A model interpretation op the correlation between the activation enthalpy and the breath op electric modulus spectrum on varying the compositions in alkali aluminogermanate and other glasses

1987 ◽  
Vol 95-96 ◽  
pp. 873-880 ◽  
Author(s):  
K.L. Ngai ◽  
J.N. Mundy ◽  
H. Jain ◽  
G. Balzer-Jollenbeck ◽  
O. Kanert
Keyword(s):  
Electric Modulus ◽  
Activation Enthalpy ◽  
Model Interpretation ◽  
Modulus Spectrum

Conductivity, Dielectric and Modulus study of NH4PF6 based Zwitterionic polymer electrolyte

2020 ◽  
Vol 13 ◽  
Author(s):  
Manindra Kumar ◽  
Neelabh Srivastava
Keyword(s):  
Polymer Electrolyte ◽  
Power Law ◽  
Loss Tangent ◽  
Electric Modulus ◽  
Relaxation Behavior ◽  
Conductivity Data ◽  
Frequency Range ◽  
Zwitterionic Polymer ◽  
Power Law Equation ◽  
Jonscher’S Power Law

Background and Objective: Zwitterionic polymer electrolyte has been successfully synthesized using NH4PF6 salt. The conductivity of the synthesized polymer membrane is found to be of the order of 10-3Scm-1. Dielectric and Modulus properties of the polymer electrolyte have also been studied which showed well relaxation peaks with both temperature and salt concentrations. Result: This is well depicted with the loss tangent curve. Debye type relaxation behavior has observed from the electric modulus. Conclusion: Frequency dependent conductivity data (fitted with Jonscher's power law equation) confirmed the presence of NCL/SLPL type behavior in the studied frequency range.

Electrical conductivity studies on silica phases and the effects of phase transformation

2019 ◽  
Vol 104 (12) ◽  
pp. 1800-1805
Author(s):  
George M. Amulele ◽  
Anthony W. Lanati ◽  
Simon M. Clark
Keyword(s):  
Electrical Conductivity ◽  
Activation Volume ◽  
Activation Enthalpy ◽  
Hydrogen Charge ◽  
Charge Compensation ◽  
Composition Analysis ◽  
Conductivity Measurements ◽  
Pressure System ◽  
Electrical Conductivities ◽  
Silica Phases

Abstract Starting with the same sample, the electrical conductivities of quartz and coesite have been measured at pressures of 1, 6, and 8.7 GPa, respectively, over a temperature range of 373–1273 K in a multi-anvil high-pressure system. Results indicate that the electrical conductivity in quartz increases with pressure as well as when the phase change from quartz to coesite occurs, while the activation enthalpy decreases with increasing pressure. Activation enthalpies of 0.89, 0.56, and 0.46 eV, were determined at 1, 6, and 8.7 GPa, respectively, giving an activation volume of –0.052 ± 0.006 cm3/mol. FTIR and composition analysis indicate that the electrical conductivities in silica polymorphs is controlled by substitution of silicon by aluminum with hydrogen charge compensation. Comparing with electrical conductivity measurements in stishovite, reported by Yoshino et al. (2014), our results fall within the aluminum and water content extremes measured in stishovite at 12 GPa. The resulting electrical conductivity model is mapped over the magnetotelluric profile obtained through the tectonically stable Northern Australian Craton. Given their relative abundances, these results imply potentially high electrical conductivities in the crust and mantle from contributions of silica polymorphs. The main results of this paper are as follows:The electrical conductivity of silica polymorphs is determined by impedance spectroscopy up to 8.7 GPa.The activation enthalpy decreases with increasing pressure indicating a negative activation volume across the silica polymorphs.The electrical conductivity results are consistent with measurements observed in stishovite at 12 GPa.

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

Complex dielectric permittivity, electric modulus and electrical conductivity analysis of Au/Si3N4/p-GaAs (MOS) capacitor

2021 ◽  
Author(s):  
Sema Türkay ◽  
Adem Tataroğlu
Keyword(s):  
Electrical Conductivity ◽  
Dielectric Permittivity ◽  
Electric Modulus ◽  
Arrhenius Equation ◽  
Rf Magnetron Sputtering ◽  
Oxide Semiconductor ◽  
Complex Dielectric Permittivity ◽  
Mos Capacitor ◽  
Universal Power Law ◽  
Complex Electrical Conductivity

AbstractRF magnetron sputtering was used to grow silicon nitride (Si3N4) thin film on GaAs substrate to form metal–oxide–semiconductor (MOS) capacitor. Complex dielectric permittivity (ε*), complex electric modulus (M*) and complex electrical conductivity (σ*) of the prepared Au/Si3N4/p-GaAs (MOS) capacitor were studied in detail. These parameters were calculated using admittance measurements performed in the range of 150 K-350 K and 50 kHz-1 MHz. It is found that the dielectric constant (ε′) and dielectric loss (ε″) value decrease with increasing frequency. However, as the temperature increases, the ε′ and ε″ increased. Ac conductivity (σac) was increased with increasing both temperature and frequency. The activation energy (Ea) was determined by Arrhenius equation. Besides, the frequency dependence of σac was analyzed by Jonscher’s universal power law (σac = Aωs). Thus, the value of the frequency exponent (s) were determined.

scholarly journals The Difficulty of Predicting Eastern Spruce Dwarf Mistletoe in Lowland Black Spruce: Model Benchmarking in Northern Minnesota, USA

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 843
Author(s):  
Ella R. Gray ◽  
Matthew B. Russell ◽  
Marcella A. Windmuller-Campione
Keyword(s):  
Stand Structure ◽  
Black Spruce ◽  
Forest Health ◽  
Basal Area ◽  
Predictive Performance ◽  
Stand Age ◽  
Management Decisions ◽  
Dwarf Mistletoe ◽  
Growing Seasons ◽  
Model Interpretation

Insects, fungi, and diseases play an important role in forest stand development and subsequently, forest management decisions and treatments. As these disturbance agents commonly occur within and across landscapes, modeling has often been used to inform forest planning and management decisions. However, models are rarely benchmarked, leaving questions about their utility. Here, we assessed the predictive performance of a Bayesian hierarchical model through on–the-ground sampling to explore what features of stand structure or composition may be important factors related to eastern spruce dwarf mistletoe (Arceuthobium pusillum Peck) presence in lowland black spruce (Picea mariana (Mill.) B. S. P.). Twenty-five state-owned stands included in the predictive model were sampled during the 2019 and 2020 growing seasons. Within each stand, data related to the presence of eastern spruce dwarf mistletoe, stand structure, and species composition were collected. The model accurately predicted eastern spruce dwarf mistletoe occurrence for 13 of the 25 stands. The amount of living and dead black spruce basal area differed significantly based on model prediction and observed infestation, but trees per hectare, total living basal area, diameter at breast height, stand age, and species richness were not significantly different. Our results highlight the benefits of model benchmarking to improve model interpretation as well as to inform our understanding of forest health problems across diverse stand conditions.

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