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- 저자 : Alshanqiti, Abdullah (검색결과 2 건)
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Bisphenol A and the related alkylphenol contaminants in crustaceans and their potential bioeffects
Zuo, Yuegang,Zhu, Zhuo,Alshanqiti, Mohammed,Michael, Joseph,Deng, Yiwei Techno-Press 2015 Advances in environmental research Vol.4 No.1
Bisphenol A is widely used in plastic and other industrial consumer products. Release of bisphenol A and its analogues into the aquatic environment during manufacture, use and disposal has been a great scientific and public concern due to their toxicity and endocrine disrupting effects on aquatic wildlife and even human beings. More recent studies have shown that these alkylphenols may affect the molting processes and survival of crustacean species such as American lobster, crab and shrimp. In this study, we have developed gas chromatography with flame ionization detection (GC-FID) and gas chromatography-mass spectrometric (GC-MS) methods for the determination of bisphenol A and its analogues in shrimp Macrobrachium rosenbergii, blue crab Callinectes sapidus and American lobster Homarus americanus samples. Bisphenol A, 2,4-bis-(dimethylbenzyl)phenol and 4-cumylphenol were found in shrimp in the concentration ranges of 0.67-5.51, 0.36-1.61, and < LOD (the limit of detection)-1.96 ng/g (wet weight), and in crab of 0.10-0.44, 0.13-0.62, and 0.26-0.58 ng/g (wet weight), respectively. In lobster tissue samples, bisphenol A, 2-t-butyl-4-(dimethylbenzyl)phenol, 2,6-bis-(t-butyl)-4-(dimethylbenzyl)phenol, 2,4-bis-(dimethybenzyl)phenol, 2,4-bis-(dimethylbenzyl)-6-t-butylphenol and 4-cumylphenol were determined at the concentration ranges of 4.48-7.01, 1.23-2.63, 2.71-9.10, 0.35-0.91, 0.64-3.25, and 0.44-1.00 ng/g (wet weight), respectively. At these concentration levels, BPA and its analogs may interfere the reproduction and development of crustaceans, such as larval survival, molting, metamorphosis and shell hardening.
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A Remote Sensing Scene Classification Model Based on EfficientNetV2L Deep Neural Networks
Aljabri, Atif A.,Alshanqiti, Abdullah,Alkhodre, Ahmad B.,Alzahem, Ayyub,Hagag, Ahmed International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10
Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.
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