Standard Normal Method Research Articles

Fluidized-bed OCM reaction: A promising Mn2O3-Na2WO4/TiO2 catalyst and a numerical study

The fluidized-bed reactor with excellent heat and mass transfer features has great appeal for application in the oxidative coupling of methane (FB-OCM), whereas qualified fluid catalysts represent a grand challenge. Herein, we report a high-performance Mn2O3-Na2WO4/TiO2 fluid catalyst (5.4 wt% Mn2O3 and 2 wt% Na2WO4), which was obtained by the incipient-wetness impregnation method and examined in the OCM reaction in an electric-heating quartz fluidized-bed reactor (i.d., 2 cm). The standard normalization method on the basis of carbon atom was used for calculating the CH4 conversion and C2-C3 selectivity. This catalyst achieves 35.5 % CH4 conversion, 66 % C2-C3 selectivity and particularly 15.1 % single-pass yield of C2H4 for a feed of CH4/O2/H2O=3/1/1 with a catalyst dosage of 20 mL at 700 °C and a total GHSV of 7200 h−1. Such catalyst is stable for at least 50 h without signs of deactivation and agglomeration/defluidization. The catalyst particle size is crucial for the FB-OCM reaction with respect to the reaction light-off and catalyst fluidization. The catalyst with preferable particle size of 450–600 μm can not only trigger the OCM reaction at a relatively low temperature of 630 °C but also warrant active bubbling fluidization. Furthermore, thanks to the low amount of Na2WO4 and interpenetrating structure of TiO2-nanorod, such catalyst achieves good anti-aggregation and robust mechanical strength (with a low attrition index of 1.9 %). The effects of water-adding amount in feedstock and catalyst particle size distribution on the hydrodynamics behaviour were investigated in a laboratory-scale fluidized-bed OCM reactor by using numerical simulation method.

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy (LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350 °C, 1400 °C, and 1450 °C. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350 °C, 1400 °C, and 1450 °C. The normalized full spectrum combined with partial least squares (PLS) quantification modeling was used, using the Ca II 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450 °C has been found to yield superior results compared to both 1350 °C and 1400 °C, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Improving Network Training on Resource-Constrained Devices via Habituation Normalization

As a technique for accelerating and stabilizing training, the batch normalization (BN) is widely used in deep learning. However, BN cannot effectively estimate the mean and the variance of samples when training/fine-tuning with small batches of data on resource-constrained devices. It will lead to a decrease in the accuracy of the deep learning model. In the fruit fly olfactory system, the algorithm based on the "negative image" habituation model can filter redundant information and improve numerical stability. Inspired by the circuit mechanism, we propose a novel normalization method, the habituation normalization (HN). HN first eliminates the "negative image" obtained by habituation and then calculates the statistics for normalizing. It solves the problem of accuracy degradation of BN when the batch size is small. The experiment results show that HN can speed up neural network training and improve the model accuracy on vanilla LeNet-5, VGG16, and ResNet-50 in the Fashion MNIST and CIFAR10 datasets. Compared with four standard normalization methods, HN keeps stable and high accuracy in different batch sizes, which shows that HN has strong robustness. Finally, the applying HN to the deep learning-based EEG signal application system indicates that HN is suitable for the network fine-tuning and neural network applications under limited computing power and memory.

Cell-tak coating interferes with DNA-based normalization of metabolic flux data

Metabolic flux investigations of cells and tissue samples are a rapidly advancing tool in diverse research areas. Reliable methods of data normalization are crucial for an adequate interpretation of results and to avoid a misinterpretation of experiments and incorrect conclusions. The most common methods for metabolic flux data normalization are to cell number, DNA and protein. Data normalization may be affected by a variety of factors, such as density, healthy state, adherence efficiency, or proportional seeding of cells. The mussel-derived adhesive Cell-Tak is often used to immobilize poorly adherent cells. Here we demonstrate that this coating strongly affects the fluorescent detection of DNA leading to an incorrect and highly variable normalization of metabolic flux data. Protein assays are much less affected and cell counting can virtually completely remove the effect of the coating. Cell-Tak coating also affects cell shape in a cell line-specific manner and may change cellular metabolism. Based on these observations we recommend cell counting as a gold standard normalization method for Seahorse metabolic flux measurements with protein content as a reasonable alternative.

Using GIS-based spatial analysis to determine urban greenspace accessibility for different racial groups in the backdrop of COVID-19: a case study of four US cities.

As the United States leads COVID-19 cases on global charts, its spatial distribution pattern offers a unique opportunity for studying the social and ecological factors that contribute to the pandemic’s scale and size. We use a GIS-data-based approach to evaluate four American cities—Anchorage (Alaska), Atlanta (Georgia), Phoenix (Arizona), and Portland (Oregon) characterized by the significant composition of different racial and ethnic group populations. Building upon previous studies that investigated urban spatial inequalities using the environmental justice framework, we examine: (1) the relative racial vulnerability of Census Block Groups (CBG) and ZIP Code Tabulation Areas (ZCTA) to COVID-19 (2) green space distribution at CBG and ZCTA scale. Using standard normalization methods, we ranked racial vulnerability against % available green space for each city. Our results highlight the legacy of past and present urban planning injustices. The project is useful from environmental justice, public health management, and urban planning perspectives.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10708-021-10538-8.

ALCN: Adaptive Local Contrast Normalization

To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different lighting conditions. Unfortunately, creating such a training set is a very unwieldy and complex task. We therefore propose a novel illumination normalization method that can easily be used for different problems with challenging illumination conditions. Our preliminary experiments show that among current normalization methods, the Difference-of-Gaussians method remains a very good baseline, and we introduce a novel illumination normalization model that generalizes it. Our key insight is then that the normalization parameters should depend on the input image, and we aim to train a Convolutional Neural Network to predict these parameters from the input image. This, however, cannot be done in a supervised manner, as the optimal parameters are not known a priori. We thus designed a method to train this network jointly with another network that aims to recognize objects under different illuminations: The latter network performs well when the former network predicts good values for the normalization parameters. We show that our method significantly outperforms standard normalization methods and would also be appear to be universal since it does not have to be re-trained for each new application. Our method improves the robustness to light changes of state-of-the-art 3D object detection and face recognition methods.

GFS: fuzzy preprocessing for effective gene expression analysis

BackgroundGene expression data produced on high-throughput platforms such as microarrays is susceptible to much variation that obscures useful biological information. Therefore, preprocessing data with a suitable normalization method is necessary, and has a direct and massive impact on the quality of downstream data analysis. However, it is known that standard normalization methods perform poorly, specially in the presence of substantial batch effects and heterogeneity in gene expression data.ResultsWe present Gene Fuzzy Score (GFS), a simple preprocessing technique, that is able to largely reduce obscuring variation while retaining useful biological information. Using four sets of publicly available datasets containing batch effects and heterogeneity, we compare GFS with three standard normalization techniques as well as raw gene expression. Each method is evaluated with respect to the quality, consistency, and biological coherence of its processed output. It is found that GFS outperforms other transformation techniques in all three aspects.ConclusionOur approach to preprocessing is a stronger alternative to popular normalization techniques. We demonstrate that it achieves the essential goal of preprocessing – it is effective at making expression values from multiple samples comparable, even when they are from separate platforms, in independent batches, or belong to a heterogeneous phenotype.

Quantitative analysis of ammonium salts in coking industrial liquid waste treatment process based on Raman spectroscopy**Project supported by the National Natural Science Foundation of China (Grant Nos. 41405022 and 61475068).

Quantitative analysis of ammonium salts in the process of coking industrial liquid waste treatment is successfully performed based on a compact Raman spectrometer combined with partial least square (PLS) method. Two main components (NH4SCN and (NH4)2S2O3) of the industrial mixture are investigated. During the data preprocessing, wavelet denoising and an internal standard normalization method are employed to improve the predicting ability of PLS models. Moreover, the PLS models with different characteristic bands for each component are studied to choose a best resolution. The internal and external calibration results of the validated model show a mass percentage error below 1% for both components. Finally, the repeatabilities and reproducibilities of Raman and reference titration measurements are also discussed.

Normalization of gene expression data using support vector machine approach

Normalization of gene expression data refers the process of minimizing non biological variation in measured probe intensity levels so that biological differences in gene expression can be appropriately detected. Several linear normalization within arrays approaches has already been proposed. Recently, use of non-linear methods has been gained quite attention. In this study, our objective is to formulate non-linear normalization methods using support vector regression (SVR) and support vector machine quantile regression (SVMQR) approaches, more easier way and, assess the consistency of these methods with respect to other standard normalization methods for further application in gene expression data. SVR and SVMQR normalization methods have been implemented and their performance have been evaluated with respect to other standard normalization methods namely, locally weighted scatter plot smoothing and Kernel regression. It has been found that the normalized data based on proposed methods are capable of producing minimum variances within replicate groups and also able to detect truly expressible significant genes with respect to above mentioned other normalized data.

Normalizing Hip Muscle Strength: Establishing Body-Size-Independent Measurements

Bazett-Jones DM, Cobb SC, Joshi MN, Cashin SE, Earl JE. Normalizing hip muscle strength: establishing body-size-independent measurements. Objective To investigate the effectiveness of computing body-size-independent hip strength measures using muscle-specific allometric scaling and ratio standard normalization methods. Design Cross-sectional study. Setting University laboratory. Participants A convenience sample of healthy participants (N=113; 42 men, 71 women). Interventions Not applicable. Main Outcome Measures Anthropometric measurements of the leg and thigh were obtained, and maximal hip strength was tested (medial and lateral rotation, abduction, adduction, flexion, extension). Strength was measured isometrically as force (kg) and then converted to torque (Nm). Results The allometric scaling analysis resulted in exponents for normalizing body mass (BM) in each muscle group assessed. In addition, a 6-muscle average exponent was also computed (bavg) for force (men, .554; women, .335) and torque (men, .792; women, .482). The nonsignificant results of the linear regression analysis revealed that normalizing hip strength to BM bavg (hip strength/BM bavg) effectively removed the influence of BM on force and torque. However, sex should be factored into analyses of allometric scaling because men have higher b-values than women for both force and torque. The linear regression analyses also demonstrated that force normalized to BM ( P=.162–.895) and torque normalized to BM × Height ( P=.146–.889) were body-size-independent measures. Force normalized to BM 0.67 ( P=.001–.191) and body mass index (BMI) ( P=<.001–.066), and torque normalized to BM ( P=.004–.415) and BMI ( P<.001) were significantly related to BM and therefore were not body-size independent. Conclusions Normalizing force and torque to BM bavg is the most effective method of removing body-size dependence and allowing comparisons of persons with differing body sizes.

Improved tomographic reconstructions using adaptive time-dependent intensity normalization

The first processing step in synchrotron-based micro-tomography is the normalization of the projection images against the background, also referred to as a white field. Owing to time-dependent variations in illumination and defects in detection sensitivity, the white field is different from the projection background. In this case standard normalization methods introduce ring and wave artefacts into the resulting three-dimensional reconstruction. In this paper the authors propose a new adaptive technique accounting for these variations and allowing one to obtain cleaner normalized data and to suppress ring and wave artefacts. The background is modelled by the product of two time-dependent terms representing the illumination and detection stages. These terms are written as unknown functions, one scaled and shifted along a fixed direction (describing the illumination term) and one translated by an unknown two-dimensional vector (describing the detection term). The proposed method is applied to two sets (a stem Salix variegata and a zebrafish Danio rerio) acquired at the parallel beam of the micro-tomography station 2-BM at the Advanced Photon Source showing significant reductions in both ring and wave artefacts. In principle the method could be used to correct for time-dependent phenomena that affect other tomographic imaging geometries such as cone beam laboratory X-ray computed tomography.

Using spectral flux ratios to standardize SN Ia luminosities

We present a new method to standardize Type Ia supernova (SN Ia) luminosities to ~<0.13 magnitudes using flux ratios from a single flux-calibrated spectrum per SN. Using Nearby Supernova Factory spectrophotomery of 58 SNe Ia, we performed an unbiased search for flux ratios which correlate with SN Ia luminosity. After developing the method and selecting the best ratios from a training sample, we verified the results on a separate validation sample and with data from the literature. We identified multiple flux ratios whose correlations with luminosity are stronger than those of light curve shape and color, previously identified spectral feature ratios, or equivalent width measurements. In particular, the flux ratio R(642/443) = F(642 nm) / F(443 nm) has a correlation of 0.95 with SN Ia absolute magnitudes. Using this single ratio as a correction factor produces a Hubble diagram with a residual scatter standard deviation of 0.125 +- 0.011 mag, compared with 0.161 +- 0.015 mag when fit with the SALT2 light curve shape and color parameters x1 and c. The ratio R(642/443) is an effective correction factor for both extrinsic dust reddening and instrinsic variations such as those of SN 1991T-like and SN 1999aa-like SNe. When combined with broad-band color measurements, spectral flux ratios can standardize SN Ia magnitudes to ~0.12 mag. These are the first spectral metrics that improve over the standard normalization methods based upon light curve shape and color and they provide among the lowest scatter Hubble diagrams ever published.

Modelling and Forecasting Dynamic VaR Thresholds for Risk Management and Regulation

The paper describes alternative methods of estimating Value-at-Risk (VaR) thresholds based on two calibrated models and three conditional volatility or GARCH models. The five models of volatility are used to estimate and forecast the VaR thresholds of an equally-weighted portfolio, comprising four financial stock indexes, namely S&P500, CAC40, FTSE100 a Swiss market index (SMI). On the basis of the number of (non-)violations of the Basel Accord thresholds, the best performing model is PS-GARCH, followed closely by VARMA-AGARCH, neither of which would lead to the imposition of any penalties. The next best performing threshold forecasts are given by the Portfolio-GARCH and RiskmetricsTM-EWMA models, both of which would have a penalty of 0.5. Not surprisingly, the worst forecasts are obtained from the standard normal method based on historical variances.