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FlatTop vs. 2-Cell in a dramatic tractor pull. For more information, see my previous post on Solarollers.
See the drag race between FlatTop and 2-Cell at YouTube.
The advantage in both cases goes to 2-Cell, although FlatTop does a lot better when heading south due to the angle of the Sun and 2-Cell's two solar cells.
These graphs show total live listings per day for for sale, jobs, services and housing categories. The cities are grouped by size with the largest Craigslist sites in the first graph and some smaller sites in the second. The categories are chosen to closely mirror categories of classified ads newspapers offer.
The data (tab-delimited windows text file) are harvested from the Craigslist home-page-reported listings for each property by a Python script.
These are fun and easy to build. The solar engine is made from two transistors and a threshold "switch" that allows the two capacitors to be charged by the solar cell. When the voltage across the capacitors reaches the threshold of the switch, the energy stored in the capacitor is released moving the Roller as current flows to the motor. This allows the vehicle to move in a wide range of light intensities.
Solar Roller and BEAM resources:
Alex Weber over at Instructables built a great programmable LED based on the AVR chip. Since I am tooled for PIC, I thought it would be fun to replicate what he has done. This simple project is fun and it provides a great building block for playing with collective behaviors.
Download detailed instructions and software LEDRec.c, LEDRec.h, LEDRec.hex for a PIC-based programmable LED.
At the New Yorker, a small article hits the point I was trying to make in my previous post pretty squarely. Here is the last paragraph from Risk Management by Lauren Collins:
He acknowledged that the paragraph had come from a forwarded e-mail, but said that, before pasting it into his pitch, he had done “some simple calculations” that supported its conclusions. “In what I’ve seen in dealing with the war and the misperceptions of it,” he said, “it seemed to me like those would be the right numbers.” He went on, “I work in D.C. on a daily basis, and I’m afraid to get out of my car in a lot of places. I hear about police officers being murdered every day in D.C. and Baltimore. And I’ve had thousands of friends and colleagues go to Iraq and come back safely.”
Even without the explanation of the particulars of the question (murder rate in Washington DC vs soldier death rate in Iraq) this quote illustrates two habits of humans: our feelings about our personal experiences count for more than data (anecdotal evidence weighs a lot, passion over data!) and we are profoundly unskilled at probability and statistics. Policies made on this kind of thinking may have good or bad results. But they will be consistent only with the bias, not the way the world works.
My friend Hal over at halshop, has a quote from Bell Hooks' Where We Stand: Class Matters regarding housing on his blog today. Here is a small excerpt:
More than any other issue facing our nation, housing will be the concern that will force citizens to face the reality of class. Every day citizens of this nation buy houses they cannot afford and will not own in their lifetime. Ironically, in many parts of our nation the houses get bigger and grander even as incomes dwindle. The gap between those who have and those who have not will be registered by the revolt of citizens who once believed that they would always have the right to own, confronting the reality that housing is rapidly becoming a luxury that only those with unlimited resources can hold on to.
While my experience (anecdotal evidence) says that Hooks is pointing to something that is happening to people and that it is bad, some additional information would be helpful.
First, what is happening? And what does bad man? Is it bad to die in debt? Are the few people who own their homes outright better off? In what ways are they better off? What other quality-of -life vectors are affected?
Second, trends and statistics would give us hints about who this is happening to, and how it is happening. Correlations would tell us where to look for causes or at least coincidences. This is an inherently complex issue. I don’t mean complex in the sense that the causes and effects are a difficult-to-sort-out ball of spaghetti. What I mean is that small changes in initial conditions may result in (nonlinear!) large changes in outcomes.
I tend to agree with Hooks; therefore I tend to believe this understanding should inform behavior, policy. But saying so over and over—or hearing a story that supports the bias—doesn’t make it so. I admire Hooks’ passion; I long for a world with both passion and rigorous analysis.
