平顶高斯超短脉冲激光的时间部分相干性对相干合成的影响

 利用准单色光干涉理论,计算了2×2平顶高斯超短脉冲激光在远场的干涉图样,分析了激光的时间部分相干性及其存在束间相位差时对相干合成的影响。计算结果表明,当单路激光输出光束线宽小于100 nm时,理想波前的远场光斑图样和峰值光强基本保持不变,当光束线宽大于100 nm时,干涉减弱衍射效应逐渐增强;光束间存在相位差时,时间部分相干性将破坏远场图样随束间相位差的周期性变化,相位差越大干涉越弱;当超过相干长度时,则为非相干合成,但1/6倍相干长度范围内,仍可视为较理想的相干合成,此时焦斑的斯特列尔比大于0.9,对超短脉冲的相干合成影响可忽略。     

A Selective Release System Based on Dual‐Drug‐Loaded Mesoporous Silica for Nanoparticle‐Assisted Combination Therapy

A selective release system was demonstrated with a dual‐cargo loaded MSNs. When stimulated by different signals (UV or H⁺), this system could selectively release different kinds of cargoes individually. Furthermore, this system has been used to provide a combination of chemotherapy and biotherapy for cancer treatment. This controlled release system could be an important step in the development of more effective and sophisticated nanomedicine and nanodevices, due to the possibility of selective release of a complex multi‐drug.     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

楔形体破坏模式下红层边坡岩体质量SMR法评价

简要介绍了边坡岩体质量分类的SMR法,将之应用于2个典型楔形体破坏模式下红层岩体高边坡稳定性宏观评价。 指出在评价过程中要注意对边坡起控制作用的优势结构面,并将其作为SMR法中结构面项评分的主要依据,这样得出的评分 值与边坡的实际情况才会比较相符。对地应力的影响进行了定性的分析,指出高地应力地区边坡SMR法应该慎用。     

Detection of Anomalous Diffusion with Deep Residual Networks

Identification of the diffusion type of molecules in living cells is crucial to deduct their driving forces and hence to get insight into the characteristics of the cells. In this paper, deep residual networks have been used to classify the trajectories of molecules. We started from the well known ResNet architecture, developed for image classification, and carried out a series of numerical experiments to adapt it to detection of diffusion modes. We managed to find a model that has a better accuracy than the initial network, but contains only a small fraction of its parameters. The reduced size significantly shortened the training time of the model. Moreover, the resulting network has less tendency to overfitting and generalizes better to unseen data.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

On-line combination of single-drop liquid–liquid–liquid microextraction with capillary electrophoresis for sample cleanup and preconcentration: A simple and efficient approach to determining trace analyte in real matrices

In order to improve the sensitivity of capillary electrophoresis (CE) and overcome the deficiency of commercial CE instruments in handling complex matrices directly, we proposed a novel technique which combined single-drop liquid–liquid–liquid microextraction (SD-LLLME) with CE on-line. In this technique, SD-LLLME was realized using a commercial CE instrument and, to further concentrate the target analyte, large-volume sample stacking combined sweeping without polarity switching was utilized. Even though without agitating the donor phase in the extraction process, the model compound, adenine was enriched 550-fold in only 10 min. The enrichment factors were 760 and 1030 when the extraction time was extended to 30 and 60 min, respectively. The relative standard deviations (RSDs) of adenine were 5.24% and 2.29% for peak area and migration time, respectively, which indicated that this method was much more reproducible compared to the existing methods that combined sample-preparation strategies with CE. In addition, this approach was selective while cleaning up target analyte. These mentioned advantages allowed the developed method to be an attractive approach to determining trace target compounds in complex real samples.     

Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data

Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed on low quality data, in an interval or a fuzzy number. A cooperative-competitive genetic algorithm searches for the knowledge base whose fitness is primal with respect to a precedence relation between the values of this interval or fuzzy valued risk. In addition to this, the numerical estimation of this risk depends on the entrywise product of cost and confusion matrices. These have been, in turn, generalized to vague data. The flexible assignment of values to the cost function is also tackled, owing to the fact that the use of linguistic terms in the definition of the misclassification cost is allowed.     

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.     

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.