Inflammation plays a major role in etiology of multiple diseases and it has become an imperative therapeutic target in pharmacological interventions. Over the years, natural products originating from plants have made great contributions in the drug discovery process. Belonging to the Asteraceae family, Stevia rebaudiana (S. rebaudiana) is exploited at a large scale for its purpose as a natural sweetener. Even so, researchers have begun to notice other bioactive potential use of stevia such as anti-inflammatory and anti-cancer activities, which are conferred by compounds present in the leaves including stevioside, rebaudioside and isosteviol. In this review, we provide a brief overview of S. rebaudiana plant and its bioactive compounds and highlight their anti-inflammatory potential for therapeutic applications.
Manual or conventional image processing algorithms are commonly used to detect surface problems on mobile phone screens. However, inefficiency and inflexibility are disadvantages. Although the semantic segmentation method has high adaptability and accuracy, it also has a low defect detection efficiency due to its excessive parameters. In order to increase defect detection efficiency, a novel efficient encoder-decoder architecture termed MB encoder-decoder architecture based on MBConv blocks, and that it reduces the number of parameters used in semantic segmentation methods i presented. In addition, by applying the MB encoder-decoder design to the U-Net, the efficient U-Net (EU-Net) is proposed. It confirms the MB encoder-decoder architecture's superiority. Then, EU-Net to mobile phone surface defect detection in real industrial scenarios. Experimental results on a dataset show the superiority of the proposed algorithm and it can meet the real-time requirement of industrial production.
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