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Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became avail-able, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings.

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This paper compares the motion sensations of a subject rotated about a vertical axis for two fixed visual fields (a large peripheral field and a single central spot) and in darkness. Motion sensation is described in terms of threshold, frequency response, and subjective displacement and velocity. The perception of angular acceleration showed significantly lower threshold and reduced latency time for the illuminated presentation. The level of illumination, however, produced no significant difference in threshold. The subjective frequency response, measured by a nulling method, showed a higher gain in the illuminated presentation, particularly at low frequencies and accelerations. With the subject rotating a pointer to maintain a fixed heading during triangular velocity stimuli, subjective displacements showed no difference for all different visual cues. Magnitude estimates of the after-rotation associated with deceleration from a constant velocity showed a quicker rising speed, larger subjective velocity and longer duration in the illuminated presentation. All the results suggest that the oculogyral illusion is principally responsible for producing a lower threshold in the illuminated presentation, although the fixed peripheral visual field tends to reduce reliance upon vestibular signals. At lower intensity rotation stimuli, this effect is especially apparent.

Coupled structure-electrical nonlinear panel flutter equations of motion are derived using the finite element method for composite panels with embedded piezoelectric layers subjected to thermal loads. The von Karman large-deflection strain-displacement relations, quasisteady first-order piston theory aerodynamics, quasisteady thermal stress theory, and linear piezoelectricity theory are used. Following a modal transformation and reduction, a set of coupled nonlinear modal equations of motion is obtained. By using the linear optimal control design for the linearized modal equations, an optimal shape and location of piezoelectric actuators can be determined. Numerical simulations based on the nonlinear modal equations show that the maximum flutter-free dynamic pressure with the linear optimal control can be increased as high as six times of the critical dynamic pressure. The panel's largeamplitude limit-cycle motions, as well as periodic and chaotic motions at moderate temperatures, are shown to be completely suppressed within the maximum flutter-free dynamic pressure region. Flutter suppression on composite panels with different aspect ratios, boundary conditions, and thermal effects are also studied. The results reveal that piezoelectric actuators are effective in nonlinear panel flutter suppression. Pa Q,R q U, V, W [ X Nomenclature extension, coupling, and bending panel stiffness matrices system and element aerodynamic influence matrices panel length and width modal aerodynamic damping matrix air-panel mass ratio over Mach number electric displacement and field electromechanical coefficients modal force vector system and element aerodynamic damping matrices nondimensional aerodynamic damping thickness of piezoelectric layer and panel modal stiffness matrix system and element linear stiffness matrices first-order nonlinear stiffness matrices second-order nonlinear stiffness matrices feedback control gain matrix modal mass matrix system and element mass matrices Mach number system and element load vectors aerodynamic pressure penalty matrices modal amplitude vector dynamic pressure time displacement system and element nodal displacement vectors state space vector jc ¥ = width of piezoelectric material placed at the leading edge [a] = thermal expansion coefficients AT = temperature change £, Y = strain components [©]/» [®L/ = linear and nonlinear electromechanical coupling matrices [K] = curvature vector A = nondimensional dynamic pressure /x = air-panel mass ratio p = density a,r = stress components X I / ,

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