A quantitative driver model of pre-crash brake onset and control

An existing modelling framework is leveraged to create a driver braking model for use in simulations of critical longitudinal scenarios with a slower or braking lead vehicle. The model applies intermittent brake adjustments to minimize accumulated looming prediction error. It is here applied to the simulation of a set of lead vehicle scenarios. The imulation results in terms of brake initiation timing and brake jerk are demonstrated to capture well the specific types of kinematics-ependencies that have been recently reported from naturalistic near-crashes and crashes

REMOVING SLUDGE HEELS FROM SAVANNAH RIVER SITE WASTE TANKS BY OXALIC ACID DISSOLUTION

The Savannah River Site (SRS) will remove sludge as part of waste tank closure operations. Typically the bulk sludge is removed by mixing it with supernate to produce a slurry, and transporting the slurry to a downstream tank for processing. Experience shows that a residual heel may remain in the tank that cannot be removed by this conventional technique. In the past, SRS used oxalic acid solutions to disperse or dissolve the sludge heel to complete the waste removal. To better understand the actual conditions of oxalic acid cleaning of waste from carbon steel tanks, the authors developed and conducted an experimental program to determine its effectiveness in dissolving sludge, the hydrogen generation rate, the generation rate of other gases, the carbon steel corrosion rate, the impact of mixing on chemical cleaning, the impact of temperature, and the types of precipitates formed during the neutralization process. The test samples included actual SRS sludge and simulated SRS sludge. The authors performed the simulated waste tests at 25, 50, and 75 C by adding 8 wt % oxalic acid to the sludge over seven days. They conducted the actual waste tests at 50 and 75 C by adding 8 wt % oxalic acid to the sludge as a single batch. Following the testing, SRS conducted chemical cleaning with oxalic acid in two waste tanks. In Tank 5F, the oxalic acid (8 wt %) addition occurred over seven days, followed by inhibited water to ensure the tank contained enough liquid to operate the mixer pumps. The tank temperature during oxalic acid addition and dissolution was approximately 45 C. The authors analyzed samples from the chemical cleaning process and compared it with test data. The conclusions from the work are: (1) Oxalic acid addition proved effective in dissolving sludge heels in the simulant demonstration, the actual waste demonstration, and in SRS Tank 5F. (2) The oxalic acid dissolved {approx} 100% of the uranium, {approx} 100% of the iron, and {approx} 40% of the manganese during a single contact in the simulant demonstration. (The iron dissolution may be high due to corrosion of carbon steel coupons.) (3) The oxalic acid dissolved {approx} 80% of the uranium, {approx} 70% of the iron, {approx} 50% of the manganese, and {approx} 90% of the aluminum in the actual waste demonstration for a single contact. (4) The oxalic acid dissolved {approx} 100% of the uranium, {approx} 15% of the iron, {approx} 40% of the manganese, and {approx} 80% of the aluminum in Tank 5F during the first contact cycle. Except for the iron, these results agree well with the demonstrations. The data suggest that a much larger fraction of the iron in the sludge dissolved, but it re-precipitated with the oxalate added to Tank 5F. (5) The demonstrations produced large volumes (i.e., 2-14 gallons of gas/gallon of oxalic acid) of gas (primarily carbon dioxide) by the reaction of oxalic acid with sludge and carbon steel. (6) The reaction of oxalic acid with carbon steel produced hydrogen in the simulant and actual waste demonstrations. The volume produced varied from 0.00002-0.00100 ft{sup 3} hydrogen/ft{sup 2} carbon steel. The hydrogen production proved higher in unmixed tanks than in mixed tanks

Voice-selective prediction alterations in nonclinical voice hearers

Auditory verbal hallucinations (AVH) are a cardinal symptom of psychosis but also occur in 6-13% of the general population. Voice perception is thought to engage an internal forward model that generates predictions, preparing the auditory cortex for upcoming sensory feedback. Impaired processing of sensory feedback in vocalization seems to underlie the experience of AVH in psychosis, but whether this is the case in nonclinical voice hearers remains unclear. The current study used electroencephalography (EEG) to investigate whether and how hallucination predisposition (HP) modulates the internal forward model in response to self-initiated tones and self-voices. Participants varying in HP (based on the Launay-Slade Hallucination Scale) listened to self-generated and externally generated tones or self-voices. HP did not affect responses to self vs. externally generated tones. However, HP altered the processing of the self-generated voice: increased HP was associated with increased pre-stimulus alpha power and increased N1 response to the self-generated voice. HP did not affect the P2 response to voices. These findings confirm that both prediction and comparison of predicted and perceived feedback to a self-generated voice are altered in individuals with AVH predisposition. Specific alterations in the processing of self-generated vocalizations may establish a core feature of the psychosis continuum.The Authors gratefully acknowledge all the participants who collaborated in the study, and particularly Dr. Franziska Knolle for feedback on stimulus generation, Carla Barros for help with scripts for EEG time-frequency analysis, and Dr. Celia Moreira for her advice on mixed linear models. This work was supported by the Portuguese Science National Foundation (FCT; grant numbers PTDC/PSI-PCL/116626/2010, IF/00334/2012, PTDC/MHCPCN/0101/2014) awarded to APP

Connectivity of the Primate Superior Colliculus Mapped by Concurrent Microstimulation and Event-Related fMRI

Background: Neuroanatomical studies investigating the connectivity of brain areas have heretofore employed procedures in which chemical or viral tracers are injected into an area of interest, and connected areas are subsequently identified using histological techniques. Such experiments require the sacrifice of the animals and do not allow for subsequent electrophysiological studies in the same subjects, rendering a direct investigation of the functional properties of anatomically identified areas impossible. Methodology/Principal Findings: Here, we used a combination of microstimulation and fMRI in an anesthetized monkey preparation to study the connectivity of the superior colliculus (SC). Microstimulation of the SC resulted in changes in the blood oxygenation level-dependent (BOLD) signals in the SC and in several cortical and subcortical areas consistent with the known connectivity of the SC in primates. Conclusions/Significance: These findings demonstrates that the concurrent use of microstimulation and fMRI can be used to identify brain networks for further electrophysiological or fMRI investigation

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts