3D tomographic analysis of equatorial plasma bubble using GNSS-TEC data from Indonesian GNSS Network

Equatorial Plasma Bubbles(EPBs)are ionospheric irregularities that take place near the magnetic equator.EPBs most commonly occur after sunset during the equinox months,although they can also be observed during other seasons.The phenomenon significantly disrupts radio wave signals essential to communication and navigation systems.The national network of Global Navigation Satellite System(GNSS)receivers in Indonesia(>30°longitudinal range)provides an opportunity for detailed EPB studies.To explore this,we conducted preliminary 3D tomography of total electron content(TEC)data captured by GNSS receivers following a geomagnetic storm on December 3,2023,when at least four EPB clusters occurred in the Southeast Asian sector.TEC and extracted TEC depletion with a 120-minute running average were then used as inputs for a 3D tomography program.Their 2D spatial distribution consistently captured the four EPB clusters over time.These tomography results were validated through a classical checkerboard test and comparisons with other ionospheric data sources,such as the Global Ionospheric Map(GIM)and International Reference Ionosphere(IRI)profile.Validation of the results demonstrates the capability of the Indonesian GNSS network to measure peak ionospheric density.These findings highlight the potential for future three-dimensional research of plasma bubbles in low-latitude regions using existing GNSS networks,with extensive longitudinal coverage.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.

Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm

Esophageal disease is a common disorder of the digestive system that can severely affect the quality of life andprognosis of patients. Esophageal stenting is an effective treatment that has been widely used in clinical practice.However, esophageal stents of different types and parameters have varying adaptability and effectiveness forpatients, and they need to be individually selected according to the patient’s specific situation. The purposeof this study was to provide a reference for clinical doctors to choose suitable esophageal stents. We used 3Dprinting technology to fabricate esophageal stents with different ratios of thermoplastic polyurethane (TPU)/(Poly-ε-caprolactone) PCL polymer, and established an artificial neural network model that could predict the radial forceof esophageal stents based on the content of TPU, PCL and print parameter. We selected three optimal ratios formechanical performance tests and evaluated the biomechanical effects of different ratios of stents on esophagealimplantation, swallowing, and stent migration processes through finite element numerical simulation and in vitrosimulation tests. The results showed that different ratios of polymer stents had different mechanical properties,affecting the effectiveness of stent expansion treatment and the possibility of postoperative complications of stentimplantation.

Three-dimensional numerical analysis of plant-soil hydraulic interactions on pore water pressure of vegetated slope under different rainfall patterns

Understanding the pore water pressure distribution in unsaturated soil is crucial in predicting shallow landslides triggered by rainfall,mainly when dealing with different temporal patterns of rainfall intensity.However,the hydrological response of vegetated slopes,especially three-dimensional(3D)slopes covered with shrubs,under different rainfall patterns remains unclear and requires further investigation.To address this issue,this study adopts a novel 3D numerical model for simulating hydraulic interactions between the root system of the shrub and the surrounding soil.Three series of numerical parametric studies are conducted to investigate the influences of slope inclination,rainfall pattern and rainfall duration.Four rainfall patterns(advanced,bimodal,delayed,and uniform)and two rainfall durations(4-h intense and 168-h mild rainfall)are considered to study the hydrological response of the slope.The computed results show that 17%higher transpiration-induced suction is found for a steeper slope,which remains even after a short,intense rainfall with a 100-year return period.The extreme rainfalls with advanced(PA),bimodal(PB)and uniform(PU)rainfall patterns need to be considered for the short rainfall duration(4 h),while the delayed(PD)and uniform(PU)rainfall patterns are highly recommended for long rainfall durations(168 h).The presence of plants can improve slope stability markedly under extreme rainfall with a short duration(4 h).For the long duration(168 h),the benefit of the plant in preserving pore-water pressure(PWP)and slope stability may not be sufficient.

Three-dimensional stability calculation method for high and large composite slopes formed by mining stope and inner dump in adjacent open pits

The 2D limit equilibrium method is widely used for slope stability analysis.However,with the advancement of dump engineering,composite slopes often exhibit significant 3D mechanical effects.Consequently,it is of significant importance to develop an effective 3D stability calculation method for composite slopes to enhance the design and stability control of open-pit slope engineering.Using the composite slope formed by the mining stope and inner dump in Baiyinhua No.1 and No.2 open-pit coal mine as a case study,this research investigates the failure mode of composite slopes and establishes spatial shape equations for the sliding mass.By integrating the shear resistance and sliding force of each row of microstrip columns onto the bottom surface of the strip corresponding to the main sliding surface,a novel 2D equivalent physical and mechanical parameters analysis method for the strips on the main sliding surface of 3D sliding masses is proposed.Subsequently,a comprehensive 3D stability calculation method for composite slopes is developed,and the quantitative relationship between the coordinated development distance and its 3D stability coefficients is examined.The analysis reveals that the failure mode of the composite slope is characterized by cutting-bedding sliding,with the arc serving as the side interface and the weak layer as the bottom interface,while the destabilization mechanism primarily involves shear failure.The spatial form equation of the sliding mass comprises an ellipsoid and weak plane equation.The analysis revealed that when the coordinated development distance is 1500 m,the error rate between the 3D stability calculation result and the 2D stability calculation result of the composite slope is less than 8%,thereby verifying the proposed analytical method of equivalent physical and mechanical parameters and the 3D stability calculation method for composite slopes.Furthermore,the3D stability coefficient of the composite slope exhibits an exponential correlation with the coordinated development distance,with the coefficient gradually decreasing as the coordinated development distance increases.These findings provide a theoretical guideline for designing similar slope shape parameters and conducting stability analysis.

Design,progress and challenges of 3D carbon-based thermally conductive networks

The advent of the 5G era has stimulated the rapid development of high power electronics with dense integration.Three-dimensional(3D)thermally conductive networks,possessing high thermal and electrical conductivities and many different structures,are regarded as key materials to improve the performance of electronic devices.We provide a critical overview of carbonbased 3D thermally conductive networks,emphasizing their preparation-structure-property relationships and their applications in different scenarios.A detailed discussion of the microscopic principles of thermal conductivity is provided,which is crucial for increasing it.This is followed by an in-depth account of the construction of 3D networks using different carbon materials,such as graphene,carbon foam,and carbon nanotubes.Techniques for the assembly of two-dimensional graphene into 3D networks and their effects on thermal conductivity are emphasized.Finally,the existing challenges and future prospects for 3D carbon-based thermally conductive networks are discussed.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

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.

Phase II Clinical Study of Three-Dimensional Printed Coplanar Template Combined with CT-Guided Percutaneous Core Needle Biopsy of Pulmonary Nodules in Elderly Patients

Background: As the population age structure gradually ages, more and more elderly people were found to have pulmonary nodules during physical examinations. Most elderly people had underlying diseases such as heart, lung, brain and blood vessels and cannot tolerate surgery. Computed tomography (CT)-guided percutaneous core needle biopsy (CNB) was the first choice for pathological diagnosis and subsequent targeted drugs, immune drugs or ablation treatment. CT-guided percutaneous CNB requires clinicians with rich CNB experience to ensure high CNB accuracy, but it was easy to cause complications such as pneumothorax and hemorrhage. Three-dimensional (3D) printing coplanar template (PCT) combined with CT-guided percutaneous pulmonary CNB biopsy has been used in clinical practice, but there was no prospective, randomized controlled study. Methods: Elderly patients with lung nodules admitted to the Department of Oncology of our hospital from January 2019 to January 2023 were selected. A total of 225 elderly patients were screened, and 30 patients were included after screening. They were randomly divided into experimental group (Group A: 30 cases) and control group (Group B: 30 cases). Group A was given 3D-PCT combined with CT-guided percutaneous pulmonary CNB biopsy, Group B underwent CT-guided percutaneous pulmonary CNB. The primary outcome measure of this study was the accuracy of diagnostic CNB, and the secondary outcome measures were CNB time, number of CNB needles, number of pathological tissues and complications. Results: The diagnostic accuracy of group A and group B was 96.67% and 76.67%, respectively (P = 0.026). There were statistical differences between group A and group B in average CNB time (P = 0.001), number of CNB (1 vs more than 1, P = 0.029), and pathological tissue obtained by CNB (3 vs 1, P = 0.040). There was no statistical difference in the incidence of pneumothorax and hemorrhage between the two groups (P > 0.05). Conclusions: 3D-PCT combined with CT-guided percutaneous CNB can improve the puncture accuracy of elderly patients, shorten the puncture time, reduce the number of punctures, and increase the amount of puncture pathological tissue, without increasing pneumothorax and hemorrhage complications. We look forward to verifying this in a phase III randomized controlled clinical study. .

Three-Dimensional Ocean Sensor Networks:A Survey

The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

Three-dimensional Fusion of Spaceborne and Ground Radar Reflectivity Data Using a Neural Network–Based Approach

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

Three-dimensional gravity inversion based on 3D U-Net++

The gravity inversion is to restore genetic density distribution of the underground target to be explored for explaining the internal structure and distribution of the Earth.In this paper,we propose a new 3D gravity inversion method based on 3D U-Net++.Compared with two-dimensional gravity inversion,three-dimensional(3D)gravity inversion can more precisely describe the density distribution of underground space.However,conventional 3D gravity inversion method input is two-dimensional,the input and output of the network proposed in our method are three-dimensional.In the training stage,we design a large number of diversifi ed simulation model-data pairs by using the random walk method to improve the generalization ability of the network.In the test phase,we verify the network performance by using the model-data pairs generated by the simulation.To further illustrate the eff ectiveness of the algorithm,we apply the method to the inversion of the San Nicolas mining area,and the inversion results are basically consistent with the borehole measurement results.Moreover,the results of the 3D U-Net++inversion and the 3D U-Net inversion are compared.The density models of the 3D U-Net++inversion have higher resolution,more concentrated inversion results,and a clearer boundary of the density model.

Three-Dimensional Cooperative Localization via Space-Air-Ground Integrated Networks

The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.

A Novel Hydrogen-bonded Three-dimensional Network Complex Containing Nickel

A novel complex, (H 3O) 2[Ni(2,6-pydc) 2]·2H 2O was synthesized in an aqueous solution and characterized by means of single-crystal X-ray diffraction, elemental analyses and IR spectra. The X-ray structural analysis revealed that the novel compound forms three-dimensional(3D) networks by both π-π stacking and hydrogen-bonding interactions. The crystal data for the complex are a=13.853(3) nm, b=9.6892(19) nm, c=13.732(3) nm, α=90.00°, β=115.52(3)°, γ=90.00°, Z=3, R 1=0.0786, wR 2=0.1522.

Small-world networks of neuroblastoma cells cultured in three-dimensional polymeric scaffolds featuring multi-scale roughness

Understanding the mechanisms underlying cell-surface interaction is of fundamental importance for the rational design of scaffolds aiming at tissue engineering,tissue repair and neural regeneration applications.Here,we examined patterns of neuroblastoma cells cultured in three-dimensional polymeric scaffolds obtained by two-photon lithography.Because of the intrinsic resolution of the technique,the micrometric cylinders composing the scaffold have a lateral step size of^200 nm,a surface roughness of around 20 nm,and large values of fractal dimension approaching 2.7.We found that cells in the scaffold assemble into separate groups with many elements per group.After cell wiring,we found that resulting networks exhibit high clustering,small path lengths,and small-world characteristics.These values of the topological characteristics of the network can potentially enhance the quality,quantity and density of information transported in the network compared to equivalent random graphs of the same size.This is one of the first direct observations of cells developing into 3D small-world networks in an artificial matrix.

A Three-Dimensional (3D) Environment to Maintain the Integrity of Mouse Testicular Can Cause the Occurrence of Meiosis

Adhesions between different cells and extracellular matrix have been studied extensively in vitro, but little is known about their functions in testicular tissue counterparts. Spermatogonia and their companion somatic cells maintain a close association throughout spermatogenesis and this association is necessary for normal spermatogenesis. In order to keep the relative integrity of the testicular tissues, and to detect the development in vitro, culture testicular tissues in a three- dimensional （3D） agarose matrix was examined. Testicular tissues isolated from 6.5 d postpartum （dpp） mouse were cultured on the top of the matrix for 26 d with a medium height up to 4/5 of the 3D agarose matrix. The results showed that in this 3D culture environment, each type of testicular cells kept the same structure, localization and function as in vivo and might be more biologically relevant to living organisms. After culture, germ cell marker VASA and meiosis markers DAZL and SCP3 showed typical positive analysed by immunofluorescence staining and RT-PCR. It demonstrated that this 3D culture system was able to maintain the number of germ cells and promote the meiosis initiation of male germ cells.