Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,exi...Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.展开更多
The Internet of Vehicles(IoV)operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms.Existing methods often suffer from com...The Internet of Vehicles(IoV)operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms.Existing methods often suffer from complex certificate management,inefficient consensus protocols,and poor resilience in high-frequency communication,resulting in high latency,poor scalability,and unstable network performance.To address these issues,this paper proposes a secure and efficient distributed authentication scheme for IoV with reputation-driven consensus and SM9.First,this paper proposes a decentralized authentication architecture that utilizes the certificate-free feature of SM9,enabling lightweight authentication and key negotiation,thereby reducing the complexity of key management.To ensure the traceability and global consistency of authentication data,this scheme also integrates blockchain technology,applying its inherent invariance.Then,this paper introduces a reputation-driven dynamic node grouping mechanism that transparently evaluates and groups’node behavior using smart contracts to enhance network stability.Furthermore,a new RBSFT(Reputation-Based SM9 Friendly-Tolerant)consensus mechanism is proposed for the first time to enhance consensus efficiency by optimizing the PBFT algorithm.RBSFT aims to write authentication information into the blockchain ledger to achieve multi-level optimization of trust management and decision-making efficiency,thereby significantly improving the responsiveness and robustness in high-frequency IoV scenarios.Experimental results show that it excels in authentication,communication efficiency,and computational cost control,making it a feasible solution for achieving IoV security and real-time performance.展开更多
目的:通过NCBI(National Center for Biotechnology Information)数据库筛选可以应用于洗涤工业的蛋白酶,使用地衣芽孢杆菌进行高效表达。方法:利用同源重组的方式将蛋白酶基因整合到地衣芽孢杆菌基因组中,研究重组蛋白酶的酶学特性和...目的:通过NCBI(National Center for Biotechnology Information)数据库筛选可以应用于洗涤工业的蛋白酶,使用地衣芽孢杆菌进行高效表达。方法:利用同源重组的方式将蛋白酶基因整合到地衣芽孢杆菌基因组中,研究重组蛋白酶的酶学特性和洗涤效果。结果:该重组酶的最适作用温度为60℃,最适pH为11.0;AH-101具有良好的耐热性,可以在20~55℃范围内保持稳定,在60℃保温1 h,剩余活性仍高于30%;此外,该酶与表面活性剂和液体洗涤剂具有良好的兼容性,在终浓度为0.1%和0.5%的表面活性剂和液体洗涤剂中孵育2 h,重组酶的酶活力损失小于30%。结论:成功构建了一株含有外源蛋白酶的地衣芽孢杆菌重组菌株,对重组蛋白酶的特性和洗涤效果进行研究,该重组蛋白酶可以作为环保型酶制剂应用到洗涤工业中。展开更多
基金supported by National Natural Science Foundation of China(62466045)Inner Mongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.
基金supported by the National Natural Science Foundation of China(Grant No.61762071,Grant No.61163025).
文摘The Internet of Vehicles(IoV)operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms.Existing methods often suffer from complex certificate management,inefficient consensus protocols,and poor resilience in high-frequency communication,resulting in high latency,poor scalability,and unstable network performance.To address these issues,this paper proposes a secure and efficient distributed authentication scheme for IoV with reputation-driven consensus and SM9.First,this paper proposes a decentralized authentication architecture that utilizes the certificate-free feature of SM9,enabling lightweight authentication and key negotiation,thereby reducing the complexity of key management.To ensure the traceability and global consistency of authentication data,this scheme also integrates blockchain technology,applying its inherent invariance.Then,this paper introduces a reputation-driven dynamic node grouping mechanism that transparently evaluates and groups’node behavior using smart contracts to enhance network stability.Furthermore,a new RBSFT(Reputation-Based SM9 Friendly-Tolerant)consensus mechanism is proposed for the first time to enhance consensus efficiency by optimizing the PBFT algorithm.RBSFT aims to write authentication information into the blockchain ledger to achieve multi-level optimization of trust management and decision-making efficiency,thereby significantly improving the responsiveness and robustness in high-frequency IoV scenarios.Experimental results show that it excels in authentication,communication efficiency,and computational cost control,making it a feasible solution for achieving IoV security and real-time performance.
文摘目的 探讨肝硬化合并糖尿病患者发生低血糖的原因及临床指标的特点。方法 选取首都医科大学附属北京佑安医院2017年1月-2019年6月收治的肝硬化合并糖尿病患者共50例为研究对象,其中发生1次低血糖的25例为试验组,未发生低血糖的25例为对照组。对两组患者肝肾功能、空腹血糖、糖化血红蛋白及Child-Pugh分级进行评估,并分析低血糖发生的时间段及可能原因。计量资料两组比较采用独立样本t检验或Mann-Whitney U检验,计数资料两组间比较采用χ2检验。结果 试验组空腹血糖明显低于对照组[6.10(3.45~8.96)mmol/L vs 8.12(6.18±12.59)mmol/L, Z=-2.687, P=0.007],ChE明显低于对照组[3009.00(1788.50~4493.50)U/L vs 4936.00(4051.00~6740.50)U/L, Z=-3.095, P=0.002],Alb明显低于对照组[(32.02±7.07)g/L vs (35.89±5.49)g/L, t=2.161,P=0.036],糖化血红蛋白明显低于对照组[(6.97±1.64)mmol/L vs (8.04±1.78)mmol/L,t=2.047,P=0.047]。试验组Child-Pugh分级以B级(36%)及C级(36%)为主,对照组以A级(56%)及B级(40%)为主,两组Child-Pugh分级差异有统计学意义(χ^2=8.786,P=0.012)。肝硬化合并糖尿病患者大部分低血糖发生在晨起空腹及白天,原因以胰岛素过多(44%)及进食或热量补充不足(40%)为主,部分患者有空腹无症状性低血糖(16%)。结论 临床上应重视肝硬化合并糖尿病患者的血糖监测及管理,减少低血糖事件的发生。
文摘目的:通过NCBI(National Center for Biotechnology Information)数据库筛选可以应用于洗涤工业的蛋白酶,使用地衣芽孢杆菌进行高效表达。方法:利用同源重组的方式将蛋白酶基因整合到地衣芽孢杆菌基因组中,研究重组蛋白酶的酶学特性和洗涤效果。结果:该重组酶的最适作用温度为60℃,最适pH为11.0;AH-101具有良好的耐热性,可以在20~55℃范围内保持稳定,在60℃保温1 h,剩余活性仍高于30%;此外,该酶与表面活性剂和液体洗涤剂具有良好的兼容性,在终浓度为0.1%和0.5%的表面活性剂和液体洗涤剂中孵育2 h,重组酶的酶活力损失小于30%。结论:成功构建了一株含有外源蛋白酶的地衣芽孢杆菌重组菌株,对重组蛋白酶的特性和洗涤效果进行研究,该重组蛋白酶可以作为环保型酶制剂应用到洗涤工业中。
