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Predicting the response of complex fluids to different flow conditions has been the focal point of rheology and is generally done via constitutive relations. There are, nonetheless, scenarios in which not much is known from the material mathematically, while data collection from samples is elusive, resource-intensive, or both. In such cases, meta-modeling of observables using a parametric surrogate model called multi-fidelity neural networks (MFNNs) may obviate the constitutive equation development step by leveraging only a handful of high-fidelity (Hi-Fi) data collected from experiments (or high-resolution simulations) and an abundance of low-fidelity (Lo-Fi) data generated synthetically to compensate for Hi-Fi data scarcity. To this end, MFNNs are employed to meta-model the material responses of a thermo-viscoelastic (TVE) fluid, consumer product Johnson’s® Baby Shampoo, under four flow protocols: steady shear, step growth, oscillatory, and small/large amplitude oscillatory shear (S/LAOS). In addition, the time–temperature superposition (TTS) of the material response and MFNN predictions are explored. By applying simple linear regression (without induction of any constitutive equation) on log-spaced Hi-Fi data, a series of Lo-Fi data were generated and found sufficient to obtain accurate material response recovery in terms of either interpolation or extrapolation for all flow protocols except for S/LAOS. This insufficiency is resolved by informing the MFNN platform with a linear constitutive model (Maxwell viscoelastic) resulting in simultaneous interpolation and extrapolation capabilities in S/LAOS material response recovery. The roles of data volume, flow type, and deformation range are discussed in detail, providing a practical pathway to multifidelity meta-modeling of different complex fluids.

 

The evening primrose family (Onagraceae) includes 664 species (803 taxa) with a center of diversity in the Americas, especially western North America. Ongoing research in Onagraceae includes exploring striking variation in floral morphology, scent composition, and breeding system, as well as the role of these traits in driving diversity among plants and their interacting pollinators and herbivores. However, these efforts are limited by the lack of a comprehensive, well-resolved phylogeny. Previous phylogenetic studies based on a few loci strongly support the monophyly of the family and the sister relationship of the two largest tribes but fail to resolve several key relationships.

We used a target enrichment approach to reconstruct the phylogeny of Onagraceae using 303 highly conserved, low-copy nuclear loci. We present a phylogeny for Onagraceae with 169 individuals representing 152 taxa sampled across the family, including extensive sampling within the largest tribe, Onagreae. Deep splits within the family are strongly supported, whereas relationships among closely related genera and species are characterized by extensive conflict among individual gene trees.

This phylogenetic resource will augment current research projects focused throughout the family in genomics, ecology, coevolutionary dynamics, biogeography, and the evolution of characters driving diversification in the family.

 

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high-$p_T$charged hadrons,$D$mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient$\widehat{q}$. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses.

Published by the American Physical Society2024 

Mask wearing and physical distancing are effective at preventing COVID-19 transmission. Little is known about the practice of these behaviors during physical activity (PA). In this longitudinal study, direct observation was used to describe COVID-19 prevention behaviors among physically active individuals. The Viral Transmission Scan (VT-Scan) was used to assess COVID-19 prevention behaviors of people standing, sitting, walking, jogging, and cycling in educational, retail, and residential areas. The VT-Scan was performed once per week over 22 weeks between 11:00 a.m. and 2:30 p.m. Information was manually extracted from videos collected during VT-Scans. A total of 4153 people were described, of which 71.2% were physically active, 80.0% were 18–30 years of age, 14.0% were non-white, 61.0% were female, and were 19.6% obese. Individuals not engaged in PA were less compliant with COVID-19 prevention behaviors than physically active people. Compliance differed by PA type, with walkers less compliant with COVID-19 prevention behaviors than joggers and cyclists. Among those physically active, non-compliance with COVID-19 prevention behaviors was higher in 18–30-year-olds, whites, and men. Engagement in COVID-19 prevention behaviors varies as a function of PA. Efforts to promote compliance with recommendations may benefit from tailored messaging, taking into account PA participation, PA type, and characteristics of physically active individuals. 

Due to the potential impact on the diagnosis and treatment of various cardiovascular diseases, work on the rheology of blood has significantly expanded in the last decade, both experimentally and theoretically. Experimentally, blood has been confirmed to demonstrate a variety of non-Newtonian rheological characteristics, including pseudoplasticity, viscoelasticity, and thixotropy. New rheological experiments and the development of more controlled experimental protocols on more extensive, broadly physiologically characterized, human blood samples demonstrate the sensitivity of aspects of hemorheology to several physiological factors. For example, at high shear rates the red blood cells elastically deform, imparting viscoelasticity, while at low shear rates, they form “rouleaux” structures that impart additional, thixotropic behavior. In addition to the advances in experimental methods and validated data sets, significant advances have also been made in both microscopic simulations and macroscopic, continuum, modeling, as well as novel, multiscale approaches. We outline and evaluate the most promising of these recent developments. Although we primarily focus on human blood rheology, we also discuss recent observations on variations observed across some animal species that provide some indication on evolutionary effects. 

The COVID-19 pandemic severely affected many aspects of human life. While most health agencies agree mask wearing and physical distancing reduce viral transmission, efforts to improve the assessment of these behaviors are lacking. This study aimed to develop a direct observation video method [Viral Transmission (VT)-Scan] for assessing COVID-19 transmission behaviors and related factors (e.g., environmental setting). A wearable video device (WVD) was used to obtain videos of outdoor, public areas. The videos were examined to extract relevant information. All outcomes displayed good to excellent intra- and inter-reliability with intra-class correlation coefficients ranging from 0.836 to 0.997. The majority of people had a mask (60.8%) but 22.1% of them wore it improperly, 45.4% were not physical distancing, and 27.6% were simultaneously mask and physical distancing non-compliant. Transmission behaviors varied by demographics with white, obese males least likely to be mask-compliant and white, obese females least likely to physical distance. Certain environments (e.g., crosswalks) were identified as “hot spots” where higher rates of adverse transmission behaviors occurred. This study introduces a reliable method for obtaining objective data on COVID-19 transmission behaviors and related factors which may be useful for agent-based modeling and policy formation. 

Oenothera sect. Calylophus is a North American group of 13 recognized taxa in the evening primrose family (Onagraceae) with an evolutionary history that may include independent origins of bee pollination, edaphic endemism, and permanent translocation heterozygosity. Like other groups that radiated relatively recently and rapidly, taxon boundaries within Oenothera sect. Calylophus have remained challenging to circumscribe. In this study, we used target enrichment, flanking noncoding regions, gene tree/species tree methods, tests for gene flow modified for target-enrichment data, and morphometric analysis to reconstruct phylogenetic hypotheses, evaluate current taxon circumscriptions, and examine character evolution in Oenothera sect. Calylophus. Because sect. Calylophus comprises a clade with a relatively restricted geographic range, we were able to extensively sample across the range of geographic, edaphic, and morphological diversity in the group. We found that the combination of exons and flanking noncoding regions led to improved support for species relationships. We reconstructed potential hybrid origins of some accessions and note that if processes such as hybridization are not taken into account, the number of inferred evolutionary transitions may be artificially inflated. We recovered strong evidence for multiple evolutionary origins of bee pollination from ancestral hawkmoth pollination, edaphic specialization on gypsum, and permanent translocation heterozygosity. This study applies newly emerging techniques alongside dense infraspecific sampling and morphological analyses to effectively reconstruct the recalcitrant history of a rapid radiation. [Gypsum endemism; Oenothera sect. Calylophus; Onagraceae; phylogenomics; pollinator shift; recent radiation; target enrichment.]