Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remot...Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.展开更多
目的构建Ad-VS-1基因转染HUVEC的体外缺氧/复氧模型,探讨VS-1转基因治疗对血管内皮细胞缺氧/复氧损伤的保护作用及其机制。方法①建立HUVEC缺氧/复氧(H/R)损伤模型,分为4组:空白组,H/R组,空载腺病毒转染+H/R组,Ad-VS-1转染+H/R组;②测...目的构建Ad-VS-1基因转染HUVEC的体外缺氧/复氧模型,探讨VS-1转基因治疗对血管内皮细胞缺氧/复氧损伤的保护作用及其机制。方法①建立HUVEC缺氧/复氧(H/R)损伤模型,分为4组:空白组,H/R组,空载腺病毒转染+H/R组,Ad-VS-1转染+H/R组;②测定内皮细胞活力(MTT),超氧化物歧化酶活力(SOD)、丙二醛(MDA)、eNOS、NO表达、炎症因子ICAM-1、VCAM-1、TNF-α的表达情况;③在Ad-VS-1转染+H/R组基础上增设Hb、KT5823、SB203580和W ortm an in 4个信号抑制组,流式细胞仪测定细胞凋亡率进行统计学分析。结果①成功构建Ad-VS-1转染HUVEC表达后H/R模型;②与空白组比较,H/R组和空腺病毒感染+H/R组的MTT、SOD含量、eNOS、NO表达显著降低,而MDA、ICAM-1、VCAM-1、TNF-α含量显著增加(P<0.05);与H/R组和空载腺病毒感染+H/R组比较,VS-1转染+H/R组MTT、SOD、eNOS、NO表达含量明显回升,而MDA、ICAM-1,TNF-α生成量明显回落(P<0.05);③与H/R组和空载腺病毒感染+H/R组比较,VS-1转染组细胞凋亡率明显降低,而4组信号抑制剂组的细胞凋亡率较VS-1转染组均明显回升(P<0.05)。结论Ad-VS-1成功转染HUVEC,并通过PI3K/Akt-eNOS-NO-cGMP-PKG和P38MPAK等信号途径,抑制氧化应激和炎症反应,产生显著的抗血管内皮细胞缺氧/复氧损伤作用,为今后VS-1抗心肌缺血/再灌注损伤研究奠定基础。展开更多
This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was f...This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.展开更多
Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out se...Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.展开更多
文摘Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.
文摘目的构建Ad-VS-1基因转染HUVEC的体外缺氧/复氧模型,探讨VS-1转基因治疗对血管内皮细胞缺氧/复氧损伤的保护作用及其机制。方法①建立HUVEC缺氧/复氧(H/R)损伤模型,分为4组:空白组,H/R组,空载腺病毒转染+H/R组,Ad-VS-1转染+H/R组;②测定内皮细胞活力(MTT),超氧化物歧化酶活力(SOD)、丙二醛(MDA)、eNOS、NO表达、炎症因子ICAM-1、VCAM-1、TNF-α的表达情况;③在Ad-VS-1转染+H/R组基础上增设Hb、KT5823、SB203580和W ortm an in 4个信号抑制组,流式细胞仪测定细胞凋亡率进行统计学分析。结果①成功构建Ad-VS-1转染HUVEC表达后H/R模型;②与空白组比较,H/R组和空腺病毒感染+H/R组的MTT、SOD含量、eNOS、NO表达显著降低,而MDA、ICAM-1、VCAM-1、TNF-α含量显著增加(P<0.05);与H/R组和空载腺病毒感染+H/R组比较,VS-1转染+H/R组MTT、SOD、eNOS、NO表达含量明显回升,而MDA、ICAM-1,TNF-α生成量明显回落(P<0.05);③与H/R组和空载腺病毒感染+H/R组比较,VS-1转染组细胞凋亡率明显降低,而4组信号抑制剂组的细胞凋亡率较VS-1转染组均明显回升(P<0.05)。结论Ad-VS-1成功转染HUVEC,并通过PI3K/Akt-eNOS-NO-cGMP-PKG和P38MPAK等信号途径,抑制氧化应激和炎症反应,产生显著的抗血管内皮细胞缺氧/复氧损伤作用,为今后VS-1抗心肌缺血/再灌注损伤研究奠定基础。
基金National Natural Science Foundation of China(Nos.61702094 and 62301142)“Chenguang Program”Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.18CG38)。
文摘This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.
基金supported by the People’s Public Security University of China central basic scientific research business program(No.2021JKF206).
文摘Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.
