Refinement of retained austenite in super-bainitic steel by a deep cryogenic treatment

The effect of a deep cryogenic treatment on the microstructure of a super-bainitic steel was investigated. It was shown that quenching the super-bainitc steel in -196°C liquid nitrogen resulted in the transformation of retained austenite to two phases: ~20 nm thick martensite films and some nano carbides with a ~25 nm diameter. Some refinement of the retained austenite occurred, due to formation of fine martensite laths within the retained austenite. The evolution of these new phases resulted in an increase in the average hardness of the super-bainitic steel from 641 to ~670 HV1. © 2014 ISIJ.

Development of a compact rectenna for wireless powering of a head-mountable deep brain stimulation device

Design of a rectangular spiral planar inverted-F antenna (PIFA) at 915 MHz for wireless power transmission applications is proposed. The antenna and rectifying circuitry form a rectenna, which can produce dc power from a distant radio frequency energy transmitter. The generated dc power is used to operate a low-power deep brain stimulation pulse generator. The proposed antenna has the dimensions of 10 mm × 12.5 mm × 1.5 mm and resonance frequency of 915 MHz with a measured bandwidth of 15 MHz at return loss of -10 dB. A dielectric substrate of FR-4 of εr = 4.8 and δ = 0.015 with thickness of 1.5 mm is used for both antenna and rectifier circuit simulation and fabrication because of its availability and low cost. An L-section impedance matching circuit is used between the PIFA and voltage doubler rectifier. The impedance matching circuit also works as a low-pass filter for elimination of higher order harmonics. Maximum dc voltage at the rectenna output is 7.5 V in free space and this rectenna can drive a deep brain stimulation pulse generator at a distance of 30 cm from a radio frequency energy transmitter, which transmits power of 26.77 dBm.

Sky high, skin deep: dark technologies of mediation

Media has become responsive architecture. Intelligent media artefacts are now embedded into the very fabric of our existence; they have become the structure of society itself. Ubiquitous computing creates informational environments in which material structures of communication become alive with agency. McLuhan's light bulb is now everyware: [1] technology that mediates by its mere presence. Pervasive mediation, a combination of mobile networks and systems of material translation such as 3D printers and programmable matter--is our current regime of mediation.

Radio frequency energy harvesting from a feeding source in a passive deep brain stimulation device for murine preclinical research

This paper presents the development of an energy harvesting circuit for use with a head-mountable deep brain stimulation (DBS) device. It consists of a circular planar inverted-F antenna (PIFA) and a Schottky diode-based Cockcroft-Walton 4-voltage rectifier. The PIFA has the volume of π × 10(2) × 1.5 mm(3), resonance frequency of 915 MHz, and bandwidth of 16 MHz (909-925 MHz) at a return loss of -10 dB. The rectifier offers maximum efficiency of 78% for the input power of -5 dBm at a 5 kΩ load resistance. The developed rectenna operates efficiently at 915 MHz for the input power within -15 dBm to +5 dBm. For operating a DBS device, the DC voltage of 2 V is recorded from the rectenna terminal at a distance of 55 cm away from a 26.77 dBm transmitter in free space. An in-vitro test of the DBS device is presented.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

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