The effects of multiple aerospace environmental stressors on human performance

An extended Fitt's law paradigm reaction time (RT) task was used to evaluate the effects of acceleration on human performance in the Dynamic Environment Simulator (DES) at Armstrong Laboratory, Wright-Patterson AFB, Ohio. This effort was combined with an evaluation of the standard CSU-13 P anti-gravity suit versus three configurations of a 'retrograde inflation anti-G suit'. Results indicated that RT and error rates increased 17 percent and 14 percent respectively from baseline to the end of the simulated aerial combat maneuver and that the most common error was pressing too few buttons

Important Considerations in Plasmon-Enhanced Electrochemical Conversion at Voltage-Biased Electrodes.

In this perspective we compare plasmon-enhanced electrochemical conversion (PEEC) with photoelectrochemistry (PEC). PEEC is the oxidation or reduction of a reactant at the illuminated surface of a plasmonic metal (or other conductive material) while a potential bias is applied. PEC uses solar light to generate photoexcited electron-hole pairs to drive an electrochemical reaction at a biased or unbiased semiconductor photoelectrode. The mechanism of photoexcitation of charge carriers is different between PEEC and PEC. Here we explore how this difference affects the response of PEEC and PEC systems to changes in light, temperature, and surface morphology of the photoelectrode

Towards the production of radiotherapy treatment shells on 3D printers using data derived from DICOM CT and MRI: preclinical feasibility studies

Background: Immobilisation for patients undergoing brain or head and neck radiotherapy is achieved using perspex or thermoplastic devices that require direct moulding to patient anatomy. The mould room visit can be distressing for patients and the shells do not always fit perfectly. In addition the mould room process can be time consuming. With recent developments in three-dimensional (3D) printing technologies comes the potential to generate a treatment shell directly from a computer model of a patient. Typically, a patient requiring radiotherapy treatment will have had a computed tomography (CT) scan and if a computer model of a shell could be obtained directly from the CT data it would reduce patient distress, reduce visits, obtain a close fitting shell and possibly enable the patient to start their radiotherapy treatment more quickly. Purpose: This paper focuses on the first stage of generating the front part of the shell and investigates the dosimetric properties of the materials to show the feasibility of 3D printer materials for the production of a radiotherapy treatment shell. Materials and methods: Computer algorithms are used to segment the surface of the patient’s head from CT and MRI datasets. After segmentation approaches are used to construct a 3D model suitable for printing on a 3D printer. To ensure that 3D printing is feasible the properties of a set of 3D printing materials are tested. Conclusions: The majority of the possible candidate 3D printing materials tested result in very similar attenuation of a therapeutic radiotherapy beam as the Orfit soft-drape masks currently in use in many UK radiotherapy centres. The costs involved in 3D printing are reducing and the applications to medicine are becoming more widely adopted. In this paper we show that 3D printing of bespoke radiotherapy masks is feasible and warrants further investigation

In Situ ATR-SEIRAS of Carbon Dioxide Reduction at a Plasmonic Silver Cathode.

Illumination of a voltage-biased plasmonic Ag cathode during CO2 reduction results in a suppression of the H2 evolution reaction while enhancing CO2 reduction. This effect has been shown to be photonic rather than thermal, but the exact plasmonic mechanism is unknown. Here, we conduct an in situ ATR-SEIRAS (attenuated total reflectance-surface-enhanced infrared absorption spectroscopy) study of a sputtered thin film Ag cathode on a Ge ATR crystal in CO2-saturated 0.1 M KHCO3 over a range of potentials under both dark and illuminated (365 nm, 125 mW cm-2) conditions to elucidate the nature of this plasmonic enhancement. We find that the onset potential of CO2 reduction to adsorbed CO on the Ag surface is -0.25 VRHE and is identical in the light and the dark. As the production of gaseous CO is detected in the light near this onset potential but is not observed in the dark until -0.5 VRHE, we conclude that the light must be assisting the desorption of CO from the surface. Furthermore, the HCO3- wavenumber and peak area increase immediately upon illumination, precluding a thermal effect. We propose that the enhanced local electric field that results from the localized surface plasmon resonance (LSPR) is strengthening the HCO3- bond, further increasing the local pH. This would account for the decrease in H2 formation and increase the CO2 reduction products in the light

Enhancing electrochemical intermediate solvation through electrolyte anion selection to increase nonaqueous Li-O2_2 battery capacity

Among the 'beyond Li-ion' battery chemistries, nonaqueous Li-O$_2$ batteries have the highest theoretical specific energy and as a result have attracted significant research attention over the past decade. A critical scientific challenge facing nonaqueous Li-O$_2$ batteries is the electronically insulating nature of the primary discharge product, lithium peroxide, which passivates the battery cathode as it is formed, leading to low ultimate cell capacities. Recently, strategies to enhance solubility to circumvent this issue have been reported, but rely upon electrolyte formulations that further decrease the overall electrochemical stability of the system, thereby deleteriously affecting battery rechargeability. In this study, we report that a significant enhancement (greater than four-fold) in Li-O$_2$ cell capacity is possible by appropriately selecting the salt anion in the electrolyte solution. Using $^7$Li nuclear magnetic resonance and modeling, we confirm that this improvement is a result of enhanced Li$^+$ stability in solution, which in turn induces solubility of the intermediate to Li$_2$O$_2$ formation. Using this strategy, the challenging task of identifying an electrolyte solvent that possesses the anti-correlated properties of high intermediate solubility and solvent stability is alleviated, potentially providing a pathway to develop an electrolyte that affords both high capacity and rechargeability. We believe the model and strategy presented here will be generally useful to enhance Coulombic efficiency in many electrochemical systems (e.g. Li-S batteries) where improving intermediate stability in solution could induce desired mechanisms of product formation.Comment: 22 pages, 5 figures and Supporting Informatio

Algorithmic Bayesian Persuasion

Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff assuming a perfectly rational receiver. We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithm, and a "simple" $(1-1/e)$-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. We show that this result is the best possible in the black-box model for information-theoretic reasons

Can we improve the prediction of hip fracture by assessing bone structure using shape and appearance modelling?

Copyright 2013 Elsevier B.V., All rights reserved.Peer reviewedPreprin

Leaf-inspired microcontact printing vascular patterns.

The vascularization of tissue grafts is critical for maintaining viability of the cells within a transplanted graft. A number of strategies are currently being investigated including very promising microfluidics systems. Here, we explored the potential for generating a vasculature-patterned endothelial cells that could be integrated into distinct layers between sheets of primary cells. Bioinspired from the leaf veins, we generated a reverse mold with a fractal vascular-branching pattern that models the unique spatial arrangement over multiple length scales that precisely mimic branching vasculature. By coating the reverse mold with 50 μg ml-1 of fibronectin and stamping enabled selective adhesion of the human umbilical vein endothelial cells (HUVECs) to the patterned adhesive matrix, we show that a vascular-branching pattern can be transferred by microcontact printing. Moreover, this pattern can be maintained and transferred to a 3D hydrogel matrix and remains stable for up to 4 d. After 4 d, HUVECs can be observed migrating and sprouting into Matrigel. These printed vascular branching patterns, especially after transfer to 3D hydrogels, provide a viable alternative strategy to the prevascularization of complex tissues

Memory Aware Synapses: Learning what (not) to forget

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule,which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting $$ triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.Comment: ECCV 201