Situation cognition theory describes the context of a learning activity’s effect on learner’s cognition. In this paper, we use situated cognition theory to examine the effect of product dissection on product redesign activities. Two research questions were addressed: 1) Does situated cognition, in the form of product dissection, improve product functionality during redesign exercise?, and 2) Does situation cognition, again in the form of product dissection, affect the creativity of product redesigns? In this study, three sections of first year students in two different locations — The Pennsylvania State University (Penn State) and Missouri University of Science and Technology (S&T) — performed product redesign using either an electric toothbrush or a coffee maker. The redesigned products have been analyzed with respect to both depth (detail level) and creativity.

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The demand on emergency departments (ED) is variable and ever increasing, often leaving them overcrowded. Many hospitals are utilizing triage algorithms to rapidly sort and classify patients based on the severity of their injury or illness, however, most current triage methods are prone to over- or under-triage. In this paper, the group technology (GT) concept is applied to the triage process to develop a dynamic grouping and prioritization (DGP) algorithm. This algorithm identifies most appropriate patient groups and prioritizes them according to patient- and system-related information. Discrete event simulation (DES) has been implemented to investigate the impact of the DGP algorithm on the performance measures of the ED system. The impact was studied in comparison with the currently used triage algorithm, i.e., emergency severity index (ESI). The DGP algorithm outperforms the ESI algorithm by shortening patients' average length of stay (LOS), average time to bed (TTB), time in emergency room, and lowering the percentage of tardy patients and their associated risk in the system.

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Fiber volume fraction is a driving factor in mechanical properties of composites. Micromechanical models are typically used to predict the effective properties of composites with different fiber volume fractions. Since the microstructure of 3D-printed composites is intrinsically different than conventional composites, such predictions need to be evaluated for 3D-printed composites. This investigation evaluates the ability of the Voigt, Reuss, and Halpin–Tsai models to capture the dependence of modulus and strength of 3D-printed composites on varying fiber volume fraction. Tensile coupons were printed with continuous carbon fiber-reinforced Onyx matrix using a Markforged Mark Two printer. Specimens were printed at five different volume fractions with unidirectional fibers oriented at either [Formula: see text] to obtain longitudinal, shear, and transverse properties, respectively. It is shown that the Voigt model provides an excellent fit for the longitudinal tensile strength and a reasonable fit for the longitudinal modulus with varied fiber content. For the transverse direction, while the Reuss model fails to capture the transverse modulus trend, the Halpin–Tsai model provides a reasonable fit as it incorporates more experimental parameters. Like conventional composites, addition of fibers degrades the transverse strength, and the transverse strength decreases with increasing fiber volume fraction. The shear modulus variation with fiber content could not be fitted reasonably with either Halpin–Tsai model or Reuss model.