Flow singularity and slip velocity in plane extrudate swell computations

It is common knowledge that flows of viscoelastic liquids with stress singularities, like the extrudate swell flow, pose formidable obstacles to numerical computations at relatively low Weissenberg number. This paper describes an effort toward alleviating the stress singularity by means of a slip boundary condition at the die wall. The Oldoyd-B and the upper-convected Maxwell differential constitutive equations were used for simplicity and computational efficiency. With a no-slip boundary condition it was found that for Newtonian, upper-convected Maxwell and Oldroyd-B liquids the global solution was always mesh-dependent until the Newton iteration diverged at very fine tessellations in the vicinity of the static contact line. With a natural slip boundary condition the global solution became mesh-independent at the same tessellations. Moreover, the macroscopic predictions became independent of the amount of slip in a relatively broad region of slip coefficient. The Newton iteration converged up to Weissenberg number 0.6 with a no-slip boundary condition and up to 1.7 with a lip boundary condition for the upper-convected Maxwell liquid. For the Oldroyd-B liquid the maximum Weissenberg number was 0.85 without slip and 1.866 with slip. Although slip velocity, surface tension and Newtonian viscosity (or retardation time) enhanced some numerical stability in general, it appears unlikely that they could advance viscoelastic computations significantly. In the limiting case of no swelling, at infinitely large surface tension, the analytical solution for Newtonian and, a second order fluid showed:(a) elasticity increases the strength of the singularity that exists for Newtonian liquid at the contact line, and thus Newton iteration is expected to diverge at coarser and coarser tessellations as the elasticity increases in agreement with the finite element findings.(b) Finite element predictions for the same flow agreed with the analytical solution in the vicinity of the singularity only when a slip boundary condition was employed.(c) Slip boundary condition in the vicinity of the contact line alleviates the stress singularity. However, it forces the stress to go through a maximum which is equally catastrophic of the Newton iteration convergence.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27521/1/0000565.pd

TPSDicyc: Improved deformation invariant cross-domain medical image synthesis

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant model based on the deformation-invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods

An Assessment of the Efficiency of Dust Regional Modelling to Predict Saharan Dust Transport Episodes

Aerosol levels at Mediterranean Basin are significantly affected by desert dust that is eroded in North Africa and is transported northwards. This study aims to assess the performance of the Dust REgional Atmospheric Model (BSC-DREAM8b) in the prediction of dust outbreaks near the surface in Eastern Mediterranean. For this purpose, model PM10 predictions covering a 7-year period and PM10 observations at five surface monitoring sites in Greece are used. A quantitative criterion is set to select the significant dust outbreaks defined as those when the predicted PM10 surface concentration exceeds 12 μg/m3. The analysis reveals that significant dust transport is usually observed for 1–3 consecutive days. Dust outbreak seasons are spring and summer, while some events are also forecasted in autumn. The seasonal variability of dust transport events is different at Finokalia, where the majority of events are observed in spring and winter. Dust contributes by 19–25% to the near surface observed PM10 levels, which can be increased to more than 50 μg/m3 during dust outbreaks, inducing violations of the air quality standards. Dust regional modeling can be regarded as a useful tool for air quality managers when assessing compliance with air quality limit values

Analysis of plane extrudate-swell of highly elastic liquids with molecular constitutive equations

Plane, two-dimensional, polymer extrusion is analyzed by means of the Curtiss-Bird integral constitutive equation, streamlined finite-elements and Newton iteration. The Reynolds number is zero, the surface tension negligible and the melt does not slip at the wall. Starting from the Newtonian liquid of zero elasticity, the Newton iteration converged, within three to five iterations, up to a maximum Weissenberg number beyond 3500. The predicted values of the die-swell at low elasticity are in agreement with those reported in the literature. At hihger elasticities, the die-swell increases monotonically and levels off. Two other models examined, the Doi-Edwards and the Papanastasiou-Scriven-Macosko models, diverged at low Weissenberg numbers, however, the actual point of divergence was a function of the number of relaxation times. It appears that the second term of the Curtiss-Bird model, which incorporates the link tension coefficient, [var epsilon], enhances significant numerical stability, in addition to the one due to the relaxation spectrum, as its different convergence behavior from the Doi-Edwards model implies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28143/1/0000595.pd

The oxidizing power of the dark side: Rapid nocturnal aging of biomass burning as an overlooked source of oxidized organic aerosol

Oxidized organic aerosol (OOA) is a major component of ambient particulate matter, substantially affecting both climate and human health. A considerable body of evidence has established that OOA is readily produced in the presence of daylight, thus leading to the association of high concentrations of OOA in the summer or mid-afternoon. However, this current mechanistic understanding fails to explain elevated OOA concentrations during night or wintertime periods of low photochemical activity, thus leading atmospheric models to under predict OOA concentrations by a factor of 3-5. Here we show that fresh emissions from biomass burning rapidly forms OOA in the laboratory over a few hours and without any sunlight. The resulting OOA chemical composition is consistent with the observed OOA in field studies in major urban areas. To estimate the contribution of nocturnally aged OOA in the ambient atmosphere, we incorporate this nighttime-aging mechanism into a chemical-transport model and find that over much of the United States greater than 75% of the OOA formed from fresh biomass burning emissions underwent nighttime aging processes. Thus, the conceptual framework that OOA is predominantly formed in the presence of daylight fails to account for a substantial and rapid oxidation process occurring in the dark

Multimodality Quantitative Assessments of Myocardial Perfusion Using Dynamic Contrast Enhanced Magnetic Resonance and 15O-Labeled Water Positron Emission Tomography Imaging

Kinetic modeling of myocardial perfusion imaging data allows the absolute quantification of myocardial blood flow (MBF) and can improve the diagnosis and clinical assessment of coronary artery disease (CAD). Positron emission tomography (PET) imaging is considered the reference standard technique for absolute quantification, whilst oxygen-15 (15O)-water has been extensively implemented for MBF quantification. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has also been used for MBF quantification and showed comparable diagnostic performance against (¹⁵ O)-water PET studies. We investigated for the first time the diagnostic performance of two different PET MBF analysis softwares PMOD and Carimas, for obstructive CAD detection against invasive clinical standard methods in 20 patients with known or suspected CAD. Fermi and distributed parameter modeling-derived MBF quantification from DCE-MRI was also compared against (15O)-water PET, in a subgroup of six patients. The sensitivity and specificity for PMOD was significantly superior for obstructive CAD detection in both per vessel (0.83, 0.90) and per patient (0.86, 0.75) analysis, against Carimas (0.75, 0.65) and (0.81, 0.70), respectively. We showed strong, significant correlations between MR and PET MBF quantifications (r = 0.83 - 0.92). However, DP and PMOD analysis demonstrated comparable and higher hemodynamic differences between obstructive versus (no, minor, or non)-obstructive CAD, against Fermi and Carimas analysis. Our MR method assessments against the optimum PET reference standard technique for perfusion analysis showed promising results in per segment level and can support further multimodality assessments in larger patient cohorts. Further MR against PET assessments may help to determine their comparative diagnostic performance for obstructive CAD detection

Rapid dark aging of biomass burning as an overlooked source of oxidized organic aerosol

To quantify the full implications of biomass burning emissions on the atmosphere, it is essential to accurately represent the emission plume after it has undergone chemical aging in the atmosphere. Atmospheric models typically consider the predominant aging pathway of biomass burning emissions to take place in the presence of sunlight (via the OH radical); however, this mechanism leads to consistent underpredictions of oxidized organic aerosol in wintertime urban areas. Here, we show, through a combination of laboratory experiments, ambient field measurements, and chemical transport modeling, that biomass burning emission plumes exposed to NO2 and O3 age rapidly without requiring any sunlight, thus providing an overlooked source of oxidized organic aerosol previously not accounted for in models

A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data

BACKGROUND: Bone marrow adipose tissue (BMAT) represents &gt; 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT's true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances.OBJECTIVE: To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data.MATERIALS AND METHODS: We studied males and females aged 60-69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61-64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10-12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis.RESULTS: Model accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics.CONCLUSIONS: We have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.</p