Genome-wide identification of the WRKY gene family in Camellia oleifera and expression analysis under phosphorus deficiency

Camellia oleifera Abel. is an economically important woody edible-oil species that is mainly cultivated in hilly areas of South China. The phosphorus (P) deficiency in the acidic soils poses severe challenges for the growth and productivity of C. oleifera. WRKY transcription factors (TFs) have been proven to play important roles in biological processes and plant responses to various biotic/abiotic stresses, including P deficiency tolerance. In this study, 89 WRKY proteins with conserved domain were identified from the C. oleifera diploid genome and divided into three groups, with group II further classified into five subgroups based on the phylogenetic relationships. WRKY variants and mutations were detected in the gene structure and conserved motifs of CoWRKYs. Segmental duplication events were considered as the primary driver in the expanding process of WRKY gene family in C. oleifera. Based on transcriptomic analysis of two C. oleifera varieties characterized with different P deficiency tolerances, 32 CoWRKY genes exhibited divergent expression patterns in response to P deficiency stress. qRT-PCR analysis demonstrated that CoWRKY11, -14, -20, -29 and -56 had higher positive impact on P-efficient CL40 variety compared with P-inefficient CL3 variety. Similar expression trends of these CoWRKY genes were further observed under P deficiency with longer treatment period of 120d. The result indicated the expression sensitivity of CoWRKYs on the P-efficient variety and the C. oleifera cultivar specificity on the P deficiency tolerance. Tissue expression difference showed CoWRKYs may play a crucial role in the transportation and recycling P in leaves by affecting diverse metabolic pathways. The available evidences in the study conclusively shed light on the evolution of the CoWRKY genes in C. oleifera genome and provided a valuable resource for further investigation of functional characterization of WRKY genes involved to enhance the P deficiency tolerance in C. oleifera

Reflective Safety Clothes Wearing Detection in Hydraulic Engineering Using YOLOv3-CCD

The construction site of the hydraulic engineering has a high danger factor and the correct wearing of reflective safety clothes ensures the safety of workers. Therefore, the detection and testing of reflective safety clothes wearing is an important task in the construction site of a hydraulic engineering. However, the traditional manual supervision strategy has the problems of low efficiency, narrow scope, and poor real-time performance in complex working conditions. Based on YOLOv3 that is a classical target detection model, this paper proposes a reflective safety clothes detection algorithm (YOLOv3-CCD) based on an attention mechanism and an improved loss function. By adding the CA (Coordinate Attention) mechanism module to the backbone network, the characterization ability of the target feature is enhanced, so as to solve the Long-Term dependencies problem in the detection process; The loss function is changed from IOU-Loss (Intersection Over Union Loss) to CIOU-Loss (Complete-IOU Loss), so that the network takes the aspect ratio into consideration when selecting the prediction box, which improves the accuracy of target positioning; In the post-processing of the algorithm, we improved NMS (Non-Maximum Suppression) to solve the problem of dense target detection being missed. Experimental results show that compared with the original YOLOv3 network model, the algorithm has stronger robustness and the overall detection accuracy is 1.8% higher than that of the original network. Moreover, the detection speed is 32 frames per second, which is faster than the original network

Experimental Investigation of a Self-Sensing Hybrid GFRP-Concrete Bridge Superstructure with Embedded FBG Sensors

A self-sensing hybrid GFRP-concrete bridge superstructure, which consists of two bridge decks and each bridge deck is comprised of four GFRP box sections combined with a thin layer of concrete in the compression zone, was developed by using eight embedded FBG sensors in the top and bottom flanges of the four GFRP box sections at midspan section of one bridge deck along longitudinal direction, respectively. The proposed self-sensing hybrid bridge superstructure was tested in 4-point loading to investigate its flexural behavior and verify the operation of the embedded FBG sensors. The longitudinal strains of the hybrid bridge superstructure were recorded using the embedded FBG sensors as well as the surface-bonded electric resistance strain gauges. The experimental results indicate that the embedded FBG sensors can faithfully record the longitudinal strains of the hybrid bridge superstructure in tension at bottom flanges and in compression at top flanges of the four GFRP box sections over the entire loading range, as compared with the surface-bonded strain gauges. So, the proposed self-sensing hybrid GFRP-concrete bridge superstructure can reveal its internal strains in serviceability limit state as well as in strength limit state, and it will have wide applications for long-term monitoring in civil engineering

Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5

COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%—an improvement of 2.1% compared to the baseline model (YOLOv5)