[ Objective] This study aimed to investigate the preparation of red component in secondary metabolites of recombinant Hansenula anomala strain N6076 and its GC-MS detection. [ Method] Thin-layer chromatography method ...[ Objective] This study aimed to investigate the preparation of red component in secondary metabolites of recombinant Hansenula anomala strain N6076 and its GC-MS detection. [ Method] Thin-layer chromatography method was applied for large-scale preparation of red component in the secondary metabolites of re- combinant H. anomala strain N6076. The red component was dissolved in anhydrous ethanol for GC-MS detection and chemical structure comparison in the data- base to identify its type. [ Resultl The red component is preliminarily identified as a quinane compound, while no compound with exactly the same structure as the red component has been found in WILEY, Nist and Nbs compound libraries. [ Conclusion] This study laid the foundation for further NMR structure identification of the obtained red component and investigation of the relationship between its structure and biolo#cal effects.展开更多
油料作物由于含油量高、基质复杂,导致其弱极性多环芳烃类化合物提取率低,成为准确检测高油样品中多环芳烃的瓶颈。本文对比了16种多环芳烃的GC-MS/MS检测条件SIM(Single Ion Monitoring)模式和SRM(Selective Reaction Monitoring)模式...油料作物由于含油量高、基质复杂,导致其弱极性多环芳烃类化合物提取率低,成为准确检测高油样品中多环芳烃的瓶颈。本文对比了16种多环芳烃的GC-MS/MS检测条件SIM(Single Ion Monitoring)模式和SRM(Selective Reaction Monitoring)模式质谱信号响应,SRM模式干扰峰更少,检出限更低;对比了QuEChERS和超声辅助提取方法对大豆、油菜籽、花生三种油料中16种多环芳烃的提取效果,超声辅助提取的基质效应很高,部分多环芳烃基质减弱80%以上,且油菜籽的提取稳定性差,部分相对标准偏差达到32%~45%。并比较了乙腈和丙酮作为QuCEhERS方法提取溶剂的提取效果。结果表明,QuCEhERS方法中乙腈作为提取溶剂,在极性最弱的多环芳烃回收率低,如苯并[b]荧蒽、苯并[k]荧蒽等,回收率甚至小于10%。而丙酮作为QuCEhERS方法提取溶剂,而在极性弱的多环芳烃中,回收率提高了3~5倍,适合提取高油样品中多环芳烃。三种油料基质匹配标准曲线的相关系数均在0.99以上。16种多环芳烃均能获得较好的回收率(58%~100%),相对标准偏差为0.4%~10.6%,方法稳定性好。展开更多
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.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
基金Supported by National Natural Science Foundation of China (30960006)Natural Science Fund of Xinjiang University (BS080120)Postdoctoral Station of Geography from Xinjiang University
文摘[ Objective] This study aimed to investigate the preparation of red component in secondary metabolites of recombinant Hansenula anomala strain N6076 and its GC-MS detection. [ Method] Thin-layer chromatography method was applied for large-scale preparation of red component in the secondary metabolites of re- combinant H. anomala strain N6076. The red component was dissolved in anhydrous ethanol for GC-MS detection and chemical structure comparison in the data- base to identify its type. [ Resultl The red component is preliminarily identified as a quinane compound, while no compound with exactly the same structure as the red component has been found in WILEY, Nist and Nbs compound libraries. [ Conclusion] This study laid the foundation for further NMR structure identification of the obtained red component and investigation of the relationship between its structure and biolo#cal effects.
文摘油料作物由于含油量高、基质复杂,导致其弱极性多环芳烃类化合物提取率低,成为准确检测高油样品中多环芳烃的瓶颈。本文对比了16种多环芳烃的GC-MS/MS检测条件SIM(Single Ion Monitoring)模式和SRM(Selective Reaction Monitoring)模式质谱信号响应,SRM模式干扰峰更少,检出限更低;对比了QuEChERS和超声辅助提取方法对大豆、油菜籽、花生三种油料中16种多环芳烃的提取效果,超声辅助提取的基质效应很高,部分多环芳烃基质减弱80%以上,且油菜籽的提取稳定性差,部分相对标准偏差达到32%~45%。并比较了乙腈和丙酮作为QuCEhERS方法提取溶剂的提取效果。结果表明,QuCEhERS方法中乙腈作为提取溶剂,在极性最弱的多环芳烃回收率低,如苯并[b]荧蒽、苯并[k]荧蒽等,回收率甚至小于10%。而丙酮作为QuCEhERS方法提取溶剂,而在极性弱的多环芳烃中,回收率提高了3~5倍,适合提取高油样品中多环芳烃。三种油料基质匹配标准曲线的相关系数均在0.99以上。16种多环芳烃均能获得较好的回收率(58%~100%),相对标准偏差为0.4%~10.6%,方法稳定性好。
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
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.
