Independent Orbiter Assessment (IOA): Analysis of the crew equipment subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical (PCIs) items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results coresponding to the Orbiter crew equipment hardware are documented. The IOA analysis process utilized available crew equipment hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Of the 352 failure modes analyzed, 78 were determined to be PCIs

Ultrasonic Evaluation of Low Density Powder Compacts

Ultrasonic evaluation of powder compacts provides valuable information about the density of the powder and incipient defects in the compact. Since the specimens would be contaminated if they were inspected with traditional immersion or contact techniques, procedures to dry couple or vacuum bag and immerse the powder samples were implemented. With these methods ultrasonic energy was propagated through the powder compacts and the acoustic velocity and attenuation were measured. The ability to immerse the powder samples not only provides consistent coupling but allows scanning which is a valuable tool to image variations and defects in the compacts

Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Both the excitability of a neuron's membrane, driven by active ion channels, and dendritic morphology contribute to neuronal firing dynamics, but the relative importance and interactions between these features remain poorly understood. Recent modeling studies have shown that different combinations of active conductances can evoke similar firing patterns, but have neglected how morphology might contribute to homeostasis. Parameterizing the morphology of a cylindrical dendrite, we introduce a novel application of mathematical sensitivity analysis that quantifies how dendritic length, diameter, and surface area influence neuronal firing, and compares these effects directly against those of active parameters. The method was applied to a model of neurons from goldfish Area II. These neurons exhibit, and likely contribute to, persistent activity in eye velocity storage, a simple model of working memory. We introduce sensitivity landscapes, defined by local sensitivity analyses of firing rate and gain to each parameter, performed globally across the parameter space. Principal directions over which sensitivity to all parameters varied most revealed intrinsic currents that most controlled model output. We found domains where different groups of parameters had the highest sensitivities, suggesting that interactions within each group shaped firing behaviors within each specific domain. Application of our method, and its characterization of which models were sensitive to general morphologic features, will lead to advances in understanding how realistic morphology participates in functional homeostasis. Significantly, we can predict which active conductances, and how many of them, will compensate for a given age- or development-related structural change, or will offset a morphologic perturbation resulting from trauma or neurodegenerative disorder, to restore normal function. Our method can be adapted to analyze any computational model. Thus, sensitivity landscapes, and the quantitative predictions they provide, can give new insight into mechanisms of homeostasis in any biological system

Learning Queuing Networks by Recurrent Neural Networks

It is well known that building analytical performance models in practice is difficult because it requires a considerable degree of proficiency in the underlying mathematics. In this paper, we propose a machine-learning approach to derive performance models from data. We focus on queuing networks, and crucially exploit a deterministic approximation of their average dynamics in terms of a compact system of ordinary differential equations. We encode these equations into a recurrent neural network whose weights can be directly related to model parameters. This allows for an interpretable structure of the neural network, which can be trained from system measurements to yield a white-box parameterized model that can be used for prediction purposes such as what-if analyses and capacity planning. Using synthetic models as well as a real case study of a load-balancing system, we show the effectiveness of our technique in yielding models with high predictive power

Aberrant Neuronal Dynamics during Working Memory Operations in the Aging HIV-Infected Brain

Impairments in working memory are among the most prevalent features of HIV-associated neurocognitive disorders (HAND), yet their origins are unknown, with some studies arguing that encoding operations are disturbed and others supporting deficits in memory maintenance. The current investigation directly addresses this issue by using a dynamic mapping approach to identify when and where processing in working memory circuits degrades. HIV-infected older adults and a demographically-matched group of uninfected controls performed a verbal working memory task during magnetoencephalography (MEG). Significant oscillatory neural responses were imaged using a beamforming approach to illuminate the spatiotemporal dynamics of neuronal activity. HIV-infected patients were significantly less accurate on the working memory task and their neuronal dynamics indicated that encoding operations were preserved, while memory maintenance processes were abnormal. Specifically, no group differences were detected during the encoding period, yet dysfunction in occipital, fronto-temporal, hippocampal, and cerebellar cortices emerged during memory maintenance. In addition, task performance in the controls covaried with occipital alpha synchronization and activity in right prefrontal cortices. In conclusion, working memory impairments are common and significantly impact the daily functioning and independence of HIV-infected patients. These impairments likely reflect deficits in the maintenance of memory representations, not failures to adequately encode stimuli

Aberrant Neuronal Dynamics during Working Memory Operations in the Aging HIV-Infected Brain.

Impairments in working memory are among the most prevalent features of HIV-associated neurocognitive disorders (HAND), yet their origins are unknown, with some studies arguing that encoding operations are disturbed and others supporting deficits in memory maintenance. The current investigation directly addresses this issue by using a dynamic mapping approach to identify when and where processing in working memory circuits degrades. HIV-infected older adults and a demographically-matched group of uninfected controls performed a verbal working memory task during magnetoencephalography (MEG). Significant oscillatory neural responses were imaged using a beamforming approach to illuminate the spatiotemporal dynamics of neuronal activity. HIV-infected patients were significantly less accurate on the working memory task and their neuronal dynamics indicated that encoding operations were preserved, while memory maintenance processes were abnormal. Specifically, no group differences were detected during the encoding period, yet dysfunction in occipital, fronto-temporal, hippocampal, and cerebellar cortices emerged during memory maintenance. In addition, task performance in the controls covaried with occipital alpha synchronization and activity in right prefrontal cortices. In conclusion, working memory impairments are common and significantly impact the daily functioning and independence of HIV-infected patients. These impairments likely reflect deficits in the maintenance of memory representations, not failures to adequately encode stimuli