Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC–MS/MS

The chemical composition of polyphenols in tobacco waste was identified by HPLC-PDA–ESI/MS/MS and the contents of chlorogenic acids and rutin in 10 varieties of tobacco wastes were determined by HPLC–UV. The relationships between the contents of active polyphenols and the varieties of tobacco wastes were interpreted by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results showed that 15 polyphenols were identified in a methanolic extract of dried tobacco waste. The tobacco wastes were characterized by high levels of chlorogenic acids (3-CQA, 5-CQA, and 4-CQA) and rutin; their ranges in the 10 tobacco varieties were 0.116–0.196, 0.686–1.781, 0.094–0.192, and 0.413–0.998 %, respectively. According to multivariate statistics models, two active compound variables can be considered important for the discrimination of the varieties of tobacco wastes: chlorogenic acids and rutin. Consequently, samples of 10 tobacco varieties were characterized into three groups by HCA based on the PCA pattern. In conclusion, tobacco waste could be used as a new pharmaceutical material for the production of natural chlorogenic acids and rutin in the ethnopharmacological industry

Investigation of the kinetics and mechanism of the glycerol chlorination reaction using gas chromatography–mass spectrometry

As a primary by-product in biodiesel production, glycerol can be used to prepare an important fine chemical, epichlorohydrin, by the glycerol chlorination reaction. Although this process has been applied in industrial production, unfortunately, less attention has been paid to the analysis and separation of the compounds in the glycerol chlorination products. In this study, a convenient and accurate method to determine the products in glycerol chlorination reaction was established and based on the results the kinetic mechanism of the reaction was investigated. The structure of main products, including 1,3--dichloropropan-2-ol, 2,3-dichloropropan-1-ol, 3-chloro-1,2-propanediol, 2-chloro-1,3-propanediol and glycerol was ascertained by gas chromatography–mass spectrometry and the isomers of the products were distinguished. Apidic acid was considered as the best catalyst because of its excellent catalytic effect and high boiling point. The mechanism of the glycerol chlorination reaction was proposed and a new kinetic model was developed. Kinetic equations of the process in the experimental range were obtained by data fitting and the activation energies of each tandem reaction were 30.7, 41.8, 29.4 and 49.5 kJ mol-1, respectively. This study revealed the process and mechanism of the kinetics and provides the theoretical basis for engineering problems

Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks

Web-based DNNs provide accurate object recognition to the mobile Web AR, which is newly emerging as a lightweight mobile AR solution. Webbased DNNs are attracting a great deal of attention. However, balancing the UX against the computing cost for DNN-based object recognition on the Web is difficult for both self-contained and cloud-based offloading approaches, as it is a latency-sensitive service but also has high requirements in terms of computing and networking abilities. Fortunately, the emerging 5G networks promise not only bandwidth and latency improvement but also the pervasive deployment of edge servers which are closer to the users. In this article, we propose the first edge-based collaborative object recognition solution for mobile Web AR in the 5G era. First, we explore the finegrained and adaptive DNN partitioning for the collaboration between the cloud, the edge, and the mobile Web browser. Second, we propose a differentiated DNN computation scheduling approach specially designed for the edge platform. On one hand, performing part of DNN computations on mobile Web without decreasing the UX (i.e., keep response latency below a specific threshold) will effectively reduce the computing cost of the cloud system; on the other hand, performing the remaining DNN computations on the cloud (including remote and edge cloud) will also improve the inference latency and thus UX when compared to the self-contained solution. Obviously, our collaborative solution will balance the interests of both users and service providers. Experiments have been conducted in an actually deployed 5G trial network, and the results show the superiority of our proposed collaborative solution.</p

Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks

Web-based DNNs provide accurate object recognition to the mobile Web AR, which is newly emerging as a lightweight mobile AR solution. Webbased DNNs are attracting a great deal of attention. However, balancing the UX against the computing cost for DNN-based object recognition on the Web is difficult for both self-contained and cloud-based offloading approaches, as it is a latency-sensitive service but also has high requirements in terms of computing and networking abilities. Fortunately, the emerging 5G networks promise not only bandwidth and latency improvement but also the pervasive deployment of edge servers which are closer to the users. In this article, we propose the first edge-based collaborative object recognition solution for mobile Web AR in the 5G era. First, we explore the finegrained and adaptive DNN partitioning for the collaboration between the cloud, the edge, and the mobile Web browser. Second, we propose a differentiated DNN computation scheduling approach specially designed for the edge platform. On one hand, performing part of DNN computations on mobile Web without decreasing the UX (i.e., keep response latency below a specific threshold) will effectively reduce the computing cost of the cloud system; on the other hand, performing the remaining DNN computations on the cloud (including remote and edge cloud) will also improve the inference latency and thus UX when compared to the self-contained solution. Obviously, our collaborative solution will balance the interests of both users and service providers. Experiments have been conducted in an actually deployed 5G trial network, and the results show the superiority of our proposed collaborative solution

with LC–MS/MS

Discrimination and classification of tobacco wastes by identification and quantification of polyphenol