Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSP...Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.展开更多
The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology...The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.展开更多
In order to achieve a wider range of ionizing radiations detection,novel fluorescence sensing materials have been developed that utilize the fluorescence enhancement phenomenon caused by the intramolecular photoinduce...In order to achieve a wider range of ionizing radiations detection,novel fluorescence sensing materials have been developed that utilize the fluorescence enhancement phenomenon caused by the intramolecular photoinduced electron transfer(PET)effect.Two perylene diimide isomers PDI-P and PDI-B were designed and synthesized,and their molecular structures were characterized by high-resolution Fourier transform mass spectrometry(HRMS),nuclear magnetic resonance hydrogen and carbon spectroscopy(~1H and~(13)C NMR).The interaction between ionizing radiation and fluorescent molecules was simulated by HCl titration.The results show that combining PDIs and HCl can improve fluorescence through the retro-PET process.Despite the similarities in chemical structures,the fluorescent enhancement multiple of PDI-B with aromatic amine as electron donor is much higher than that of PDI-P with alkyl amine.In the direct irradiation experiments of ionizing radiation,the emission enhancement multiples of PDI-P and PDI-B are 2.01 and 45.4,respectively.Furthermore,density functional theory(DFT)and time-dependent density functional theory(TDDFT)calculations indicate that the HOMO and HOMO-1 energy ranges of PDI-P and PDI-B are 0.54 e V and 1.13 e V,respectively.A wider energy range has a stronger driving force on electrons,which is conducive to fluorescence quenching.Both femtosecond transient absorption spectroscopy(fs-TAS)and transient fluorescence spectroscopy(TFS)tests show that PDI-B has shorter charge separation lifetime and higher electron transfer rate constant.Although both isomers can significantly reduce LOD during PET process,PDI-B with aromatic amine has a wider detection range of 0.118—240 Gy due to its larger emission enhancement,which is a leap of three orders of magnitude.It breaks through the detection range of gamma radiation reported in existing studies,and provides theoretical support for the further study of sensitive and effective new materials for ionizing radiation detection.展开更多
Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three t...Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannanbinding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP.Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays.Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05).Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.展开更多
In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance...In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.展开更多
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laborat...Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.展开更多
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b...Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.展开更多
The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-r...The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.展开更多
The deep earth,deep sea,and deep space are the main parts of the national“three deep”strategy,which is in the forefront of the strategic deployment clearly defined in China’s 14th Five-Year Plan(2021-2025)and the L...The deep earth,deep sea,and deep space are the main parts of the national“three deep”strategy,which is in the forefront of the strategic deployment clearly defined in China’s 14th Five-Year Plan(2021-2025)and the Long-Range Objectives Through the Year 2035.It is important to reveal the evolutionary process and mechanism of deep tectonics to understand the earth’s past,present and future.The academic con-notation of Geology in Time has been given for the first time,which refers to the multi-field evolution response process of geological bodies at different time and spatial scales caused by geological processes inside and outside the Earth.Based on the deep in situ detection space and the unique geological envi-ronment of China Jinping Underground Laboratory,the scientific issue of the correlation mechanism and law between deep internal time-varying and shallow geological response is given attention.Innovative research and frontier exploration on deep underground in situ geo-information detection experiments for Geology in Time are designed to be carried out,which will have the potential to explore the driving force of Geology in Time,reveal essential laws of deep earth science,and explore innovative technologies in deep underground engineering.展开更多
BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some ...BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.展开更多
Herein,a novel interference-free surface-enhanced Raman spectroscopy(SERS)strategy based on magnetic nanoparticles(MNPs)and aptamer-driven assemblies was proposed for the ultrasensitive detection of histamine.A core-s...Herein,a novel interference-free surface-enhanced Raman spectroscopy(SERS)strategy based on magnetic nanoparticles(MNPs)and aptamer-driven assemblies was proposed for the ultrasensitive detection of histamine.A core-satellite SERS aptasensor was constructed by combining aptamer-decorated Fe_(3)O_(4)@Au MNPs(as the recognize probe for histamine)and complementary DNA-modified silver nanoparticles carrying 4-mercaptobenzonitrile(4-MBN)(Ag@4-MBN@Ag-c-DNA)as the SERS signal probe for the indirect detection of histamine.Under an applied magnetic field in the absence of histamine,the assembly gave an intense Raman signal at“Raman biological-silent”region due to 4-MBN.In the presence of histamine,the Ag@4-MBN@Ag-c-DNA SERS-tag was released from the Fe_(3)O_(4)@Au MNPs,thus decreasing the SERS signal.Under optimal conditions,an ultra-low limit of detection of 0.65×10^(-3)ng/mL and a linear range 10^(-2)-10^5 ng/mL on the SERS aptasensor were obtained.The histamine content in four food samples were analyzed using the SERS aptasensor,with the results consistent with those determined by high performance liquid chromatography.The present work highlights the merits of indirect strategies for the ultrasensitive and highly selective SERS detection of small biological molecules in complex matrices.展开更多
This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian mod...This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.展开更多
Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two line...Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.展开更多
Microwave reflectometry is a powerful diagnostic that can measure the density profile and localized turbulence with high spatial and temporal resolution and will be used in ITER,so understanding the influence of plasm...Microwave reflectometry is a powerful diagnostic that can measure the density profile and localized turbulence with high spatial and temporal resolution and will be used in ITER,so understanding the influence of plasma perturbations on the reflect signal is important.The characteristics of the reflect signal from profile reflectometry,the time-of-flight(TOF)signal associated with the MHD instabilities,are investigated in EAST.Using a 1D full-wave simulation code by the Finite-DifferenceTime-Domain(FDTD)method,it is well validated that the local density flattening could induce the discontinuity of the simulated TOF signal and an obvious change of reflect amplitude.Experimental TOF signals under different types of MHD instabilities(sawtooth,sawtooth precursors and tearing mode)are studied in detail and show agreement with the simulation.Two new improved algorithms for detecting and localizing the radial positions of the low-order rational surface,the cross-correlation and gradient threshold(CGT)method and the 2D convolutional neural network approach(CNN)are presented for the first time.It is concluded that TOF signal analysis from profile reflectometry can provide a straightforward and localized measurement of the plasma perturbation from the edge to the core simultaneously and may be a complement or correction to the q-profile control,which will be beneficial for the advanced tokamak operation.展开更多
Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misr...Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.展开更多
This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst inte...This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.展开更多
In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerabi...In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.展开更多
In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the s...In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.展开更多
The influence of the longitudinal acceleration and the angular acceleration of detecting target based on vortex electromagnetic waves in keyhole space are analyzed.The spectrum spreads of different orbital angular mom...The influence of the longitudinal acceleration and the angular acceleration of detecting target based on vortex electromagnetic waves in keyhole space are analyzed.The spectrum spreads of different orbital angular momentum(OAM)modes in different non-line-of-sight situations are simulated.The errors of target accelerations in detection are calculated and compared based on the OAM spectra spreading by using two combinations of composite OAM modes in the keyhole space.According to the research,the effects about spectrum spreads of higher OAM modes are more obvious.The error in detection is mainly affected by OAM spectrum spreading,which can be reduced by reasonably using different combinations of OAM modes in different practical situations.The above results provide a reference idea for investigating keyhole effect when vortex electromagnetic wave is used to detect accelerations.展开更多
基金supported by the Scientific and Innovative Action Plan of Shanghai(21N31900800)Shanghai Rising-Star Program(23QB1403500)+4 种基金the Shanghai Sailing Program(20YF1443000)Shanghai Science and Technology Commission,the Belt and Road Project(20310750500)Talent Project of SAAS(2023-2025)Runup Plan of SAAS(ZP22211)the SAAS Program for Excellent Research Team(2022(B-16))。
文摘Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.
基金supported by the Stable-Support Scientific Project of the China Research Institute of Radio-wave Propagation(Grant No.A13XXXXWXX)the National Natural Science Foundation of China(Grant Nos.42174210,4207202,and 42188101)the Strategic Pioneer Program on Space Science,Chinese Academy of Sciences(Grant No.XDA15014800)。
文摘The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.
基金financial support from the National Natural Science Foundation of China(Grant No.21801016)the Science and Technology on Applied Physical Chemistry Laboratory(Grant No.6142602220304)。
文摘In order to achieve a wider range of ionizing radiations detection,novel fluorescence sensing materials have been developed that utilize the fluorescence enhancement phenomenon caused by the intramolecular photoinduced electron transfer(PET)effect.Two perylene diimide isomers PDI-P and PDI-B were designed and synthesized,and their molecular structures were characterized by high-resolution Fourier transform mass spectrometry(HRMS),nuclear magnetic resonance hydrogen and carbon spectroscopy(~1H and~(13)C NMR).The interaction between ionizing radiation and fluorescent molecules was simulated by HCl titration.The results show that combining PDIs and HCl can improve fluorescence through the retro-PET process.Despite the similarities in chemical structures,the fluorescent enhancement multiple of PDI-B with aromatic amine as electron donor is much higher than that of PDI-P with alkyl amine.In the direct irradiation experiments of ionizing radiation,the emission enhancement multiples of PDI-P and PDI-B are 2.01 and 45.4,respectively.Furthermore,density functional theory(DFT)and time-dependent density functional theory(TDDFT)calculations indicate that the HOMO and HOMO-1 energy ranges of PDI-P and PDI-B are 0.54 e V and 1.13 e V,respectively.A wider energy range has a stronger driving force on electrons,which is conducive to fluorescence quenching.Both femtosecond transient absorption spectroscopy(fs-TAS)and transient fluorescence spectroscopy(TFS)tests show that PDI-B has shorter charge separation lifetime and higher electron transfer rate constant.Although both isomers can significantly reduce LOD during PET process,PDI-B with aromatic amine has a wider detection range of 0.118—240 Gy due to its larger emission enhancement,which is a leap of three orders of magnitude.It breaks through the detection range of gamma radiation reported in existing studies,and provides theoretical support for the further study of sensitive and effective new materials for ionizing radiation detection.
基金funded by the National Key R&D Program of China[2021YFC2301102]National Natural Science Foundation of China[82202593]Key R&D Program of Hebei Province[223777100D].
文摘Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannanbinding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP.Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays.Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05).Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.
基金supported by the National Natural Science Foundation of China(62171447)。
文摘In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
基金supported by the National Key Research and Development Program(grant number:2022YFC2305304).
文摘Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.
基金the National Natural Science Foundation of China(62003298,62163036)the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009)the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。
文摘Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
基金funded by Anhui Provincial Natural Science Foundation(No.2208085ME128)the Anhui University-Level Special Project of Anhui University of Science and Technology(No.XCZX2021-01)+1 种基金the Research and the Development Fund of the Institute of Environmental Friendly Materials and Occupational Health,Anhui University of Science and Technology(No.ALW2022YF06)Anhui Province New Era Education Quality Project(Graduate Education)(No.2022xscx073).
文摘The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.
基金supported by the National Natural Science Foundation of China(Nos.52125402 and 52174084)the Natural Science Foundation of Sichuan Province of China(No.2022NSFSC0005).
文摘The deep earth,deep sea,and deep space are the main parts of the national“three deep”strategy,which is in the forefront of the strategic deployment clearly defined in China’s 14th Five-Year Plan(2021-2025)and the Long-Range Objectives Through the Year 2035.It is important to reveal the evolutionary process and mechanism of deep tectonics to understand the earth’s past,present and future.The academic con-notation of Geology in Time has been given for the first time,which refers to the multi-field evolution response process of geological bodies at different time and spatial scales caused by geological processes inside and outside the Earth.Based on the deep in situ detection space and the unique geological envi-ronment of China Jinping Underground Laboratory,the scientific issue of the correlation mechanism and law between deep internal time-varying and shallow geological response is given attention.Innovative research and frontier exploration on deep underground in situ geo-information detection experiments for Geology in Time are designed to be carried out,which will have the potential to explore the driving force of Geology in Time,reveal essential laws of deep earth science,and explore innovative technologies in deep underground engineering.
基金The Shanxi Provincial Administration of Traditional Chinese Medicine,No.2023ZYYDA2005.
文摘BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.
基金financially supported by the National Natural Science Foundation of China(31972149)funding support from the MacDiarmid Institute for Advanced Materials and Nanotechnologythe Dodd-Walls Centre for Photonic and Quantum Technologies。
文摘Herein,a novel interference-free surface-enhanced Raman spectroscopy(SERS)strategy based on magnetic nanoparticles(MNPs)and aptamer-driven assemblies was proposed for the ultrasensitive detection of histamine.A core-satellite SERS aptasensor was constructed by combining aptamer-decorated Fe_(3)O_(4)@Au MNPs(as the recognize probe for histamine)and complementary DNA-modified silver nanoparticles carrying 4-mercaptobenzonitrile(4-MBN)(Ag@4-MBN@Ag-c-DNA)as the SERS signal probe for the indirect detection of histamine.Under an applied magnetic field in the absence of histamine,the assembly gave an intense Raman signal at“Raman biological-silent”region due to 4-MBN.In the presence of histamine,the Ag@4-MBN@Ag-c-DNA SERS-tag was released from the Fe_(3)O_(4)@Au MNPs,thus decreasing the SERS signal.Under optimal conditions,an ultra-low limit of detection of 0.65×10^(-3)ng/mL and a linear range 10^(-2)-10^5 ng/mL on the SERS aptasensor were obtained.The histamine content in four food samples were analyzed using the SERS aptasensor,with the results consistent with those determined by high performance liquid chromatography.The present work highlights the merits of indirect strategies for the ultrasensitive and highly selective SERS detection of small biological molecules in complex matrices.
基金supported by the National Natural Science Foundation of China(62371382,62071346)the Science,Technology&Innovation Project of Xiong’an New Area(2022XAGG0181)the Special Funds for Creative Research(2022C61540)。
文摘This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.
基金supported by the National Natural Science Foundation of China(61971432)Taishan Scholar Project of Shandong Province(tsqn201909156)the Outstanding Youth Innovation Team Program of University in Shandong Province(2019KJN031)。
文摘Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.
基金supported by the Open Fund of Magnetic Confinement Laboratory of Anhui Province(No.2023 AMF03005)the China Postdoctoral Science Foundation(No.2021M703256)+4 种基金the Director Funding of Hefei Institutes of Physical Science,Chinese Academy of Sciences(No.YZJJ2022QN16)the National Key R&D Program of China(Nos.2022YFE03050003,2019YFE03080200,2019Y FE03040002,and 2022YFE03070004)National Natural Science Foundation of China(Nos.12075284,12175277,12275315 and 12275311)the National Magnetic Confinement Fusion Science Program of China(No.2022YFE03040001)the Science Foundation of the Institute of Plasma Physics,Chinese Academy of Sciences(No.DSJJ-2021-08)。
文摘Microwave reflectometry is a powerful diagnostic that can measure the density profile and localized turbulence with high spatial and temporal resolution and will be used in ITER,so understanding the influence of plasma perturbations on the reflect signal is important.The characteristics of the reflect signal from profile reflectometry,the time-of-flight(TOF)signal associated with the MHD instabilities,are investigated in EAST.Using a 1D full-wave simulation code by the Finite-DifferenceTime-Domain(FDTD)method,it is well validated that the local density flattening could induce the discontinuity of the simulated TOF signal and an obvious change of reflect amplitude.Experimental TOF signals under different types of MHD instabilities(sawtooth,sawtooth precursors and tearing mode)are studied in detail and show agreement with the simulation.Two new improved algorithms for detecting and localizing the radial positions of the low-order rational surface,the cross-correlation and gradient threshold(CGT)method and the 2D convolutional neural network approach(CNN)are presented for the first time.It is concluded that TOF signal analysis from profile reflectometry can provide a straightforward and localized measurement of the plasma perturbation from the edge to the core simultaneously and may be a complement or correction to the q-profile control,which will be beneficial for the advanced tokamak operation.
基金This study was funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)was also supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.
基金supported by the National Key Laboratory of Wireless Communications Foundation,China (IFN20230204)。
文摘This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
基金funded by the Major PublicWelfare Special Fund of Henan Province(No.201300210200)the Major Science and Technology Research Special Fund of Henan Province(No.221100210400).
文摘In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.
基金funded by Hunan Provincial Natural Science Foundation of China with Grant Numbers(2022JJ50016,2023JJ50096)Innovation Platform Open Fund of Hengyang Normal University Grant 2021HSKFJJ039Hengyang Science and Technology Plan Guiding Project with Number 202222025902.
文摘In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11804073 and 61775050).
文摘The influence of the longitudinal acceleration and the angular acceleration of detecting target based on vortex electromagnetic waves in keyhole space are analyzed.The spectrum spreads of different orbital angular momentum(OAM)modes in different non-line-of-sight situations are simulated.The errors of target accelerations in detection are calculated and compared based on the OAM spectra spreading by using two combinations of composite OAM modes in the keyhole space.According to the research,the effects about spectrum spreads of higher OAM modes are more obvious.The error in detection is mainly affected by OAM spectrum spreading,which can be reduced by reasonably using different combinations of OAM modes in different practical situations.The above results provide a reference idea for investigating keyhole effect when vortex electromagnetic wave is used to detect accelerations.
