Quality of resolution of black multicomponent systems

In this work, we define the quality of selective regions in a data matrix acquired by two-way instrumental methods. We name the quality parameter as the accumulated analytical signal (AAS) and link this quality measure to the resolution quality. The AAS is calculated as the first singular value divided by the second from a singular value decomposition of the selective region. We also extend this measure to systems containing more than two analytes and define the quality of zero-concentration windows (ZCWs). These regions are crucial in the resolution step. The quality parameter of this region is named as the net accumulated analytical signal (NAAS). It is calculated as the last significant singular value divided by the first non-significant singular value from a singular value decomposition of the ZCW. Since it is sometimes difficult to decide the elution regions by local rank analysis, we introduce a shifting procedure. The different elution regions are shifted and the system is resolved using the new elution windows. Indication of a good resolution is found when a stable solution appears.     

Kernel spectral clustering with memory effect

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.     

Evolving chemometric models for predicting dynamic process parameters in viscose production

In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H₂SO₄, Na₂SO₄ and Z_nSO₄. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi–Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual forgetting mechanisms may be integrated in order to out-date older learned relations and to account for more flexibility of the models. The results show that our approach is able to overcome the huge prediction errors produced by various state-of-the-art chemometric models. It achieves a high correlation between observed and predicted target values in the range of [0.95,0.98] over a 3 months period while keeping the relative error below the reference error value of 3%. In contrast, the off-line techniques achieved correlations below 0.5, ten times higher error rates and the more deteriorate, the more time passes by.     

Relational Evolution of the Degrees of Freedom of Generally Covariant Quantum Theories

We study the classical and quantum dynamics of generally covariant theories with vanishing Hamiltonian and with a finite number of degrees of freedom. In particular, the geometric meaning of the full solution of the relational evolution of the degrees of freedom is displayed, which means the determination of the total number of evolving constants of motion required. Also a method to find evolving constants is proposed. The generalized Heisenberg picture needs M time variables, as opposed to the Heisenberg picture of standard quantum mechanics where one time variable t is enough. As an application, we study the parametrized harmonic oscillator and the SL(2, R) model with one physical degree of freedom that mimics the constraint structure of general relativity where a Schrödinger equation emerges in its quantum dynamics.     

渐进因子分析－pH－分光法测定多元酸的离解常数

根据多元酸平衡体系中型体具有先出现先消失的特点,提出了用渐进因子分析与化学平衡结合迭代,从pH-分光矩阵中求解多元酸离解常数的新方法。用该法测定了多元酸茜素S和铬天青S的离解常数,并讨论了确定型体存在区域的经验式。结果表明该方法稳定、可靠、算法收敛快,所测得的离解常数与文献值吻合。     

Markov chain-based degree distributions of evolving networks

In this paper, we study a class of stochastic processes, called evolving network Markov chains, in evolving networks. Our approach is to transform the degree distribution problem of an evolving network to a corresponding problem of evolving network Markov chains. We investigate the evolving network Markov chains, thereby obtaining some exact formulas as well as a precise criterion for determining whether the steady degree distribution of the evolving network is a power-law or not. With this new method, we finally obtain a rigorous, exact and unified solution of the steady degree distribution of the evolving network.     

GC-MS与正交投影法或渐进窗口正交投影法用于羌活挥发油成分的测定

中药化学组成的定性定量测定是药物活性理论的基础,联用色谱及其相关的化学计量学方法为中药复杂体系的分离和分辨提供了强有力的工具.采用GC-MS法对传统中药羌活中的挥发油成分进行了分离测定,并对其中重叠色谱峰根据其重叠程度,采用迭代的正交投影法(OPA)和非迭代的渐进窗口正交投影法(EWOP)进行了分辨,得到每个组分的纯色谱和光谱曲线,共分辨出98个色谱峰,通过质谱库检索得到其中65个组分的定性定量结果,占总含量的92.13%.     

Modelling of weighted evolving networks with community structures

Many social and biological networks consist of communities–groups of nodes within which links are dense but among which links are sparse. It turns out that most of these networks are best described by weighted networks, whose properties and dynamics depend not only on their structures but also on the link weights among their nodes. Recently, there are considerable interests in the study of properties as well as modelling of such networks with community structures. To our knowledge, however, no study of any weighted network model with such a community structure has been presented in the literature to date. In this paper, we propose a weighted evolving network model with a community structure. The new network model is based on the inner-community and inter-community preferential attachments and preferential strengthening mechanism. Simulation results indicate that this network model indeed reflect the intrinsic community structure, with various power-law distributions of the node degrees, link weights, and node strengths.     

渐进因子分析—分光光度法测定Fe^3＋—SCN^—络合物的稳定常数

本文利用渐进因子分析与化学平衡相结合的方法迭代求解逐级生成络合物的各级稳定常数，对Ｆｅ＾３＋－ＳＣＮ＾－体系在水溶液及５０％丙酮溶液中的配体浓度－波长的两维双线性吸光度数据矩阵进行处理，求得水溶液中该络合物第一、二级稳定常数为１ｇｋ１＝２．２６，１ｇｋ２＝１．３０。同时该法还可确定各种组分的存在区间及吸收光谱，本文还对丙酮的存在对Ｆｅ＾３＋－ＳＣＮ＾－络合物的影响作了研究，结果表明丙酮，对高配位络     

二维重叠色谱峰解析方法的比较

利用渐进因子分析方法(EFA)和固定尺寸移动窗口因子分析方法(FSMWEFA)与局部正交投影方法(LOPA)相结合,用于模拟的HPLC—DAD二维数据解析。通过比较谱峰部分重叠、光谱完全重叠、色谱拖尾3种情况的结果显示,在谱峰部分重叠和色谱拖尾两种情况下两种方法都可以较好的解析重叠峰。但是在光谱完全重叠时,前一种方法不能得出正确的结论,而后一种方法仍能得到满意的结果。