Unique Set Of Parameters Research Articles

Mutual dependence of experimental and data analysis features in characterization of fiber‐matrix interface via microdroplets

AbstractThe accurate determination of interfacial strength in composites poses a significant challenge, a critical factor yet often complex due to the multitude of unknowns that affect the microbond (MB) test. This complexity arises not only from different individual parameters, but also from the combined effects of these parameters interdepending with each other. This study presents a thorough analysis of the MB test, carried out through numerous finite element simulations that take into account a wide range of parameters. This was achieved in the context of an experimentally validated reference test. In this study, 624 different numerical simulations were performed, each using a unique set of parameters defined for this investigation. The study demonstrates that even minor modifications to features such as a change in droplet behavior (change in ductility – simulated via different material models), the normalized error can range from −2.5% to 20.6% and −10% to 80%, for peak force () and force‐displacement area (), respectively. The negative percent values indicate lower magnitudes than the reference experimental data, while positive values suggest higher predicted magnitudes. In addition, residual stresses are identified as the second strongest in terms of mutual interaction and a key feature that interacts with other parameters. The results also show that typical comparisons using and are inadequate and misleading, suggesting the use of critical stress and critical energy release rate for more accurate qualitative comparisons about the interface. In this work, the interdependencies between the selected features are investigated with the reasoning based on the interfacial crack propagation and associated dissipative phenomena of the droplet.

Ductile fracture prediction of HPDC aluminum alloy based on a shear-modified GTN damage model

In this paper, we investigate how the shear-modified Gurson-Tvergaard-Needleman (GTN) model can be used to reveal the effect of manufacturing-process-induced porosity on the scatter of ductile fracture properties of a high-pressure die-casting aluminum alloy. The GTN model exhibits great advantages in predicting the variation of failure strain/displacement by altering the initial void volume fraction (f0), compared with uncoupled ductile damage models. For a specific metallic material, one unique set of model parameters is usually determined from experiments, which leads to the fact that only a deterministic failure strain/displacement can be obtained under a fixed porosity. To overcome this shortcoming of the shear-modified GTN model, a novel parameter calibration scheme is proposed and validated in the present study. Following the physical background of the GTN model, the twelve material parameters of the shear-modified GTN model have been determined based on a combination of macroscopic mechanical tests covering a wide range of stress states and micromechanics-based unit cell simulations. Different from the existing calibration strategies, the simulation results of unit cells embedded with a spherical void have revealed a mutual dependence between the f0 and critical void volume fraction (fc). By conducting interrupted shear tests, the evolution of secondary nucleated void volume fraction was identified according to the statistical results of the fractured brittle silicon particles. The remaining parameters have been further iteratively determined. In the end, the effectiveness of the proposed parameter determination approach is confirmed based on the accurate prediction of stochastic ductile failure properties (both the global failure displacements and the local failure patterns) in different fracture tests. Given the proposed parameters identification strategy, the GTN material models have been enriched to predict the ductile failure behavior of metallic materials, which possess relatively high porosities and more pronounced scattering in failure properties.

Real-Time Determination of the Glass Transition Temperature during Reversible Network Formation Based on Furan–Maleimide Diels–Alder Cycloadditions Using Dielectric Spectroscopy

The molecular dynamics of the reversible network 4F230-2M230 were studied systematically by dielectric relaxation spectroscopy (DRS) at frequencies between 10–1 and 107 Hz under both isothermal and non-isothermal conditions, focusing on the cooperative segmental dynamics as sensed by the dielectric α-relaxation. This reversible network is based on the furan–maleimide Diels–Alder reaction of a 4-functional furan-functionalized Jeffamine coupled with a 2-functional maleimide. Different strategies for extracting a “dielectric” glass transition temperature (Tg) were employed and compared. First, relaxation times τα were derived by conventional Havriliak–Negami fits of dielectric spectra and then fitted to the Vogel–Fulcher–Tammann (VFT) equation, which yields the dynamic Tg of partially cured samples (approach I). Alternatively, the thermal glass transition temperature was derived from the dielectric spectra ε′(f, T) using a fine structure analysis by bivariate differential sampling. Both the dynamic and thermal Tgs revealed an excellent agreement, confirming the stability of the VFT fits as well as the equivalence of both quantities as usually expected for bulk glass-forming materials. Two attempts were made to find a unique set of VFT parameters based on a global fit considering DRS spectra from partially cured samples during heating from −120 to 100 °C. Via a first procedure (approach II), a unique set of VFT parameters EV and τ∞ was obtained that well describes the relaxation times during non-isothermal cure at temperatures where the effects of ongoing cure kinetics can be neglected. To overcome this restriction, a refined approach III was developed that includes the earlier reported kinetic model for the DA reaction and allows accurate predictions for the relaxation time evolution during both isothermal and non-isothermal cure experiments. Based on this unique description, a new technique for continuous cure monitoring was proposed and tested. This technique allows us to compute in real-time dynamic Tg from the relaxation time τα at any stage of the isothermal or non-isothermal cure process using two unique VFT parameters from the global fit procedure. This approach might stimulate new applications of DRS-based cure monitoring.

Parahydrogen in Reversible Exchange Induces Long-Lived 15N Hyperpolarization of Anticancer Drugs Anastrozole and Letrozole.

Hyperpolarization modalities overcome the sensitivity limitations of NMR and unlock new applications. Signal amplification by reversible exchange (SABRE) is a particularly cheap, quick, and robust hyperpolarization modality. Here, we employ SABRE for simultaneous chemical exchange of parahydrogen and nitrile-containing anticancer drugs (letrozole or anastrozole) to enhance 15N polarization. Distinct substrates require unique optimal parameter sets, including temperature, magnetic field, or a shaped magnetic field profile. The fine tuning of these parameters for individual substrates is demonstrated here to maximize 15N polarization. After optimization, including the usage of pulsed μT fields, the 15N nuclei on common anticancer drugs, letrozole and anastrozole, can be polarized within 1-2 min. The hyperpolarization can exceed 10%, corresponding to 15N signal enhancement of over 280,000-fold at a clinically relevant magnetic field of 1 T. This sensitivity gain enables polarization studies at naturally abundant 15N enrichment level (0.4%). Moreover, the nitrile 15N sites enable long-lasting polarization storage with [15N]T1 over 9 min, enabling signal detection from a single hyperpolarization cycle for over 30 min.

Constituent-based quasi-linear viscoelasticity: a revised quasi-linear modelling framework to capture nonlinear viscoelasticity in arteries

Arteries exhibit fully nonlinear viscoelastic behaviours (i.e. both elastically and viscously nonlinear). While elastically nonlinear arterial models are well established, effective mathematical descriptions of nonlinear viscoelasticity are lacking. Quasi-linear viscoelasticity (QLV) offers a convenient way to mathematically describe viscoelasticity, but its viscous linearity assumption is unsuitable for whole-wall vascular applications. Conversely, application of fully nonlinear viscoelastic models, involving deformation-dependent viscous parameters, to experimental data is impractical and often reduces to identifying specific solutions for each tested loading condition. The present study aims to address this limitation: By applying QLV theory at the wall constituent rather than at the whole-wall level, the deformation-dependent relative contribution of the constituents allows to capture nonlinear viscoelasticity with a unique set of deformation-independent model parameters. Five murine common carotid arteries were subjected to a protocol of quasi-static and harmonic, pseudo-physiological biaxial loading conditions to characterise their viscoelastic behaviour. The arterial wall was modelled as a constrained mixture of an isotropic elastin matrix and four families of collagen fibres. Constituent-based QLV was implemented by assigning different relaxation functions to collagen- and elastin-borne parts of the wall stress. Nonlinearity in viscoelasticity was assessed via the pressure dependency of the dynamic-to-quasi-static stiffness ratio. The experimentally measured ratio increased with pressure, from 1.03 pm 0.03 (mean pm standard deviation) at 80–40 mmHg to 1.58 pm 0.22 at 160–120 mmHg. Constituent-based QLV captured well this trend by attributing the wall viscosity predominantly to collagen fibres, whose recruitment starts at physiological pressures. In conclusion, constituent-based QLV offers a practical and effective solution to model arterial viscoelasticity.

A numerical model for basic creep of concrete with aging and damage on beams

This work presents a methodology to model the basic creep of concrete, considering aging and damage. Continuum elements with viscoelastic material represent the basic creep with aging using the solidification theory; interface elements with a cohesive zone model describe damage behavior through a linear softening law. Given the challenges of obtaining a unique set of parameters from experimental tests, a calibration process based on decoupling the physical phenomena is adopted. In addition, an inverse analysis is used when characterization tests are not available, allowing an indirect model calibration. Afterward, numerical results are compared with experimental data from bending creep tests. These results show that the proposed methodology can accurately represent concrete creep behavior at different load levels and specimen sizes besides predicting the beams’ residual capacity observed in tests with high load levels.

Accountability Issues, Online Covert Hate Speech, and the Efficacy of Counter‐Speech

Concerning individual or institutional accountability for online hate speech, research has revealed that most such speech is covert (veiled or camouflaged expressions of hate) and cannot be addressed with existing measures (e.g., deletion of messages, prosecution of the perpetrator). Therefore, in this article, we examine another way to respond to and possibly deflect hate speech: counter-speech. Counter-narratives aim to influence those who write hate speech, to encourage them to rethink their message, and to offer to all who read hate speech a critical deconstruction of it. We created a unique set of parameters to analyze the strategies used in counter-speech and their impact. Upon analysis of our database (manual annotations of 15,000 Twitter and YouTube comments), we identified the rhetoric most used in counter-speech, the general impact of the various counter-narrative strategies, and their specific impact concerning several topics. The impact was defined by noting the number of answers triggered by the comment and the tone of the answers (negative, positive, or neutral). Our data reveal an overwhelming use of argumentative strategies in counter-speech, most involving reasoning, history, statistics, and examples. However, most of these argumentative strategies are written in a hostile tone and most dialogues triggered are negative. We also found that affective strategies (based on displaying positive emotions, for instance) led to a positive outcome, although in most cases these narratives do not receive responses. We recommend that education or training—even machine learning such as empathetic bots—should focus on strategies that are positive in tone, acknowledging grievances especially.

Identifiability of soft tissue constitutive parameters from in-vivo macro-indentation

Reliable identification of soft tissue material parameters is frequently required in a variety of applications, particularly for biomechanical simulations using finite element analysis (FEA). However, determining representative constitutive laws and material parameters is challenging and often comprises a bottleneck that hinders the successful implementation of FEA. Soft tissues exhibit a nonlinear response and are commonly modeled using hyperelastic constitutive laws. In-vivo material parameter identification, for which standard mechanical tests (e.g., uniaxial tension and compression) are inapplicable, is commonly achieved using finite macro-indentation test. Due to the lack of analytical solutions, the parameters are commonly identified using inverse FEA (iFEA), in which simulated results and experimental data are iteratively compared. However, determining what data must be collected to accurately identify a unique parameter set remains unclear. This work investigates the sensitivities of two types of measurements: indentation force-depth data (e.g., measured using an instrumented indenter) and full-field surface displacements (e.g., using digital image correlation). To eliminate model fidelity and measurement-related errors, we employed an axisymmetric indentation FE model to produce synthetic data for four 2-parameter hyperelastic constitutive laws: compressible Neo-Hookean, and nearly incompressible Mooney-Rivlin, Ogden, and Ogden-Moerman models. For each constitutive law, we computed the objective functions representing the discrepancies in the reaction force, the surface displacement, and their combination, and visualized them for hundreds of parameter sets, spanning a representative range as found in the literature for the bulk soft tissue complex in human lower limbs. Moreover, we quantified three identifiability metrics, which provided insights into the uniqueness (or lack thereof) and the sensitivities. This approach provides a clear and systematic evaluation of the parameter identifiability, which is independent of the selection of the optimization algorithm and initial guesses required in iFEA. Our analysis indicated that the indenter’s force-depth data, despite being commonly used for parameter identification, was insufficient for reliably and accurately identifying both parameters for all the investigated material models and that the surface displacement data improved the parameter identifiability in all cases, although the Mooney-Rivlin parameters remained poorly identifiable. Informed by the results, we then discuss several identification strategies for each constitutive model. Finally, we openly provide the codes used in this study, to allow others to further investigate the indentation problem according to their specifications (e.g., by modifying the geometries, dimensions, mesh, material models, boundary conditions, contact parameters, or objective functions).

A Comparative Review on Single Phase Transformerless Inverter Topologies for Grid-Connected Photovoltaic Systems

The uses of grid-connected photovoltaic (PV) inverters are increasing day by day due to the scarcity of fossil fuels such as coal and gas. On the other hand, due to their superior efficiency, lower cost, smaller size, and lighter weight when compared to inverters with transformers, transformerless inverters for low-voltage single-phase grid-tied photovoltaic (PV) systems have recently attracted more interest. However, there are some specific challenges, especially leakage current issues, which must be adequately addressed to ensure the safety standards of the grid codes. A variety of topologies has been presented in the literature to eliminate the leakage current using the decoupling or clamping technique. However, choosing an appropriate topology after comparison is challenging because each topology has a unique set of parameters. In this paper, the authors have selected a common set of parameters and simulated all the selected eighteen well-known topologies in MATLAB/Simulink to fairly analyze and compare their common mode characteristics and other output parameters. In addition, the power loss distribution of every switch of each of the eighteen topologies was calculated and presented to better understand the insight of the topologies.

Simplified Approach to the Parameterization of the NRTL Model for Partially Miscible Binary Systems: Tττ LLE Methodology

A new approach to parameterizing the NRTL model for binary liquid–liquid equilibria (LLE) is presented. It allows for the incorporation of a priori knowledge into the parameterization procedure rather than using the “black box” approach often employed. The method first converts the compositional Txx data to a set of unique binary interaction parameters, Tττ data. Thereafter, the second step improves the parameterization of frequently used temperature-dependent parameters (TDPs) by reducing the traditional nonlinear regression problem to simpler linear regression. This method is less susceptible to poor initial guesses, finding local minima, and significantly reduces computational requirements, with comparable/improved performance to published parameters. A total of 29 binary systems, including upper and/or lower critical solution temperatures, were evaluated using the approach to provide generalized recommendations and understanding of the TDP requirements for each system type. Inclusion of such information and use of the approach will significantly simplify multicomponent LLE model parameterization.

Insights from an electro-mechanical heart failure cell model: Role of SERCA enhancement on arrhythmogenesis and myocyte contraction

Background and objectiveStructural and electrical remodeling in heart failure predisposes the heart to ventricular arrhythmias. Computer modeling approaches, used to complement experimental results, can provide a more mechanistic knowledge of the biophysical phenomena underlying cardiac pathologies. Indeed, previous in-silico studies have improved the understanding of the electrical correlates of heart failure involved in arrhythmogenesis; however, information on the crosstalk between electrical activity, intracellular Ca2+ and contraction is still incomplete. This study aims to investigate the electro-mechanical behavior of virtual failing human ventricular myocytes to help in the development of therapies, which should ideally target pump failure and arrhythmias at the same time. MethodsWe implemented characteristic remodeling of heart failure with reduced ejection fraction by including reported changes in ionic conductances, sarcomere function and cell structure (e.g. T-tubules disarray). Model parametrization was based on published experimental data and the outcome of simulations was validated against experimentally observed patterns. We focused on two aspects of myocardial dysfunction central in heart failure: altered force-frequency relationship and susceptibility to arrhythmogenic early afterdepolarizations. Because biological variability is a major problem in the generalization of in-silico findings based on a unique set of model parameters, we generated and evaluated a population of models. ResultsThe population-based approach is crucial in robust identification of parameters at the core of abnormalities and in generalizing the outcome of their correction. As compared to non-failing ones, failing myocytes had prolonged repolarization, a higher incidence of early afterdepolarizations, reduced contraction and a shallower force-frequency relationship, all features peculiar of heart failure. Component analysis applied to the model population identified reduced SERCA function as a relevant contributor to most of these derangements, which were largely reverted or diminished by restoration of SERCA function alone. ConclusionsThese simulated results encourage the development of strategies comprising SERCA stimulation and highlight the need to evaluate both electrical and mechanical outcomes.

Effects of Processing Parameters on the Microstructure and Mechanical Properties of Intermetallic Matrix Composites

In the realm of advanced materials, Intermetallic Matrix Composites (IMCs) have garnered significant attention due to their potential for high-temperature applications. This study systematically investigates the influence of various processing parameters on the microstructure and mechanical properties of IMCs. Utilizing a combination of powder metallurgy and subsequent heat treatments, samples were prepared under varied conditions. The microstructural evolution was meticulously examined using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), revealing distinct morphological changes as a function of processing parameters. Quantitative analysis demonstrated a direct correlation between processing conditions and the distribution, size, and morphology of the reinforcing phases. Mechanical testing, including tensile, compression, and hardness tests, was conducted to evaluate the resultant properties. The findings indicate that specific processing conditions can be optimized to achieve a desirable balance between ductility and strength. Notably, a unique set of parameters was identified that yielded an unprecedented combination of high strength and ductility, challenging the conventional trade-off paradigm in composite materials. This research underscores the critical role of processing in tailoring the microstructure and, consequently, the mechanical performance of IMCs, paving the way for their application in demanding environments.

Meta-Dynamic Network Modelling for Biochemical Networks.

ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.

Effectiveness of various musical parameters in Carnatic raga identification

Raga are vital parts of Carnatic music and offer the performer a musical structure to improvise within them. Raga identification is considered to be a tedious task even for expert music listeners. As of now, we do not know a unique set of parameters which can precisely determine the raga of a Carnatic music. Even though several machine learning techniques are available to perform raga recognition, they all suffer from several issues like less recognition accuracy, poor scalability etc. In this work, we suggest a non machine model which has a high recognition accuracy. The model works on extracting five features of the music and then computing a similarity measure between the feature vectors of the input audio with the corresponding feature vectors stored in the database for raga recognition. Even though our system is not excellent in scalability, it is simple to implement and its recognition accuracy can be increased by increasing the feature vectors stored in the database.

Gaussian AMP Aided Model-Driven Learning for OTFS System

Orthogonal time frequency space (OTFS) modulation, which converts the fast time-varying channel in the time-frequency domain into a time-invariant channel in the delay-Doppler(DD) domain, can mitigate the fading caused by the dynamic change of the channel. Nevertheless, due to the influence of the Doppler effect, it is challenging for traditional algorithms to acquire the desired detection results. To this end, this letter proposes a Gaussian approximate message passing (GAMP) aided model-driven signal detection algorithm. It first expands the existing GAMP algorithm into a corresponding deep neural network. Then, a unique set of trainable parameters, including one damping factor and two stepping factors, are constructed to improve the algorithm’s convergence. Simulation results demonstrate that the designed model-driven signal detection algorithm outperforms the original GAMP by about 2 dB at the bit error ratio (BER) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 10^{-4}$ </tex-math></inline-formula> .

Multiple-criteria decision analysis to substantiate the prospects of industrial and solid municipal wastes as slurry fuel components.

This article investigates the recovery of typical wastes (coal slime, sawdust, cardboard and tire pyrolysis residue) as part of high-moisture slurry fuels. Using a laboratory furnace, the ignition and combustion characteristics of fuels as well as NOx and SOx emissions were determined. Using multiple-criteria decision-making (MCDM) methods and experimental results, we access the performance of four different slurry fuels in comparison with bituminous coal. The novelty of the study is based on the following features: we consider a unique set of parameters of the fuels (economic, environmental, safety and energy indicators), as well as three countries for their potential use (the USA, India and Russia); three different methods for calculating the efficiency indicator of each fuel were used. Despite rather low energy performance, the summarizing efficiency indicator of waste-based slurries was 53-93% higher than that of coal. The use of cardboard in the composition of a fuel blend showed the best complex result (the increase in the efficiency indicator was 80-93% relative to coal). The least promising additive was the pyrolysis residue of automobile tires. Its addition resulted in a 10-15% decrease in overall efficiency relative to a slurry without additives. The research results are useful for optimizing the component composition of waste-based slurries, technical and economic development of projects for the incineration of various wastes in the form of high-moisture fuel slurries.

Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.

Pinpointing the origin of the increased driving voltage during prolonged operation in a phosphorescent OLED based on an exciplex host

We report on the origin of the reduced power efficiency in a red phosphorescent OLED with an exciplex host after prolonged operation. The power efficiency is reduced solely by an increased driving voltage while the radiant flux remains constant. An electrical model describing the driving voltage increase is, thus, sufficient to explain the reduced power efficiency. The electrical model of the fresh OLED and at different stages of degradation was devised from four different measurement methods. Using multiple measurement methods to determine the model parameters results in a rather unique set of model parameters, despite the large number of model parameters (38) as revealed by a correlation analysis. The increase in driving voltage could be reproduced by modifying only 7 out of the 38 model parameters. A sensitivity analysis identified the parameters with the largest effect (66%) on the driving voltage increase to be the trap density and the mobility of the employed hole transporting layer. This work highlights the benefit of using multiple measurement methods to derive reliable model parameters and the use of a sensitivity analysis to pinpoint the origin of the investigated property. • Increased driving voltage after prolonged operation. • Radiant flux remained constant, so an electrical model is sufficient. • Determination of reliable model parameters by using multiple measurement methods. • Sensitivity analysis reveals origin of the increased driving voltage. • Hole traps in TAPC are the main cause of the increased driving voltage.

Influence of particle size on the small-strain stiffness in granular soils: experimental observations and micromechanical interpretation

The aim of the current study is to provide evidence regarding the influence of particle size on the dynamic properties in granular materials. Experiments using a resonant column (RC) device were conducted on glass beads having similar coefficient of uniformity, but four different mean grain sizes to describe the tendencies in the magnitude of Gmax and Emax. It can be clearly seen that for similar particle gradation the influence of particle size is significant, in particular for larger mean grain sizes d50 exceeding 1·5 mm. The experimental data were fitted using existing empirical relationships with corresponding fitting parameters. The fitting parameters describing the void ratio and pressure dependence to estimate maximum stiffness were found to depend on grain size to a certain extent. This influence was diminished for the materials with a d50 &lt; 1·5 mm. It was therefore possible to use a unique set of fitting parameters for these materials with a satisfying degree of accuracy. The results were compared with some previous studies to highlight the size dependency of Gmax and Emax for materials with mean size greater than 2 mm, but also the relatively small influence on materials with lower mean particle sizes (d50 &lt; 2 mm) is acknowledged. The results are furthermore discussed through a micromechanical interpretation highlighting why larger grain sizes influence stiffness values.

Fully automated background phase correction using M-estimate SAmple consensus (MSAC)-Application to2Dand 4D flow.

Flow quantification by phase-contrast MRI is hampered by spatially varying background phase offsets. Correction performance by polynomial regression on stationary tissue may be affected by outliers such as wrap-around or constant flow. Therefore, we propose an alternative, M-estimate SAmple Consensus (MSAC) to reject outliers, and improve and fully automate background phase correction. The MSAC technique fits polynomials to randomly drawn small samples from the image. Over several trials, it aims to find the best consensus set of valid pixels by rejecting outliers to the fit and minimizing the residuals of the remaining pixels. The robustness of MSAC to its few parameters was investigated and verified using third-order polynomial correction fits on a total of 118 2D flow (97 with wrap-around) and 18 4D flow data sets (14 with wrap-around), acquired at 1.5 T and 3T. Background phase was compared with standard stationary correction and phantom correction. Pulmonary/systemic flow ratios in 2D flow were derived, and exemplary 4D flow analysis was performed. The MSAC technique is robust over a range of parameter choices, and a unique set of parameters is suitable for both 2D and 4D flow. In 2D flow, phase errors were significantly reduced by MSAC compared with stationary correction (p=0.005), and stationary correction shows larger errors in pulmonary/systemic flow ratios compared with MSAC. In 4D flow, MSAC shows similar performance as stationary correction. The MSAC method provides fully automated background phase correction to 2D and 4D flow data and shows improved robustness over stationary correction, especially with outliers present.