Nacre-like block lattice metamaterials with targeted phononic band gap and mechanical properties.

The extraordinary quasi-static mechanical properties of nacre-like composite metamaterials, such as high specific strength, stiffness, and toughness, are due to the periodic arrangement of two distinct phases in a "brick and mortar" structure. It is also theorized that the hierarchical periodic structure of nacre structures can provide wider band gaps at different frequency scales. However, the function of hierarchy in the dynamic behavior of metamaterials is largely unknown, and most current investigations are focused on a single objective and specialized applications. Nature, on the other hand, appears to develop systems that represent a trade-off between multiple objectives, such as stiffness, fatigue resistance, and wave attenuation. Given the wide range of design options available to these systems, a multidisciplinary strategy combining diverse objectives may be a useful opportunity provided by bioinspired artificial systems. This paper describes a class of hierarchically-architected block lattice metamaterials with simultaneous wave filtering and enhanced mechanical properties, using deep learning based on artificial neural networks (ANN), to overcome the shortcomings of traditional design methods for forward prediction, parameter design, and topology design of block lattice metamaterial. Our approach uses ANN to efficiently describe the complicated interactions between nacre geometry and its attributes, and then use the Bayesian optimization technique to determine the optimal geometry constants that match the given fitness requirements. We numerically demonstrate that complete band gaps, that is attributed to the coupling effects of local resonances and Bragg scattering, exist. The coupling effects are naturally influenced by the topological arrangements of the continuous structures and the mechanical characteristics of the component phases. We also demonstrate how we can tune the frequency of the complete band gap by modifying the geometrical configurations and volume fraction distribution of the metamaterials. This research contributes to the development of mechanically robust block lattice metamaterials and lenses capable of controlling acoustic and elastic waves in hostile settings.

The frequency and nature of medication errors in hospitalized patients with acute coronary syndrome.

Background Cardiovascular medications have been commonly associated with medication errors. Objective The objective of this study was to investigate the incidence and predictors of medication errors in patients with acute coronary syndrome. Setting the coronary care unit of a university teaching hospital. Methods This was a prospective observational study on 150 patients admitted to the coronary care unit between August 2014 and July 2015. Main outcome measure The principal outcome was the number (frequency) of encountered medication errors. Results Of total 5790 prescription items reviewed, 547 (9.4%) potential medication errors were identified of which 523 (9.0%) were prescribing errors and 24 were monitoring errors. The most frequent prescribing errors were dosing errors (231, 42.2%) followed by loading dose omission error (91, 16.6%), omission of essential drugs on 1st day (43, 7.9%), and timing error (40, 7.3%). Errors frequently encountered with drugs such as aspirin, enoxaparin, beta-blockers followed by angiotensin-converting enzyme inhibitors and clopidogrel. Multivariate logistic regression analysis revealed that renal impairment (OR 6.02; 95% CI 1.4-35.4; p = 0.02) and longer duration of hospital stay (OR 4.01; 95% CI 1.5-10.7; p = 0.005) were predictors of the higher incidence of medication errors. Conclusion Prescribing and monitoring errors in coronary care unit are frequent and avoidable, with the majority of errors were ranked to be of mild to moderate severity. Dosing errors, omission of essential drugs and monitoring errors were most common error types encountered. Dosage adjustment based on estimation of the glomerular filtration rate immediately after admission help avoiding dosage-related errors.