This just one of the fascinating examples in this article of how epidemiologists tracked down very specific examples of COVID-19 spread in different situations:
Some really great shoe-leather epidemiology demonstrated clearly the effect of a single asymptomatic carrier in a restaurant environment (see below). The infected person (A1) sat at a table and had dinner with 9 friends. Dinner took about 1 to 1.5 hours. During this meal, the asymptomatic carrier released low-levels of virus into the air from their breathing. Airflow (from the restaurant’s various airflow vents) was from right to left. Approximately 50% of the people at the infected person’s table became sick over the next 7 days. 75% of the people on the adjacent downwind table became infected. And even 2 of the 7 people on the upwind table were infected (believed to happen by turbulent airflow). No one at tables E or F became infected, they were out of the main airflow from the air conditioner on the right to the exhaust fan on the left of the room. (Ref)
Apparently, the decision has been made at NASA that supersonic heavy load parachutes just won’t work. So instead, a rocket to Mars will enter the Mars atmosphere at supersonic speeds using the shockwave from a blasting “retro” rocket to plow through the air instead of the old fashioned heat shield (“retro” here meaning opposite the direction of travel, not old fashioned)
Given this decision it’s interesting that SpaceX’s last booster reentry intentionally started it’s reentry burn high to simulate supersonic reentry burn conditions in the Mars atmosphere. And NASA mobilized special thermal cameras to record this experiment (that otherwise would’ve cost NASA hundreds of millions to test/simulate)
Development of Supersonic Retro-Propulsion for Future Mars Entry, Descent, and Landing Systems (PDF)
And a video at:
Supersonic Retropropulsion (wind tunnel) Test, Mach 4.6 Schlieren Video (youtube)
I like saying “Supersonic Retro-Propulsion”
From the New England Journal of Medicine:
Also see hilarious spurious data correlations by Tyler Vigen such as the correlation between:
“US spending on science, space, and technology” and “Suicides by hanging, strangulation and suffocation”
According to the former director of the NIH, Dr. Elias Zerhouni (in the Washington Post):
The NIH is facing an 8.2 percent across-the-board cut in future years (5.3 percent for 2013).
The most impacted are the young, new investigator scientists, who are coming into science, and will now abandon the field of science. There will be a generational gap created.
This article makes the point that the consensus on the health benefits of fat reduction in the diet are not scientifically born out. Instead it’s the result of the moral equivalent of the herding instinct for scientists.
Could this be true? A basic dietary assumption of the past 40 years was a whoops? And if scientists were wrong about something this fundamental, what else could they be wrong about.
Maybe Woody Allen’s predictions in the movie “Sleeper” really will come true (Ie., in the future, steak and cigarettes are determined to be healthy)
Continue reading “Diet and Fat: A Severe Case of Mistaken Consensus (NYTimes.com)”
In the article Why Can’t A Computer Be More Like A Brain? (spectrum.ieee.org) author Jeff Hawkins proposes a theory of how the brain works and how it could be implemented in computers:
“Memory of what a dog looks like is not stored in one location. Low-level visual details such as fur, ears, and eyes are stored in low-level nodes, and high-level structure, such as head or torso, are stored in higher-level nodes.the low-level nodes learn first. Representations in high-level nodes then share what was previously learned in low-level nodes.
each node in the hierarchy learns common, sequential patterns, analogous to learning a melody. When a new sequence comes along, the [lower level] node matches the input to previously learned patterns, analogous to recognizing a melody. Then the [lower level] node outputs a constant pattern representing the best matched sequences, analogous to naming a melody. Given that the output of nodes at one [lower] level becomes input to nodes at the next [higher] level, the hierarchy learns sequences of sequences of sequences.”
A graphic (PDF) from the article demonstrates the point:
What the article sort of talks about is the intrinsic role of time based feedback loops as part of how a node recognizes a pattern. I’m guessing that rather than process a wide bit pattern directly, “nodes” process the wide bit pattern in chunks remembering the node’s state based on the previous sequence of chunks and determining the new state based on the previous state and the current new chunk (and maybe neighboring/parent node states)