离散记录时间的分布

使用组合数学与概率论的方法研究了离散和的纪录时间的分布,并得出两个纪录时间L(m)和L(m+1)的条件分布.     

Temperature Dependence of the Properties of Water and Its Solutes,Including the Supercooled Region

An earlier study on the properties of supercooled water and some solutes obtained by simple graphical extrapolation of data available in the literature, determined above the freezing point, led to the hypothesis that water could have a temperature of structural arrest at t₀=−30 °C for H₂O and −22 °C for D₂O. From the present investigation of many more properties of a widely varied nature, this discovery is confirmed throughout. The method is compared with the classical logarithmic Arrhenius and van′t Hoff methods that predict t₀ always to occur at 0 K, and with the more recently used power law dependence method. With the latter, extrapolated plots intersect the temperature axis at or near to a value of t_s=−45 °C, that is, systematically more negative than t₀. It is argued that this t_s value, which is the so-called singularity temperature, is likely to be an artefact. It is suggested that, instead of using modified van ’t Hoff or Arrhenius relationships to describe temperature dependences in the full temperature range, polynomials in (t−t₀) should be used.     

食双星仙女座BX轨道周期长时标变化的观测研究

通过对W UMa型食双星BX And的观测得到了6个新的光度极小时刻,结合文献中的光变极小时刻数据,得出了O-C图和演化模型新的拟合参数,以及更高精度的轨道解,首次提出BX And轨道周期的长期减小性,并对其中一种可能的物理机制进行了讨论.     

中国共产党的生命线、胜利线——解放思想、实事求是、与时俱进、求真务实、人民为本

解放思想、实事求是、与时俱进、求真务实,是中国共产党的正确思想路线。人民为本,是中国共产党正确的政治路线。这条思想路线和政治路线,是中国共产党的生命线、胜利线。中国革命的成功,社会主义建设过程中的重大失误,改革开放的成功,从哲学上反思,归根结底就是是否坚持解放思想、实事求是、与时俱进、求真务实、人民为本的思想路线和政治路线。     

大学生党员马克思主义理论教育研究——基于培养领导者视角

大学生党员是大学生群体中的优秀分子。然而,一些大学生党员却不能深刻领会马克思主义本质和全面把握中国特色社会主义理论体系实质,对马克思主义中国化时代化大众化更是缺乏认识。因此面对现实又立足长远,当前高校应加强对大学生党员的马克思主义理论教育,提高大学生党员的理论素养、组织大学生党员深入群众实践,并勇于求索创新,使之成为推进马克思主义中国化时代化大众化的重要力量。     

Hitting Times,Cover Cost,and the Wiener Index of a Tree

We exhibit a close connection between hitting times of the simple random walk on a graph, the Wiener index, and related graph invariants. In the case of trees, we obtain a simple identity relating hitting times to the Wiener index. It is well known that the vertices of any graph can be put in a linear preorder so that vertices appearing earlier in the preorder are “easier to reach” by a random walk, but “more difficult to get out of.” We define various other natural preorders and study their relationships. These preorders coincide when the graph is a tree, but not necessarily otherwise. Our treatise is self‐contained, and puts some known results relating the behavior or random walk on a graph to its eigenvalues in a new perspective.     

Trilinearity deviation ratio: A new metric for chemometric analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry data

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC–TOFMS) is a well-established instrumental platform for complex samples. However, chemometric data analysis is often required to fully extract useful information from the data. We demonstrate that retention time shifting from one modulation to the next, Δ²t_R, is not sufficient alone to quantitatively describe the trilinearity of a single GC × GC–TOFMS run for the purpose of predicting the performance of the chemometric method parallel factor analysis (PARAFAC). We hypothesize that analyte peak width on second dimension separations, ²W_b, also impacts trilinearity, along with Δ²t_R. The term trilinearity deviation ratio, TDR, which is Δ²t_R normalized by ²W_b, is introduced as a quantitative metric to assess accuracy for PARAFAC of a GC × GC–TOFMS data cube. We explore how modulation ratio, M_R, modulation period, P_M, temperature programming rate, T_ramp, sampling phase (in-phase and out-of-phase), and signal-to-noise ratio, S/N, all play a role in PARAFAC performance in the context of TDR. Use of a P_M in the 1–2 s range provides an optimized peak capacity for the first dimension separation (500–600) for a 30 min run, with an adequate peak capacity for the second dimension separation (12–15), concurrent with an optimized two-dimensional peak capacity (6000–7500), combined with sufficiently low TDR values (0–0.05) to facilitate low quantitative errors with PARAFAC (0–0.5%). In contrast, use of a P_M in the 5 s or greater range provides a higher peak capacity on the second dimension (30–35), concurrent with a lower peak capacity on the first dimension (100–150) for a 30 min run, and a slightly reduced two-dimensional peak capacity (3000–4500), and furthermore, the data are not sufficiently trilinear for the more retained second dimension peaks in order to directly use PARAFAC with confidence.     

Analytical chemistry of the persistent organic pollutants identified in the Stockholm Convention: A review

Persistent organic pollutants (POPs) are major environmental concern due to their persistence, long-range transportability, bio-accumulation and potentially adverse effects on living organisms. Analytical chemistry plays an essential role in the measurement of POPs and provides important information on their distribution and environmental transformations. Much effort has been devoted during the last two decades to the development of faster, safer, more reliable and more sensitive analytical techniques for these pollutants. Since the Stockholm Convention (SC) on POPs was adopted 12 years ago, analytical methods have been extensively developed. This review article introduces recent analytical techniques and applications for the determination of POPs in environmental and biota samples, and summarizes the extraction, separation and instrumental analyses of the halogenated POPs. Also, this review covers important aspects for the analyses of SC POPs (e.g. lipid determination and quality assurance/quality control (QA/QC)), and finally discusses future trends for improving the POPs analyses and for potential new POPs.     

A general method for finding the optimal threshold in discrete time

We develop an approach for solving one-sided optimal stopping problems in discrete time for general underlying Markov processes on the real line. The main idea is to transform the problem into an auxiliary problem for the ladder height variables. In case that the original problem has a one-sided solution and the auxiliary problem has a monotone structure, the corresponding myopic stopping time is optimal for the original problem as well. This elementary line of argument directly leads to a characterization of the optimal boundary in the original problem. The optimal threshold is given by the threshold of the myopic stopping time in the auxiliary problem. Supplying also a sufficient condition for our approach to work, we obtain solutions for many prominent examples in the literature, among others the problems of Novikov-Shiryaev, Shepp-Shiryaev, and the American put in option pricing under general conditions. As a further application we show that for underlying random walks (and Lévy processes in continuous time), general monotone and log-concave reward functions g lead to one-sided stopping problems.     

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In software defect prediction with a regression model, too many metrics extracted from static code and aggregated (sum, avg, max, min) from methods into classes can be candidate features, and the classical feature selection methods, such as AIC, BIC, should be processed at a given model. As a result, the selected feature sets are significantly different for various models without a reasonable interpretation. Maximal information coefficient (MIC) presented by Reshef et al.\ucite{4} is a novel method to measure the degree of the interdependence between two continuous variables, and an available computing method is also given based on the observations. This paper firstly use the MIC between defect counts and each feature to select features, and then conduct the power transformation on the selected features, and finally build up the principal component Poisson and negative binomial regression model. All experiments are conducted on KC1 data set in NASA repository on the level of class. The block-regularized $m\times 2$ cross-validated sequential $t$-test is employed to test the difference of performance of two models. The performance measures of a model in this paper are FPA, AAE, ARE. The experimental results show that 1) the aggregated features, such as sum, avg, max, are selected by MIC except min, which are significantly different from AIC, BIC; 2) the power transformation to the features can improve the performance for majority of models; 3) after PCA and factorial analysis, two clear factors are obtained in the model. One corresponds to the aggregated features via avg and max, and the other corresponds to the aggregated features with sum. Therefore, the model owns a reasonable interpretation. Conclusively, the aggregated features with sum, avg, max are significantly effective for software defect prediction, and the regression model based on the selected features by MIC has some advantages.