To describe the dynamic propcrty of trust relationship, wt propose atime-related trust model and extend Joang's subjective logic to fit for time-related trust model.The extension includes prepositional conjunction...To describe the dynamic propcrty of trust relationship, wt propose atime-related trust model and extend Joang's subjective logic to fit for time-related trust model.The extension includes prepositional conjunction, disjunction and negation for traditional logic anddiscounting and consensus operators that are evidential operators specially designed for thepropagation and computation of trust relationships. With the extension of subjective logic fortime-related trust, our time-related trust modelis suitable to model the dynamic trust relationshipin practice. Finally an example of reputation assessment is offered to demonstrate the usage of ourtrust model.展开更多
In networked mobile commerce network transactions,trust is the prerequisite and key to a smooth transaction.The measurement of trust between entities involves factors such as transaction amount,transaction time,person...In networked mobile commerce network transactions,trust is the prerequisite and key to a smooth transaction.The measurement of trust between entities involves factors such as transaction amount,transaction time,personal income of consumer entities and their risk attitude towards trust,etc.,so it is difficult to accurately calculate quantitatively.In order to find out the essential characteristics of this trust relationship,based on the research background of mobile commerce in the mobile network environment,a dynamic trust mechanism is proposed through the research of trust in the mobile network environment,trust influencing factors and trust mechanism.The calculation model of mobile interactive services based on mobile service business transactions.The model calculates feedback credibility through feedback deviation and feedback robustness,and combines transaction context factors and trust mapping mechanism to judge the seller’s credibility.This model better reflects the degree of influence of subjective factors such as personal preferences and risk attitudes on trust calculations,And the sensitivity of trust algorithms and transaction attributes has been greatly improved.After a large number of experiments and theoretical analysis,this mechanism provides an effective explanation for solving the problem of network trust computing.and provides valuable new ideas for the study of secure transactions in the mobile Internet environment.展开更多
为了探讨5-甲基胞嘧啶(5-methylcytosine,m5C)相关基因在三阴性乳腺癌(triple negative breast cancer,TNBC)患者治疗及预后中的潜在价值,构建了基于m5C相关基因的预后预测模型,用于评估TNBC患者的预后和生存状况。从基因表达总库(gene ...为了探讨5-甲基胞嘧啶(5-methylcytosine,m5C)相关基因在三阴性乳腺癌(triple negative breast cancer,TNBC)患者治疗及预后中的潜在价值,构建了基于m5C相关基因的预后预测模型,用于评估TNBC患者的预后和生存状况。从基因表达总库(gene expression omnibus,GEO)数据库和癌症基因组图谱(the cancer genome atlas,TCGA)数据库中下载TNBC基因表达谱和相应的临床数据。通过Pearson分析确定了99个m5C相关基因,进一步采用单因素Cox分析鉴定出5个与预后有关的m5C相关基因(SLC6A14、BCL11A、UGT8、LMO4、PSAT1)并构建了风险评分(risk score)预测模型,根据风险评分中位值将患者划分为高风险组和低风险组。使用Kaplan-Meier(K-M)生存分析、受试者工作特征(receiver operating characteristic,ROC)曲线、多变量Cox回归分析、构建列线图和校准曲线评估了模型的预测效能。训练集和验证集的K-M生存曲线、受试者工作特征曲线下面积(area under the curve,AUC)分析均验证了模型具有良好的预测能力。多变量Cox回归分析显示,风险评分可作为独立的预后生物标志物。使用ssGSEA、免疫评分分析和化疗药物对高低风险组患者的半最大抑制浓度(half maximal inhibitory concentration,IC50)值差异分析显示,免疫细胞和免疫检查点基因以及大多数化疗药物的IC50值在不同风险组之间的表达存在显著差异。研究结果构建了基于5个m5C相关基因的风险评分预后预测模型,这将有助于阐明TNBC中m5C相关基因的作用机制,进而提供更有价值的预后及诊断的生物标志物和潜在的治疗靶点,为TNBC患者临床个性化治疗提供理论指导。展开更多
文摘To describe the dynamic propcrty of trust relationship, wt propose atime-related trust model and extend Joang's subjective logic to fit for time-related trust model.The extension includes prepositional conjunction, disjunction and negation for traditional logic anddiscounting and consensus operators that are evidential operators specially designed for thepropagation and computation of trust relationships. With the extension of subjective logic fortime-related trust, our time-related trust modelis suitable to model the dynamic trust relationshipin practice. Finally an example of reputation assessment is offered to demonstrate the usage of ourtrust model.
基金The author is very grateful for the financial support of the new retail virtual reality technology(2017TP1026)of the key laboratory in Hunan Province.
文摘In networked mobile commerce network transactions,trust is the prerequisite and key to a smooth transaction.The measurement of trust between entities involves factors such as transaction amount,transaction time,personal income of consumer entities and their risk attitude towards trust,etc.,so it is difficult to accurately calculate quantitatively.In order to find out the essential characteristics of this trust relationship,based on the research background of mobile commerce in the mobile network environment,a dynamic trust mechanism is proposed through the research of trust in the mobile network environment,trust influencing factors and trust mechanism.The calculation model of mobile interactive services based on mobile service business transactions.The model calculates feedback credibility through feedback deviation and feedback robustness,and combines transaction context factors and trust mapping mechanism to judge the seller’s credibility.This model better reflects the degree of influence of subjective factors such as personal preferences and risk attitudes on trust calculations,And the sensitivity of trust algorithms and transaction attributes has been greatly improved.After a large number of experiments and theoretical analysis,this mechanism provides an effective explanation for solving the problem of network trust computing.and provides valuable new ideas for the study of secure transactions in the mobile Internet environment.
文摘为了探讨5-甲基胞嘧啶(5-methylcytosine,m5C)相关基因在三阴性乳腺癌(triple negative breast cancer,TNBC)患者治疗及预后中的潜在价值,构建了基于m5C相关基因的预后预测模型,用于评估TNBC患者的预后和生存状况。从基因表达总库(gene expression omnibus,GEO)数据库和癌症基因组图谱(the cancer genome atlas,TCGA)数据库中下载TNBC基因表达谱和相应的临床数据。通过Pearson分析确定了99个m5C相关基因,进一步采用单因素Cox分析鉴定出5个与预后有关的m5C相关基因(SLC6A14、BCL11A、UGT8、LMO4、PSAT1)并构建了风险评分(risk score)预测模型,根据风险评分中位值将患者划分为高风险组和低风险组。使用Kaplan-Meier(K-M)生存分析、受试者工作特征(receiver operating characteristic,ROC)曲线、多变量Cox回归分析、构建列线图和校准曲线评估了模型的预测效能。训练集和验证集的K-M生存曲线、受试者工作特征曲线下面积(area under the curve,AUC)分析均验证了模型具有良好的预测能力。多变量Cox回归分析显示,风险评分可作为独立的预后生物标志物。使用ssGSEA、免疫评分分析和化疗药物对高低风险组患者的半最大抑制浓度(half maximal inhibitory concentration,IC50)值差异分析显示,免疫细胞和免疫检查点基因以及大多数化疗药物的IC50值在不同风险组之间的表达存在显著差异。研究结果构建了基于5个m5C相关基因的风险评分预后预测模型,这将有助于阐明TNBC中m5C相关基因的作用机制,进而提供更有价值的预后及诊断的生物标志物和潜在的治疗靶点,为TNBC患者临床个性化治疗提供理论指导。
