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Bioavailability is an important indicator for evaluating the nutritional value of protein. In this study, we investigated the effect of ultrasound on improving the bioavailability of daily-consumed dietary proteins (rice, oat, corn, and soy protein). The results indicated that there was a significant difference (P

The existing photosynthetic rate prediction models consider only a single growing season. However, a photosynthetic rate prediction model intended for full growth of crops is needed. Therefore, a photosynthetic rate prediction model based on artificial neural networks (ANN), which establishes the prediction of the entire photosynthetic process, is presented in this article. The proposed model was developed using the multi-factor photosynthetic rate data obtained by experiments on cucumber seedlings and flowering stage. The ANN model was trained with the Levenberg-Marquardt (LM) training algorithm. In contrast to the single-phase photosynthetic rate prediction models, in the proposed model a fusion of parameters of all growing stages was applied, whereat all growing parameters were merged into one six-dimensional input signal (temperature, CO2 concentration, light intensity, relative humidity, chlorophyll content, and growth stage). Verification of model accuracy and performance has shown that merging of growing parameters has obvious advantage. Moreover, the proposed model satisfied the requirement in terms of training error. In addition, the determination correlation between measured and estimated values was 0.9517, thus, good correlation and estimation were achieved. Besides, the test average absolute error was 1.1454, which proves a high accuracy of the proposed model. Therefore, the proposed prediction model can provide the theoretical basis for the facilities light regulation and technical support. Keywords: Artificial neural networks, Cucumber, Full growth period, Photosynthetic rate, Prediction model.

Village gazetteers are among the most important primary sources for the study of Chinese village history and development. Gazetteers contain copious information on a village's administration, economy, culture, education, dialect, etc. that often cannot be found elsewhere. Contemporary Chinese Village Gazetteer Data (CCVG Data, 数字村庄) is a project initiated by the University Library System to collect data from village gazetteers and present it in open-access datasets, supporting teaching and research on Chinese village history and development. The data coverage period is 1949 to the present, though the final year of coverage for each village varies due to varying gazetteer compilation and publication dates. There are 18 datasets included in CCVG Data: Gazetteer Information; Village Information; Natural Environment; Natural Disasters; Year of First Availability; Last Names; Ethnic Groups; Population and Migration; Military, Politics and Management; Economy; Family Planning; Education. All are available for free download. For user’s convenience, CCVG Data is bilingual (Chinese/English). Data collection began in August 2018; by June 2020, data on 1,000 villages had been collected and entered into a customized platform to create the datasets. CCVG Data’s goal is to collect and input data on 2,500 to 3,000 villages by the end of 2021. Data will be updated every four to six months, with updated datasets offered in .csv format for download. A website supporting this dataset, including a data dictionary, information on the data structure, citation information, as well as a CCVG Data Map is established., 村志是研究中国乡村历史及发展的最重要的原始文献之一，因为它包含了关于村庄管理，经济，文化，教育，方言等大量材料，且这些材料往往是在其他地方找不到的。Contemporary Chinese Village Gazetteer Data (CCVG Data 数字村庄) 项目由匹兹堡大学图书馆系统原创和开发，旨在从村志中提取数据，以支持中国村庄历史和发展的教学和研究。数据覆盖期为1949年至每部村志编辑和出版的年份。18个数据集包括村志信息，村庄信息，自然环境，自然灾害，首次拥有年，姓氏，民族，人口与人口迁移，军事、政治和管理，经济，计划生育，教育等等，供用户免费下载。为方便使用，此数据集提供中英两种语言。截至2020年6月底，1,000个村庄的数据被提取并被输入定制平台。CCVG数据项目的目标是在2021年底之前完成2,500至3,000个村庄的数据提取和输入工作。每四到六个月将更新一次数据，并提供CSV格式下载入口。CCVG数据网站已经创建，包括数字村庄地图、使用者手册、数据结构、如何引用等内容。