Computer-assisted optimization of selectivity by tuning temperature and carrier gas pressure drop in two GC capillary columns coupled in series

The paper shows a computer-assisted procedure for the optimization of selectivity of two columns coupled in series by tuning the working temperature (using the isothermal mode) and columns coupling-point pressure at constant inlet and outlet carrier gas pressures. The optimization procedure validation was tested by the separation of 32 hydrocarbons in a column series with the aim to resolve the maximum number of components in the shortest possible analysis time.Dedicated to Professor J. F. K. Huber on the occasion of his 65th birthday     

多柱串联高温HPLC系统的构建及应用

在高效液相色谱(HPLC)中,使用多柱串联能够极大地增加柱效和改善分离效果,提高温度能够明显降低色谱柱反压、加快分析速度.将色谱柱串联与提高柱温相结合构建了多柱串联高温HPLC系统,该系统常温下分析标准样品时(以芴计),绝对柱效达7.6×104塔板数;柱温升高到120℃,色谱柱反压从35.3MPa降低到15.4MPa,分析时间从78.39min缩短为19.26min.采用构建的系统分析中药六味地黄丸,常温下出峰94个,升高温度至100℃出峰80个,分离效果较常规的单柱系统有明显提高;升高温度,使主要组分的出峰时间缩短,可获得更为详尽的组成信息.     

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.     

PPCP用固态脉冲电源的实验研究

 介绍了一种采用半导体开关与磁开关、可饱和脉冲变压器相结合技术的固态脉冲电源,此电源可用于脉冲放电等离子体烟气治理。在理论分析的基础上建立了实验模型,通过实验验证了此类电源的可行性,解决了8支晶闸管开关串联的动静态均压及开通同步性问题,并对可饱和脉冲变压器及磁开关的工作特性进行了分析计算。电源在阻性负载上得到峰值电压37.5 kV、前沿101 ns、脉宽1 μs的脉冲,重复频率300 Hz,输出功率10 kW。     

Multivariate Time Series Imputation: An Approach Based on Dictionary Learning

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods.     

中国散裂中子源直线射频功率源系统的研制

中国散裂中子源加速器质子束流加速能量为1.6 GeV,重复频率为25 Hz,撞击固体金属靶产生散射中子,一期工程的打靶束流功率为100 kW。直线加速器的设计束流流强为15 mA,输出能量为81 MeV。射频加速和聚束系统包括一台射频四极场加速器、中能束流传输线的两个聚束器、四节漂移管直线加速器加速腔和直线-环束流传输线的一个散束器,与之相对应,共有8个单元在线运行的射频功率源为其提供所需的射频功率。目前,直线射频功率源系统预研项目已全部完成,各项性能参数均已达到设计指标,当前正处在批产安装调试阶段。151013     

磁致冷材料钆硅锗系合金中Si和Gd的重量法联测的研究

参考了HClO4脱水重量法测定Si及草酸盐重量法测定Gd的方法,建立了钆硅锗系合金中主成分Si和Gd联合测定的方法,并详细考查了共存元素及杂质元素对测定的影响。合金共存元素Ge在HCl的存在下,绝大部分可以GeCl4的形式被蒸发,达到与被测元素分离的目的。熔样过程引入的杂质元素Ni可与被测元素Gd,用氨水沉淀分离,残留Ni不足以影响Gd的测定。方法回收率在98.5%以上,Si和Gd的测量相对标准偏差分别在2.6%~3.0%和0.4%~0.5%。     

含能材料的密度、爆速、爆压和静电感度的理论研究

用密度泛函理论(DFT) B3LYP方法, 在6-31G*基组水平下, 全优化计算了系列硝胺类和硝基芳烃类爆炸物的几何构型, 用Monte-Carlo方法和自编程序, 基于0.001 e•bohr^－3等电子密度面所包围的体积空间求得分子平均摩尔体积(V)和理论密度(ρ). 用Kamlet-Jacobs方程基于理论密度(ρ)和PM3计算生成焓(ΔH_f)估算标题物的爆速(D)和爆压(p), 发现多环硝胺类化合物的爆轰性能优于芳烃硝基类化合物, 故此, 在寻求高能量密度材料(HEDM)时, 我们应特别关注多环硝胺化合物. 与ρ和D文献值比较, 表明本理论计算方法和结果是适用可靠的. 将爆速(D)和爆压(p)计算值与静电感度实验值(E_ES)进行比较和关联, 发现: 若对化合物进行细致分类讨论, 则它们之间存在较好的线性关系. 据此建议, 在含能材料分子设计中, 可通过理论计算爆轰性质(D或p)去预估难以定量求得或尚未合成的含能材料的静电火花感度值(E_ES). 此外, 我们还讨论了取代基对ρ, D和p的影响, 也有助于分子设计.     

Cu离子A位掺杂对LaSrCoO4复合氧化物氧化性能的影响

采用聚丙烯酰胺法制备了La1-xCuxSrCoO4(x=0.2-0.8)复合氧化物,考察了Cu离子掺杂量(x)对CO及C3H8氧化反应活性的影响,并运用XRD、IR、TPR和TPD等多种手段对复合氧化物催化剂进行了表征。结果表明,催化剂活性随x的变化而变化,Cu部分取代A位La能提高LaSrCoO4催化剂的CO及C3H8氧化反应活性,当x=0.4时La1-xCuxSrCoO4催化剂的晶格氧和Co3 含量较多,晶格氧的活动性较高,催化活性最佳。     

Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis

Body temperature is usually employed in clinical practice by strict binary thresholding, aiming to classify patients as having fever or not. In the last years, other approaches based on the continuous analysis of body temperature time series have emerged. These are not only based on absolute thresholds but also on patterns and temporal dynamics of these time series, thus providing promising tools for early diagnosis. The present study applies three time series entropy calculation methods (Slope Entropy, Approximate Entropy, and Sample Entropy) to body temperature records of patients with bacterial infections and other causes of fever in search of possible differences that could be exploited for automatic classification. In the comparative analysis, Slope Entropy proved to be a stable and robust method that could bring higher sensitivity to the realm of entropy tools applied in this context of clinical thermometry. This method was able to find statistically significant differences between the two classes analyzed in all experiments, with sensitivity and specificity above 70% in most cases.