The efficacy of shape control is the core of this technology and the main basis of automatic shape control system model designing. This passage constructs the three-dimensional elastic deformation model of CVCplus rol...The efficacy of shape control is the core of this technology and the main basis of automatic shape control system model designing. This passage constructs the three-dimensional elastic deformation model of CVCplus roll system in 2250 mm hot rolling mill. Comparing and analyzing different influence of working factors on control characteristic, the shape control characteristic of CVCplus roll system in its whole work time is studied, and the cause is analyzed and the difference of the roll gap curve and crown adjustable area in early and latter work time is compared. The outcome has crucial meaning in both theory and production.展开更多
目的:建立基于自噬和铁死亡相关基因的预后模型,并基于肾透明细胞癌(ccrCC)的自噬基因(autophagy related genes,ARGs)和铁死亡基因(ferroptosis related genes,FRGs)模型评估预后。方法:通过癌症基因组图谱(TCGA)数据库中的ccRCC数据...目的:建立基于自噬和铁死亡相关基因的预后模型,并基于肾透明细胞癌(ccrCC)的自噬基因(autophagy related genes,ARGs)和铁死亡基因(ferroptosis related genes,FRGs)模型评估预后。方法:通过癌症基因组图谱(TCGA)数据库中的ccRCC数据集识别与风险相关的ARGs和FRGs,进行功能富集和肿瘤分型分析,通过单变量和多变量Cox回归建立537例患者的预后风险模型。多指标ROC用于评估模型的准确性。最后使用GSE29609数据集验证。结果:共发现37个差异表达的基因。单变量和多变量Cox回归确定了8个与OS相关的风险相关基因:CASP4、PRKCQ、BNIP3、BAG1、BIRC5、CHAC1、ATG16L2、EIF4EBP1。Kaplan-Meier生存分析显示,高危组患者生存率较低,多指标ROC曲线下面积>0.75,说明模型预测准确率较高。然后基于CIBERSORT算法进行免疫细胞浸润评估。结论:基于8个ARGs和FRGs的肾透明细胞癌相关基因预后模型具有一定的准确性,可更准确地指导临床治疗。展开更多
To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection...To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.展开更多
文摘The efficacy of shape control is the core of this technology and the main basis of automatic shape control system model designing. This passage constructs the three-dimensional elastic deformation model of CVCplus roll system in 2250 mm hot rolling mill. Comparing and analyzing different influence of working factors on control characteristic, the shape control characteristic of CVCplus roll system in its whole work time is studied, and the cause is analyzed and the difference of the roll gap curve and crown adjustable area in early and latter work time is compared. The outcome has crucial meaning in both theory and production.
文摘目的:建立基于自噬和铁死亡相关基因的预后模型,并基于肾透明细胞癌(ccrCC)的自噬基因(autophagy related genes,ARGs)和铁死亡基因(ferroptosis related genes,FRGs)模型评估预后。方法:通过癌症基因组图谱(TCGA)数据库中的ccRCC数据集识别与风险相关的ARGs和FRGs,进行功能富集和肿瘤分型分析,通过单变量和多变量Cox回归建立537例患者的预后风险模型。多指标ROC用于评估模型的准确性。最后使用GSE29609数据集验证。结果:共发现37个差异表达的基因。单变量和多变量Cox回归确定了8个与OS相关的风险相关基因:CASP4、PRKCQ、BNIP3、BAG1、BIRC5、CHAC1、ATG16L2、EIF4EBP1。Kaplan-Meier生存分析显示,高危组患者生存率较低,多指标ROC曲线下面积>0.75,说明模型预测准确率较高。然后基于CIBERSORT算法进行免疫细胞浸润评估。结论:基于8个ARGs和FRGs的肾透明细胞癌相关基因预后模型具有一定的准确性,可更准确地指导临床治疗。
基金supported in part by the National Key R&D Program of China(No.2022YFB3904503)National Natural Science Foundation of China(No.62172418)。
文摘To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.
