- 著者
- Chao ZHAO Kun ZHAO Xiaoyan LIU Yi-fan HUANG Bin LIU
- 出版者
- Japanese Society for Food Science and Technology
- 雑誌
- Food Science and Technology Research (ISSN:13446606)
- 巻号頁・発行日
- vol.19, no.4, pp.661-667, 2013 (Released:2013-09-05)
- 参考文献数
- 35
- 被引用文献数
- 6 10
Orthogonal experiment was used to optimize the extraction conditions of Flammulina velutipes mycelia polysaccharides (FvP). Four independent variables (ratio of water to raw material, initial pH value, extracting temperature, and extracting time) were taken into consideration. A yield of FvP of 2.19% was obtained under an optimized condition (ratio of water to material of 50:1, initial pH value of 6.0, extracting temperature of 85°C, and extracting time of 6 h). Subsequently, antioxidative properties of FvP-2 (crude polysaccharides) and FvP-3 (deproteinized polysaccharides) prepared from F. velutipes mycelia were evaluated by monitoring the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical and hydroxyl radical scavenging activity, together with antitumor activity against the human hepatocellular carcinoma BEL-7402 cells. DPPH scavenging rate of 65.85% and hydroxyl radical scavenging rate of 71.24% were obtained at 200 μg/mL of FvP-3. The inhibition rate of BEL-7402 cells was up to 45% at 640 μg/mL of FvP-2. These results suggested that FvP possesses potent antioxidant and antitumor properties. The polysaccharide may be useful as a functional food additive and an antioxidant and antitumor agent.
- 著者
- Yifei Kang Chunmiao Ma Simin Wang Weiguo Wu Kun Zhao
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- IEICE Electronics Express (ISSN:13492543)
- 巻号頁・発行日
- vol.19, no.21, pp.20220291, 2022-11-10 (Released:2022-11-10)
- 参考文献数
- 32
- 被引用文献数
- 1
Nowadays, data centers are critical infrastructure for the information industry. Thermal security is one of the most concerning problems of the data center efficiently providing service. The temperature prediction method is an effective way, which overcomes the lagging of the feedback control and rewards a high prediction accuracy. While the current LSTM based prediction methods are limited in accuracy and restricted in scalability due to the lack of knowledge of physical properties and consideration of time constant differences of features. To address this, we propose a data center temperature prediction model with two-segment LSTM for prediction separately for IT equipment load and other heat-related variables with different time constants. The model takes into account the physical properties of the equipment and achieves higher prediction accuracy. The experimental results show that the prediction accuracy of our method is 27.27% higher than the state-of-art single segment LSTM method.