“.NET Framework2.0程序设计”教学改革的探索与实践

".NET Framework2.0程序设计"是实践性、综合性、应用性很强的课程,必须充分重视教学改革。本文旨在探索".NET Framework2.0程序设计"教学改革的方式方法。文中详细阐述了本课程的课程定位、改革传统教学方式和教学方法等内容。

SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型

非小细胞肺癌严重损害人类健康,早期非小细胞肺癌CT(Computed Tomography)图像中的肿瘤结节体积小,不易发现,极易造成漏诊和误诊.为了精确分割非小细胞肺癌CT图像中的小体积肿瘤结节,本文提出SOSNet(Small Object Segmentation Networks)自动分割模型,利用ResNet(Residual Network)基础层和空洞卷积构造非对称编码器-解码器结构作为分割主网络,利用轴向取反注意力模块ARA(Axial Reverse Attention)逐步擦除背景中对分割有影响的结构,再使用结构细化模块SR(Structure Refinement)对主网络输出的粗略特征图进行结构细化,从而实现非小细胞肺癌肿瘤结节分割.在非小细胞肺癌公开数据集的实验测试表明,本文提出的小目标自动分割模型SOSNet可以有效分割出非小细胞肺癌CT图像中的小体积肿瘤结节,其mDice(mean-Dice)、mIoU(mean Intersection over Union)、Sensitivity、F1、Specificity、平均绝对误差MAE(Mean Absolute Error)均优于当前最先进的小目标分割模型CaraNet(Context Axial Reverse Attention Network).

Carbon nanocages bridged with graphene enable fast kinetics for dual-carbon lithium-ion capacitors

Lithium-ion capacitors(LICs) combining the advantages of lithium-ion batteries and supercapacitors are considered a promising nextgeneration energy storage device. However, the sluggish kinetics of battery-type anode cannot match the capacitor-type cathode, restricting the development of LICs. Herein, hierarchical carbon framework(HCF) anode material composed of 0D carbon nanocage bridged with 2D graphene network are developed via a template-confined synthesis process. The HCF with nanocage structure reduces the Li^(+) transport path and benefits the rapid Li^(+) migration, while 2D graphene network can promote the electron interconnecting of carbon nanocages. In addition, the doped N atoms in HCF facilitate to the adsorption of ions and enhance the pseudo contribution, thus accelerate the kinetics of the anode. The HCF anode delivers high specific capacity, remarkable rate capability. The LIC pouch-cell based on HCF anode and active HCF(a-HCF) cathode can provide a high energy density of 162 Wh kg^(-1) and a superior power density of 15.8 kW kg^(-1), as well as a long cycling life exceeding 15,000cycles. This study demonstrates that the well-defined design of hierarchical carbon framework by incorporating 0D carbon nanocages and 2D graphene network is an effective strategy to promote LIC anode kinetics and hence boost the LIC electrochemical performance.

All‑Covalent Organic Framework Nanofilms Assembled Lithium‑Ion Capacitor to Solve the Imbalanced Charge Storage Kinetics

Free-standing covalent organic framework(COFs)nanofilms exhibit a remarkable ability to rapidly intercalate/de-intercalate Li^(+) in lithium-ion batteries,while simultaneously exposing affluent active sites in supercapacitors.The development of these nanofilms offers a promising solution to address the persistent challenge of imbalanced charge storage kinetics between battery-type anode and capacitor-type cathode in lithium-ion capacitors(LICs).Herein,for the first time,custom-made COFBTMB-TP and COFTAPB-BPY nanofilms are synthesized as the anode and cathode,respectively,for an all-COF nanofilm-structured LIC.The COFBTMB-TP nanofilm with strong electronegative–CF3 groups enables tuning the partial electron cloud density for Li^(+) migration to ensure the rapid anode kinetic process.The thickness-regulated cathodic COFTAPB-BPY nanofilm can fit the anodic COF nanofilm in the capacity.Due to the aligned 1D channel,2D aromatic skeleton and accessible active sites of COF nanofilms,the whole COFTAPB-BPY//COFBTMB-TP LIC demonstrates a high energy density of 318 mWh cm^(−3) at a high-power density of 6 W cm^(−3),excellent rate capability,good cycle stability with the capacity retention rate of 77%after 5000-cycle.The COFTAPB-BPY//COFBTMB-TP LIC represents a new benchmark for currently reported film-type LICs and even film-type supercapacitors.After being comprehensively explored via ex situ XPS,7Li solid-state NMR analyses,and DFT calculation,it is found that the COFBTMB-TP nanofilm facilitates the reversible conversion of semi-ionic to ionic C–F bonds during lithium storage.COFBTMB-TP exhibits a strong interaction with Li^(+) due to the C–F,C=O,and C–N bonds,facilitating Li^(+) desolation and absorption from the electrolyte.This work addresses the challenge of imbalanced charge storage kinetics and capacity between the anode and cathode and also pave the way for future miniaturized and wearable LIC devices.

Accelerating Oxygen Electrocatalysis Kinetics on Metal-Organic Frameworks via Bond Length Optimization

Metal-organic frameworks(MOFs)have been developed as an ideal platform for exploration of the relationship between intrinsic structure and catalytic activity,but the limited catalytic activity and stability has hampered their practical use in water splitting.Herein,we develop a bond length adjustment strategy for optimizing naphthalene-based MOFs that synthesized by acid etching Co-naphthalenedicarboxylic acid-based MOFs(donated as AE-CoNDA)to serve as efficient catalyst for water splitting.AE-CoNDA exhibits a low overpotential of 260 mV to reach 10 mA cm^(−2)and a small Tafel slope of 62 mV dec^(−1)with excellent stability over 100 h.After integrated AE-CoNDA onto BiVO_(4),photocurrent density of 4.3 mA cm^(−2)is achieved at 1.23 V.Experimental investigations demonstrate that the stretched Co-O bond length was found to optimize the orbitals hybridization of Co 3d and O 2p,which accounts for the fast kinetics and high activity.Theoretical calculations reveal that the stretched Co-O bond length strengthens the adsorption of oxygen-contained intermediates at the Co active sites for highly efficient water splitting.

Rational construction of heterointerfaces in biomass sugarcane-derived carbon for superior electromagnetic wave absorption

The pervasive adoption of 5th generation mobile communication technology propels electromagnetic wave(EW)absorbents to achieve high-level performance.The heterointerface construction is crucial to the improvement of absorption ability.Herein,a series of ultralight composites with rational heterointerfaces(Co/ZnO@N-doped C/layer-stacked C,MSC)is fabricated by calcination with ration-al construction of sugarcane and CoZn-zeolitic imidazolate framework(ZIF).The components and structures of as-prepared composites were investigated,and their electromagnetic parameters could be adjusted by the content of CoZn-ZIFs.All composites possess excellent EW absorption performance,especially MSC-3.The optimal minimum reflection loss and effective absorption band of MSC-3 can reach−42 dB and 7.28 GHz at the thickness of only 1.6 mm with 20wt%filler loading.This excellent performance is attributed to the syner-gistic effect of dielectric loss stemming from the multiple heterointerfaces and magnetic loss induced by magnetic single Co.The sugar-cane-derived layer-stacked carbon has formed consecutive conductive networks and has further dissipated the electromagnetic energy through multiple reflection and conduction losses.Moreover,the simulated radar cross section(RCS)technology manifests that MSC-3 possesses outstanding EW attenuation capacity under realistic far-field conditions.This study provides a strategy for building efficient ab-sorbents based on biomass.

Electrostatic Interaction-directed Construction of Hierarchical Nanostructured Carbon Composite with Dual Electrical Conductive Networks for Zinc-ion Hybrid Capacitors with Ultrastability

Metal-organic framework(MOF)-derived carbon composites have been considered as the promising materials for energy storage.However,the construction of MOF-based composites with highly controllable mode via the liquid-liquid synthesis method has a great challenge because of the simultaneous heterogeneous nucleation on substrates and the self-nucleation of individual MOF nanocrystals in the liquid phase.Herein,we report a bidirectional electrostatic generated self-assembly strategy to achieve the precisely controlled coatings of single-layer nanoscale MOFs on a range of substrates,including carbon nanotubes(CNTs),graphene oxide(GO),MXene,layered double hydroxides(LDHs),MOFs,and SiO_(2).The obtained MOF-based nanostructured carbon composite exhibits the hierarchical porosity(V_(meso)/V_(micro)∶2.4),ultrahigh N content of 12.4 at.%and"dual electrical conductive networks."The assembled aqueous zinc-ion hybrid capacitor(ZIC)with the prepared nanocarbon composite as a cathode shows a high specific capacitance of 236 F g^(-1)at 0.5 A g^(-1),great rate performance of 98 F g^(-1)at 100 A g^(-1),and especially,an ultralong cycling stability up to 230000 cycles with the capacitance retention of 90.1%.This work develops a repeatable and general method for the controlled construction of MOF coatings on various functional substrates and further fabricates carbon composites for ZICs with ultrastability.

基于ASP.NET MVC架构在精炼炉RH二级客户端设计与实现

为了实现基于B/S框架的精炼炉RH二级客户端系统,采用基于.NET平台的ASP.NET MVC的Web开发框架,使用了MVC(Model-View-Controller)三层模式,利用基于ADO.NET为基础的Entity Framework对象关系映射器(简称ORM)框架技术,与数据库之间进行交互,Web界面采用Razor视图引擎,使得代码开发更加简洁、规范。基于MVC分层模式开发的精炼炉RH二级客户端系统,具有较好的可复用性、可扩展性,并且易于部署和维护。

Hollow Metal-Organic Framework/MXene/Nanocellulose Composite Films for Giga/Terahertz Electromagnetic Shielding and Photothermal Conversion

With the continuous advancement of communication technology,the escalating demand for electromagnetic shielding interference(EMI)materials with multifunctional and wideband EMI performance has become urgent.Controlling the electrical and magnetic components and designing the EMI material structure have attracted extensive interest,but remain a huge challenge.Herein,we reported the alternating electromagnetic structure composite films composed of hollow metal-organic frameworks/layered MXene/nanocellulose(HMN)by alternating vacuum-assisted filtration process.The HMN composite films exhibit excellent EMI shielding effectiveness performance in the GHz frequency(66.8 dB at Kaband)and THz frequency(114.6 dB at 0.1-4.0 THz).Besides,the HMN composite films also exhibit a high reflection loss of 39.7 dB at 0.7 THz with an effective absorption bandwidth up to 2.1 THz.Moreover,HMN composite films show remarkable photothermal conversion performance,which can reach 104.6℃under 2.0 Sun and 235.4℃under 0.8 W cm^(−2),respectively.The unique micro-and macrostructural design structures will absorb more incident electromagnetic waves via interfacial polarization/multiple scattering and produce more heat energy via the local surface plasmon resonance effect.These features make the HMN composite film a promising candidate for advanced EMI devices for future 6G communication and the protection of electronic equipment in cold environments.

Photocatalytic degradation of methylene blue over MIL-100(Fe)/GO composites: a performance and kinetic study

Adsorption coupled with photocatalytic degradation is proposed to fulfill the removal and thorough elimination of organic dyes.Herein,we report a facile hydrothermal synthesis of MIL-100(Fe)/GO photocatalysts.The adsorption and photocatalytic degradation process of methylene blue(MB)on MIL‐100(Fe)/GO composites were systematically studied from performance and kinetic perspectives.A possible adsorption‐photocatalytic degradation mechanism is proposed.The optimized 1M8G composite achieves 95%MB removal(60.8 mg/g)in 210 min and displays well recyclability over ten cycles.The obtained MB adsorption and degradation results are well fitted onto Langmuir isotherm and pseudo‐second order kinetic model.This study shed light on the design of MOFs based composites for water treatment.

Expediting^(*)OH accumulation kinetics on metal-organic frameworks-derived CoOOH with CeO_(2) “accelerator” for electrocatalytic 5-hydroxymethylfurfural oxidation valorization

In this work,nickel foam supported CeO_(2)-modified CoBDC(BDC stands for terephthalic acid linker)metal-organic frameworks(NF/CoBDC@CeO_(2)) are prepared by hydrothermal and subsequent impregnation methods,which can be further transformed to NF/CoOOH@CeO_(2) by reconstruction during the electrocatalytic test.The obtained NF/CoOOH@CeO_(2) exhibits excellent performance in electrocatalytic oxidation of 5-hydroxymethylfurfural(HMF) because the introduction of CeO_(2) can optimize the electronic structure of the heterointerface and accelerate the accumulation of ^(*)OH.It requires only a potential of 1.290 V_(RHE) to provide a current density of 50 mA cm^(-2) in 1.0 M KOH+50 mM HMF,which is 222 mV lower than that required in 1,0 M KOH(1.512 V_(RHE)).In addition,density-functional theory calculation results demonstrate that CeO_(2) biases the electrons to the CoOOH side at the heterointerface and promotes the adsorption of ^(*)OH and ^(*)HMF on the catalyst surface,which lower the reaction energy barrier and facilitate the electrocata lytic oxidation process.

Tectonic framework of Qilian orogen: reveal from an aeromagnetic anomaly feature

The Qilian Orogenic belt is one of the typical orogenic belts globally and a natural laboratory for studying plate tectonics.Many researchers have studied the ophiolite and high pressure and ultra-high pressure metamorphic rocks in the Qilian orogen and obtained valuable achievements.However,a hot debate exists on the basement property,the distribution of ophiolite,and the boundaries of tectonic units.Large-scale high-precision aeromagnetic surveys have recently been conducted in the Qilian Orogenic belt and adjacent areas.In this study,we are trying to analysis the tectonic framework of the Qilian Orogen using 1:500,000 aeromagnetic data.The results provide geophysical perspectives for studying the structural framework and deformation of this area.According to the aeromagnetic∆T anomaly map,the central and Southern Qilian have the same magnetic anomaly feature that noticeably differs from the North Qilian Orogenic belt and the Qaidam Block.This result indicates that the central and Southern Qilian have a unified magnetic basement and differ from the North Qilian orogenic belt and Qaidam Block.The map shows the distribution of ophiolite in the North Qilian orogenic belt.Linear magnetic anomalies represent the ophiolites because the mafic–ultramafic rocks usually have high magnetic susceptibility.The ophiolite belts are continuously distributed in the western part of North Qilian orogenic belt and have a large scale.However,the scale of the ophiolite belt and the outcropping of mafic–ultramafic rocks reduces when they pass through Qilian County to the east.The results indicate differences in the evolution process between the eastern and western parts of North Qilian,with Qilian County as the transition zone.This study also systematically defines the geophysical boundaries of the Qaidam Block,Qilian Block,North Qilian Orogenic belt,and Alxa block.It is proposed that the sinistral displacement of the Altun Fault is adjusted and absorbed by the series of NE-trending faults in the Qilian orogen and merge into the Longshoushan–Gushi Fault.The extension of the North Qilian Orogenic belt is strengthened by the neotectonics movement along the shearing direction,which separated the North Qilian Orogenic belt into several segments and formed a series of northeast-trending faults.

Research on the Legal Framework of Content Regulations for Network Platforms

In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactively enforce content regulations in accordance with legal censorship obligations.Additionally,platform policies and user agreements augment their authority in content regulation.The platforms can achieve cost-effective and highly efficient content regulation by leveraging their strategic advantages enabled by their own technical capabilities and extensive coverage.The platform self-regulation model,however,still faces challenges.First,accurately evaluating content remains a formidable task;second,ensuring effective platform publicity through self-regulation poses difficulties;third,users may potentially face disadvantages due to the platform’s right of self-regulation;and fourth,digital copyright owners face challenges when defending digital copyright disputes under the safe harbor rule.Therefore,it is imperative to establish,review,and revise the legal framework for content regulation of network platforms in order to enhance the efficiency of their governance systems.The formulation of the legal framework for content regulation of network platforms may encompass the following aspects:rationalizing obligations pertaining to platform content regulations,enhancing supervision over platform self-regulation,and establishing a dual-track responsibility system for digital copyright content regulation.This will ensure a harmonious balance among public interests,users’personal rights and interests,and commercial benefits through regulating the content on network platforms.

Laser-forged transformation and encapsulation of nanoalloys:pioneering robust wideband electromagnetic wave absorption and shielding from GHz to THz

The emergence of the internet of things has promoted wireless communication’s evolution towards multi-band and multi-area utilization.Notably,forthcoming sixth-generation(6G)communication standards,incorporating terahertz(THz)frequencies alongside existing gigahertz(GHz)modes,drive the need for a versatile multi-band electromagnetic wave(EMW)absorbing and shielding material.This study introduces a pivotal advance via a new strategy,called ultrafast laser-induced thermal-chemical transformation and encapsulation of nanoalloys(LITENs).Employing multivariate metal-organic frameworks,this approach tailors a porous,multifunctional graphene-encased magnetic nanoalloy(GEMN).By fine-tuning pulse laser parameters and material components,the resulting GEMN excels in low-frequency absorption and THz shielding.GEMN achieves a breakthrough of minimum reflection loss of−50.6 dB in the optimal C-band(around 4.98 GHz).Computational evidence reinforces GEMN’s efficacy in reducing radar cross sections.Additionally,GEMN demonstrates superior electromagnetic interference shielding,reaching 98.92 dB under THz band(0.1–2 THz),with the mean value result of 55.47 dB.These accomplishments underscore GEMN’s potential for 6G signal shielding.In summary,LITEN yields the remarkable EMW controlling performance,holding promise in both GHz and THz frequency domains.This contribution heralds a paradigm shift in EM absorption and shielding materials,establishing a universally applicable framework with profound implications for future pursuits.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks

The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.

City Network Evolution Characteristics of Smart Industry: Evidence from Yangtze River Delta, China

The dual-path model of industrial evolution and spatial progression has been widely acknowledged and incorporated into the strategic planning to promote the development of urban industries and regional collaborations.However,current research on inter-enter-prise city networks mainly focuses on the single sector of flows on all enterprise branches,such as product value chains and production factors,but neglects that of particular industry department.Built upon the new economic geography and city networks theory,this paper develops a methodological framework that focuses on the analysis of city network evolution characteristics of smart industry.Particu-larly,a conceptual model of smart industry enterprise-industry-city is proposed and then applied to a case study of smart industry in the Yangtze River Delta Region,China.Using enterprise supplier-customer data,a city network of smart industry is constructed and sub-sequently analyzed with the proposed model.Findings indicate that the smart industry network in Yangtze River Delta Region exhibits a hierarchical structure and the expansion of the network presents a small-world network characteristic.The study not only makes a meth-odological contribution for revealing the industrial and spatial evolution path of the current smart industry,but also provides empirical support for the formulation of new economic development policies focused on smart industries,demonstrating the role of city clusters as carriers of regional synergistic development.

Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

.NET平台集成CLIPS的实现方法研究

针对CLIPS语言框架及其不足,研究了在.NET中实现CLIPS托管程序集的方法,详细介绍了实现.NET平台CLIPS托管程序集的基本思想和关键技术。概述了CLIPS框架特点及其不足,分析了CLIPS与.NET混合编程的必要性。在保持CLIPS语言框架最大相似性的情况下,采用.NET的平台调用服务完成了对CLIPS框架类、接口的封装和调用。对生成的程序集进行引用,测试了其可行性。测试结果表明,该方法能够确保CLIPS框架的完成结构,实现对框架的完整二次开发和包装,继承了其推理结构的可靠性和系统的完整性,对专家系统的开发具有重要意义。

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.