Normal Method Research Articles

Aspekti prikladnosti trostupanjskog miješanja gotove betonske smjese

Several studies have indicated that three-stage mixing (TM) methods improve the mechanical strength and slump of concrete compared to those of normal mixing methods, particularly when a recycled concrete aggregate is used. However, most studies did not address the technological aspects of ready-mixed concrete, including continuous mixing during site delivery. In this study, the properties of concrete (slump and compressive strength) based on the time lapse after mixing via the TM method (i.e. immediately, 45 min, and 90 min after mixing) were investigated, and the samples were tested after three years of curing. The results indicate some positive effects of TM on ready-mixed concrete and its characteristics on a delivery to the construction site, particularly a less severe loss of consistency compared to that of conventional mixing, as well as a positive effect on the compressive strength when fly ash is used as an aggregate coating additive in the first stage of concrete mixing.

"Olympic Hope": Fucoldian interpretations

The study of literary works devoted to running makes it possible to determine their plot dominant as a conflict between the feeling of freedom generated by running and such properties of professional sports as fierce competition of competitive activities and authoritarian regulation of the training process. Sociocultural tensions of this kind were fruitfully conceptualized in the works of Michel Foucault using the concepts of bio-power as a unity of anatomical and biopolitics; disciplinary practices as methods of normalization of bodies; supervision and panopticism as means of control, as well as technologies of self and subjectivation aimed at dampening the effects of excessive domination. The article uses the fantastic sports novel by Danish writer Knud Lundberg "Olympic Hope: The Story that Happened at the 1996 Olympic Games" published in 1955 and not translated into Russian as an object of application of the Fucoldian means of analyzing the above conflict situation. The research methodology is based on a conceptual analysis of M. Foucault's theoretical legacy and the reception of the French philosopher's ideas by Russian and Western scientists. The study revealed that the novel presents four variants of bio-power: German (genetic eugenics), American (hormonal eugenics), Soviet (crippling practices) and Danish (based on the free development of natural predisposition), used to organize the birth and training of top-level runners. The disciplinary practices of runner training are most pronounced in totalitarian states. The methods of their implementation are specialized closed training spaces; strict daily routine; daily monotonous running exercises; constant differentiation of athletes by rating; exams in the form of competitions; medical experiments. The realization of the technologies of selfhood and subjectivation by runners is carried out in the forms of resistance and transgression. K. Lundberg associates his Olympic hopes in sports running not with cruel professionals who are ready to give up their health (the problem of doping) and even their lives for the sake of personal awards and increasing the prestige of their states, but with educated means of humanistic sports by amateur athletes, for whom running has existential significance as a way of holistic, bodily and spiritual self-realization in the ethically loaded sphere of freedom.

An efficient decision-making model for evaluating irrigation systems under uncertainty: Toward integrated approaches to sustainability

Agriculture is essential for many countries that depend on crops for food production and security. So, evaluating irrigation systems and selecting the best one is critical and gives various benefits such as water use efficiency, productivity, and agricultural development. This paper proposed a framework for conserving water and increasing effectiveness by using a suitable irrigation system. We used the multi-criteria decision-making (MCDM) methodology to deal with conflict criteria in the evaluation process. We used two MCDM methods such as Criteria Importance Through Inter-Criteria Correlation (CRITIC) method to compute the weights of the irrigation system criteria, and the spherical fuzzy double normalization-based multiple aggregations (DNMA) method to rank the irrigation systems (alternatives). The main advantage of CRITIC method computes the conflict and variability of the criteria by calculate weights of them. The main advantage of DNMA is used the two normalization method to rank the alternatives. These methods are integrated with spherical fuzzy set (SFS) fuzzy information in the assessment process. It has three values: membership, non-membership, and hesitant degrees to overcome uncertainty in the assessment steps. The proposed methodology is applied to a case study to show its performance. This study used 20 criteria of irrigation systems and 10 irrigation systems (alternatives) to select the best alternative. The results are discussed from the perspective of five experts. The sensitivity analysis is conducted to show the stability of the results. The comparative analysis is performed to show the validity and effectiveness of the proposed methodology. The results show the proposed methodology is more robust compared to other methods.

Action-angle Variables for Axisymmetric Potentials via Birkhoff Normalization

We describe a method for calculating action-angle (AA) variables in axisymmetric galactic potentials using Birkhoff normalization, a technique from Hamiltonian perturbation theory. An advantageous feature of this method is that it yields explicit series expressions for both the forward and inverse transformations between the AA variables and position–velocity data. It also provides explicit expressions for the Hamiltonian and dynamical frequencies as functions of the action variables. We test this method by examining orbits in a Milky Way model potential and compare it to the popular Stäckel approximation method. When vertical actions are not too large, the Birkhoff normalization method achieves fractional errors smaller than a part in 103 and outperforms the Stäckel approximation. We also show that the range over which Birkhoff normalization provides accurate results can be extended by constructing Padé approximants from the perturbative series expressions developed with the method. Numerical routines in Python for carrying out the Birkhoff normalization procedure are made available.

State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model’s performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH.

Analyzing RNA-Seq data from Chlamydia with super broad transcriptomic activation: challenges, solutions, and implications for other systems

BackgroundRNA sequencing (RNA-Seq) offers profound insights into the complex transcriptomes of diverse biological systems. However, standard differential expression analysis pipelines based on DESeq2 and edgeR encounter challenges when applied to the immediate early transcriptomes of Chlamydia spp., obligate intracellular bacteria. These challenges arise from their reliance on assumptions that do not hold in scenarios characterized by extensive transcriptomic activation and limited repression.ResultsStandard analyses using unique chlamydial RNA-Seq reads alone identify nearly 300 upregulated and about 300 downregulated genes, significantly deviating from actual RNA-Seq read trends. By incorporating both chlamydial and host reads or adjusting for total sequencing depth, the revised normalization methods each detected over 700 upregulated genes and 30 or fewer downregulated genes, closely aligned with observed RNA-Seq data. Further validation through qRT-PCR analysis confirmed the effectiveness of these adjusted approaches in capturing the true extent of transcriptomic activation during the immediate early phase of chlamydial infection.ConclusionsThis study highlights the limitations of standard RNA-Seq analysis tools in scenarios with extensive transcriptomic activation, such as in Chlamydia spp. during early infection. Our revised normalization methods, incorporating host reads or total sequencing depth, provide a more accurate representation of gene expression dynamics. These approaches may inform similar adjustments in other systems with unbalanced gene expression dynamics, enhancing the accuracy of transcriptomic analysis.

PathoTracker: an online analytical metagenomic platform for Klebsiella pneumoniae feature identification and outbreak alerting

Clinical metagenomics (CMg) Nanopore sequencing can facilitate infectious disease diagnosis. In China, sub-lineages ST11-KL64 and ST11-KL47 Carbapenem-resistant Klebsiella pneumoniae (CRKP) are widely prevalent. We propose PathoTracker, a specially compiled database and arranged method for strain feature identification in CMg samples and CRKP traceability. A database targeting high-prevalence horizontal gene transfer in CRKP strains and a ST11-only database for distinguishing two sub-lineages in China were created. To make the database user-friendly, facilitate immediate downstream strain feature identification from raw Nanopore metagenomic data, and avoid the need for phylogenetic analysis from scratch, we developed data analysis methods. The methods included pre-performed phylogenetic analysis, gene-isolate-cluster index and multilevel pan-genome database and reduced storage space by 10-fold and random-access memory by 52-fold compared with normal methods. PathoTracker can provide accurate and fast strain-level analysis for CMg data after 1 h Nanopore sequencing, allowing early warning of outbreaks. A user-friendly page (http://PathoTracker.pku.edu.cn/) was developed to facilitate online analysis, including strain-level feature, species identifications and phylogenetic analyses. PathoTracker proposed in this study will aid in the downstream analysis of CMg.

Capacity Optimization of Pumped–Hydro–Wind–Photovoltaic Hybrid System Based on Normal Boundary Intersection Method

Introducing pumped storage to retrofit existing cascade hydropower plants into hybrid pumped storage hydropower plants (HPSPs) could increase the regulating capacity of hydropower. From this perspective, a capacity configuration optimization method for a multi-energy complementary power generation system comprising hydro, wind, and photovoltaic power is developed. Firstly, to address the uncertainty of wind and photovoltaic power outputs, the K-means clustering algorithm is applied to deal with historical data on load and photovoltaic, wind, and water inflow within a specific region over the past year. This process helps reduce the number of scenarios, resulting in 12 representative scenarios and their corresponding probabilities. Secondly, with the aim of enhancing outbound transmission channel utilization and decreasing the peak–valley difference for the receiving-end power grid’s load curve, a multi-objective optimization model based on the normal boundary intersection (NBI) algorithm is developed for the capacity optimization of the multi-energy complementary power generation system. The result shows that retrofitting cascade hydropower plants with pumped storage units to construct HPSPs enhances their ability to accommodate wind and photovoltaic power. The optimal capacity of wind and photovoltaic power is increased, the utilization rate of the system’s transmission channel is improved, and the peak-to-valley difference for the residual load of the receiving-end power grid is reduced.

Financial and Environmental Performance Analysis of Logistics company on the Fortune 500 By MEREC-based AROMAN Methods

This study aims to examine the financial and environmental performance of a logistics company listed in the Fortune 500 Türkiye over the years. The current study is carried out within the scope of Ekol Logistics, one of Türkiye’s leading logistics companies. The data is obtained from the Fortune 500 Türkiye and Ekol’s Sustainability reports. In this study, a hybrid model integrating the method based on the removal effects of criteria (MEREC) and alternative ranking order method accounting for two-step normalization (AROMAN) method is employed. The MEREC findings showed that net sales change was the most significant criterion, while equity was the least significant criterion in the assessment of financial performance. Moreover, number of vehicles equipped with EURO 6 technology was the most significant criterion, while water consumption was the least significant criterion in the assessment of environmental performance. The AROMAN findings pointed out that Ekol's achieved the most outstanding financial results in the years 2020, 2021, and 2022. Additionally, Ekol achieved notable environmental performance in 2022. Moreover, sensitivity and comparative analysis are conducted to observe the validity of the results.

Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging (nano-DESI MSI): A Tutorial Review.

Nanospray desorption electrospray ionization (nano-DESI) is a liquid-based ambient mass spectrometry imaging (MSI) technique that enables visualization of analyte distributions in biological samples down to cellular-level spatial resolution. Since its inception, significant advancements have been made to the nano-DESI experimental platform to facilitate molecular imaging with high throughput, deep molecular coverage, and spatial resolution better than 10 μm. The molecular selectivity of nano-DESI MSI has been enhanced using new data acquisition strategies, the development of separation and online derivatization approaches for isobar separation and isomer-selective imaging, and the optimization of the working solvent composition to improve analyte extraction and ionization efficiency. Furthermore, nano-DESI MSI research has underscored the importance of matrix effects and established normalization methods for accurately measuring concentration gradients in complex biological samples. This tutorial offers a comprehensive guide to nano-DESI experiments, detailing fundamental principles and data acquisition and processing methods and discussing essential operational parameters.

A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github.

GABA Analogue HSK16149 in Chinese Patients With Diabetic Peripheral Neuropathic Pain

Many patients with diabetic peripheral neuropathic pain (DPNP) experience inadequate relief, despite best available medical treatments. There are no approved and effective therapies for patients with DPNP in China. To evaluate the efficacy and safety of capsules containing γ-aminobutyric acid (GABA) analogue HSK16149 in the treatment of Chinese patients with DPNP. This phase 2 to 3 adaptive randomized clinical trial was multicenter, double blind, and placebo and pregabalin controlled. The trial started on December 10, 2020, and concluded on July 8, 2022. In stage 1, various doses of HSK16149 were evaluated to determine safety and efficacy for stage 2. The second stage then validated the efficacy and safety of the recommended dose. In stage 1, enrolled patients (n = 363) were randomized 1:1:1:1:1:1 to 4 HSK16149 doses (40, 80, 120, or 160 mg/d), pregabalin (300 mg/d), or placebo. In stage 2, patients (n = 362) were randomized 1:1:1 to receive HSK16149, 40 or 80 mg/d, or placebo. The final efficacy and safety analysis pooled data from patients receiving the same treatment. The primary efficacy end point in stage 1 was the change from baseline in average daily pain score (ADPS) at week 5. The primary efficacy end point in stage 2 was the change from baseline in ADPS at week 13. When the final statistical analysis was performed, the P values calculated from the independent data of each phase were combined using the weighted inverse normal method to make statistical inferences. Of 725 randomized patients in the full-analysis set (393 men [54.2%]; mean [SD] age, 58.80 [9.53] years; 700 [96.6%] of Han Chinese ethnicity), 177 received placebo; 178, HSK16149, 40 mg/d; 179, HSK16149, 80 mg/d; 66, HSK16149, 120 mg/d; 63, HSK16149, 160 mg/d; and 62, pregabalin, 300 mg/d. A total of 644 patients (88.8%) completed the study. The 40- and 80-mg/d doses of HSK16149 were recommended in stage 2. At week 13, the ADPS mean (SD) change from baseline was -2.24 (1.55) for the 40-mg/d and -2.16 (1.79) for 80-mg/d groups and -1.23 (1.68) for the placebo group, showing statistical significance for both HSK16149 doses vs placebo (both P < .001). In a safety set (n = 726), 545 patients (75.1%) had adverse events, which were generally mild to moderate, with dizziness and somnolence being the most common. Forty- and eighty-mg/d doses of HSK16149 were recommended for treating patients with DPNP in China. The efficacy of HSK16149 capsules was superior to placebo in all groups for relieving DPNP and appeared well tolerated. ClinicalTrials.gov Identifier: NCT04647773.

Plant biodiversity assessment of locally cultivated crops and household food security in Northern Iran

BackgroundLocally cultivated crops play an important role in the food security. The biodiversity of these crops can be important for the livelihood of households in current and future generations. This research aims to study the socio-economic, agronomic, and ecological aspects which contribute to the maintenance of crop diversification and food security in the study area.MethodBased on latitude and topography 10 villages were randomly selected, which was done in a study with 227 household farm managers. Food security was evaluated based on the species richness, and area under cultivation of food groups, and the probit logistic regression model was used for evaluation. Probit regression, also known as probit models, is used when the output or dependent variable of the model is bivariate. In inverse probit models, the standard normal distribution is modeled as a linear combination of predictor variables. In this situation, the application of normal regression methods is not applicable because the distribution occurred in two levels. In this study, it is assumed that the protection of biodiversity of local plants is related to food security, and for this reason, we have used this model.ResultsThe findings showed that socio-economic status of different households and farms in this coastal area is one of the key factors affecting the biodiversity of locally cultivated crops. Local cultivars, especially those of rice, are resistant to most environmental factors and contribute to family food security. Food security and rice abundance are significantly correlated in all of the research area’s communities.ConclusionsTo ensure the sustainability, and health of production, and to ensure food security, planting various crops in this study area is recommended. Future research is needed to focus on solutions and technologies rich in diversity tailored to the socio-economic and environmental factors of locally cultivated crops.

Dynamics of chains of a large number of fully coupled oscillators, as well as oscillators with one-way and two-way delay couplings

This articles considers chains consisting of identical nonlinear equations of second order with couplings in linear elements. It is assumed that there is a large delay in the coupling elements. Moreover, we assume that the chain possesses a large number of elements. Therefore, instead of the original system with many elements, we can study a second order nonlinear integro-differential equation with periodic boundary conditions. The main study is focused on the local dynamics of chains with one-sided, two-sided couplings, as well as fully coupled chains. Also, we study the stability of equilibrium states and identify the critical cases. The condition of a sufficiently large delay helps to determine the parameters for the implementation of critical cases explicitly. Our methodology is based on the infinite-dimensional normalisation method proposed by the author, namely the method of quasinormal forms. We construct quasinormal forms for the chains under consideration that determine the asymptotics of the leading terms of the asymptotic expansions of the solutions to the original system.

Changes in Human Meibum Lipid Composition Related to the Presence and Severity of Meibomian Gland Dysfunction.

Purpose: Changes in meibum composition and quantity in meibomian gland dysfunction (MGD) result in tear film instability and dry eye. This exploratory study aimed to identify changes in (O-acyl)-ω-hydroxy fatty acid (OAHFA) and hydrocarbon chain (HC) unsaturation levels in meibum related to the presence and severity of MGD. Methods: Meibum samples were collected from 3 cohorts of adults with no MGD, mild-to-moderate MGD, and severe MGD in a noninterventional clinical trial (NCT01979887). OAHFAs, cholesterol esters (CE), HC unsaturation, and HC length in the meibum samples were quantified with 1H-nuclear magnetic resonance spectroscopy using 2 methods of normalization. Results: Meibum samples from 62 subjects were analyzed: 21 non-MGD, 21 mild-to-moderate MGD, and 20 severe MGD. Meibum OAHFA and CE levels and HC unsaturation were reduced with increasing severity of MGD, with most pairwise comparisons significant (P < 0.05, t-tests), following the order non-MGD > mild-to-moderate MGD > severe MGD. Regardless of the resonances used for normalization, each pairwise comparison of OAHFA, CE, and HC unsaturation levels in MGD (combined severities) versus non-MGD samples was significant (P < 0.01, t-test). Analysis using various normalization equations showed reductions of 20%-22% for OAHFAs, 51%-57% for CE, and 36%-66% for HC unsaturation in MGD (combined severities) compared with non-MGD. HC length was not altered in MGD (combined severities) compared with non-MGD samples (t-test). Conclusions: Meibum OAHFA, CE, and HC unsaturation levels were reduced in MGD and were lowest in the severe MGD cohort. These findings may contribute to the understanding of the pathophysiology of MGD.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Hopf-Hopf bifurcation, period [formula omitted] solutions, slow-fast phenomena, and chimera of an optoelectronic reservoir computing system with single delayed feedback loop

In this paper, we investigate the co-dimension two bifurcations and complicated dynamics of an optoelectronic reservoir computing (RC) system with single delayed feedback loop. We focuses primarily on its underlying system’s Hopf-Hopf bifurcation. Firstly, we apply DDE-BIFTOOL built in Matlab to sketch the bifurcation diagrams with respect to two bifurcation parameters, namely feedback strength β and time delay τ, and find the existence of the Hopf-Hopf bifurcation points. Then, using the multiple scales method, we obtain their normal forms, and using the normal form method, we unfold and classify their local dynamics. Then numerical simulations are conducted to verify these results. We discover rich dynamical behaviors of the system in specific regions. Besides, other complicated dynamics, such as fast-slow phenomena, Period n solutions, and chimera, are found in the system. All these rich dynamical phenomena can provide excellent performance potentially for this optoelectronic reservoir computing system with single delayed feedback loop.

Self-normalization for a 1 mm3 resolution clinical PET system using deep learning

Objective. This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditional generative adversarial networks (cGANs). Approach. We evaluated different approaches by exploring each of the following three methodologies. First, we used images that were either unnormalized or corrected for geometric factors, which encompass all time-invariant factors, as input data types. Second, we set the input tensor shape as either a single axial slice (2D) or three contiguous axial slices (2.5D). Third, we chose either Pix2Pix or polarized self-attention (PSA) Pix2Pix, which we developed for this work, as a deep learning network. The targets for all approaches were the axial slices of images normalized using the direct normalization method. We performed Monte Carlo simulations of ten voxelized phantoms with the SimSET simulation tool and produced 26,000 pairs of axial image slices for training and testing. Main results. The results showed that 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the best performance among all the methods we tested. All approaches improved general image quality figures of merit peak signal to noise ratio (PSNR) and structural similarity index (SSIM) from ∼15 % to ∼55 %, and 2.5D PSA Pix2Pix showed the highest PSNR (28.074) and SSIM (0.921). Lesion detectability, measured with region of interest (ROI) PSNR, SSIM, normalized contrast recovery coefficient, and contrast-to-noise ratio, was generally improved for all approaches, and 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the highest ROI PSNR (28.920) and SSIM (0.973). Significance. This study demonstrates the potential of an image-based end-to-end self-normalization framework using cGANs for improving PET image quality and lesion detectability without the need for separate normalization scans.

Predicting the entire pyrolysis process of representative charring material infiltrated with kerosene using an improved artificial neural network

It is crucial to predict the entire pyrolysis process of charring materials infiltrated with flammable liquids to provide guidance for reducing the fire hazards and implementing numerical simulations of fire accidents caused by the leakage of flammable liquids. In the present study, pyrolysis data obtained from the thermogravimetric experiments were preprocessed using linear interpolation and data normalization methods. The processed data set was then used to train the artificial neural network (ANN) framework to predict the pyrolysis behaviors of typical charring material (Mongolian Scots pine) infiltrated with typical flammable liquid (kerosene) under three different conditions: (1) pyrolysis at a constant mass fraction of kerosene with multiple heating rates; (2) pyrolysis at multiple mass fractions of kerosene with a constant heating rate; and (3) pyrolysis at multiple mass fractions of kerosene and multiple heating rates. The ANN, with the double hidden layer structure (6−3), was capable to well predict the entire pyrolysis behaviors of Mongolian Scots pine under all the three scenarios. Besides, five data transformation function (multiplication, exponential function with base e, logarithmic function with base e, square root, and exponentiation function) were applied to the present three input variables (temperature, mass fraction of kerosene, and heating rate) to generate new input variables in order to extract more characteristics among the pyrolysis data. Therein, the ANN utilizing the data transformation functions of multiplication and exponential function with base e exhibited the best predictive ability. Among the three input variables in the ANN (temperature, mass fraction of kerosene, and heating rate), temperature was identified as the most sensitive one to the prediction performance, followed by mass fraction of kerosene, with heating rate being the least sensitive.

Area normalization of HERFD-XANES spectra.

The normalization of X-ray absorption near-edge structure (XANES) spectra is required for comparing spectral features and extracting quantitative information in analytical techniques such as linear combination analysis, principal component analysis and multivariate curve resolution. Most published data are normalized to the edge-jump, but normalization to the spectral area has also been applied. The latter is particularly attractive if only a small energy range around the absorption can be recorded reliably. Here, the two normalization methods are compared at the L3-edge of Pt, Pd and Rh, and at the Ni K-edge using experimental and calculated spectra. Normalization to the spectral area is found to be a viable approach if the range for the area normalization is sufficiently large.