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
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
Objective:To evaluate the clinical efficacy of high-throughput real-time mass spectrometry detection technology for exhaled breath in the rapid diagnosis of pulmonary tuberculosis(PTB),providing a novel technological ...Objective:To evaluate the clinical efficacy of high-throughput real-time mass spectrometry detection technology for exhaled breath in the rapid diagnosis of pulmonary tuberculosis(PTB),providing a novel technological support for early screening and diagnosis of PTB.Methods:A total of 120 PTB patients admitted to a hospital from January 2023 to June 2024 were selected as the case group,and 150 healthy individuals and patients with non-tuberculous pulmonary diseases during the same period were selected as the control group.Exhaled breath samples were collected from all study subjects,and the types and concentrations of volatile organic compounds(VOCs)in the samples were detected using a high-throughput real-time mass spectrometer.A diagnostic model was constructed using machine learning algorithms,and core indicators such as diagnostic sensitivity,specificity,and area under the curve(AUC)of this technology were analyzed and compared with the efficacy of traditional sputum smear examination,sputum culture,and GeneXpert MTB/RIF detection.Results:The diagnostic sensitivity of the high-throughput real-time mass spectrometry diagnostic model for exhaled breath in diagnosing PTB was 92.5%,the specificity was 94.0%,and the AUC was 0.978,which were significantly higher than those of sputum smear examination(sensitivity 58.3%,specificity 90.0%,AUC 0.741).Compared with GeneXpert technology,its specificity was comparable(94.0%vs 93.3%),and the detection time was shortened to less than 15 minutes.The model achieved an accuracy of 91.3%in distinguishing PTB from other pulmonary diseases and was not affected by demographic factors such as age and gender.Conclusion:High-throughput real-time mass spectrometry detection technology for exhaled breath has the advantages of being non-invasive,rapid,highly sensitive,and highly specific,and holds significant clinical application value in the rapid diagnosis and large-scale screening of PTB,warranting further promotion.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
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
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
Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classification...Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.展开更多
A reverse-transcription loop-mediated isothermal amplification （RT-LAMP） method was established for the detection of wheat streak mosaic virus （WSMV）. Ac-cording to the conservative regions of the genes that encod...A reverse-transcription loop-mediated isothermal amplification （RT-LAMP） method was established for the detection of wheat streak mosaic virus （WSMV）. Ac-cording to the conservative regions of the genes that encode the coat protein of WSMV, 2 pairs of primers were designed. Final y, the 1st pair of primers was select-ed through the specificity test. The sensitivity test showed the sensitivity of RT-LAMP method was 10 times higher than that of RT-PCR. In addition, the amplifica-tion of target gene could be judged visual y from the presence of fluorescence （cal-cein） in the final reaction system. The RT-LAMP method, established in this study, was rapid, easy, specific and sensitive. Moreover, it did not require sophisticated equip-ment. The RT-LAMP was suitable for the rapid detection of WSMV.展开更多
Tobacco leaf shapes including the length,width,area,perimeter and roundness parameters and so on,Only obtain exact boundaries of the leaf information to calculate a large number of leaf parameters.This paper introduce...Tobacco leaf shapes including the length,width,area,perimeter and roundness parameters and so on,Only obtain exact boundaries of the leaf information to calculate a large number of leaf parameters.This paper introduces the classical edge detection Methods,bug method is used to track the boundaries of tobacco leaf extractly.The test shows that the algorithm has a good edge extraction capability.展开更多
It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （C...It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.展开更多
Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the...Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.展开更多
Tunneling machines, or excavators, are large and good conductors and affect the reliability of data gathering and interpretation in advanced detection using transient electromagnetic methods. In our experiment, we use...Tunneling machines, or excavators, are large and good conductors and affect the reliability of data gathering and interpretation in advanced detection using transient electromagnetic methods. In our experiment, we used a coincident-loop and central loop type of configuration, where the coil plane l） vertical to and 2） parallel to the working face. A SIROTEM instrument at different locations was used to observe the transient electromagnetic responses of the excavator and to analyze the response amplitudes. The result shows that the tunneling machine affects the advanced detection data and is related to the way the coil is coupled. When the excavator is 6 m from the observatory, the interference of tunneling machine can be ignored.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
[Objectives ] The paper was to explore enzyme inhibition rate method for rapid detection of organophosphorus and carbamate pesticides in cowpea. [ Methods ] Acetylcholinesterase （ACHE） was added to cowpea extract, t...[Objectives ] The paper was to explore enzyme inhibition rate method for rapid detection of organophosphorus and carbamate pesticides in cowpea. [ Methods ] Acetylcholinesterase （ACHE） was added to cowpea extract, to determine the inhibition rate of extract against enzyme. The influences of different sampiing methods and sampling parts on detection results were compared. [ Results] The positive rate of standard sampling was 18.18% higher than that of non-stand- ard sampling, and the positive rate of samples collected from cowpea tail was 16.67% higher than that collected from other parts. [ Condmions] Enzyme inhibi- tion rate method is suitable for rapid detection of organophosphorus and carbamate pesticides in cowpea.展开更多
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Science and Technology Plan of Heilongjiang Provincial Health Commission,Study on the Efficacy of High-Throughput Real-Time Mass Spectrometry Detection of Exhaled Breath for Rapid Diagnosis of Pulmonary Tuberculosis(Project No.:20230303110014)。
文摘Objective:To evaluate the clinical efficacy of high-throughput real-time mass spectrometry detection technology for exhaled breath in the rapid diagnosis of pulmonary tuberculosis(PTB),providing a novel technological support for early screening and diagnosis of PTB.Methods:A total of 120 PTB patients admitted to a hospital from January 2023 to June 2024 were selected as the case group,and 150 healthy individuals and patients with non-tuberculous pulmonary diseases during the same period were selected as the control group.Exhaled breath samples were collected from all study subjects,and the types and concentrations of volatile organic compounds(VOCs)in the samples were detected using a high-throughput real-time mass spectrometer.A diagnostic model was constructed using machine learning algorithms,and core indicators such as diagnostic sensitivity,specificity,and area under the curve(AUC)of this technology were analyzed and compared with the efficacy of traditional sputum smear examination,sputum culture,and GeneXpert MTB/RIF detection.Results:The diagnostic sensitivity of the high-throughput real-time mass spectrometry diagnostic model for exhaled breath in diagnosing PTB was 92.5%,the specificity was 94.0%,and the AUC was 0.978,which were significantly higher than those of sputum smear examination(sensitivity 58.3%,specificity 90.0%,AUC 0.741).Compared with GeneXpert technology,its specificity was comparable(94.0%vs 93.3%),and the detection time was shortened to less than 15 minutes.The model achieved an accuracy of 91.3%in distinguishing PTB from other pulmonary diseases and was not affected by demographic factors such as age and gender.Conclusion:High-throughput real-time mass spectrometry detection technology for exhaled breath has the advantages of being non-invasive,rapid,highly sensitive,and highly specific,and holds significant clinical application value in the rapid diagnosis and large-scale screening of PTB,warranting further promotion.
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.
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.
基金The National High Technology Research and Develop-ment Program of China(863Program)(No.2006AA04Z416)the Na-tional Science Fund for Distinguished Young Scholars(No.50725828)the Excellent Dissertation Program for Doctoral Degree of Southeast University(No.0705)
文摘Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.
文摘A reverse-transcription loop-mediated isothermal amplification （RT-LAMP） method was established for the detection of wheat streak mosaic virus （WSMV）. Ac-cording to the conservative regions of the genes that encode the coat protein of WSMV, 2 pairs of primers were designed. Final y, the 1st pair of primers was select-ed through the specificity test. The sensitivity test showed the sensitivity of RT-LAMP method was 10 times higher than that of RT-PCR. In addition, the amplifica-tion of target gene could be judged visual y from the presence of fluorescence （cal-cein） in the final reaction system. The RT-LAMP method, established in this study, was rapid, easy, specific and sensitive. Moreover, it did not require sophisticated equip-ment. The RT-LAMP was suitable for the rapid detection of WSMV.
基金Supported by Key Technologies R & D Program of Henan Province(082102210065)Natural Science Research Project of Henan Educational Committee(2007210005)~~
文摘Tobacco leaf shapes including the length,width,area,perimeter and roundness parameters and so on,Only obtain exact boundaries of the leaf information to calculate a large number of leaf parameters.This paper introduces the classical edge detection Methods,bug method is used to track the boundaries of tobacco leaf extractly.The test shows that the algorithm has a good edge extraction capability.
基金Chinese Ministry of Science and Technology and National Natural Science Foundation Under Grant No. 2006DFB71680
文摘It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.
基金Supported by National Natural Science Foundation of China(Grant No.51607180)
文摘Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.
基金support received from the National Basic Research Program of China (No2007CB209400)the National Natural Science Foundation of China (No50774085)the Young Scientists Fund of the School Science Foundation of CUMT (No2008A046)
文摘Tunneling machines, or excavators, are large and good conductors and affect the reliability of data gathering and interpretation in advanced detection using transient electromagnetic methods. In our experiment, we used a coincident-loop and central loop type of configuration, where the coil plane l） vertical to and 2） parallel to the working face. A SIROTEM instrument at different locations was used to observe the transient electromagnetic responses of the excavator and to analyze the response amplitudes. The result shows that the tunneling machine affects the advanced detection data and is related to the way the coil is coupled. When the excavator is 6 m from the observatory, the interference of tunneling machine can be ignored.
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.
文摘[Objectives ] The paper was to explore enzyme inhibition rate method for rapid detection of organophosphorus and carbamate pesticides in cowpea. [ Methods ] Acetylcholinesterase （ACHE） was added to cowpea extract, to determine the inhibition rate of extract against enzyme. The influences of different sampiing methods and sampling parts on detection results were compared. [ Results] The positive rate of standard sampling was 18.18% higher than that of non-stand- ard sampling, and the positive rate of samples collected from cowpea tail was 16.67% higher than that collected from other parts. [ Condmions] Enzyme inhibi- tion rate method is suitable for rapid detection of organophosphorus and carbamate pesticides in cowpea.
