the mantle derived magmas westwards with deformation in a complex contractional setting with shearing structures involving western KC bodies and basement from 1415 Ma to 1340 Ma;(iii)NNW-SSE extensional structures are relocated westwards,involving mantle magmas,negative flower structures and depression that led to the formation of late Mesoproterozoic basins from 1325 Ma to 1170 Ma.Additionally,we detect several first and second order structures to place the structuring of the KC in a craton-scale context in relation to the crustal structures detected in NW Namibia.展开更多
Research on scale effects on flows over weirs has been conducted on a limited basis, primarily focusing on flows upstream of a single-type weir, such as ogee, broad-crested, and sharp-crested (linear and non-linear) w...Research on scale effects on flows over weirs has been conducted on a limited basis, primarily focusing on flows upstream of a single-type weir, such as ogee, broad-crested, and sharp-crested (linear and non-linear) weirs. However, the scale effects downstream of these single-type weirs have not been thoroughly investigated. This study examined the scale effects on flows over a combined weir system consisting of an ogee weir and a sharp-crested weir, both upstream and downstream, utilizing physical modeling at a 1:33.33 scale based on Froude similarity and three-dimensional (3D) computational fluid dynamics (CFD) modeling. The sharp-crested weir in this study was represented by two sluice gates that remain closed and submerged during flood events. The experimental data confirmed that the equivalent discharge coefficients of the combined weir system behaved similarly to those of a sharp-crested weir across various H/P (where H is the total head, and P is the weir height) values. However, scale effects on the discharge rating curve due to surface tension and viscosity could only be minimized when H/P > 0.4, Re > 26 959, and We > 240 (where Re and We are the Reynolds and Weber numbers, respectively), provided that the water depth exceeded 0.042 m above the crest. Additionally, Re greater than 4 × 104 was necessary to minimize scale effects caused by viscosity in flows in the spillway channel and stilling basin (with baffle blocks). The limiting criteria aligned closely with existing literature. This study offers valuable insights for practical applications in hydraulic engineering in the future.展开更多
The popularity of deep learning has boosted computer-generated holography(CGH)as a vibrant research field,particularly physics-driven unsupervised learning.Nevertheless,present unsupervised CGH models have not yet exp...The popularity of deep learning has boosted computer-generated holography(CGH)as a vibrant research field,particularly physics-driven unsupervised learning.Nevertheless,present unsupervised CGH models have not yet explored the potential of generating full-color 3D holograms through a unified framework.In this study,we propose a lightweight multiwavelength network model capable of high-fidelity and efficient full-color hologram generation in both 2D and 3D display,called IncepHoloRGB.The high-speed simultaneous generation of RGB holograms at 191 frames per second(FPS)is based on Inception sampling blocks and multi-wavelength propagation module integrated with depth-traced superimposition,achieving an average structural similarity(SSIM)of 0.88 and peak signal-to-noise ratio(PSNR)of 29.00 on the DIV2K test set in reconstruction.Full-color reconstruction of numerical simulations and optical experiments shows that IncepHoloRGB is versatile to diverse scenarios and can obtain authentic full-color holographic 3D display within a unified network model,paving the way for applications towards real-time dynamic naked-eye 3D display,virtual and augmented reality(VR/AR)systems.展开更多
Anticancer drug resistance remains a major challenge in cancer treatment hindering the efficacy of chemotherapy and targeted therapies.Conventional two-dimensional(2D)cell cultures cannot replicate the complexity of t...Anticancer drug resistance remains a major challenge in cancer treatment hindering the efficacy of chemotherapy and targeted therapies.Conventional two-dimensional(2D)cell cultures cannot replicate the complexity of the in vivo tumor microenvironment(TME),limiting their utility for drug resistance research.Therefore,three-dimensional(3D)tumor models have proven to be a promising alternative for investigating chemoresistance mechanisms.In this review,various cancer 3D models,including spheroids,organoids,scaffold-based models,and bioprinted models,are comprehensively evaluated with a focus on their application in drug resistance studies.We discuss the materials,properties,and advantages of each model,highlighting their ability to better mimic the TME and represent complex mechanisms of drug resistance such as epithelial-mesenchymal transition(EMT),drug efflux,and tumor-stroma interactions.Furthermore,we investigate the limitations of these models,including scalability,reproducibility and technical challenges,as well as their potential therapeutic impact on personalized medicine.Through a thorough comparison of model performance,we provide insights into the strengths and weaknesses of each approach and offer guidance for model selection based on specific research needs.展开更多
The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in so...The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in southwestern China as the engineering prototype,large-scale three-dimensional(3D)physical model tests were conducted on a 3D-printed complex geological model containing two faults.Based on the selfdeveloped 3D loading system and excavation device,the macroscopic failure of fault-slip rockbursts was simulated indoors.The stress,strain,and fracturing characteristics of the surrounding rock near the two faults were systematically evaluated during excavation and multistage loading.The test results effectively revealed the evolution and triggering mechanism of fault-slip rockbursts.After the excavation of a highstress tunnel,stress readjustment occurred.Owing to the presence of these two faults,stress continued to accumulate in the rock mass between them,leading to the accumulation of fractures.When the shear stress on a fault surface exceeded its shear strength,sudden fault slip and dislocation occurred,thus triggering rockbursts.Rockbursts occurred twice in the vault between the two faults,showing obvious intermittent characteristics.The rockburst pit was controlled by two faults.When the faults remained stable,tensile failure predominated in the surrounding rock.However,when the fault slip was triggered,shear failure in the surrounding rock increased.These findings provide valuable insights for enhancing the comprehension of fault-slip rockbursts.展开更多
Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors pr...Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.展开更多
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)。
文摘With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.
基金financial support from the National Key R&D Program of China (No. 2021YFC3000600)National Natural Science Foundation of China (No. 41872206)National Nonprofit Fundamental Research Grant of China, Institute of Geology, China, Earthquake Administration (No. IGCEA2010)
文摘The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards.Utilising the SKUA GoCAD software,we constructed detailed seismic fault models for the 2021 M_(S)6.4 Yangbi earthquake in Yunnan,China,using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow.Our analysis revealed a NW-striking main fault with a high-angle SW dip,accompanied by two branch faults.Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault,whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern.Additionally,a third ENE-striking short fault was identified NE of the main fault.In combination with the spatial distribution of pre-existing faults,our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW-and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone.The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones,through either cascade or conjugate rupture modes,can cause unexpected moderate-large earthquakes and severe disasters,necessitating attention in regions like southeast Xizang,which have complex fault systems.
文摘This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.
基金supported by the Key Research and DevelopmentPlan in Shanxi Province of China(No.201803D421045)the Natural Science Foundation of Shanxi Province(No.2021-0302-123104)。
文摘This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are obtained.Pore structures are segmented by the U-shaped network(U-Net)neural network integrated with the Canny edge detection operator,ensuring accurate pore delineation and edge extraction.The trained U-Net achieves 98.55%accuracy.The 2D data are superimposed and processed into 3D point clouds,enabling reconstruction of the pore structure and aluminum skeleton.Analysis of pore 01 shows the cross-sectional area initially increases,and then decreases with milling depth,with a uniform point distribution of 40 per layer.The reconstructed model exhibits a porosity of 77.5%,with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%,confirming high fidelity.Equivalent sphere diameters decrease with size,averaging 1.95 mm.Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations,and the stresses in the reconstruction model concentrate on thin cell walls,leading to localized deformations.This method accurately restores the aluminum foam’s complex internal structure,improving reconstruction preci-sion and simulation reliability.The approach offers a cost-efficient,high-precision technique for optimizing material performance in engineering applications.
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
文摘Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.
基金National Undergraduate Training Program for Innovation and Entrepreneurship(Project No.:202310407006)。
文摘Highway planning requires geological surveys and stability analysis of the surrounding area.In the early stage of the survey,the modeling and stability analysis of the survey area can be carried out by using GIS software to intuitively understand the topography of the study area.The use of DEM to extract terrain factors can be used for simple stability analysis and the source data is easy to obtain,simple to operate,fast to analyze,and reliable analysis results.In this paper,taking the X104 road section in Ganxian County as an example,the ArcGIS platform is used to carry out 3D modeling visualization and stability analysis,and the stability evaluation map of the study area is obtained.
文摘With the widespread application of 3D visualization in digital exhibition halls and virtual reality,achieving efficient rendering and high-fidelity presentation has become a key challenge.This study proposes a hybrid point cloud generation method that combines traditional sampling with 3D Gaussian splatting,aiming to address the issues of rendering delay and missing details in existing 3D displays.By improving the OBJ model parsing process and incorporating an adaptive area-weighted sampling algorithm,we achieve adaptive point cloud generation based on triangle density.Innovatively,we advance the ellipsoidal parameter estimation process of 3D Gaussian splatting to the point cloud generation stage.By establishing a mathematical relationship between the covariance matrix and local curvature,the generated point cloud naturally exhibits Gaussian distribution characteristics.Experimental results show that,compared to traditional methods,our approach reduces point cloud data by 38% while maintaining equivalent visual quality at a 4096×4096 texture resolution.By introducing mipmap texture optimization strategies and a GPU-accelerated rasterization pipeline,stable rendering at 60 frames per second is achieved in a WebGL environment.Additionally,we quantize and compress the spherical harmonic function parameters specific to 3D Gaussian splatting,reducing network transmission bandwidth to 52% of the original data.This study provides a new technical pathway for fields requiring high-precision display,such as the digitization of cultural heritage.
基金supported by National key research and development program of China(2022YFF1102500)。
文摘Given the severe toxicity and widespread presence of cadmium(Cd)in staple foods such as rice,accurate dietary exposure assessments are imperative for public health.In vitro bioavailability is commonly used to adjust dietary exposure levels of risk factors;however,traditional planar Transwell models have limitations,such as cell dedifferentiation and lack of key intestinal components,necessitating a more physiologically relevant in vitro platform.This study introduces an innovative three-dimensional(3D)intestinal organoid model using a microfluidic chip to evaluate Cd bioavailability in food.Caco-2 cells were cultured on the chip to mimic small intestinal villi's 3D structure,mucus production,and absorption functions.The model's physiological relevance was thoroughly characterized,demonstrating the formation of a confluent epithelial monolayer with well-developed tight junctions(ZO-1),high microvilli density(F-actin),and significant mucus secretion(Alcian blue staining),closely resembling the physiological intestinal epithelium.Fluorescent particle tracking confirmed its ability to simulate intestinal transport and diffusion.The Cd bioavailability in rice measured by the 3D intestinal organoid model((9.07±0.21)%)was comparable to the mouse model((12.82±3.42)%)but significantly lower than the Caco-2 monolayer model((26.97±1.11)%).This 3D intestinal organoid model provides a novel and reliable strategy for in vitro assessment of heavy metal bioavailability in food,with important implications for food safety and risk assessment.
基金supported by the National Natural Science Foundation of China(Nos.52074246,52275390,52375394)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)the Key R&D Program of Shanxi Province(No.202102050201011).
文摘Accurate retrieval of casting 3D models is crucial for process reuse.Current methods primarily focus on shape similarity,neglecting process design features,which compromises reusability.In this study,a novel deep learning retrieval method for process reuse was proposed,which integrates process design features into the retrieval of casting 3D models.This method leverages the comparative language-image pretraining(CLIP)model to extract shape features from the three views and sectional views of the casting model and combines them with process design features such as modulus,main wall thickness,symmetry,and length-to-height ratio to enhance process reusability.A database of 230 production casting models was established for model validation.Results indicate that incorporating process design features improves model accuracy by 6.09%,reaching 97.82%,and increases process similarity by 30.25%.The reusability of the process was further verified using the casting simulation software EasyCast.The results show that the process retrieved after integrating process design features produces the least shrinkage in the target model,demonstrating this method’s superior ability for process reuse.This approach does not require a large dataset for training and optimization,making it highly applicable to casting process design and related manufacturing processes.
基金funded by the Chinese State Grid Jiangsu Electric Power Co.,Ltd.Science and Technology Project Funding,Grant Number J2023031.
文摘Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask segmentation. Then, based on the obtained shape masks, a vector extraction network guided by heatmaps is designed to extract structural vectors by fusing the features from node heatmap and skeleton heatmap, respectively. Compared with the state-of-the-art methods, experiment results illustrate that the proposed semantic segmentation method can effectively eliminate the interference of many elements on drawings to segment the shape masks effectively, meanwhile, the model trained by the proposed vector extraction network can accurately extract the vectors such as nodes and line connections, avoiding redundant vector detection. The proposed method lays a solid foundation for automatic 3D model reconstruction and contributes to technological advancements in relevant fields.
基金supported in part by the National Key Research and Development Project of China(No.2022YFC3004602)in part by the National Natural Science Foundation of China(Nos.52121003 and 52342403).
文摘Multistage fracturing technology has been used to enhance tight hydrocarbon resource recovery.Determining the proper well spacing and fracturing strategy is crucial for generating a complex fracture network that facilitates oil and gas flow in reservoirs.The stress-shadow effect that occurs between multiple wells significantly affects the development of fracture networks in reservoirs.However,the quantification of the stress-shadow effect and its influence on fracture networks has not been satisfactorily resolved because of the difficulties in detecting and identifying fracture propagation and reorientation in reservoirs.In this study,based on the geological information from the Shengli oilfield,we applied a hybrid finite element-discrete element method to analyze engineering-scale three-dimensional fracture propagation and reorientation by altering well spacings and fracturing strategies.The results indicate that the fracturing area generated by the synchronous fracturing scheme is much smaller than those generated by the sequential and alternative schemes.An alternative hydrofracturing scheme is optimal with respect to fracturing area.The stress-blind area was defined to quantify the mechanical disturbance between adjacent wells.Our study improves the understanding of the effect of fracturing schemes on fracture networks and the impact of independent factors contributing to stress-shadow effects.
基金supported by the subsidiary programme“Ayudas Extraordinarias Menciones Excelencia Severo Ochoa”of the CN IGME-CSIC(project AECEX2021,grant 15903)the University of Minnesota and National Science Foundation(award NSF-EAR 2153786)+1 种基金the Portuguese Foundation for Science and Technology(FCT)support,Geosciences Center project UIDB/00073/2020(doi:10.54499/UIDB/00073/2020)University of Coimbra and and GeoBioTec project UIDB/04035/2020(doi:10.54499/UIDB/04035/2020),Nova School of Science and Technology.
文摘The Kunene Complex(KC)represents a very large Mesoproterozoic igneous body,mainly composed of anorthosites and gabbroic rocks that extends from SW Angola to NW Namibia(outcropping 18,000 km^(2),NE-SW trend,and ca.350 km long and up to 50 km wide).Little is known about its structure at depth.Here,we use recently acquired aerogeophysical data to accurately determine its hidden extent and to unravel its morphology at depth.These data have been interpreted and modelled to investigate the unexposed KC boundaries,reconstructing the upper crustal structure(between 0 and 15 km depth)overlain by the thin sedimentary cover of the Kalahari Basin.The modelling reveals that the KC was emplaced in the upper crust and extends in depth up to ca.5 km,showing a lobular geometry and following a large NE-SW to NNE-SSW linear trend,presumably inherited from older Paleoproterozoic structures.The lateral continuation of the KC to the east(between 50 and 125 km)beneath the Kalahari Cenozoic sediments suggests an overall size three times the outcropping dimension(about 53,500 km^(2)).This affirmation clearly reinforces the economic potential of this massif,related to the prospecting of raw materials and certain types of economic mineralization(Fe-Ti oxides,metallic sulphides or platinum group minerals).Up to 11 lobes have been isolated with dimensions ranging from 135.5 to 37.3 km in length and 81.9 to 20.7 km in width according to remanent bodies revealed by TMI mapping.A total volume of 65,184 km3 was calculated only for the magnetically remanent bodies of the KC.A long-lasting complex contractional regime,where large strike-slip fault systems were involved,occurred in three kinematic pulses potentially related to a change of velocity or convergence angle acting on previous Paleoproterozoic inherited sutures.The coalescent magmatic pulses can be recognized by means of magnetic anomalies,age of the bodies as well as the lineations inferred in this work:(i)Emplacement of the eastern mafic bodies and granites in a stage of significant lateral extension in a transtensional context between 1500 Ma and 1420 Ma;(ii)Migration of the mantle derived magmas westwards with deformation in a complex contractional setting with shearing structures involving western KC bodies and basement from 1415 Ma to 1340 Ma;(iii)NNW-SSE extensional structures are relocated westwards,involving mantle magmas,negative flower structures and depression that led to the formation of late Mesoproterozoic basins from 1325 Ma to 1170 Ma.Additionally,we detect several first and second order structures to place the structuring of the KC in a craton-scale context in relation to the crustal structures detected in NW Namibia.
基金supported by the Ministry of Public Works and Housing of Indonesia and Parahyangan Catholic University(Grant No.II/PD/2023-07/02-SJ).
文摘Research on scale effects on flows over weirs has been conducted on a limited basis, primarily focusing on flows upstream of a single-type weir, such as ogee, broad-crested, and sharp-crested (linear and non-linear) weirs. However, the scale effects downstream of these single-type weirs have not been thoroughly investigated. This study examined the scale effects on flows over a combined weir system consisting of an ogee weir and a sharp-crested weir, both upstream and downstream, utilizing physical modeling at a 1:33.33 scale based on Froude similarity and three-dimensional (3D) computational fluid dynamics (CFD) modeling. The sharp-crested weir in this study was represented by two sluice gates that remain closed and submerged during flood events. The experimental data confirmed that the equivalent discharge coefficients of the combined weir system behaved similarly to those of a sharp-crested weir across various H/P (where H is the total head, and P is the weir height) values. However, scale effects on the discharge rating curve due to surface tension and viscosity could only be minimized when H/P > 0.4, Re > 26 959, and We > 240 (where Re and We are the Reynolds and Weber numbers, respectively), provided that the water depth exceeded 0.042 m above the crest. Additionally, Re greater than 4 × 104 was necessary to minimize scale effects caused by viscosity in flows in the spillway channel and stilling basin (with baffle blocks). The limiting criteria aligned closely with existing literature. This study offers valuable insights for practical applications in hydraulic engineering in the future.
基金supports from National Natural Science Foundation of China(Grant No.62205117,52275429)National Key Research and Development Program of China(Grant No.2021YFF0502700)+2 种基金Young Elite Scientists Sponsorship Program by CAST(Grant No.2022QNRC001)West Light Foundation of the Chinese Academy of Sciences(Grant No.xbzg-zdsys-202206)Hubei Natural Science Foundation Innovative Research Group Project(2024AFA025).
文摘The popularity of deep learning has boosted computer-generated holography(CGH)as a vibrant research field,particularly physics-driven unsupervised learning.Nevertheless,present unsupervised CGH models have not yet explored the potential of generating full-color 3D holograms through a unified framework.In this study,we propose a lightweight multiwavelength network model capable of high-fidelity and efficient full-color hologram generation in both 2D and 3D display,called IncepHoloRGB.The high-speed simultaneous generation of RGB holograms at 191 frames per second(FPS)is based on Inception sampling blocks and multi-wavelength propagation module integrated with depth-traced superimposition,achieving an average structural similarity(SSIM)of 0.88 and peak signal-to-noise ratio(PSNR)of 29.00 on the DIV2K test set in reconstruction.Full-color reconstruction of numerical simulations and optical experiments shows that IncepHoloRGB is versatile to diverse scenarios and can obtain authentic full-color holographic 3D display within a unified network model,paving the way for applications towards real-time dynamic naked-eye 3D display,virtual and augmented reality(VR/AR)systems.
基金funded by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia(grant numbers 451-03-136/2025-03/200007 and 451-03-136/2025-03/200042).
文摘Anticancer drug resistance remains a major challenge in cancer treatment hindering the efficacy of chemotherapy and targeted therapies.Conventional two-dimensional(2D)cell cultures cannot replicate the complexity of the in vivo tumor microenvironment(TME),limiting their utility for drug resistance research.Therefore,three-dimensional(3D)tumor models have proven to be a promising alternative for investigating chemoresistance mechanisms.In this review,various cancer 3D models,including spheroids,organoids,scaffold-based models,and bioprinted models,are comprehensively evaluated with a focus on their application in drug resistance studies.We discuss the materials,properties,and advantages of each model,highlighting their ability to better mimic the TME and represent complex mechanisms of drug resistance such as epithelial-mesenchymal transition(EMT),drug efflux,and tumor-stroma interactions.Furthermore,we investigate the limitations of these models,including scalability,reproducibility and technical challenges,as well as their potential therapeutic impact on personalized medicine.Through a thorough comparison of model performance,we provide insights into the strengths and weaknesses of each approach and offer guidance for model selection based on specific research needs.
基金funding support from the National Natural Science Foundation of China(Grant Nos.42177136 and 52309126).
文摘The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in southwestern China as the engineering prototype,large-scale three-dimensional(3D)physical model tests were conducted on a 3D-printed complex geological model containing two faults.Based on the selfdeveloped 3D loading system and excavation device,the macroscopic failure of fault-slip rockbursts was simulated indoors.The stress,strain,and fracturing characteristics of the surrounding rock near the two faults were systematically evaluated during excavation and multistage loading.The test results effectively revealed the evolution and triggering mechanism of fault-slip rockbursts.After the excavation of a highstress tunnel,stress readjustment occurred.Owing to the presence of these two faults,stress continued to accumulate in the rock mass between them,leading to the accumulation of fractures.When the shear stress on a fault surface exceeded its shear strength,sudden fault slip and dislocation occurred,thus triggering rockbursts.Rockbursts occurred twice in the vault between the two faults,showing obvious intermittent characteristics.The rockburst pit was controlled by two faults.When the faults remained stable,tensile failure predominated in the surrounding rock.However,when the fault slip was triggered,shear failure in the surrounding rock increased.These findings provide valuable insights for enhancing the comprehension of fault-slip rockbursts.
基金the National Science and Technology Council(NSTC)of the Republic of China,Taiwan,for financially supporting this research under Contract No.NSTC 112-2637-M-131-001.
文摘Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.
