Traditional Detection Methods Research Articles

Weighted spectral correlation angle target detection method for land-based hyperspectral imaging

In land-based spectral imaging, the spectra of ground objects are inevitably affected by the imaging conditions (weather conditions, atmospheric conditions, light conditions, zenith and azimuth angle conditions) and spatial distribution of targets, leading to uncertainties featured by “same object different spectrum”. That is, the spectrum of a ground object may change within a certain range under different imaging conditions. Traditional target detection (TD) methods are mainly based on similarity measurements and do not fully account for the spectral uncertainties. These detection methods are prone to false detections or missed detections. Therefore, reducing the impact of spectral uncertainties on TD is an important research topic in hyperspectral imaging. In this paper, we first review traditional TD methods and compare their principles and characteristics. It is found that the spectral correlation angle (SCA) method has good adaptability in land-based imaging. The shortcoming of the SCA method that it cannot reflect the local spectrum characteristics, is also analyzed. As the effect of spectral uncertainties cannot be completely overcome by the SCA method, a new similarity measurement method, the weighted spectral correlation angle (WSCA) method, is proposed. It can reduce the influence of spectral uncertainties on TD by increasing the weight of particular bands. Finally, we use two sets of experiments to analyze the effect of the WSCA method on TD. Its performance in overcoming spectral uncertainties caused by variations in imaging conditions or uneven spatial distributions of targets is tested. The results show that the WSCA method can effectively reduce the influence of spectral uncertainties and obtain a good detection result.Graphical

Intensive Malware Detection Approach based on Data Mining

Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. While malware has always been a problem in the digital age, its effects have gotten increasingly serious. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful codes and can be located using traditional signature- and rule-based detection methods. The research focuses on developing malware detectors using data mining techniques. The proposed method outlined below sets itself apart by emphasizing the processing of malware behaviors significantly dependent on aspects. Finding more dependable intelligent detecting techniques is a crucial component of this paper. In order to identify the cluster of the most essential malware features and use decision tree classifiers for malware detection, the study, a common methodology for creating malware detectors based on data mining, is implemented and investigated. Our approach can identify the most significant features of malware that can significantly determine and detect a malware code.

Anomaly Detection of Axial Piston Pump Based on the DTW-RCK-IF Composite Method Using Pressure Signals

Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use.

A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid.

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.

CNN Based Rat Detection using Thermal Sensor

The detection and control of rats in commercial buildings and industries are crucial issues due to the damage they can cause to Godowns and equipment. Traditional methods of rat detection and control can be time-consuming and expensive and may not always be effective. This has brought the exploration of machine learning-based approaches, which can provide more accurate and efficient detection of rats. One such approach is the use of thermal sensors in conjunction with machine learning algorithms to detect rats in commercial buildings, industries, etc. Thermal sensors can detect the body heat of rats, and machine learning algorithms can be trained to analyze thermal data and accurately identify the presence of rats. This approach has several advantages over traditional methods, including higher accuracy, long-range and faster detection. The machine learning algorithms used in this approach can be trained using large datasets of thermal images of rats, which can be obtained using thermal cameras

Recent Studies and Applications of Hydrogel-Based Biosensors in Food Safety.

Food safety has increasingly become a human health issue that concerns all countries in the world. Some substances in food that can pose a significant threat to human health include, but are not limited to, pesticides, biotoxins, antibiotics, pathogenic bacteria, food quality indicators, heavy metals, and illegal additives. The traditional methods of food contaminant detection have practical limitations or analytical defects, restricting their on-site application. Hydrogels with the merits of a large surface area, highly porous structure, good shape-adaptability, excellent biocompatibility, and mechanical stability have been widely studied in the field of food safety sensing. The classification, response mechanism, and recent application of hydrogel-based biosensors in food safety are reviewed in this paper. Furthermore, the challenges and future trends of hydrogel biosensors are also discussed.

Research on the application of the Sleep EEG Net model based on domain adaptation transfer in the detection of driving fatigue

Fatigue detection in driving faces challenges stemming from data scarcity and difficulty in data acquisition, which poses a significant challenge to traditional fatigue detection methods. To address this issue, this study introduces a Sleep EEG Net model based on domain adaptation transfer learning. This model was pre-trained using the publicly available Sleep-EDF dataset, and domain adaptation transfer training techniques were employed to train the feature extractor of the pre-trained model, enabling cross-domain knowledge transfer. As a result, the model has been successfully applied to the task of fatigue detection in driving with only a limited amount of fatigue driving data. Experimental results demonstrate that this approach achieves a recognition accuracy of 91.5% in fatigue detection tasks. Furthermore, the model exhibits strong generalization capabilities and robustness, achieving high recognition accuracy in both simulated and real driving environments, thereby validating its effectiveness in practical applications.

Precision Isolation of Circulating Leukemia Cells in Chronic Myelogenous Leukemia Patients Using a Novel Microfluidic Device and Its Clinical Applications

Chronic Myelogenous Leukemia (CML) is a prevalent hematologic malignancy characterized by the malignant transformation of myeloid cells and their proliferation in the peripheral blood. The management of CML poses significant challenges, particularly in detecting and eradicating minimal residual disease, which is crucial for preventing relapse and improving survival outcomes. Traditional minimal residual disease detection methods, such as bone marrow aspiration, are invasive and have limitations which include the potential for sampling errors and false negatives. This study introduces a novel label-free microfluidic chip designed for the segregation and recovery of circulating leukemia cells, offering a non-invasive liquid biopsy approach with potential applications in precision medicine. Over July 2021 to October 2023, we recruited 56 CML patients across various disease stages and collected blood samples for analysis using our microfluidic device. The device demonstrated high efficacy in isolating circulating leukemia cells, with an optimal capture efficiency of 78% at a sample flow rate of 3 mL/h. Our results indicate that the microfluidic device can efficiently segregate and quantify circulating leukemia cells, providing a detailed understanding of CML progression and treatment response. The significant reduction in circulating leukemia cell counts in patients in complete remission highlights the device’s potential in monitoring treatment efficacy. Furthermore, the device’s sensitivity in detecting minimal residual disease could offer a more reliable prognostic tool for therapeutic decision-making in CML management.

Does mesocosm validation of environmental DNA methods translate to natural environment monitoring applications? A case study detecting a high-profile invader; the red eared slider turtle, Trachemys scripta elegans, in Australia

AbstractEnvironmental DNA (eDNA) surveys have gained popularity as a highly sensitive detection tool that generally outperform traditional detection techniques. eDNA surveys can provide a cost-effective means to identify species’ distributions and recent incursions, informing the control or containment of invasive species. The red-eared slider turtle, Trachemys scripta elegans, is one of the world’s most invasive species and is listed as a priority pest species for management in Australia. In this study, we validate two eDNA assays to detect this invasive turtle in Australia. We demonstrate high sensitivity in a laboratory setting and perfect detection rates in mesocosms for one of these eDNA assays but show that this does not translate to high detection rates in urban waterbodies at sites of known occupancy. In fact, our results suggest eDNA surveys provide sub-optimal performance compared to traditional detection methods for T.s. elegans. We suggest the capacity for eDNA surveys to provide a highly sensitive detection tool must be evaluated in natural environments on a species-by-species basis to understand any limitations and to avoid high error rates from eDNA surveys leading to wasted resources or inappropriate management decisions. For management of T.s. elegans in Australia, clearly defining the utility of certain eDNA based approaches to detect T.s. elegans and their incursions is vital for effective management of this pest species.

A sewer disease grading method based on entropy power theory

Nowadays, urban sewers are becoming more and more dense, and a large number of pipes are inevitably suffering from various kinds of diseases due to their old age and long exposure to a wet environment, which creates various hidden dangers for people’s property safety. The identification of the type and degree of damage to sewers is still mainly manual, which requires a lot of accumulated experience to determine the level of damage and is time-consuming and laborious. To address these problems, we collected and constructed a sewer disease dataset named sewerdata, which contains over 300K images labeled by professionals and contains three common disease categories: crack, corrosion, and misalignment. Meanwhile, a sewer disease grading method based on entropy power theory is proposed. The method designs three disease grading indicators for the characteristics of sewer diseases, and its experimental results show that the detection accuracy is significantly improved compared with the traditional detection method. A disease grading method based on entropy power theory proposed in this paper aims to provide a new scientific research idea in the field of sewer disease grading and detection.

3D Simulation and Leakage Detection Technique for Deep Foundation Pits Based on Electric Field

To solve the problems of poor accuracy, poor anti-interference ability and unclear distribution of leakage electric field response characteristics of the traditional ground DC method for leakage detection of underground diaphragm walls in foundation pits, this paper proposes a deep foundation pit leakage detection technology based on the electric field. Secondly, it constructed the multi-field coupling equations and boundary conditions of the leakage electric field coupled with the leakage flow field and ion diffusion field, and then realized the three-dimensional forward evolution of the leakage electric field of the pit using the nonstructural mesh dissection method and the finite-element-infinite-element discretization method. Finally, physical model experiments and numerical simulations are used to confirm the reliability and accuracy of the foundation pit leakage detection technology and system. The results shows that monitoring the continuous change of the electric field for a period of time, the electric field detection technology for foundation pit leakage can effectively identify the location of hidden leakage and overcome the site’s strong industrial interference, resulting in a new technical solution for foundation pit leakage detection.

Intelligent anomaly detection method for power and electricity data based on cubic exponential smoothing model

During the operation of the power system, abnormal electricity data are inevitably generated. How to efficiently detect and identify these abnormal data is a crucial component of power system state estimation, and it is also the foundation for the safety and stability of power system operation. Traditional anomaly detection methods only focus on specific power system characteristics, and there are problems with high computational complexity and low accuracy. To address the shortcomings of traditional methods for anomaly detection in power and electricity data, a smart anomaly detection method for power and electricity data based on a cubic exponential smoothing model is proposed. The cubic exponential smoothing model uses historical data to predict the current regional electricity consumption and then subtracts the predicted value from the true value to obtain the residual term. Finally, the DBSCAN density clustering algorithm is used to cluster the residual term, achieving the recognition of abnormal electricity data. We conduct an experimental comparison of regional electricity consumption data of a certain power grid. The results indicate that the proposed method has achieved good results in both detection rate and false alarm rate indicators in the intelligent detection of abnormal data in power and electricity consumption.

Detection of skin defects on mangoes based on hyperspectral imaging combined with band ratio and improved Otsu method

Skin defects are one of the major concerns in post-harvest grading and processing of mangoes. Skin defects can cause mangoes to be easily damaged during transportation and storage, and also raise the risk of infection for other mangoes in the batch. To solve the problems of inefficiency and time-consuming of traditional detection methods of skin defects on mangoes, the hyperspectral imaging combined with band ratio and improved Otsu method for detecting on mangoes was proposed in this study. Firstly, Principal Component Analysis was used to the three regions of Vis-NIR (450–1000 nm), Vis (450–780 nm), and NIR (780–1000 nm). It was found that the PC2 image of the Vis-NIR region and the PC1 image of the Vis region were more effective in distinguishing defect areas. Next, based on the weight coefficient curves derived from the selected PC images, four feature wavelengths (642 nm, 702 nm, 744 nm and 942 nm) were determined. Additionally, the PC images and band ratio images were obtained from these feature wavelength images. The band ratio Q744/942 image was determined to be the most suitable for detecting skin defects on mangoes. Finally, the segmentation results of global thresholding and the improved Otsu method were compared, and it was found that the improved Otsu method had better segmentation results. Mis-segmentation due to the stem-end region and the defective region had the same gray scale features. The band ratio Q744/702 image based on the linear stretch transformation could be used to effectively distinguish the stem-end region and the defect region. The final overall accuracy was 96.67 % with no false positives. The results indicate that the proposed detection method in this study has the potential for online detection of skin defects on mangoes.

Detection and identification of oil spill species based on polarization information.

Aiming at the problem of poor oil identification accuracy in existing oil spill detection technologies, the polarization degree model of oil spill on rough sea surface under different azimuths and zenith angles was established based on Fresnel theory. The analytical expressions of visible light polarization degree in calm and fluctuating water surface were derived respectively, and the polarization degree model of oil spill in reflection space was constructed. The effectiveness of the method and its influence on the polarization distribution of oil spill were analyzed by simulation. A portable turntable was designed to test the polarization characteristics of the experiment, and the visible light polarization detection experiment was carried out. The visible light polarization images of five typical oil spills at different observation azimuth and zenth angles were obtained. The differences in the polarization degrees of different oil species were analyzed, and the correctness of the theoretical model was proved by experiments. The polarization detection experiment of visible light pBRDF was completed, which more intuitively showed the variation law of the polarization characteristics of light reflected by different oil spills in different spatial positions. Using polarization information to distinguish oil species is a useful supplement to the traditional oil spill detection method and has important significance to improve the marine pollution control ability.

Locational detection of the false data injection attacks via semi-supervised multi-label adversarial network

False data injection attacks (FDIAs) pose a significant threat to the healthy and safe operation of smart grids. Traditional fdia detection methods are difficult to deal with complex and harsh scenarios. Considering the increasing demand for attack location services in practical power grid management, this paper proposes a new adversarial scheme called Semi-Supervised Multi-Label Adversarial Network (SMAN). To better adapt to harsh learning conditions with only a few labeled samples, this scheme combines semi-supervised mechanisms with state-of-the-art generative adversarial networks. The showdown between generator and discriminator aims to optimize the model for high detection accuracy in semi-supervised training. Among them, for the non-Euclidean structure of the power system, we propose a graph attention-based generation mechanism to improve the generator and enhance the authenticity of the generated samples. Furthermore, the classification network based on label correlation of FDIAs is proposed to capture the inconsistency and co-occurrence dependency in the measurements due to the potential attacks, which is applied to detect the exact injection locations with high dimension data. To this end, we perform label transformation and hierarchical training on the localization task to quickly localize data attacks based on multi-label classification. Extensive simulations and comparisons implemented on IEEE 14-bus and 118-bus power systems demonstrate the superiority of this scheme in localization detection. And it has been proved that it has high robustness and generalization ability under harsh conditions.

MSA R-CNN: A comprehensive approach to remote sensing object detection and scene understanding

Remote sensing scene understanding is crucial for extracting valuable information from high-resolution images, including object detection and classification. Traditional object detection methods face challenges in handling the diverse scales, orientations, and complex backgrounds present in remote sensing data. In this paper, we propose a novel remote sensing scene understanding system called multiscale-attention R-CNN (MSA R-CNN), which incorporates a super multiscale feature extraction network (SMENet) for enhanced feature extraction from multiscale images, an adaptive dynamic inner lateral (ADIL) connection module to tackle information loss in feature pyramid networks (FPN), and a distributed lightweight attention module (DLAM) to refine feature information processing. Furthermore, a new dataset combining the DIOR and DOTA datasets is introduced to extract the background information of detected objects and evaluate the proposed system’s performance. MSA R-CNN achieved an mAP of 74.37% on the DIOR dataset when the gamma value was set to 0.2 and 81.97% on the DOTA dataset when the gamma value was set to 0.1 with the same learning rate, outperforming state-of-the-art models on both datasets. The proposed system demonstrates significant improvements in both object detection and background information extraction, providing a comprehensive solution for remote sensing scene understanding.

A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring

The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments.

Label-free fluorescence detection of human 8-oxoguanine DNA glycosylase activity amplified by target-induced rolling circle amplification

BackgroundHuman 8-oxoG DNA glycosylase 1 (hOGG1) is one of the important members of DNA glycosylase for Base excision repair (BER), the abnormal activity of which can lead to the failure of BER and the appearance of various diseases, such as breast cancer, bladder cancer, Parkinson's disease and lung cancer. Therefore, it is important to detect the activity of hOGG1. However, traditional detection methods suffer from time consuming, complicated operation, high false positive results and low sensitivity. Thus, it remains a challenge to develop simple and sensitive hOGG1 analysis strategies to facilitate early diagnosis and treatment of the relative disease. ResultsA target-induced rolling circle amplification (TIRCA) strategy for label-free fluorescence detection of hOGG1 activity was proposed with high sensitivity and specificity. The TIRCA strategy was constructed by a hairpin probe (HP) containing 8-oxoG site and a primer probe (PP). In the presence of hOGG1, the HP transformed into dumbbell DNA probe (DDP) after the 8-oxoG site of which was removed. Then the DDP formed closed circular dumbbell probe (CCDP) by ligase. CCDP could be used as amplification template of RCA to trigger RCA. The RCA products containing repeated G4 sequences could combine with ThT to produce enhanced fluorescence, achieving label-free fluorescence sensing of hOGG1. Given the high amplification efficiency of RCA and the high fluorescence quantum yield of the G4/ThT, the proposed TIRCA achieved highly sensitive measurement of hOGG1 activity with a detection limit of 0.00143 U/mL. The TIRCA strategy also exhibited excellent specificity for hOGG1 analysis over other interference enzymes. SignificanceThis novel TIRCA strategy demonstrates high sensitivity and high specificity for the detection of hOGG1, which has also been successfully used for the screening of inhibitors and the analysis of hOGG1 in real samples. We believe that this TIRCA strategy provides new insight into the use of the isothermal nucleic acid amplification as a useful tool for hOGG1 detection and will play an important role in disease early diagnosis and treatment.

Application of cfDNA-NGS Test in MRD Monitoring of Acute Leukemia Patients with CAR-T Cell Therapy

Background: The minimal residual disease (MRD) plays an important role in the comprehensive management of CAR-T cell therapy. However, some reasons hinder the application of MRD monitoring in CAR-T cell therapy. Dynamic monitoring of cfDNA in solid tumors and hematological malignancies can effectively evaluate the efficacy of treatment and predict long-term prognosis. However, there is no relevant researches on whether cfDNA can replace bone marrow for MRD monitoring after CAR-T cell therapy for acute leukemia. Methods: 23 patients with acute leukemia who received CAR-T cell therapy from January 2021 to October 2022 in the First Affiliated Hospital of Suzhou University were collected retrospectively, including 14 AML patients and 9 ALL patients. The CAR-T cell targets included CD38, CD7, CLL-1 and CD19/CD22. Bone marrow and peripheral blood samples were collected before CAR-T cell therapy, +28d and every month after CAR-T cell therapy. We used QIAamp Circulating Nucleic Acid Kit to extract cfDNA, and then conducted high-throughput sequencing through Nextseq550 sequencing platform. We compared the test results of cfDNA-NGS and BM-NGS in terms of gene mutation types and variant allele frequency (VAF), and analyzed the efficacy of cfDNA-NGS, BM-NGS and FCM on the prediction of remission and replase of CAR-T cell therapy by the receiver operating characteristic curve (ROC curve). Results: In this study, a total of 123 peripheral blood samples were collected and cfDNA was extracted from all samples. The results of 121 cfDNA-NGS and BM-NGS test were consistent, with a consistency rate of 98.4% (121/123), and the consistency rate with bone marrow FCM detection was 85.4% (105/123). cfDNA-NGS test was a good indicator for predicting the efficacy and replase after CAR-T cell therapy: compared with BM-NGS and FCM, cfDNA-NGS test before CAR-T cell therapy had the highest prediction efficiency for CR (AUC=0.767, 95%CI: 0.576-0.958, P=0.039). Survival analysis showed no difference in 1-year OS and LFS between cfDNA-NGS (+), BM-NGS (+) , FCM (+) patients at +28 days after CAR-T cell therapy (OS: 27.3% vs 33.3% vs 30%, P=0.949; LFS: 27.3% vs 33.3% vs 30%, P=0.970). At +28 days after CAR T treatment, there was no difference in 1-year OS and LFS between patients with cfDNA-NGS (-), BM-NGS (-), FCM (-) (OS: 91.7% vs 76.2% vs 83.3%, P=0.671; LFS: 73.3% vs 76.2% vs 82.1%, P=0.726). Then we assessed the long-term prognosis by cfDNA-NGS combined with traditional MRD detection methods after CAR-T cell therapy: Survival analysis showed that 1-year OS and LFS of patients with cfDNA (-), BM-MRD (-) and cfDNA1 (-), BM-MRD (+) were significantly better than those with cfDNA (+), BM-MRD (-) and cfDNA1 (+), BM-MRD (+) (OS: 90.9% vs 100% vs 50% vs 22.2%, P=0.008: LFS：68.2% vs 100% vs 50% vs 22.2%, P=0.017). Conclusions: The cfDNA-NGS test was a good indicator for predicting the efficacy and replase after CAR-T cell therapy, and was not inferior to BM-NGS and FCM detection in evaluating efficacy, replase and long-term prognosis. Combination cfDNA-NGS test with traditional MRD detection methods could evaluate the long-term prognosis of CAR-T cell therapy more comprehensively.

WFRE-YOLOv8s: A New Type of Defect Detector for Steel Surfaces

During the production of steel, in view of the manufacturing engineering, transportation, and other factors, a steel surface may produce some defects, which will endanger the service life and performance of the steel. Therefore, the detection of defects on a steel surface is one of the indispensable links in production. The traditional defect detection methods have trouble in meeting the requirements of high detection accuracy and detection efficiency. Therefore, we propose the WFRE-YOLOv8s, based on YOLOv8s, for detecting steel surface defects. Firstly, we change the loss function to WIoU to address quality imbalances between data. Secondly, we newly designed the CFN in the backbone to replace C2f to reduce the number of parameters and FLOPs of the network. Thirdly, we utilized RFN to complete a new neck RFN to reduce the computational overhead and, at the same time, to fuse different scale features well. Finally, we incorporate the EMA attention module into the backbone to enhance the extraction of valuable features and improve the detection accuracy of the model. Extensive experiments are carried out on the NEU-DET to prove the validity of the designed module and model. The mAP0.5 of our proposed model reaches 79.4%, which is 4.7% higher than that of YOLOv8s.