Bamboo surface defect detection provides quality assurance for bamboo product manufacture in industrial scenarios, an integral part of the overall manufacturing process. Currently, bamboo defect inspection predominantly relies on manual operation, but manual inspection is very time-consuming as well as labor-intensive, and the quality of inspection is not guaranteed. In recent years, a few visual inspection systems based on traditional image processing have been deployed in some factories. However, traditional machine vision algorithms extract features in tedious steps and have poor performance along with poor adaptability in the face of complex defects. Accordingly, many scholars are committed to seeking deep learning methods to accomplish surface defect detection. However, existing deep learning object detectors struggle with specific industrial defects when directly applied to industrial defect detection, such as sliver defects, especially for ones with extreme aspect ratios. To this end, this paper proposes an improved model based on the advanced object detector YOLOV4-CSP, which introduces asymmetric convolution and attention mechanism. The introduction of asymmetric convolution enhances the feature extraction in the horizontal direction of the bamboo strip surface, improving the performance in detecting sliver defects. In addition, convolutional block attention module(CBAM), a hybrid attention module, which combines channel attention with spatial attention, is utilized to promote the representation ability of the model by increasing the weights of crucial channels and regions. The proposed model achieves outstanding performance in the general categories and excels in the hard-to-detect categories. Some enterprise's bamboo strip dataset experiments verify that the model can reach 96.74% mAP for the typical six surface defects. Meanwhile, we also observe significant improvements when extending our model to aluminum datasets with similar characteristics.
Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.
Background Human cytomegalovirus (HCMV), a member of the β-herpesvirus family, causes the establishment of a latent infection that persists throughout the life of the host and can be reactivated when immunity is weakened. To date, there is no vaccine to prevent HCMV infection, and clinically approved drugs target the stage of viral replication and have obvious adverse reactions. Thus, development of novel therapeutics is urgently needed. Methods In the current study, we identified a naturally occurring pterostilbene that inhibits HCMV Towne strain replication in human diploid fibroblast WI-38 cells through Western blotting, qPCR, indirect immunofluorescence assay, tissue culture infective dose assays. The time-of-addition experiment was carried out to identify the stage at which pterostilbene acted. Finally, the changes of cellular senescence biomarkers and reactive oxygen species production brought by pterostilbene supplementation were used to partly elucidate the mechanism of anti-HCMV activity. Results Our findings revealed that pterostilbene prevented lytic cytopathic changes, inhibited the expression of viral proteins, suppressed the replication of HCMV DNA, and significantly reduced the viral titre in WI-38 cells. Furthermore, our data showed that pterostilbene predominantly acted after virus cell entry and membrane fusion. The half-maximal inhibitory concentration was determined to be 1.315 μM and the selectivity index of pterostilbene was calculated as 26.73. Moreover, cell senescence induced by HCMV infection was suppressed by pterostilbene supplementation, as shown by a decline in senescence-associated β-galactosidase activity, decreased production of reactive oxygen species and reduced expression of p16, p21 and p53, which are considered biomarkers of cellular senescence. Conclusion Together, our findings identify pterostilbene as a novel anti-HCMV agent that may prove useful in the treatment of HCMV replication.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.