Tomato brown rugose fruit virus(ToBRFV) is a novel tobamovirus firstly reported in 2015 and poses a severe threat to the tomato industry. So far, it has spread to 10 countries in America, Asia, and Europe. In 2019, To...Tomato brown rugose fruit virus(ToBRFV) is a novel tobamovirus firstly reported in 2015 and poses a severe threat to the tomato industry. So far, it has spread to 10 countries in America, Asia, and Europe. In 2019, ToBRFV was identified in Shandong Province(ToBRFV-SD), China. In this study, it was shown that ToBRFV-SD induced mild to severe mosaic and blistering on leaves, necrosis on sepals and pedicles, and deformation, yellow spots, and brown rugose necrotic lesions on fruits. ToBRFV-SD induced distinct symptoms on plants of tomato, Capsicum annumm, and Nicotiana benthamiana, and caused latent infection on plants of Solanum tuberosum, Solanum melongena, and N. tabacum cv. Zhongyan 102. All the 50 tomato cultivars tested were highly sensitive to ToBRFV-SD. The complete genomic sequence of ToBRFV-SD shared the highest nucleotide and amino acid identities with isolate IL from Israel. In the phylogenetic tree constructed with the complete genomic sequence, all the ToBRFV isolates were clustered together and formed a sister branch with tobacco mosaic virus(TMV). Furthermore, a quadruplex RT-PCR system was developed that could differentiate ToBRFV from other economically important viruses affecting tomatoes, such as TMV, tomato mosaic virus, and tomato spotted wilt virus. The findings of this study enhance our understanding of the biological and molecular characteristics of ToBRFV and provide an efficient and effective detection method for multiple infections, which is helpful in the management of ToBRFV.展开更多
This study is aimed at establishing a sensitive approach to detect disseminated tumor cells in peripheral blood and evaluate its clinical significance. A total of 198 blood samples including 168 from colorectal carcin...This study is aimed at establishing a sensitive approach to detect disseminated tumor cells in peripheral blood and evaluate its clinical significance. A total of 198 blood samples including 168 from colorectal carcinoma (CRC) patients and 30 from healthy volunteers were examined by quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR) to evaluate the expression of carcinoembryonic antigen (CEA), cytokeratin 20 (CK20) and cytokeratin 19 (CK19) mRNA. CEA mRNA was detected in 35.8% of patients and 3.3% of controls, CK20 mRNA in 28.3% of patients and 6.7% of controls, and CK19 mRNA in 41.9% of patients and 3.3% of controls. CEA and CK20 mRNA positive ratio increased with the advancing Dukes stages, but there was no significant difference in positive ratio between any two stages (P>0.05). Also, relatively high positive ratio of CEA, CK20 and CK19 mRNA expression was observed in some CRC patients with earlier Dukes stages. A higher positive ratio was obtained when two or three detection markers were combined compared to a single marker. Our study indicates that quanti-tative real-time RT-PCR detection for CEA, CK20 and CK19 mRNA in peripheral blood is a valuable tool for monitoring early stage dissemination of CRC cells in blood circulation.展开更多
Reverse transcription-polymerase chain reaction(RT-PCR)is an essential method for specific diagnosis of SARS-CoV-2 infection.Unfortunately,false negative test results are often reported.In this study,we attempted to d...Reverse transcription-polymerase chain reaction(RT-PCR)is an essential method for specific diagnosis of SARS-CoV-2 infection.Unfortunately,false negative test results are often reported.In this study,we attempted to determine the principal causes leading to false negative results of RT-PCR detection of SARS-CoV-2 RNAs in respiratory tract specimens.Multiple sputum and throat swab specimens from 161 confirmed COVID-19 patients were tested with a commercialfluorescent RT-PCR kit targeting the ORF1 ab and N regions of SARS-CoV-2 genome.The RNA level of a cellular housekeeping gene ribonuclease P/MRP subunit p30(RPP30)in these specimens was also assessed by RT-PCR.Data for a total of 1052 samples were retrospectively re-analyzed and a strong association between positive results in SARS-CoV-2 RNA tests and high level of RPP30 RNA in respiratory tract specimens was revealed.By using the ROC-AUC analysis,we identified Ct cutoff values for RPP30 RT-PCR which predicted false negative results for SARS-CoV-2 RT-PCR with high sensitivity(95.03%–95.26%)and specificity(83.72%–98.55%)for respective combination of specimen type and ampli-fication reaction.Using these Ct cutoff values,false negative results could be reliably identified.Therefore,the presence of cellular materials,likely infected host cells,are essential for correct SARS-CoV-2 RNA detection by RT-PCR in patient specimens.RPP30 could serve as an indicator for cellular content,or a surrogate indicator for specimen quality.In addition,our results demonstrated that false negativity accounted for a vast majority of contradicting results in SARS-CoV-2 RNA test by RT-PCR.展开更多
Tomato mottle mosaic virus(ToMMV), an economically important species of the genus Tobamovirus, causes significant loss in yield and quality of tomato fruits. Here, we identified the Shandong isolate of ToMMV(ToMMV-SD)...Tomato mottle mosaic virus(ToMMV), an economically important species of the genus Tobamovirus, causes significant loss in yield and quality of tomato fruits. Here, we identified the Shandong isolate of ToMMV(ToMMV-SD) collected from symptomatic tomato fruits in Weifang, Shandong Province of China. ToMMV-SD caused symptoms such as severe mosaic, mottling, and necrosis of tomato leaves, yellow spot and necrotic lesions on tomato fruits. The obtained full genome of ToMMV-SD was 6 399 nucleotides(accession number MW373515) and had the highest identity of 99.5% with that of isolate SC13-051 from the United States of America at the genomic level. The infectious clone of ToMMV-SD was constructed and induced clear mosaic and necrotic symptoms onto Nicotiana benthamiana leaves. Several commercial tomato cultivars, harboring Tm-2~2 resistance gene, and pepper cultivars, containing L resistance gene, were susceptible to ToMMV-SD. Plants of Solanum melongena(eggplant) and Brassica pekinensis(napa cabbage) showed mottling symptoms, while N. tabacum cv. Zhongyan 100 displayed latent infection. ToMMV-SD did not infect plants of N. tabacum cv. Xanthi NN, Brassica rapa ssp. chinensis(bok choy), Raphanus sativus(radish), Vigna unguiculata cv. Yuanzhong 28-2(cowpea), or Tm-2~2 transgenic N. benthamiana. A quintuplex RT-PCR system differentiated ToMMV from tomato mosaic virus, tomato brown rugose fruit virus, tobacco mosaic virus, and tomato spotted wilt virus, with the threshold amount of 0.02 pg. These results highlight the threat posed by ToMMV to tomato and pepper cultivation and offer an efficient detection system for the simultaneous detection of four tobamoviruses and tomato spotted wilt virus infecting tomato plants in the field.展开更多
This study was conducted to rapidly detect clinical infection condition of duck reovirus. A pair of specific primers was designed according to gene se- quence of σC protein of duck reovirus, and a specific RT-PCR det...This study was conducted to rapidly detect clinical infection condition of duck reovirus. A pair of specific primers was designed according to gene se- quence of σC protein of duck reovirus, and a specific RT-PCR detection method of duck reovirus was established with genome of duck reovirus as template. Differ- ent samples were collected from ducks infected by suspected reovirus in Jiangsu Province and subjected to PCR detection. The results showed that the established RT-PCR method could specifically amplify the 438 bp sequence of the conservative region of σC gene, and detec the DNA of duck rcovirus as low as 1gf, with a detection rate of 100%. The RT-PCR method could be used for rapid clinical diagnosis of duck reovirus.展开更多
Ret finger protein（RFP） is a member of the tripartite motif family, which is characterized by a conserved RING finger of motif, a B-box, and a coiled-coil domain（they are called RBCC generally）. Although RFP was k...Ret finger protein（RFP） is a member of the tripartite motif family, which is characterized by a conserved RING finger of motif, a B-box, and a coiled-coil domain（they are called RBCC generally）. Although RFP was known to be an oncogene when its RBCC moiety was connected with a tyrosine kinase domain by DNA rearrangement, its biological function was not well defined. In this study, by using real-time RT-PCR, the RFP expressions in human and mouse normal tissues, and in the cervical squamous cell carcinoma, endometrial adenocarcinoma, gastric adenocarcinoma, esophageal squamous cell carcinoma, and brain cancer tissues were analyzed. The result of the study proved that the highest level of mRNA reverse transcription appeared in the normal testical tissue, whereas that in other normal tissues of human and mice were low. The mRNA reverse transcription level of RFP was higher in the endometrial adenocarcinoma tissue than in the cervical squamous cell carcinoma tissue; the mRNA reverse transcription level of RFP in the gastric adenocarcinoma tissue was significantly higher than that in the esophageal squamous cell carcinoma tissue. It was also found that the mRNA reverse transcription level of RFP in the brain cancer tissue was higher than that in the normal brain tissue. These results suggested that RFP could possibly be a useful molecular target for the development of new therapeutics for malignant tumors.展开更多
A competitive internal control(IC)adapted to RT-PCR in-house assay was developed for HCV RNA detection in human pooled plasma.Also,it was applied in a multiplex RT-PCR for the HIV-1 and HCV RNA screening in human pool...A competitive internal control(IC)adapted to RT-PCR in-house assay was developed for HCV RNA detection in human pooled plasma.Also,it was applied in a multiplex RT-PCR for the HIV-1 and HCV RNA screening in human pooled plasma and plasma-derived products.A 258-bp PCR product from the 5´non-coding region of HCV genome was obtained.A competitive IC template was constructed by inserting a 52-bp double strand sequence into the NheI site of the 258-bp amplicon.This sequence was cloned and the obtained plasmid was used to generate a synthetic RNA.The IC/RNA was incorporated in in-house HCV and/or HIV PCR technique to monitor the efficiency of extraction,reverse transcription,and PCR amplification steps.IC was also used to detect all major genotypes of HCV and HIV-1 strains with similar sensitivity.The detection limit of the assay for HCV and HIV-1 was 52.7 IU/mL and 164.2 IU/mL,respectively.These techniques have been evaluated in international programs of external quality assurance with highly satisfactory results.This IC is an essential reagent in PCR techniques to detect and identify HCV and HIV-1 in pooled plasma samples involved in the manufacture of plasma-derived products as well as in the field of clinical microbiology with limited resources.展开更多
Objective:This paper focuses on the multiple detection RT-PCR technology of H5,H7,AND H9 subtype avian influenza viruses and Newcastle disease virus,and points out the specific detection methods and detection procedur...Objective:This paper focuses on the multiple detection RT-PCR technology of H5,H7,AND H9 subtype avian influenza viruses and Newcastle disease virus,and points out the specific detection methods and detection procedures of avian influenza and Newcastle disease virus.Methods:The genes of Newcastle disease virus carrying out the HA gene sequence of H5,H7 and H9 subtype AIV in GenBank were used to establish a strategy for simultaneous detection of three subtypes of avian influenza virus and Newcastle disease virus.Results:The results showed that the program can detect and distinguish H5,H7 and H9 subtype avian influenza viruses and Newcastle disease virus at one time.Conclusion:Multiple RT-PCR detection method has high detection sensitivity and can detect and determine different subtypes of avian influenza virus and Newcastle disease virus quickly and accurately,therefore,it has a crucial role in the detection and control of avian influenza H5,H7 and H9 subtypes and Newcastle disease.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propo...Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.展开更多
Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving d...Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving detection model.Firstly,the Contrast-Limited Adaptive Histogram Equalisation(CLAHE)image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target;then,on the basis of the YOLOv5 model,the Kmeans++clustering algorithm is introduced to obtain a suitable anchor frame,and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection.Finally,an improved SEAM(Separated and Enhancement Attention Module)attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes.Compared with the original model,the improved YOLO-LKSDS model achieves a 13.3%improvement in accuracy,a 1.7%improvement in mAP,and 240,000 fewer parameters on the BDD100K dataset.In order to validate the generalization of the improved algorithm,we selected the KITTI dataset for experimentation,which shows that YOLOv5’s accuracy improves by 21.1%,recall by 36.6%,and mAP50 by 29.5%,respectively,on the KITTI dataset.The deployment of this paper’s algorithm is verified by an edge computing platform,where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W,demonstrating high real-time capability and energy efficiency.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to ...Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.展开更多
基金supported by the grants from the National Natural Science Foundation of China (31720103912 and 31801704)the ’Taishan Scholar’ Construction Project, China (TS201712023)。
文摘Tomato brown rugose fruit virus(ToBRFV) is a novel tobamovirus firstly reported in 2015 and poses a severe threat to the tomato industry. So far, it has spread to 10 countries in America, Asia, and Europe. In 2019, ToBRFV was identified in Shandong Province(ToBRFV-SD), China. In this study, it was shown that ToBRFV-SD induced mild to severe mosaic and blistering on leaves, necrosis on sepals and pedicles, and deformation, yellow spots, and brown rugose necrotic lesions on fruits. ToBRFV-SD induced distinct symptoms on plants of tomato, Capsicum annumm, and Nicotiana benthamiana, and caused latent infection on plants of Solanum tuberosum, Solanum melongena, and N. tabacum cv. Zhongyan 102. All the 50 tomato cultivars tested were highly sensitive to ToBRFV-SD. The complete genomic sequence of ToBRFV-SD shared the highest nucleotide and amino acid identities with isolate IL from Israel. In the phylogenetic tree constructed with the complete genomic sequence, all the ToBRFV isolates were clustered together and formed a sister branch with tobacco mosaic virus(TMV). Furthermore, a quadruplex RT-PCR system was developed that could differentiate ToBRFV from other economically important viruses affecting tomatoes, such as TMV, tomato mosaic virus, and tomato spotted wilt virus. The findings of this study enhance our understanding of the biological and molecular characteristics of ToBRFV and provide an efficient and effective detection method for multiple infections, which is helpful in the management of ToBRFV.
基金Project (No. 021103004) supported by the Science and TechnologyDevelopment Program of Zhejiang Province, China
文摘This study is aimed at establishing a sensitive approach to detect disseminated tumor cells in peripheral blood and evaluate its clinical significance. A total of 198 blood samples including 168 from colorectal carcinoma (CRC) patients and 30 from healthy volunteers were examined by quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR) to evaluate the expression of carcinoembryonic antigen (CEA), cytokeratin 20 (CK20) and cytokeratin 19 (CK19) mRNA. CEA mRNA was detected in 35.8% of patients and 3.3% of controls, CK20 mRNA in 28.3% of patients and 6.7% of controls, and CK19 mRNA in 41.9% of patients and 3.3% of controls. CEA and CK20 mRNA positive ratio increased with the advancing Dukes stages, but there was no significant difference in positive ratio between any two stages (P>0.05). Also, relatively high positive ratio of CEA, CK20 and CK19 mRNA expression was observed in some CRC patients with earlier Dukes stages. A higher positive ratio was obtained when two or three detection markers were combined compared to a single marker. Our study indicates that quanti-tative real-time RT-PCR detection for CEA, CK20 and CK19 mRNA in peripheral blood is a valuable tool for monitoring early stage dissemination of CRC cells in blood circulation.
基金supported by the Natural Science Foundation of Anhui Province(Grant No.1608085MH162)。
文摘Reverse transcription-polymerase chain reaction(RT-PCR)is an essential method for specific diagnosis of SARS-CoV-2 infection.Unfortunately,false negative test results are often reported.In this study,we attempted to determine the principal causes leading to false negative results of RT-PCR detection of SARS-CoV-2 RNAs in respiratory tract specimens.Multiple sputum and throat swab specimens from 161 confirmed COVID-19 patients were tested with a commercialfluorescent RT-PCR kit targeting the ORF1 ab and N regions of SARS-CoV-2 genome.The RNA level of a cellular housekeeping gene ribonuclease P/MRP subunit p30(RPP30)in these specimens was also assessed by RT-PCR.Data for a total of 1052 samples were retrospectively re-analyzed and a strong association between positive results in SARS-CoV-2 RNA tests and high level of RPP30 RNA in respiratory tract specimens was revealed.By using the ROC-AUC analysis,we identified Ct cutoff values for RPP30 RT-PCR which predicted false negative results for SARS-CoV-2 RT-PCR with high sensitivity(95.03%–95.26%)and specificity(83.72%–98.55%)for respective combination of specimen type and ampli-fication reaction.Using these Ct cutoff values,false negative results could be reliably identified.Therefore,the presence of cellular materials,likely infected host cells,are essential for correct SARS-CoV-2 RNA detection by RT-PCR in patient specimens.RPP30 could serve as an indicator for cellular content,or a surrogate indicator for specimen quality.In addition,our results demonstrated that false negativity accounted for a vast majority of contradicting results in SARS-CoV-2 RNA test by RT-PCR.
基金supported by the grants from the National Natural Science Foundation of China(32072387)the‘Taishan Scholar’Construction Project,China(TS201712023)。
文摘Tomato mottle mosaic virus(ToMMV), an economically important species of the genus Tobamovirus, causes significant loss in yield and quality of tomato fruits. Here, we identified the Shandong isolate of ToMMV(ToMMV-SD) collected from symptomatic tomato fruits in Weifang, Shandong Province of China. ToMMV-SD caused symptoms such as severe mosaic, mottling, and necrosis of tomato leaves, yellow spot and necrotic lesions on tomato fruits. The obtained full genome of ToMMV-SD was 6 399 nucleotides(accession number MW373515) and had the highest identity of 99.5% with that of isolate SC13-051 from the United States of America at the genomic level. The infectious clone of ToMMV-SD was constructed and induced clear mosaic and necrotic symptoms onto Nicotiana benthamiana leaves. Several commercial tomato cultivars, harboring Tm-2~2 resistance gene, and pepper cultivars, containing L resistance gene, were susceptible to ToMMV-SD. Plants of Solanum melongena(eggplant) and Brassica pekinensis(napa cabbage) showed mottling symptoms, while N. tabacum cv. Zhongyan 100 displayed latent infection. ToMMV-SD did not infect plants of N. tabacum cv. Xanthi NN, Brassica rapa ssp. chinensis(bok choy), Raphanus sativus(radish), Vigna unguiculata cv. Yuanzhong 28-2(cowpea), or Tm-2~2 transgenic N. benthamiana. A quintuplex RT-PCR system differentiated ToMMV from tomato mosaic virus, tomato brown rugose fruit virus, tobacco mosaic virus, and tomato spotted wilt virus, with the threshold amount of 0.02 pg. These results highlight the threat posed by ToMMV to tomato and pepper cultivation and offer an efficient detection system for the simultaneous detection of four tobamoviruses and tomato spotted wilt virus infecting tomato plants in the field.
基金Supported by the Twelfth Batch of"Top Talent Project"of Jiangsu Province(NY023)Jiangsu Provincial Natural Science Research Project(16KJB-23004)
文摘This study was conducted to rapidly detect clinical infection condition of duck reovirus. A pair of specific primers was designed according to gene se- quence of σC protein of duck reovirus, and a specific RT-PCR detection method of duck reovirus was established with genome of duck reovirus as template. Differ- ent samples were collected from ducks infected by suspected reovirus in Jiangsu Province and subjected to PCR detection. The results showed that the established RT-PCR method could specifically amplify the 438 bp sequence of the conservative region of σC gene, and detec the DNA of duck rcovirus as low as 1gf, with a detection rate of 100%. The RT-PCR method could be used for rapid clinical diagnosis of duck reovirus.
基金Supported by the National Natural Science Foundation of China(No. 30371757)
文摘Ret finger protein（RFP） is a member of the tripartite motif family, which is characterized by a conserved RING finger of motif, a B-box, and a coiled-coil domain（they are called RBCC generally）. Although RFP was known to be an oncogene when its RBCC moiety was connected with a tyrosine kinase domain by DNA rearrangement, its biological function was not well defined. In this study, by using real-time RT-PCR, the RFP expressions in human and mouse normal tissues, and in the cervical squamous cell carcinoma, endometrial adenocarcinoma, gastric adenocarcinoma, esophageal squamous cell carcinoma, and brain cancer tissues were analyzed. The result of the study proved that the highest level of mRNA reverse transcription appeared in the normal testical tissue, whereas that in other normal tissues of human and mice were low. The mRNA reverse transcription level of RFP was higher in the endometrial adenocarcinoma tissue than in the cervical squamous cell carcinoma tissue; the mRNA reverse transcription level of RFP in the gastric adenocarcinoma tissue was significantly higher than that in the esophageal squamous cell carcinoma tissue. It was also found that the mRNA reverse transcription level of RFP in the brain cancer tissue was higher than that in the normal brain tissue. These results suggested that RFP could possibly be a useful molecular target for the development of new therapeutics for malignant tumors.
基金Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina(CONICET),Agencia Nacional de Promoción Ciencia y Técnica(FONCYT),Ministerio de Ciencia de la Provincia de Córdoba and Secretaría de Ciencia y Tecnología de la Universidad Nacional de Córdoba(SECyT-UNC)and by own funds of the Laboratorio de Hemoderivados de la Universidad Nacional de Córdoba.S.G-R.and L.R.are a Career Investigator and a Graduate Fellow of CONICET,respectively.
文摘A competitive internal control(IC)adapted to RT-PCR in-house assay was developed for HCV RNA detection in human pooled plasma.Also,it was applied in a multiplex RT-PCR for the HIV-1 and HCV RNA screening in human pooled plasma and plasma-derived products.A 258-bp PCR product from the 5´non-coding region of HCV genome was obtained.A competitive IC template was constructed by inserting a 52-bp double strand sequence into the NheI site of the 258-bp amplicon.This sequence was cloned and the obtained plasmid was used to generate a synthetic RNA.The IC/RNA was incorporated in in-house HCV and/or HIV PCR technique to monitor the efficiency of extraction,reverse transcription,and PCR amplification steps.IC was also used to detect all major genotypes of HCV and HIV-1 strains with similar sensitivity.The detection limit of the assay for HCV and HIV-1 was 52.7 IU/mL and 164.2 IU/mL,respectively.These techniques have been evaluated in international programs of external quality assurance with highly satisfactory results.This IC is an essential reagent in PCR techniques to detect and identify HCV and HIV-1 in pooled plasma samples involved in the manufacture of plasma-derived products as well as in the field of clinical microbiology with limited resources.
文摘Objective:This paper focuses on the multiple detection RT-PCR technology of H5,H7,AND H9 subtype avian influenza viruses and Newcastle disease virus,and points out the specific detection methods and detection procedures of avian influenza and Newcastle disease virus.Methods:The genes of Newcastle disease virus carrying out the HA gene sequence of H5,H7 and H9 subtype AIV in GenBank were used to establish a strategy for simultaneous detection of three subtypes of avian influenza virus and Newcastle disease virus.Results:The results showed that the program can detect and distinguish H5,H7 and H9 subtype avian influenza viruses and Newcastle disease virus at one time.Conclusion:Multiple RT-PCR detection method has high detection sensitivity and can detect and determine different subtypes of avian influenza virus and Newcastle disease virus quickly and accurately,therefore,it has a crucial role in the detection and control of avian influenza H5,H7 and H9 subtypes and Newcastle disease.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金supported by project ZR2022MF330 supported by Shandong Provincial Natural Science Foundationthe National Natural Science Foundation of China under Grant No.61701286.
文摘Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.
基金supported by the Key R&D Program of Shaanxi Province(No.2025CYYBXM-078).
文摘Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving detection model.Firstly,the Contrast-Limited Adaptive Histogram Equalisation(CLAHE)image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target;then,on the basis of the YOLOv5 model,the Kmeans++clustering algorithm is introduced to obtain a suitable anchor frame,and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection.Finally,an improved SEAM(Separated and Enhancement Attention Module)attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes.Compared with the original model,the improved YOLO-LKSDS model achieves a 13.3%improvement in accuracy,a 1.7%improvement in mAP,and 240,000 fewer parameters on the BDD100K dataset.In order to validate the generalization of the improved algorithm,we selected the KITTI dataset for experimentation,which shows that YOLOv5’s accuracy improves by 21.1%,recall by 36.6%,and mAP50 by 29.5%,respectively,on the KITTI dataset.The deployment of this paper’s algorithm is verified by an edge computing platform,where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W,demonstrating high real-time capability and energy efficiency.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金supported by the High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(IITP-2025-RS-2023-00259497)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Republic of Korea government(MSIT)(No.IITP-2025-RS-2023-00254129+1 种基金Graduate School of Metaverse Convergence(Sungkyunkwan University))was supported by the Basic Science Research Program of the National Research Foundation(NRF)funded by the Republic of Korean government(MSIT)(No.RS-2024-00346737).
文摘Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.
