New trends in how medical devices are made and how they deliver value is fundamentally changing, devices are moving more and more into the world of the Internet of Things, utilizing highly sophisticated chipsets, processing capabilities and sensors. They are mobile and connected like never before, delivering solutions in innovative new areas such as patient-specific devices and ‘Lab on a Chip’ electronic diagnostic testing. What does the future of manufacturing medical devices, efficiently and profitably, look like? Or, should we say manufacturing the ‘Internet of Medical Things’ (IoMT)?
After a 20 year mission, the probe known as Cassini, will take a victory lap and on its final orbit, plunge into Saturn’s atmosphere, sending back new and unique data to the very end including valuable insights into Saturn’s weather. Dr. Peter Delamere is a Professor of Space Physics at the University of Alaska Fairbanks. His research focuses on comparative magnetospheric physics with an emphasis on the numerical simulation of space plasmas using hybrid (kinetic ion, fluid electron) and multi-fluid techniques. Dr. Delamere has studied the solar wind interaction with the giant magnetospheres of Jupiter and Saturn, comets, Pluto, and the plasma interaction at Io. In addition, he has developed models to study the flow of mass and energy through the inner magnetospheres of Jupiter and Saturn to study the internally-driven dynamics of these systems. As part of celebrating this event with the scientific community, we caught up with Dr. Delamere and asked him to share his insights.
Actually, this is about two R versions (standard and improved), a Python version, and a Perl version of a new machine learning technique recently published here. We asked for help to translate the original Perl script to Python and R, and finally decided to work with Naveenkumar Ramaraju, who is currently pursuing a master's in Data Science at Indiana University. So the Python and R versions are from him. We believe that this code comparison and translation will be very valuable to anyone learning Python or R with the purpose of applying it to data science and machine learning.
Li metal has been considered an outstanding candidate for anode materials in Li-ion batteries (LIBs) due to its exceedingly high specific capacity and extremely low electrochemical potential, but addressing the problem of Li dendrite formation has remained a challenge for its practical rechargeable applications. In this work, we used a porous carbon material made from asphalt (Asp), specifically untreated gilsonite, as an inexpensive host material for Li plating. The ultrahigh surface area of >3000 m2/g (by BET, N2) of the porous carbon ensures that Li was deposited on the surface of the Asp particles, as determined by scanning electron microscopy, to form Asp–Li. Graphene nanoribbons (GNRs) were added to enhance the conductivity of the host material at high current densities, to produce Asp–GNR–Li. Asp–GNR–Li has demonstrated remarkable rate performance from 5 A/gLi (1.3C) to 40 A/gLi (10.4C) with Coulombic efficiencies >96%. Stable cycling was achieved for more than 500 cycles at 5 A/gLi, and the areal capacity reached up to 9.4 mAh/cm2 at a highest discharging/charging rate of 20 mA/cm2 that was 10× faster than that of typical LIBs, suggesting use in ultrafast charging systems. Full batteries were also built combining the Asp–GNR–Li anodes with a sulfurized carbon cathode that possessed both high power density (1322 W/kg) and high energy density (943 Wh/kg).