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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of music information retrieval (MIR), classification according to music genres is a challenging task. Traditional audio feature engineering methods requires manually selecting and extracting music signal features for processing, resulting in complex feature extraction process, unstable model performance and poor generalization. The method combining deep learning with spectrogram also has some problems such as unsuitable model for some data and difficulty in global feature extraction. This paper proposes a music genre classification model based on convolutional attention mechanism, called MGTN. MGTN combines two music genre classification methods: input spectrogram and audio signal feature extraction, to construct audio time series data, which greatly improves the model's ability to extract features and generalization, and provides a new idea for music genre classification. Experimental results on GTZAN and Ballroom datasets show that the MGTN model can effectively fuse input data from two different modalities. Compared with dozens of benchmark models, the MGTN model has strong advantages. [ABSTRACT FROM AUTHOR]

Air pollution seriously affects human health and social sustainable development. However, the multi-variable air quality data obtained by sensors often have missing values, which brings difficulties to data analysis and processing. Currently, many analysis methods for changes in a certain air component only rely on time data and spatial data of this attribute, ignoring the influence of other air components on the trend of this attribute in the same time interval. In addition, it is difficult to achieve ideal results in filling discrete missing data. This paper proposes a Time Attention Model (TAM) based on deep learning, which uses attention mechanism to focus on the correlation between different timestamps and the correlation between different feature time series, and combines short-term historical data to fill missing values in multi-variable air quality data. The proposed model is evaluated using air quality data from Beijing, and the experimental results show that TAM has advantages over ten other baseline models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Capital Institute of Physical Education is the property of Shoudu Tiyu Xueyuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plastics Science & Technology / Suliao Ke-Ji is the property of Plastics Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Capital Institute of Physical Education is the property of Shoudu Tiyu Xueyuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sediment yield from the Northwest part of Hekouzhen-Longmen region in the Yellow River basin has decreased by about 94% since 1999, so it is of important significant to analyze the vegetation variation and its driving forces which would be helpful to understand the causes of sediment decrease. Based on the remote sensing survey data, this paper analyzed the degree of vegetation variation and its changing process of the area from 1978 to 2017 and the driving forces of the vegetation variation during this period. The research shows that the vegetation had been continuously improving in the past 40 years and the period from 1998 to 2017 contributed nearly 80%. In 2016, the vegetation coverage of grass forest land in the study area reached 59.7% and the percentage of effective vegetation in the area reached 48.8%, which are three times of them in 1978. Before 2000, human destruction on the grass-forest vegetation was the decisive factor to the area’s vegetation status. But after 2000, human destruction was slight and the rainfall degree has become the key factor to affect the vegetation status. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Education / Tiyu Xuekan is the property of Journal of Physical Education Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Capital Institute of Physical Education is the property of Shoudu Tiyu Xueyuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)