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基于大语言模型Function-calling架构的中医舌象辅助问诊系统 认领 引用
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作者 施浩然 王月明 +3 位作者 石磊 王瑾 谷宇 李琦 《科学技术与工程》 EI 北大核心 2026年第13期5583-5593,共11页
针对大语言模型缺乏视觉能力及现有多模态大模型在中医舌象分析中准确率不足的问题,构建了一个中医舌象辅助问诊系统,实现舌象的自动分类与中医辅助诊疗方案的生成。在利用UNet进行舌象分割的基础上,使用TransNeXt主干网络构建舌象多标... 针对大语言模型缺乏视觉能力及现有多模态大模型在中医舌象分析中准确率不足的问题,构建了一个中医舌象辅助问诊系统,实现舌象的自动分类与中医辅助诊疗方案的生成。在利用UNet进行舌象分割的基础上,使用TransNeXt主干网络构建舌象多标签分类系统,实现舌体分割与舌象多标签分类。进一步,结合舌象多标签分类系统与大语言模型,并基于微调后的Function-calling架构,实现了中医舌象辅助问诊系统。在舌象多标签分类系统的有效性验证实验中,UNet模型在舌象分割的平均精确度、平均召回率和平均交并比指标上表现较好,分别达到97.58%,98.61%和96.25%;基于TransNeXt主干网络开发的舌象多标签分类模型在舌象识别的子集准确率、精确度、召回率、F1方面表现更佳,分别达到75.69%、91.18%、91.41%、91.28%。在中医辅助诊疗方案的生成实验中,甄选出的最佳大语言模型相较于多模态大模型在Bleu-4、Rouge-1、Rouge-2、Rouge-L同样表现更佳,分别达到79.03%、82.46%、76.00%、86.46%。结合舌象多标签分类系统以及大语言模型构建的中医舌象辅助问诊系统能够辅助中医进行舌诊,为中医辅助诊断方案的生成提供技术支持。 展开更多
关键词 大语言模型 Function-calling架构 舌象识别 中医知识问答
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基于Graph RAG语义融合的知名科学家学术与社会影响问答研究 认领 引用 被引量：2
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作者 吴志祥 沙焕旭 +1 位作者 尹璐璐 毛进 《情报理论与实践》 CSSCI 北大核心 2026年第3期160-169,共10页
[目的/意义]知名科学家影响力的认知建构面临学术与社会影响割裂、表达碎片化的问题,制约了跨语境理解。本文尝试聚合多源文本语料中的结构化信息,实现科学家影响的语义融合与统一表达。[方法/过程]基于Graph RAG框架,设计多源数据融合... [目的/意义]知名科学家影响力的认知建构面临学术与社会影响割裂、表达碎片化的问题,制约了跨语境理解。本文尝试聚合多源文本语料中的结构化信息,实现科学家影响的语义融合与统一表达。[方法/过程]基于Graph RAG框架,设计多源数据融合方法,构建跨域知识图谱;引入人智协同方案生成多用户、深层次问题集;开展覆盖240万字语料的实验评估,从用户适配能力、回答质量与语义融合效果三个角度分析模型表现。[结果/结论]Graph RAG在跨语境语义融合方面表现优异,能有效缓解科学家数据分散与语义分割问题。其中,DeepSeek-V3-8B与bge-m3组合模型效果最佳,支持生成结构清晰、回答深入的科学家影响描述。本文为数智支撑的科学家与社会关系研究提供情报学方案。 展开更多
关键词 知名科学家 学术与社会影响 语义融合 Graph RAG 大语言模型
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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Modularized Graph Convolutional Network 认领 引用
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作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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Spatio-temporal feature extraction with a global-local Transformer model for video scene graph generation 认领 引用
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作者 Rongsen Wu Jie Xu +4 位作者 Hao Zheng Zhiyuan Xu Zixuan Li Shixue Cheng Shumao Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期364-374,共11页
In the field of video scene graph generation,spatio-temporal feature extraction and the long-tail effect in relationship classification are core research issues.This paper proposes extracting spatio-temporal features ... In the field of video scene graph generation,spatio-temporal feature extraction and the long-tail effect in relationship classification are core research issues.This paper proposes extracting spatio-temporal features using the global-local Transformer model for video scene graph generation.Methods based on the Transformer architecture and attention mechanism enrich the semantic information of spatio-temporal features in videos,thereby improving the accuracy of relationship classification.In the feature processing module,pose features are introduced to strengthen the semantic representation of objects.In the spatial feature encoding module,a local spatial visibility matrix based on bounding boxes and key points of human pose features is proposed to add the issue of insufficient attention to local details in traditional Transformer encoders.In the temporal feature encoding module,a global random frame extraction strategy is proposed,which considers global temporal features while also taking computational complexity into account.In the relation classification module,to address the uneven distribution of object and relation categories in the Action Genome dataset,a relation classification loss function based on bipartite graph matching and Focal Loss is proposed,which alleviates the long-tail effect in relation classification and improves the accuracy. 展开更多
关键词 Video scene graph generation Transformer Pose features Visibility matrix Bipartite graph matching
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Graph Attention Networks for Skin Lesion Classification with CNN-Driven Node Features 认领 引用
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作者 Ghadah Naif Alwakid Samabia Tehsin +3 位作者 Mamoona Humayun Asad Farooq Ibrahim Alrashdi Amjad Alsirhani 《Computers, Materials & Continua》 SCIE EI 2026年第1期1964-1984,共21页
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ... Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems. 展开更多
关键词 Graph neural network image classification DermaMNIST dataset graph representation
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A node importance prediction algorithm based on graph attention and contrastive learning 认领 引用
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作者 Jun Ai Yuming Zhang +2 位作者 Zhan Su Chenye Guo Mingsong Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期671-684,共14页
In complex network analysis,node ranking is vital for propagation prediction,structural optimization,and intervention strategy design,yet existing methods often fail to effectively integrate community information in d... In complex network analysis,node ranking is vital for propagation prediction,structural optimization,and intervention strategy design,yet existing methods often fail to effectively integrate community information in dynamic settings.To address this,this paper proposes a node ranking method that combines graph attention mechanisms with contrastive learning.Community detection is employed to extract node-level community features,and a joint embedding module is designed to fuse global and local structures,thereby incorporating community information into node representations.Based on this,a multi-layer graph attention network adaptively learns node and neighborhood features,while contrastive learning mitigates interference from dynamic evolution and strengthens the model's ability to capture multi-scale structural differences.Experiments on multiple dynamic network datasets show that the proposed method significantly outperforms existing approaches in ranking accuracy,particularly in networks with higher average degrees and clearer community structures.These results validate the effectiveness of the method in enhancing feature representation and modeling multi-scale dynamic node influence. 展开更多
关键词 node importance community features graph attention contrastive learning
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Few-Shot Knowledge Graph Completion with Structure-Aware Graph Attention Network 认领 引用
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作者 YANG Rongtai SHAO Yubin +2 位作者 DU Qingzhi ZHANG Feng QI Yuting 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期1024-1033,I0019,共10页
Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity'... Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity's neighborhood topology holds potential to address this,its significance is overlooked in current research.In this paper,we propose a structure-aware graph attention network for few-shot knowledge graph completion.Firstly,to enhance entity representations,we design a structure-aware graph attention encoder to capture the graph's structural features of nodes,generating embedding for entity pairs.Secondly,a semantic prototype matching network is employed to compute the prediction score.Experiments on the NELL-One and Wiki-One datasets show that our proposed model outperforms the best baseline models by 0.021,0.026,0.039,0.032 and 0.016,0.064,0.043,0.040 in terms of MRR,Hits@10,Hits@5,and Hits@1 metrics,respectively.This demonstrates that our model can effectively leverage neighborhood topological information to improve the accuracy of knowledge completion,and achieve a better generalization. 展开更多
关键词 knowledge graph completion neighborhood topology structure-aware graph attention entity representations semantic prototype
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A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification 认领 引用
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作者 Yuquan Gan Siyu Wu +3 位作者 Chang Su Nan Xiang Zhijie Xu Yushan Pan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期464-482,共19页
Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and com... Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and computational costs from calculating correlations between all tokens(especially as image size or spectral bands increase)and limited ability to model local boundary information due to lacking explicit enhancement mechanisms.This paper proposes a novel method,bridge transformer network fused with deep graph convolution(BTDGC),to address these issues.The framework includes three components as follows:a double random masking mechanism(DRMM)that forces the model to infer masked features from context during training,a bridge transformer(BT)module with bridge tokens for cross-region feature interaction and a Deep Graph Convolutional Pooling(DGCP)module that preserves spatial topology while aggregating hierarchical information.Experiments on standard hyperspectral datasets show BTDGC outperforms mainstream methods in classification accuracy and robustness,effectively balancing global modelling and local boundary representation.The code is available at http://gffzz188fe103f8f1460aspovbw6oqw59q6nuc.ffgz.tsg.suse.edu.cn/jenny3489/BTDGC. 展开更多
关键词 convolution graph convolutional network masking mechanism transforms
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Spatial-Temporal Graph Fusion with Dual-Scale Convolution for Traffic Flow Prediction 认领 引用
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作者 Dan Wang Mengyi Cui +1 位作者 Zhenhua Yu Yukang Liu 《Computers, Materials & Continua》 SCIE EI 2026年第6期1375-1396,共22页
Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal f... Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal features,particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks.This paper proposes a novel framework of traffic flow prediction,referred to as Adaptive Graph Fusion Dual-scale Convolutional Network(AGFDCN),which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks.Specifically,we introduce a Dual-Scale Temporal Network,which combines long-and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns across multiple temporal scales.Furthermore,we design a Dynamic Adaptive Graph Module,which models complex spatial dependencies in traffic networks through an adaptive graph fusion mechanism and a dual-path attention-gated module.Finally,the temporal and spatial representations are integrated by employing a gated fusion mechanism,enhancing the overall prediction performance.Experimental results obtained based on three highway datasets(i.e.,PEMS04,PEMS07 and PEMS08)verify that the proposed model outperforms several state-of-the-art baselines in various evaluation metrics.Compared to the spatial-temporal graph model AGCRN with best performance in the baseline models,the proposed model exhibits significant improvements across all datasets:it achieves reduces of MAE by 42.07%and RMSE by 35.43%on PEMS04;MAE by 28.35%and RMSE by 29.28%on PEMS07;and MAE by 30.52%and RMSE by 30.73%on PEMS08,respectively,validating its effectiveness in modeling complex spatial-temporal traffic data and its robustness in handling sudden traffic changes. 展开更多
关键词 Dual-scale convolution dual-path attention-gated module adaptive graph fusion spatial-temporal dynamic graph traffic flow prediction
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Tunnel Mapping in Low-Light Environments:A Synergistic Scheme of Image Enhancement and Multi-Source Factor Graph Optimization 认领 引用
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作者 Qilong Wang Ning Wang +3 位作者 Shuhan Luo Xiang Gao Yuqian Lu Min He 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期988-1012,共25页
Tunnel environments often suffer from GPS denial,uneven illumination,and structural uniformity,which lead to feature degradation,loop closure failure,and long-distance drift in SLAM systems.To solve these problems,thi... Tunnel environments often suffer from GPS denial,uneven illumination,and structural uniformity,which lead to feature degradation,loop closure failure,and long-distance drift in SLAM systems.To solve these problems,this study aims to propose a high-precision SLAM method suitable for tunnel structural health monitoring.Firstly,an ABA-CLAHE image enhancement algorithm is proposed,which adopts cascaded processing of nonlinear brightness adjustment in HSV space and CLAHE local contrast optimization to improve low-light image quality and enhance feature stability.Then,SURF feature matching combined with the RANSAC algorithm is used to ensure feature matching accuracy.Finally,a factor graph model is constructed by integrating IMU pre-integration,laser odometry,visual odometry,and loop closure constraints,and iSAM2 incremental optimization is employed to achieve globally consistent mapping.Municipal tunnel tests show that the loop closure error is reduced to O.096 m and the global reprojection error is l.10 pixels,and the structural continuity of the constructed dense 3D map is significantly improved.This method provides a technical solution with centimeter-level accuracy for tunnel structural health monitoring,which is demonstrating strong practical potential for engineering applications. 展开更多
关键词 SLAM municipal tunnels image enhancement factor graph
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Dual Channel Graph Convolutional Networks via Personalized PageRank 认领 引用
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作者 Longlong Lin Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期221-223,共3页
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat... Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications. 展开更多
关键词 convolutional node feature similarity graph convolutional framework learning graph representations neural networks gnns networks graph personalized
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Experience replay with cohesive-subgraph awareness for continual graph learning in IoT 认领 引用
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作者 Zhenzhen Xie Qi Luo +3 位作者 Yan Huang Yongqi Yin Jiaqi Zhang Junjie Pang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期417-427,共11页
In the Internet-of-Things(IoT)scenarios,Continual Graph Learning(CGL)has become a key technique for capturing the complex relational structures underlying diverse applications including sensor networks,road systems an... In the Internet-of-Things(IoT)scenarios,Continual Graph Learning(CGL)has become a key technique for capturing the complex relational structures underlying diverse applications including sensor networks,road systems and biomedical networks.However,the structural changes in these evolving graphs introduce instability,making catastrophic forgetting a primary challenge for CGL.Experience replay is currently a promising method,as it strikes a balance between new and old knowledge.It also provides CGL models with a human-like memory capability.However,prior work rarely leverages the graph’s intrinsic properties to proactively identify the critical patterns hiding in the evolving graphs.To this end,we propose a unified framework that integrates cohesionsubgraph awareness into existing CGL mechanisms.We propose a novel cohesive structure-aware experience replay framework that leverages intrinsic graph properties,such as κ-core and κ-truss metrics,to guide the selection of representative historical nodes for replay.Unlike conventional replay strategies that rely on random sampling or task-driven node selection,our approach systematically identifies structurally significant nodes that encapsulate the evolving patterns of streaming graphs.By integrating these cohesive subgraph properties into the experience replay process,our method effectively preserves critical historical knowledge while adapting to new graph structures with low computational overhead.The experimental results demonstrate that our method consistently outperforms existing replay strategies in mitigating catastrophic forgetting and maintaining classification performance.On the PubMed dataset,our-core-based replay strategy improves the F1 score by 3.7% compared to random sampling,while reducing training time by up to 85% compared to full retraining.Similarly,on the Cora dataset,our approach achieves a 98.3% F1 score,surpassing baseline methods by 4.5%. 展开更多
关键词 Graph neural networks Continual learning Experience replay Cohesive subgraph
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KIG:A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation 认领 引用
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作者 Siyuan Xu Dong Pan +3 位作者 Zhaohui Jiang Zhiwen Chen Haoyang Yu Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期327-345,共19页
Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider... Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS. 展开更多
关键词 Blast furnace ironmaking process graph neural network(GNN) knowledge graph(KG) multisensor time series(MTS) temporal alignment time-delay estimation(TDE)
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SSAG:Situational Semantic Augmented Graph for Active SLAM in Object-Goal Navigation 认领 引用
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作者 Shasha Tian Zhengyang Chen +4 位作者 Kai Ren Na Li Chongwei Ruan Zhijia Cui Mian Wu 《Computers, Materials & Continua》 SCIE EI 2026年第9期452-473,共22页
To address the issues of low exploration efficiency and“geometric myopia”caused by the lack of highlevel environmental structure modeling for mobile robots in complex indoor environments,this paper proposes an activ... To address the issues of low exploration efficiency and“geometric myopia”caused by the lack of highlevel environmental structure modeling for mobile robots in complex indoor environments,this paper proposes an active SLAMobject navigationmethod based on Situational Semantic Augmented Graph(SSAG).Unlikemethods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association,this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions.First,an online room segmentation algorithmis employed to transformunstructured sensory data into a structured graph representation that characterizes room-level functional attributes and topological associations.Subsequently,a Graph Attention Network(GAT)is utilized to perform explicit reasoning on the semantic attributes of unobserved regions,providing decision-making priors for long-range exploration.Building upon this,we introduce a Product of Experts(PoE)mechanism within the Proximal Policy Optimization(PPO)framework to deeply fuse semantic reasoning heatmaps with geometric hard constraints.This integration optimizes the target selection strategy,generating long-term navigation goals with superior semantic consistency and physical reachability.Experimental results on the Habitat simulator and Gibson dataset demonstrate that the proposed SSAG achieves a Success weighted by Path Length(SPL)of 0.340,outperforming SemExp and SemGO by 15.3%and 4.9%,respectively,with a total success rate of 68.4%.Notably,for object search tasks with strong spatial correlations(e.g.,“bed”and“toilet”),the success rates reach 73.2%and 72.5%,representing a performance gain of over 20%compared to baseline methods.These results validate the effectiveness of room-level semantic reasoning for object navigation and demonstrate the capability of the proposed method to achieve efficient autonomous navigation in unknown,complex scenarios. 展开更多
关键词 Active SLAM object navigation situational semantic augmented graph graph attention network deep reinforcement learning
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From data to insight:Building a knowledge graph for risk analysis of hazardous chemical accidents 认领 引用
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作者 Kai Zhao Xilei Lu +7 位作者 Lining Wan Linlin Zhang Yulong Jin Pengtao Wen Jinhao Gao Miao He Qibo Wang Li Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期92-110,共19页
A substantial amount hazardous chemical accident(HCA)data have been accumulated in the form of unstructured textual data,making systematic analysis and utilization challenging.More importantly,manually identifying and... A substantial amount hazardous chemical accident(HCA)data have been accumulated in the form of unstructured textual data,making systematic analysis and utilization challenging.More importantly,manually identifying and analyzing key information from a considerable quantity of accident data is inefficient and highly susceptible to subjective bias.To efficiently unlock the value of HCA investigation reports and uncover underlying accident patterns,a semi-automated method for knowledge graph(KG)construction has been developed to model the HCA data.First,an ontology that accurately expresses key factors of HCAs is established.Second,an automated method is developed for the identification,standardization,and enhancement of accident factors,which combines deep learning(DL)and natural language processing(NLP)techniques.Specifically,the deep neural network model,named interaction region and type information(IRTI)is proposed to extract accident factors and their relationships from lengthy HCA data with complex overlapping issues.Non-standard accident factors are standardized using ChatGPT-4 in combination with the proposed text clustering model,named contrastive learningbased short text clustering(CLSTC).The processed accident factors are used to construct the hazardous chemical accident knowledge graph(HCAKG).Finally,the risk factors in the HCAKG are statistically analyzed,and their internal topological relationships are explored to facilitate quantitative analysis.Data from HCA investigation reports are used to demonstrate the effectiveness of this method.The result shows that it improves the accuracy and efficiency of accident data analysis,promoting better risk assessment and management strategies. 展开更多
关键词 Chemical processes Safety Neural networks Knowledge graph Hazardous chemical accident Risk analysis
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Balaban index of bicyclic chain graphs 认领 引用
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作者 LV Xue-zheng MA Meng-yu WANG Cheng-rui 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2026年第1期194-213,共20页
Chain graphs are{2K2,C3,C5}-free graphs.Balaban index and sum-Balaban index are two important topological indices.In this paper,we concentrate on the subclass of bicyclic connected chain graphs,identifying th... Chain graphs are{2K2,C3,C5}-free graphs.Balaban index and sum-Balaban index are two important topological indices.In this paper,we concentrate on the subclass of bicyclic connected chain graphs,identifying the extremal graphs that exhibit the minimum or maximum Balaban index and sum-Balaban index within this class.Moreover,we provide a systematic ordering of all bicyclic connected chain graphs according to the magnitude of their Balaban index and sum-Balaban index. 展开更多
关键词 Balaban index sum-Balaban index chain graph
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FedGNN:Federated Graph Neural Networks for Privacy-Preserving Cyber-Resilient Energy Optimization in IoT-Based Smart Grids 认领 引用
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作者 Alanoud Al Mazroa Fahad Masood +3 位作者 Bakri Hussain Awaji Mohammad Alhefdi Abeer Aljohani Jawad Ahmad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1462-1483,共22页
The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and priv... The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids. 展开更多
关键词 Cyber security energy optimization graph neural networks IoT smart grids
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Vertex-Degree Function Indices of Unicyclic Graphs with Fixed Girth or Fixed Diameter 认领 引用
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作者 Jianwei DU Xiaoling SUN +1 位作者 Yinzhen MEI Mengyuan FAN 《Journal of Mathematical Research with Applications》 CSCD 2026年第3期304-312,共9页
The vertex-degree function index of a graph G is defined as Hf(G)=∑x∈V(G)f(dG(x)),where dG(x)denotes the degree of vertex x in G and f(x)is a real-valued function.In this work,we determine the extremal v... The vertex-degree function index of a graph G is defined as Hf(G)=∑x∈V(G)f(dG(x)),where dG(x)denotes the degree of vertex x in G and f(x)is a real-valued function.In this work,we determine the extremal values of the vertex-degree function index of unicyclic graphs with fixed girth or with fixed diameter when f(x)is strictly convex(resp.,strictly concave).Moreover,we apply the results directly to some famous topological indices which belong to the family of vertex-degree function indices,such as the first and second multiplicative Zagreb indices,zeroth-order general Randic index,sum lordeg index,variable sum exdeg index. 展开更多
关键词 vertex-degree function index unicyclic graph girth diameter
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基于大语言模型和GraphRAG的科技文献图谱式摘要生成研究 认领 引用 被引量：1
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作者 马玮璐 孙坦 +1 位作者 赵瑞雪 鲜国建 《数据分析与知识发现》 EI CSSCI CSCD 北大核心 2026年第3期113-128,共16页
【目的】简化科技文献摘要撰写流程,生成结构化的图谱式摘要以辅助科研。【方法】选取PMC数据库的水稻育种相关论文,构建4276条“全文-摘要”问答对;通过实验遴选最优的提示词和温度系数,对Qwen2.5-7B-Instruct大模型进行有监督微调;将... 【目的】简化科技文献摘要撰写流程,生成结构化的图谱式摘要以辅助科研。【方法】选取PMC数据库的水稻育种相关论文,构建4276条“全文-摘要”问答对;通过实验遴选最优的提示词和温度系数,对Qwen2.5-7B-Instruct大模型进行有监督微调;将微调后的模型接入GraphRAG框架,生成每篇论文的图谱式摘要,并利用优选提示词通过全局查询生成文本摘要。【结果】在水稻育种测试集中,本文方法在ROUGE-1、ROUGE-2和ROUGE-L的F1值上较LightRAG基线模型分别提升44.16、61.36和54.87个百分点;在5分制人工评测得分平均提升1.78分,生成的图谱式摘要更能直观地揭示知识点间的逻辑关联。【局限】受限于算力资源,本研究选取的大模型参数量较小,模型生成能力仍有提升空间;此外,GraphRAG索引构建耗时较长,在实际应用中需进一步优化推理效率。【结论】知识图谱增强的检索生成技术能够深入捕捉论文中的远距离隐藏信息,生成更加全面且准确的论文摘要和层次清晰的图谱式摘要,便于提高科研人员的阅读效率,助力科研工作。 展开更多
关键词 文本摘要 图谱式摘要 大语言模型 GraphRAG
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