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Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences 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 China Brewing is the property of China Brewing 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 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.)

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.)

Copyright of Food Research & Development is the property of Food Research & Development 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.)

To study the body composition of patients with chronic kidney disease, to analyze and evaluate their health status, and to provide strategy for disease management of chronic kidney disease patients. Methods The convenience sampling method was used to select elderly patients hospitalized in the nephrology department as the research subjects. The body impedance analysis was performed using the bioelectrical impedance method to collect health scores and body composition indicators. Results The interquartile range of health scores for elderly patients with chronic kidney disease was 74 (69, 79) points. Correlation analysis showed that health scores were correlated with obesity, nutritional status, and bone conditions. Conclusion The health score of elderly patients with chronic kidney disease is not optimistic. The body components are gradually losing. Medical staff should pay attention to the influencing factors of patients' health score, and formulate targeted intervention to health management. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing traffic flow velocity prediction model used a unique data set and a single model, this paper proposed a prediction model combining time series and artificial neural network. The model predicted real-time data and historical data by time series, and used artificial neural network to adjust the predicted values of real-time data and historical data. The experimental results show that the prediction model can control the prediction error within 7 % and can effectively predict the short-term traffic flow speed under different input parameters. [ABSTRACT FROM AUTHOR]