The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient

X band model of Venus atmosphere permittivity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94912/1/rds5697.pd

Sunlight refraction in the mesosphere of Venus during the transit on June 8th, 2004

Many observers in the past gave detailed descriptions of the telescopic aspect of Venus during its extremely rare transits across the Solar disk. In particular, at the ingress and egress, the portion of the planet's disk outside the Solar photosphere has been repeatedly perceived as outlined by a thin, bright arc ("aureole"). Those historical visual observations allowed inferring the existence of Venus' atmosphere, the bright arc being correctly ascribed to the refraction of light by the outer layers of a dense atmosphere. On June 8th, 2004, fast photometry based on electronic imaging devices allowed the first quantitative analysis of the phenomenon. Several observers used a variety of acquisition systems to image the event -- ranging from amateur-sized to professional telescopes and cameras -- thus collecting for the first time a large amount of quantitative information on this atmospheric phenomenon. In this paper, after reviewing some elements brought by the historical records, we give a detailed report of the ground based observations of the 2004 transit. Besides confirming the historical descriptions, we perform the first photometric analysis of the aureole using various acquisition systems. The spatially resolved data provide measurements of the aureole flux as a function of the planetocentric latitude along the limb. A new differential refraction model of solar disk through the upper atmosphere allows us to relate the variable photometry to the latitudinal dependency of scale-height with temperature in the South polar region, as well as the latitudinal variation of the cloud-top layer altitude. We compare our measurements to recent analysis of the Venus Express VIRTIS-M, VMC and SPICAV/SOIR thermal field and aerosol distribution. Our results can be used a starting point for new, more optimized experiments during the 2012 transit event.Comment: Icarus, in pres

Modeled O 2 nightglow distributions in the Venusian atmosphere

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96410/1/jgre3128.pd