Application and validation of the models Theseus and Opus with two field experimental data sets

2007 ◽  
pp. 37-49
Author(s):  
Martin Wegehenkel ◽  
Wilfried Mirschel
Keyword(s):  
Experimental Data ◽  
Data Sets

scholarly journals Polyconvex hyperelastic modeling of rubberlike materials

2021 ◽  
Vol 43 (7) ◽  
Author(s):  
Cyprian Suchocki ◽  
Stanisław Jemioło
Keyword(s):  
Experimental Data ◽  
Power Law ◽  
Curve Fitting ◽  
Constitutive Relations ◽  
Data Sets ◽  
Power Law Model ◽  
Stored Energy ◽  
Data Approximation ◽  
Exponential Power ◽  
Hyperelastic Models

AbstractIn this work a number of selected, isotropic, invariant-based hyperelastic models are analyzed. The considered constitutive relations of hyperelasticity include the model by Gent (G) and its extension, the so-called generalized Gent model (GG), the exponential-power law model (Exp-PL) and the power law model (PL). The material parameters of the models under study have been identified for eight different experimental data sets. As it has been demonstrated, the much celebrated Gent’s model does not always allow to obtain an acceptable quality of the experimental data approximation. Furthermore, it is observed that the best curve fitting quality is usually achieved when the experimentally derived conditions that were proposed by Rivlin and Saunders are fulfilled. However, it is shown that the conditions by Rivlin and Saunders are in a contradiction with the mathematical requirements of stored energy polyconvexity. A polyconvex stored energy function is assumed in order to ensure the existence of solutions to a properly defined boundary value problem and to avoid non-physical material response. It is found that in the case of the analyzed hyperelastic models the application of polyconvexity conditions leads to only a slight decrease in the curve fitting quality. When the energy polyconvexity is assumed, the best experimental data approximation is usually obtained for the PL model. Among the non-polyconvex hyperelastic models, the best curve fitting results are most frequently achieved for the GG model. However, it is shown that both the G and the GG models are problematic due to the presence of the locking effect.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

Validation of FLACS against experimental data sets from the model evaluation database for LNG vapor dispersion

2010 ◽  
Vol 23 (6) ◽  
pp. 857-877 ◽  
Author(s):  
Olav R. Hansen ◽  
Filippo Gavelli ◽  
Mathieu Ichard ◽  
Scott G. Davis
Keyword(s):  
Experimental Data ◽  
Model Evaluation ◽  
Data Sets ◽  
Vapor Dispersion

New analysis of the low-energy π±p differential cross-sections of the CHAOS Collaboration

2015 ◽  
Vol 24 (07) ◽  
pp. 1550050 ◽  
Author(s):  
E. Matsinos ◽  
G. Rasche
Keyword(s):  
Experimental Data ◽  
Cross Sections ◽  
Differential Cross ◽  
Form Factors ◽  
Partial Wave Analysis ◽  
Meson Mass ◽  
Data Sets ◽  
Differential Cross Sections ◽  
Pion Nucleon ◽  
Meson Factory

In a previous paper, we reported the results of a partial-wave analysis (PWA) of the pion–nucleon (πN) differential cross-sections (DCSs) of the CHAOS Collaboration and came to the conclusion that the angular distribution of their π+p data sets is incompatible with the rest of the modern (meson factory) database. The present work, re-addressing this issue, has been instigated by a number of recent improvements in our analysis, namely regarding the inclusion of the theoretical uncertainties when investigating the reproduction of experimental data sets on the basis of a given "theoretical" solution, modifications in the parametrization of the form factors of the proton and of the pion entering the electromagnetic part of the πN amplitude, and the inclusion of the effects of the variation of the σ-meson mass when fitting the ETH model of the πN interaction to the experimental data. The new analysis of the CHAOS DCSs confirms our earlier conclusions and casts doubt on the value for the πN Σ term, which Stahov, Clement and Wagner have extracted from these data.

scholarly journals Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3811
Author(s):  
Iosif Sorin Fazakas-Anca ◽  
Arina Modrea ◽  
Sorin Vlase
Keyword(s):  
Experimental Data ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Reactivity Ratios ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Integration Algorithm ◽  
Data Set ◽  
Parametric Regression ◽  
Non Parametric

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

scholarly journals Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data

2017 ◽  
Author(s):  
Alexander P. Browning ◽  
Scott W. McCue ◽  
Rachelle N. Binny ◽  
Michael J. Plank ◽  
Esha T. Shah ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cell Migration ◽  
Spatial Clustering ◽  
Cancer Cell Line ◽  
Movement Direction ◽  
Data Sets ◽  
Collective Cell Migration ◽  
Rejection Sampling ◽  
Data Set ◽  
Cell Migration And Proliferation

AbstractCollective cell spreading takes place in spatially continuous environments, yet it is often modelled using discrete lattice-based approaches. Here, we use data from a series of cell proliferation assays, with a prostate cancer cell line, to calibrate a spatially continuous individual based model (IBM) of collective cell migration and proliferation. The IBM explicitly accounts for crowding effects by modifying the rate of movement, direction of movement, and the rate of proliferation by accounting for pair-wise interactions. Taking a Bayesian approach we estimate the free parameters in the IBM using rejection sampling on three separate, independent experimental data sets. Since the posterior distributions for each experiment are similar, we perform simulations with parameters sampled from a new posterior distribution generated by combining the three data sets. To explore the predictive power of the calibrated IBM, we forecast the evolution of a fourth experimental data set. Overall, we show how to calibrate a lattice-free IBM to experimental data, and our work highlights the importance of interactions between individuals. Despite great care taken to distribute cells as uniformly as possible experimentally, we find evidence of significant spatial clustering over short distances, suggesting that standard mean-field models could be inappropriate.

Retrieval of Bio-Geophysical Parameters from Remotely Sensing Data by Using Bayesian Methodology

2007 ◽  
pp. 222-252
Author(s):  
C. Notarnicola
Keyword(s):  
Experimental Data ◽  
Theoretical Model ◽  
Theoretical Models ◽  
Data Sets ◽  
Model Errors ◽  
Sources Of Information ◽  
Remotely Sensed Data ◽  
Bayesian Methodology ◽  
Information Models ◽  
Bayesian Methodologies

This chapter introduces the use of Bayesian methodology for inversion purposes: the extraction of bio-geophysical parameters from remotely sensed data. Multisources information, such as different polarizations, frequencies, and sensors are fundamental to the development of operationally useful inversion systems. In this context, Bayesian methodologies offer a convenient tool of combining two or more disparate sources of information, models, and data. The chapter describes the development of a general model starting from a theoretical model, including the sensor noise and the model errors, by using a Bayesian approach. Furthermore, the developed procedure is applied to some experimental data sets. The author hopes that considering theoretical models and experimental data in many different configurations can give an idea of the versatility and robustness of the Bayesian framework.

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