A Predictive Model of Cognitive Performance Under Acceleration Stress

Extreme acceleration maneuvers encountered in modern agile fighter aircraft can wreak havoc on human physiology thereby significantly influencing cognitive task performance. Increased acceleration causes a shift in local arterial blood pressure and profusion causing declines in regional cerebral oxygen saturation. As oxygen content continues to decline, activity of high order cortical tissue reduces to ensure sufficient metabolic resources are available for critical life-sustaining autonomic functions. Consequently, cognitive abilities reliant on these affected areas suffer significant performance degradations. This goal of this effort was to develop and validate a model capable of predicting human cognitive performance under acceleration stress. An Air Force program entitled, Human Information Processing in Dynamic Environments (HIPDE) evaluated cognitive performance across twelve tasks under various levels of acceleration stress. Data sets from this program were leveraged for model development and validation. Development began with creation of a proportional control cardiovascular model that produced predictions of several hemodynamic parameters including eye-level blood pressure. The relationship between eye-level blood pressure and regional cerebral oxygen saturation (rSO2) was defined and validated with objective data from two different HIPDE experiments. An algorithm was derived to relate changes in rSO2 within specific brain structures to performance on cognitive tasks that require engagement of different brain areas. Data from two acceleration profiles (3 and 7 Gz) in the Motion Inference experiment were used in algorithm development while the data from the remaining two profiles (5 and 7 Gz SACM) verified model predictions. Data from the precision timing experiment were then used to validate the model predicting cognitive performance on the precision timing task as a function of Gz profile. Agreement between the measured and predicted values were defined as a correlation coefficient close to 1, linear best-fit slope on a plot of measured vs. predicted values close to 1, and low mean percent error. Results showed good overall agreement between the measured and predicted values for the rSO2 (Correlation Coefficient: 0.7483-0.8687; Linear Best-Fit Slope: 0.5760-0.9484; Mean Percent Error: 0.75-3.33) and cognitive performance models (Motion Inference Task - Correlation Coefficient: 0.7103-0.9451; Linear Best-Fit Slope: 0.7416-0.9144; Mean Percent Error: 6.35-38.21; Precision Timing Task - Correlation Coefficient: 0.6856 - 0.9726; Linear Best-Fit Slope: 0.5795 - 1.027; Mean Percent Error: 6.30 - 17.28). The evidence suggests that the model is an accurate predictor of cognitive performance under high acceleration stress across tasks, the first such model to be developed. Applications of the model include Air Force mission planning, pilot training, improved adversary simulation, analysis of astronaut launch and reentry profiles, and safety analysis of extreme amusement rides

Advancements in Real-Time Simulation of Power and Energy Systems

Modern power and energy systems are characterized by the wide integration of distributed generation, storage and electric vehicles, adoption of ICT solutions, and interconnection of different energy carriers and consumer engagement, posing new challenges and creating new opportunities. Advanced testing and validation methods are needed to efficiently validate power equipment and controls in the contemporary complex environment and support the transition to a cleaner and sustainable energy system. Real-time hardware-in-the-loop (HIL) simulation has proven to be an effective method for validating and de-risking power system equipment in highly realistic, flexible, and repeatable conditions. Controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are the two main HIL simulation methods used in industry and academia that contribute to system-level testing enhancement by exploiting the flexibility of digital simulations in testing actual controllers and power equipment. This book addresses recent advances in real-time HIL simulation in several domains (also in new and promising areas), including technique improvements to promote its wider use. It is composed of 14 papers dealing with advances in HIL testing of power electronic converters, power system protection, modeling for real-time digital simulation, co-simulation, geographically distributed HIL, and multiphysics HIL, among other topics

1979-80, 1980-81 GENERAL ISSUE- BULLETIN

1979-80, 1980-81 GENERAL ISSUE- BULLETINhttps://digitalrepository.unm.edu/course_catalogs/1089/thumbnail.jp

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse