DENG Qixin;CHEN Hui;LIN Yan;ZHANG Tinggui;ZHENG Quanxing;LAI Weiyang;MA Pengfei;LAN Hongqiao;LIU Zechun;XIE Wei
Tobacco Science & Technology,2022,No. 08
This study aims to investigate the effect of temperature on the release of acidic components from heated tobacco products. To be specific, the types and amount of acidic components in the aerosol of three different heated tobacco products at different heating temperatures were analyzed and compared with those of conventional cigarettes. The results are as follows. (1) The release and types of acidic components in mainstream aerosol of the heated tobacco products were lower than those of cigarette smoke, and the release of acidic components from the different heated tobacco products was significantly different as well. (2) Formic acid, acetic acid, and lactic acid accounted for a relatively large proportion of the acidic components in mainstream aerosol of heated tobacco products, and the released and migrated amount of these acids surged at the heating temperature of 200–300 °C, while the increase slowed down when the temperature was above 300 °C. The amount of released and migrated palmitic acid spiked at the heating temperature of 200–350 °C, while the increase decelerated when the temperature was higher than 350 °C. (3) The migration rates of free organic acids in the heated tobacco products were related to the boiling points of the acids. The higher the boiling points were, the lower the amount of migrated free organic acids would be.
MAO Duobin;HUANG Xiaoyu;ZHOU Lifeng;LIU Qiang;CHEN Zhifei;FENG Yingjie;HUANG Shen;ZHANG Junling
Tobacco Science & Technology,2022,No. 08
This study aims to explore the effects of Bacillus subtilis on the chemical composition and sensory quality of tobacco leaves. To be specific, a microorganism (H11) was screened from the cured tobacco leaves. Based on the analysis of strain morphology and 16S rDNA sequence, H11 was preliminarily identified as B. subtilis subsp. subtilis. It was used to ferment 20 kg low-grade tobacco leaves (2017 DCFB) from Pingdingshan, Henan Province for 30 h, and the variation in sensory quality, routine chemical components, and neutral aroma components of tobacco leaves before and after the fermentation was comparatively analyzed. Metagenomic sequencing technology was adopted to analyze the changes in microbial population on the tobacco leaves before and after fermentation, and the roles of enzymes in the leaf fermentation were discussed. Compared with no fermentation, the fermentation improved the volatility, sweet taste, aroma quality, and aroma quantity of tobacco smoke, increased the content (mass fraction) of water-soluble total sugar and reducing sugar by 2.52% and 1.92%, respectively, raised the content (mass fractions) of neutral aroma components such as (E)-β-damascenone, dihydroactinidiolide, and megastigmatrienone C by 25.92%, 10.57%, and 12.15%, respectively. The amount of microorganisms on the tobacco leaves decreased significantly after fermentation, and the relative abundance of Bacillus increased to 76% of the total amount of microorganisms. B. subtilis has important influence on the chemical composition and sensory quality of tobacco leaves, and it is characterized by a short fermentation time and high throughput, which can provide references for the microbiological treatment of low-grade tobacco leaves.
CHEN Yulun;CHEN Danni;LI Xi
Geomatics and Information Science of Wuhan University,2022,No. 08
Objectives: Most of the remotely sensed night light data is acquired by satellites, and the spatial resolution is always coarse and the overpass time is fixed. These limitations hinder understanding night light patterns with insight. Compared to satellite images, camera image data has higher temporal and spatial resolution, and web camera images provide more information on night light and details of urban economic activities. However, such data has rarely been reported for academic research. Methods: For real-time video data, we design a specific Crawler program for downloading images from the Earth Camera website. Consequently, the acquired time series camera images are analyzed to find different trend components and their spatial distribution by using principal component analysis (PCA). Results: Generally, the Crawler program can be run stably to obtain public camera image data from the Earth Camera website. Through analyzing city light in a region inside Tokyo based on the proposed PCA method, we find that the city light dynamic is complex and random in some extent, and the revealed pattern shows a general and significant decreasing trend, while some regions have more complicated temporal patterns such as increasing at first and then decreasing. In addition, the different building façades have different city light dynamic as well. Conclusions: This study proposes a technical framework for acquiring and analyzing urban public camera images at night, and it suggests that urban camera can effectively provide city light change information from a micro perspective, which will provide new data source for supporting quantitative remote sensing of night light.
GAO Jialiang;LU Feng;PENG Peng;XU Yang
Geomatics and Information Science of Wuhan University,2022,No. 08
Objectives: The rapid development of information and communication technology has facilitated the online tourism service and massive web text, which provides a new opportunity for tourism sector planning and personalized recommendation. However, owing to the characteristics of semantic vagueness and low signal-to-noise ratio, the web text is difficult to get utilized directly. Therefore, how to integrate the technologies of knowledge engineering, natural language processing and machine learning, so as to form a formalized domain knowledge graph from abundant tourism text, has attracted much attention. Methods: This paper proposes a tourism knowledge graph construction method based on tourism domain ontology and transfer learning. Firstly, the ontology of tourist attractions is defined based on the domain specifications and standards, which support a comprehensive and systematic description of the semantic characteristics of attractions. Secondly, a transfer learning method is adopted to transform the pre-training language model into a customized knowledge extractor to acquire knowledge triples accurately from web text, which is integrated with the scattered tourism-related information including tourist check-ins and POI (point of interest) attributes to build a systematic knowledge graph. Results: Experimental results show that the proposed knowledge extractor improves the accuracy (average area under the curve) and integrity (the number of semantic characteristics) of acquisition of semantic knowledge by 50.7% and 670%, respectively, compared with the common LDA (latent Dirichlet allocation) model. The constructed knowledge graph of tourist attractions contained 77 039 entities, 16 types of relationship, and total 10 971 810 triples. Conclusions: Through the unified organization paradigm of triplet knowledge, the study realizes the fusion and integration of multi-source heterogeneous tourism data, and addresses the potential systemic risk in the decision-making process based on a single data source. It is argued that the constructed knowledge graph can fully capture the real tourism scene, support in-depth analysis of tourist behaviors and demands at different scales and granularities, and provide decision support for sustainable developments of tourist destinations.
LI Pengcheng;BAI Wenhao
Geomatics and Information Science of Wuhan University,2022,No. 08
Objective: Decryption is the key to ensuring the safe sharing of remote sensing resources. To solve the problems of incomplete target detection, unreliable complementary results, high resource consumption and difficulty of training in the traditional methods of sensitive target hiding in remote sensing images, an automatic hiding method of sensitive targets in remote sensing images is proposed based on the ability of Transformer structure to deal with global information. Methods: Firstly, the optimized Cascade Mask R–CNN instance segmentation model with Swin Transformer as the backbone network is used to detect sensitive targets and generate mask regions. After improving the generalization capability of the model, RSMosaic (remote sense Mosaic), a data synthesis method to reduce the dependence on manually labeled data is designed. Secondly, the mask region is expanded by using the shadow detection model based on HSV (hue–saturation–value) space, and the MAE(masked autoencoders) model is introduced to achieve target background generation. Finally, the generated images are spliced with the original images to obtain the decrypted images. Results: The sub-meter remote sensing images collected by Google Earth are used as test data, and the results show that this proposed method generates reliable hiding results while reducing dataset dependence and training resource consumption. Compared with the traditional method, the AP (average precision) values of bounding box and pixel mask are improved by 13.2% and 11.2%respectively in sensitive target instance segmentation, and the AP values can be improved by another 9.39% and 14.16% respectively after using RSMosaic, which is better than other repair models in terms of objective index and index variance in the field of image repair, especially in mean absolute error and maximum mean discrepancy indexes which are improved by more than 80%. It achieves the effect of automatic hiding of sensitive targets with reasonable structure and clear texture. Conclusions: The proposed method reduces manpower, data and computing resources, and achieves better results in both subjective visual effects and objective indexes, which can provide technical support for real remote sensing image sharing.
