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
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
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
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
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
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
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
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
Mycotoxins and their derivatives since their discoveries and until the present time are behind unspecified economic and medical damages.Aflatoxins are classified according to their physical–chemical and toxicological...Mycotoxins and their derivatives since their discoveries and until the present time are behind unspecified economic and medical damages.Aflatoxins are classified according to their physical–chemical and toxicological characters in the most dangerous row of the mycotoxins.These aflatoxins are in part responsible,of irreversible medical disasters that are not easily manageable such as cancer of the liver and kidneys,and in the other part,of losses in the stored cereal products.Based on these crucial findings,monitoring of this toxin became imperative in post-harvest food products,during storage,during transformation chain and even during the long phases of conservation.Vigilance of this toxin is delivered by detection methods using very advanced technologies to respond in the shortest possible times.In addition,the knowledge of factors supporting the biosynthesis of aflatoxins such as the temperature,moisture content,concentration of nitrogen and carbon,and the molecules responsible for the genetic control of the synthesis will be reflected later in the choice of bio-control techniques.This control is currently based on new strategies using the bioactives substances of the plants,the lactic bacteria and some strains of actinomycetes that have good inhibiting activity against aflatoxins with fewer side effects on Man.On the other hand,this brief review summarizes the results of new studies demonstrating the toxicity of the toxin,new detection methods and bio-control.展开更多
Real-time liquefaction monitoring and warning techniques are new ways to mitigate liquefaction hazard. A key point is to establish a reverse liquefaction detection method based on seismic records. However, the existin...Real-time liquefaction monitoring and warning techniques are new ways to mitigate liquefaction hazard. A key point is to establish a reverse liquefaction detection method based on seismic records. However, the existing methods are quite limited and the reliability requires verification. On Feb. 22, 2011 an earthquake of magnitude 6.3 struck at New Zealand＇s South Island. Remarkable liquefaction phenomena were reported, which provide an opportunity to verify the existing liquefaction detection methods. 27 acceleration records within 50 km to the epicenter were selected to perform a blind detection by using the existing methods, including Miyajima method, Suzuki method, Kostadinov-Yamazaki method and Yuan-Sun method. The blind detection results indicate that Yuan-Sun method gives correct results for seven confirmed sites, and Suzuki method and Yuan-Sun method yield correct detection for a reported non-liquefied site. Four methods including the Yuan-Sun method give identical detection for four sites and three methods also including the Yuan-Sun method give identical detection for ten sites. Besides, there are five sites, for which the four methods give opposite detection.展开更多
As an effective and universal acaricide, amitraz is widely used on beehives against varroasis caused by the mite Varroa jacobsoni. Its residues in honey pose a great danger to human health. In this study, a sensitive,...As an effective and universal acaricide, amitraz is widely used on beehives against varroasis caused by the mite Varroa jacobsoni. Its residues in honey pose a great danger to human health. In this study, a sensitive, rapid, and environmentally friendly surface-enhanced Raman spectroscopy method (SERS) was developed for the determination of trace amount of amitraz in honey with the use of silver nanorod (AgNR) array substrate. The AgNR array substrate fabricated by an oblique angle deposition technique exhibited an excellent SERS activity with an enhancement factor of -10^7. Density function theory was employed to assign the characteristic peak of amitraz. The detection of amitraz was further explored and amitraz in honey at concentrations as low as 0.08 mg/kg can be identified. Specifically, partial least square regression analysis was employed to correlate the SERS spectra in full-wavelength with Camitraz to afford a multiple-quantitative amitraz predicting model. Preliminary results show that the predicted concentrations of amitraz in honey samples are in good agreement with their real concentrations. Compared with the conventional univariate quantitative model based on single peak’s intensity, the proposed multiple-quantitative predicting model integrates all the characteristic peaks of amitraz, thus offering an improved detecting accuracy and anti-interference ability.展开更多
Surface irradiance measurements with high temporal resolution can be used to detect clear skies,which is a critical step for further study,such as aerosol and cloud radiative effects.Twenty-one clear-sky detection(CSD...Surface irradiance measurements with high temporal resolution can be used to detect clear skies,which is a critical step for further study,such as aerosol and cloud radiative effects.Twenty-one clear-sky detection(CSD)methods are assessed based on five years of 1-min surface irradiance data at Xianghe—a heavily polluted station on the North China Plain.Total-sky imager(TSI)discrimination results corrected by manual checks are used as the benchmark for the evaluation.The performance heavily relies on the criteria adopted by the CSD methods.Those with higher cloudy-sky detection accuracy rates produce lower clear-sky accuracy rates,and vice versa.A general tendency in common among all CSD methods is the detection accuracy deteriorates when aerosol loading increases.Nearly all criteria adopted in CSD methods are too strict to detect clear skies under polluted conditions,which is more severe if clear-sky irradiance is not properly estimated.The mean true positive rate(CSD method correctly detects clear sky)decreases from 45%for aerosol optical depth(AOD)≤0.2%to 6%for AOD>0.5.The results clearly indicate that CSD methods in a highly polluted region still need further improvements.展开更多
<i>Entamoeba histolytica</i> is an anaerobic parasitic protozoan and well known as a human pathogen, while its close relative, <i>Entamoeba dispar</i>, also possesses similar characteristics as...<i>Entamoeba histolytica</i> is an anaerobic parasitic protozoan and well known as a human pathogen, while its close relative, <i>Entamoeba dispar</i>, also possesses similar characteristics as an infectious agent. These microorganisms are generally transmitted in fecal-contaminated water. However, <i>E. dispar</i> present in industrial wastewater is also capable of creating biofilms that can cause adverse impacts in piping networks. Therefore, it is important to detect both of these protozoan species in water and to find a cost-effective technique for inactivation or management control. This review article summarizes the available detection methods in water and wastewater matrices along with feasible disinfection techniques.展开更多
Human respiratory system is harbored by a vast array of transient and normal microbial flora.A number of pathogenic viruses were diagnosed from samples in different occasions from mild to severe infections of respirat...Human respiratory system is harbored by a vast array of transient and normal microbial flora.A number of pathogenic viruses were diagnosed from samples in different occasions from mild to severe infections of respiratory tract.Molecular methods were developed for detection of these viruses during last two decades.Nucleic acid amplification methods were introduced for rapid and accurate diagnosis of pathogenic viruses.Multiplex detection systems were employed to identify a panel of pathogenic viruses,which requires specialized kits and instruments in some cases.This review summarizes different types of molecular approaches which were developed with time and applied for the detection of pathogenic viruses associated with infections of the respiratory system.展开更多
[Objective] More accurate, rapid and sensitive method of melamine and cyanuricacid residue in dairy products and feedstuff were re- viewed. [ Method] Physicochemical properties, metabolism, uses, harm and detection me...[Objective] More accurate, rapid and sensitive method of melamine and cyanuricacid residue in dairy products and feedstuff were re- viewed. [ Method] Physicochemical properties, metabolism, uses, harm and detection methods of melamine and cyanuric acid were analyzed and described. [ Result] Melamine and cyanuric acid, when used alone, were slightly toxic, but long -term intake could lead to animal reproductive and urinary system damage. [ Condusion] Establishing a more sensitive, fast and easy to popularize detection method for elarnine and cyanuricacid res- idue in dairy products and feedstuff was necessary.展开更多
Bactrocera dorsalis Hendel （Diptera： Tephritidae） is an invasive pest around the world. The paper summarizes biological and ecological characteristics of B, dorsalis, and reviews its detection methods from the aspe...Bactrocera dorsalis Hendel （Diptera： Tephritidae） is an invasive pest around the world. The paper summarizes biological and ecological characteristics of B, dorsalis, and reviews its detection methods from the aspects of morphological identification, acoustic detection and molecular detection, in order to provide a reference for further research and development of new detection methods. The hot issues in the study of B. dorsalis, such as ecological adaptation pattern, diffusion pathways and mechanisms, sustainable control measures, are also put forward in the paper.展开更多
Koi herpes virus is a new virus found in the aquaculture production of Cryprinus carpiod and common carp in recent years. Currently, virus isolation and identification is still the traditional method for the detection...Koi herpes virus is a new virus found in the aquaculture production of Cryprinus carpiod and common carp in recent years. Currently, virus isolation and identification is still the traditional method for the detection of Koi herpes virus, while molecular biology detection method has become the current developmental di- rection due to its characteristics of more sensitive, specific and rapid. Furthermore, people are still committed to exploring new detection methods for the detection of Koi herpes vires. In this paper, traditional and newly-developed detection methods of Koi herpes vires in recent years were summarized, in order to provide refer- ence for further exploring rapid and accurate diagnostic detection method.展开更多
Dengue virus infections are increasing worldwide generally and in Asia,Central and South America and Africa,particularly.It poses a serious threat to the children population.The rapid and accurate diagnostic systems a...Dengue virus infections are increasing worldwide generally and in Asia,Central and South America and Africa,particularly.It poses a serious threat to the children population.The rapid and accurate diagnostic systems are essentially required due to lack of effective vaccine against dengue virus and the progressive spread of the dengue virus infection.The recent progress in developing micro-and nano-fabrication techniques has led to low cost and scale down the biomedical point-of-care devices.Starting from the conventional and modern available methods for the diagnosis of dengue infection,this review examines several emerging rapid and point-of-care diagnostic devices that hold significant potential for the progress in smart diagnosis tools.The given review revealed that an effective vaccine is required urgently against all the dengue virus serotypes.However,the rapid detection methods of dengue virus help in early treatment and significantly reduce the dengue virus outbreak.展开更多
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.
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 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.
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.
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 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Mycotoxins and their derivatives since their discoveries and until the present time are behind unspecified economic and medical damages.Aflatoxins are classified according to their physical–chemical and toxicological characters in the most dangerous row of the mycotoxins.These aflatoxins are in part responsible,of irreversible medical disasters that are not easily manageable such as cancer of the liver and kidneys,and in the other part,of losses in the stored cereal products.Based on these crucial findings,monitoring of this toxin became imperative in post-harvest food products,during storage,during transformation chain and even during the long phases of conservation.Vigilance of this toxin is delivered by detection methods using very advanced technologies to respond in the shortest possible times.In addition,the knowledge of factors supporting the biosynthesis of aflatoxins such as the temperature,moisture content,concentration of nitrogen and carbon,and the molecules responsible for the genetic control of the synthesis will be reflected later in the choice of bio-control techniques.This control is currently based on new strategies using the bioactives substances of the plants,the lactic bacteria and some strains of actinomycetes that have good inhibiting activity against aflatoxins with fewer side effects on Man.On the other hand,this brief review summarizes the results of new studies demonstrating the toxicity of the toxin,new detection methods and bio-control.
基金National Natural Science Foundation of China Under Grant No.50078165
文摘Real-time liquefaction monitoring and warning techniques are new ways to mitigate liquefaction hazard. A key point is to establish a reverse liquefaction detection method based on seismic records. However, the existing methods are quite limited and the reliability requires verification. On Feb. 22, 2011 an earthquake of magnitude 6.3 struck at New Zealand＇s South Island. Remarkable liquefaction phenomena were reported, which provide an opportunity to verify the existing liquefaction detection methods. 27 acceleration records within 50 km to the epicenter were selected to perform a blind detection by using the existing methods, including Miyajima method, Suzuki method, Kostadinov-Yamazaki method and Yuan-Sun method. The blind detection results indicate that Yuan-Sun method gives correct results for seven confirmed sites, and Suzuki method and Yuan-Sun method yield correct detection for a reported non-liquefied site. Four methods including the Yuan-Sun method give identical detection for four sites and three methods also including the Yuan-Sun method give identical detection for ten sites. Besides, there are five sites, for which the four methods give opposite detection.
基金supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (No.16KJB510009 and No.17KJB510017)Jiangsu Province Natural Science Foundation of China (BK20150228)
文摘As an effective and universal acaricide, amitraz is widely used on beehives against varroasis caused by the mite Varroa jacobsoni. Its residues in honey pose a great danger to human health. In this study, a sensitive, rapid, and environmentally friendly surface-enhanced Raman spectroscopy method (SERS) was developed for the determination of trace amount of amitraz in honey with the use of silver nanorod (AgNR) array substrate. The AgNR array substrate fabricated by an oblique angle deposition technique exhibited an excellent SERS activity with an enhancement factor of -10^7. Density function theory was employed to assign the characteristic peak of amitraz. The detection of amitraz was further explored and amitraz in honey at concentrations as low as 0.08 mg/kg can be identified. Specifically, partial least square regression analysis was employed to correlate the SERS spectra in full-wavelength with Camitraz to afford a multiple-quantitative amitraz predicting model. Preliminary results show that the predicted concentrations of amitraz in honey samples are in good agreement with their real concentrations. Compared with the conventional univariate quantitative model based on single peak’s intensity, the proposed multiple-quantitative predicting model integrates all the characteristic peaks of amitraz, thus offering an improved detecting accuracy and anti-interference ability.
基金supported by the National Key R&D Program of China grant number 2017YFA0603504the Strategic Priority Research Program of the Chinese Academy of Sciences grant number XDA17010101the National Natural Science Foundation of Chinagrant number 41875183。
文摘Surface irradiance measurements with high temporal resolution can be used to detect clear skies,which is a critical step for further study,such as aerosol and cloud radiative effects.Twenty-one clear-sky detection(CSD)methods are assessed based on five years of 1-min surface irradiance data at Xianghe—a heavily polluted station on the North China Plain.Total-sky imager(TSI)discrimination results corrected by manual checks are used as the benchmark for the evaluation.The performance heavily relies on the criteria adopted by the CSD methods.Those with higher cloudy-sky detection accuracy rates produce lower clear-sky accuracy rates,and vice versa.A general tendency in common among all CSD methods is the detection accuracy deteriorates when aerosol loading increases.Nearly all criteria adopted in CSD methods are too strict to detect clear skies under polluted conditions,which is more severe if clear-sky irradiance is not properly estimated.The mean true positive rate(CSD method correctly detects clear sky)decreases from 45%for aerosol optical depth(AOD)≤0.2%to 6%for AOD>0.5.The results clearly indicate that CSD methods in a highly polluted region still need further improvements.
文摘<i>Entamoeba histolytica</i> is an anaerobic parasitic protozoan and well known as a human pathogen, while its close relative, <i>Entamoeba dispar</i>, also possesses similar characteristics as an infectious agent. These microorganisms are generally transmitted in fecal-contaminated water. However, <i>E. dispar</i> present in industrial wastewater is also capable of creating biofilms that can cause adverse impacts in piping networks. Therefore, it is important to detect both of these protozoan species in water and to find a cost-effective technique for inactivation or management control. This review article summarizes the available detection methods in water and wastewater matrices along with feasible disinfection techniques.
文摘Human respiratory system is harbored by a vast array of transient and normal microbial flora.A number of pathogenic viruses were diagnosed from samples in different occasions from mild to severe infections of respiratory tract.Molecular methods were developed for detection of these viruses during last two decades.Nucleic acid amplification methods were introduced for rapid and accurate diagnosis of pathogenic viruses.Multiplex detection systems were employed to identify a panel of pathogenic viruses,which requires specialized kits and instruments in some cases.This review summarizes different types of molecular approaches which were developed with time and applied for the detection of pathogenic viruses associated with infections of the respiratory system.
文摘[Objective] More accurate, rapid and sensitive method of melamine and cyanuricacid residue in dairy products and feedstuff were re- viewed. [ Method] Physicochemical properties, metabolism, uses, harm and detection methods of melamine and cyanuric acid were analyzed and described. [ Result] Melamine and cyanuric acid, when used alone, were slightly toxic, but long -term intake could lead to animal reproductive and urinary system damage. [ Condusion] Establishing a more sensitive, fast and easy to popularize detection method for elarnine and cyanuricacid res- idue in dairy products and feedstuff was necessary.
基金Supported by International Cooperation Project of the Ministry of Science and Technology(2011DFB30040)Key Project of Science and Technology Development Fund of Guangxi Academy of Agricultural Sciences(2012JZ08)Scientific and Technological Projects of Nanning Municipal Science and Technology Bureau(20132308)
文摘Bactrocera dorsalis Hendel （Diptera： Tephritidae） is an invasive pest around the world. The paper summarizes biological and ecological characteristics of B, dorsalis, and reviews its detection methods from the aspects of morphological identification, acoustic detection and molecular detection, in order to provide a reference for further research and development of new detection methods. The hot issues in the study of B. dorsalis, such as ecological adaptation pattern, diffusion pathways and mechanisms, sustainable control measures, are also put forward in the paper.
基金Supported by Project of Jilin Provincial Science and Technology Commission(20080218)
文摘Koi herpes virus is a new virus found in the aquaculture production of Cryprinus carpiod and common carp in recent years. Currently, virus isolation and identification is still the traditional method for the detection of Koi herpes virus, while molecular biology detection method has become the current developmental di- rection due to its characteristics of more sensitive, specific and rapid. Furthermore, people are still committed to exploring new detection methods for the detection of Koi herpes vires. In this paper, traditional and newly-developed detection methods of Koi herpes vires in recent years were summarized, in order to provide refer- ence for further exploring rapid and accurate diagnostic detection method.
基金supported by the Scientific Research Fund of the Shenzhen International cooperation projects under Grant Nos.(GJHZ20190819151403615)the Natural Science Youth Foundation of China(61801307).
文摘Dengue virus infections are increasing worldwide generally and in Asia,Central and South America and Africa,particularly.It poses a serious threat to the children population.The rapid and accurate diagnostic systems are essentially required due to lack of effective vaccine against dengue virus and the progressive spread of the dengue virus infection.The recent progress in developing micro-and nano-fabrication techniques has led to low cost and scale down the biomedical point-of-care devices.Starting from the conventional and modern available methods for the diagnosis of dengue infection,this review examines several emerging rapid and point-of-care diagnostic devices that hold significant potential for the progress in smart diagnosis tools.The given review revealed that an effective vaccine is required urgently against all the dengue virus serotypes.However,the rapid detection methods of dengue virus help in early treatment and significantly reduce the dengue virus outbreak.
