A high-sensitivity,low-noise single photon avalanche diode(SPAD)detector was presented based on a 180 nm BCD process.The proposed device utilizes a p-implant layer/high-voltage n-well(HVNW)junction to form a deep aval...A high-sensitivity,low-noise single photon avalanche diode(SPAD)detector was presented based on a 180 nm BCD process.The proposed device utilizes a p-implant layer/high-voltage n-well(HVNW)junction to form a deep avalanche multiplication region for near-infrared(NIR)sensitivity enhancement.By optimizing the device size and electric field of the guard ring,the fill factor(FF)is significantly improved,further increasing photon detection efficiency(PDE).To solve the dark noise caused by the increasing active diameter,a field polysilicon gate structure connected to the p+anode was investigated,effectively suppressing dark count noise by 76.6%.It is experimentally shown that when the active diameter increases from 5 to 10μm,the FF is significantly improved from 20.7%to 39.1%,and thus the peak PDE also rises from 13.3%to 25.8%.At an excess bias voltage of 5 V,a NIR photon detection probability(PDP)of 6.8%at 905 nm,a dark count rate(DCR)of 2.12 cps/μm^(2),an afterpulsing probability(AP)of 1.2%,and a timing jitter of 216 ps are achieved,demonstrating excellent single photon detection performance.展开更多
We here report a high system detection efficiency(SDE)superconducting single-photon detector(SSPD)at 2μm wavelength.The device integrates a SiO_(2)/Ta_(2)O_(5)distributed Bragg reflector(DBR)and a sandwich-structured...We here report a high system detection efficiency(SDE)superconducting single-photon detector(SSPD)at 2μm wavelength.The device integrates a SiO_(2)/Ta_(2)O_(5)distributed Bragg reflector(DBR)and a sandwich-structured double-layer NbN nanowire to enhance the optical absorption efficiency.A cold development technique is implemented to optimize the superconducting nanowires with sub-40-nm linewidths,thus enhancing the intrinsic detection efficiency(IDE).The fabricated SSPD shows an SDE exceeding 90% at 2μm wavelength.Moreover,the detector allows an operational working temperature of 2.2 K provided by a compact GM cryo-cooler.This detector delivers excellent performance at the 2μm wavelength,and its optimized structural design implies promising potential for extending detection toward longer infrared bands.It thus holds value for advancing high-sensitivity quantum technologies,mid-infrared optical communications,and dark matter detection research.展开更多
The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framewo...The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are imp...Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.展开更多
Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,ex...Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.展开更多
Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.Howe...Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.展开更多
For the problem of cooperative strike against multiple maneuvering targets,in order to improve the detection efficiency of multi-missile systems,this paper proposes a Group Cooperative Midcourse Guidance Law(GCMGL)for...For the problem of cooperative strike against multiple maneuvering targets,in order to improve the detection efficiency of multi-missile systems,this paper proposes a Group Cooperative Midcourse Guidance Law(GCMGL)for heterogeneous missile formation with optimal detection efficiency.Firstly,considering the adverse impact of target maneuvering on the guidance system,a Super-Twisting Disturbance Observer(STDO)is introduced to estimate target acceleration.Secondly,to avoid chattering in the system,a reaching law is combined with the design of the midcourse guidance law and cooperative detection control law for the leader missiles.This approach provides reference information for follower missiles and forms an optimal detection formation.Then,to achieve cooperative engagement of targets by follower missiles in groups,a group consensus protocol is introduced in the Line-of-Sight(LOS)direction to design the GCMGL.Simultaneously,in the direction normal to the LOS,when follower missiles cannot obtain the LOS angle combination information from the leader missiles,a distributed extended state observer is introduced to estimate it.Finally,a time-varying LOS angle Formation Tracking Midcourse Guidance Law(FTMGL)is designed based on this estimated information.The guidance law’s stability is validated using Lyapunov theory,and simulation experiments are performed to confirm its effectiveness and advantages.展开更多
Complex-valued double-sideband direct detection(DD)can reconstruct the optical field and achieve a high electrical spectral efficiency(ESE)comparable to that of a coherent homodyne receiver,and DD does not require a c...Complex-valued double-sideband direct detection(DD)can reconstruct the optical field and achieve a high electrical spectral efficiency(ESE)comparable to that of a coherent homodyne receiver,and DD does not require a costly local oscillator laser.However,a fundamental question remains if there is an optimal DD receiver structure with the simplest design to approach the performance of the coherent homodyne detection.This study derives the optimal DD receiver structure with an optimal transfer function to recover a quadrature amplitude modulation(QAM)signal with a near-zero guard band at the central frequency of the signal.We derive the theoretical ESE limit for various detection schemes by invoking Shannon’s formula.Our proposed scheme is closest to coherent homodyne detection in terms of the theoretical ESE limit.By leveraging a WaveShaper to construct the optimal transfer function,we conduct a proof-of-concept experiment to transmit a net 228.85-Gb/s 64-QAM signal over an 80-km single-mode fiber with a net ESE of 8.76 b/s/Hz.To the best of our knowledge,this study reports the highest net ESE per polarization per wavelength for DD transmission beyond 40-km single-mode fiber.For a comprehensive metric,denoted as 2ESE×Reach,we achieve the highest 2ESE×Reach per polarization per wavelength for DD transmission.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
Silver ion(Ag^(+))is a highly toxic metal ion,and its monitoring in water or food resources has become extraordinarily necessary within the scope of human health.In the light of the fact of Ag^(+)-induced folding stru...Silver ion(Ag^(+))is a highly toxic metal ion,and its monitoring in water or food resources has become extraordinarily necessary within the scope of human health.In the light of the fact of Ag^(+)-induced folding structure of specific peptides,an unlabeled and highselectivity Ag^(+)assay is presented by means of intrinsic fluorescence of peptides.Under the quenching effect of gold nanoparticles(AuNPs),characteristic fluorescence of peptides could be considerably reduced by rapid modification.Along with the Ag adding,the fluorescence signals of peptide-AuNPs are largely enhanced by the behavior between peptides and Agt.This is basically involving the formation of 4-coordinated complexes,generating the changes of peptides in structure and fluorescence properties.Under this circumstance,the adverse influence of plenty of interfering ions is suppressed,including the toxic Hg^(2+),Pb^(2+).The results highlight that Ag ions could be selectively recognized as low as 2.4 nmol/L with a linear range of 5 to 800 nmol/L.In comparison with other programs,the given approach declares simplicity,sensitivity,and superior selectivity.Furthermore,the biosensor excels in the practical application in water samples(e.g.,lake,tap and drinking water)owing to its non-interference and on-site rapid determination.展开更多
To address the problem of instability and inaccuracy when the Unmanned Aerial Vehicles(UAVs) formation equipped with bearing-only sensor network tracks a maneuvering target,this paper proposes a distributed cooperativ...To address the problem of instability and inaccuracy when the Unmanned Aerial Vehicles(UAVs) formation equipped with bearing-only sensor network tracks a maneuvering target,this paper proposes a distributed cooperative tracking control method considering the effectiveness of passive detection. First, the system model of passive detection in UAV formation is constructed.Then, the Geometric Dilution of Precision(GDOP) of bearing-only sensor nodes pair on the observation plane is analyzed. Building on this foundation, the pairwise form is expanded to obtain the optimal geometric configuration for the entire network. Subsequently, the Distributed Cubature Information Filtering(DCIF) is integrated with the weighted average consensus protocol to design the distributed cooperative observer suitable for the system model, enabling state estimation of the target. Finally, within the distributed architecture, the Nonlinear Model Predictive Controller(NMPC) is designed. This controller autonomously assembles the UAV formation during the assembly phase and forms an optimal detection array. The UAV formation then tracks the target using the virtual geometric center based on the established rigid geometric configuration. The simulation experiments validate that the proposed model and method can enhance the passive detection effectiveness of the UAV formation, thereby achieving stable and efficient distributed cooperative tracking for the maneuvering target.展开更多
By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out so...By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out some limitations of the APFD series metrics. These limitations include APFD series metrics having inaccurate physical explanations and being unable to precisely describe the process of fault detection. To avoid the limitations of existing metrics, this paper proposes two improved metrics for evaluating fault detection efficiency of a test suite, including relative-APFD and relative-APFDc. The proposed metrics refer to both the speed of fault detection and the constraint of the testing source. The case study shows that the two proposed metrics can provide much more precise descriptions of the fault detection process and the fault detection efficiency of the test suite.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propo...Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propose SparseMoE-MFN,a novel unified framework that integrates sparse attention with a sparse-activated Mixture of-Experts(MoE)architecture.This framework aims to enhance the efficiency,inferential depth,and interpretability of multimodal fake news detection.Sparse MoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing.The sparse attention module adaptively filters irrelevant tokens and focuses on key regions,reducing computational costs and noise.The sparse MoE module dynamically routes inputs to specialized experts(visual,language,cross-modal alignment)based on content heterogeneity.This expert specialization design boosts computational efficiency and semantic adaptability,enabling precise processing of complex content and improving performance on ambiguous categories.Evaluated on the large-scale,multilingualMR2 dataset,SparseMoEMFN achieves state-of-the-art performance.It obtains an accuracy of 86.7%and a macro-averaged F1 score of 0.859,outperforming strong baselines like MiniGPT-4 by 3.4%and 3.2%,respectively.Notably,it shows significant advantages in the“unverified”category.Furthermore,SparseMoE-MFN demonstrates superior computational efficiency,with an average inference latency of 89.1 ms and 95.4 GFLOPs,substantially lower than existing models.Ablation studies and visualization analyses confirm the effectiveness of both sparse attention and sparse MoE components in improving accuracy,generalization,and efficiency.展开更多
Oil and gas pipelines are a vital long-distance liquid and natural gas carrier,but their functionality is being assessed from a two-fold perspective of power economy and environmentalism.This review concurs on the way...Oil and gas pipelines are a vital long-distance liquid and natural gas carrier,but their functionality is being assessed from a two-fold perspective of power economy and environmentalism.This review concurs on the way these outcomes are interdependent throughout the pipeline lifecycle by contending that the efficiency,emissions,reliability,and environmental risk are jointly determined through the shared design decisions,operating plans,integrity platforms,and monitoring and response plans.Our initial conceptualization is pipeline systems and performance measures,which are characterized by boundary and comparability issues of particular energy consumption,methane intensity,and release consequence measures.Next,we look at hydraulic and station optimization,focusing on the need to look at the importance of equipment performance at part loads,constraints consciousness dispatch,and transient management to prevent the erosion of integrity levels by efficiency gains.The integrity management is appraised as one of the key enablers of stewardship that connects the corrosion prevention,in-line inspection and verification,and the risk-based mitigation to less likely failure,less disruptive interventions,and reduced emissions during maintenance.We compare the leak and spill prevention,detection,quantification,and response of the SCADA(supervisory control and data acquisition)-based computational monitoring,distributed sensing,as well as aerial/satellite,focusing on the validation,characterization of uncertainty,and the operational parameters modulating the time-to-detect and isolation performance.Environmental impacts of the lifecycle,not related to releases,are explained,such as routing and construction disturbance,management of right-of-way,station externalities,decommissioning,and climate resilience.Lastly,we assess new technologies,such as continuous monitoring networks,electrification,superior materials,and multi-objective decision-making that collaborates to increase energy,reliability,and environmental performance in heterogeneous pipeline networks.展开更多
【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided tech...【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.展开更多
In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identi...In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identification of immature fruits or early stage disease spots.These objects pose significant difficulties due to their small pixel coverage,limited feature information,substantial scale variations,and high susceptibility to complex background interference.These challenges frequently result in inadequate accuracy and robustness in current detection models.This study addresses two critical needs in the cashew cultivation industry—fruitmaturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model.The method introduces three key technological innovations:(1)The SDDil module is designed and integrated into the backbone network.This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost,effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects.Simultaneously,the SDmodule dynamically enhances discriminative features and suppresses background noise,significantly improving the model’s feature discrimination capability in complex environments;(2)The introduction of the DynamicScalSeq-Zoom_cat neck network,significantly improving multi-scale feature fusion;and(3)The optimization of the Minimum Point Distance Intersection over Union(MPDIoU)loss function,which enhances bounding box localization accuracy byminimizing vertex distance.Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model:mAP50 reaches 0.825,a 4.6% increase compared to the originalYOLOv11;mAP50-95 improves to 0.624,a 6.5% increase;and recall rises to 0.777,a 2.4%increase.This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.展开更多
Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).Ho...Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).展开更多
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec...In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62171233the Natural Science Foundation of China,Jiangsu Province under Grant BK20241891the Jiangsu Province Graduate Research and Practice Innovation Plan under Grants SJCX24_0313 and KYCX24_1169。
文摘A high-sensitivity,low-noise single photon avalanche diode(SPAD)detector was presented based on a 180 nm BCD process.The proposed device utilizes a p-implant layer/high-voltage n-well(HVNW)junction to form a deep avalanche multiplication region for near-infrared(NIR)sensitivity enhancement.By optimizing the device size and electric field of the guard ring,the fill factor(FF)is significantly improved,further increasing photon detection efficiency(PDE).To solve the dark noise caused by the increasing active diameter,a field polysilicon gate structure connected to the p+anode was investigated,effectively suppressing dark count noise by 76.6%.It is experimentally shown that when the active diameter increases from 5 to 10μm,the FF is significantly improved from 20.7%to 39.1%,and thus the peak PDE also rises from 13.3%to 25.8%.At an excess bias voltage of 5 V,a NIR photon detection probability(PDP)of 6.8%at 905 nm,a dark count rate(DCR)of 2.12 cps/μm^(2),an afterpulsing probability(AP)of 1.2%,and a timing jitter of 216 ps are achieved,demonstrating excellent single photon detection performance.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA0520403)Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)+1 种基金Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300100)the National Natural Science Foundation of China(Grant Nos.U24A20320 and 62401554)。
文摘We here report a high system detection efficiency(SDE)superconducting single-photon detector(SSPD)at 2μm wavelength.The device integrates a SiO_(2)/Ta_(2)O_(5)distributed Bragg reflector(DBR)and a sandwich-structured double-layer NbN nanowire to enhance the optical absorption efficiency.A cold development technique is implemented to optimize the superconducting nanowires with sub-40-nm linewidths,thus enhancing the intrinsic detection efficiency(IDE).The fabricated SSPD shows an SDE exceeding 90% at 2μm wavelength.Moreover,the detector allows an operational working temperature of 2.2 K provided by a compact GM cryo-cooler.This detector delivers excellent performance at the 2μm wavelength,and its optimized structural design implies promising potential for extending detection toward longer infrared bands.It thus holds value for advancing high-sensitivity quantum technologies,mid-infrared optical communications,and dark matter detection research.
基金supported by the National Natural Science Foundation of China(No.52106080)the Jilin City Science and Technology Innovation Development Plan Project(No.20240302014)+2 种基金the Jilin Provincial Department of Education Science and Technology Research Project(No.JJKH20230135K)the Jilin Province Science and Technology Development Plan Project(No.YDZJ202401640ZYTS)the Northeast Electric Power University Teaching Reform Research Project(No.J2427)。
文摘The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金supported by University of Malaya and Ministry of High Education-Malaysia via Fundamental Research Grant Scheme No.FRGS/1/2023/TK10/UM/02/3.
文摘Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.
基金supported by the National Natural Science Foundation of China(Grant No.62172292).
文摘Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.
基金the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300100)the National Key Research and Development Program of China(Grant Nos.2023YFB3809600 and 2023YFC3007801)+1 种基金the National Natural Science Foundation of China(Grant Nos.62301543 and U24A20320)the Shanghai Sailing Program(Grant No.21YF1455700).
文摘Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.
基金supported by the National Natural Science Foundation of China(Nos.61973253 and 62003264).
文摘For the problem of cooperative strike against multiple maneuvering targets,in order to improve the detection efficiency of multi-missile systems,this paper proposes a Group Cooperative Midcourse Guidance Law(GCMGL)for heterogeneous missile formation with optimal detection efficiency.Firstly,considering the adverse impact of target maneuvering on the guidance system,a Super-Twisting Disturbance Observer(STDO)is introduced to estimate target acceleration.Secondly,to avoid chattering in the system,a reaching law is combined with the design of the midcourse guidance law and cooperative detection control law for the leader missiles.This approach provides reference information for follower missiles and forms an optimal detection formation.Then,to achieve cooperative engagement of targets by follower missiles in groups,a group consensus protocol is introduced in the Line-of-Sight(LOS)direction to design the GCMGL.Simultaneously,in the direction normal to the LOS,when follower missiles cannot obtain the LOS angle combination information from the leader missiles,a distributed extended state observer is introduced to estimate it.Finally,a time-varying LOS angle Formation Tracking Midcourse Guidance Law(FTMGL)is designed based on this estimated information.The guidance law’s stability is validated using Lyapunov theory,and simulation experiments are performed to confirm its effectiveness and advantages.
基金supported by the National Natural Science Foundation of China(62341508).
文摘Complex-valued double-sideband direct detection(DD)can reconstruct the optical field and achieve a high electrical spectral efficiency(ESE)comparable to that of a coherent homodyne receiver,and DD does not require a costly local oscillator laser.However,a fundamental question remains if there is an optimal DD receiver structure with the simplest design to approach the performance of the coherent homodyne detection.This study derives the optimal DD receiver structure with an optimal transfer function to recover a quadrature amplitude modulation(QAM)signal with a near-zero guard band at the central frequency of the signal.We derive the theoretical ESE limit for various detection schemes by invoking Shannon’s formula.Our proposed scheme is closest to coherent homodyne detection in terms of the theoretical ESE limit.By leveraging a WaveShaper to construct the optimal transfer function,we conduct a proof-of-concept experiment to transmit a net 228.85-Gb/s 64-QAM signal over an 80-km single-mode fiber with a net ESE of 8.76 b/s/Hz.To the best of our knowledge,this study reports the highest net ESE per polarization per wavelength for DD transmission beyond 40-km single-mode fiber.For a comprehensive metric,denoted as 2ESE×Reach,we achieve the highest 2ESE×Reach per polarization per wavelength for DD transmission.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金Supported by the National Natural Science Foundation of China(21775114,21874102)。
文摘Silver ion(Ag^(+))is a highly toxic metal ion,and its monitoring in water or food resources has become extraordinarily necessary within the scope of human health.In the light of the fact of Ag^(+)-induced folding structure of specific peptides,an unlabeled and highselectivity Ag^(+)assay is presented by means of intrinsic fluorescence of peptides.Under the quenching effect of gold nanoparticles(AuNPs),characteristic fluorescence of peptides could be considerably reduced by rapid modification.Along with the Ag adding,the fluorescence signals of peptide-AuNPs are largely enhanced by the behavior between peptides and Agt.This is basically involving the formation of 4-coordinated complexes,generating the changes of peptides in structure and fluorescence properties.Under this circumstance,the adverse influence of plenty of interfering ions is suppressed,including the toxic Hg^(2+),Pb^(2+).The results highlight that Ag ions could be selectively recognized as low as 2.4 nmol/L with a linear range of 5 to 800 nmol/L.In comparison with other programs,the given approach declares simplicity,sensitivity,and superior selectivity.Furthermore,the biosensor excels in the practical application in water samples(e.g.,lake,tap and drinking water)owing to its non-interference and on-site rapid determination.
基金supported by the National Natural Science Foundation of China (Nos. 62176214 and 61973253)。
文摘To address the problem of instability and inaccuracy when the Unmanned Aerial Vehicles(UAVs) formation equipped with bearing-only sensor network tracks a maneuvering target,this paper proposes a distributed cooperative tracking control method considering the effectiveness of passive detection. First, the system model of passive detection in UAV formation is constructed.Then, the Geometric Dilution of Precision(GDOP) of bearing-only sensor nodes pair on the observation plane is analyzed. Building on this foundation, the pairwise form is expanded to obtain the optimal geometric configuration for the entire network. Subsequently, the Distributed Cubature Information Filtering(DCIF) is integrated with the weighted average consensus protocol to design the distributed cooperative observer suitable for the system model, enabling state estimation of the target. Finally, within the distributed architecture, the Nonlinear Model Predictive Controller(NMPC) is designed. This controller autonomously assembles the UAV formation during the assembly phase and forms an optimal detection array. The UAV formation then tracks the target using the virtual geometric center based on the established rigid geometric configuration. The simulation experiments validate that the proposed model and method can enhance the passive detection effectiveness of the UAV formation, thereby achieving stable and efficient distributed cooperative tracking for the maneuvering target.
基金The National Natural Science Foundation of China(No.61300054)the Natural Science Foundation of Jiangsu Province(No.BK2011190,BK20130879)+1 种基金the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.13KJB520018)the Science Foundation of Nanjing University of Posts&Telecommunications(No.NY212023)
文摘By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out some limitations of the APFD series metrics. These limitations include APFD series metrics having inaccurate physical explanations and being unable to precisely describe the process of fault detection. To avoid the limitations of existing metrics, this paper proposes two improved metrics for evaluating fault detection efficiency of a test suite, including relative-APFD and relative-APFDc. The proposed metrics refer to both the speed of fault detection and the constraint of the testing source. The case study shows that the two proposed metrics can provide much more precise descriptions of the fault detection process and the fault detection efficiency of the test suite.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金supported by the National Social Science Fund of China(20BXW101).
文摘Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propose SparseMoE-MFN,a novel unified framework that integrates sparse attention with a sparse-activated Mixture of-Experts(MoE)architecture.This framework aims to enhance the efficiency,inferential depth,and interpretability of multimodal fake news detection.Sparse MoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing.The sparse attention module adaptively filters irrelevant tokens and focuses on key regions,reducing computational costs and noise.The sparse MoE module dynamically routes inputs to specialized experts(visual,language,cross-modal alignment)based on content heterogeneity.This expert specialization design boosts computational efficiency and semantic adaptability,enabling precise processing of complex content and improving performance on ambiguous categories.Evaluated on the large-scale,multilingualMR2 dataset,SparseMoEMFN achieves state-of-the-art performance.It obtains an accuracy of 86.7%and a macro-averaged F1 score of 0.859,outperforming strong baselines like MiniGPT-4 by 3.4%and 3.2%,respectively.Notably,it shows significant advantages in the“unverified”category.Furthermore,SparseMoE-MFN demonstrates superior computational efficiency,with an average inference latency of 89.1 ms and 95.4 GFLOPs,substantially lower than existing models.Ablation studies and visualization analyses confirm the effectiveness of both sparse attention and sparse MoE components in improving accuracy,generalization,and efficiency.
文摘Oil and gas pipelines are a vital long-distance liquid and natural gas carrier,but their functionality is being assessed from a two-fold perspective of power economy and environmentalism.This review concurs on the way these outcomes are interdependent throughout the pipeline lifecycle by contending that the efficiency,emissions,reliability,and environmental risk are jointly determined through the shared design decisions,operating plans,integrity platforms,and monitoring and response plans.Our initial conceptualization is pipeline systems and performance measures,which are characterized by boundary and comparability issues of particular energy consumption,methane intensity,and release consequence measures.Next,we look at hydraulic and station optimization,focusing on the need to look at the importance of equipment performance at part loads,constraints consciousness dispatch,and transient management to prevent the erosion of integrity levels by efficiency gains.The integrity management is appraised as one of the key enablers of stewardship that connects the corrosion prevention,in-line inspection and verification,and the risk-based mitigation to less likely failure,less disruptive interventions,and reduced emissions during maintenance.We compare the leak and spill prevention,detection,quantification,and response of the SCADA(supervisory control and data acquisition)-based computational monitoring,distributed sensing,as well as aerial/satellite,focusing on the validation,characterization of uncertainty,and the operational parameters modulating the time-to-detect and isolation performance.Environmental impacts of the lifecycle,not related to releases,are explained,such as routing and construction disturbance,management of right-of-way,station externalities,decommissioning,and climate resilience.Lastly,we assess new technologies,such as continuous monitoring networks,electrification,superior materials,and multi-objective decision-making that collaborates to increase energy,reliability,and environmental performance in heterogeneous pipeline networks.
基金National Natural Science Foundation of China(32270237)Guangxi Key Research and Development Plan Project(Guike AB21238002)Basic Scientific Research Project of Guangxi Academy of Agricultural Sciences(Guinongke 2024YP082)。
文摘【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.
基金supported by Hebei North University Doctoral Research Fund Project(No.BSJJ202315)the Youth Research Fund Project of Higher Education Institutions in Hebei Province(No.QN2024146).
文摘In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identification of immature fruits or early stage disease spots.These objects pose significant difficulties due to their small pixel coverage,limited feature information,substantial scale variations,and high susceptibility to complex background interference.These challenges frequently result in inadequate accuracy and robustness in current detection models.This study addresses two critical needs in the cashew cultivation industry—fruitmaturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model.The method introduces three key technological innovations:(1)The SDDil module is designed and integrated into the backbone network.This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost,effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects.Simultaneously,the SDmodule dynamically enhances discriminative features and suppresses background noise,significantly improving the model’s feature discrimination capability in complex environments;(2)The introduction of the DynamicScalSeq-Zoom_cat neck network,significantly improving multi-scale feature fusion;and(3)The optimization of the Minimum Point Distance Intersection over Union(MPDIoU)loss function,which enhances bounding box localization accuracy byminimizing vertex distance.Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model:mAP50 reaches 0.825,a 4.6% increase compared to the originalYOLOv11;mAP50-95 improves to 0.624,a 6.5% increase;and recall rises to 0.777,a 2.4%increase.This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.
基金supported by the Jilin Science and Technology Development Program,China (20240602032RC)the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZD001)+1 种基金the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZY012)the Jilin Province Development and Reform Commission-Project for Improving the Independent Innovation Capacity of Major Grain Crops,China (2024C002)。
文摘Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).
文摘In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.
