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.

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.

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。

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.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

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。

ZIF-8@ZIF-67衍生的Co_(3)O_(4)-GN-CNT网络用作高性能锂离子电池负极

Co_(3)O_(4)由于较高的理论容量近年来被视为锂离子电池新型负极材料的热门候选之一,然而其较差的电导率和循环性能制约了其进一步发展。以ZIF-8@ZIF-67为自模板,三聚氰胺和g-C_(3)N_(4)为碳源,通过碳化和氧化处理制备了碳纳米管和石墨烯作为导电桥梁和外壳的Co_(3)O_(4)/C三维导电网络。颗粒纳米化的策略和锌的高温挥发造孔使其在0.5、2 A/g的电流密度下循环200、800圈后仍具有1 139.7、1 002.1 mAh/g的比容量,从0.2 A/g逐渐增大充放电的电流密度至10 A/g又恢复到0.2 A/g后比容量仍有初始容量的94.9%。该网络结构和同类材料相比表现出较为优异的循环和倍率性能。

Simiao Wan alleviates obesity-associated insulin resistance via PKCε/IRS-1/PI3K/Akt signaling pathway based on network pharmacology analysis and experimental validation

Background:The purpose of the study was to investigatethe active ingredients and potential biochemicalmechanisms of Simiao Wan(SMW)in obesity-associated insulin resistance.Methods:An integrated network pharmacology method to screen the active compoundsand candidate targets,construct the protein-protein-interaction network,and ingredients-targets-pathways network was constructed for topological analysis to identify core targets and main ingredients.To find the possible signaling pathways,enrichment analysis was performed.Further,a model of insulin resistance in HL-7702 cells was established to verify the impact of SMW and the regulatory processes.Results:An overall of 63 active components and 151 candidate targets were obtained,in which flavonoids were the main ingredients.Enrichment analysis indicated that the PI3K-Akt signaling pathway was the potential pathway regulated by SMW in obesity-associated insulin resistance treatment.The result showed that SMW could significantly ameliorate insulin sensitivity,increase glucose synthesis and glucose utilization and reduce intracellular lipids accumulation in hepatocytes.Also,SMW inhibited diacylglycerols accumulation-induced PKCεactivity and decreased its translocation to the membrane.Conclusion:SMW ameliorated obesity-associated insulin resistance through PKCε/IRS-1/PI3K/Akt signaling axis in hepatocytes,providing a new strategy for metabolic disease treatment.

La^(3+)掺杂TiO_(2)粉体的高分子网络凝胶法制备工艺优化及其光催化降解甲基橙性能(英文)

采用高分子网络凝胶法制备La^(3+)掺杂TiO_(2)粉体,通过正交实验优化制备工艺,采用比表面积测试(BET)、X射线衍射仪(XRD)、紫外-可见光谱仪(UV-Vis)和扫描透射电子显微镜(STEM)对TiO_(2)粉体的晶体结构和光吸收性能进行了表征,并以甲基橙为目标进行了光催化降解实验。结果表明:高分子网络凝胶法制备La^(3+)掺杂TiO_(2)粉体时对TiO_(2)光吸收波长影响最大的是煅烧温度,其次是掺杂量,最后是保温时间;正交实验得到的最优制备条件为:La^(3+)掺杂量为0.5%mol、煅烧温度为800℃,保温时间为30 min;煅烧温度过高与过低都不利于提高La^(3+)掺杂TiO_(2)的光催化活性,其存在一个最佳的温度范围:650~700℃;La^(3+)掺杂抑制了锐钛矿相向金红石相的转变,也抑制了晶粒的长大,但当La^(3+)掺杂量为1.0%mol时,其光催化效果最好;二氧化钛光催化降解甲基橙是零级反应,掺杂并没有改变光催化反应类型。

基于3D-torus结构的点到点通信路由算法

多计算机系统的性能很大程度上决定于点对点的通信代价,在多计算机系统中大都采用三维的torus网。当系统的通信量很高时,最短路径的选择是非常重要的。

A network lightweighting method for difficult segmentation of 3D medical images

Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.

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。

基于改进麻雀搜索算法的3DDV-Hop定位算法

针对经典3DDV-Hop算法定位精度不高的问题,提出一种基于改进麻雀搜索算法优化的改进的3DDV-Hop算法;算法首先通过多通信半径优化传感器节点之间跳数,并且在平均跳距计算过程中引入动态加权因子提高平均跳距计算精度,其次在麻雀搜索算法的基础上融合反向学习策略与萤火虫算法分别对麻雀搜索算法的种群与位置更新迭代进行优化,最后将未知节点坐标计算问题转化成改进后的麻雀搜索算法寻优问题,利用改进后的麻雀搜索算法替代最小二乘法计算未知节点坐标,进一步提高未知节点位置计算精度;经过MATLAB仿真验证,改进算法对比于经典3DDV-Hop算法和相关算法,定位精度得到有效提高。