Subjects: Nuclear Science and Technology >> Nuclear Detection Technology and Nuclear Electronics submitted time 2024-03-20
Fu Shengwei
Long Jinbiao
Zhan Meiqiang
Lu Chengui
Guo Yufei
Yang Junjie
Zhang Zhe
Yu Yuhong
Lu Fenhua
Liu Tuoqi
Li Zhiyao
Xu Yingfeng
Sun Zhiyu
Yang Herun
Abstract： The properties of working gases in gas detectors,such as the average ionization energy,Fano factor,and drift velocity,have a significant impact on the preliminary simulation,parameter design,and trajectory reconstruction of the detectors.SeF6,as the target working gas in domestic neutrinoless double beta decay experiments,has unknown parameters that need to be investigated.To study the relevant parameters of this gas,a measurement scheme was designed and the accuracy and reliability of the experimental plan were tested using Ar/CH4=90/10(P10) as the working gas.In the experiment,the average ionization energy of P10 was measured using a grid ionization chamber with an 𝛼 source,yielding a value of 27.10±0.04 eV,and the Fano factor was determined to be 0.175±0.001 when the energy resolution reached 0.91% after subtracting noise through calibration electronics.Additionally,the drift velocity was measured using a 266 nm laser and a time projection chamber,and the results were consistent with the Garfield++ simulation results.The experimental results indicate the feasibility of the measurement scheme and demonstrate high reliability of
the measurement results.This provides a solid foundation for further research on the properties of SeF6.
Subjects: Computer Science >> Computer Application Technology submitted time 2022-04-26
Abstract：
Load forecasting can effectively balance the load at both ends of supply and demand. Statistical model and deep learning are two common ways of constructing forecasting methods, but few load forecasting methods are constructed from the perspective of explanation. In this paper, a deep smoothing factor model, termed as DeepES, is proposed based on the nonlinear fitting ability of deep neural network and the explicable property of exponential smoothing model. According to the experimental results using actual load series data, the DeepES model achieves the best prediction. Moreover, compared with the traditional RNNs network with a single factor as the network input, DeepES has a more accurate and better explanatory for load prediction.
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Peer Review Status:
Awaiting Review
Subjects: Biology >> Bioengineering submitted time 2017-07-24 Cooperative journals: 《中国生物工程杂志》
Abstract：研究证明人白血病抑制因子（hLIF）是一种对多种不同类型的细胞和组织具有重要功能的细胞因子，其独特的生物学特性使其被广泛应用。本文介绍了自制的具有生物活性的hLIF，将hLIF基因克隆到pET32a中，并利用硫氧还蛋白（Trx）作为融合配体，在大肠杆菌中成功表达可溶性融合蛋白Trx-hLIF。亲和层析纯化后利用SDS-PAGE和Western blot对纯化结果进行检验。利用肠激酶（EK酶）切割融合蛋白，释放hLIF，随后通过简单的阳离子交换得到纯度高达98.1％的hLIF 4.75mg。小鼠M1髓系白血病细胞增殖分析测定纯化的rhLIF的功能与hLIF具有相似的生物活性，EC50为5ng/mL，对应的比活性为0.5×107 IU/mg。
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