Symmetry classification of quasi-linear PDE’s containing arbitrary functions

We consider the problem of performing the preliminary “symmetry classification” of a class of quasi-linear PDE’s containing one or more arbitrary functions: we provide an easy condition involving these functions in order that nontrivial Lie point symmetries be admitted, and a “geometrical” characterization of the relevant system of equations determining these symmetries. Two detailed examples will elucidate the idea and the procedure: the first one concerns a nonlinear Laplace-type equation, the second a generalization of an equation (the Grad–Schlüter–Shafranov equation) which is used in magnetohydrodynamics.     

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.     

聚光光伏系统散热研究进展

对聚光光伏系统的发展过程进行了回顾.根据电池和聚光系统分类介绍了相匹配的散热冷却方式.电池组合方式简单、聚光倍数低的系统,使用传统风冷、水冷的被动散热即可满足要求;电池组合方式复杂、聚光倍数高的系统,常利用液浸冷却、射流冲击、通道冷却或多方法组合的新型冷却方式.指出在控制成本的前提下,选择系统可靠性高、结构简单的换热方...     

基于3-吡唑-5吡啶-1,2,4-三唑和均苯四甲酸构筑的两个同构配位聚合物的晶体结构和磁性

以3-吡唑-5吡啶-1,2,4-三唑(H2L)和均苯四甲酸(H_4btec)为配体合成了2个新的同构配位聚合物[M(btec)_(0.5)(H_2L)]_n(M=Co(Ⅱ)(1),Cu(Ⅱ)(2)),通过X射线单晶衍射、元素分析、红外光谱等进行了结构表征。晶体结构分析表明配合物1和2是同构的,都属于正交晶系,空间群为Pbca。2个配位聚合物都是二维结构,通过N…H…O氢键形成三维超分子框架结构。此外对上述配合物进行了磁性研究,结果表明配合物1内通过羧基桥连的金属钴离子之间是弱的铁磁相互作用;配合物2中存在典型的順磁行为。     

分类问题的在线顺序双并联快速学习机

双并联前馈神经网络模型是单层感知机和单隐层前馈神经网络的混合结构,本文构造了一种双并联快速学习机算法,与其他类似算法比较,提出的算法能利用较少的隐层单元及更少的待定参数,获得近似的学习性能.数值实验表明,对很多实际分类问题,提出的算法具备更佳的泛化能力,因而可以作为快速学习机算法的有益补充.     

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.     

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.     

Causal structure and algebraic classification of non-dissipative linear optical media

In crystal optics and quantum electrodynamics in gravitational vacua, the propagation of light is not described by a metric, but an area metric geometry. In this article, this prompts us to study conditions for linear electrodynamics on area metric manifolds to be well-posed. This includes an identification of the timelike future cones and their duals associated to an area metric geometry, and thus paves the ground for a discussion of the related local and global causal structures in standard fashion. In order to provide simple algebraic criteria for an area metric manifold to present a consistent spacetime structure, we develop a complete algebraic classification of area metric tensors up to general transformations of frame. This classification, valuable in its own right, is then employed to prove a theorem excluding the majority of algebraic classes of area metrics as viable spacetimes. Physically, these results classify and drastically restrict the viable constitutive tensors of non-dissipative linear optical media.     

决策树分类模型在工程项目评标风险预警中的应用

本文将数据挖掘中的决策树分类方法运用到工程项目评标数据分析,从200多个天津市工程项目招投标打分数据中,随机抽取15个招投标项目中的67个承包商的评标专家打分数据进行分析,得到中标承包商技术和商务评分分界点,进而得到工程项目潜在风险的预警阈值,然后借助因子分析辨识出风险来源并进行预警。     

数量化理论在金银花产地分类中的应用

采用数量化理论的方法.以金银花中微量元素含量作为变量,进行了产地分类,分析结果表明,金银花按产地可分3类:新密、封丘和平邑为一类;南京为一类;昆明和桂林为一类.结果与生产实际一致.