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.

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.

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.

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.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography(OCT)imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues.However,it still faces the following challenges:including data processing speed,image quality,and improvements in three-dimensional(3D)visualization effects.OCT technology,especially functional imaging techniques like optical coherence tomography angiography(OCTA),requires a long acquisition time and a large data size.Despite the substantial increase in the acquisition speed of swept source optical coherence tomography(SS-OCT),it still poses significant challenges for data processing.Additionally,during in situ acquisition,image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis.Firstly,a customized frequency domainfilter with anti-banding suppression parameters was designed to suppress artifact noises.Then,this study proposed a graphics processing unit(GPU)-based real-time data processing pipeline for SS-OCT,achieving a measured line-process rate of 800 kHz for 3D fast and high-quality data visualization.Furthermore,a GPU-based realtime data processing for CC-OCTA was integrated to acquire dynamic information.Moreover,a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials,with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network.OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation.Therefore,GPU-based OCT enables real-time processing and visualization with artifact suppression,making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

3D打印生物墨水在组织修复与再生医学中的应用

背景:通过选用合适的生物墨水,3D打印技术可用以制造人体组织和器官的代替物,并在人体内发挥作用。近些年来3D打印技术发展迅速,在再生医学中有着巨大的应用潜力。目的:介绍3D打印用生物墨水的类型,并综述生物墨水的分类、应用、优缺点及未来愿景。方法:以“3D printing,Biological ink,Tissue engineering,hydrogel,Synthetic material,Cytoactive factor,3D打印、生物墨水、组织工程”为检索词,运用计算机检索2000-2022年以来发表在PubMed、CNKI数据库中的相关文献,最终纳入83篇进行综述。结果与结论:在过去的几十年里,生物3D打印技术发展迅速,在组织工程和生物医学等各个领域都受到了极大的关注。相对于传统生物支架制造方法在功能性及结构方面受到的限制,3D打印可以更好地模拟生物组织复杂的结构,并且具有合适的力学、流变学和生物学特性。生物墨水是3D打印中必不可少的一部分,通过生物材料制备的生物墨水,经打印后产生的生物支架在组织修复和再生医学等方面有着巨大的科研潜力及临床意义,其材料的研究本身也越来越受到专家们的重视。3D打印生物墨水的材料各种各样,有天然材料也有合成材料,还有一些不需要任何额外生物材料的细胞聚集体,并且各种材料在实际运用中的功效各不相同。未来会有越来越多的生物墨水被研制用于组织工程,需要通过充足的实验模拟及设备测试来对生物墨水的可打印性进行分析,从而满足实际的医用需求。

3D打印导板技术联合多次去旋转治疗重度僵硬性脊柱侧凸

背景:近年来,随着3D打印技术的发展,使得外科手术走向个性化、精准化。3D打印导板技术可实现术前规划、术中导航,使得外科手术更加精准。临床中重度僵硬性脊柱侧弯矫形术中仍面临置钉准确性不高导致螺钉松动甚至引起神经并发症的问题,现有关于3D打印导板技术指导重度僵硬性脊柱侧弯术中置钉的研究不多。目的:评价3D打印导向模板技术联合后路多次去旋转治疗重度僵硬性脊柱侧凸的临床效果。方法:回顾性分析3D打印导向模板椎弓根螺钉置入后联合施行后路多次转棒去旋转技术治疗重度脊柱侧凸6例患者的临床资料,男3例,女3例,手术时年龄15-23岁,平均(18.17±3.49)岁。分析术后2周和术后18个月时脊柱侧弯相关参数的变化,进行统计学分析。结果与结论:(1)手术时间280-540 min,平均(340.83±102.20)min,术中出血量1000-4000 mL,平均(2000.00±1073.70)mL,固定节段9-14个椎体,平均(11.83±1.72)个椎体,矫形过程中未出现螺钉松动;(2)所有患者均获得随访,术后2周全脊柱正侧位片显示冠状位主弯的cobb角、冠状面C_(7)铅垂线和S1正中线的距离、矢状面C_(7)铅垂线和S1后缘的距离、顶椎偏移、胸椎后凸角、腰椎前凸角均获得明显矫正,主弯的cobb角平均矫正率62.22%,术后18个月随访各参数较术后2周无明显变化,矫形效果满意,无感染和内固定断裂;(3)围术期切口延迟愈合1例,经过换药处理瘢痕愈合,未出现神经并发症;(4)结果表明3D打印导向模板结合后路多次转棒去旋转技术治疗重度僵硬性脊柱侧凸畸形安全有效,矫形效果满意。

3D MERGE与3D SPACE STIR序列在腰椎间盘突出症检查中的应用比较

目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。

3D打印技术在腓骨头上入路治疗复杂胫骨平台后外侧骨折中的临床应用

目的探讨3D打印技术在腓骨头上入路治疗复杂胫骨平台后外侧骨折中的临床应用价值。方法回顾性分析67例接受腓骨头上入路治疗的复杂胫骨平台后外侧骨折患者的临床资料,根据术前是否采用3D打印模拟手术将患者分为3D打印组35例和常规组32例。比较2组患者的手术时间、术中出血量、术中透视次数,观察术后切口感染、腘血管损伤、腓总神经损伤等并发症发生情况。随访骨折愈合时间,术后6个月评估Rasmussen评分,末次随访时评估美国特种外科医院(HSS)膝关节功能评分。结果67例患者随访时间为14~22个月;2组各有1例患者发生术后切口感染,均未发生腘血管损伤、腓总神经损伤、下肢深静脉血栓等并发症;3D打印组患者手术时间、术中出血量、术中透视次数均短于或少于常规组,差异有统计学意义(P<0.05);2组患者骨折愈合时间、术后6个月Rasmussen评分、末次随访时HSS评分比较,差异无统计学意义(P>0.05)。结论3D打印技术应用于复杂胫骨平台后外侧骨折患者的腓骨头上入路治疗中,可以优化手术方案,缩短手术时间,减少术中出血量和透视次数。

3D打印混凝土永久模板叠合柱的抗压性能数值模拟研究

为深入研究3D打印混凝土永久模板叠合柱的抗压性能,基于3D打印混凝土永久模板叠合柱及同尺寸整体现浇对照柱试验建立构件数值模型,模拟分析其轴压荷载-位移响应及失效形态。针对界面粘结性能、现浇混凝土抗压强度、打印模板厚度、荷载偏心距等参数开展3D打印混凝土永久模板叠合柱的抗压性能计算分析,研究表明:叠合柱轴压极限承载力随着薄弱界面剪切强度、刚度及现浇混凝土抗压强度的增大而增大。由于打印材料的抗压强度高于现浇混凝土,叠合柱抗压极限承载力提升率与打印模板厚度呈近似线性关系,叠合圆柱的抗压极限承载力随着荷载偏心距的增大而降低,呈近似线性负相关。此外,偏心距对叠合圆柱极限承载力下降幅度的影响大于现浇圆柱。

沉浸式3D虚拟仿真实验平台构建

为解决传统实践教学在时间、空间上的局限性,增强实践教学的互动性,以虚拟智慧城市中的物联网创新应用为研究对象,结合虚拟现实技术,在实验内容和教学模式中融入价值创造和创业素养,构建融入“创新实验路径”和“多层次综合实验项目”的物联网专业沉浸式3D虚拟仿真实验平台。采用布鲁姆教学目标分类法设计3D虚拟仿真实验教学目标,并按照IAPVE的实施模型,构建基于3D虚拟仿真实验平台的教学实施模型和考核评价模型。实施结果表明,该3D虚拟仿真实验平台及教学实施和考核评价模型可指导实践教学改革,实现学生综合能力的全面协同提升。

3D打印共面模板在局部进展型胰腺癌125I粒子植入治疗中的疗效观察

局部进展型胰腺癌(locally advanced pancreatic cancer,LAPC)指肿瘤局部广泛浸润,伴有严重的血管侵犯而无远处转移的胰腺癌,占全部胰腺癌的30%~50%[1],病人平均生存期6~12个月,难以手术切除[2],需要先采用转化治疗,当其成为可切除肿瘤时再行手术切除,但转化成功率一般较低。顽固性腰腹痛常常是LAPC病人就诊的首要且最痛苦的症状,严重影响病人的生活。目前临床主要的治疗方法为药物、神经毁损等,但只能暂时缓解症状而不针对肿瘤本身。

基于正逆向设计结合的3D打印实践教学

以装配体零件为载体,以正逆向设计结合为思想,以项目式教学为依托的3D打印实践教学项目,引导学生自主创新,注重产品质量。实践过程中部分零件采用正向设计并打印制作成型,其余零件则采用逆向设计并打印制作成型,组装获得完整装配体。教学实施效果表明,实践环节中的产品废品率显著下降,同时学生的课堂参与度显著提升。基于正逆向设计结合的教学方法突破传统的3D打印实践教学思路,锻炼了学生项目管理、自主创新、主动思考解决问题等综合能力,贯彻落实了学生质量观意识的培养。

融合Unity3D的缆索起重机安全运行数字孪生模型构建方法

为降低缆索起重机(下文简称为缆机)运行的安全风险,分析缆机运行流程,提取缆机安全运行知识语义,通过物联网技术获取实时数据,利用3DS Max软件构建大坝缆机运行初始场景,实现孪生模型三维可视化,将模型导入Unity3D引擎,使用高清渲染管线对模型进行渲染,改善视觉效果,编写C#脚本对缆机运行全过程进行安全模拟仿真。研究结果表明:应用本文所构建的缆机安全运行数字孪生模型监测隐患与故障,综合准确率达到96.7%,可有效实现缆机运行过程的安全监控、参数化控制以及可视化展示。研究结果可为缆机施工过程的安全控制提供技术支持。

明胶/氧化纳米纤维素高弹性模量高孔隙皮肤支架的3D打印工艺

背景:在采用主动修复治疗手段应对皮肤创伤时,需要使用组织工程技术生成新的组织来代替坏死组织,皮肤支架在创伤修复领域具有良好的应用前景。皮肤支架需要呈现具有一定力学强度的三维多孔结构,以满足细胞增殖分裂的需求,而目前使用的明胶基生物材料力学强度弱,无法达到皮肤支架的使用要求。目的:针对明胶/氧化纳米纤维素复合材料制备组织工程皮肤支架时使用的3D打印工艺进行研究,重点研究不同工艺参数下制备皮肤支架的孔隙率与其力学强度之间的关系。方法:从葎草中提取10%浓度的氧化纳米纤维素晶须,再与5%的明胶复合得到明胶/氧化纳米纤维素复合材料,检测明胶与明胶/氧化纳米纤维素复合材料的弹性模量。以明胶/氧化纳米纤维素复合材料为基材,采用3D打印挤压成型方法制备皮肤支架,通过对材料进行力学性能测试和流变特性测试确定挤压成型工艺参数(填充间隙1.5-2.5 mm,0.1 mm均布;气压160-200 kPa),并以此制备具有三维多孔结构的皮肤支架。对皮肤支架进行了抗压性能的测试并与有限元分析结果相对比,论证了支架打印时的填充间隙与支架孔隙率及力学强度之间的关系。结果与结论:①通过实验得出,加入10%浓度的氧化纳米纤维素晶须使5%明胶的弹性模量度提升了8.84倍;在气压160 kPa下挤出成型可以得到1 mm直径的丝状凝胶,此时填充间隙从1.5 mm增大到2.5 mm会使支架的理论孔隙率从33%上升到60%,但抗压强度从230000 Pa降低到95000 Pa;②结果显示,使用2 mm填充间隙制备得到了理论孔隙率为50%、弹性模量160000 Pa的皮肤支架,该支架具有清晰的三维多孔结构。