Detection Method Research Articles

Structural damage detection based on structural macro-strain mode shapes extracted from non-stationary output responses

Abstract Long-gauge fiber Bragg grating strain sensors have been widely employed because of their broader measuring range and higher sensitivity. However, current structural damage detection methods using macro-strain modal parameters are based on structural frequency response function or stationary power spectrum density, which are not applicable to non-stationary responses. To overcome this limitation, an improved method is proposed in this paper for structural damage detection based on structural macro-strain responses under unknown multi-point non-stationary excitations. First, a new concept of macro-strain energy spectrum transmissibility (MEST) is proposed using structural non-stationary macro-strain responses, and it is derived that MEST at a certain system pole equals the ratio of macro-strain mode shape. Then, the singular value decomposition technique is adopted for the MEST matrix to identify structural natural frequencies and macro-strain mode shapes. Finally, two damage detection indicators are constructed based on the identified normalized macro-strain mode shape (NMMS). The first indicator is the difference in structural NMMS before and after structural damage. The second one is based on the curvatures of structural NMMS, which can be used for structures without intact baseline. Numerical verifications are conducted to identify beam-type structural damage under multi-point non-stationary excitations or vehicle loads. Five damage scenarios with different measurement noise levels are investigated, and damage detection results validate the effectiveness of the proposed method.

Fraud Detection in Bank Loans with KYC Verification System

In today's technologically-driven financial landscape, bank loan fraud poses a significant threat to both financial institutions and their customers. While traditional methods of fraud detection have been moderately effective, the increasing sophistication of fraudsters necessitates the implementation of more advanced measures. This research proposes an integrated system for bank loan fraud detection that leverages the robustness of Know Your Customer (KYC) verification. By combining traditional KYC processes with advanced machine learning algorithms, this system seeks to provide a more comprehensive approach to detecting and preventing fraudulent loan applications. Initial results indicate a marked reduction in successful fraud attempts, as well as a decrease in false positives compared to conventional systems. Furthermore, the integrated system offers enhanced customer experience by streamlining the loan application process, reducing verification times, and ensuring greater security of personal data. As financial institutions continue to grapple with the challenges of fraud, this research underscores the importance of integrating traditional verification methods with cutting-edge technological solutions for optimal results

Highly Sensitive and Selective Detection of L-Tryptophan by ECL Using Boron-Doped Diamond Electrodes.

L-tryptophan is an amino acid that is essential to the metabolism of humans. Therefore, there is a high interest for its detection in biological fluids including blood, urine, and saliva for medical studies, but also in food products. Towards this goal, we report on a new electrochemiluminescence (ECL) method for L-tryptophan detection involving the in situ production of hydrogen peroxide at the surface of boron-doped diamond (BDD) electrodes. We demonstrate that the ECL response efficiency is directly related to H2O2 production at the electrode surface and propose a mechanism for the ECL emission of L-tryptophan. After optimizing the analytical conditions, we show that the ECL response to L-tryptophan is directly linear with concentration in the range of 0.005 to 1 µM. We achieved a limit of detection of 0.4 nM and limit of quantification of 1.4 nM in phosphate buffer saline (PBS, pH 7.4). Good selectivity against other indolic compounds (serotonin, 3-methylindole, tryptamine, indole) potentially found in biological fluids was observed, thus making this approach highly promising for quantifying L-tryptophan in a broad range of aqueous matrices of interest.

Reference Material Production and Milk Protein Concentration as Elements to Improve Bluetongue Serological Diagnosis in Bulk Tank Milk.

The serological surveillance of bluetongue in bulk tank milk is an efficient and cost-effective method for the early detection of bluetongue virus incursions in unvaccinated free areas of the disease. In addition, the availability of standardized and reliable reagents and refined diagnostic procedures with high sensitivity and specificity are essential for surveillance purposes. However, no available reference materials for bluetongue virus serological surveillance in bulk tank milk exist. This study shows the production and characterization of reference material for the implementation of a commercially available bluetongue milk ELISA test in official laboratories, as well as the evaluation of a procedure to increase the sensitivity in samples with low levels of antibodies. This procedure, based on milk protein concentration, allowed us to notably increase the ELISA test's analytical sensitivity, which is useful for milk samples from farms with low within-herd prevalence or pools of bulk tank milk samples. The standardized milk reference material produced here, together with the evaluated procedure to improve analytical sensitivity, could be applied as tools to ensure an accurate diagnosis by official laboratories in bluetongue unvaccinated free areas.

A comparative evaluation of five phenotypic methods for identification of carbapenemase-producing Enterobacteriaceae: a modified carbapenemase detection test.

Rapid detection of carbapenemase-producing Enterobacteriaceae (CPE) is urgently needed to prevent their spread in healthcare settings. Here, we have evaluated the performance of the phenotypic methods for detection of carbapenemase production directly from bacterial cultures. A total of 99 clinical and rectal Enterobacteriaceae isolates were included (81 carrying known carbapenemase-encoding genes and 18 without carbapenemase production). All isolates were subjected to the five phenotypic tests including in-house Carba NP (iCarba NP), modified-Carba NP, E-Test MBL, modified Hodge test (MHT), and commercial combination disk test. Test results were read at different time points for iCarba NP and modified-Carba (1 min, 5 min, 15 min, 1 h and 2 h). The sensitivity and specificity of the iCarba NP were 78.87% and 100%, respectively, whereas those of the modified-Carba NP test were 95.06% and 94.44%, respectively. False-negative results were detected in four OXA-48 isolates with the use of modified-Carba NP, whereas one non-carbapenemase isolate had false-positive results. The sensitivity/specificity was 91.30%/100% and 80.25%/83.33% for the E-Test MBL and MHT, respectively. The sensitivity and specificity of the aminophenylboronic acid synergy test were 100% and 97.94%, respectively, whereas those of the dipicolinic acid synergy test were 82.61% and 96.23%, respectively. Rapid, simple, and reliable methods are needed for laboratory detection of CPE isolates to improve the detection and surveillance of these clinically relevant pathogens in an epidemiological context. We conclude that the modified-Carba NP test can be one of the reliable tests for the prediction of carbapenemase-producing bacteria.IMPORTANCEThe emergence of carbapenem resistance among Gram-negative bacteria is a serious global health threat. Here, we investigate the performance of the five phenotypic assays against carbapenemase-producing and carbapenemase-non-producing Enterobacteriaceae. Accurate and rapid detection of CPE isolates is critically required for clinical management and treatment of infections caused by these organisms. Among the five evaluated phenotypic tests, the mCNP test presented the highest sensitivity (95.06%) and, therefore, can be considered the best test to be used as a screening phenotypic methodology.

One-Step Synthesis of Chitosan Hydrogel as Electrochemical Chemosensor for Hydrogen Sulfide Detection in Pregnancy-Induced Hypertension Syndrome Serum Sample.

Oxidative stress caused by pregnancy-induced hypertension syndrome significantly affects the health of pregnant women. Hydrogen sulfide is a typical gaseous signal molecule against oxidative stress, and it is of profound significance to develop a detection method. In this study, a stimuli-responsive hydrogel was constructed based on the coordination and bonding principle of metal ions and chitosan (CS) to realize the quantitative detection of hydrogen sulfide (H2S). The chain of CS contains a large number of amino groups and hydroxyl groups, which can form the coordination structure with Cu2+, triggering CS to form a stable hydrogel. The hydrogel can be formed within about 5 s, which has the characteristics of rapid preparation. In the presence of target H2S, the cross-linking agent Cu2+ in the hydrogel can compete out, resulting in the collapse of the hydrogel and the release of the electrochemical probe. By detecting the concentration of the released electrochemical probe, the quantitative detection of H2S can be achieved. The prepared hydrogel has a good linear relationship with the concentration of H2S from 1 μM to 60 μm. At the same time, the hydrogel has good specificity and stability, and it can be applied to the detection of H2S in serum samples.

Detection of Gene Doping Using Dried Blood Spots from a Mouse Model with rAAV9 Vector-Mediated Human Erythropoietin Expression as a Pilot Study

Rapid advancements in gene technology have raised concerns regarding the potential abuse of techniques, such as gene doping, for enhancing athletic performance. To identify this possibility, a reliable procedure for detecting doping genes is required. Although detection methods for doping genes have been created, there are still areas for further improvement. One significant challenge is the high storage and transport costs of the test samples. For this issue, the dried blood spot (DBS) method can be a cost-effective solution. This study aimed to assess the practicality of incorporating DBSs into the gene doping detection process as a pilot study. Whole-blood samples were initially collected from mice engineered to express human erythropoietin from the rAAV vector. Then, the blood was placed in filter papers and left to dry at room temperature for five hours to form DBSs. These DBSs were subsequently preserved in sealed plastic bags at room temperature. After the extraction of DNA, DBSs were formed, and TaqMan-qPCR was utilized to detect the presence of rAAV vector-derived DNA. The finding confirmed that doping gene-specific fragments were successfully detected in DBSs. This outcome suggests that the DBS method is an effective approach to be considered when developing a comprehensive protocol for gene doping detection.

Efficiently localizing system anomalies for cloud infrastructures: a novel Dynamic Graph Transformer based Parallel Framework

Cloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.

Superior oxidase-mimetic activity of FeCo-NC dual-atom nanozyme for smartphone-based visually colorimetric assay of organophosphorus pesticides.

A colorimetric analysis platformhas been successfully developedbased on FeCo-NC dual-atom nanozyme (FeCo-NC DAzyme) for the detection of organophosphorus pesticides (OPPs). The FeCo-NC DAzyme exhibited exceptional oxidase-like activity (OXD), enabling the catalysis of colorless TMB to form blue oxidized TMB (oxTMB) without the need for H2O2 involvement. By combining acid phosphatase (ACP) hydrolase with FeCo-NC DAzyme, a "FeCo-NC DAzyme + TMB + ACP + SAP" colorimetric system was constructed, which facilitated the rapid detection of malathion. Thechromogenic system was appliedto detect malathion using a smartphone-based app and an auxiliary imaging interferogram device for colorimetric measurements, which have a linear range of 0.05-4.0 µM and a limit of detection (LOD) as low as 15 nM in real samples, comparable to UV-Vis and HPLC-DAD detection methods. Overall, these findings present a novel approach for convenient, rapid, and on-site monitoring of OPPs.

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

Recognition and analysis system of steel stamping character based on machine vision

During the packaging process, it is essential to detect the steel stamping characters inside the box to identify any missing or repeated characters. Currently, manual detection suffers from low efficiency and a high false detection rate. To address these challenges, a steel stamping character recognition and analysis system based on machine vision has been developed. The enhanced YOLOv7 detection method was employed for character identification, complemented by a statistical analysis approach to achieve automated judgment and detection. To address the issue of size disparity between large and small characters, a small size anchor box and a larger detection head were integrated. Furthermore, modifications were made to the output structure of the YOLOv7 prediction network to enhance multi-scale detection capabilities. The inclusion of the location attention convolution module bolstered global feature extraction, thereby enhancing the detection accuracy of similar characters. Moreover, the utilization of a hash table was used to improve the efficiency of mapping steel stamping character recognition sequences. The experimental results demonstrate that the enhanced model achieves an accuracy of 99.83%, with a processing efficiency of 10.5 ms per single frame. These findings align with the performance criteria for automatic recognition and analysis of steel stamping characters.

PARP14 and PARP9/DTX3L regulate interferon-induced ADP-ribosylation.

PARP-catalysed ADP-ribosylation (ADPr) is important in regulating various cellular pathways. Until recently, PARP-dependent mono-ADP-ribosylation has been poorly understood due to the lack of sensitive detection methods. Here, we utilised an improved antibody to detect mono-ADP-ribosylation. We visualised endogenous interferon (IFN)-induced ADP-ribosylation and show that PARP14 is a major enzyme responsible for this modification. Fittingly, this signalling is reversed by the macrodomain from SARS-CoV-2 (Mac1), providing a possible mechanism by which Mac1 counteracts the activity of antiviral PARPs. Our data also elucidate a major role of PARP9 and its binding partner, the E3 ubiquitin ligase DTX3L, in regulating PARP14 activity through protein-protein interactions and by the hydrolytic activity of PARP9 macrodomain 1. Finally, we also present the first visualisation of ADPr-dependent ubiquitylation in the IFN response. These approaches should further advance our understanding of IFN-induced ADPr and ubiquitin signalling processes and could shed light on how different pathogens avoid such defence pathways.

A detection method for road network interchanges with the MeshCNN based on Delaunay triangulation

ABSTRACT Due to their unstructured characteristics, mature convolutional neural network (CNN) models often have difficulty performing spatial analysis with vector data. Current studies used graph neural network (GCN) models to address this problem. However, the definition of cognition factors involves uncertainties, making it challenging to accurately and comprehensively define these factors. In this paper, the road interchange detection task is taken as an example to introduce the MeshCNN, a deep learning model based on triangular mesh data, aiming to provide a new solution for spatial analysis of vector data. A triangular edge classification model is first trained with simple input features. Then, interchanges are detected based on the classification results with an adaptive method. Experiments were conducted on real-world road network data from four cities. The results reveal that the proposed method outperformed the existing methods with precision and recall rate of 89.36% and 79.25% for interchange detection on the total datasets. Furthermore, our proposed method can also detect interchanges in other regions more easily than the GCN method.

Emerging MXene-based electrocatalysts for efficient nitrate reduction to ammonia: recent advance, challenges, and prospects

Ammonia (NH3) plays an irreplaceable role in traditional agriculture and emerging renewable energy. Its preparation in industry mainly relies on the energy-intensive Haber-Bosch process, which is associated with high energy consumption and large CO2 emissions. Recently, the nitrate reduction reaction (NO3-RR) driven by renewable energy has received extensive attention. This reaction can efficiently synthesize NH3 with water as a hydrogen source and NO3- as a nitrogen source under mild conditions, which is conducive to reducing energy consumption and promoting the carbon cycle. It is well known that the properties of electrocatalysts determine the performance of NO3-RR. As an emerging two-dimensional material, MXenes (transition metal carbides/nitrides/carbon nitrides) possess excellent electrical conductivity, large specific surface area and controllable surface functional groups, which shows great application potential in the field of NO3-RR. Herein, this review summarized the structure, properties and synthesis strategies of MXenes to elucidate the possibilities from foundation to application. Then, the latest research progress in applying MXene-based electrocatalysts to NO3-RR was summarized and the applicability of different NH3 detection methods was analyzed. Finally, the present challenges and future prospects of NO3-RR were presented. This review aimed to provide thoughtful insights into the rational design of MXene-based electrocatalysts for sustainable NH3 synthesis.

Crack Detection of Curved Surface Structure Based on Multi-Image Stitching Method

The crack detection method based on image processing has been a new achievement in the field of civil engineering inspection in recent years. Column piers are generally used in bridge structures. When a digital camera collects cracks on the pier surface, the loss of crack dimension information leads to errors in crack detection results. In this paper, an image stitching method based on Speed-Up Robust Features (SURFs) is adopted to stitch the surface crack images captured from different angles into a complete crack image to improve the accuracy of the crack detection method based on image processing in curved structures. Based on the proposed method, simulated crack tests of vertical, inclined, and transverse cracks on five different structural surfaces were conducted. The results showed that the influence of structural curvature on the measurement results of vertical cracks is very small and can be ignored. Nevertheless, the loss of depth information at both ends of curved cracks will lead to errors in crack measurement outcomes, and the factors that affect the precision of crack detection include the curvature of the surface and the length of the crack. Compared with inclined cracks, the structural curvature significantly influences the measurement results of transverse cracks, especially the length measurement results of transverse cracks. The image stitching method can effectively reduce the errors caused by the structural curved surface, and the stitching effect of three images is better than that of two images.

Tire defect detection based on low and high-level feature fusion

Recently, object detection based on deep learning has made great progress in the field of defect detection. Due to its complex texture background and varied defect features, existing defect detection methods based on object detection face great challenges in the detection of tire internal defects. In this paper, a tire defect detection model based on low and high-level feature fusion is proposed. First, a multi-head feature extraction module is proposed, which extracts abundant effective information from multiple dimensions. Second, a spatial semantic fusion upsampling module is proposed to alleviate the problem of information loss in the upsampling process. Finally, a novel prediction head is designed to expand the receptive field by compressing the size of the feature map to improve the detection accuracy of large defects. Experimental results show that the mAP of the proposed method achieves 94.03% on the tire internal defect dataset, and the average detection time is 36.74 ms, which can meet the needs of industrial online detection.

Unraveling minnan imagery: a comprehensive analysis of traditional and modern minnan nursery rhymes through complex networks

Nursery rhymes provide insights into the traditions, beliefs, and values of a culture, thereby making it an integral part of a community’s heritage. As representative linguistic resources within the intangible cultural heritage of the Hoklo people, Minnan nursery rhymes (MNRs) play a crucial role in connecting the Chinese mainland, Taiwan Strait, and overseas Chinese communities. This study delves into features of 617 traditional and 289 modern pieces through text mining techniques, including text segmentation, the TF-IDF (term frequency-inverse document frequency) method, and the complex network analysis. We examine the frequency and emotional purity of lyrics at a larger scale than previous studies using a small set of manually annotated samples. Furthermore, we analyze the patterns of MNRs by assessing the overall, individual, core-periphery structures of the constructed MNR networks, considering key terms as nodes and co-occurrence relationships between nodes as links. Our investigation reveals the heterogeneous nature of terms in both traditional and modern MNR networks. Moreover, through the community detection method, we identify five primary imagery features presented in MNRs. Traditional MNRs place emphasis on family relationships, folk culture, and food culture, reflecting enduring aspects of Minnan cultural heritage. In contrast, modern MNRs pivot towards themes of children’s emotions and natural scenery, indicative of evolving societal values. This study represents the first large-scale complex network analysis of MNRs, providing valuable insights into the embedded Minnan culture and serving as a foundation for further research into the societal dynamics reflected in these cherished MNRs resources.

Topological Edge State Ring Resonator for Mid-Infrared(MI) Refractive Indices Biosensor for detection of Brain Tumors

The unique properties of light underlie the perspectives of quantum photonic technologies, optical interconnects, and a wide range of new sensors.Some of the most dangerous and deadly diseases are tumors, cancers, and brain lesions, which are expensive to detect and treat. Therefore, a low-cost and accurate method to diagnose them can prevent and treat the progress of this disease. In this work, we used the precise topological valley photonic crystal (TVPC) method for detection. TVPC is an important method for transmitting and controlling light in the optical device. The valley-spin locking in the topology state provides robust transfer and low propagation loss at the desired path.In order to design, by finding the edge band within the first bulk band gap of 146.4 THz–155.9 THz, the model and design of the topological ring resonator (TRS)were realized. We proposed a topological biosensorbyusing the hexagonal lattice air holes in the silicon slab with a compact size of 22.95 μm × 10 μm. the quality factor and sensitivity at best value are 2.905 × 104 and 9021 nm RIU−1 respectively. This design can be implemented on the Complementary Metal-Oxide-Semiconductor (CMOS) technology as a high-sensitivity optical device.

SVulDetector: Vulnerability detection based on similarity using tree-based attention and weighted graph embedding mechanisms

Vulnerability detection by comparing similarities with known vulnerable code is an important method for improving code security, and is particularly effective in detecting vulnerabilities caused by code reuse. However, vulnerability detection is made difficult by the existence of some different and vulnerability-unrelated statements between codes with the same vulnerability pattern, as well as the small differences between vulnerable and fixed non-vulnerable codes. To address these challenges, we believe that more attention needs to be paid to some core syntactic and semantic information about vulnerabilities, which can help models more accurately identify vulnerable code. Hence, we propose a novel code-similarity-based vulnerability detection approach named SVulDetector. First, it contains a code representation, called Slice Composite Graphs (SCGs), which can represent rich syntactic and semantic information related to vulnerable statements while minimizing the interference from similar vulnerability irrelevant information as much as possible. Next, a tree-based attention mechanism is used to highlight certain key syntactic information in vulnerable code and fixed non-vulnerable code. Finally, SVulDetector highlights key vulnerable node information in the graph-based code representation via a weighted graph embedding mechanism. We extensively evaluated SVulDetector on an improved real-world dataset using both two-class and multi-class vulnerability detection tasks, and the proposed SVulDetector outperforms existing state-of-the-art detection methods.

Snow‐ or ice‐covered road detection in winter road surface conditions using deep neural networks

AbstractTraffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method.