scholarly journals Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

2020 ◽  
Vol 1 ◽  
Author(s):  
Pavan Inguva ◽  
Lachlan R. Mason ◽  
Indranil Pan ◽  
Miselle Hengardi ◽  
Omar K. Matar
Keyword(s):  
Polymer Blends ◽  
High Performance ◽  
Molar Fraction ◽  
Principal Component ◽  
Engineering Practice ◽  
Data Set ◽  
Pattern Clustering ◽  
Polymer Precipitation ◽  
Multicomponent Polymer ◽  
Process Classification

Abstract Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.

scholarly journals CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Reny Pratiwi ◽  
Aijaz Ahmad Malik ◽  
Nalini Schaduangrat ◽  
Virapong Prachayasittikul ◽  
Jarl E. S. Wikberg ◽  
...  
Keyword(s):  
Amino Acids ◽  
Physicochemical Properties ◽  
Predictive Model ◽  
High Performance ◽  
Web Server ◽  
Principal Component ◽  
Large Set ◽  
Data Set ◽  
Diverse Range ◽  
Ice Binding

Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.

scholarly journals Multidimensional Comparative Analysis of Bioactive Phenolic Compounds of Honeys of Various Origin

Antioxidants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 530
Author(s):  
Michał Gośliński ◽  
Dariusz Nowak ◽  
Artur Szwengiel
Keyword(s):  
Comparative Analysis ◽  
Phenolic Compounds ◽  
Antioxidant Capacity ◽  
High Performance ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Total Content ◽  
Multidimensional Analysis ◽  
Manuka Honey ◽  
Data Set

Honey is a natural product which owes its health benefits to its numerous bioactive compounds. The composition of honey is highly diverse and depends on the type of honey and its origin. Antioxidant capacity arises mainly from the total content of polyphenols and their composition. The aim of this study was to perform a multidimensional comparative analysis of phenolic compounds of honeys of various origins. Honeydew, buckwheat, manuka, Malaysian and goldenrod honeys had the highest antioxidant capacity (above 400 mg Trolox equivalents kg−1). These honeys were also characterized by the highest total polyphenol content (about 2500 mg gallic acid equivalents (GAE) kg−1) and the highest total flavonoid content (1400–1800 mg catechin equivalents (CAE) kg−1). Other honeys had much lower antioxidant properties. A multidimensional analysis of the profiles of phenolic compounds showed that honeys constitute a non-homogeneous data set and manuka honey was in contrast to other samples. Principal component analysis (PCA) (based on 18 phenolic compounds) distinguished honeys into five groups. Manuka, Malaysian and honeydew honeys created their own separate groups and the location of other honeys was variable. Ultra-high-performance liquid chromatography (UHPLC) analysis demonstrated that profiles of polyphenols in honeys were highly varied. Caffeic acid, datiscetin and rhamnetin were characteristic compounds for manuka honey. Quercetin, kaempferol and apigenin were present in all honeys except Malaysian honey. The antioxidant properties and the profiles of bioactive phenolic compounds of honeys were miscellaneous. The richest sources of polyphenols were local buckwheat and honeydew honeys, alongside exotic manuka and Malaysian honeys. These honeys could provide valuable ingredients to the human diet, helping to prevent diseases.

Extraction, characterization and antioxidant properties of phenolic compounds in açaí juçara (Euterpe edulis Mart.) from Atlantic Forest

2021 ◽  
Vol 24 ◽  
Author(s):  
Leticia Dangui da Silva ◽  
Rafael Sari ◽  
Camila Diedrich ◽  
Celeide Pereira ◽  
Vanderlei Aparecido de Lima ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Phenolic Compounds ◽  
Atlantic Forest ◽  
High Performance ◽  
Antioxidant Activities ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Data Set ◽  
Antioxidant Power ◽  
Euterpe Edulis

Abstract The açaí is a popular Brazilian fruit, however, already part of the world's healthy eating habits owing to its antioxidant properties. The study aimed to determine the effect of solvent in extracting phenolic compounds with antioxidant potential in açaí juçara (Euterpe edulis Mart.) using a Completely Randomized Design (CRD). The phenolic compound profile was quantified by High-Performance Liquid Chromatography (HPLC), and the data set was analyzed by Principal Component Analysis (PCA). The PCA was applied to evidence the relationships between the concentration of phenolic compounds and the solvents. Furthermore, the antioxidant activity was also determined by 2,2’-diphenyl-1-picrylhydrazyl (DPPH), 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and Ferric Reducing Antioxidant Power (FRAP) methods. The solvent ethanol: water 70% was more efficient in extracting phenolic compounds with high antioxidant activity. In this extract, salicylic acid was found in high concentrations as well as catechin, epicatechin, and coumaric acid. Based on a consensus that phenolic compounds are associated with the most powerful antioxidant activities of fruits, the “açaí juçara” from the Atlantic Forest is a potential source of polyphenols. They could be used as natural antioxidants for application in the food and pharmaceutical industry in order to substitute the synthetic antioxidants.

Dimensionality Reduction for Countermovement Jump Metrics

2020 ◽  
pp. 1-4
Author(s):  
Lachlan P. James ◽  
Haresh Suppiah ◽  
Michael R. McGuigan ◽  
David L. Carey
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
High Performance ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Countermovement Jump ◽  
Data Set ◽  
Jump Performance

Purpose: Dozens of variables can be derived from the countermovement jump (CMJ). However, this does not guarantee an increase in useful information because many of the variables are highly correlated. Furthermore, practitioners should seek to find the simplest solution to performance testing and reporting challenges. The purpose of this investigation was to show how to apply dimensionality reduction to CMJ data with a view to offer practitioners solutions to aid applications in high-performance settings. Methods: The data were collected from 3 cohorts using 3 different devices. Dimensionality reduction was undertaken on the extracted variables by way of principal component analysis and maximum likelihood factor analysis. Results: Over 90% of the variance in each CMJ data set could be explained in 3 or 4 principal components. Similarly, 2 to 3 factors could successfully explain the CMJ. Conclusions: The application of dimensional reduction through principal component analysis and factor analysis allowed for the identification of key variables that strongly contributed to distinct aspects of jump performance. Practitioners and scientists can consider the information derived from these procedures in several ways to streamline the transfer of CMJ test information.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

The Utilization of Ftir (Fourier Transform Infra-Red) Method Combined with Chemometrics For Authentication of Indonesian Coffee Powder

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Firmansyah A. ◽  
Winingsih W. ◽  
Soebara Y S
Keyword(s):  
Fourier Transform ◽  
Natural Product ◽  
High Performance ◽  
Principal Component ◽  
Raw Material ◽  
Attenuated Total Reflectance ◽  
Coffee Bean ◽  
Product Analysis ◽  
Total Reflectance ◽  
And Cluster Analysis

Analysis of natural product remain challenging issues for analytical chemist, since natural products are complicated system of mixture. The most popular methods of choice used for quality control of raw material and finished product are high performance liquid chromatography (HPLC), gas chromatography (GC) and mass spectrometry (MS). The utilization of FTIR-ATR (Fourier Transform Infrared-Attenuated Total Reflectance) method in natural product analysis is still limited. This study attempts to expand the use of FTIR spectroscopy in authenticating Indonesian coffee powder.The coffee samples studied were taken from nine regions in Indonesia, namely Aceh Gayo, Flores, Kintamani, Mandheling, Papua, Sidikalang, Toraja, Kerinci and Lampung.The samples in the form of coffee bean from various regions were powdered . The next step conducted was to determine the spectrum using the FTIR-ATR (Attenuated Total Reflectance) using ZnSe crystal of 8000 resolution. Spectrum samples, then, were analyzed using chemometrics. The utilized chemometric model was the principal component analysis (PCA) and cluster analysis (CA). Based on the chemometric analysis, there are similarities between Aceh Gayo coffee with Toraja coffee, Mandailing coffee, Kintamani coffee and Flores coffee. Sidikalang coffee has a similarity to Flores coffee; Papua coffee has a similarity to Sidikalang coffee; Lampung coffee has a similarity to Sidikalang coffee, while Kerinci coffee has a similarity to Papua coffee.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

scholarly journals Hugoniot Data and Equation of State Parameters for an Ultra-High Performance Concrete

2021 ◽  
Author(s):  
C. Sauer ◽  
F. Bagusat ◽  
M.-L. Ruiz-Ripoll ◽  
C. Roller ◽  
M. Sauer ◽  
...  
Keyword(s):  
Equation Of State ◽  
High Performance ◽  
High Performance Concrete ◽  
Applied Pressure ◽  
High Strength ◽  
Parameter Study ◽  
Ultra High Performance Concrete ◽  
Data Set ◽  
Shock Response ◽  
Particle Velocities

AbstractThis work aims at the characterization of a modern concrete material. For this purpose, we perform two experimental series of inverse planar plate impact (PPI) tests with the ultra-high performance concrete B4Q, using two different witness plate materials. Hugoniot data in the range of particle velocities from 180 to 840 m/s and stresses from 1.1 to 7.5 GPa is derived from both series. Within the experimental accuracy, they can be seen as one consistent data set. Moreover, we conduct corresponding numerical simulations and find a reasonably good agreement between simulated and experimentally obtained curves. From the simulated curves, we derive numerical Hugoniot results that serve as a homogenized, mean shock response of B4Q and add further consistency to the data set. Additionally, the comparison of simulated and experimentally determined results allows us to identify experimental outliers. Furthermore, we perform a parameter study which shows that a significant influence of the applied pressure dependent strength model on the derived equation of state (EOS) parameters is unlikely. In order to compare the current results to our own partially reevaluated previous work and selected recent results from literature, we use simulations to numerically extrapolate the Hugoniot results. Considering their inhomogeneous nature, a consistent picture emerges for the shock response of the discussed concrete and high-strength mortar materials. Hugoniot results from this and earlier work are presented for further comparisons. In addition, a full parameter set for B4Q, including validated EOS parameters, is provided for the application in simulations of impact and blast scenarios.

Sign in / Sign up

Export Citation Format

Share Document