The performance and price of copper-based micro linear products are determined by the diameter uniformity.How to accurately detect the wire diameter of long-length copper based micro linear products without cutting or...The performance and price of copper-based micro linear products are determined by the diameter uniformity.How to accurately detect the wire diameter of long-length copper based micro linear products without cutting or damage has always been a technical concern for production enterprises.Herein,a novel approach was developed for nondestructive detection of the average diameter at any given segment of a long copper wire by assessing the adsorption capacity of arginine on its surface.The amount of adsorbent on the surface of the copper wire exhibits a positive correlation with the area,which can be detected by extractive electrospray ionization mass spectrometry(EESI-MS)after online elution with ammonia.The experimental results demonstrated that the analysis can be completed within 15 min,with a good linear relationship between copper wires with different diameters and the adsorption capacity of arginine.The linear correlation coefficient R2was 0.995,the relative standard deviation was 1.10%-2.81%,and the detection limit reached 2.5μm(length of segment=4 cm),showing potential applications for facile measurement of the average diameter of various metal wires.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
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
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
This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview ...This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.展开更多
[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field.However,traditional detection methods struggle to maintain high accuracy and efficiency under c...[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field.However,traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions,such as strong light exposure and weed interference.The aims are to develop an effective crop line extraction method by combining YOLOv8-G,Affinity Propagation,and the Least Squares method to enhance detection accuracy and performance in complex field environments.[Methods]The proposed method employs machine vision techniques to address common field challenges.YOLOv8-G,an improved object detection algorithm that combines YOLOv8 and Ghost‐NetV2 for lightweight,high-speed performance,was used to detect the central points of crops.These points were then clustered using the Affinity Propagation algorithm,followed by the application of the Least Squares method to extract the crop lines.Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework,and ablation studies were performed to validate the enhancements made in YOLOv8-G.[Results and Discussions]The performance of the proposed method was compared with classical object detection and clustering algorithms.The YOLOv8-G algorithm achieved average precision(AP)values of 98.22%,98.15%,and 97.32%for corn detection at 7,14,and 21 days after emergence,respectively.Additionally,the crop line extraction accuracy across all stages was 96.52%.These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field.[Conclusions]The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference,enabling rapid and accurate crop identification.This approach supports the automatic navigation of agricultural machinery,offering significant improvements in the precision and efficiency of field operations.展开更多
During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms...During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms, aging processes, and safety performance. However, there is currently no non-destructive and quantitative detection method for migration of plasticizers in propellant liner. In this study, we developed a HTPB sensing liner by incorporating conductive fillers-namely carbon black(CB), carbon nanotubes(CNTs), and graphene nanoplatelets(GNP)-into the HTPB matrix. The synergistic interaction between CNTs and GNP facilitates the formation of a tunneling conductive network that imparts electrical conductivity to the HTPB liner. To elucidate the functional relationship between conductivity and nitroglycerin(NG) migration, we applied the HTPB sensing liner onto double base propellant surfaces and measured both the conductivity of the sensing layer and NG migration during a 71°C accelerated aging experiment. The results shows that when CNTs/GNP content reaches 3wt%, there is an exponential correlation between conductivity and NG migration with a fitting degree of 0.9652;the average response sensitivity of ΔR/R0 relative to NG migration is calculated as 41.69, with an average deviation of merely5.67% between NG migrations derived from conductivity fittings compared to those obtained via TGA testing results. Overall, this sensing liner exhibits excellent capabilities for detecting NG migration nondestructively and quantitatively while offering a novel approach for assessing interfacial component migrations as well as debonding defects in propellants-a promising avenue for future self-monitoring strategies regarding propellant integrity.展开更多
Perfluoroalkyl and polyfluoroalkyl substances(PFASs)pollution with their toxicity,carcinogenicity,and persistence has been concerned worldwide.Research into novel materials for detecting and removing PFASs has increas...Perfluoroalkyl and polyfluoroalkyl substances(PFASs)pollution with their toxicity,carcinogenicity,and persistence has been concerned worldwide.Research into novel materials for detecting and removing PFASs has increased rapidly,particularly in regard to the emergence of well-characterized single-atom materials(SAMs).Owing to the high selectivity,high atom utilization,and abundant active sites of SAMs,these materials exhibit remarkable efficacy in the detection and removal of PFASs.In this work,recent advances in the synthesis of SAMs for the detection and removal PFASs are reviewed.In-depth discussions of the structure-activity relationship and reaction mechanisms have demonstrated the high efficiency,activity,and selectivity of SAMs for the detection,adsorption,and degradation of PFASs.To optimize the application of SAMs in PFASs remediation,this review comprehensively surveys SAMs applications for PFASs and analyzes potential design strategies based on synthesis methods and corresponding properties.Synthesis strategies such as wet-chemistry,which offer ease of operation and high potential for large-scale production,are recommended for the further exploration of specific SAMs for the detection and removal of PFASs.Finally,this review identifies the challenges and opportunities for development of SAMs for the detection and remediation of PFASs,providing an outlook on strategic goals for a green economy and sustainable development.展开更多
Lithium-plating-type defects in lithium-ion batteries pose severe safety risks due to their potential to trigger thermal runaway.To prevent defective batteries from entering the market,developing in-line detection met...Lithium-plating-type defects in lithium-ion batteries pose severe safety risks due to their potential to trigger thermal runaway.To prevent defective batteries from entering the market,developing in-line detection methods during manufacturing is critical yet challenging.This study introduces a deep learning-based method for detecting lithium-plating-type defects using formation and capacity grading data,enabling accurate identification of defective batteries without additional equipment.First,lithiumplating-type defect batteries with various types and area ratios are fabricated.Formation and capacity grading data from 154 batteries(48 defective,106 normal)are collected to construct a dataset.Subsequently,a dual-task deep learning model is then developed,where the reconstruction task learns latent representations from the features,while the classification task identifies the defective batteries.Shapley value analysis further quantifies feature importance,revealing that defective batteries exhibit reduced coulombic efficiency(attributed to irreversible lithium loss)and elevated open-circuit voltage/K-values(linked to self-equalization effects).These findings align with the electrochemical mechanisms of lithium plating,enhancing the model's interpretability.Validated on statistically robust samples shows that the framework achieves a recall of 97.14%for defective batteries and an overall accuracy of 97.42%.This deep learning-driven solution provides a scalable and cost-effective quality control strategy for battery manufacturing.展开更多
The development of artificial intelligence(AI)technologies creates a great chance for the iteration of railway monitoring.This paper proposes a comprehensive method for railway utility pole detection.The framework of ...The development of artificial intelligence(AI)technologies creates a great chance for the iteration of railway monitoring.This paper proposes a comprehensive method for railway utility pole detection.The framework of this paper on railway systems consists of two parts:point cloud preprocessing and railway utility pole detection.Thismethod overcomes the challenges of dynamic environment adaptability,reliance on lighting conditions,sensitivity to weather and environmental conditions,and visual occlusion issues present in 2D images and videos,which utilize mobile LiDAR(Laser Radar)acquisition devices to obtain point cloud data.Due to factors such as acquisition equipment and environmental conditions,there is a significant amount of noise interference in the point cloud data,affecting subsequent detection tasks.We designed a Dual-Region Adaptive Point Cloud Preprocessing method,which divides the railway point cloud data into track and non-track regions.The track region undergoes projection dimensionality reduction,with the projected results being unique and subsequently subjected to 2D density clustering,greatly reducing data computation volume.The non-track region undergoes PCA-based dimensionality reduction and clustering operations to achieve preprocessing of large-scale point cloud scenes.Finally,the preprocessed results are used for training,achieving higher accuracy in utility pole detection and data communication.Experimental results show that our proposed preprocessing method not only improves efficiency but also enhances detection accuracy.展开更多
Microplastics are plastic particles or fibers with a diameter of less than 5 mm,and they widely exist in the environment and pose potential risks to the ecosystem and human health.Microplastics detection can provide b...Microplastics are plastic particles or fibers with a diameter of less than 5 mm,and they widely exist in the environment and pose potential risks to the ecosystem and human health.Microplastics detection can provide basic data for formulating effective environmental protection strategies.In this paper,the physical,chemical and biological detection methods of microplastics are reviewed,and the advantages and disadvantages of different methods are analyzed.The problems and challenges encountered in microplastics detection are analyzed,and the future research is discussed.展开更多
As a new type of environmental pollutants,microplastics have gradually attracted people's attention.A large number of plastics discharged into the environment by human beings are constantly aging and breaking,and ...As a new type of environmental pollutants,microplastics have gradually attracted people's attention.A large number of plastics discharged into the environment by human beings are constantly aging and breaking,and finally become microplastics.Microplastics can adsorb pollutants in the environment,and their components have certain toxicity,which can cause different degrees of harm to organisms.Due to the structural characteristics of microplastic particles,such as small particle size,large specific surface area,and their distribution in different environmental media,it is very difficult to accurately detect microplastics.Reliable collection and detection methods are the key to the study of environmental behavior of microplastics.In this study,the collection and detection methods of microplastics in the environment were reviewed,and the development direction of microplastics detection technology in the future was prospected.This study has a certain reference value for the related research and the prevention and treatment of micro-plastic pollution.展开更多
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
The dual transmitter implements the equivalent anti-magnetic flux transient electromagnetic method, which can effectively reduce the scope of the transient electromagnetic detection blind area. However, this method is...The dual transmitter implements the equivalent anti-magnetic flux transient electromagnetic method, which can effectively reduce the scope of the transient electromagnetic detection blind area. However, this method is rarely reported in the detection of pipelines in urban geophysical exploration and the application of coal mines. Based on this, this paper realizes the equivalent anti-magnetic flux transient electromagnetic method based on the dual launcher. The suppression effect of this method on the blind area is analyzed by physical simulation. And the detection experiment of underground pipelines is carried out outdoors. The results show that the dual launcher can significantly reduce the turn-off time, thereby effectively reducing the impact of the blind area on the detection results, and the pipeline detection results verify the device’s effectiveness. Finally, based on the ground experimental results, the application prospect of mine advanced detection is discussed. Compared with other detection fields, the formation of blind areas is mainly caused by the equipment. If the dual launcher can be used to reduce the blind area, the accuracy of advanced detection can be improved more effectively. The above research results are of great significance for improving the detection accuracy of the underground transient electromagnetic method.展开更多
Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis...Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.展开更多
Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,...Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,astronomy applications,semiconductor technology and superconductiong electronics. In this article,we present a reviewof the principle and performance of typical terahertz sources,detectors and non-destructive testing applications. On this basis,the newdevelopment and trends of terahertz radiation detectors are also discussed.展开更多
Within the roadway advanced detection methods, DC resistivity method has an extensive application because of its simple principle and operation. Numerical simulation of the effect of focusing current on advanced detec...Within the roadway advanced detection methods, DC resistivity method has an extensive application because of its simple principle and operation. Numerical simulation of the effect of focusing current on advanced detection was carried out using a three-dimensional finite element method （FEM）, meanwhile the electric-field distribution of the point source and nine-point power source were calculated and analyzed with the same electric charges. The results show that the nine-point power source array has a very good ability to focus, and the DC focus method can be used to predict the aquifer abnormality body precisely. By comparing the FEM modelling results with physical simulation results from soil sink, it is shown that the accuracy of forward simulation meets the requirement and the artificial disturbance from roadway has no impact on the DC focus method.展开更多
基金supported by the National Natural Science Foundation of China(No.22422402)National Key Research and Development Program of China(No.2022YFF0705300)Key Research and Development Program of Jiangxi Province(No.20232BBG70004)。
文摘The performance and price of copper-based micro linear products are determined by the diameter uniformity.How to accurately detect the wire diameter of long-length copper based micro linear products without cutting or damage has always been a technical concern for production enterprises.Herein,a novel approach was developed for nondestructive detection of the average diameter at any given segment of a long copper wire by assessing the adsorption capacity of arginine on its surface.The amount of adsorbent on the surface of the copper wire exhibits a positive correlation with the area,which can be detected by extractive electrospray ionization mass spectrometry(EESI-MS)after online elution with ammonia.The experimental results demonstrated that the analysis can be completed within 15 min,with a good linear relationship between copper wires with different diameters and the adsorption capacity of arginine.The linear correlation coefficient R2was 0.995,the relative standard deviation was 1.10%-2.81%,and the detection limit reached 2.5μm(length of segment=4 cm),showing potential applications for facile measurement of the average diameter of various metal wires.
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 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.
文摘This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.
文摘[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field.However,traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions,such as strong light exposure and weed interference.The aims are to develop an effective crop line extraction method by combining YOLOv8-G,Affinity Propagation,and the Least Squares method to enhance detection accuracy and performance in complex field environments.[Methods]The proposed method employs machine vision techniques to address common field challenges.YOLOv8-G,an improved object detection algorithm that combines YOLOv8 and Ghost‐NetV2 for lightweight,high-speed performance,was used to detect the central points of crops.These points were then clustered using the Affinity Propagation algorithm,followed by the application of the Least Squares method to extract the crop lines.Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework,and ablation studies were performed to validate the enhancements made in YOLOv8-G.[Results and Discussions]The performance of the proposed method was compared with classical object detection and clustering algorithms.The YOLOv8-G algorithm achieved average precision(AP)values of 98.22%,98.15%,and 97.32%for corn detection at 7,14,and 21 days after emergence,respectively.Additionally,the crop line extraction accuracy across all stages was 96.52%.These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field.[Conclusions]The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference,enabling rapid and accurate crop identification.This approach supports the automatic navigation of agricultural machinery,offering significant improvements in the precision and efficiency of field operations.
基金funded by Zhijian Laboratory Open Fund,Rocket Force University of Engineering(Grant No.2023-ZJSYS-KF01-03).
文摘During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms, aging processes, and safety performance. However, there is currently no non-destructive and quantitative detection method for migration of plasticizers in propellant liner. In this study, we developed a HTPB sensing liner by incorporating conductive fillers-namely carbon black(CB), carbon nanotubes(CNTs), and graphene nanoplatelets(GNP)-into the HTPB matrix. The synergistic interaction between CNTs and GNP facilitates the formation of a tunneling conductive network that imparts electrical conductivity to the HTPB liner. To elucidate the functional relationship between conductivity and nitroglycerin(NG) migration, we applied the HTPB sensing liner onto double base propellant surfaces and measured both the conductivity of the sensing layer and NG migration during a 71°C accelerated aging experiment. The results shows that when CNTs/GNP content reaches 3wt%, there is an exponential correlation between conductivity and NG migration with a fitting degree of 0.9652;the average response sensitivity of ΔR/R0 relative to NG migration is calculated as 41.69, with an average deviation of merely5.67% between NG migrations derived from conductivity fittings compared to those obtained via TGA testing results. Overall, this sensing liner exhibits excellent capabilities for detecting NG migration nondestructively and quantitatively while offering a novel approach for assessing interfacial component migrations as well as debonding defects in propellants-a promising avenue for future self-monitoring strategies regarding propellant integrity.
基金supported by Shenzhen Science and Technology Program(NO.JCYJ20210324094000001).
文摘Perfluoroalkyl and polyfluoroalkyl substances(PFASs)pollution with their toxicity,carcinogenicity,and persistence has been concerned worldwide.Research into novel materials for detecting and removing PFASs has increased rapidly,particularly in regard to the emergence of well-characterized single-atom materials(SAMs).Owing to the high selectivity,high atom utilization,and abundant active sites of SAMs,these materials exhibit remarkable efficacy in the detection and removal of PFASs.In this work,recent advances in the synthesis of SAMs for the detection and removal PFASs are reviewed.In-depth discussions of the structure-activity relationship and reaction mechanisms have demonstrated the high efficiency,activity,and selectivity of SAMs for the detection,adsorption,and degradation of PFASs.To optimize the application of SAMs in PFASs remediation,this review comprehensively surveys SAMs applications for PFASs and analyzes potential design strategies based on synthesis methods and corresponding properties.Synthesis strategies such as wet-chemistry,which offer ease of operation and high potential for large-scale production,are recommended for the further exploration of specific SAMs for the detection and removal of PFASs.Finally,this review identifies the challenges and opportunities for development of SAMs for the detection and remediation of PFASs,providing an outlook on strategic goals for a green economy and sustainable development.
基金supported by the National Natural Science Foundation of China(NSFC,52277223 and 51977131)the Shanghai Pujiang Programme(23PJD062)。
文摘Lithium-plating-type defects in lithium-ion batteries pose severe safety risks due to their potential to trigger thermal runaway.To prevent defective batteries from entering the market,developing in-line detection methods during manufacturing is critical yet challenging.This study introduces a deep learning-based method for detecting lithium-plating-type defects using formation and capacity grading data,enabling accurate identification of defective batteries without additional equipment.First,lithiumplating-type defect batteries with various types and area ratios are fabricated.Formation and capacity grading data from 154 batteries(48 defective,106 normal)are collected to construct a dataset.Subsequently,a dual-task deep learning model is then developed,where the reconstruction task learns latent representations from the features,while the classification task identifies the defective batteries.Shapley value analysis further quantifies feature importance,revealing that defective batteries exhibit reduced coulombic efficiency(attributed to irreversible lithium loss)and elevated open-circuit voltage/K-values(linked to self-equalization effects).These findings align with the electrochemical mechanisms of lithium plating,enhancing the model's interpretability.Validated on statistically robust samples shows that the framework achieves a recall of 97.14%for defective batteries and an overall accuracy of 97.42%.This deep learning-driven solution provides a scalable and cost-effective quality control strategy for battery manufacturing.
文摘The development of artificial intelligence(AI)technologies creates a great chance for the iteration of railway monitoring.This paper proposes a comprehensive method for railway utility pole detection.The framework of this paper on railway systems consists of two parts:point cloud preprocessing and railway utility pole detection.Thismethod overcomes the challenges of dynamic environment adaptability,reliance on lighting conditions,sensitivity to weather and environmental conditions,and visual occlusion issues present in 2D images and videos,which utilize mobile LiDAR(Laser Radar)acquisition devices to obtain point cloud data.Due to factors such as acquisition equipment and environmental conditions,there is a significant amount of noise interference in the point cloud data,affecting subsequent detection tasks.We designed a Dual-Region Adaptive Point Cloud Preprocessing method,which divides the railway point cloud data into track and non-track regions.The track region undergoes projection dimensionality reduction,with the projected results being unique and subsequently subjected to 2D density clustering,greatly reducing data computation volume.The non-track region undergoes PCA-based dimensionality reduction and clustering operations to achieve preprocessing of large-scale point cloud scenes.Finally,the preprocessed results are used for training,achieving higher accuracy in utility pole detection and data communication.Experimental results show that our proposed preprocessing method not only improves efficiency but also enhances detection accuracy.
基金Project of National Center of Technology Innovation for Dairy"Study on the Key Technologies of Microplastics Detection for New Pollutants in Dairy Ingredient Water"(2023-KFKT-24).
文摘Microplastics are plastic particles or fibers with a diameter of less than 5 mm,and they widely exist in the environment and pose potential risks to the ecosystem and human health.Microplastics detection can provide basic data for formulating effective environmental protection strategies.In this paper,the physical,chemical and biological detection methods of microplastics are reviewed,and the advantages and disadvantages of different methods are analyzed.The problems and challenges encountered in microplastics detection are analyzed,and the future research is discussed.
基金Supported by Project of National Center of Technology Innovation for Dairy"Study on the Key Technologies of Microplastics Detection for New Pollutants in Dairy Ingredient Water"(2023-KFKT-24).
文摘As a new type of environmental pollutants,microplastics have gradually attracted people's attention.A large number of plastics discharged into the environment by human beings are constantly aging and breaking,and finally become microplastics.Microplastics can adsorb pollutants in the environment,and their components have certain toxicity,which can cause different degrees of harm to organisms.Due to the structural characteristics of microplastic particles,such as small particle size,large specific surface area,and their distribution in different environmental media,it is very difficult to accurately detect microplastics.Reliable collection and detection methods are the key to the study of environmental behavior of microplastics.In this study,the collection and detection methods of microplastics in the environment were reviewed,and the development direction of microplastics detection technology in the future was prospected.This study has a certain reference value for the related research and the prevention and treatment of micro-plastic pollution.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
文摘The dual transmitter implements the equivalent anti-magnetic flux transient electromagnetic method, which can effectively reduce the scope of the transient electromagnetic detection blind area. However, this method is rarely reported in the detection of pipelines in urban geophysical exploration and the application of coal mines. Based on this, this paper realizes the equivalent anti-magnetic flux transient electromagnetic method based on the dual launcher. The suppression effect of this method on the blind area is analyzed by physical simulation. And the detection experiment of underground pipelines is carried out outdoors. The results show that the dual launcher can significantly reduce the turn-off time, thereby effectively reducing the impact of the blind area on the detection results, and the pipeline detection results verify the device’s effectiveness. Finally, based on the ground experimental results, the application prospect of mine advanced detection is discussed. Compared with other detection fields, the formation of blind areas is mainly caused by the equipment. If the dual launcher can be used to reduce the blind area, the accuracy of advanced detection can be improved more effectively. The above research results are of great significance for improving the detection accuracy of the underground transient electromagnetic method.
基金supported by the scientific and technological key project in Henan Province (No.212102210148)Open fund of Key Laboratory of Grain Information Processing and Control (No.KFJJ-2018-101)
文摘Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.
基金supported by the Cooperative Innovation Center of Terahertz Science , the National Basic Research Program of China (Grant No. 2014CB339800)the National Natural Science Foundation of China (Grant Nos. 61138001, 61420106006)+1 种基金the Program for Changjiang Scholars and Innovative Research Team in University (grant No. IRT13033)the Major National Development Project of Scientific Instruments and Equipment of China (Grant No. 2011YQ150021)
文摘Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,astronomy applications,semiconductor technology and superconductiong electronics. In this article,we present a reviewof the principle and performance of typical terahertz sources,detectors and non-destructive testing applications. On this basis,the newdevelopment and trends of terahertz radiation detectors are also discussed.
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.
