In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
For the characteristics of small,dense distribution,high diversity of defects and high precision and fast detection in the process of PCB(Printed Circuit Board)defect detection,a defect detection algorithm based on YO...For the characteristics of small,dense distribution,high diversity of defects and high precision and fast detection in the process of PCB(Printed Circuit Board)defect detection,a defect detection algorithm based on YOLOv8n was proposed in this study.Firstly,the original C2f module of YOLOv8n was improved into a C2FFaster-EMA module to reduce the number of parameters and floating-point operations(FLOPs).Additionally,the WIoUv3 loss function was introduced to mitigate the negative impact of low-quality defect images on model training.Consequently,a reduction in model size and an enhancement in detection precision were achieved.Finally,the ablation and comparative experiments were conducted on an augmented Deep PCB dataset,and the generalization experiments were performed on the PCB Defect-Augmented dataset.The results indicated that the proposed model reduces the number of parameters by 23.3%and FLOPs by 20%,P by 0.7%,mAP@0.5 by 0.3%,and mAP@0.5:0.95 by 3.9%,respectively,compared to the original YOLOv8n model.Furthermore,the comparative experiments demonstrated that the proposed model achieves higher accuracy and mAP compared to YOLOv5n and YOLOv5s.It was concluded that the proposed method satisfies the requirements for both accuracy and speed in PCB defect detection.展开更多
In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOL...In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOLO)v8n network is proposed.First,a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual(DWR)module and a dilated reparameterization block(DRB)to replace the C2f module at the high level of the backbone network,enriching the gradient flow information and increasing the effective receptive field(ERF).Second,an efficient local attention(ELA)mechanism is fused with the high-level screening-feature pyramid networks(HS-FPN)module,and an ELA_HSFPN is designed to replace the original feature fusion module,enhancing the ability of the network to cope with multiscale detection tasks.Moreover,a lightweight shared convolutional detection head(SCDH)is introduced to reduce the number of parameters and the computational complexity of the module while enhancing the performance and generalizability of the model.Finally,the soft intersection over union(SIoU)replaces the original loss function to improve the convergence speed and prediction accuracy of the model.Experimental results show that compared with that of the original YOLOv8n model,the mAP@0.5 of the improved algorithm is increased by 5.1%,the number of parameters and computational complexity are reduced by 33.3%and 32.1%,respectively,and the FPS is increased by 4.9%.Compared with other mainstream object detection algorithms,the improved algorithm still leads in terms of core indicators and has good generalizability for surface defects encountered in other industrial scenarios.展开更多
A novel hydrangea-like boron and nitrogen co-doped carbon material synthesised by the cross-linking reaction of spiny spherical polymers and co-doped with boron and nitrogen(B/N)via high-temperature calcination was us...A novel hydrangea-like boron and nitrogen co-doped carbon material synthesised by the cross-linking reaction of spiny spherical polymers and co-doped with boron and nitrogen(B/N)via high-temperature calcination was used to construct an electrochemical sensor for the detection of aristolochic acid.Under optimal conditions,the sensor showed good electrochemical response to aristolochic acid,with a theoretical detection limit of 47.3 nmol/L and the sensitivity reaching 0.31μA Lμmol^(-1)cm^(-2).Moreover,the sensor was successfully applied to the detection of aristolochic acid in the extracts of Chinese herbal medicine samples,and the detection results were consistent with those of high-performance liquid chromatography.With a strong selectivity for substances to be measured in complex environments,this study provides a new and efficient method by which to detect aristolochic acid in Chinese herbal medicine,which greatly expands the application field of B/N heteroatom-doped carbon materials.展开更多
Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional B...Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional Block Attention Module(CBAM),and Deformable Convolutional Networks v2(DCNv2).The Ghost Module streamlines feature generation to reduce redundancy,CBAM applies channel and spatial attention to improve feature focus,and DCNv2 enables adaptability to geometric variations in vehicle shapes.These components work together to improve both accuracy and computational efficiency.Evaluated on the KITTI dataset,the proposed model achieves 95.4%mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision,93.7% recall,and a 94.93%F1-score.Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics.An ablation study is also conducted to quantify the individual and combined contributions of GhostModule,CBAM,and DCNv2,highlighting their effectiveness in improving detection performance.By addressing feature redundancy,attention refinement,and spatial adaptability,the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios.展开更多
Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges ...Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.展开更多
Aromatic nitro compounds present substantial health and environmental concerns due to their toxic nature and potential explosive properties.Consequently,the development of host–vip molecular recognition systems for...Aromatic nitro compounds present substantial health and environmental concerns due to their toxic nature and potential explosive properties.Consequently,the development of host–vip molecular recognition systems for these compounds serves a dual-purpose:enabling the fabrication of high-performance sensors for detection and guiding the design of efficient adsorbents for environmental remediation.This study investigated the host–vip recognition behavior of perethylated pillar[n]arenes toward two aromatic nitro molecules,1-chloro-2,4-dinitrobenzene and picric acid.Various techniques including^(1)H NMR,2D NOESY NMR,and UV-vis spectroscopy were employed to explore the binding behavior between pillararenes and aromatic nitro vips in solution.Moreover,valuable single crystal structures were obtained to elucidate the distinct solid-state assembly behaviors of these vips with different pillararenes.The assembled solid-state supramolecular structures observed encompassed a 1:1 host–vip inclusion complex,an external binding complex,and an exo-wall tessellation complex.Furthermore,based on the findings from these systems,a pillararene-based test paper was developed for efficient picric acid detection,and the removal of picric acid from solution was also achieved using pillararenes powder.This research provides novel insights into the development of diverse host–vip systems toward hazardous compounds,offering potential applications in environmental protection and explosive detection domains.展开更多
Porcine deltacoronavirus(PDCoV)is an emerging swine enteropathogenic coronavirus that can cause acute diarrhea and vomiting in newborn piglets and poses a potential risk for cross-species transmission.It is necessary ...Porcine deltacoronavirus(PDCoV)is an emerging swine enteropathogenic coronavirus that can cause acute diarrhea and vomiting in newborn piglets and poses a potential risk for cross-species transmission.It is necessary to develop an effective serological diagnostic tool for the surveillance of PDCoV infection and vaccine immunity effects.In this study,we developed a monoclonal antibody-based competitive ELISA(cELISA)that selected the purified recombinant PDCoV nucleocapsid(N)protein as the coating antigen to detect PDCoV antibodies.To evaluate the diagnostic performance of the cELISA,122 swine serum samples(39 positive and 83 negative)were tested and the results were compared with an indirect immunofluorescence assay(IFA)as the reference method.By receiver operating characteristic(ROC)curve analysis,the optimum cutoff value of percent inhibition(PI)was determined to be 26.8%,which showed excellent diagnostic performance,with an area under the curve(AUC)of 0.9919,a diagnostic sensitivity of 97.44%and a diagnostic specificity of 96.34%.Furthermore,there was good agreement between the cELISA and virus neutralization test(VNT)for the detection of PDCoV antibodies,with a coincidence rate of 92.7%,and theκanalysis showed almost perfect agreement(κ=0.851).Overall,the established cELISA showed good diagnostic performance,including sensitivity,specificity and repeatability,and can be used for diagnostic assistance,evaluating the response to vaccination and assessing swine herd immunity.展开更多
Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predic...Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.展开更多
Echinops latifolius Tausch(ELT)is the traditional Mongolian medicine for the treatment of osteoporosis,and the ambiguous composition of active ingredients is an important factor in restricting the modernization and gl...Echinops latifolius Tausch(ELT)is the traditional Mongolian medicine for the treatment of osteoporosis,and the ambiguous composition of active ingredients is an important factor in restricting the modernization and globalization of this herb.Considering the traditional activity screening strategy is time-consuming and labor intensive,online HPLC active ingredient detection coupled with ESI-IT-TOF-MS^(n) strategy was employed in this study to isolate,identify and screen active compounds from the herbal medicines at the same time.The structure-activity relationship of these compounds was elucidated as well.Owing to the association of osteoporosis progression and oxidative stress,the antioxidants screening from ELT could be a good interpretive of the active substance in this herb.Meanwhile,DPPH equivalent method was an indicative of the most powerful antioxidant in ELT.Consequently,the screening and identification of the antioxidants in ELT was performed by using on-line HPLC-radical scavenging detection coupled with ESI-IT-TOF-MS^(n) strategy,and the structure-activity relationship was investigated based on DPPH equivalent method.Finally,20 constituents(including apigenin glucosides,caffeic acid,biscaffeoylquinic acids,biscaffeoylquinic acid methyl esters,ect.)were characterized in ELT extracts,and 18 components showed appreciable radical scavenging capacity.In addition,the structure-activity relationship study was carried out based on 14 compounds isolated from our laboratory,and the structural requirements of the compounds on antioxidant activity were obtained:(1)compounds with phenolic hydroxyl groups could have antioxidant activity;(2)the antioxidant activity could not be facilitated by the number of hydroxyl groups,but affected by the number of caffeoyl groups;(3)the substitution position of caffeoyl on quinic acid had a greater influence on DPPH activity;(4)methoxy groups could reduce the antioxidant activity.Collectively,this work provided the biochemical perspective to link active compounds and anti-osteoporosis action of ELT,and further explained how ELT worked in osteoporosis patients with bone loss.展开更多
To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight arc...To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.展开更多
The macrocyclic family comprising pillar[n]arenes and cucurbit[n]urils have received much attention recently. However, studies on the construction of supramolecular complexes formed directly with derivatized pillar[n]...The macrocyclic family comprising pillar[n]arenes and cucurbit[n]urils have received much attention recently. However, studies on the construction of supramolecular complexes formed directly with derivatized pillar[n]arenes and cucurbit[n]urils are scant. Given the interest in such systems, herein we have synthesized a new type of naphthalene-derivatized pillar[n]arene NTP5 and selected Q[10] as the host molecule. The 4-[2-(1-naphthalenyl)ethenyl]pyridine of NTP5 is encapsulated by Q[10] and formed a hostvip complex in water-acetic acid(1:1) solution accompanied by enhanced fluorescence, which changed the morphology of NTP5 from a sphere to a porous form. In addition, the fluorescence of Q[10]-NTP5 can be quenched by the addition of the highly toxic pesticide paraquat(PQ), and the mechanism was shown to be the formation of a new charge transfer ternary system of Q[10]-NTP5-PQ. This work provides new ideas for the contribution of supramolecular assemblies based on derivatized pillar[n]arenes and their combination with cucurbit[n]urils and reveals their potential applications.展开更多
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-samp...We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned.展开更多
This paper reviews three complex interactions between microwave energy and microorganisms (bacteria, fungi, and viruses). The first interaction comprises the detection of viruses within human blood using a 50-Ohm tran...This paper reviews three complex interactions between microwave energy and microorganisms (bacteria, fungi, and viruses). The first interaction comprises the detection of viruses within human blood using a 50-Ohm transmission-line vector net-analyzer (typically 0 to 10 dBm @ 2 to 8.5 GHz) where the blood is placed within a test chamber that acts as a non-50-Ohm discontinuity. The second interaction employs 1 to 6.5 W @ 8 to 26 GHz for microwave feed-horn illumination to inactivate microorganisms at an applied power density of 10 to 100 mW<sup>-2</sup>. The third interaction is within multi-mode microwave ovens, where microorganism cell membrane disruption occurs at a few 100 s of W @ 2.45 GHz and microorganism inactivation between 300 to 1800 W @ 2.45 GHz. Within the first microwave interaction, blood relaxation processes are examined. Whereas in the latter two microwave interactions, the following disruption, and inactivation mechanisms are examined: chemical cellular lysis and, microwave resonant absorption causing cell wall rupture, and thermodynamic analysis in terms of process energy budget and suspension energy density. In addition, oven-specific parameters are discussed.展开更多
BACKGROUND The global outbreak of human severe acute respiratory syndrome coronavirus(SARS-CoV)-2 infection represents an urgent need for readily available,accurate and rapid diagnostic tests.Nucleic acid testing of r...BACKGROUND The global outbreak of human severe acute respiratory syndrome coronavirus(SARS-CoV)-2 infection represents an urgent need for readily available,accurate and rapid diagnostic tests.Nucleic acid testing of respiratory tract specimens for SARS-CoV-2 is the current gold standard for diagnosis of coronavirus disease 2019(COVID-19).However,the diagnostic accuracy of reverse transcription polymerase chain reaction(RT-PCR)tests for detecting SARS-CoV-2 nucleic acid may be lower than optimal.The detection of SARS-CoV-2-specific antibodies should be used as a serological non-invasive tool for the diagnosis and management of SARS-CoV-2 infection.AIM To investigate the diagnostic value of SARS-CoV-2 IgM/IgG and nucleic acid detection in COVID-19.METHODS We retrospectively analyzed 652 suspected COVID-19 patients,and 206 non-COVID-19 patients in Wuhan Integrated TCM and Western Medicine Hospital.Data on SARS-CoV-2 nucleic acid tests and serum antibody tests were collected to investigate the diagnostic value of nucleic acid RT-PCR test kits and immunoglobulin(Ig)M/IgG antibody test kits.The j2 test was used to compare differences between categorical variables.A 95%confidence interval(CI)was provided by the Wilson score method.All analyses were performed with IBM SPSS Statistics version 22.0(IBM Corp.,Armonk,NY,United States).RESULTS Of the 652 suspected COVID-19 patients,237(36.3%)had positive nucleic acid tests,311(47.7%)were positive for IgM,and 592(90.8%)were positive for IgG.There was a significant difference in the positive detection rate between the IgM and IgG test groups(P<0.001).Using the RT-PCR results as a reference,the specificity,sensitivity,and accuracy of IgM/IgG combined tests for SARS-CoV-2 infection were 98.5%,95.8%,and 97.1%,respectively.Of the 415 suspected COVID-19 patients with negative nucleic acid test results,366 had positive IgM/IgG tests with a positive detection rate of 88.2%.CONCLUSION Our data indicate that serological IgM/IgG antibody combined test had high sensitivity and specificity for the diagnosis of SARS-CoV-2 infection,and can be used in combination with RT-PCR for the diagnosis of SARS-CoV-2 infection.展开更多
In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,wh...In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.展开更多
Reman spectroscopy is used as a tool to monitor the reaction between N,N'-di(pmethyl)monothioxamides and 1,3-diamine trimethylene and to detect the reaction intermediate. By observing changes of 1024 cm^(-1) C=S b...Reman spectroscopy is used as a tool to monitor the reaction between N,N'-di(pmethyl)monothioxamides and 1,3-diamine trimethylene and to detect the reaction intermediate. By observing changes of 1024 cm^(-1) C=S band and appearance of a new bend at around 1720 cm^(-1), the reaction mechanism is discussed.展开更多
[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were ...[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were obtained in Escherichia coli prokaryotic expression system by optimizing codons and expression conditions of E.coli.Furthermore,based on the purified soluble N protein and NH fusion protein,a high-sensitivity fluorescence immunoassay kit for detecting the antibody against PPR V was established.[Results]The method could quickly and quantitatively detect PPR V antibody in sheep serum,with high sensitivity and specificity,without any cross reaction to other related sheep pathogens.The intra-batch and inter-batch coefficients of variation were less than 10%and 15%,respectively,and the method had good repeatability.Through detection on 292 clinical serum samples,it was compared with the French IDVET competitive ELISA kit,and the coincidence rate of the two methods reached 93.84%.Compared with the serum neutralization test,the detected titer value of the high-sensitivity rapid fluorescence quantitative detection method was basically consistent with the tilter value obtained by the neutralization test on the standard positive serum(provided by the WOAH Brucellosis Reference Laboratory of France).[Conclusions]This method can realize rapid quantitative detection of PPR V antibody on site,and has high practical value and popularization value.展开更多
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
文摘For the characteristics of small,dense distribution,high diversity of defects and high precision and fast detection in the process of PCB(Printed Circuit Board)defect detection,a defect detection algorithm based on YOLOv8n was proposed in this study.Firstly,the original C2f module of YOLOv8n was improved into a C2FFaster-EMA module to reduce the number of parameters and floating-point operations(FLOPs).Additionally,the WIoUv3 loss function was introduced to mitigate the negative impact of low-quality defect images on model training.Consequently,a reduction in model size and an enhancement in detection precision were achieved.Finally,the ablation and comparative experiments were conducted on an augmented Deep PCB dataset,and the generalization experiments were performed on the PCB Defect-Augmented dataset.The results indicated that the proposed model reduces the number of parameters by 23.3%and FLOPs by 20%,P by 0.7%,mAP@0.5 by 0.3%,and mAP@0.5:0.95 by 3.9%,respectively,compared to the original YOLOv8n model.Furthermore,the comparative experiments demonstrated that the proposed model achieves higher accuracy and mAP compared to YOLOv5n and YOLOv5s.It was concluded that the proposed method satisfies the requirements for both accuracy and speed in PCB defect detection.
基金supported by the Jiangsu Province Science and Technology Policy Guidance Program(Industry-University-Research Cooperation)/Forward-Looking Joint Research Project(BY2016005-05).
文摘In response to themissed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects,a detection algorithm based on an improved You Only Look Once(YOLO)v8n network is proposed.First,a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual(DWR)module and a dilated reparameterization block(DRB)to replace the C2f module at the high level of the backbone network,enriching the gradient flow information and increasing the effective receptive field(ERF).Second,an efficient local attention(ELA)mechanism is fused with the high-level screening-feature pyramid networks(HS-FPN)module,and an ELA_HSFPN is designed to replace the original feature fusion module,enhancing the ability of the network to cope with multiscale detection tasks.Moreover,a lightweight shared convolutional detection head(SCDH)is introduced to reduce the number of parameters and the computational complexity of the module while enhancing the performance and generalizability of the model.Finally,the soft intersection over union(SIoU)replaces the original loss function to improve the convergence speed and prediction accuracy of the model.Experimental results show that compared with that of the original YOLOv8n model,the mAP@0.5 of the improved algorithm is increased by 5.1%,the number of parameters and computational complexity are reduced by 33.3%and 32.1%,respectively,and the FPS is increased by 4.9%.Compared with other mainstream object detection algorithms,the improved algorithm still leads in terms of core indicators and has good generalizability for surface defects encountered in other industrial scenarios.
基金funded by the National Natural Science Foundation of China(No.22476170)Science and Technology Talent and Platform Program of Yunnan Provincial Science and Technology Department(No.202505AW340011)。
文摘A novel hydrangea-like boron and nitrogen co-doped carbon material synthesised by the cross-linking reaction of spiny spherical polymers and co-doped with boron and nitrogen(B/N)via high-temperature calcination was used to construct an electrochemical sensor for the detection of aristolochic acid.Under optimal conditions,the sensor showed good electrochemical response to aristolochic acid,with a theoretical detection limit of 47.3 nmol/L and the sensitivity reaching 0.31μA Lμmol^(-1)cm^(-2).Moreover,the sensor was successfully applied to the detection of aristolochic acid in the extracts of Chinese herbal medicine samples,and the detection results were consistent with those of high-performance liquid chromatography.With a strong selectivity for substances to be measured in complex environments,this study provides a new and efficient method by which to detect aristolochic acid in Chinese herbal medicine,which greatly expands the application field of B/N heteroatom-doped carbon materials.
文摘Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional Block Attention Module(CBAM),and Deformable Convolutional Networks v2(DCNv2).The Ghost Module streamlines feature generation to reduce redundancy,CBAM applies channel and spatial attention to improve feature focus,and DCNv2 enables adaptability to geometric variations in vehicle shapes.These components work together to improve both accuracy and computational efficiency.Evaluated on the KITTI dataset,the proposed model achieves 95.4%mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision,93.7% recall,and a 94.93%F1-score.Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics.An ablation study is also conducted to quantify the individual and combined contributions of GhostModule,CBAM,and DCNv2,highlighting their effectiveness in improving detection performance.By addressing feature redundancy,attention refinement,and spatial adaptability,the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios.
文摘Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.
基金supported by the fundamental research funds of Zhejiang Sci-Tech University(No.22212286-Y)the Natural Science Foundation of Zhejiang Province(No.LQ24B040003)。
文摘Aromatic nitro compounds present substantial health and environmental concerns due to their toxic nature and potential explosive properties.Consequently,the development of host–vip molecular recognition systems for these compounds serves a dual-purpose:enabling the fabrication of high-performance sensors for detection and guiding the design of efficient adsorbents for environmental remediation.This study investigated the host–vip recognition behavior of perethylated pillar[n]arenes toward two aromatic nitro molecules,1-chloro-2,4-dinitrobenzene and picric acid.Various techniques including^(1)H NMR,2D NOESY NMR,and UV-vis spectroscopy were employed to explore the binding behavior between pillararenes and aromatic nitro vips in solution.Moreover,valuable single crystal structures were obtained to elucidate the distinct solid-state assembly behaviors of these vips with different pillararenes.The assembled solid-state supramolecular structures observed encompassed a 1:1 host–vip inclusion complex,an external binding complex,and an exo-wall tessellation complex.Furthermore,based on the findings from these systems,a pillararene-based test paper was developed for efficient picric acid detection,and the removal of picric acid from solution was also achieved using pillararenes powder.This research provides novel insights into the development of diverse host–vip systems toward hazardous compounds,offering potential applications in environmental protection and explosive detection domains.
基金supported by the National Key Research and Development Program(2023YFD1800501)the National Natural Science Foundation of China(32373030,32202787)+5 种基金the S&T Program of Hebei(21322401D)the Jiangsu Province Natural Sciences Foundation(BK20221432,BK20210158)the Jiangsu Agricultural Science and Technology Innovation Fund(CX(22)3028)the Special Project of Northern Jiangsu(SZ-LYG202109)the Open Fund of Shaoxing Academy of Biomedicine of Zhejiang Sci-Tech University(SXAB202215)the Open Fund of Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases,Ministry of Agriculture and Rural Affairs(YDWS202213).
文摘Porcine deltacoronavirus(PDCoV)is an emerging swine enteropathogenic coronavirus that can cause acute diarrhea and vomiting in newborn piglets and poses a potential risk for cross-species transmission.It is necessary to develop an effective serological diagnostic tool for the surveillance of PDCoV infection and vaccine immunity effects.In this study,we developed a monoclonal antibody-based competitive ELISA(cELISA)that selected the purified recombinant PDCoV nucleocapsid(N)protein as the coating antigen to detect PDCoV antibodies.To evaluate the diagnostic performance of the cELISA,122 swine serum samples(39 positive and 83 negative)were tested and the results were compared with an indirect immunofluorescence assay(IFA)as the reference method.By receiver operating characteristic(ROC)curve analysis,the optimum cutoff value of percent inhibition(PI)was determined to be 26.8%,which showed excellent diagnostic performance,with an area under the curve(AUC)of 0.9919,a diagnostic sensitivity of 97.44%and a diagnostic specificity of 96.34%.Furthermore,there was good agreement between the cELISA and virus neutralization test(VNT)for the detection of PDCoV antibodies,with a coincidence rate of 92.7%,and theκanalysis showed almost perfect agreement(κ=0.851).Overall,the established cELISA showed good diagnostic performance,including sensitivity,specificity and repeatability,and can be used for diagnostic assistance,evaluating the response to vaccination and assessing swine herd immunity.
基金supported by the Science for Earthquake Resilience of China(No.XH18027)Research and Development of Comprehensive Geophysical Field Observing Instrument in China's Mainland(No.Y201703)Research Fund Project of Shandong Earthquake Agency(Nos.JJ1505Y and JJ1602)
文摘Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.
基金National Natural Science Foundation of China(Grant No.81860756,81960758)Natural Science Foundation of Inner Mongolia Autonomous Region(Grant No.2017MS08122,2019MS08111 and 2019MS08119)+2 种基金Inner Mongolia Science and Technology Innovation Guide Project(Grant No.02039001)Rolling Support Plan for Grassland Talents Project in Inner Mongolia Autonomous RegionInner Mongolia Autonomous Region Higher Education Science Research Project(Grant No.NJZY19099)。
文摘Echinops latifolius Tausch(ELT)is the traditional Mongolian medicine for the treatment of osteoporosis,and the ambiguous composition of active ingredients is an important factor in restricting the modernization and globalization of this herb.Considering the traditional activity screening strategy is time-consuming and labor intensive,online HPLC active ingredient detection coupled with ESI-IT-TOF-MS^(n) strategy was employed in this study to isolate,identify and screen active compounds from the herbal medicines at the same time.The structure-activity relationship of these compounds was elucidated as well.Owing to the association of osteoporosis progression and oxidative stress,the antioxidants screening from ELT could be a good interpretive of the active substance in this herb.Meanwhile,DPPH equivalent method was an indicative of the most powerful antioxidant in ELT.Consequently,the screening and identification of the antioxidants in ELT was performed by using on-line HPLC-radical scavenging detection coupled with ESI-IT-TOF-MS^(n) strategy,and the structure-activity relationship was investigated based on DPPH equivalent method.Finally,20 constituents(including apigenin glucosides,caffeic acid,biscaffeoylquinic acids,biscaffeoylquinic acid methyl esters,ect.)were characterized in ELT extracts,and 18 components showed appreciable radical scavenging capacity.In addition,the structure-activity relationship study was carried out based on 14 compounds isolated from our laboratory,and the structural requirements of the compounds on antioxidant activity were obtained:(1)compounds with phenolic hydroxyl groups could have antioxidant activity;(2)the antioxidant activity could not be facilitated by the number of hydroxyl groups,but affected by the number of caffeoyl groups;(3)the substitution position of caffeoyl on quinic acid had a greater influence on DPPH activity;(4)methoxy groups could reduce the antioxidant activity.Collectively,this work provided the biochemical perspective to link active compounds and anti-osteoporosis action of ELT,and further explained how ELT worked in osteoporosis patients with bone loss.
基金funded by the General Project of Key Research and Develop-ment Plan of Shaanxi Province(No.2022NY-087).
文摘To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.
基金supported by the National Natural Science Foundation of China(No.21861011)the Innovation Program for Highlevel Talents of Guizhou Province(No.2016-5657)+1 种基金the Science and Technology Fund of Guizhou Province(No.[2020]-1Y046)University of Hull for support。
文摘The macrocyclic family comprising pillar[n]arenes and cucurbit[n]urils have received much attention recently. However, studies on the construction of supramolecular complexes formed directly with derivatized pillar[n]arenes and cucurbit[n]urils are scant. Given the interest in such systems, herein we have synthesized a new type of naphthalene-derivatized pillar[n]arene NTP5 and selected Q[10] as the host molecule. The 4-[2-(1-naphthalenyl)ethenyl]pyridine of NTP5 is encapsulated by Q[10] and formed a hostvip complex in water-acetic acid(1:1) solution accompanied by enhanced fluorescence, which changed the morphology of NTP5 from a sphere to a porous form. In addition, the fluorescence of Q[10]-NTP5 can be quenched by the addition of the highly toxic pesticide paraquat(PQ), and the mechanism was shown to be the formation of a new charge transfer ternary system of Q[10]-NTP5-PQ. This work provides new ideas for the contribution of supramolecular assemblies based on derivatized pillar[n]arenes and their combination with cucurbit[n]urils and reveals their potential applications.
文摘We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned.
文摘This paper reviews three complex interactions between microwave energy and microorganisms (bacteria, fungi, and viruses). The first interaction comprises the detection of viruses within human blood using a 50-Ohm transmission-line vector net-analyzer (typically 0 to 10 dBm @ 2 to 8.5 GHz) where the blood is placed within a test chamber that acts as a non-50-Ohm discontinuity. The second interaction employs 1 to 6.5 W @ 8 to 26 GHz for microwave feed-horn illumination to inactivate microorganisms at an applied power density of 10 to 100 mW<sup>-2</sup>. The third interaction is within multi-mode microwave ovens, where microorganism cell membrane disruption occurs at a few 100 s of W @ 2.45 GHz and microorganism inactivation between 300 to 1800 W @ 2.45 GHz. Within the first microwave interaction, blood relaxation processes are examined. Whereas in the latter two microwave interactions, the following disruption, and inactivation mechanisms are examined: chemical cellular lysis and, microwave resonant absorption causing cell wall rupture, and thermodynamic analysis in terms of process energy budget and suspension energy density. In addition, oven-specific parameters are discussed.
基金Natural Science Foundation of Hubei Province,China,No.2016CFB596and Wuhan City Medical Research Project,China,No.WX17Q39 and No.WX15B14.
文摘BACKGROUND The global outbreak of human severe acute respiratory syndrome coronavirus(SARS-CoV)-2 infection represents an urgent need for readily available,accurate and rapid diagnostic tests.Nucleic acid testing of respiratory tract specimens for SARS-CoV-2 is the current gold standard for diagnosis of coronavirus disease 2019(COVID-19).However,the diagnostic accuracy of reverse transcription polymerase chain reaction(RT-PCR)tests for detecting SARS-CoV-2 nucleic acid may be lower than optimal.The detection of SARS-CoV-2-specific antibodies should be used as a serological non-invasive tool for the diagnosis and management of SARS-CoV-2 infection.AIM To investigate the diagnostic value of SARS-CoV-2 IgM/IgG and nucleic acid detection in COVID-19.METHODS We retrospectively analyzed 652 suspected COVID-19 patients,and 206 non-COVID-19 patients in Wuhan Integrated TCM and Western Medicine Hospital.Data on SARS-CoV-2 nucleic acid tests and serum antibody tests were collected to investigate the diagnostic value of nucleic acid RT-PCR test kits and immunoglobulin(Ig)M/IgG antibody test kits.The j2 test was used to compare differences between categorical variables.A 95%confidence interval(CI)was provided by the Wilson score method.All analyses were performed with IBM SPSS Statistics version 22.0(IBM Corp.,Armonk,NY,United States).RESULTS Of the 652 suspected COVID-19 patients,237(36.3%)had positive nucleic acid tests,311(47.7%)were positive for IgM,and 592(90.8%)were positive for IgG.There was a significant difference in the positive detection rate between the IgM and IgG test groups(P<0.001).Using the RT-PCR results as a reference,the specificity,sensitivity,and accuracy of IgM/IgG combined tests for SARS-CoV-2 infection were 98.5%,95.8%,and 97.1%,respectively.Of the 415 suspected COVID-19 patients with negative nucleic acid test results,366 had positive IgM/IgG tests with a positive detection rate of 88.2%.CONCLUSION Our data indicate that serological IgM/IgG antibody combined test had high sensitivity and specificity for the diagnosis of SARS-CoV-2 infection,and can be used in combination with RT-PCR for the diagnosis of SARS-CoV-2 infection.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003).
文摘In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.
文摘Reman spectroscopy is used as a tool to monitor the reaction between N,N'-di(pmethyl)monothioxamides and 1,3-diamine trimethylene and to detect the reaction intermediate. By observing changes of 1024 cm^(-1) C=S band and appearance of a new bend at around 1720 cm^(-1), the reaction mechanism is discussed.
基金Supported by The National Project for the Prevention and Control of Major Exotic Animal Diseases(2022YFD1800500)National Mutton Sheep Industrial Technology System(CARS39).
文摘[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were obtained in Escherichia coli prokaryotic expression system by optimizing codons and expression conditions of E.coli.Furthermore,based on the purified soluble N protein and NH fusion protein,a high-sensitivity fluorescence immunoassay kit for detecting the antibody against PPR V was established.[Results]The method could quickly and quantitatively detect PPR V antibody in sheep serum,with high sensitivity and specificity,without any cross reaction to other related sheep pathogens.The intra-batch and inter-batch coefficients of variation were less than 10%and 15%,respectively,and the method had good repeatability.Through detection on 292 clinical serum samples,it was compared with the French IDVET competitive ELISA kit,and the coincidence rate of the two methods reached 93.84%.Compared with the serum neutralization test,the detected titer value of the high-sensitivity rapid fluorescence quantitative detection method was basically consistent with the tilter value obtained by the neutralization test on the standard positive serum(provided by the WOAH Brucellosis Reference Laboratory of France).[Conclusions]This method can realize rapid quantitative detection of PPR V antibody on site,and has high practical value and popularization value.
