从姜黄及姜黄浸膏中提取的挥发油化学成分研究

姜黄(Curcuma longa)是姜科姜黄属植物中的一个品种,据《中华人民共和国药典2005版一部》记载[1],姜黄中姜黄挥发油含量不少于7.0 mL/100 g.     

2-[-1′-(4′-甲基-3′-戊烯基)]-2-甲基苯并呋喃的合成研究

许多天然产物(如黄酮、香豆素、苯丙素)分子结构中含有2-[-1′-(4′-甲基-3′-戊烯基)]-2-甲基苯并呋喃(1a)和1b的结构单元,如,具有抗肿瘤生物活性的Sanggenol L 4和Kuwanol 5[1].因此寻找1a和1b便利有效的合成方法具有重要的合成价值.我们采用2,4,6-三羟基苯乙酮(2a)[或2,4-二羟基苯乙酮(2b)]为原料,与柠檬醛3在有机碱(吡啶、三乙胺、N,N-二乙基苯胺)的作用下,一步反应以较好的收率40%～70%分别获得1a和1b结构单元.     

基于计算机拟合的三棱镜色散率测量

利用分光计对三棱镜顶角和汞光灯、钠光灯的不同波长光线的最小偏向角进行测量,并且计算不同波长光线的折射率,再利用计算机编程拟合实验曲线,得到关于三棱镜的色散曲线,最后求导得到色散率函数。     

Determining the input dimension of a neural network for nonlinear time series prediction

Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it ,the paper pressents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Fimally,some validation examples and results are given.     

Position difference regularity of corresponding R-wave peaks for maternal ECG components from different abdominal points

We collected 343 groups of abdominal electrocardiogram(ECG) data from 78 pregnant women and deleted the channels unable for experts to determine R-wave peaks from them; then, based on these filtered data, the statistics of position difference of corresponding R-wave peaks for different maternal ECG components from different points were studied. The resultant statistics showed the regularity that the position difference of corresponding maternal R-wave peaks between different abdominal points does not exceed the range of 30 ms. The regularity was also proved using the fECG data from MIT–BIH PhysioBank. Additionally, the paper applied the obtained regularity, the range of position differences of the corresponding maternal R-wave peaks, to accomplish the automatic detection of maternal R-wave peaks in the recorded all initial 343 groups of abdominal signals, including the ones with the largest fetal ECG components, and all 55 groups of ECG data from MIT–BIH PhysioBank, achieving the successful separation of the maternal ECGs.     

Wavelet optimization for applying continuous wavelet transform to maternal electrocardiogram component enhancing

In the procedure of non-invasive fetal electrocardiogram(ECG) extraction, high-quality maternal R wave peak detection demands enhancing the maternal ECG component firstly. Among all the enhancing algorithms, the one based on the continuous wavelet transform(CWT) is very important and its effectiveness depends on the optimization of the used wavelet. However, up to now, there is still no clear conclusion on the optimal wavelet(including type and scale) for CWT to enhance the maternal ECG component of an abdominal ECG signal. To solve this problem, in this paper, we select several common used types of wavelets to carry out our research on what the optimal wavelets are. We first establish big-enough training datasets with different sampling rates and make a maternal QRS template for each signal in the training datasets.Second, for each type of selected wavelets, we find its optimal scale corresponding to each QRS template in a training dataset based on the principle of maximal correlation. Then calculating the average of all optimized wavelet scales results in the mean optimal wavelet of this type for the dataset. We use two original abdominal ECG databases to train and test the optimized mean optimal wavelets. The test results show that, as a whole, the mean optimal wavelets obtained are superior to the wavelets used in other publications for applying CWT to maternal ECG component enhancing.     

用TEAOH-C4H9NO复合模板剂合成SAPO-34分子筛的研究I．SAPO-34分子筛的合成与表征

以吗啉(C4H9NO)为主要模板剂，以少量四乙基氢氧化铵(TEAOH)为辅助模板剂合成了SAPO-34分子筛，并采用X射线衍射、扫描电镜、傅里叶变换红外光谱和热重-差热分析等手段对合成的SAPO-34分子筛进行了表征．结果表明，TEAOH在导向生成SAPO-34分子筛骨架过程中表现活跃，占据了较多的平衡骨架负电荷的位置，而吗啉主要起到填充分子筛孔道的作用．用TEAOH—C4H9NO复合模板剂合成的SAPO-34分子筛，其晶粒远小于单用吗啉模板剂合成的分子筛．     

以两亲性单季铵盐分子为模板剂合成的多级孔SAPO-34分子筛

设计合成两亲性的单季铵盐分子作为助结构导向剂,两亲性分子具有晶体生长抑制剂或者致孔剂作用,最终导向合成多级孔/小晶粒SAPO-34分子筛。助结构导向剂减少Si进入AlPO₄骨架,降低分子筛的强酸酸性。相较于传统的SAPO-34分子筛(CS),添加助结构导向剂合成的多级孔SAPO-34颗粒尺寸较小,介孔孔体积较高,强酸酸性较弱,其在甲醇制烯烃反应中的双烯(乙烯和丙烯)选择性提高约4%,催化剂的寿命延长一倍。     

co-*-模和1-余倾斜模

给出了co-*-模的定义和研究了1-余倾斜模与co-*-模之间的关系,并且设APR是有限型余倾斜双模,如果有fin.dimR<∞或fin.dimA<∞,则|fin.dim R-fin.dim A|≤1.     

总体经验模态分解能量向量用于ECG能量分布的研究

总体经验模态分解(EEMD)改进了经验模态分解(EMD)存在的模态混叠问题, 依据信号自身的波动特点将信号分解, 特别适合非线性非平稳信号的分析处理. ECG信号能量分布有一定的规律, 疾病会引起能量分布的变化, 研究ECG能量分布的改变对心脏疾病的研究和临床诊断有重要意义. 本文将ECG信号通过EEMD方法分解为多个本征模态函数(IMF)分量, 观察IMF分量的波动规律, 指出了ECG信号在不同时间尺度上的波动特点和物理意义. 将IMF分量分别计算能量, 得到ECG的能量向量, 并对健康人和三种心脏疾病患者能量向量进行对比分析. 结果表明心脏疾病导致EEMD能量向量的高频分量显著降低, 尤其是p₁分量具有较好的区分度, 可以作为心脏疾病诊断的参考依据. 相比较传统的频域分析方法单纯关注频率而忽略信号自身特点和信号成分之间的相互作用, EEMD的分解结果依赖于ECG信号本身, 因此更能够反映ECG信号的真实情况, 揭示年龄和疾病对ECG能量分布的影响.