Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.

Point-of-care testing methods for the rapid and sensitive screening of pathogenic bacteria are urgently needed because of the high number of outbreaks of microbial infections and foodborne diseases. In this study, we developed a highly sensitive and multiplex lateral flow assay (LFA) for the simultaneous detection of Pseudomonas aeruginosa and Salmonella typhimurium in complex samples by using wheat germ agglutinin (WGA)-modified magnetic quantum dots (Mag@QDs) as a universal detection nanoprobe. The Mag@QDs-WGA tag with a 200 nm Fe₃O₄ core and multiple QD-formed shell was introduced into the LFA biosensor for the universal capture of the two target bacteria and provided the dual amplification effect of fluorescence enhancement and magnetic enrichment for ultra-sensitivity detection. Meanwhile, two antibacterial antibodies were separately sprayed onto the two test lines of the LFA strip to ensure the specific identification of P. aeruginosa and S. typhimurium through one test. The proposed LFA exhibited excellent analytical performance, including high capture rate (>80%) to the target pathogens, low detection limit (<30 cells/mL), short testing time (<35 min), and good reproducibility (relative standard deviation < 10.4%). Given these merits, the Mag@QDs-WGA-based LFA has a great potential for the on-site and real-time diagnosis of bacterial samples.

This study established a portable and ultrasensitive detection method based on recombinase polymerase amplification (RPA) combined with high-sensitivity multilayer quantum dot (MQD)-based immunochromatographic assay (ICA) to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The RPA-MQD-based ICA method is reported for the first time and has the following advantages: (i) RPA is free from the constraints of instruments and can be promoted in point-of-care testing (POCT) scenarios, (ii) fluorescence ICA enhances the portability of detection operation so that the entire operation time is controlled within 1 h, and (iii) compared with common colorimetric-based RPA-ICA, the proposed assay used MQD to provide strong and quantifiable fluorescence signal, thus enhancing the detection sensitivity. With this strategy, the proposed RPA-MQD-based ICA can amplify and detect the SARS-CoV-2 nucleic acid on-site with a sensitivity of 2 copies/reaction, which is comparable to the sensitivity of commercial reverse transcription quantitative polymerase chain reaction (RT-qPCR) kits. Moreover, the designed primers did not cross-react with other common respiratory viruses, including adenovirus, influenza virus A, and influenza virus B, suggesting high specificity. Thus, the established portable method can sensitively detect SARS-CoV-2 nucleic acid without relying on equipment, having good application prospects in SARS-CoV-2 detection scenarios under non-lab conditions.