Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detec...Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detection, is facing a lack of simple and fast sensing methods that are compatible with field applications in resource-limited areas. In this work, we developed a sensing approach to identify PCR-amplified Salmonella genomic DNA with the naked eye in a snapshot. Based on the ratiometric fiuorescence signals from SYBR Green Ⅰ and Hydroxyl naphthol blue, positive samples stood out from negative ones with a distinct color pattern under UV exposure. The proposed sensing scheme enabled highly specific identification of Salmonella with a detection limit at the single-copy level. Also, as a supplement to the intuitive naked-eye visualization results, numerical analysis of the colored images was available with a smartphone app to extract RGB values from colored images. This work provides a simple, rapid, and user-friendly solution for PCR identification, which promises great potential in molecular diagnosis of Salmonella and other pathogens in field.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
Developing an accurate and visual sensing strategy for trace levels of fluoroquinolone residues that pose threat to food safety and human health is highly desired but remains challenging.Herein,a target selfcalibratio...Developing an accurate and visual sensing strategy for trace levels of fluoroquinolone residues that pose threat to food safety and human health is highly desired but remains challenging.Herein,a target selfcalibration ratiometric fluorescent sensing platform has been designed for sensitive visual detection of levofloxacin(LEV)based on fluorescent europium metal-organic framework(Eu-MOF)probe.Specifically,the Eu-MOF was facilely synthesized via directly mixing Eu^(3+)with 1,10-phenanthroline-2,9-dicarboxylic acid(PDA)ligand at room temperature,which exhibited well-stable red fluorescence at 612 nm.Upon the addition of target LEV,the significant fluorescence quenching from Eu^(3+)was observed owing to the inner filter effect between the Eu-MOF and LEV.While the intrinsic fluorescence for LEV at 462nm was gradually enhanced,thereby realizing the self-calibration ratiometric fluorescence responses to LEV.Through this strategy,LEV can be detected down to 27 nmol/L.Furthermore,a test paper-based Eu-MOF integrated with the smartphone assisted RGB color analysis was exploited for the quantitative monitoring of LEV through the multi-color changes from red to blue,thus achieved portable,convenient and visual detection of LEV in honey and milk samples.Therefore,the developed strategy could provide a useful tool for supporting the practical on-site test in food samples.展开更多
Tetracycline(TC)is a broad-spectrum antibiotic,and its residues in the environment and food are harmful to human health.Therefore,it is essential to rapidly,sensitively,and conveniently detect TC.In this work,we devel...Tetracycline(TC)is a broad-spectrum antibiotic,and its residues in the environment and food are harmful to human health.Therefore,it is essential to rapidly,sensitively,and conveniently detect TC.In this work,we developed a portable silicon nanoparticles chelated Europium(Ⅲ)-based polyacrylonitrile(Eu-SiNPs/PAN)nanofiber membrane for rapid,sensitive,and convenient detection of TC.The Eu-SiNPs were synthesized with a facile one-pot method.The Eu-SiNPs/PAN nanofiber membrane was fabricated by electrospinning,combining Eu-SiNPs and PAN with three-dimensional porous membrane structures and UV resistance.Both the Eu-SiNPs and the Eu-SiNPs/PAN nanofiber membranes have good selectivity and anti-interference ability towards TC.The combined merits of rapid response,long storage life,easy portability,and naked-eye recognition of TC make the Eu-SiNPs/PAN nanofiber membrane a promising material for convenient TC detection applications.The practicability of these nanofiber membranes was further verified by detecting TC in real samples,such as lake water,drinking water and honey,and achieved quantitative detection.展开更多
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.展开更多
With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers ...With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly urgent.Currently,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and adaptability.To address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color image.The Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE images.As shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.展开更多
People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces.Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial aw...People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces.Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users.This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects.The system architecture includes four main layers:Wearable Internet of Things(IoT),Network,Cloud,and Indoor Object Detection Layers.The wearable hardware prototype is assembled using a Raspberry Pi 4,while the indoor object detection approach exploits YOLOv11.YOLOv11 represents the cutting edge of deep learning models optimized for both speed and accuracy in recognizing objects and powers the research prototype.In this work,we used a prototype implementation,comparative experiments,and two datasets compiled from Furniture Detection(i.e.,from Roboflow Universe)and Kaggle,which comprises 3000 images evenly distributed across three object categories,including bed,sofa,and table.In the evaluation process,the Raspberry Pi is only used for a feasibility demonstration of real-time inference performance(e.g.,latency and memory consumption)on embedded hardware.We also evaluated YOLOv11 by comparing its performance with other current methodologies,which involved a Convolutional Neural Network(CNN)(MobileNet-Single Shot MultiBox Detector(SSD))model together with the RTDETR Vision Transformer.The experimental results show that YOLOv11 stands out by reaching an average of 99.07%,98.51%,97.96%,and 98.22%for the accuracy,precision,recall,and F1-score,respectively.This feasibility study highlights the effectiveness of Raspberry Pi 4 and YOLOv11 in real-time indoor object detection,paving the way for structured user studies with visually impaired people in the future to evaluate their real-world use and impact.展开更多
Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed...Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed areas.Fire detection becomes crucial as it directly impacts human safety and the environment.While modern technology requires precise techniques for early detection to prevent damage and loss,few research has focused on artificial intelligence(AI)-based early fire alert systems for BVI individuals in indoor settings.To prevent such fire incidents,it is crucial to identify fires accurately and promptly,and alert BVI personnel using a combination of smart glasses,deep learning(DL),and computer vision(CV).The most recent technologies require effective methods to identify fires quickly,preventing damage and physical loss.In this manuscript,an Enhanced Fire Detection System for Blind and Visually Challenged People using Artificial Intelligence with Deep Convolutional Neural Networks(EFDBVC-AIDCNN)model is presented.The EFDBVC-AIDCNN model presents an advanced fire detection system that utilizes AI to detect and classify fire hazards for BVI people effectively.Initially,image pre-processing is performed using the Gabor filter(GF)model to improve texture details and patterns specific to flames and smoke.For the feature extractor,the Swin transformer(ST)model captures fine details across multiple scales to represent fire patterns accurately.Furthermore,the Elman neural network(ENN)technique is implemented to detect fire.The improved whale optimization algorithm(IWOA)is used to efficiently tune ENN parameters,improving accuracy and robustness across varying lighting and environmental conditions to optimize performance.An extensive experimental study of the EFDBVC-AIDCNN technique is accomplished under the fire detection dataset.A short comparative analysis of the EFDBVC-AIDCNN approach portrayed a superior accuracy value of 96.60%over existing models.展开更多
Highlights The RT-RAA-VF assay developed for the NiV P gene can perform rapid detection of NiV within 20 min at 42℃ with high specificity.This assay is capable of attaining sensitivity to a single copy of NiV RNA tra...Highlights The RT-RAA-VF assay developed for the NiV P gene can perform rapid detection of NiV within 20 min at 42℃ with high specificity.This assay is capable of attaining sensitivity to a single copy of NiV RNA transcripts.This assay effectively avoids false positives caused by aerosol contamination with a sealed disposable nucleic acid visualization test paper device.展开更多
This article presents a human fall detection system that addresses two critical challenges:privacy preservation and detection accuracy.We propose a comprehensive framework that integrates state-of-the-art machine lear...This article presents a human fall detection system that addresses two critical challenges:privacy preservation and detection accuracy.We propose a comprehensive framework that integrates state-of-the-art machine learning models,multimodal data fusion,federated learning(FL),and Karush-Kuhn-Tucker(KKT)-based resource optimization.The systemfuses data fromwearable sensors and cameras using Gramian Angular Field(GAF)encoding to capture rich spatial-temporal features.To protect sensitive data,we adopt a privacy-preserving FL setup,where model training occurs locally on client devices without transferring raw data.A custom convolutional neural network(CNN)is designed to extract robust features from the fused multimodal inputs under FL constraints.To further improve efficiency,a KKT-based optimization strategy is employed to allocate computational tasks based on device capacity.Evaluated on the UP-Fall dataset,the proposed system achieves 91%accuracy,demonstrating its effectiveness in detecting human falls while ensuring data privacy and resource efficiency.This work contributes to safer,scalable,and real-world-applicable fall detection for elderly care.展开更多
Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature...Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature phosphorescence(RTP)detection in single system due to the formidable synthesis.Herein,a multifunctional composite of Eu&CDs@p RHO has been designed by co-assembly strategy and prepared via a facile calcination and impregnation treatment.Eu&CDs@p RHO exhibits intense fluorescence(FL)and RTP coming from two individual luminous centers,Eu3+in the free pores and CDs in the interrupted structure of RHO zeolite.Unique four-mode color outputs including pink(Eu^(3+),ex.254 nm),light violet(CDs,ex.365 nm),blue(CDs,254 nm off),and green(CDs,365 nm off)could be realized,on the basis of it,a preliminary application of advanced information encoding has been demonstrated.Given the free pores of matrix and stable RTP in water of confined CDs,a visual RTP detection of Fe^(3+)ions is achieved with the detection limit as low as 9.8μmol/L.This work has opened up a new perspective for the strategic amalgamation of luminous vips with porous zeolite to construct the advanced functional materials.展开更多
Room-temperature phosphorescence(RTP)materials exhibiting long emission lifetimes have gained increasing attention owing to their potential applications in encryption,anti-counterfeiting,and sensing.However,most polym...Room-temperature phosphorescence(RTP)materials exhibiting long emission lifetimes have gained increasing attention owing to their potential applications in encryption,anti-counterfeiting,and sensing.However,most polymers exhibit a short RTP lifetime(<1 s)because of their unstable triplet excitons.Herein,a new strategy of polymer chain stabilized phosphorescence(PCSP),which yields a new kind of RTP polymers with an ultralong lifetime and a sensitive oxygen response,has been reported.The rigid polymer chains of poly(methyl mathacrylate)(PMMA)immobilize the emitter molecules through multiple interactions between them,giving rise to efficient RTP.Meanwhile,the loosely-packed amorphous polymer chains allow oxygen to diffuse inside,endowing the doped polymers with oxygen sensitivity.Flexible and transparent polymer films exhibited an impressive ultralong RTP lifetime of 2.57 s at room temperature in vacuum,which was among the best performance of PMMA.Intriguingly,their RTP was rapidly quenched in the presence of oxygen.Furthermore,RTP microparticles with a diameter of 1.63μm were synthesized using in situ dispersion polymerization technique.Finally,oxygen sensors for quick,visual,and quantitative oxygen detection were developed based on the RTP microparticles through phosphorescence lifetime and image analysis.With distinctive advantages such as an ultralong lifetime,oxygen sensitivity,ease of fabrication,and cost-effectiveness,PCSP opens a new avenue to sensitive materials for oxygen detection.展开更多
Icing detection is critically important for preventing safety accidents and economic losses,especially concerning ice formation from invalidated anti-icing fluids(water and ethylene glycol)under extreme conditions.Tra...Icing detection is critically important for preventing safety accidents and economic losses,especially concerning ice formation from invalidated anti-icing fluids(water and ethylene glycol)under extreme conditions.Traditional technologies like ultrasonics and capacitor-antenna face challenges with limited detection areas,lower accuracy,and susceptibility to electromagnetic interference.Here,we introduce a novel viscosity-ultrasensitive fluorescent probe 40,4‴-(2,2-diphenyle-thene-1,1-diyl)bis-(3,5-dicarboxylate)(TPE-2B4C)based on AIEgens for moni-toring ice formation of anti-icing fluids in low-temperature environments.TPE-2B4C,consisting of four sodium carboxylate groups and multiple freely rotating benzene rings,demonstrates outstanding solubility in anti-icing fluids and exhibits no fluorescent background signal even at low temperatures(<−20°C).Upon freezing,TPE-2B4C relocates from the water phase to higher viscosity ethylene glycol,causing restriction of benzene rings and a significantly increased green fluorescence signal.TPE-2B4C can successfully determine whether the anti-icing fluids are icing from−5 to−20°C with a high contrast ratio.Due to its simple setup,fast operation,and broad applicability,our new method is anticipated to be employed for rapid,real-time,and large-scale icing detection.展开更多
In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks,this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane's surface...In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks,this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane's surface using unmanned aerial vehicles(UAv).Our objective is to attain a sufficiently high coverage rate with the least number of viewpoints.The contributions of this work are enumerated as follows.Firstly,the 3D model of the target aircraft is spatially extended in accordance with the depth range of the camera mounted on the drone,thereby confining the sampling range of 3D viewpoints.Next,a candidate set of viewpoints is generated through random sampling and the probabilistic potential field technique.Subsequently,we propose a novel hyper-heuristic algorithm.In this algorithm,a genetic algorithm serves as a high-level heuristic strategy,in tandem with multiple low-level heuristic operators devised for combinatorial optimization.This not only augments the capacity to seek the global optimal solution but also expedites the convergence rate,aiming to ascertain the optimal subset of viewpoints.Moreover,we devise a new fitness function for appraising candidate solution vectors in the set covering problem(ScP),strengthening the evolutionary guidance for genetic algorithms.Eventually,experimental findings on the simulated and real airplanes corroborate the efficacy of the proposed method,i.e.,it markedly diminishes the requisite number of viewpoints and augments inspection efficiency.展开更多
Norfloxacin is widely used owing to its strong bactericidal effect on Gram-negative bacteria.However,the residual norfloxacin in the environment can be biomagnified via food chain andmay damage the human liver and del...Norfloxacin is widely used owing to its strong bactericidal effect on Gram-negative bacteria.However,the residual norfloxacin in the environment can be biomagnified via food chain andmay damage the human liver and delay the bone development ofminors.Present work described a reliable and sensitive smartphone colorimetric sensing system based on cobaltdoped Fe_(3)O_(4) magnetic nanoparticles(Co-Fe_(3)O_(4) MNPs)for the visual detection of norfloxacin.Compared with Fe_(3)O_(4),Co-Fe_(3)O_(4) MNPs earned more remarkably peroxidase-like activity and TMB(colorless)was rapidly oxidized to oxTMB(blue)with the presence of H_(2)O_(2).Interestingly,the addition of low concentration of norfloxacin can accelerate the color reaction process of TMB,and blue deepening of the solution can be observed with the naked eye.However,after adding high concentration of norfloxacin,the activity of nanozymewas inhibited,resulting in the gradual fading of the solution.Based on this principle,a colorimetric sensor integrated with smartphone RGB mode was established.The visual sensor exhibited good linearity for norfloxacin monitoring in the range of 0.13-2.51μmol/L and 17.5-100μmol/L.The limit of visual detectionwas 0.08μmol/L.In the actualwater sample analysis,the spiked recoveries of norfloxacin were over the range of 95.7%-104.7%.These results demonstrated that the visual sensor was a convenient and fast method for the efficient and accurate detection of norfloxacin in water,which may have broad application prospect.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
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
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
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
基金supported by the Macao Science and Technology Development Fund(FDCT)(Nos.FDCT 0029/2021/A1,FDCT0002/2021/AKP,004/2023/SKL,0036/2021/APD)University of Macao(No.MYRG-GRG2023-00034-IME,SRG2024-00057IME)+2 种基金Dr.Stanley Ho Medical Development Foundation(No.SHMDF-OIRFS/2024/001)Zhuhai Huafa Group(No.HF-006-2021)Guangdong Science and Technology Department(No.2022A0505030022)。
文摘Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detection, is facing a lack of simple and fast sensing methods that are compatible with field applications in resource-limited areas. In this work, we developed a sensing approach to identify PCR-amplified Salmonella genomic DNA with the naked eye in a snapshot. Based on the ratiometric fiuorescence signals from SYBR Green Ⅰ and Hydroxyl naphthol blue, positive samples stood out from negative ones with a distinct color pattern under UV exposure. The proposed sensing scheme enabled highly specific identification of Salmonella with a detection limit at the single-copy level. Also, as a supplement to the intuitive naked-eye visualization results, numerical analysis of the colored images was available with a smartphone app to extract RGB values from colored images. This work provides a simple, rapid, and user-friendly solution for PCR identification, which promises great potential in molecular diagnosis of Salmonella and other pathogens in field.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
基金supported by the National Natural Science Foundation of China(Nos.32260247 and 22064010)the Natural Science Foundation of Jiangxi Province(Nos.20232BAB215071 and 20224BAB213009).
文摘Developing an accurate and visual sensing strategy for trace levels of fluoroquinolone residues that pose threat to food safety and human health is highly desired but remains challenging.Herein,a target selfcalibration ratiometric fluorescent sensing platform has been designed for sensitive visual detection of levofloxacin(LEV)based on fluorescent europium metal-organic framework(Eu-MOF)probe.Specifically,the Eu-MOF was facilely synthesized via directly mixing Eu^(3+)with 1,10-phenanthroline-2,9-dicarboxylic acid(PDA)ligand at room temperature,which exhibited well-stable red fluorescence at 612 nm.Upon the addition of target LEV,the significant fluorescence quenching from Eu^(3+)was observed owing to the inner filter effect between the Eu-MOF and LEV.While the intrinsic fluorescence for LEV at 462nm was gradually enhanced,thereby realizing the self-calibration ratiometric fluorescence responses to LEV.Through this strategy,LEV can be detected down to 27 nmol/L.Furthermore,a test paper-based Eu-MOF integrated with the smartphone assisted RGB color analysis was exploited for the quantitative monitoring of LEV through the multi-color changes from red to blue,thus achieved portable,convenient and visual detection of LEV in honey and milk samples.Therefore,the developed strategy could provide a useful tool for supporting the practical on-site test in food samples.
基金supported by the Natural Science Foundation of Tianjin(Nos.18JCQNJC72400 and 22JCQNJC01510).
文摘Tetracycline(TC)is a broad-spectrum antibiotic,and its residues in the environment and food are harmful to human health.Therefore,it is essential to rapidly,sensitively,and conveniently detect TC.In this work,we developed a portable silicon nanoparticles chelated Europium(Ⅲ)-based polyacrylonitrile(Eu-SiNPs/PAN)nanofiber membrane for rapid,sensitive,and convenient detection of TC.The Eu-SiNPs were synthesized with a facile one-pot method.The Eu-SiNPs/PAN nanofiber membrane was fabricated by electrospinning,combining Eu-SiNPs and PAN with three-dimensional porous membrane structures and UV resistance.Both the Eu-SiNPs and the Eu-SiNPs/PAN nanofiber membranes have good selectivity and anti-interference ability towards TC.The combined merits of rapid response,long storage life,easy portability,and naked-eye recognition of TC make the Eu-SiNPs/PAN nanofiber membrane a promising material for convenient TC detection applications.The practicability of these nanofiber membranes was further verified by detecting TC in real samples,such as lake water,drinking water and honey,and achieved quantitative detection.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
基金supported by the Natural Science Foundation of Henan Province(Grant No.242300420297)awarded to Yi Sun.
文摘With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly urgent.Currently,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and adaptability.To address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color image.The Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE images.As shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.
基金funded by the King Salman Center for Disability Research through Research Group No.KSRG-2024-140.
文摘People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces.Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users.This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects.The system architecture includes four main layers:Wearable Internet of Things(IoT),Network,Cloud,and Indoor Object Detection Layers.The wearable hardware prototype is assembled using a Raspberry Pi 4,while the indoor object detection approach exploits YOLOv11.YOLOv11 represents the cutting edge of deep learning models optimized for both speed and accuracy in recognizing objects and powers the research prototype.In this work,we used a prototype implementation,comparative experiments,and two datasets compiled from Furniture Detection(i.e.,from Roboflow Universe)and Kaggle,which comprises 3000 images evenly distributed across three object categories,including bed,sofa,and table.In the evaluation process,the Raspberry Pi is only used for a feasibility demonstration of real-time inference performance(e.g.,latency and memory consumption)on embedded hardware.We also evaluated YOLOv11 by comparing its performance with other current methodologies,which involved a Convolutional Neural Network(CNN)(MobileNet-Single Shot MultiBox Detector(SSD))model together with the RTDETR Vision Transformer.The experimental results show that YOLOv11 stands out by reaching an average of 99.07%,98.51%,97.96%,and 98.22%for the accuracy,precision,recall,and F1-score,respectively.This feasibility study highlights the effectiveness of Raspberry Pi 4 and YOLOv11 in real-time indoor object detection,paving the way for structured user studies with visually impaired people in the future to evaluate their real-world use and impact.
基金the King Salman Centre for Disability Research for funding this work through Research Group No.KSRG-2024-068。
文摘Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed areas.Fire detection becomes crucial as it directly impacts human safety and the environment.While modern technology requires precise techniques for early detection to prevent damage and loss,few research has focused on artificial intelligence(AI)-based early fire alert systems for BVI individuals in indoor settings.To prevent such fire incidents,it is crucial to identify fires accurately and promptly,and alert BVI personnel using a combination of smart glasses,deep learning(DL),and computer vision(CV).The most recent technologies require effective methods to identify fires quickly,preventing damage and physical loss.In this manuscript,an Enhanced Fire Detection System for Blind and Visually Challenged People using Artificial Intelligence with Deep Convolutional Neural Networks(EFDBVC-AIDCNN)model is presented.The EFDBVC-AIDCNN model presents an advanced fire detection system that utilizes AI to detect and classify fire hazards for BVI people effectively.Initially,image pre-processing is performed using the Gabor filter(GF)model to improve texture details and patterns specific to flames and smoke.For the feature extractor,the Swin transformer(ST)model captures fine details across multiple scales to represent fire patterns accurately.Furthermore,the Elman neural network(ENN)technique is implemented to detect fire.The improved whale optimization algorithm(IWOA)is used to efficiently tune ENN parameters,improving accuracy and robustness across varying lighting and environmental conditions to optimize performance.An extensive experimental study of the EFDBVC-AIDCNN technique is accomplished under the fire detection dataset.A short comparative analysis of the EFDBVC-AIDCNN approach portrayed a superior accuracy value of 96.60%over existing models.
基金supported by the National Key Research and Development Program of China(2021YFF0703600)。
文摘Highlights The RT-RAA-VF assay developed for the NiV P gene can perform rapid detection of NiV within 20 min at 42℃ with high specificity.This assay is capable of attaining sensitivity to a single copy of NiV RNA transcripts.This assay effectively avoids false positives caused by aerosol contamination with a sealed disposable nucleic acid visualization test paper device.
基金supported by King Fahd University of Petroleum&Minerals,Dhahran,31261,SaudiArabiaTheauthors at KFUP Macknowledge the Interdisciplinary Research Center for Intelligent Secure Systems(IRC-ISS)for the support received under Grant No.INSS2516.
文摘This article presents a human fall detection system that addresses two critical challenges:privacy preservation and detection accuracy.We propose a comprehensive framework that integrates state-of-the-art machine learning models,multimodal data fusion,federated learning(FL),and Karush-Kuhn-Tucker(KKT)-based resource optimization.The systemfuses data fromwearable sensors and cameras using Gramian Angular Field(GAF)encoding to capture rich spatial-temporal features.To protect sensitive data,we adopt a privacy-preserving FL setup,where model training occurs locally on client devices without transferring raw data.A custom convolutional neural network(CNN)is designed to extract robust features from the fused multimodal inputs under FL constraints.To further improve efficiency,a KKT-based optimization strategy is employed to allocate computational tasks based on device capacity.Evaluated on the UP-Fall dataset,the proposed system achieves 91%accuracy,demonstrating its effectiveness in detecting human falls while ensuring data privacy and resource efficiency.This work contributes to safer,scalable,and real-world-applicable fall detection for elderly care.
基金supported by the National Natural Science Foundation of China(No.22288101)the 111 Project(No.B17020)。
文摘Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature phosphorescence(RTP)detection in single system due to the formidable synthesis.Herein,a multifunctional composite of Eu&CDs@p RHO has been designed by co-assembly strategy and prepared via a facile calcination and impregnation treatment.Eu&CDs@p RHO exhibits intense fluorescence(FL)and RTP coming from two individual luminous centers,Eu3+in the free pores and CDs in the interrupted structure of RHO zeolite.Unique four-mode color outputs including pink(Eu^(3+),ex.254 nm),light violet(CDs,ex.365 nm),blue(CDs,254 nm off),and green(CDs,365 nm off)could be realized,on the basis of it,a preliminary application of advanced information encoding has been demonstrated.Given the free pores of matrix and stable RTP in water of confined CDs,a visual RTP detection of Fe^(3+)ions is achieved with the detection limit as low as 9.8μmol/L.This work has opened up a new perspective for the strategic amalgamation of luminous vips with porous zeolite to construct the advanced functional materials.
基金National Natural Science Foundation of China(No.22475241)Guangdong Basic and Applied Basic Research Foundation(Nos.2022A1515010826 and 2023A1515012696)the Fundamental Research Funds for the Central Universities(Nos.17lgjc03 and 18lgpy04).
文摘Room-temperature phosphorescence(RTP)materials exhibiting long emission lifetimes have gained increasing attention owing to their potential applications in encryption,anti-counterfeiting,and sensing.However,most polymers exhibit a short RTP lifetime(<1 s)because of their unstable triplet excitons.Herein,a new strategy of polymer chain stabilized phosphorescence(PCSP),which yields a new kind of RTP polymers with an ultralong lifetime and a sensitive oxygen response,has been reported.The rigid polymer chains of poly(methyl mathacrylate)(PMMA)immobilize the emitter molecules through multiple interactions between them,giving rise to efficient RTP.Meanwhile,the loosely-packed amorphous polymer chains allow oxygen to diffuse inside,endowing the doped polymers with oxygen sensitivity.Flexible and transparent polymer films exhibited an impressive ultralong RTP lifetime of 2.57 s at room temperature in vacuum,which was among the best performance of PMMA.Intriguingly,their RTP was rapidly quenched in the presence of oxygen.Furthermore,RTP microparticles with a diameter of 1.63μm were synthesized using in situ dispersion polymerization technique.Finally,oxygen sensors for quick,visual,and quantitative oxygen detection were developed based on the RTP microparticles through phosphorescence lifetime and image analysis.With distinctive advantages such as an ultralong lifetime,oxygen sensitivity,ease of fabrication,and cost-effectiveness,PCSP opens a new avenue to sensitive materials for oxygen detection.
基金support from the National Natural Science Foundation of China(9235630033,22105069)Shanghai Pujiang Program(20PJ1402900)+2 种基金Shanghai Natural Science Foundation(21ZR1418400)Innovation Program of Shanghai Municipal Education Commission(2023FGS01)Natural Science Foundation of Jiangsu Province(BK20231225).
文摘Icing detection is critically important for preventing safety accidents and economic losses,especially concerning ice formation from invalidated anti-icing fluids(water and ethylene glycol)under extreme conditions.Traditional technologies like ultrasonics and capacitor-antenna face challenges with limited detection areas,lower accuracy,and susceptibility to electromagnetic interference.Here,we introduce a novel viscosity-ultrasensitive fluorescent probe 40,4‴-(2,2-diphenyle-thene-1,1-diyl)bis-(3,5-dicarboxylate)(TPE-2B4C)based on AIEgens for moni-toring ice formation of anti-icing fluids in low-temperature environments.TPE-2B4C,consisting of four sodium carboxylate groups and multiple freely rotating benzene rings,demonstrates outstanding solubility in anti-icing fluids and exhibits no fluorescent background signal even at low temperatures(<−20°C).Upon freezing,TPE-2B4C relocates from the water phase to higher viscosity ethylene glycol,causing restriction of benzene rings and a significantly increased green fluorescence signal.TPE-2B4C can successfully determine whether the anti-icing fluids are icing from−5 to−20°C with a high contrast ratio.Due to its simple setup,fast operation,and broad applicability,our new method is anticipated to be employed for rapid,real-time,and large-scale icing detection.
基金supported by the Trade Union Employee Innovation Foundation of China(2022270024).
文摘In order to enhance the efficiency of visual inspection and effectively carry out 3D visual coverage tasks,this paper focuses on the 3D view planning problem concerning the visual coverage of an airplane's surface using unmanned aerial vehicles(UAv).Our objective is to attain a sufficiently high coverage rate with the least number of viewpoints.The contributions of this work are enumerated as follows.Firstly,the 3D model of the target aircraft is spatially extended in accordance with the depth range of the camera mounted on the drone,thereby confining the sampling range of 3D viewpoints.Next,a candidate set of viewpoints is generated through random sampling and the probabilistic potential field technique.Subsequently,we propose a novel hyper-heuristic algorithm.In this algorithm,a genetic algorithm serves as a high-level heuristic strategy,in tandem with multiple low-level heuristic operators devised for combinatorial optimization.This not only augments the capacity to seek the global optimal solution but also expedites the convergence rate,aiming to ascertain the optimal subset of viewpoints.Moreover,we devise a new fitness function for appraising candidate solution vectors in the set covering problem(ScP),strengthening the evolutionary guidance for genetic algorithms.Eventually,experimental findings on the simulated and real airplanes corroborate the efficacy of the proposed method,i.e.,it markedly diminishes the requisite number of viewpoints and augments inspection efficiency.
基金supported by the National Natural Science Foundation of China (No.21976211).
文摘Norfloxacin is widely used owing to its strong bactericidal effect on Gram-negative bacteria.However,the residual norfloxacin in the environment can be biomagnified via food chain andmay damage the human liver and delay the bone development ofminors.Present work described a reliable and sensitive smartphone colorimetric sensing system based on cobaltdoped Fe_(3)O_(4) magnetic nanoparticles(Co-Fe_(3)O_(4) MNPs)for the visual detection of norfloxacin.Compared with Fe_(3)O_(4),Co-Fe_(3)O_(4) MNPs earned more remarkably peroxidase-like activity and TMB(colorless)was rapidly oxidized to oxTMB(blue)with the presence of H_(2)O_(2).Interestingly,the addition of low concentration of norfloxacin can accelerate the color reaction process of TMB,and blue deepening of the solution can be observed with the naked eye.However,after adding high concentration of norfloxacin,the activity of nanozymewas inhibited,resulting in the gradual fading of the solution.Based on this principle,a colorimetric sensor integrated with smartphone RGB mode was established.The visual sensor exhibited good linearity for norfloxacin monitoring in the range of 0.13-2.51μmol/L and 17.5-100μmol/L.The limit of visual detectionwas 0.08μmol/L.In the actualwater sample analysis,the spiked recoveries of norfloxacin were over the range of 95.7%-104.7%.These results demonstrated that the visual sensor was a convenient and fast method for the efficient and accurate detection of norfloxacin in water,which may have broad application prospect.
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funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
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This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
