Dose–Response Relationship between High-Fidelity Simulation and Intensive Care Nursing Students’ Learning Outcomes: An Italian Multimethod Study

Background: The best application modality of high-fidelity simulation in graduate critical care nursing courses is still rarely investigated in nursing research. This is an important issue since advanced nursing skills are necessary to effectively respond to critically ill patients’ care needs. The aim of the study was to examine the influence of a modified teaching model based on multiple exposures to high-fidelity simulations on both the learning outcomes and the perceptions of graduate students enrolled in a critical care nursing course. Methods: A multimethod study involving a sample of graduate critical care nursing students was conducted. A theoretical teaching model focused on multiple exposures to high-fidelity simulations is currently applied as a teaching method in an Italian critical care nursing course. According to the Kirkpatrick model for evaluating training programs, the performance, self-efficacy, and self-confidence in managing critically ill patients were considered learning outcomes, while satisfaction with learning and students’ lived experiences during the experimental phases were considered students’ perceptions. Results: Multiple exposures to high-fidelity simulations significantly improved performance, self-efficacy, and self-confidence in managing virtual critically ill patients’ care needs. The satisfaction level was high, while lived experiences of participants were positive and allowed for better explanation of quantitative results of this study. Conclusions: Multiple exposures to high-fidelity simulations can be considered a valuable teaching method that can improve the learning outcomes of graduate nurses enrolled in an intensive care course.

Super-resolution of low-fidelity flow solutions via generative adversarial networks

While computational fluid dynamics (CFD) can solve a wide variety of fluid flow problems, accurate CFD simulations require significant computational resources and time. We propose a general method for super-resolution of low-fidelity flow simulations using deep learning. The approach is based on a conditional generative adversarial network (GAN) with inexpensive, low-fidelity solutions as inputs and high-fidelity simulations as outputs. The details, including the flexible structure, unique loss functions, and handling strategies, are thoroughly discussed, and the methodology is demonstrated using numerical simulations of incompressible flows. The distinction between low- and high-fidelity solutions is made in terms of discretization and physical modeling errors. Numerical experiments demonstrate that the approach is capable of accurately forecasting high-fidelity simulations.

Development of a Fast-Running Algorithm to Approximate Incident Blast Parameters Using Body-Mounted Sensor Measurements

ABSTRACT Introduction The Office of Naval Research sponsored the Blast Load Assessment-Sense and Test program to develop a rapid, in-field solution that could be used by team leaders, commanders, and medical personnel to make science-based stand-down decisions for service members exposed to blast overpressure. However, a critical challenge to this goal was the reliable interpretation of surface pressure data collected by body-worn blast sensors in both combat and combat training scenarios. Without an appropriate standardized metric, exposures from different blast events cannot be compared and accumulated in a service member’s unique blast exposure profile. In response to these challenges, we developed the Fast Automated Signal Transformation, or FAST, algorithm to automate the processing of large amounts of pressure–time data collected by blast sensors and provide a rapid, reliable approximation of the incident blast parameters without user intervention. This paper describes the performance of the FAST algorithms developed to approximate incident blast metrics from high-explosive sources using only data from body-mounted blast sensors. Methods and Materials Incident pressure was chosen as the standardized output metric because it provides a physiologically relevant estimate of the exposure to blast that can be compared across multiple events. In addition, incident pressure serves as an ideal metric because it is not directionally dependent or affected by the orientation of the operator. The FAST algorithms also preprocess data and automatically flag “not real” traces that might not be from blasts events (false positives). Elimination of any “not real” blast waveforms is essential to avoid skewing the results of subsequent analyses. To evaluate the performance of the FAST algorithms, the FAST results were compared to (1) experimentally measured pressures and (2) results from high-fidelity numerical simulations for three representative real-world events. Results The FAST results were in good agreement with both experimental data and high-fidelity simulations for the three case studies analyzed. The first case study evaluated the performance of FAST with respect to body shielding. The predicted incident pressure by FAST for a surrogate facing the charge, side on to charge, and facing away from the charge was examined. The second case study evaluated the performance of FAST with respect to an irregular charge compared to both pressure probes and results from high-fidelity simulations. The third case study demonstrated the utility of FAST for detonations inside structures where reflections from nearby surfaces can significantly alter the incident pressure. Overall, FAST predictions accounted for the reflections, providing a pressure estimate typically within 20% of the anticipated value. Conclusions This paper presents a standardized approach—the FAST algorithms—to analyze body-mounted blast sensor data. FAST algorithms account for the effects of shock interactions with the body to produce an estimate of incident blast conditions, allowing for direct comparison of individual exposure from different blast events. The continuing development of FAST algorithms will include heavy weapons, providing a singular capability to rapidly interpret body-worn sensor data, and provide standard output for analysis of an individual’s unique blast exposure profile.