In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic...In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.展开更多
As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose signific...As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.展开更多
The advancement of various types of fluorescent nanoparticles is crucial for enhancing the application of lateral flow immunoassays(LFIA)across multiple fields.Currently,the fluorescent nanoparticles utilized in LFIA ...The advancement of various types of fluorescent nanoparticles is crucial for enhancing the application of lateral flow immunoassays(LFIA)across multiple fields.Currently,the fluorescent nanoparticles utilized in LFIA predominantly consist of traditional dye-doped nanoparticles or aggregation-induced luminescence dye-doped nanoparticles.The reliance on specific types of nanoparticles limits the diversity of signal reporting groups available for LFIA.Herein,we developed a solid-state luminescent dye-doped nanoparticles(SLDNPs)-based LFIA system with exceptional stability for the detection of C-reactive protein(CRP)in serum.The synthesis of SLD_(520)NP_(S)was simplicity,efficient and eco-friendly,which was ideal for large-scale production of the LFIA test strip.And the SLD_(520)NP_(S)exhibits superior fluorescence quantum yield(49%),fully guarantees the performance of the LFIA test strip.The constructed SLD_(520)NPsm Ab1-based LFIA demonstrated a satisfactory linear relationship with CRP concentrations ranging from 0.5 ng/mL to 100 ng/mL,with limits of detection(LOD)of 0.78 ng/mL and a visible LOD of 1 ng/mL using a handheld 405 nm lamp.Furthermore,the developed LFIA exhibited excellent recoveries in serum,ranging from 94.45%to 102.5%.Overall,the outstanding performance of the SLD_(520)NPs-mAb1-based LFIA indicates that solid-state luminescent dyes have significant potential applications in the field of LFIA.展开更多
Sacbrood virus(SBV)is one of the most pathogenic honeybee viruses with host specificity and regional variation.The SBV strain infecting the Chinese honeybee(Apis cerana)is known as Chinese sacbrood virus(CSBV).The ext...Sacbrood virus(SBV)is one of the most pathogenic honeybee viruses with host specificity and regional variation.The SBV strain infecting the Chinese honeybee(Apis cerana)is known as Chinese sacbrood virus(CSBV).The extensively used CSBV detection methods require professionals and expensive equipment;thus,they are unsuitable for rapid onsite CSBV detection.To achieve early and rapid detection of CSBV,we developed a lateral flow detection(LFD)strip method for CSBV detection via clustered regularly interspaced short palindromic repeats(CRISPR)and the Cas13a technique.On the basis of the conserved CSBV VP2 gene nucleotide region,we designed 3 recombinant enzyme-assisted amplification(RAA)primer pairs and prepared 3 corresponding crRNAs.We investigated key performance metrics,including the sensitivity,specificity,and accuracy of LFD strips.The results demonstrated that the LFD strip based on the optimal combination(primer 2+crRNA 2)presented the lowest detection limit(2.80×101 copies/μL),and this strip could complete CSBV detection within 1 h.Furthermore,this strip exhibited excellent detection specificity,with no cross-reactivity with four other honeybee viruses.A test of 100 clinical samples indicated the feasibility of the LFD method for CSBV detection.A comparison of various CSBV detection methods revealed that the CRISPR-Cas13a-based LFD method was more accurate,efficient,and sensitive than the other methods were,indicating great application prospects in onsite CSBV detection.Our developed method is highly important for preventing and controlling CSBV infection as well as maintaining honeybee health.展开更多
The hydrocarbon detection techniques used currently are generally based on the theory of single-phase medium, but hydrocarbon reservoir mostly is multi-phase medium, therefore, multisolutions and uncertainties are exi...The hydrocarbon detection techniques used currently are generally based on the theory of single-phase medium, but hydrocarbon reservoir mostly is multi-phase medium, therefore, multisolutions and uncertainties are existed in the result of hydrocarbon detection. This paper presents a fast way to detect hydrocarbon in accordance with BOIT theory and laboratory data. The technique called DHAF technique has been applied to several survey area and obtained good result where the coincidence rate for hydrocarbon detection is higher than other similar techniques. The method shows a good prospect of the application in hydrocarbon detecting at exploration stage and in reservoir monitoring at production stage.展开更多
This work reports the single-molecule detection of gossypol by flow injection analysis with chemiluminescence method. The method is based on the reaction of luminol with ferricyanid in sodium hydroxide medium sensitiz...This work reports the single-molecule detection of gossypol by flow injection analysis with chemiluminescence method. The method is based on the reaction of luminol with ferricyanid in sodium hydroxide medium sensitized by gossypol. Under the optimum conditions, the CL intensity is proportional to the concentration of gossypol over the range of 1.11×10^-17-2.78×10^-16 mol/L in acid solution and 8.00×10^-11-7.39×10^-8mol/L in neutral solution with correlation coefficients 0.9983 and 0.9905, respectively. The detection limits is 1.60×10^-18 mol/L （S/N=3）. The proposed method has been applied for the determination of the gossypol in cottonseeds and pharmaceutical formulations with satisfactory results.展开更多
With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great signific...With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great significance in reducing system crashes and maintaining system security and integrity. This paper focuses on detecting three common types of vulnerabilities: Unused_Variable, Use_of_Uninitialized_Variable, and Use_After_ Free. We propose a method for software vulnerability detection based on an improved control flow graph(ICFG) and several predicates of vulnerability properties for each type of vulnerability. We also define a set of grammar rules for analyzing and deriving the three mentioned types of vulnerabilities, and design three vulnerability detection algorithms to guide the process of vulnerability detection. In addition, we conduct cases studies of the three mentioned types of vulnerabilities with real vulnerability program segments from Common Weakness Enumeration(CWE). The results of the studies show that the proposed method can detect the vulnerability in the tested program segments. Finally, we conduct manual analysis and experiments on detecting the three types of vulnerability program segments(30 examples for each type) from CWE, to compare the vulnerability detection effectiveness of the proposed method with that of the existing detection tool Cpp Check. The results show that the proposed method performs better. In summary, the method proposed in this paper has certain feasibility and effectiveness in detecting the three mentioned types of vulnerabilities, and it will also have guiding significance for the detection of other common vulnerabilities.展开更多
The application of pesticides (mostly insecticides and fungicides) during the tea-planting process will undoubtedly increase the dietary risk associated with drinking tea. Thus, it is necessary to ascertain whether pe...The application of pesticides (mostly insecticides and fungicides) during the tea-planting process will undoubtedly increase the dietary risk associated with drinking tea. Thus, it is necessary to ascertain whether pesticide residues in tea products exceed the maximum residue limits. However, the complex matrices present in tea samples comprise a major challenge in the analytical detection of pesticide residues. In this study, nine types of lateral flow immunochromatographic strips (LFICSs) were developed to detect the pesticides of interest (fenpropathrin, chlorpyrifos, imidacloprid, thiamethoxam, acetamiprid, carbendazim, chlorothalonil, pyraclostrobin, and iprodione). To reduce the interference of tea substrates on the assay sensitivity, the pretreatment conditions for tea samples, including the extraction solvent, extraction time, and purification agent, were optimized for the simultaneous detection of these pesticides. The entire testing procedure (including pretreatment and detection) could be completed within 30 min. The detected results of authentic tea samples were confirmed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), which suggest that the LFICS coupled with sample rapid pretreatment can be used for on-site rapid screening of the target pesticide in tea products prior to their market release.展开更多
Objective To develop an in situ PCR in combination with flow cytometry （ISPCR-FCM） for monitoring cholera toxin positive Vibrio cholerae. Methods In running this method, 4% paraformaldehyde was used to fix the Vibri...Objective To develop an in situ PCR in combination with flow cytometry （ISPCR-FCM） for monitoring cholera toxin positive Vibrio cholerae. Methods In running this method, 4% paraformaldehyde was used to fix the Vibrio cholerae cells and 1 mg/mL lysozyme for 20 min to permeabilize the cells. Before the PCR thermal cycling, 2.5% glycerol was added into the PCR reaction mixture in order to protect the integrality of the cells. Results A length of 1037bp DNA sequence was amplified, which is specific for the cholera toxin gene （ctxAB gene）. Cells subjected to ISPCR showed the presences of ctxAB gene both in epifluorescence microscopy and in flow cytometric analysis. The specificity and sensitivity of the method were investigated. The sensitivity was relatively low （10^5 cells/mL）, while the specificity was high. Conclusion We have successfully developed a new technique for detection of toxigenic Vibrio cholerae strains. Further study is needed to enhance its sensitivities. ISPCR-FCM shows a great promise in monitoring specific bacteria and their physiological states in environmental samples.展开更多
A strip reader based lateral flow immunoassay （LFIA） was established for the rapid and quantitative detection of ractopamine （RAC） in swine urine. The ratio of the optical densities （ODs） of the test line （AT）...A strip reader based lateral flow immunoassay （LFIA） was established for the rapid and quantitative detection of ractopamine （RAC） in swine urine. The ratio of the optical densities （ODs） of the test line （AT） to that of the control line （Ac） was used to effectively minimize interference among strips and sample variations. The linear range for the quantitative detection of RAC was 0.2 ng/mL to 3.5 ng/mL with a median inhibitory concentration （IC50） of 0.59＋0.06 ng/mL. The limit of detection （LOD） of the LFIA was 0.13 ng/mL. The intra-assay recovery rates were 92.97%, 97.25%, and 107.41%, whereas the inter-assay rates were 80.07%, 108.17%, and 93.7%, respectively.展开更多
Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,e...Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,ephemeral gully mapping remains a challenge.In this study,we proposed a flow-directional detection for identifying ephemeral gullies from high-resolution images and digital elevation models(DEMs).Ephemeral gullies exhibit clear linear features in high-resolution images.An edge detection operator was initially used to identify linear features from high-resolution images.Then,according to gully erosion mechanism,the flow-directional detection was designed.Edge images obtained from edge detection and flow directions obtained from DEMs were used to implement the flow-directional detection that detects ephemeral gullies along the flow direction.Results from ten study areas in the Loess Plateau of China showed that ranges of precision,recall,and Fmeasure are 6 o.66%-90.47%,65.74%-94.98%,and63.10%-91.93%,respectively.The proposed method is flexible and can be used with various images and DEMs.However,analysis of the effect of DEM resolution and accuracy showed that DEM resolution only demonstrates a minor effect on the detection results.Conversely,DEM accuracy influences the detection result and is more important than the DEM resolution.The worse the vertical accuracy of DEM,the lower the performance of the flow-directional detection will be.This work is beneficial to research related to monitoring gully erosion and assessing soil loss.展开更多
A privilege flow oriented intrusion detection method based on HSMM(Hidden semi-Markov Model)is discussed.The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS(Intrusion D...A privilege flow oriented intrusion detection method based on HSMM(Hidden semi-Markov Model)is discussed.The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS(Intrusion Detection System).Using the dataset of DARPA 1998,our experiment results reveal good detection performance and acceptable computation cost.展开更多
The expressway traffc incidents have the characteristics of high harmful, strong destructive and refractory.Incident detection can guarantee smooth operation of the expressway, reduce traffc congestion and avoid secon...The expressway traffc incidents have the characteristics of high harmful, strong destructive and refractory.Incident detection can guarantee smooth operation of the expressway, reduce traffc congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffc ratio at the entrances and crossway in the network. The expressway traffc simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffc network states so as to provide advice for the highway management department.展开更多
Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input t...Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.展开更多
Objective To establish a sensitive,simple and rapid detection method for African swine fever virus(ASFV)B646L gene.Methods A recombinase-aided amplification-lateral flow dipstick(RAA-LFD)assay was developed in this st...Objective To establish a sensitive,simple and rapid detection method for African swine fever virus(ASFV)B646L gene.Methods A recombinase-aided amplification-lateral flow dipstick(RAA-LFD)assay was developed in this study.Recombinase-aided amplification(RAA)is used to amplify template DNA,and lateral flow dipstick(LFD)is used to interpret the results after the amplification is completed.The lower limits of detection and specificity of the RAA assay were verified using recombinant plasmid and pathogenic nucleic acid.In addition,30 clinical samples were tested to evaluate the performance of the RAA assay.Results The RAA-LFD assay was completed within 15 min at 37°C,including 10 min for nucleic acid amplification and 5 minutes for LFD reading results.The detection limit of this assay was found to be 200 copies per reaction.And there was no cross-reactivity with other swine viruses.Conclusion A highly sensitive,specific,and simple RAA-LFD method was developed for the rapid detection of the ASFV.展开更多
Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance te...Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.展开更多
Bacterial endotoxin(a type of lipopolysaccharide,LPS)that acts as the strongest immune stimulant exhibits high toxicity to human health.The golden standard detection methods rely heavily on the use of a large amount o...Bacterial endotoxin(a type of lipopolysaccharide,LPS)that acts as the strongest immune stimulant exhibits high toxicity to human health.The golden standard detection methods rely heavily on the use of a large amount of tachypleus amebocyte lysate(TAL)reagents,extracted from the unique blue blood of legally protected horseshoe crabs.Herein,a cost-effective distance-based lateral flow(D-LAF)sensor is demonstrated for the first time based on the coagulation cascade process of TAL induced by endotoxin,which causes the generation of gel-state TAL.The gelation process can increase the amount of trapped water molecules and shorten the lateral flow distance of the remaining free water on the pH paper.The water flow distance is directly correlated to the concentration of endotoxin.Noteworthy,the D-LAF sensor allows the detection of endotoxin with the reduced dosage of TAL reagents than the golden standard detection methods.The detection limit of endotoxin is calculated to be 0.0742 EU/mL.This method can be applied to the detection of endotoxin in real samples such as household water and clinical injection solution with excellent performance comparable to the commercial ELISA kit.展开更多
To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved...To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.展开更多
Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately ...Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.展开更多
In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,...In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of tampering.Although deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational efficiency.To address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection techniques.In contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream.This design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level interaction.Furthermore,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region detection.Compared with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets.In addition,it has been extensively validated on other datasets,including CASIA and DIS25k.Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51704327)
文摘In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00351898 and No.RS-2025-02263915)the MOTIE under Training Industrial Security Specialist forHigh-Tech Industry(RS-2024-00415520)supervised by theKorea Institute for Advancement of Technology(KIAT)+1 种基金the MSIT under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.
基金supported by the National Natural Science Foundation of China(Nos.22064014,21765013)the Science and Technology Development Plan Project of Lanzhou(No.20211-146)+3 种基金the Science and Technology Project of Gansu Province(Nos.21YF5FA071,21JR7RA538)the Industrial Support Programme for Higher Education Institutions Project(Nos.2023CYZC-69,2024CYCZ-05)the 2023 Gansu Provincial Key Talent Project(No.2023RCXM26)a Gansu province postdoctoral grant(No.00247)。
文摘The advancement of various types of fluorescent nanoparticles is crucial for enhancing the application of lateral flow immunoassays(LFIA)across multiple fields.Currently,the fluorescent nanoparticles utilized in LFIA predominantly consist of traditional dye-doped nanoparticles or aggregation-induced luminescence dye-doped nanoparticles.The reliance on specific types of nanoparticles limits the diversity of signal reporting groups available for LFIA.Herein,we developed a solid-state luminescent dye-doped nanoparticles(SLDNPs)-based LFIA system with exceptional stability for the detection of C-reactive protein(CRP)in serum.The synthesis of SLD_(520)NP_(S)was simplicity,efficient and eco-friendly,which was ideal for large-scale production of the LFIA test strip.And the SLD_(520)NP_(S)exhibits superior fluorescence quantum yield(49%),fully guarantees the performance of the LFIA test strip.The constructed SLD_(520)NPsm Ab1-based LFIA demonstrated a satisfactory linear relationship with CRP concentrations ranging from 0.5 ng/mL to 100 ng/mL,with limits of detection(LOD)of 0.78 ng/mL and a visible LOD of 1 ng/mL using a handheld 405 nm lamp.Furthermore,the developed LFIA exhibited excellent recoveries in serum,ranging from 94.45%to 102.5%.Overall,the outstanding performance of the SLD_(520)NPs-mAb1-based LFIA indicates that solid-state luminescent dyes have significant potential applications in the field of LFIA.
基金supported by the National Key Research and Development Program of China(NO.2022YFD1600202)the Lush Mountain Public Welfare Protection Action of the China Environmental Protection Foundation(CEPFQS202169-14).
文摘Sacbrood virus(SBV)is one of the most pathogenic honeybee viruses with host specificity and regional variation.The SBV strain infecting the Chinese honeybee(Apis cerana)is known as Chinese sacbrood virus(CSBV).The extensively used CSBV detection methods require professionals and expensive equipment;thus,they are unsuitable for rapid onsite CSBV detection.To achieve early and rapid detection of CSBV,we developed a lateral flow detection(LFD)strip method for CSBV detection via clustered regularly interspaced short palindromic repeats(CRISPR)and the Cas13a technique.On the basis of the conserved CSBV VP2 gene nucleotide region,we designed 3 recombinant enzyme-assisted amplification(RAA)primer pairs and prepared 3 corresponding crRNAs.We investigated key performance metrics,including the sensitivity,specificity,and accuracy of LFD strips.The results demonstrated that the LFD strip based on the optimal combination(primer 2+crRNA 2)presented the lowest detection limit(2.80×101 copies/μL),and this strip could complete CSBV detection within 1 h.Furthermore,this strip exhibited excellent detection specificity,with no cross-reactivity with four other honeybee viruses.A test of 100 clinical samples indicated the feasibility of the LFD method for CSBV detection.A comparison of various CSBV detection methods revealed that the CRISPR-Cas13a-based LFD method was more accurate,efficient,and sensitive than the other methods were,indicating great application prospects in onsite CSBV detection.Our developed method is highly important for preventing and controlling CSBV infection as well as maintaining honeybee health.
基金The project is sponsored by the Innovation Foundation of Key Lab of Geophysical Exploration under CNPC.
文摘The hydrocarbon detection techniques used currently are generally based on the theory of single-phase medium, but hydrocarbon reservoir mostly is multi-phase medium, therefore, multisolutions and uncertainties are existed in the result of hydrocarbon detection. This paper presents a fast way to detect hydrocarbon in accordance with BOIT theory and laboratory data. The technique called DHAF technique has been applied to several survey area and obtained good result where the coincidence rate for hydrocarbon detection is higher than other similar techniques. The method shows a good prospect of the application in hydrocarbon detecting at exploration stage and in reservoir monitoring at production stage.
基金financial support from the National Natural Science Foundation of China(Grant No.20075017)and from Tianjin Normal University.
文摘This work reports the single-molecule detection of gossypol by flow injection analysis with chemiluminescence method. The method is based on the reaction of luminol with ferricyanid in sodium hydroxide medium sensitized by gossypol. Under the optimum conditions, the CL intensity is proportional to the concentration of gossypol over the range of 1.11×10^-17-2.78×10^-16 mol/L in acid solution and 8.00×10^-11-7.39×10^-8mol/L in neutral solution with correlation coefficients 0.9983 and 0.9905, respectively. The detection limits is 1.60×10^-18 mol/L （S/N=3）. The proposed method has been applied for the determination of the gossypol in cottonseeds and pharmaceutical formulations with satisfactory results.
基金Supported by the National Natural Science Foundation of China(61202110 and 61502205)the Project of Jiangsu Provincial Six Talent Peaks(XYDXXJS-016)
文摘With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great significance in reducing system crashes and maintaining system security and integrity. This paper focuses on detecting three common types of vulnerabilities: Unused_Variable, Use_of_Uninitialized_Variable, and Use_After_ Free. We propose a method for software vulnerability detection based on an improved control flow graph(ICFG) and several predicates of vulnerability properties for each type of vulnerability. We also define a set of grammar rules for analyzing and deriving the three mentioned types of vulnerabilities, and design three vulnerability detection algorithms to guide the process of vulnerability detection. In addition, we conduct cases studies of the three mentioned types of vulnerabilities with real vulnerability program segments from Common Weakness Enumeration(CWE). The results of the studies show that the proposed method can detect the vulnerability in the tested program segments. Finally, we conduct manual analysis and experiments on detecting the three types of vulnerability program segments(30 examples for each type) from CWE, to compare the vulnerability detection effectiveness of the proposed method with that of the existing detection tool Cpp Check. The results show that the proposed method performs better. In summary, the method proposed in this paper has certain feasibility and effectiveness in detecting the three mentioned types of vulnerabilities, and it will also have guiding significance for the detection of other common vulnerabilities.
基金supported by grants from Shanghai Agriculture Applied Technology Development Program,China(Grant No.:2020-02-08-00-08-F01456)the Key Research and Development Program of Zhejiang Province,China(Grant No.:2020C02024-2).
文摘The application of pesticides (mostly insecticides and fungicides) during the tea-planting process will undoubtedly increase the dietary risk associated with drinking tea. Thus, it is necessary to ascertain whether pesticide residues in tea products exceed the maximum residue limits. However, the complex matrices present in tea samples comprise a major challenge in the analytical detection of pesticide residues. In this study, nine types of lateral flow immunochromatographic strips (LFICSs) were developed to detect the pesticides of interest (fenpropathrin, chlorpyrifos, imidacloprid, thiamethoxam, acetamiprid, carbendazim, chlorothalonil, pyraclostrobin, and iprodione). To reduce the interference of tea substrates on the assay sensitivity, the pretreatment conditions for tea samples, including the extraction solvent, extraction time, and purification agent, were optimized for the simultaneous detection of these pesticides. The entire testing procedure (including pretreatment and detection) could be completed within 30 min. The detected results of authentic tea samples were confirmed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), which suggest that the LFICS coupled with sample rapid pretreatment can be used for on-site rapid screening of the target pesticide in tea products prior to their market release.
基金This work was supported by the Natural Sciences Foundation of China (Grant No. NSFC. 40176036).
文摘Objective To develop an in situ PCR in combination with flow cytometry （ISPCR-FCM） for monitoring cholera toxin positive Vibrio cholerae. Methods In running this method, 4% paraformaldehyde was used to fix the Vibrio cholerae cells and 1 mg/mL lysozyme for 20 min to permeabilize the cells. Before the PCR thermal cycling, 2.5% glycerol was added into the PCR reaction mixture in order to protect the integrality of the cells. Results A length of 1037bp DNA sequence was amplified, which is specific for the cholera toxin gene （ctxAB gene）. Cells subjected to ISPCR showed the presences of ctxAB gene both in epifluorescence microscopy and in flow cytometric analysis. The specificity and sensitivity of the method were investigated. The sensitivity was relatively low （10^5 cells/mL）, while the specificity was high. Conclusion We have successfully developed a new technique for detection of toxigenic Vibrio cholerae strains. Further study is needed to enhance its sensitivities. ISPCR-FCM shows a great promise in monitoring specific bacteria and their physiological states in environmental samples.
基金supported by the national science and technology support program in the 12th Five Year Plan(2011BAK10B04 and 2011BAK10B01)the national natural science foundation of China(Grant No.31160323)the research program of the state key laboratory of food science and technology,Nanchang University(SKLF-ZZB-201306)
文摘A strip reader based lateral flow immunoassay （LFIA） was established for the rapid and quantitative detection of ractopamine （RAC） in swine urine. The ratio of the optical densities （ODs） of the test line （AT） to that of the control line （Ac） was used to effectively minimize interference among strips and sample variations. The linear range for the quantitative detection of RAC was 0.2 ng/mL to 3.5 ng/mL with a median inhibitory concentration （IC50） of 0.59＋0.06 ng/mL. The limit of detection （LOD） of the LFIA was 0.13 ng/mL. The intra-assay recovery rates were 92.97%, 97.25%, and 107.41%, whereas the inter-assay rates were 80.07%, 108.17%, and 93.7%, respectively.
基金funded by the National Natural Science Foundation of China (Grant No. 41930102, 41971333, 41771415, and 41701449)the Priority Academic Program Development of Jiangsu Higher Education Institutions (Grant No. 164320H116)the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEPA04)。
文摘Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,ephemeral gully mapping remains a challenge.In this study,we proposed a flow-directional detection for identifying ephemeral gullies from high-resolution images and digital elevation models(DEMs).Ephemeral gullies exhibit clear linear features in high-resolution images.An edge detection operator was initially used to identify linear features from high-resolution images.Then,according to gully erosion mechanism,the flow-directional detection was designed.Edge images obtained from edge detection and flow directions obtained from DEMs were used to implement the flow-directional detection that detects ephemeral gullies along the flow direction.Results from ten study areas in the Loess Plateau of China showed that ranges of precision,recall,and Fmeasure are 6 o.66%-90.47%,65.74%-94.98%,and63.10%-91.93%,respectively.The proposed method is flexible and can be used with various images and DEMs.However,analysis of the effect of DEM resolution and accuracy showed that DEM resolution only demonstrates a minor effect on the detection results.Conversely,DEM accuracy influences the detection result and is more important than the DEM resolution.The worse the vertical accuracy of DEM,the lower the performance of the flow-directional detection will be.This work is beneficial to research related to monitoring gully erosion and assessing soil loss.
基金Supported by Tianjin Natural Science Foundation Key Projects(013600711,033800611)
文摘A privilege flow oriented intrusion detection method based on HSMM(Hidden semi-Markov Model)is discussed.The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS(Intrusion Detection System).Using the dataset of DARPA 1998,our experiment results reveal good detection performance and acceptable computation cost.
基金Supported by the National Natural Science Foundation of China under Grant Nos.71871130,71471104,71771019,71571109the University Science and Technology Program Funding Projects of Shandong Province under Grant No.J17KA211the Project of Public Security Department of Shandong Province under Grant No.GATHT2015-236
文摘The expressway traffc incidents have the characteristics of high harmful, strong destructive and refractory.Incident detection can guarantee smooth operation of the expressway, reduce traffc congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffc ratio at the entrances and crossway in the network. The expressway traffc simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffc network states so as to provide advice for the highway management department.
基金supported in part by the Major Project for New Generation of AI (2018AAA0100400)the National Natural Science Foundation of China (61836014,U21B2042,62072457,62006231)the InnoHK Program。
文摘Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.
基金supported by National Key R&D Program of China[2017YFC200503]National Natural Science Foundation of China[No.42077399].
文摘Objective To establish a sensitive,simple and rapid detection method for African swine fever virus(ASFV)B646L gene.Methods A recombinase-aided amplification-lateral flow dipstick(RAA-LFD)assay was developed in this study.Recombinase-aided amplification(RAA)is used to amplify template DNA,and lateral flow dipstick(LFD)is used to interpret the results after the amplification is completed.The lower limits of detection and specificity of the RAA assay were verified using recombinant plasmid and pathogenic nucleic acid.In addition,30 clinical samples were tested to evaluate the performance of the RAA assay.Results The RAA-LFD assay was completed within 15 min at 37°C,including 10 min for nucleic acid amplification and 5 minutes for LFD reading results.The detection limit of this assay was found to be 200 copies per reaction.And there was no cross-reactivity with other swine viruses.Conclusion A highly sensitive,specific,and simple RAA-LFD method was developed for the rapid detection of the ASFV.
基金Supported by the National Natural Science Foundation of China(51704327)
文摘Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.
基金supported by the National Natural Science Foundation of China(No.T2250410382)Natural Science Foundation of Shandong Province(Nos.ZR2020QB153 and ZR2022YQ12)+3 种基金Taishan Scholars Program(Nos.tsqn201812088 and ts20190948)Shandong Scientific and Technical Small and Medium-sized Enterprises Innovation Capacity Improvement Project(No.2022TSGC2533)The Science,Education and Industry Integration of Basic Research Project of Qilu University of Technology(No.2023PY058)Qilu University of Technology Talent Research Project(No.2023RCKY087)。
文摘Bacterial endotoxin(a type of lipopolysaccharide,LPS)that acts as the strongest immune stimulant exhibits high toxicity to human health.The golden standard detection methods rely heavily on the use of a large amount of tachypleus amebocyte lysate(TAL)reagents,extracted from the unique blue blood of legally protected horseshoe crabs.Herein,a cost-effective distance-based lateral flow(D-LAF)sensor is demonstrated for the first time based on the coagulation cascade process of TAL induced by endotoxin,which causes the generation of gel-state TAL.The gelation process can increase the amount of trapped water molecules and shorten the lateral flow distance of the remaining free water on the pH paper.The water flow distance is directly correlated to the concentration of endotoxin.Noteworthy,the D-LAF sensor allows the detection of endotoxin with the reduced dosage of TAL reagents than the golden standard detection methods.The detection limit of endotoxin is calculated to be 0.0742 EU/mL.This method can be applied to the detection of endotoxin in real samples such as household water and clinical injection solution with excellent performance comparable to the commercial ELISA kit.
基金National Natural Science Foundation of China(61701029)。
文摘To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.
基金This work was supported in part by the National Key R&D Program of China 2021YFE0110500in part by the National Natural Science Foundation of China under Grant 62062021in part by the Guiyang Scientific Plan Project[2023]48-11.
文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
基金supported by National Natural Science Foundation of China(No.61502274).
文摘In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of tampering.Although deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational efficiency.To address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection techniques.In contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream.This design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level interaction.Furthermore,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region detection.Compared with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets.In addition,it has been extensively validated on other datasets,including CASIA and DIS25k.Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.
