Scientific Machine Learning for Modeling and Simulating Complex Fluids

The formulation of rheological constitutive equations -- models that relate internal stresses and deformations in complex fluids -- is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine learning constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data, and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these 'digital fluid twins' to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation -- a task that is not achievable using any previously developed data-driven rheological equation of state.Comment: 13 pages, 4 figure

The Medium Amplitude Response of Nonlinear Maxwell-Oldroyd Type Models in Simple Shear

A general framework for Maxwell-Oldroyd type differential constitutive models is examined, in which an unspecified nonlinear function of the stress and rate-of-deformation tensors is incorporated into the well-known corotational version of the Jeffreys model discussed by Oldroyd. For medium amplitude simple shear deformations, the recently developed mathematical framework of medium amplitude parallel superposition (MAPS) rheology reveals that this generalized nonlinear Maxwell model can produce only a limited number of distinct signatures, which combine linearly in a well-posed basis expansion for the third order complex viscosity. This basis expansion represents a library of MAPS signatures for distinct constitutive models that are contained within the generalized nonlinear Maxwell model. We describe a framework for quantitative model identification using this basis expansion, and discuss its limitations in distinguishing distinct nonlinear features of the underlying constitutive models from medium amplitude shear stress data. The leading order contributions to the normal stress differences are also considered, revealing that only the second normal stress difference provides distinct information about the weakly nonlinear response space of the model. After briefly considering the conditions for time-strain separability within the generalized nonlinear Maxwell model, we apply the basis expansion of the third order complex viscosity to derive the medium amplitude signatures of the model in specific shear deformation protocols. Finally, we use these signatures for estimation of model parameters from rheological data obtained by these different deformation protocols, revealing that three-tone oscillatory shear deformations produce data that is readily able to distinguish all features of the medium amplitude, simple shear response space of this generalized class of constitutive models.Comment: 26 pages, 11 figure

Computer-assisted diagnosis in the noninvasive evaluation of patients with suspected coronary artery disease

A microcomputer program called CADENZA, which employs Bayes' theorem to analyze and report the results of various clinical descriptors and noninvasive tests relative to the diagnosis of coronary artery disease, was evaluated in 1,097 consecutive patients without previous myocardial infarction. With this program, each patient was characterized by a probability for coronary artery disease, based on Framingham risk factor analysis, symptom characterization, electrocardiographic stress testing, cardiokymography, cardiac fluoroscopy, thallium perfusion scintigraphy and technetium equilibrium-gated blood pool scintigraphy. A total of 11,808 probability estimates derived from various combinations of the available observations were analyzed: 2,180 in 170 patients undergoing coronary angiography and 9,628 in 969 patients who completed a 1 year follow-up for coronary events.The predicted probability of disease correlated linearly with observed angiographic prevalence in the 170 patients who subsequently had coronary angiography (prevalence = [0.001 ± 0.011] + [0.966 ± 0.019] x probability). The difference between probability and prevalence averaged 3.1%, and the magnitude of this correlation was not affected by the type or amount of data analyzed. The prevalence of multivessel disease in these patients increased as a monotonic function of disease probability. Below a probability of 25%, single vessel disease was slightly more common than multivessel disease. Above a probability of 75%, multivessel disease predominated. In the 969 patients followed up for 1 year from the date of testing, the incidence of cardiac death and nonfatal infarction increased as a cubic function of disease probability (from approximately 0 to 8% per year for each). Above a probability of 90%, however, the standard deviation for predicting these events was wide.These data indicate that Bayes' theorem in general— and CADENZA in particular—is an accurate, clinically applicable means for quantifying the prevalence of angiographic coronary artery disease, the risk of multivessel disease and the incidence of morbid coronary events in the year after testing

The Perception of "Training Availability" Among Certified Nurse Aides: Relationship to CNA Performance, Turnover, Attitudes, Burnout, and Empowerment

Article on the perception of "training availability" among certified nurse aides (CNAs) and the relationship to CNA performance, turnover, attitudes, burnout, and empowerment

Increased physical activity, physician recommendation, and senior center participation

This article employs the Andersen Behavioral Model to explore increased physical activity and participation in three types of senior center activities: physical fitness, dance/aerobic classes, and chair exercises

Semen quality in relation to biomarkers of pesticide exposure.

We previously reported reduced sperm concentration and motility in fertile men in a U.S. agrarian area (Columbia, MO) relative to men from U.S. urban centers (Minneapolis, MN; Los Angeles, CA; New York, NY). In the present study we address the hypothesis that pesticides currently used in agriculture in the Midwest contributed to these differences in semen quality. We selected men in whom all semen parameters (concentration, percentage sperm with normal morphology, and percentage motile sperm) were low (cases) and men in whom all semen parameters were within normal limits (controls) within Missouri and Minnesota (sample sizes of 50 and 36, respectively) and measured metabolites of eight current-use pesticides in urine samples provided at the time of semen collection. All pesticide analyses were conducted blind with respect to center and case-control status. Pesticide metabolite levels were elevated in Missouri cases, compared with controls, for the herbicides alachlor and atrazine and for the insecticide diazinon [2-isopropoxy-4-methyl-pyrimidinol (IMPY)]; for Wilcoxon rank test, p = 0.0007, 0.012, and 0.0004 for alachlor, atrazine, and IMPY, respectively. Men from Missouri with high levels of alachlor or IMPY were significantly more likely to be cases than were men with low levels [odds ratios (ORs) = 30.0 and 16.7 for alachlor and IMPY, respectively], as were men with atrazine levels higher than the limit of detection (OR = 11.3). The herbicides 2,4-D (2,4-dichlorophenoxyacetic acid) and metolachlor were also associated with poor semen quality in some analyses, whereas acetochlor levels were lower in cases than in controls (p = 0.04). No significant associations were seen for any pesticides within Minnesota, where levels of agricultural pesticides were low, or for the insect repellent DEET (N,N-diethyl-m-toluamide) or the malathion metabolite malathion dicarboxylic acid. These associations between current-use pesticides and reduced semen quality suggest that agricultural chemicals may have contributed to the reduction in semen quality in fertile men from mid-Missouri we reported previously