International audienceIn this article, we experimentally investigate the structure-property relationships of an acrylonitrile butadiene styrene (ABS) copolymer for fatigue and use a microstructure-based multistage fatigue (MSF) model to predict material failure. The MSF model comprises three stages of fatigue damage (crack incubation, small crack growth, and long crack growth) that was originally used for metal alloys. This study shows for the first time that the MSF theory is general enough to apply to polymer systems like ABS. The experimental study included monotonic testing (compression and tension) and fully reversed uniaxial cyclic tests at two frequencies (1 Hz and 10 Hz) with a range of strain amplitudes of 0.006 to 0.04. Cyclical softening was observed in the ABS copolymer. Fractography studies of failed specimens revealed that particles were responsible for crack incubation. Although polymeric materials can be argued to be more complex in terms of failure modes and thermo-mechano-chemical sensitivity when compared with most metal alloys, results showed that the MSF model could be extended successfully to capture microstructural effects to polymeric materials

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This article provides a sequential calibration methodology for correlating the Modified Embedded Atom Method (MEAM) potential parameters to lower length scale calculation results or experimental data. We developed a graphical interactive MATLAB program called the MEAM Potential Calibration (MPC) tool that provides an interface with the large-scale atomistic/molecular massively parallel simulator. The MPC tool supports a rigorous yet fairly simple calibration methodology for determining the MEAM potential parameters. A pure aluminum system is used as an example to demonstrate the bridging methodology; however, the tool can be used for any material.

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Background Health care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual’s health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States. Objective This study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient’s record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method. Methods We outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. Results Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients. Conclusions Upon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients.