Power from bumps in the road

Wednesday, February 11th, 2009

MIT students hope to harness power from bumps in the road:

The project came about because “we wanted to figure out where energy is being wasted in a vehicle,” senior Zack Anderson explains. Some hybrid cars already do a good job of recovering the energy from braking, so the team looked elsewhere, and quickly homed in on the suspension.

They began by renting a variety of different car models, outfitting the suspension with sensors to determine the energy potential, and driving around with a laptop computer recording the sensor data. Their tests showed “a significant amount of energy” was being wasted in conventional suspension systems, Anderson says, “especially for heavy vehicles.”

Once they realized the possibilities, the students set about building a prototype system to harness the wasted power. Their prototype shock absorbers use a hydraulic system that forces fluid through a turbine attached to a generator. The system is controlled by an active electronic system that optimizes the damping, providing a smoother ride than conventional shocks while generating electricity to recharge the batteries or operate electrical equipment.

In their testing so far, the students found that in a 6-shock heavy truck, each shock absorber could generate up to an average of 1 kW on a standard road — enough power to completely displace the large alternator load in heavy trucks and military vehicles, and in some cases even run accessory devices such as hybrid trailer refrigeration units.

They filed for a patent last year and formed a company, called Levant Power Corp., to develop and commercialize the product. They are currently doing a series of tests with their converted Humvee to optimize the system’s efficiency. They hope their technology will help give an edge to the military vehicle company in securing the expected $40 billion contract for the new army vehicle called the Joint Light Tactical Vehicle, or JLTV.

Michelin has already integrated similar technology into their electric wheel.

The Iceberg Secret, Revealed

Wednesday, February 11th, 2009

The Iceberg Secret, Revealed:

You know how an iceberg is 90% underwater? Well, most software is like that too — there’s a pretty user interface that takes about 10% of the work, and then 90% of the programming work is under the covers. And if you take into account the fact that about half of your time is spent fixing bugs, the UI only takes 5% of the work. And if you limit yourself to the visual part of the UI, the pixels, what you would see in PowerPoint, now we’re talking less than 1%.

That’s not the secret. The secret is that People Who Aren’t Programmers Do Not Understand This.

This leads to some Important Corollaries, like Important Corollary Two:

If you show a nonprogrammer a screen which has a user interface which is 100% beautiful, they will think the program is almost done.

Joel’s advice?

Once you understand the Iceberg Secret, it’s easy to work with it. Understand that any demos you do in a darkened room with a projector are going to be all about pixels. If you can, build your UI in such a way that unfinished parts look unfinished. For example, use scrawls for the icons on the toolbar until the functionality is there. As you’re building your web service, you may want to consider actually leaving out features from the home page until those features are built. That way people can watch the home page go from 3 commands to 20 commands as more things get built.

Bill Gates on whether improving health in poor countries will lead to a population explosion

Wednesday, February 11th, 2009

Bill Gates on whether improving health in poor countries will lead to a population explosion:

When the Gates Foundation started out, it emphasized “reproductive health,” a.k.a. population control. (Someone once noted that you can always convince poor people that rich people have too much money and rich people that poor people have too many children.) But they soon discovered that there’s a direct relationship between improving health and slowing population growth. The most effective way to get people to have fewer children is to rapidly improve the health prospects of those kids. Speed is important, because it gives parents a chance to react and have smaller families.

This implies that aid should be concentrated, to get a small group out of the Malthusian Trap, rather than to grow a larger group a bit faster than usual without improving their long-term situation.

Does a Big Economy Need Big Power Plants?

Wednesday, February 11th, 2009

Does a Big Economy Need Big Power Plants? Amory B. Lovins says, no:

Thermal power stations burn fuel or fission atoms to boil water to turn turbines that spin generators, making 92 percent of U.S. electricity. Over a century, local combined-heat-and-power plants serving neighborhoods evolved into huge, remote, electricity-only generators serving whole regions. Electrons were dispatched hundreds of miles from central stations to dispersed users through a grid that the National Academy of Engineering ranked as its profession’s greatest achievement of the 20th century.

This evolution made sense at first, because power stations were costlier and less reliable than the grid, so by backing each other up through the grid and melding customers’ diverse loads, they could save capacity and achieve reliability. But these assumptions have reversed: central thermal power plants now cost less than the grid, and are so reliable that about 98 percent to 99 percent of all power failures originate in the grid. Thus the original architecture is raising, not lowering, costs and failure rates: cheap and reliable power must now be made at or near customers.

If central thermal power plants are so efficient and so reliable because they’re big and centralized, then numerous small plants might not help the situation. Rod Adams, publisher of Atomic Insights, makes this point — and others:

He talks about how most power failures occur in the grid, not the power plant, and then advises that a microgrid of small, distributed units can be more reliable than our current model. The problem with that statement is that central station power plant reliability is partially a result of careful engineering, redundancy and professionally trained operators that would not exist if units are too small. Microgrids also have many of the same vulnerabilities of the existing grid, but they will be less carefully engineered and less carefully maintained.

Lovins likes to use the evolution of computers as an analogy, but anyone who is commenting here who has paid close attention to the computer revolution knows that reliability has not been its strongest measure of effectiveness.
[...]
As William Tucker pointed out, Lovins is not totally wrong — there are some significant advantages to right sized power plants that can be manufactured in a factory rather than stick built and that can be delivered in far less time than is typically assumed for a large central station power plant of any kind. There are at least three companies who have publicly announced plans to build nuclear power plants in unit sizes of less than 50 MWe (150 MW thermal). They are Toshiba, which has designed a 10 MWe unit that can run for 30 years without new fuel; Hyperion, which has designed a 70 MW thermal heat source useful for assisting in enhanced oil recovery, district heating and which can be connected to a 27 MWe steam turbine for power production; and NuScale, which has designed a 45 MWe power plant that can be delivered as a single 300 ton unit to a site that has water or rail access.

For my money, those smaller nuclear plants have a huge advantage over the types of systems that Lovins advocates — they produce reliable power without producing any polluting emissions at all. They also need very little in the way of fuel delivery infrastructure. In a world powered by Lovins microgrids, there will be a large demand for diesel fuel and natural gas to fuel the generators that must back-up intermittent wind and solar power. That vision also includes a whole lot of excess capacity that must sit idle for much of its existence.

Huge study boosts disappointment on multivitamins

Wednesday, February 11th, 2009

Huge study boosts disappointment on multivitamins:

The largest study ever of multivitamin use in older women found the pills did nothing to prevent common cancers or heart disease. The eight-year study in 161,808 postmenopausal women echoes recent disappointing vitamin studies in men.

Recency Bias and Economic Crisis

Wednesday, February 11th, 2009

Nick Silver examines the perceived risk of an economic crash based on recent memory:

I decided to set up a rudimentary statistical experiment. Suppose it’s January 2008 and you’re an investor — or an economist — trying to forecast the probability of a major downturn in the United States economy. We’ll define such a downturn as occurring any time real GDP falls at an annualized rate of 4 percent in any one quarter, which is about equal to the decline experienced in the fourth quarter of 2008. Between 1947 and 2007, quarterly GDP fell by this amount on eleven separate occasions.

About the simplest economic model one can build is to assume that fluctuations in GDP occur randomly and follow a normal bell-curve distribution. From there, it is reasonably straightforward to calculate the percentage chance of a “crash” — a 4 percent decline in GDP — in any given quarter.

If you had done this calculation in January 2008, using data from the past sixty years — roughly since the end of World War II — you would have estimated the chance of a crash in the upcoming quarter to be 3.17 percent, implying about one crash for every eight years of economic activity. Not exactly an everyday occurrence, but certainly something you would need to be prepared for.

But suppose instead that your time horizon is shorter. You decide to look only at data from the past twenty years — or essentially since Alan Greenspan took over as chairman of the Federal Reserve in 1987. With that time horizon, the risk of a crash appears to be very low indeed. In fact, you would assess the probability to be just 0.04 percent — meaning one crash for every 624 years. This deceptively simple choice of assumptions, then, turns out to make a huge amount of difference. If you’d looked at data from the mid-eighties onward, instead of from World War II onward, you’d have underestimated the risk of a crash by a factor of about 80.

Moreover, if you were to conduct this “crash-risk” calculation retroactively and graph it over time, you’d identify a couple of inflection points at which the potential impact of recency bias increases. Using a twenty-year time horizon, your perceived risk of a crash would fall dramatically as of about 1995, once you started to “forget” about the oil crisis of the mid-1970s — this happens to coincide with the beginnings of the dot-com bubble. Then a few years later, as of 2001 or 2002, the risk of a crash would fall almost to zero, as the economic turmoil of the early 1980s begins to be written off by investors. This happens to coincide with the start of the housing bubble.

It doesn’t necessarily take an unforeseeable combination of events, then, to precipitate a market crash like the one we’re now experiencing; a short memory span would suffice.

Probabilistic CMOS

Tuesday, February 10th, 2009

I’m going to need a deeper explanation before I truly understand “probabilistic” CMOS, but it looks to provide more computational power while using less electricity than traditional Boolean chips:

Silicon transistors become increasingly ‘noisy’ as they get smaller, but engineers have historically dealt with this by boosting the operating voltage to overpower the noise and ensure accurate calculations. Chips with more and smaller transistors are consequently more power-hungry.

“PCMOS is fundamentally different,” Palem said. “We lower the voltage dramatically and deal with the resulting computational errors by embracing the errors and uncertainties through probabilistic logic.”

They designed their proof-of-concept chip specifically for encryption:

Palem said PCMOS is ideally suited for encryption, a process that relies on generating random numbers. It’s equally well-suited for graphics, but for different reasons. In a streaming video application on a cell phone, for example, it is unnecessary to conduct precise calculations. The small screen, combined with the human brain’s ability to process less-than-perfect pictures, results in a case where the picture looks just as good with a calculation that’s only approximately correct.

The Persistence of Ideology

Tuesday, February 10th, 2009

Sociologist Daniel Bell declared The End of Ideology in 1960, and Francis Fukuyama famously declared The End of History in 1989, after the fall of Communism, and yet, Theodore Dalrymple notes, ideology persists:

Who, then, are ideologists? They are people needy of purpose in life, not in a mundane sense (earning enough to eat or to pay the mortgage, for example) but in the sense of transcendence of the personal, of reassurance that there is something more to existence than existence itself. The desire for transcendence does not occur to many people struggling for a livelihood. Avoiding material failure gives quite sufficient meaning to their lives. By contrast, ideologists have few fears about finding their daily bread. Their difficulty with life is less concrete. Their security gives them the leisure, their education the need, and no doubt their temperament the inclination, to find something above and beyond the flux of daily life.

If this is true, then ideology should flourish where education is widespread, and especially where opportunities are limited for the educated to lose themselves in grand projects, or to take leadership roles to which they believe that their education entitles them. The attractions of ideology are not so much to be found in the state of the world—always lamentable, but sometimes improving, at least in certain respects—but in states of mind. And in many parts of the world, the number of educated people has risen far faster than the capacity of economies to reward them with positions they believe commensurate with their attainments. Even in the most advanced economies, one will always find unhappy educated people searching for the reason that they are not as important as they should be.

Rage can be a powerful reward in itself:

Feminists continued to see every human problem as a manifestation of patriarchy, civil rights activists as a manifestation of racism, homosexual-rights activists as a manifestation of homophobia, anti-globalists as a manifestation of globalization, and radical libertarians as a manifestation of state regulation.

How delightful to have a key to all the miseries, both personal and societal, and to know personal happiness through the single-minded pursuit of an end for the whole of humanity! At all costs, one must keep at bay the realization that came early in life to John Stuart Mill, as he described it in his Autobiography. He asked himself:

“Suppose that all your objects in life were realized; that all the changes in institutions and opinions which you are looking forward to, could be effected at this very instant: would this be a great joy and happiness to you?” And an irrepressible self-consciousness distinctly answered, “No!” At this my heart sank within me: the whole foundation on which my life was constructed fell down. All my happiness was to have been found in the continual pursuit of this end. The end had ceased to charm, and how could there ever again be any interest in the means? I seemed to have nothing left to live for.

This is the question that all ideologists fear, and it explains why reform, far from delighting them, only increases their anxiety and rage. It also explains why traditional religious belief is not an ideology in the sense in which I am using the term, for unlike ideology, it explicitly recognizes the limitations of earthly existence, what we can expect of it, and what we can do by our own unaided efforts. Some ideologies have the flavor of religion; but the absolute certainty of, say, the Anabaptists of Münster, or of today’s Islamists, is ultimately irreligious, since they claimed or claim to know in the very last detail what God requires of us.

As with most Dalrymple pieces, you should read the whole thing.

Los Angeles has the highest population density

Tuesday, February 10th, 2009

Los Angeles has the highest population density in the nation:

Yes, that was the word “highest,” not a smudge on your monitor. At 7,068 people per square mile, Los Angeles is considerably denser than New York-Newark, which ranks fourth at 5,309 people per square mile (behind San Francisco-Oakland and San Jose as well as Los Angeles). How could this be?

It is true that Los Angeles’s downtown disappoints, especially when compared with such thriving urban cores as Midtown Manhattan, Downtown San Francisco, or Chicago’s Loop. [...] However, despite the fact that Los Angeles’s center is comparatively low-density, its peripheral areas are considerably denser than the suburbs of other cities.

Los Angeles’s homes sit on very small lots, in part due to the difficulty of providing water infrastructure to new developments. (Other southwestern cities share this trait.) Moreover, Los Angeles has a large immigrant population that lives at very high densities. The area also has very few vacant lots.

So if the fundamental characteristic of sprawl is low density, Los Angeles is the least-sprawling city in the nation. (The least dense among the 40 largest metro areas is Atlanta.)

I have to agree with this comment that density figures depend greatly on scale:

Comparing LA density to the entirety of the New York metro area is disingenious. The New York metro has a proper center — NYC — with a density of of 27000/sq mi — and hence proper proper public transport. LA doesn’t. Of course if you include the Hamptons and bucolic Litchfield, Connecticut, into your statistics for New York, you can prove anything.

Making Wrong Code Look Wrong

Tuesday, February 10th, 2009

In exploring the art of Making Wrong Code Look Wrong, Joel Spolsky looks at what Hungarian notation originally meant:

It looked like Hungarian, and Simonyi was from Hungary, thus the name. In Simonyi’s version of Hungarian notation, every variable was prefixed with a lower case tag that indicated the kind of thing that the variable contained [ e.g. rwMax].

I’m using the word kind on purpose, there, because Simonyi mistakenly used the word type in his paper, and generations of programmers misunderstood what he meant.

If you read Simonyi’s paper closely, what he was getting at was the same kind of naming convention as I used in my example above where we decided that us meant “unsafe string” and s meant “safe string.” They’re both of type string. The compiler won’t help you if you assign one to the other and Intellisense won’t tell you bupkis. But they are semantically different; they need to be interpreted differently and treated differently and some kind of conversion function will need to be called if you assign one to the other or you will have a runtime bug. If you’re lucky.

Simonyi’s original concept for Hungarian notation was called, inside Microsoft, Apps Hungarian, because it was used in the Applications Division, to wit, Word and Excel. In Excel’s source code you see a lot of rw and col and when you see those you know that they refer to rows and columns. Yep, they’re both integers, but it never makes sense to assign between them. In Word, I’m told, you see a lot of xl and xw, where xl means “horizontal coordinates relative to the layout” and xw means “horizontal coordinates relative to the window.” Both ints. Not interchangeable. In both apps you see a lot of cb meaning “count of bytes.” Yep, it’s an int again, but you know so much more about it just by looking at the variable name. It’s a count of bytes: a buffer size. And if you see xl = cb, well, blow the Bad Code Whistle, that is obviously wrong code, because even though xl and cb are both integers, it’s completely crazy to set a horizontal offset in pixels to a count of bytes.
[...]
But then something kind of wrong happened.

The dark side took over Hungarian Notation.

Nobody seems to know why or how, but it appears that the documentation writers on the Windows team inadvertently invented what came to be known as Systems Hungarian.

Somebody, somewhere, read Simonyi’s paper, where he used the word “type,” and thought he meant type, like class, like in a type system, like the type checking that the compiler does. He did not. He explained very carefully exactly what he meant by the word “type,” but it didn’t help. The damage was done.

Apps Hungarian had very useful, meaningful prefixes like “ix” to mean an index into an array, “c” to mean a count, “d” to mean the difference between two numbers (for example “dx” meant “width”), and so forth.

Systems Hungarian had far less useful prefixes like “l” for long and “ul” for “unsigned long” and “dw” for double word, which is, actually, uh, an unsigned long. In Systems Hungarian, the only thing that the prefix told you was the actual data type of the variable.

This was a subtle but complete misunderstanding of Simonyi’s intention and practice, and it just goes to show you that if you write convoluted, dense academic prose nobody will understand it and your ideas will be misinterpreted and then the misinterpreted ideas will be ridiculed even when they weren’t your ideas.

Grandma’s moistening kettle may have held off flu

Tuesday, February 10th, 2009

Grandma's moistening kettle may have held off flu:

The correlation with flu and low humidity is important because in cold winter weather, when flu is most common, even a high relative humidity reading may indicate little actual moisture in the air, and the less moisture there is, the happier the flu virus seems to be.

Shaman and co-author Melvin Kohn, an epidemiologist with the Oregon Department of Health Services, reanalyzed data from a study published in 2007 in the journal PLoS Pathogens by researchers at Mount Sinai School of Medicine in New York. That report found there were more flu cases when it was colder and drier.

The Oregon researchers said relative humidity could only explain about 12 percent of the variability of influenza virus transmission and 36 percent of virus survival in the 2007 study.

In their new analysis, Shaman and Kohn said using absolute humidity explains 50 percent of influenza transmission and 90 percent of virus survival.

Warm air “holds” more water at the same relative humidity.

Marijuana Cuts Lung Cancer Tumor Growth In Half

Tuesday, February 10th, 2009

Marijuana cuts lung cancer tumor growth in half, according to Harvard researchers:

In the present study, the researchers first demonstrated that two different lung cancer cell lines as well as patient lung tumor samples express [cannabinoid receptors] CB1 and CB2, and that non-toxic doses of THC[the active ingredient in marijuana] inhibited growth and spread in the cell lines. “When the cells are pretreated with THC, they have less EGFR stimulated invasion as measured by various in-vitro assays,” Preet said.

Then, for three weeks, researchers injected standard doses of THC into mice that had been implanted with human lung cancer cells, and found that tumors were reduced in size and weight by about 50 percent in treated animals compared to a control group. There was also about a 60 percent reduction in cancer lesions on the lungs in these mice as well as a significant reduction in protein markers associated with cancer progression, Preet says.

Although the researchers do not know why THC inhibits tumor growth, they say the substance could be activating molecules that arrest the cell cycle. They speculate that THC may also interfere with angiogenesis and vascularization, which promotes cancer growth.

Darwinism Must Die So That Evolution May Live

Tuesday, February 10th, 2009

Darwinism must die so that evolution may live, proclaims Carl Safina:

Equating evolution with Charles Darwin ignores 150 years of discoveries, including most of what scientists understand about evolution. Such as: Gregor Mendel’s patterns of heredity (which gave Darwin’s idea of natural selection a mechanism — genetics — by which it could work); the discovery of DNA (which gave genetics a mechanism and lets us see evolutionary lineages); developmental biology (which gives DNA a mechanism); studies documenting evolution in nature (which converted the hypothetical to observable fact); evolution’s role in medicine and disease (bringing immediate relevance to the topic); and more.
[...]
Science has marched on. But evolution can seem uniquely stuck on its founder. We don’t call astronomy Copernicism, nor gravity Newtonism. “Darwinism” implies an ideology adhering to one man’s dictates, like Marxism. And “isms” (capitalism, Catholicism, racism) are not science. “Darwinism” implies that biological scientists “believe in” Darwin’s “theory.” It’s as if, since 1860, scientists have just ditto-headed Darwin rather than challenging and testing his ideas, or adding vast new knowledge.
[...]
Charles Darwin didn’t invent a belief system. He had an idea, not an ideology. The idea spawned a discipline, not disciples. He spent 20-plus years amassing and assessing the evidence and implications of similar, yet differing, creatures separated in time (fossils) or in space (islands). That’s science.

That’s why Darwin must go.

Keep Your Identity Small

Tuesday, February 10th, 2009

Keep your identity small, Paul Graham advises, to avoid “religious wars”:

Politics, like religion, is a topic where there’s no threshold of expertise for expressing an opinion. All you need is strong convictions.

Do religion and politics have something in common that explains this similarity? One possible explanation is that they deal with questions that have no definite answers, so there’s no back pressure on people’s opinions. Since no one can be proven wrong, every opinion is equally valid, and sensing this, everyone lets fly with theirs.

But this isn’t true. There are certainly some political questions that have definite answers, like how much a new government policy will cost. But the more precise political questions suffer the same fate as the vaguer ones.

I think what religion and politics have in common is that they become part of people’s identity, and people can never have a fruitful argument about something that’s part of their identity. By definition they’re partisan.

Which topics engage people’s identity depends on the people, not the topic. For example, a discussion about a battle that included citizens of one or more of the countries involved would probably degenerate into a political argument. But a discussion today about a battle that took place in the Bronze Age probably wouldn’t. No one would know what side to be on. So it’s not politics that’s the source of the trouble, but identity. When people say a discussion has degenerated into a religious war, what they really mean is that it has started to be driven mostly by people’s identities.

His conclusion:

The more labels you have for yourself, the dumber they make you.

Is the Tipping Point Toast?

Monday, February 9th, 2009

Is the Tipping Point Toast? Duncan Watts’ research suggests it should be:

He programmed a group of 10,000 people, all governed by a few simple interpersonal rules. Each was able to communicate with anyone nearby. With every contact, each had a small probability of “infecting” another. And each person also paid attention to what was happening around him: If lots of other people were adopting a trend, he would be more likely to join, and vice versa. The “people” in the virtual society had varying amounts of sociability — some were more connected than others. Watts designated the top 10% most-connected as Influentials; they could affect four times as many people as the average Joe. In essence, it was a virtual society run — in a very crude fashion — according to the rules laid out by thinkers like Gladwell and Keller.

Watts set the test in motion by randomly picking one person as a trendsetter, then sat back to see if the trend would spread. He did so thousands of times in a row.

The results were deeply counterintuitive. The experiment did produce several hundred societywide infections. But in the large majority of cases, the cascade began with an average Joe (although in cases where an Influential touched off the trend, it spread much further). To stack the deck in favor of Influentials, Watts changed the simulation, making them 10 times more connected. Now they could infect 40 times more people than the average citizen (and again, when they kicked off a cascade, it was substantially larger). But the rank-and-file citizen was still far more likely to start a contagion.

Why didn’t the Influentials wield more power? With 40 times the reach of a normal person, why couldn’t they kick-start a trend every time? Watts believes this is because a trend’s success depends not on the person who starts it, but on how susceptible the society is overall to the trend — not how persuasive the early adopter is, but whether everyone else is easily persuaded. And in fact, when Watts tweaked his model to increase everyone’s odds of being infected, the number of trends skyrocketed.

“If society is ready to embrace a trend, almost anyone can start one — and if it isn’t, then almost no one can,” Watts concludes. To succeed with a new product, it’s less a matter of finding the perfect hipster to infect and more a matter of gauging the public’s mood. Sure, there’ll always be a first mover in a trend. But since she generally stumbles into that role by chance, she is, in Watts’s terminology, an “accidental Influential.”

Perhaps the problem with viral marketing is that the disease metaphor is misleading. Watts thinks trends are more like forest fires: There are thousands a year, but only a few become roaring monsters. That’s because in those rare situations, the landscape was ripe: sparse rain, dry woods, badly equipped fire departments. If these conditions exist, any old match will do. “And nobody,” Watts says wryly, “will go around talking about the exceptional properties of the spark that started the fire.”

He also replicated Milgram’s famous six degrees of separation experiment, but he did not get the same results: hubs weren’t crucial.

(Hat tip to Todd.)