MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

Development of TaqMan-based Real-time PCR Assay for Detecting Transmissible Gastroenteritis Virus and Its Application in Vaccine Evaluation

[Objective] This study aimed to establish a TaqMan-based real-time PCR assay for detecting transmissible gastroenteritis virus （TGEV）. [Method] Primers and a probe were designed according to the conserved sequence of N gene in TGEV genome. After gradient dilution, the recombinant plasmid harboring the N gene was used as a standard for real-time PCR assay to establish the standard curve. [Re- sult] The results showed that the established real-time PCR assay exhibited a good linear relationship within the range of 102-10^10 copies/ul; the correlation coefficient was above 0.99 and the amplification efficiency ranged from 90% to 110%. The de- tection limit of real-time PCR assay for TGEV was 10 copies/μl, suggesting a high sensitivity; there was no cross reaction with other porcine viruses, indicating a good specificity; coefficients of variation within and among batches were lower than 3%, suggesting a good repeatability. The established real-time PCR method could be ap- plied in quantitative analysis and evaluation of the immune efficacy of TGEV vac- cines and detection of TGEV in clinical samples. [Conclusion] The TaqMan-based real-time PCR assay established in this study is highly sensitive and specific, which can provide technical means for the epidemiological survey of TGEV, development of TGEV vaccines and investigation of the pathogenesis of TGE.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Establishment and Application of a Real-time PCR Method for Detecting stx2 Gene in Shiga Toxin-producing Escherichia coli(STEC)

[Objective] This study aimed to establish a real-time PCR method for de- tecting stx2 gene in Shiga toxin-producing E. coli （STEC）. [Method] According to the known STEC stx2 gene sequences published in GenBank, PCR primers and probes were designed based on the conserved region to construct recombinant plasmid as a positive template, thus optimizing the reaction conditions and establishing the real- time PCR method. [Result] A standard curve was established based on the opti- mized real-time PCR system, indicting a good linear correlation between the initial template concentration and Ct value, with the correlation coefficient F^e of above 0.995. The established method had a good specificity, without non-specific amplifica- tion for 10 non-STEC intestinal bacterial strains; the detection limit of initial template was 1.0x102 copies/μI, indicating a high sensitivity; furthermore, the coefficients of variation within and among batches were lower than 1% and 5% respectively, sug- gesting a good repeatability. [Conclusion] In this study, a real-time PCR method was successfully established for detecting STEC stx2 gene, which provided technical means for rapid detection of STEC in samples.

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

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.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Procalcitonin and presepsin for detecting bacterial infection and spontaneous bacterial peritonitis in cirrhosis:A systematic review and meta-analysis

BACKGROUND Diagnosing bacterial infections(BI)in patients with cirrhosis can be challenging because of unclear symptoms,low diagnostic accuracy,and lengthy culture testing times.Various biomarkers have been studied,including serum procal-citonin(PCT)and presepsin.However,the diagnostic performance of these markers remains unclear,requiring further informative studies to ascertain their diagnostic value.AIM To evaluate the pooled diagnostic performance of PCT and presepsin in detecting BI among patients with cirrhosis.INTRODUCTION Bacterial infections(BI)commonly occur in patients with cirrhosis,resulting in poor outcomes,including the development of cirrhotic complications,septic shock,acute-on-chronic liver failure(ACLF),multiple organ failures,and mortality[1,2].BI is observed in 20%-30%of hospitalized patients,with and without ACLF[3].Patients with cirrhosis are susceptible to BI because of internal and external factors.The major internal factors are changes in gut microbial composition and function,bacterial translocation,and cirrhosis-associated immune dysfunction syndrome[4,5].External factors include alcohol use,proton-pump inhibitor use,frailty,readmission,and invasive procedures.Spontaneous bacterial peritonitis(SBP),urinary tract infection,pneumonia,and primary bacteremia are the common BIs in hospit-alized patients with cirrhosis[6].Early diagnosis and adequate empirical antibiotic therapy are two critical factors that improve the prognosis of BI in patients with cirrhosis.However,early detection of BI in cirrhosis is challenging due to subtle clinical signs and symptoms,low sensitivity and specificity of systemic inflammatory response syndrome criteria,and low sensitivity of bacterial cultures.Thus,effective biomarkers need to be identified for the early detection of BI.Several biomarkers have been evaluated,but their efficacy in detecting BI is unclear.Procalcitonin(PCT)is a precursor of the hormone calcitonin,which is secreted by parafollicular cells of the thyroid gland[7].In the presence of BI,PCT gene expression increases in extrathyroidal tissues,causing a subsequent increase in serum PCT level[8].Changes in serum PCT are detectable as early as 4 hours after infection onset and peaks between 8 and 24 hours,making it a valuable diagnostic biomarker for BI.Several studies have demonstrated the favorable diagnostic accuracy of PCT in the diagnosis of BI in individuals with cirrhosis[9-13]and without cirrhosis[14-16].Since 2014,two meta-analyses have been published on the diagnostic value of PCT for SBP and BI in patients with cirrhosis[17,18].Other related studies have been conducted since then[10-12,19-33].Serum presepsin has recently emerged as a promising biomarker for diagnosing BI.This biomarker is the N-terminal fraction protein of the soluble CD14 g-negative bacterial lipopolysaccharide–lipopolysaccharide binding protein(sCD14-LPS-LBP)complex,which is cleaved by inflammatory serum protease in response to BI[34].Presepsin levels increase within 2 hours and peaks in 3 hours[35].This is useful for detecting BI since presepsin levels increase earlier than serum Our systematic review and meta-analysis was performed with adherence to PRISMA guidelines[37].

FPGA implementation of 500-MHz high-count-rate high-time-resolution real-time digital neutron-gamma discrimination for fast liquid detectors

Fast neutron flux measurements with high count rates and high time resolution have important applications in equipment such as tokamaks.In this study,real-time neutron and gamma discrimination was implemented on a self-developed 500-Msps,12-bit digitizer,and the neutron and gamma spectra were calculated directly on an FPGA.A fast neutron flux measurement system with BC-501A and EJ-309 liquid scintillator detectors was developed and a fast neutron measurement experiment was successfully performed on the HL-2 M tokamak at the Southwestern Institute of Physics,China.The experimental results demonstrated that the system obtained the neutron and gamma spectra with a time accuracy of 1 ms.At count rates of up to 1 Mcps,the figure of merit was greater than 1.05 for energies between 50 keV and 2.8 MeV.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

A YOLOv8-CE-based real-time traffic sign detection and identification method for autonomous vehicles

Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Advancing PCB Quality Control:Harnessing YOLOv8 Deep Learning for Real-Time Fault Detection

Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control processes.Given the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system integrity.To address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in PCBs.Specifically,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 Nano.Through rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing process.Our results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults.The model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of 0.98.These findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Real-Time Method for Detecting Harmonic and Reactive Currents of Single-Phase Circuits

According to the characteristics of single-phase circuits and demand of using active filter for real-time detecting harmonic and reactive currents, a detecting method based on Fryze＇s power definition is proposed. The results of theoretical analysis and simula- tion show that the proposed method is effective in realtime detecting of instantaneous harmonic and reactive currents in single-phase circuits. When only detecting the total reactive currents, this method does not need a phase-locked loop circuit, and it also can be used in some special applications to provide different compensations on the ground of different requirements of electric network. Compared with the other methods based on the theory of instantaneous reactive power, this method is simple and easy to realize.

Application of a real-time PCR method for detecting and monitoring hookworm Necator americanus infections in Southern China

Objective:To develop a quautitative PCR method for detecting hookworm infection and quantification.Methods:A real-time PCR method was designed hased on the intergenic regionⅡof ribosomal DNA of the hookworm Neeator americanus.The deteetion limit of this method was compared with the microscopy-hased Kato-Katz method.The real-time PCR method was used to conduct an epidemiological survey of hookworm infection in southern Fujian Province of China.Result:The real-time PCR method was specific for detecting Necator americanus infection,and was more sensitive than conventional PCR or microscopy-based method.A preliminary survey for hookworm infection in villages of Fujian Province confirmed the high prevalence of hnokworm infections in the resident populations.In addition,the infection rate in women was significantly higher than thai of in men.Conclusions:A real-time PCR method is designed,which has increased detection sensitivity for more accurate epidemiological studies of hookworm infections,especially when intensity of the infection needs to he considered.

TaqMan Real-time RT-PCR Assay for Detecting and Differentiating Japanese Encephalitis Virus

Objective To detect Japanese encephalitis virus（JEV） rapidly and distinguish its genotypes, a TaqMan-based reverse transcriptase quantitative polymerase chain reaction（RT-PCR） detection system was developed.Methods By aligning the full-length sequences of JEV（G1-G5）, six sets of highly specific TaqMan real-time RT-PCR primers and probes were designed based on the highly conserved NS1, NS2, and M genes of JEV, which included one set for non-specific JEV detection and five sets for the detection of specific JEV genotypes. Twenty batches of mosquito samples were used to evaluate our quantitative PCR assay.Results With the specific assay, no other flavivirus were detected. The lower limits of detection of the system were 1 pfu/mL for JEV titers and 100 RNA copies/μL. The coefficients of variation of this real-time RT-PCR were all 〈 2.8%. The amplification efficiency of this method was between 90% and 103%.Conclusion A TaqMan real-time RT-PCR detection system was successfully established to detect and differentiate all five JEV genotypes.

Multiplex real-time RT-PCR for detecting chikungunya virus and dengue virus

Objective:To develop diagnostic test for detection chikungunya virus(CHIKV and Dengue virus (DENV) infection.Methods:We have performed a rapid,accurate laboratory confirmative method to simultaneously detect,quantify and differentiate CHIKV and DENV infection by single-step multiplex real-time RT-PCR.Results:The assay’s sensitivity was 97.65%,specificity was 92.59% and accuracy was 95.82%when compared to conventional RT-PCR.Additionally,there was no cross-reaction between CHIKV,DENV,Japanese encephalitis virus,hepatitis C,hepatitis A or hepatitis E virus.Conclusions:This rapid and reliable assay provides a means for simultaneous early diagnosis of CHIKV and DENV in a single-step reaction.

A Comparison Between Northern Blotting and Quantitative Real-Time PCR as a Means of Detecting the Nutritional Regulation of Genes Expressed in Roots of Arabidopsis thaliana

Quantitative real-time PCR （qRT-PCR） has become a routine and robust technique for measuring the expression of genes of interest, validating microarray experiments and monitoring biomarkers. However, concerns have been raised over the accuracy of qRT-PCR in China as well as in the rest of the world. We have previously used qRT-PCR to study the response of ANR1 and other root-expressed MADS-box genes to fluctuations in the supply of nitrate, phosphate and sulphate under hydroponic growth conditions. In this study, we have used both Northern blotting and qRT-PCR analyses to confirm the nutritional regulation of MADS-box genes in Arabidopsis thaliana and test whether both technologies produce the same results. The information obtained indicated that the qRT-PCR results are consistent with those obtained by Northern blotting hybridization for all the tested root-expressed MADS-box genes, in response to different nitrate, phosphate and sulphate growth conditions. Furthermore, our novel results showed that the expressions of AGL12, AGL18, and AGL19 were all down regulated in response to S and P re-supply in both qRT-PCR and Northern blotting analyses.

A novel real-time RT-PCR with TaqM an-MGB probes and its application in detecting BVDV infections in dairy farms

A real-time RT-PCR assay using Taq Man-MGB probes was developed to detect and type the bovine viral diarrhea virus（BVDV） in cattle.Universal primers and Taq Man-MGB probes were designed from the 5′-untranslated region of known pestiviral sequences.Prior to optimizing the assay, c RNAs were transcribed in vitro from the BVDV 1 and BVDV 2 RTPCR products to make standard curves.The detection limit of the assay was 1.72×102 copies for BVDV 1 and 2.14×102copies for BVDV 2.The specificity of the assay evaluated on several BVDV strains including bovine herpesvirus 1（BHV 1）, foot and mouth disease virus（FMDV） and several classical swine fever virus（CSFV） strains showed specific detection of the positive virus over 40 cycles.The assay was highly reproducible with the coefficient of variance ranging from 1.04 to 1.33% for BVDV 1 and from 0.83 to 1.48% for BVDV 2, respectively.Using this method, we tested a total of 2 327 cattle from three dairy farms for the presence of BVDV persistently infected（PI） animals.In this assay, each RT-PCR template contained a mixture of ten samples from different animals.The occurrence rate of PI cattle in three farms ranging from 0.9 to 2.54% could represent partly the PI rates in cattle farm in China.In conclusion, using our real-time PCR assay, we could effectively detect and type BVDV and identify PI cattle in a rapid and cost-effective manner.