Nonlinear wave interaction and spin models in the MHD regime

Here we consider the influence on the electron spin in the MHD regime. Recently developed models which include spin-velocity correlations are taken as a starting point. A theoretical argument is presented, suggesting that in the MHD regime a single fluid electron model with spin correlations is equivalent to a model with spin-up and spin-down electrons constituting different fluids, but where the spin-velocity correlations are omitted. Three wave interaction of 2 shear Alfven waves and a compressional Alfven wave is then taken as a model problem to evaluate the asserted equivalence. The theoretical argument turns out to be supported, as the predictions of the two models agree completely. Furthermore, the three wave coupling coefficients obey the Manley-Rowe relations, which give further support to the soundness of the models and the validity of the assumptions made in the derivation. Finally we point out that the proposed two-fluid model can be incorporated in standard Particle-In-Cell schemes with only minor modifications.Comment: 8 page

Regularizing velocity differences in time-lapse FWI using gradient mismatch information

We present a method for recovering time-lapse velocity changes using full waveform inversion (FWI). In a preprocessing step we invert for a single intermediate model by simultaneously minimizing the data misfit in the baseline and the monitor surveys. We record the individual FWI gradients corresponding to the baseline and the monitor datasets at each iteration of the inversion. Regions where these gradients consistently have opposing sign are likely to correspond to locations of time-lapse change. This insight is used to generate a spatially varying confidence map for time-lapse change. In a subsequent joint inversion we invert for baseline and monitor models while regularizing the difference between the models with this spatially varying confidence map. Unlike double difference full waveform inversion (DDFWI) we do not require identical source and receiver positions in the baseline and monitor surveys

Iterative estimation of reflectivity and image texture: Least-squares migration with an empirical Bayes approach

In many geophysical inverse problems, smoothness assumptions on the underlying geology are used to mitigate the effects of nonuniqueness, poor data coverage, and noise in the data and to improve the quality of the inferred model parameters. Within a Bayesian inference framework, a priori assumptions about the probabilistic structure of the model parameters can impose such a smoothness constraint, analogous to regularization in a deterministic inverse problem. We have considered an empirical Bayes generalization of the Kirchhoff-based least-squares migration (LSM) problem. We have developed a novel methodology for estimation of the reflectivity model and regularization parameters, using a Bayesian statistical framework that treats both of these as random variables to be inferred from the data. Hence, rather than fixing the regularization parameters prior to inverting for the image, we allow the data to dictate where to regularize. Estimating these regularization parameters gives us information about the degree of conditional correlation (or lack thereof) between neighboring image parameters, and, subsequently, incorporating this information in the final model produces more clearly visible discontinuities in the estimated image. The inference framework is verified on 2D synthetic data sets, in which the empirical Bayes imaging results significantly outperform standard LSM images. We note that although we evaluated this method within the context of seismic imaging, it is in fact a general methodology that can be applied to any linear inverse problem in which there are spatially varying correlations in the model parameter space.MIT Energy Initiative (Shell International Exploration and Production B.V.)ERL Founding Member Consortiu

Effects of the gg-factor in semi-classical kinetic plasma theory

A kinetic theory for spin plasmas is put forward, generalizing those of previous authors. In the model, the ordinary phase space is extended to include the spin degrees of freedom. Together with Maxwell's equations, the system is shown to be energy conserving. Analysing the linear properties, it is found that new types of wave-particle resonances are possible, that depend directly on the anomalous magnetic moment of the electron. As a result new wave modes, not present in the absence of spin, appear. The implications of our results are discussed.Comment: 4 pages, two figures, version to appear in Physical Review Letter

Drug-induced skin reactions: A 2-year study

Background: The aim of this study was to analyze the clinical characteristics of patients with adverse cutaneous drug reactions, which occur when a medicinal product results in cutaneous morbidity. Methods: The study included 308 patients who were diagnosed as having an adverse cutaneous drug reaction during the study period (2007�2009). In 84 cases, histopathologic examination of skin biopsies were also performed. Results: Patients with drug reactions were found to be more commonly female (63) than male (37). Beta-lactam antibiotics were found to be the most frequent cause of adverse cutaneous drug reactions (42.7), followed by non-steroidal anti-infammatory drugs (16.5). Acute urticaria was the most common clinical presentation (59.2) followed by fxed drug eruptions (18.5), and maculopapular eruptions (14.9). Conclusion: Adverse cutaneous drug reactions in our study population were mainly induced by beta-lactam antibiotics and non-steroidal anti-inflammatory drugs. The most common forms of cutaneous adverse drug reactions were found to be acute urticaria, fxed drug eruptions, and maculopapular rashes. © 2015 Farshchian et al

Spin induced nonlinearities in the electron MHD regime

We consider the influence of the electron spin on the nonlinear propagation of whistler waves. For this purpose a recently developed electron two-fluid model, where the spin up- and down populations are treated as different fluids, is adapted to the electron MHD regime. We then derive a nonlinear Schrodinger equation for whistler waves, and compare the coefficients of nonlinearity with and without spin effects. The relative importance of spin effects depend on the plasma density and temperature as well as the external magnetic field strength and the wave frequency. The significance of our results to various plasmas are discussed.Comment: 5 page

From extended phase space dynamics to fluid theory

We derive a fluid theory for spin-1/2 particles starting from an extended kinetic model based on a spin-projected density matrix formalism. The evolution equation for the spin density is found to contain a pressure-like term. We give an example where this term is important by looking at a linear mode previously found in a spin kinetic model.Comment: 4 page

Spin and magnetization effects in plasmas

We give a short review of a number of different models for treating magnetization effects in plasmas. In particular, the transition between kinetic models and fluid models is discussed. We also give examples of applications of such theories. Some future aspects are discussed.Comment: 18 pages, 1 figure. To appear in Plasma Physics and Controlled Fusion, Special Issue for the 37th ICPP, Santiago, Chil

Impacts of Logging-Associated Compaction on Forest Soils: A Meta-Analysis

Soil compaction associated with mechanized wood harvesting can long-lastingly disturb forest soils, ecosystem function, and productivity. Sustainable forest management requires precise and deep knowledge of logging operation impacts on forest soils, which can be attained by meta-analysis studies covering representative forest datasets. We performed a meta-analysis on the impact of logging-associated compaction on forest soils microbial biomass carbon (MBC), bulk density, total porosity, and saturated hydraulic conductivity (Ksat) affected by two management factors (machine weight and passage frequency), two soil factors (texture and depth), and the time passed since the compaction event. Compaction significantly decreased soil MBC by −29.5% only in subsoils (>30 cm). Overall, compaction increased soil bulk density by 8.9% and reduced total porosity and Ksat by −10.1 and −40.2%, respectively. The most striking finding of this meta-analysis is that the greatest disturbance to soil bulk density, total porosity, and Ksat occurs after very frequent (>20) machine passages. This contradicts the existing claims that most damage to forest soils happens after a few machine passages. Furthermore, the analyzed physical variables did not recover to the normal level within a period of 3–6 years. Thus, altering these physical properties can disturb forest ecosystem function and productivity, because they play important roles in water and air supply as well as in biogeochemical cycling in forest ecosystems. To minimize the impact, we recommend the selection of suitable logging machines and decreasing the frequency of machine passages as well as logging out of rainy seasons especially in clayey soils. It is also very important to minimize total skid trail coverage for sustainable forest management

Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images

Background: Nowadays, fatty liver is one of the commonly occurred diseases for the liver which can be observed generally in obese patients. Final results from a vari-ety of exams and imaging methods can help to identify and evaluate people affected by this condition. Objective: The aim of this study is to present a combined algorithm based on neural networks for the classification of ultrasound �images from fatty liver affected patients. Material and Methods: In experimental research can be categorized as a diagnostic study which focuses on classification of the acquired ultrasonography images for 55 patients with fatty liver. We implemented pre-trained convolutional neural networks of Inception-ResNetv2, GoogleNet, AlexNet, and ResNet101 to extract features from the images and after combining these resulted features, we provided support vector machine (SVM) algorithm to classify the liver images. Then the results are compared with the ones in implementing the algorithms independently. Results: The area under the receiver operating characteristic curve (AUC) for the introduced combined network resulted in 0.9999, which is a better result compared to any of the other introduced algorithms. The resulted accuracy for the proposed network also caused 0.9864, which seems acceptable accuracy for clinical application. Conclusion: The proposed network can be used with high accuracy to classify ultrasound images of the liver to normal or fatty. The presented approach besides the high AUC in comparison with other methods have the independence of the method from the �user or expert interference. © 2021, Shriaz University of Medical Sciences. All rights reserved