The implementation of multiple pathogen testing is essential for a rapid response to future outbreaks and for reducing disease transmission.This study introduces a 96-channel microfluidic chip,fabricated through a mol...The implementation of multiple pathogen testing is essential for a rapid response to future outbreaks and for reducing disease transmission.This study introduces a 96-channel microfluidic chip,fabricated through a molding process,which enables the batch detection of pathogens.It explores the rapid lysis and elution processes of pathogens within the microfluidic chips to ensure that nucleic acid extraction,elution,and amplification are completed entirely within the chip.This chip can extract nucleic acids from samples in just 10 min,achieving an extraction efficiency comparable to that of traditional in-tube methods.An oil phase is pre-loaded into the chip to effectively prevent aerosol contamination.This approach allows for the simultaneous detection of 21 common respiratory pathogens,with a detection limit of 10 copies per reaction.Furthermore,applications involving clinical samples demonstrate significant practicality.Compared to many traditional in-tube pathogen detection methods and molecular biology technologies that utilize microfluidic chips,this detection chip not only enables simultaneous detection of multiple pathogens but also demonstrates high sensitivity.展开更多
Objective:To develop a quautitative PCR method for detecting hookworm infection and quantification.Methods:A real-time PCR method was designed hased on the intergenic regionⅡof ribosomal DNA of the hookworm Neeator a...Objective:To develop a quautitative PCR method for detecting hookworm infection and quantification.Methods:A real-time PCR method was designed hased on the intergenic regionⅡof ribosomal DNA of the hookworm Neeator americanus.The deteetion limit of this method was compared with the microscopy-hased Kato-Katz method.The real-time PCR method was used to conduct an epidemiological survey of hookworm infection in southern Fujian Province of China.Result:The real-time PCR method was specific for detecting Necator americanus infection,and was more sensitive than conventional PCR or microscopy-based method.A preliminary survey for hookworm infection in villages of Fujian Province confirmed the high prevalence of hnokworm infections in the resident populations.In addition,the infection rate in women was significantly higher than thai of in men.Conclusions:A real-time PCR method is designed,which has increased detection sensitivity for more accurate epidemiological studies of hookworm infections,especially when intensity of the infection needs to he considered.展开更多
Against the background of the sand-flow foundation treatment engineering of Guangzhou Zhoutouzui variable cross-section immersed tunnel, a kind of sand deposit-detecting method was devised on the basis of full-scale m...Against the background of the sand-flow foundation treatment engineering of Guangzhou Zhoutouzui variable cross-section immersed tunnel, a kind of sand deposit-detecting method was devised on the basis of full-scale model test of sand-flow method. The real-time data of sand-deposit height and radius were obtained by the self-developed sand-deposit detectors. The test results show that the detecting method is simple and has high precision. In the use of sand-flow method, the sand-carrying capability of fluid is limited, and sand particles are all transported to the sand-deposit periphery through crater, gap and chutes after the sand deposit formed. The diffusion range of the particles outside the sand-deposit does not exceed 2.0 m. Severe sorting of sand particles is not observed because of the unique oblique-layered depositing process. The temporal and spatial distributions of gap and chutes directly affect the sand-deposit expansion, and the expansion trend of the average sand-deposit radius accords with quadratic time-history curve.展开更多
An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Point...An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.展开更多
The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engi...The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engineering. The first problem encountered in both its theoretical study and practical measurement is how to detect the breaking of waves.展开更多
In the aerospace field, residual stress directly affects the strength, fatigue life and dimensional stability of thin-walled structural components, and is a key factor to ensure flight safety and reliability. At prese...In the aerospace field, residual stress directly affects the strength, fatigue life and dimensional stability of thin-walled structural components, and is a key factor to ensure flight safety and reliability. At present, research on residual stress at home and abroad mainly focuses on the optimization of traditional detection technology, stress control of manufacturing process and service performance evaluation, among which research on residual stress detection methods mainly focuses on the improvement of the accuracy, sensitivity, reliability and other performance of existing detection methods, but it still faces many challenges such as extremely small detection range, low efficiency, large error and limited application range.展开更多
The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governin...The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governing equations of motion for the pipe are derived using Hamilton’s principle.The finite element method,combined with the Galerkin approach,is employed to obtain the mass,damping,and stiffness matrices.To identify damage locations through pipe mode-shape decomposition,an index called the“matching pursuit residual”is introduced as a novel contribution of this study.The proposed method facilitates damage detection at various levels and locations under different boundary conditions.The findings demonstrate that the MP residual damage index can accurately localize damage in the pipes.Furthermore,the results of the numerical and experimental tests showcase the efficiency of the proposed method,highlighting that the MP signal approximation algorithm effectively detects damage in structures.展开更多
Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodo...Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.展开更多
Legume foods are not only trendy but also rich in nutrients and offer unique health benefits.Nevertheless,allergies to soy and other legumes have emerged as critical issues in food safety,presenting significant challe...Legume foods are not only trendy but also rich in nutrients and offer unique health benefits.Nevertheless,allergies to soy and other legumes have emerged as critical issues in food safety,presenting significant challenges to the food processing industry and impacting consumer health.The complexity of legume allergens,coupled with inadequate allergen identification methods and the absence of robust detection and evaluation systems,complicates the management of these allergens.Here,we provide a comprehensive and critical review,mentioning various aspects related to legume allergies,including the types of legume allergens,the mechanisms behind these allergies,and the immunoglobulin E(Ig E)-binding epitopes involved,summarizing and discussing the detection techniques and the impact of different processing techniques on sensitization to legume proteins.Furthermore,this paper provides an overview of research advances in diagnostic and therapeutic strategies for legume allergens and discusses current challenges and prospects for studying legume allergens.展开更多
Near-Earth Asteroids posed a threat to human civilization,making their monitoring crucial.As the demand for asteroid detection technology increased,precise detection of these celestial bodies became an urgent task to ...Near-Earth Asteroids posed a threat to human civilization,making their monitoring crucial.As the demand for asteroid detection technology increased,precise detection of these celestial bodies became an urgent task to understand their characteristics and assess potential impact risks.To improve asteroid detection accuracy and efficiency,we proposed an advanced image processing method and a deep learning network for automatic asteroid detection.Specifically,we aligned star clusters and overlaid images to exploit asteroid motion rates,transforming them into object-like trajectories and improving the signal-to-noise ratio.This approach created the Asteroid Trajectory Image Data set under various conditions.We modified CenterNet2 network to develop AstroCenterNet by integrating Multi-channel Histogram Truncation for feature enhancement,using the SimAM attention mechanism to expand contextual information and suppress noise,and refining Feature Pyramid Network to improve low-level feature detection.Our results demonstrated a detection accuracy of 98.4%,a recall of 97.6%,a mean Average Precision of 94.01%,a false alarm rate of 1.6%,and a processing speed of approximately 17.86 frames per second,indicating that our method achieves high precision and efficiency.展开更多
Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real...Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.展开更多
BACKGROUND Helicobacter pylori(H.pylori),a globally prevalent pathogen,is exhibiting increasing rates of antimicrobial resistance.However,clinical implementation of pre-treatment susceptibility testing remains limited...BACKGROUND Helicobacter pylori(H.pylori),a globally prevalent pathogen,is exhibiting increasing rates of antimicrobial resistance.However,clinical implementation of pre-treatment susceptibility testing remains limited due to the organism’s fastidious growth requirements and prolonged culture time.AIM To propose a novel detection method utilizing antibiotic-supplemented media to inhibit susceptible strains,while resistant isolates were identified through urease-mediated hydrolysis of urea,inducing a phenol red color change for visual confirmation.METHODS Colombia agar was supplemented with urea,phenol red,and nickel chloride,and the final pH was adjusted to 7.35.Antibiotic-selective media were prepared by incorporating amoxicillin(0.5μg/mL),clarithromycin(2μg/mL),metronidazole(8μg/mL),or levofloxacin(2μg/mL)into separate batches.Gastric antral biopsies were homogenized and inoculated at 1.0×105 CFU onto the media,and then incubated under microaerobic conditions at 37°C for 28-36 hours.Resistance was determined based on a color change from yellow to pink,and the results were validated via broth microdilution according to Clinical and Laboratory Standards Institute guidelines.RESULTS After 28-36 hours of incubation,the drug-resistant H.pylori isolates induced a light red color change in the medium.Conversely,susceptible strains(H.pylori 26695 and G27)produced no visible color change.Compared with the conventional 11-day protocol,the novel method significantly reduced detection time.Among 201 clinical isolates,182 were successfully evaluated using the new method,resulting in a 90.5%detection rate.This was consistent with the 95.5%agreement rate observed when compared with microdilution-based susceptibility testing.The success rate of the novel approach was significantly higher than that of the comparative method(P<0.01).The accuracy of the new method was comparable to that of the dilution method.CONCLUSION The novel detection method can rapidly detect H.pylori drug resistance within 28-36 hours.With its operational simplicity and high diagnostic performance,it holds strong potential for clinical application in the management of H.pylori antimicrobial resistance.展开更多
Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods rema...Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods remains challenging,as it requires the estimation of more than eight parameters.Assuming the reservoir to be a weakly anisotropic ORT medium with small contrasts in the background elastic parameters,a new azimuthal elastic impedance equation was first derived using parameter combinations and mathematical approximations.This equation exhibited almost the same accuracy as the original equation and contained only six model parameters:the compression modulus,anisotropic shear modulus,anisotropic compression modulus,density,normal fracture weakness,and tangential fracture weakness.Subsequently,a stepwise inversion method using second-order derivatives of the elastic impedance was developed to estimate these parameters.Moreover,the Thomsen anisotropy parameter,epsilon,was estimated from the inversion results using the ratio of the anisotropic compression modulus to the compression modulus.Synthetic examples with moderate noise and field data examples confirm the feasibility and effectiveness of the inversion method.The proposed method exhibited accuracy similar to that of previous inversion strategies and could predict richer vertical fracture information.Ultimately,the method was applied to a three-dimensional work area,and the predictions were consistent with logging and geological a priori information,confirming the effectiveness of this method.Summarily,the proposed stepwise inversion method can alleviate the uncertainty of multi-parameter inversion in ORT medium,thereby improving the reliability of fracture detection.展开更多
With the gradual penetration of the internet of things(IoT)into all areas of life,the scale of IoT devices shows an explosive growth trend.The era of internet of everything is coming,and the important position of IoT ...With the gradual penetration of the internet of things(IoT)into all areas of life,the scale of IoT devices shows an explosive growth trend.The era of internet of everything is coming,and the important position of IoT security is becoming increasingly prominent.Due to the large number types of IoT devices,there may be different security vulnerabilities,and unknown attack forms and virus samples are appear.In other words,large number of IoT devices,large data volumes,and various attack forms pose a big challenge of malicious traffic identification.To solve these problems,this paper proposes a concept drift detection and adaptation(CDDA)method for IoT security framework.The AI model performance is evaluated by verifying the effectiveness of IoT traffic for data drift detection,so as to select the best AI model.The experimental test are given to confirm that the feasibility of the framework and the adaptive method in practice,and the effect on the performance of IoT traffic identification is also verified.展开更多
In this paper,a dynamic linear detecting method,that the non-linear coefficient NL% was led and the non-linearity of data were estimated continuously and dynamically and determined when NL% exceeded reference value (...In this paper,a dynamic linear detecting method,that the non-linear coefficient NL% was led and the non-linearity of data were estimated continuously and dynamically and determined when NL% exceeded reference value (5%),was used for data processing and could solve the problem caused by the phenomenon of substrate depleting occurred following the redox reaction in portable blood sugar analyzer.By contrast to the conventional end-point method,the dynamic linear detecting method is based on multipoint data collecting.Experiments of measuring the calibration glucose solution with 8 various concentrations from 50 mg/dl to 400 mg/dl were carried out with the analyzer developed by our group.The linear regression curve,whose correlation for the data was 0.9995 and the residual was 2.8080,were obtained.The obtained correlation,residual, and the computation workload are all fit for the portable blood sugar analyzer.展开更多
Network traffic anomalies refer to the traffic changed abnormally and obviously.Local events such as temporary network congestion,Distributed Denial of Service(DDoS)attack and large-scale scan,or global events such as...Network traffic anomalies refer to the traffic changed abnormally and obviously.Local events such as temporary network congestion,Distributed Denial of Service(DDoS)attack and large-scale scan,or global events such as abnormal network routing,can cause network anomalies.Network anomaly detection and analysis are very important to Computer Security Incident Response Teams(CSIRT).But wide-scale traffic anomaly detection requires extracting anomalous modes from large amounts of high-dimensional noise-rich data,and interpreting the modes;so,it is very difficult.This paper proposes a general method based on Principle Component Analysis(PCA)to analyze network anomalies.This method divides the traffic matrix into normal and anomalous subspaces,maps traffic vectors into the normal subspace,gets the distance from detected vector to average normal vector,and detects anomalies based on that distance.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
Precipitation events,which follow a life cycle of initiation,development,and decay,represent the fundamental form of precipitation.Comprehensive and accurate detection of these events is crucial for effective water re...Precipitation events,which follow a life cycle of initiation,development,and decay,represent the fundamental form of precipitation.Comprehensive and accurate detection of these events is crucial for effective water resource management and flood control.However,current investigations on their spatio-temporal patterns remain limited,largely because of the lack of systematic detection indices that are specifically designed for precipitation events,which constrains event-scale research.In this study,we defined a set of precipitation event detection indices(PEDI)that consists of five conventional and fourteen extreme indices to characterize precipitation events from the perspectives of intensity,duration,and frequency.Applications of the PEDI revealed the spatial patterns of hourly precipitation events in China and its first-and second-order river basins from 2008 to 2017.Both conventional and extreme precipitation events displayed spatial distribution patterns that gradually decreased in intensity,duration,and frequency from southeast to northwest China.Compared with those in northwest China,the average values of most PEDIs in southeast China were usually 2-10 times greater for first-order river basins and 3-15 times greater for second-order basins.The PEDI could serve as a reference method for investigating precipitation events at global,regional,and basin scales.展开更多
Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects exte...Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.展开更多
Within the roadway advanced detection methods, DC resistivity method has an extensive application because of its simple principle and operation. Numerical simulation of the effect of focusing current on advanced detec...Within the roadway advanced detection methods, DC resistivity method has an extensive application because of its simple principle and operation. Numerical simulation of the effect of focusing current on advanced detection was carried out using a three-dimensional finite element method （FEM）, meanwhile the electric-field distribution of the point source and nine-point power source were calculated and analyzed with the same electric charges. The results show that the nine-point power source array has a very good ability to focus, and the DC focus method can be used to predict the aquifer abnormality body precisely. By comparing the FEM modelling results with physical simulation results from soil sink, it is shown that the accuracy of forward simulation meets the requirement and the artificial disturbance from roadway has no impact on the DC focus method.展开更多
基金supported by grants from the National Key Research and Development Program of China(Nos.2023YFA0915200,2023YFA0915204)the Equipment Research and Development Projects of the Chinese Academy of Sciences(No.PTYQ2024YZ0010)+3 种基金the Science and Technology Commission of Shanghai Municipality Project(No.XTCX-KJ-2024-038)the Natural Science Foundation of Hebei Province of China(No.H2024206249)the Postdoctoral Fellowship Program of CPSF(No.GZC20232838)Science and Technology Commission of Shanghai Municipality(No.22S31901700).
文摘The implementation of multiple pathogen testing is essential for a rapid response to future outbreaks and for reducing disease transmission.This study introduces a 96-channel microfluidic chip,fabricated through a molding process,which enables the batch detection of pathogens.It explores the rapid lysis and elution processes of pathogens within the microfluidic chips to ensure that nucleic acid extraction,elution,and amplification are completed entirely within the chip.This chip can extract nucleic acids from samples in just 10 min,achieving an extraction efficiency comparable to that of traditional in-tube methods.An oil phase is pre-loaded into the chip to effectively prevent aerosol contamination.This approach allows for the simultaneous detection of 21 common respiratory pathogens,with a detection limit of 10 copies per reaction.Furthermore,applications involving clinical samples demonstrate significant practicality.Compared to many traditional in-tube pathogen detection methods and molecular biology technologies that utilize microfluidic chips,this detection chip not only enables simultaneous detection of multiple pathogens but also demonstrates high sensitivity.
基金supported by the Fujian Provincial Grants(2008N2005,2009-CXB-67)Xiamen City Science and Technology Grant(3502Z20094021)
文摘Objective:To develop a quautitative PCR method for detecting hookworm infection and quantification.Methods:A real-time PCR method was designed hased on the intergenic regionⅡof ribosomal DNA of the hookworm Neeator americanus.The deteetion limit of this method was compared with the microscopy-hased Kato-Katz method.The real-time PCR method was used to conduct an epidemiological survey of hookworm infection in southern Fujian Province of China.Result:The real-time PCR method was specific for detecting Necator americanus infection,and was more sensitive than conventional PCR or microscopy-based method.A preliminary survey for hookworm infection in villages of Fujian Province confirmed the high prevalence of hnokworm infections in the resident populations.In addition,the infection rate in women was significantly higher than thai of in men.Conclusions:A real-time PCR method is designed,which has increased detection sensitivity for more accurate epidemiological studies of hookworm infections,especially when intensity of the infection needs to he considered.
基金Project(51108190) supported by the National Natural Science Foundation of ChinaProject(2012ZC27) supported by the Independence Research Subject from State Key Laboratory of Subtropical Building Science,ChinaProject(GTCC 2008-253) supported by the Research Subject from Guangzhou City,China
文摘Against the background of the sand-flow foundation treatment engineering of Guangzhou Zhoutouzui variable cross-section immersed tunnel, a kind of sand deposit-detecting method was devised on the basis of full-scale model test of sand-flow method. The real-time data of sand-deposit height and radius were obtained by the self-developed sand-deposit detectors. The test results show that the detecting method is simple and has high precision. In the use of sand-flow method, the sand-carrying capability of fluid is limited, and sand particles are all transported to the sand-deposit periphery through crater, gap and chutes after the sand deposit formed. The diffusion range of the particles outside the sand-deposit does not exceed 2.0 m. Severe sorting of sand particles is not observed because of the unique oblique-layered depositing process. The temporal and spatial distributions of gap and chutes directly affect the sand-deposit expansion, and the expansion trend of the average sand-deposit radius accords with quadratic time-history curve.
文摘An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.
文摘The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engineering. The first problem encountered in both its theoretical study and practical measurement is how to detect the breaking of waves.
文摘In the aerospace field, residual stress directly affects the strength, fatigue life and dimensional stability of thin-walled structural components, and is a key factor to ensure flight safety and reliability. At present, research on residual stress at home and abroad mainly focuses on the optimization of traditional detection technology, stress control of manufacturing process and service performance evaluation, among which research on residual stress detection methods mainly focuses on the improvement of the accuracy, sensitivity, reliability and other performance of existing detection methods, but it still faces many challenges such as extremely small detection range, low efficiency, large error and limited application range.
文摘The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governing equations of motion for the pipe are derived using Hamilton’s principle.The finite element method,combined with the Galerkin approach,is employed to obtain the mass,damping,and stiffness matrices.To identify damage locations through pipe mode-shape decomposition,an index called the“matching pursuit residual”is introduced as a novel contribution of this study.The proposed method facilitates damage detection at various levels and locations under different boundary conditions.The findings demonstrate that the MP residual damage index can accurately localize damage in the pipes.Furthermore,the results of the numerical and experimental tests showcase the efficiency of the proposed method,highlighting that the MP signal approximation algorithm effectively detects damage in structures.
文摘Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.
基金financially supported by National Natural Science Foundation of China(32460627 and 32272359)the Special Research Fund of Natural Science(Special Post)of Guizhou University(2022)54。
文摘Legume foods are not only trendy but also rich in nutrients and offer unique health benefits.Nevertheless,allergies to soy and other legumes have emerged as critical issues in food safety,presenting significant challenges to the food processing industry and impacting consumer health.The complexity of legume allergens,coupled with inadequate allergen identification methods and the absence of robust detection and evaluation systems,complicates the management of these allergens.Here,we provide a comprehensive and critical review,mentioning various aspects related to legume allergies,including the types of legume allergens,the mechanisms behind these allergies,and the immunoglobulin E(Ig E)-binding epitopes involved,summarizing and discussing the detection techniques and the impact of different processing techniques on sensitization to legume proteins.Furthermore,this paper provides an overview of research advances in diagnostic and therapeutic strategies for legume allergens and discusses current challenges and prospects for studying legume allergens.
基金funded by the National Science and Technology Major Project(2022ZD0117401)the National Defense Science and Technology Innovation Special Zone Project Foundation of China(grant No.19-163-21-TS-001-067-01)support was provided by the Chinese Academy of Sciences(CAS)“Light of West China”Program(No.2020-XBQNXZ-016 and No.2022-XBQNXZ-016).
文摘Near-Earth Asteroids posed a threat to human civilization,making their monitoring crucial.As the demand for asteroid detection technology increased,precise detection of these celestial bodies became an urgent task to understand their characteristics and assess potential impact risks.To improve asteroid detection accuracy and efficiency,we proposed an advanced image processing method and a deep learning network for automatic asteroid detection.Specifically,we aligned star clusters and overlaid images to exploit asteroid motion rates,transforming them into object-like trajectories and improving the signal-to-noise ratio.This approach created the Asteroid Trajectory Image Data set under various conditions.We modified CenterNet2 network to develop AstroCenterNet by integrating Multi-channel Histogram Truncation for feature enhancement,using the SimAM attention mechanism to expand contextual information and suppress noise,and refining Feature Pyramid Network to improve low-level feature detection.Our results demonstrated a detection accuracy of 98.4%,a recall of 97.6%,a mean Average Precision of 94.01%,a false alarm rate of 1.6%,and a processing speed of approximately 17.86 frames per second,indicating that our method achieves high precision and efficiency.
基金supported by National Key Research and Development Program of China(2023YFF0906100)National Natural Science Foundation of China(52408008)Key Research and Development Program of Jiangsu Province(BE2022833).
文摘Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.
基金Supported by the Guangxi Science and Technology Major Projects,No.AA23073012the National Natural Science Foundation of China,No.32360035 and No.32060018。
文摘BACKGROUND Helicobacter pylori(H.pylori),a globally prevalent pathogen,is exhibiting increasing rates of antimicrobial resistance.However,clinical implementation of pre-treatment susceptibility testing remains limited due to the organism’s fastidious growth requirements and prolonged culture time.AIM To propose a novel detection method utilizing antibiotic-supplemented media to inhibit susceptible strains,while resistant isolates were identified through urease-mediated hydrolysis of urea,inducing a phenol red color change for visual confirmation.METHODS Colombia agar was supplemented with urea,phenol red,and nickel chloride,and the final pH was adjusted to 7.35.Antibiotic-selective media were prepared by incorporating amoxicillin(0.5μg/mL),clarithromycin(2μg/mL),metronidazole(8μg/mL),or levofloxacin(2μg/mL)into separate batches.Gastric antral biopsies were homogenized and inoculated at 1.0×105 CFU onto the media,and then incubated under microaerobic conditions at 37°C for 28-36 hours.Resistance was determined based on a color change from yellow to pink,and the results were validated via broth microdilution according to Clinical and Laboratory Standards Institute guidelines.RESULTS After 28-36 hours of incubation,the drug-resistant H.pylori isolates induced a light red color change in the medium.Conversely,susceptible strains(H.pylori 26695 and G27)produced no visible color change.Compared with the conventional 11-day protocol,the novel method significantly reduced detection time.Among 201 clinical isolates,182 were successfully evaluated using the new method,resulting in a 90.5%detection rate.This was consistent with the 95.5%agreement rate observed when compared with microdilution-based susceptibility testing.The success rate of the novel approach was significantly higher than that of the comparative method(P<0.01).The accuracy of the new method was comparable to that of the dilution method.CONCLUSION The novel detection method can rapidly detect H.pylori drug resistance within 28-36 hours.With its operational simplicity and high diagnostic performance,it holds strong potential for clinical application in the management of H.pylori antimicrobial resistance.
基金sponsorship of the National Natural Science Foundation of China(42430809,42274157,42030103,42404132)the Fund of State Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China)(SKLDOG2024-ZYTS-02)+5 种基金the Postdoctoral Fellowship Program of CPSF(GZB20240850)the Postdoctoral Project of Qingdao(QDBSH20240102082)the Fundamental Research Funds for the Central Universities(24CX07004A,24CX06036A)the CNPC Innovation Fund(2024DQ02-0505,2024DQ02-0136)the Innovation fund project for graduate student of China University of Petroleum(East China)the Fundamental Research Funds for the Central Universities(24CX04002A).
文摘Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods remains challenging,as it requires the estimation of more than eight parameters.Assuming the reservoir to be a weakly anisotropic ORT medium with small contrasts in the background elastic parameters,a new azimuthal elastic impedance equation was first derived using parameter combinations and mathematical approximations.This equation exhibited almost the same accuracy as the original equation and contained only six model parameters:the compression modulus,anisotropic shear modulus,anisotropic compression modulus,density,normal fracture weakness,and tangential fracture weakness.Subsequently,a stepwise inversion method using second-order derivatives of the elastic impedance was developed to estimate these parameters.Moreover,the Thomsen anisotropy parameter,epsilon,was estimated from the inversion results using the ratio of the anisotropic compression modulus to the compression modulus.Synthetic examples with moderate noise and field data examples confirm the feasibility and effectiveness of the inversion method.The proposed method exhibited accuracy similar to that of previous inversion strategies and could predict richer vertical fracture information.Ultimately,the method was applied to a three-dimensional work area,and the predictions were consistent with logging and geological a priori information,confirming the effectiveness of this method.Summarily,the proposed stepwise inversion method can alleviate the uncertainty of multi-parameter inversion in ORT medium,thereby improving the reliability of fracture detection.
基金supported by 2023 Teaching Research Project of the Education Department of Anhui Province:Exploration of Optimizing Teaching Strategies for Embedded Courses in the Context of“New Engineering”(Project No.2023jyxm0460)2024 High-quality Course on Ideological and Political Education Integrated into Curriculum at Anhui University of Engineering:“Data Structures and Algorithms”(Project No.2024szyzk40)Industry-University-Research Cooperation Project of Anhui University of Engineering:“Online detection of surface quality defects in high-speed wire rod”(Project No.HX-2024-11-003).
文摘With the gradual penetration of the internet of things(IoT)into all areas of life,the scale of IoT devices shows an explosive growth trend.The era of internet of everything is coming,and the important position of IoT security is becoming increasingly prominent.Due to the large number types of IoT devices,there may be different security vulnerabilities,and unknown attack forms and virus samples are appear.In other words,large number of IoT devices,large data volumes,and various attack forms pose a big challenge of malicious traffic identification.To solve these problems,this paper proposes a concept drift detection and adaptation(CDDA)method for IoT security framework.The AI model performance is evaluated by verifying the effectiveness of IoT traffic for data drift detection,so as to select the best AI model.The experimental test are given to confirm that the feasibility of the framework and the adaptive method in practice,and the effect on the performance of IoT traffic identification is also verified.
文摘In this paper,a dynamic linear detecting method,that the non-linear coefficient NL% was led and the non-linearity of data were estimated continuously and dynamically and determined when NL% exceeded reference value (5%),was used for data processing and could solve the problem caused by the phenomenon of substrate depleting occurred following the redox reaction in portable blood sugar analyzer.By contrast to the conventional end-point method,the dynamic linear detecting method is based on multipoint data collecting.Experiments of measuring the calibration glucose solution with 8 various concentrations from 50 mg/dl to 400 mg/dl were carried out with the analyzer developed by our group.The linear regression curve,whose correlation for the data was 0.9995 and the residual was 2.8080,were obtained.The obtained correlation,residual, and the computation workload are all fit for the portable blood sugar analyzer.
基金This work was funded by the High-tech Research and Development Program of China(863 Program)under Grant 2006II01Z451.
文摘Network traffic anomalies refer to the traffic changed abnormally and obviously.Local events such as temporary network congestion,Distributed Denial of Service(DDoS)attack and large-scale scan,or global events such as abnormal network routing,can cause network anomalies.Network anomaly detection and analysis are very important to Computer Security Incident Response Teams(CSIRT).But wide-scale traffic anomaly detection requires extracting anomalous modes from large amounts of high-dimensional noise-rich data,and interpreting the modes;so,it is very difficult.This paper proposes a general method based on Principle Component Analysis(PCA)to analyze network anomalies.This method divides the traffic matrix into normal and anomalous subspaces,maps traffic vectors into the normal subspace,gets the distance from detected vector to average normal vector,and detects anomalies based on that distance.
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National Key Research and Development Program of China,No.2023YFC3206605,No.2021YFC3201102National Natural Science Foundation of China,No.41971035。
文摘Precipitation events,which follow a life cycle of initiation,development,and decay,represent the fundamental form of precipitation.Comprehensive and accurate detection of these events is crucial for effective water resource management and flood control.However,current investigations on their spatio-temporal patterns remain limited,largely because of the lack of systematic detection indices that are specifically designed for precipitation events,which constrains event-scale research.In this study,we defined a set of precipitation event detection indices(PEDI)that consists of five conventional and fourteen extreme indices to characterize precipitation events from the perspectives of intensity,duration,and frequency.Applications of the PEDI revealed the spatial patterns of hourly precipitation events in China and its first-and second-order river basins from 2008 to 2017.Both conventional and extreme precipitation events displayed spatial distribution patterns that gradually decreased in intensity,duration,and frequency from southeast to northwest China.Compared with those in northwest China,the average values of most PEDIs in southeast China were usually 2-10 times greater for first-order river basins and 3-15 times greater for second-order basins.The PEDI could serve as a reference method for investigating precipitation events at global,regional,and basin scales.
基金supported by the National Natural Science Foundation of China(Grant Nos.52021005,52325904,and 51991391)。
文摘Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.
基金Project(41174103)supported by the National Natural Science Foundation of ChinaProject(20110162130008)supported by the PhD Program Foundation of Ministry of Education of ChinaProject(2011BAB04B08)supported by the National Key Technology R&D Program during the 12th Five-Year Plan of China
文摘Within the roadway advanced detection methods, DC resistivity method has an extensive application because of its simple principle and operation. Numerical simulation of the effect of focusing current on advanced detection was carried out using a three-dimensional finite element method （FEM）, meanwhile the electric-field distribution of the point source and nine-point power source were calculated and analyzed with the same electric charges. The results show that the nine-point power source array has a very good ability to focus, and the DC focus method can be used to predict the aquifer abnormality body precisely. By comparing the FEM modelling results with physical simulation results from soil sink, it is shown that the accuracy of forward simulation meets the requirement and the artificial disturbance from roadway has no impact on the DC focus method.
