固相萃取气相色谱-质谱联用法测定动物组织中的莱克多巴胺、沙丁胺醇

通过液液分配和固相萃取提取、净化动物组织中添加的莱克多巴胺、沙丁胺醇,TMS衍生化试剂BSTFA衍生化,气相色谱-质谱联用对衍生物检测分析,确定了动物组织中莱克多巴胺、沙丁胺醇的定性、定量分析方法,该法检出限为0.5μg/L,衍生物的峰面积与样品浓度在1μg/L￣1000μg/L范围内都呈良好的线性关系,线性回归系数均为0.9998。不同组织中莱克多巴胺加标回收率分别为:肝脏74.8%-85.8%,脂肪71.2%-80.6%,肌肉75.0%-82.7%,肾脏72.0%-82.9%;相对标准偏差为2.3%-5.2%。沙丁胺醇加标回收率分别为肝脏70.5%-76.7%,脂肪72.7%-79.5%,肌肉72.6%-78.5%,肾脏72.5%-75.8%;相对标准偏差为1.8%-5.2%。     

Importance sampling for signal extraction model in time series analysis

In time series analysis, signal extraction model (SEM) is used to estimate unobserved signal component from observed time series data. Since parameters of the components in SEM are often unknown in practice, a commonly used method is to estimate unobserved signal component using the maximum likelihood estimates (MLEs) of parameters of the components. This paper explores an alternative way to estimate unobserved signal component when parameters of the components are unknown. The suggested method makes use of importance sampling (IS) with Bayesian inference. The basic idea is to treat parameters of the components in SEM as a random vector and compute a posterior probability density function of the parameters using Bayesian inference. Then IS method is applied to integrate out the parameters and thus estimates of unobserved signal component, unconditional to the parameters, can be obtained. This method is illustrated with a real time series data. Then a Monte Carlo study with four different types of time series models is carried out to compare a performance of this method with that of a commonly used method. The study shows that IS method with Bayesian inference is computationally feasible and robust, and more efficient in terms of mean square errors (MSEs) than a commonly used method.     

大数据背景下CPI预测问题的文本挖掘技术设计与应用

本文创新地将半监督交互式关键词提取算法词频-逆向文件频率( Term Frequency- Inverse Document Frequency, TF-IDF )与基于 Transformer 的 双 向 编 码 表 征 ( Bidirectional Encoder Representation from Transformers,BERT)模型相结合,设计出一种扩展CPI预测种子关键词的文本挖掘技术。采用交互式TF-IDF算法,对原始CPI预测种子关键词汇广度上进行扩展,在此基础上通过BERT“两段式”检索过滤模型深入挖掘文本信息并匹配关键词,实现CPI预测关键词深度上的扩展,从而构建了CPI预测的关键词库。在此基础上,本文进一步对文本挖掘技术特征扩展前后的关键词建立预测模型进行对比分析。研究表明,相比于传统的关键词提取算法,交互式TF-IDF算法不仅无需借助语料库,而且还允许种子词的输入。同时,BERT模型通过迁移学习的方式对基础模型进行微调,学习特定领域知识,在CPI预测问题中很好地实现了语言表征、语义拓展与人机交互。相对于传统文本挖掘技术,本文设计的文本挖掘技术具有较强的泛化表征能力,在84个CPI预测关键种子词的基础上,扩充后的关键词对CPI具有更高的预测准确度和更充分的解释性。本文针对CP 预测问题设计的文本挖掘技术,也为建立其他宏观经济指标关键词词库提供新的研究思路与参考价值。     

天麻中天麻素的提取工艺

目的考察影响天麻中天麻素的提取因素。方法采用正交试验法,以提取液中醇浸膏收率及天麻素的含量为考察指标。结果提取的最佳条件70℃下,用40%乙醇提取3次,每次2h,6倍量乙醇。结论所用工艺稳定可行。     

走进郑和

明代大航海家郑和,作为回族穆斯林,有着显赫的家世、卓著的勇气、超凡的智慧,因而义不容辞地担当了出使西洋的重任,并圆满地完成了使命,创造了名垂青史的业绩。本文从回回民族史的角度诠释了郑和航海的内在必然性,为我们重新认识郑和提供了新的视角。     

南辕北辙

有个人坐着马车向北走。他告诉一个路人说他要去楚国。 路人就问他:“你要去楚国,应该向南走,怎么向北走呢?” 那人回答说:“没有关系,我的马特别好!” 路人告诉他:“你的马虽好,可你走的不是去楚国的路啊!” 那人又说:“没有关系,我的路费很多!” 路人又说:“你的路费虽多,可你走的不是去楚国的路啊!” 那人无所谓的说道:“我的车夫驾车技术非常好!” 路人听了,哭笑不得,不禁感叹道:“这人的马越好,路费越多,车夫技术越好,离楚国也就越来越远了!”     

Prediction theory for autoregressivemoving average processes

This paper reviews statistical prediction theory for autoregressive-moving average processes wing techniques developed in control theory. It demonstrates explicitly the connectioluns between the statistical and control theory literatures. Both the forecasting problem and the Single extraction problem am considered, udng linear least squares methods. Whereas the classical Statistical theory developed by Wiener and Kolmogomv is restricted to stationary stochaotic processes, the recursive techniques known as the Kalman filter are shown to provide a satisfactory treatment of the difference-stationary care and other more general cases. Complete results for non-invertible moving averages are also obtained.     

卵磷脂提取工艺的研究

研究建立了以新疆本地植物油脚为原料的卵磷脂提取工艺。水化油脚在0.065-0.085MPa、80℃条件下真空浓缩脱水,得浓缩磷脂,再经丙酮脱油、分离、干燥得粉状磷脂即粗磷脂;粗磷脂在55℃、30MPa条件下用超临界流体二氧化碳萃取2小时萃除油脂等杂质,纯化得磷脂;然后用三倍量85%的乙醇60℃下提取磷脂1小时,提取三次,最终得到以磷脂酰胆碱为主的产品即卵磷脂。大豆、葵花和胡麻三种卵磷脂的提取率分别为:17.8%、12.6%及13.2%,含磷量分别为:4.52%、2.93% 及 3.28%,磷脂酰胆碱含量分别为:64.3%、53.3% 及59.8%。     

On the multiple frontiers of extraction: excavating contemporary capitalism

Understanding the intensification and expansion of extractive industries in contemporary capitalism requires an approach attentive not only to the literal forms of extraction prevalent in mining and agribusiness but also to new fronts of extraction emerging in activities such as data mining and biocapitalism. This article introduces the concept of operations of capital to trace connections between the expansive logic of extraction and capitalist activity in the domains of logistics and finance. Arguing that extractive operations are at large across these domains, we explore their relevance for capital’s relation with its multiple outsides. The resulting analysis provides a basis for mapping struggles against the changing forms of dispossession and exploitation enabled by extraction.