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.

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.

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.

Luminescence regulation of Sb^(3+)in 0D hybrid metal halides by hydrogen bond network for optical anti-counterfeiting

The Sb^(3+) doping strategy has been proven to be an effective way to regulate the band gap and improve the photophysical properties of organic-inorganic hybrid metal halides(OIHMHs).However,the emission of Sb^(3+) ions in OIHMHs is primarily confined to the low energy region,resulting in yellow or red emissions.To date,there are few reports about green emission of Sb^(3+)-doped OIHMHs.Here,we present a novel approach for regulating the luminescence of Sb^(3+) ions in 0D C_(10)H_(2)_(2)N_(6)InCl_(7)·H_(2)O via hydrogen bond network,in which water molecules act as agents for hydrogen bonding.Sb^(3+)-doped C_(10)H_(2)2N_(6)InCl_(7)·H_(2)O shows a broadband green emission peaking at 540 nm and a high photoluminescence quantum yield(PLQY)of 80%.It is found that the intense green emission stems from the radiative recombination of the self-trapped excitons(STEs).Upon removal of water molecules with heat,C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7) generates yellow emis-sion,attributed to the breaking of the hydrogen bond network and large structural distortions of excited state.Once water molecules are adsorbed by C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7),it can subsequently emit green light.This water-induced reversible emission switching is successfully used for optical security and information encryption.Our findings expand the under-standing of how the local coordination structure influences the photophysical mechanism in Sb^(3+)-doped metal halides and provide a novel method to control the STEs emission.

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.

基于改进YOLO-v3的风力机叶片表面损伤检测识别

为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(squeeze and excitation networks,SENet)结构,使YOLO-v3算法更加关注与目标相关的特征通道,提升网络性能。结果表明,改进后算法的平均精度为84.42%,较原YOLO-v3算法提升了6.14%,检测时间减少了21 ms,改进后的YOLO-v3算法能较好地识别出风力机叶片表面损伤。

基于YOLO-V3算法的加油站不安全行为检测

为控制加油站火灾爆炸风险目标,结合事故统计和故障树分析方法,提出一种基于YOLO-V3算法的加油站不安全行为检测模型。首先在收集90起加油站火灾爆炸事故的基础上,统计分析加油站火灾爆炸事故的点火源;其次构建加油站火灾爆炸故障树,计算各基本事件的结构重要度,并确定加油站危险性较高的不安全行为;然后采用现场采集和模拟的方法收集加油站不安全行为图像数据,利用数据增强方法构建加油站不安全行为图像数据集;最后基于深度学习的方法构建加油站不安全行为检测模型,经过1000次训练迭代后得到最终模型。研究结果表明:引起加油站火灾爆炸事故的不安全行为主要有抽烟、打电话等;训练得到的检测模型在测试集上对抽烟、打电话和正常行为检测类别的平均检测精度分别为67%、85%和77%,模型的平均检测精度均值为84%。

基于改进YOLO-v3的无人机遥感图像农村地物分类

随着新农村的建设,农业用地的规划和利用也变得至关重要。针对传统地物分类方法效率低、自动化程度不高等问题,提出基于YOLO-v3改进模型的农村地物检测分类方法。该方法在YOLO-v3的Res4结构的基础上添加SPP层,有效地提升了模型对多尺寸目标的适应能力,在一定程度上提高了模型的泛化能力。同时,在FPN层添加PAN结构,增加了定位信息的语义特征,对于模型的检测精度提升明显。实验结果表明,提出的改进YOLO-v3模型在Air数据集的mAP达到了0.726,相较YOLO-v3模型,在精度、检测效率和模型的泛化能力上都有所提升。

基于改进DeepLabv3+算法的起重机锈迹检测

室外工作的起重机金属结构易产生锈蚀现象,严重的锈蚀会导致结构承载能力显著降低,从而引发灾难性事故。文中针对当前起重机人工锈迹巡检中存在的漏检、误检和费时等问题,提出一种基于改进DeepLabv3+算法的自动化锈迹检测方法。该方法依托于机器视觉,将原始DeepLabv3+的骨干网络替换为幽灵网络(GhostNet)以提升网络的轻量化程度;使用特征金字塔网络(FPN)进行特征提取,用于抑制噪声和背景对锈迹提取的不良干扰;引入空间感知独立自注意机制(SSA)来提高网络区域感知性能;最后使用特征融合(Add)代替原始网络的特征堆叠来降低算法参数量。将所提方法应用于室外起重机锈迹检测,结果表明所提算法的检测性能优于原始算法和其他经典语义分割算法,具有重要的工程应用价值。

Fang-Xia-Dihuang decoction inhibits breast cancer progression induced by psychological stress via down-regulation of PI3K/AKT and JAK2/STAT3 pathways:An in vivo and a network pharmacology assessment

Background:The development and prognosis of breast cancer are intricately linked to psychological stress.In addition,depression is the most common psychological comorbidity among breast cancer survivors,and reportedly,Fang-Xia-Dihuang decoction(FXDH)can effectively manage depression in such patients.However,its pharmacological and molecular mechanisms remain obscure.Methods:Public databases were used for obtaining active components and related targets.Main active components were further verified by ultra-high-performance liquid chromatography-high-resolution mass spectrometry(UPLC-HRMS).Protein–protein interaction and enrichment analyses were taken to predict potential hub targets and related pathways.Molecule docking was used to understand the interactions between main compounds and hub targets.In addition,an animal model of breast cancer combined with depression was established to evaluate the intervention effect of FXDH and verify the pathways screened by network pharmacology.Results:174 active components of FXDH and 163 intersection targets of FXDH,breast cancer,and depression were identified.Quercetin,methyl ferulate,luteolin,ferulaldehyde,wogonin,and diincarvilone were identified as the principal active components of FXDH.Protein–protein interaction and KEGG enrichment analyses revealed that the phosphoinositide-3-kinase–protein kinase B(PI3K/AKT)and Janus kinase/signal transducer and activator of transcription(JAK2/STAT3)signaling pathways played a crucial role in mediating the efficacy of FXDH for inhibiting breast cancer progression induced by depression.In addition,in vivo experiments revealed that FXDH ameliorated depression-like behavior in mice and inhibited excessive tumor growth in mice with breast cancer and depression.FXDH treatment downregulated the expression of epinephrine,PI3K,AKT,STAT3,and JAK2 compared with the control treatment(p<0.05).Molecular docking verified the relationship between the six primary components of FXDH and the three most important targets,including phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha(PIK3CA),AKT,and STAT3.Conclusion:This study provides a scientific basis to support the clinical application of FXDH for improving depression-like behavior and inhibiting breast cancer progression promoted by chronic stress.The therapeutic effects FXDH may be closely related to the PI3K/AKT and JAK2/STAT3 pathways.This finding helps better understand the regulatory mechanisms underlying the efficacy of FXDH.

Zuo Gui Wan Promotes Osteogenesis via PI3K/AKT Signaling Pathway:Network Pharmacology Analysis and Experimental Validation

Objective Osteogenesis is vitally important for bone defect repair,and Zuo Gui Wan(ZGW)is a classic prescription in traditional Chinese medicine(TCM)for strengthening bones.However,the specific mechanism by which ZGW regulates osteogenesis is still unclear.The current study is based on a network pharmacology analysis to explore the potential mechanism of ZGW in promoting osteogenesis.Methods A network pharmacology analysis followed by experimental validation was applied to explore the potential mechanisms of ZGW in promoting the osteogenesis of bone marrow mesenchymal stem cells(BMSCs).Results In total,487 no-repeat targets corresponding to the bioactive components of ZGW were screened,and 175 target genes in the intersection of ZGW and osteogenesis were obtained.And 28 core target genes were then obtained from a PPI network analysis.A GO functional enrichment analysis showed that the relevant biological processes mainly involve the cellular response to chemical stress,metal ions,and lipopolysaccharide.Additionally,KEGG pathway enrichment analysis revealed that multiple signaling pathways,including the phosphatidylinositol-3-kinase/protein kinase B(PI3K/AKT)signaling pathway,were associated with ZGW-promoted osteogensis.Further experimental validation showed that ZGW could increase alkaline phosphatase(ALP)activity as well as the mRNA and protein levels of ALP,osteocalcin(OCN),and runt related transcription factor 2(Runx 2).What’s more,Western blot analysis results showed that ZGW significantly increased the protein levels of p-PI3K and p-AKT,and the increases of these protein levels significantly receded after the addition of the PI3K inhibitor LY294002.Finally,the upregulated osteogenic-related indicators were also suppressed by the addition of LY294002.Conclusion ZGW promotes the osteogenesis of BMSCs via PI3K/AKT signaling pathway.

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

HL-3装置测量基准网的建立及部件定位测量

根据HL-3装置总装集成设计安装精度的要求,需要建立一个高精度的测量基准网,在总装过程中采用激光跟踪仪等先进测量设备对安装部件的空间位置进行测量。建立的基准网实现了网内基准点空间坐标最大不确定度为0.133mm。特别在对真空室、临时第一壁/限制器等部件的安装中,进行定位测量和数据反馈,然后再进行安装调整,实现了真空室∅1.84mm的同轴度精度,满足同轴度≤∅3mm的要求;标高偏差为-0.08~+0.136mm,满足标高偏差≤±1mm的要求。临时第一壁/限制器安装最大偏差值为+1.9351mm,最小偏差值为-1.8337mm,均满足各模块表面位置误差不超过±2mm的技术要求。测量基准网的建立以及安装过程中对部件高精度的定位测量,保证了HL-3装置高质量的建造。

CTCS-3线路GSM-R网络运维质量评价体系研究

针对高速铁路GSM-R网络承载行车通信业务的特点,分析CTCS-3线路GSM-R网络运维质量评价思路,提出CTCS-3线路GSM-R网络运维质量评价体系,通过GSM-R网络运行数据进行计算得出评价结果,综合反映出CTCS-3线路GSM-R网络运维质量状况。

MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

In complex traffic environment scenarios,it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance.The accuracy of 3D object detection will be affected by problems such as illumination changes,object occlusion,and object detection distance.To this purpose,we face these challenges by proposing a multimodal feature fusion network for 3D object detection(MFF-Net).In this research,this paper first uses the spatial transformation projection algorithm to map the image features into the feature space,so that the image features are in the same spatial dimension when fused with the point cloud features.Then,feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features,suppress useless features,and increase the directionality of the network to features.Finally,this paper increases the probability of false detection and missed detection in the non-maximum suppression algo-rithm by increasing the one-dimensional threshold.So far,this paper has constructed a complete 3D target detection network based on multimodal feature fusion.The experimental results show that the proposed achieves an average accuracy of 82.60%on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)dataset,outperforming previous state-of-the-art multimodal fusion networks.In Easy,Moderate,and hard evaluation indicators,the accuracy rate of this paper reaches 90.96%,81.46%,and 75.39%.This shows that the MFF-Net network has good performance in 3D object detection.

基于NS-3网络模拟器的MPQUIC协议仿真研究

随着5G移动视频应用加速落地以及国内视频流需求的迅速激增,迫切需要一种新的数据传输协议来提供可靠的安全性,以保障上层应用处理更多的接入连接以及满足更低的延时需求.多路径QUIC协议(Multipath Quick UDP Internet Connection,MPQUIC)具有拟合多条链路带宽资源、强大连接的容错能力和高可靠性等优点,被认为将在未来移动互联网数据传输中发挥重要的作用.然而,目前国内外研究人员对于MPQUIC协议的相关研究正处于初步阶段,该协议还没有一个普适性的、开源的仿真平台.因此,借助全球网络仿真领域应用最广的NS-3网络模拟器搭建了MPQUIC仿真平台(ns3-mpquic),为相关学者提供研究MPQUIC协议的开源、免费、普适的基础平台,为全球专家学者对MPQUIC协议的模拟部署和优化提供助力.

黄芩苷对慢性萎缩性胃炎小鼠JAK1、STAT3表达的影响

【目的】通过网络药理学和动物实验探讨黄芩苷对慢性萎缩性胃炎小鼠胃黏膜的修复机制。【方法】(1)应用网络药理学预测分析黄芩苷治疗慢性萎缩性胃炎的潜在关键靶点。(2)动物实验:将40只C57BL/6N小鼠随机分为正常组、模型组、维酶素组、黄芩苷组,每组10只。除正常组,其他3组小鼠采用N-甲基-N’-硝基-N-亚硝基胍(MNNG)灌胃结合饥饱失常法构建慢性萎缩性胃炎模型。给药结束后,采用苏木素-伊红(HE)染色法观察胃黏膜组织病理变化,采用酶联免疫吸附法(ELISA)检测血清中胃泌素(GAS)和前列腺素E2(PGE2)水平变化,采用实时荧光定量聚合酶链反应(qRT-PCR)法和蛋白免疫印迹(Western Blot)法检测胃黏膜组织中Janus酪氨酸激酶1(JAK1)、信号转导和转录激活子3(STAT3)的mRNA与蛋白表达水平。【结果】网络药理学结果显示,黄芩苷与核心靶点JAK1、STAT3可自发结合。动物实验结果显示:与正常组比较,模型组小鼠胃黏膜组织发生萎缩,腺体排列紊乱,存在大量淋巴细胞,胃黏膜细胞凋亡指数显著升高(P<0.05),血清中GAS与PGE2水平显著降低(P<0.05),胃黏膜组织中JAK1、STAT3的mRNA与蛋白表达水平显著升高(P<0.05);与模型组比较,维酶素组与黄芩苷组小鼠胃黏膜病变减轻,腺体排列相对整齐,结构较完整,胃黏膜细胞凋亡指数显著降低(P<0.05),血清中GAS与PGE2水平显著升高(P<0.05),胃黏膜组织中JAK1、STAT3的mRNA与蛋白表达水平显著降低(P<0.05);黄芩苷组上述各指标与维酶素组比较,差异均无统计学意义(P>0.05)。【结论】黄芩苷可有效修复慢性萎缩性胃炎小鼠胃黏膜病变,其机制可能与下调JAK1、STAT3的mRNA及蛋白表达有关。