Degree growth rates and index estimation in a directed preferential attachment model

Preferential attachment is widely used to model power-law behavior of degree distributions in both directed and undirected networks. In a directed preferential attachment model, despite the well-known marginal power-law degree distributions, not much investigation has been done on the joint behavior of the in- and out-degree growth. Also, statistical estimates of the marginal tail exponent of the power-law degree distribution often use the Hill estimator as one of the key summary statistics, even though no theoretical justification has been given. This paper focuses on the convergence of the joint empirical measure for in- and out-degrees and proves the consistency of the Hill estimator. To do this, we first derive the asymptotic behavior of the joint degree sequences by embedding the in- and out-degrees of a fixed node into a pair of switched birth processes with immigration and then establish the convergence of the joint tail empirical measure. From these steps, the consistency of the Hill estimators is obtained.     

A Reversible Dual‐Response Fluorescence Switch for the Detection of Multiple Analytes

This paper reports a reversible dual fluorescence switch for the detection of a proton target and 2,4,6‐trinitrotoluene (TNT) with opposite‐response results, based on fluorophore derivatization of silica nanoparticles. Fluorescent silica nanoparticles were synthesized through modification of the surface with a nitrobenzoxadiazole (NBD) fluorophore and an organic amine to form a hybrid monolayer of fluorophores and amino ligands; the resultant nanoparticles showed different fluorescence responses to the proton target and TNT. Protonation of the amino ligands leads to fluorescence enhancement due to inhibition of photoinduced electron transfer (PET) between the amine and fluorophore. By contrast, addition of TNT results in fluorescence quenching because a fluorescence resonance energy transfer (FRET) happens between the NBD fluorophore and the formed TNT–amine complex. The fluorescence signal is reversible through washing with the proper solvents and the nanoparticles can be reused after centrifugal separation. Furthermore, these nanoparticles were assembled into chips on an etched silicon wafer for the detection of TNT and the proton target. The assembled chip can be used as a convenient indicator of herbicide (2,4‐dichlorophenoxyacetic acid) and TNT residues with the use of only 10 μL of sample. The simple NBD‐grafted silica nanoparticles reported here show a reversible signal and good assembly flexibility; thus, they can be applied in multianalyte detection.     

Multifunctional Fe3O4@mTiO2@noble metal composite NPs as ultrasensitive SERS substrates for trace detection

Multifunctional materials have become the development trend of current material preparation. We reported a typical layer-by-layer method for the fabrication of multifunctional Fe₃O₄@mTiO₂@noble metal triplex core-shell composite nanoparticle (NP), which is composed of a magnetic Fe₃O₄ particle as the core, a mesoporous TiO₂ interlayer and a layer of Ag nanoparticles or Au nanorods as the shell. The obtained Fe₃O₄@mTiO₂@noble metal composite NPs have shown excellent surface enhanced Raman scattering (SERS) sensitivity. Raman results present that the limit of detection (LOD) for crystal violet (CV), p-aminothiophnol (p-ATP) and p-mercaptobenzoic acid (p-MBA) of the Fe₃O₄@mTiO₂@noble metal composite NPs substrates are as low as 1.0 × 10⁻⁹ M, 1.0 × 10⁻¹² M and 1.0 × 10⁻⁹ M, respectively. In addition, the composite NPs also show high reproducibility and stability across the entire area with relative standard deviations (RSD) less than 15.00%. These highly sensitivity with good reproducibility can be attributed to the presence of plentiful “hot spots” produced by magnetic aggregation and target molecules enrichment by mesoporous TiO₂ adsorption for practical application. Fe₃O₄@mTiO₂@Ag composite NPs were used for thiram detection and the detection limit can reach to 5.0 × 10⁻⁸ M (about 0.012 ppm), which is lower than the maximal residue limit of 7 ppm in fruit prescribed by the U.S. Environmental Protection Agency. These multifunctional composite NPs provide easy separation, enrichment and trace detection of the analyte, exhibiting a great prospect as a potential SERS sensor in complex environments.     

Prediction of protein structural class based on symmetrical recurrence quantification analysis

Protein structural class prediction for low similarity sequences is a significant challenge and one of the deeply explored subjects. This plays an important role in drug design, folding recognition of protein, functional analysis and several other biology applications. In this paper, we worked with two benchmark databases existing in the literature (1) 25PDB and (2) 1189 to apply our proposed method for predicting protein structural class. Initially, we transformed protein sequences into DNA sequences and then into binary sequences. Furthermore, we applied symmetrical recurrence quantification analysis (the new approach), where we got 8 features from each symmetry plot computation. Moreover, the machine learning algorithms such as Linear Discriminant Analysis (LDA), Random Forest (RF) and Support Vector Machine (SVM) are used. In addition, comparison was made to find the best classifier for protein structural class prediction. Results show that symmetrical recurrence quantification as feature extraction method with RF classifier outperformed existing methods with an overall accuracy of 100% without overfitting.     

多条转动拉曼谱线雷达测量对流层大气温度

由于对流层大气中气溶胶的存在,传统的利用大气中瑞利散射和振动拉曼散射测量大气温度的方法具有一定的局限性,然而利用大气N₂和O₂分子的的转动拉曼信号获得大气温度信息的方法不受对流层大气中气溶胶的影响。因此利用N₂和O₂分子纯转动拉曼散射激光雷达开展了测量对流层下部温度分布的实验研究。现有的转动拉曼雷达系统基本上是通过获取单一的转动拉曼谱线来反演大气温度,这就导致了系统的信噪比低,不能很好的反演温度。作者在双光栅单色仪的基础上提出了一种新的雷达信号分光系统。这种新的分光系统的每条通道所获得的信号并不是单独的转动拉曼谱线,而是多条转动拉曼谱线之和,这样就能提高整个系统的信噪比。在较小的激光能量和小口径望远镜的情况下,利用这种方法,雷达系统可以在几公里内获得较好的信噪比。最后实验得到了对流层0.3～5 km高度内的大气温度分布, 它与球载无线电探空仪比较, 二者一致性较好。     

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.     

Rolling resistance and sinkage analysis by comparing FEM and experimental data for a grape transporting vehicle

In this study a 2D FEM model was developed to analyze ruts formation, rolling resistance, and power loss for a grape transporting cart aimed to replace the use of heavy tractors while harvesting grape. The model was supported by experiments in a vineyard in South Italy. Cone penetration tests were conducted to estimate frictional and cohesive properties in three soil conditions: firm, soft, and wet saturated. A tractor pulled test rig for a single wheel was developed to measure rolling resistance and sinkage, and complete the selection of the soil parameters. Completed the model, the analysis was conducted for a range of different wheel dimensions, and the outputs analyzed through response surfaces. The results showed the different impacts that width and diameter have on ruts formation and rolling resistance for different soil conditions. Wider wheels determined a main reduction of the sinkage, while the width contribution to the rolling resistance was affected by the total soil volume deformed. Larger diameters led to lower rolling resistance, with a higher impact on more deformable soils. Contact stress was compared with the thresholds recommended in the literature to determine the acceptable designs. This analysis represents a tool to select the running gear dimensions.     

DNA模板自组装正电荷纳米金基底的增强SERS效应

本文将合成的直径为10 nm的正电荷金纳米颗粒通过静电作用高密度自组装到带负电荷的长链λ-DNA分子上, 形成了高密度的具有纳米间隙的金纳米颗粒网络结构。研究了孤立的金纳米颗粒和所自组装的金纳米颗粒-DNA复合材料作为表面增强拉曼散射(SERS)基底的活性。原本对SERS信号响应较弱的10 nm直径的金纳米颗粒, 在自组装到DNA上形成具有纳米间隙的金纳米颗粒网络后, 产生了均匀、一致、强烈的SERS增强响应。我们利用用该基底对罗丹明G(R6G)、吡啶(Py)和对巯基苯胺(4-ATP)等不同类型的小分子化合物进行SERS检测的结果表明, 此方法制备SERS基底产率高、均一, 具有较好的SERS增强效果好, SERS信号稳定性和重复性相对常规孤立的金纳米颗粒SERS基底有很大提高。     

Novel graphical representation of genome sequence and its applications in similarity analysis

In order to compare different genome sequences, an alignment-free method has been proposed. Considering the essential property of sequence is sequentiality, we define a compound transformation which transforms a genome sequence into a sparse 16 by L−1$L-1$ matrix M based on 16 kinds of 2-mer (dinucleotides). Furthermore, we found the transformation above-mentioned is an order-preserving transformation (OPT). Based on the theory of matrix analysis, we derive a 16-dimensional vector to characterize a genome sequence via singular value decomposition (SVD) of M. Finally, we analyze the similarities among multiple sequences from 20 eutherian species. The experiment results show that our approach performs well in the field of sequence analysis.     

Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition

Image fusion for visible and infrared images is a significant task in image analysis. The target regions in infrared image and abundant detail information in visible image should be both extracted into the fused result. Thus, one should preserve or even enhance the details from original images in fusion process. In this paper, an algorithm using pixel value based saliency detection and detail preserving based image decomposition is proposed. Firstly, the multi-scale decomposition is constructed using weighted least squares filter for original infrared and visible images. Secondly, the pixel value based saliency map is designed and utilized for image fusion in different decomposition level. Finally, the fusion result is reconstructed by synthesizing different scales with synthetic weights. Since the information of original signals can be well preserved and enhanced with saliency extraction and multi scale decomposition process, the fusion algorithm performs robustly and excellently. The proposed approach is compared with other state-of the-art methods on several image sets to verify the effectiveness and robustness.