Characterization of MmWave Full-Duplex Cloud-Radio Access Network (C-RAN) with RRH Selection for 5G and Beyond

In this paper, a millimeter-wave (mmWave) full-duplex (FD) cloud radio access network (C-RAN) for 5G and beyond systems is studied, where the central unit is equipped with multiple antennas, and the spatially distributed remote radio heads (RRHs) operate in FD mode. In particular, we analyze a relay selection method based on end-to-end signal to interference plus noise ratio (SINR) in the proposed system. Distinguishing features of mmWave communications such as Rician small scale fading, path loss, blockage and directivity are taken into account in both fronthaul (FH) and access links. In order to have a comparison basis, the performance of the RRH selection method is also investigated in half-duplex (HD) mmWave C-RAN. Initially, the expression of the end-to-end SINR in the proposed system is derived, and then the cumulative density function (CDF) of the end-to-end SINR is derived in both FD and HD RRHs. Subsequently, we derive performance expressions such as outage probability, average rate and energy efficiency for the RRH selection method in the proposed system in both HD and FD mmWave C-RAN. Finally, the effectiveness of the RRH selection method and the accuracy of the obtained results are verified via Monte Carlo simulation results, which show that the RRH selection method in the mmWave FD C-RAN improves the performance significantly.     

A benchmark spike‐in data set for biomarker identification in metabolomics

The development and the validation of innovative approaches for biomarker selection are of paramount importance in many ‐omics technologies. Unfortunately, the actual testing of new methods on real data is difficult, because in real data sets, one can never be sure about the “true” biomarkers. In this paper, we present a publicly available metabolomic ultra performance liquid chromatography–mass spectrometry spike‐in data set for apples. The data set consists of 10 control samples and three spiked sets of the same size, where naturally occurring compounds are added in different concentrations. In this sense, the data set can serve as a test bed to assess the performance of new algorithms and compare them with previously published results. We illustrate some of the possibilities provided by this spike‐in data set by comparing the performance of two popular biomarker‐selection methods, the univariate t‐test and the multivariate variable importance in projection. To promote a widespread use of the data, raw data files as well as preprocessed peak lists are made available. Copyright © 2012 John Wiley & Sons, Ltd.     

Identification of a lipid‐related peak set to enhance the interpretation of TOF‐SIMS data from model and cellular membranes

Principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) data enables differentiating structurally similar molecules according to linear combinations of multiple peaks in their spectra. However, in order to use PCA to correctly identify variations in lipid composition between samples, the discrimination achieved must be based on chemical differences that are related to the lipid species, and not sample‐associated contamination. Here, we identify the positive‐ion TOF‐SIMS peaks that are related to phosphatidylcholine lipid headgroups and tail groups by PCA of spectra acquired from lipid isotopologs. We demonstrate that restricting PCA to a contaminant‐free lipid‐related peak set reduces the variability in the spectra acquired from lipid samples that is due to contaminants, which enhanced differentiating different lipid standards, but adversely affected the contrast in PC scores images of phase‐separated lipid membranes. We also show that PCA of a restricted data set consisting of the peaks related to lipids and amino acids increases the likelihood that the discrimination of TOF‐SIMS data acquired from intact cells is based on differences in the lipids and proteins on the cell surface, and not sample‐specific contamination without compromising sample discrimination. We expect that the lipid‐related peak database established herein will facilitate interpreting the TOF‐SIMS data and PCA results from studies of both model and cellular membranes, and enhance identifying the origins of the peaks that contribute to discriminating different types of cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Critical aspects of urine profiling for the selection of potential biomarkers using UPLC-TOF-MS

Biomarker selection through the metabolomics approach involves the acquisition of nontargeted metabolic profiles. In this study, some critical factors that may affect this process were investigated using urine test samples and a UPLC‐TOF system. Repeated injections of a single sample demonstrated that the percentage of undetected and poorly repeatable measurements (intensity RSD > 15%) decreased from 22.5 to 5.8% and from 32.9 to 14.7%, respectively, as the scan time was increased up to 0.6 s (approximately 11 data points per peak). An additional critical factor was identified in the presence of broad concentration differences between the samples; the application of a dilution scheme that minimized these differences reduced the number of missing values in the final datasets by 36%. The impact of missing values was further investigated in the study of two groups of samples produced by using a spike as artificial marker. Missing values weakened the models used for the interpretation of the metabolic profiles, and greatly hindered the identification of possible markers. Finally, a simple strategy for an effective analysis of urine samples was outlined; it proved to limit the need for the post‐acquisition elaboration of the data. The same strategy can easily be adapted to other matrices. Copyright © 2011 John Wiley & Sons, Ltd.     

Pattern Recognition as a Procedure for Selecting Analytical Methods for Solving Analytical Problems. A Preliminary Investigation

Abstract

A first step towards developing a calculation method, predicting the analytical method suitable to solve a distinct analytical problem, is described.

With pattern recognition, a binary decision maker is calculated, deciding the analytical method from two alternatives, w-vie spectrometry and atomic absorption spectrometry. This procedure yielded a 85% predicting accuracy.     

Simultaneous Analysis of Co2+, Cu2+ Mn2+, Ni2+ and Zn2+ in The Ultraviolet Region Using 4-(Pyridil-2-AZO) Resorcinol and Multivariate Calibration

ABSTRACT

A multicomponent spectrophotometric methodology for the simultaneous determination of Co²⁺, Cu²⁺, Mn²⁺, Ni²⁺ and Zn²⁺ in aqueous solution is reported, using (4-(pyridil-2-azo) resorcinol), a diode array spectrophotometer and multivariate calibration by partial least-squares and principal component regerssions. Spectra are recorded in the UV region. The 225 – 320 nm range is selected as optimal, through a criterion based on tederivatives of the differences between individual spectra, which compares favorably with a genetic algorithm. The methodology is applied to the simultaneous determination of the five than 1.5 mgL^?1. The best result are obtanied at pH 9.0, with average absolute errors of prediction lower than 0.09 mgL^?1     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

基于在线评论和前景效用的商品等级评价及选择方法

商品属性或特征的在线评论对消费者选择商品产生重要影响,现有方法尚未关注商品级别和消费者的有限理性问题。为此,本文提出在线评论中基于前景理论的商品选择方法。首先,描述了依据在线评论的商品选择问题;然后,定义商品关于属性值级别隶属度,进而计算级别正理想点和负理想点;其次,以正理想点和负理想点为参考点,计算各个商品的前景综合级别特征值,据此提出基于二元语义的商品等级评价与选择方法。最后,通过一个商品选择案例说明该方法的有效性和合理性。     

基于稀疏主成分分析特征选择算法的山楂叶产地判别模型研究

为实现山楂叶产地的快速判别,提出一种基于稀疏主成分分析特征选择（SPCAFS）与支持向量机（SVM）建模的定性分析方法。采用近红外积分球漫反射光谱法采集6个产地共41批山楂叶123份样品的近红外光谱图,经数据预处理后,通过SPCAFS对代表性特征波段进行选择,并采用SVM建立山楂叶近红外产地判别模型。模型与连续投影（SPA）,正则化自表示（RSR）和稀疏子空间聚类（SSC）3种特征选择算法进行对比,以准确率、精确度和灵敏度作为评价标准,评估所提模型的预测性能。结果显示,SPCAFS的特征波段数相比于全波长建模从1 500减少到21,预测结果的准确率和精确度分别从78%、76%提升至97%、100%。同时,相比于SPA、RSR、SSC算法,准确率分别提升了6%、3%、3%,精确度分别提升了13%、10%、5%,模型的预测能力得到显著提升,基于SPCAFS的SVM判别模型可实现山楂叶南北产地的快速判别。     

Solidification behavior and microstructural evolution of the Cr‐Nb eutectic alloy

A series of microstructures including fully coupled eutectic, both α‐Cr and β‐Cr₂Nb primary dendrites embedded in eutectic and only β‐Cr₂Nb primary dendrites plus eutectic were observed in the arc‐melted Cr‐Cr₂Nb eutectic alloy. By employing EPMA analysis performed at the eutectic regions, the eutectic composition of the Cr‐Cr₂Nb system was indicated to contain less than 18 at.%Nb. Based on the solidification phase selection involving phase competitive nucleation and growth, the α‐Cr phase was predicted to be the primary nucleating phase and the β‐Cr₂Nb the primary growing phase. Under large undercooling conditions, the solidification process was controlled by nucleation, which led to the formation of α‐Cr primary particles. With the decrease in undercooling, the solidification process developed into growth controlling, which caused the occurrence of β‐Cr₂Nb primary phase since the actual solidification path of the alloy lay within the hypereutectic region. The explanation was confirmed by the experimental composition analysis.