Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.     

Measurement of heat transfer coefficients for steam condensation on a vertical 21.5-mm-O.D. tube in the presence of air

ABSTRACT

Governing the rate of heat transport by condenser tubes in the passive containment cooling system (PCCS), the steam condensation over a vertical cylinder in the presence of air was investigated experimentally. The main objective of this study was to explore if the condensation heat transfer coefficient relies on the tube dimension, which has been a variable missed in most condensation models or has been embraced without experimental demonstration under phase change environments. The mean heat transfer coefficient was measured in the condensation test facility named JERICHO (JNU Experimental Rig for Investigation of Condensation Heat transfer On tube). The outer diameter of the condenser tube used in this study was set to 21.5 mm. The measured heat transfer coefficients were compared to those obtained from the 40-mm-O.D. tube, and a multiplier to correct the variation of the heat transfer coefficient with the tube diameter was proposed for its application to Lee correlation. The proposed correlation was further validated against another set of experimental data obtained from a separate test facility housing the 31.8-mm-O.D. tube.     

Measurement of solubility and diffusion coefficients of ethylene in solid and molten low-density polyethylene with different melt indices

The diffusion behavior of ethylene in polyethylene is of great importance for the polymerization and degassing of polyethylene (PE) industry. Based on the gravimetric sorption and desorption measurement approach, an intelligent gravimetric analyzer is applied to obtain the solubility and diffusion coefficients of ethylene in solid low-density PE (LDPE) with different melt indices at 30°C to 70°C, 0 to 4 atm and in molten LDPE at 160°C to 230°C, 0 to 4 atm, respectively. Results indicate that both the solubility and diffusion coefficients of ethylene in solid LDPE are smaller than those in molten LDPE, while the dissolution enthalpy and diffusion activation energy of ethylene in solid LDPE are higher. In addition, one- and two-dimensional diffusion models are built and the effects of particle size, polymer properties, and operation conditions are systematically investigated on the diffusion behaviors of ethylene in solid and molten LDPE.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Optimization of quinacridone magenta,Cu-phthalocyanine cyan,and arylide yellow ink films formulated for maximum color gamut

Investigation of ink formulation options with the purpose to obtain color-gamut-optimal set of Cyan Magenta and Yellow CMY inks is reported. Implementation of the thickness dependent Kubelka-Munk model on multiple ink layers having different and well-defined thicknesses, provides characteristic absorption and scattering (K, S ) spectra of the ink ingredients. These data enable accurate computation of the reflectance spectrum and thus the L*a*b* color coordinates for any given ink thickness or substrate. Pigment materials investigated are quinacridone as magenta, copper-phthalocyanine as cyan, and arylide yellow. Scaling the peak of the absorption band to the number of molecules per unit area for the specific pigments studied in this article provides the molar extinction coefficients, 1.21 × 10⁴ , 4.7 × 10⁴ , and 3.3 × 10⁴ cm²/millimole respectively, regardless of the different ink formulations used, in accord with Avogadro's principle. Having a set of three pairs of K, S spectra is used to compute the color gamut of any CMY color combination in the L*a*b* space as a function of ink layer thickness and formulation. Using an iterative algorithm, a color-gamut-optimal set of CMY inks is obtained.     

提升生活排水系统卫生安全性能的关键问题

新冠肺炎确诊患者粪便病毒检测呈阳性,意味着新冠病毒可能会通过气溶胶的方式进行传播,使得生活排水系统的卫生安全问题时隔17年后再次摆在人们面前。以保障人的健康为出发点,从基础研究、排水机理、系统设计、施工监管、维护管理等多方面提出了提升生活排水系统卫生安全性能的关键问题,以期从全生命周期内切断排水系统可能传播病毒的隐形途径,切实保障居民居家健康。     

基于链接相似度和作弊系数的Spam网页识别算法

Spam网页主要通过链接作弊手段达到提高搜索排名而获利的目的,根据链接作弊的特征,引入链接相似度和作弊系数两个指标来判定网页作弊的可能性。借鉴BadRank算法思想,从Spam网页种子集合通过迭代计算链接相似度和作弊系数,并根据与种子集合的链接指向关系设置权重,将待判定的网页进行度量。最后选取Anti-Trust Rank等算法作对比实验,结果验证了本文算法在准确率和适应性方面优于对比算法。     

Structural and optical characterization of ZnO doped PC/PS blend nanocomposites

PC50%/PS50% polymer blend nanocomposites, undoped and doped with different concentration of ZnO nanoparticles (1, 2, 3 wt%), have been prepared using solution casting method. Structural and optical studies have been performed using X-ray Diffraction (XRD), Scanning Electron Microscope (SEM) and Ultraviolet–Visible spectroscopy (UV–Vis). ZnO nanoparticles have been synthesized by chemical route method. The nanostructure of the ZnO nanoparticles has been ascertained through X-ray Diffraction (XRD) and Transmission Electron Microscopy (TEM). Optical Absorption Spectra has been used to study optical constants of prepared blend nanocomposites. Energy band gap of PC/PS – ZnO blend nanocomposites have been calculated by using Tauc relation. The band gap of the nanocomposites decreases as ZnO wt% increases. Extinction coefficient, refractive index and real & imaginary part of dielectric constants increase with increase in ZnO nanoparticles wt%.     

Evolving accuracy: A genetic algorithm to improve election night forecasts

In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provides an argument to tighten the intersection between computer science and social science, especially political science, further. We scrutinize the performance of our algorithm's runtime behavior to evaluate its applicability in the field. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.     

基于改进ISODATA的无先验扩展目标聚类分析算法

基于弹载雷达多扩展目标检测的应用需求,在CFAR检测输出的基础上,对检测结果的聚类分析方法进行了论证分析,提出了改进的ISODATA算法。该算法摆脱了常用聚类分析算法对目标个数、聚类初值等先验信息的要求以及孤立点的影响。数字仿真实验验证了新算法的有效性。