Day: 09/01/2013

US electrical grid on the edge of failure.

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Network analysis suggests geography makes grid inherently unstable.

Facebook can lose a few users and remain a perfectly stable network, but where the national grid is concerned simple geography dictates that it is always just a few transmission lines from collapse.

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That is according to a mathematical study of spatial networks by physicists in Israel and the United States. Study co-author Shlomo Havlin of Bar-Ilan University in Ramat-Gan, Israel, says that the research builds on earlier work by incorporating a more explicit analysis of how the spatial nature of physical networks affects their fundamental stability. The upshot, published today in Nature Physics, is that spatial networks are necessarily dependent on any number of critical nodes whose failure can lead to abrupt — and unpredictable — collapse1.

The electric grid, which operates as a series of networks that are defined by geography, is a prime example, says Havlin. “Whenever you have such dependencies in the system, failure in one place leads to failure in another place, which cascades into collapse.”

The warning comes ten years after a blackout that crippled parts of the midwest and northeastern United States and parts of Canada. In that case, a series of errors resulted in the loss of three transmission lines in Ohio over the course of about an hour. Once the third line went down, the outage cascaded towards the coast, cutting power to some 50 million people. Havlin says that this outage is an example of the inherent instability his study describes, but others question whether the team’s conclusions can really be extrapolated to the real world.

“I suppose I should be open-minded to new research, but I’m not convinced,” says Jeff Dagle, an electrical engineer at the Pacific Northwest National Laboratory in Richland, Washington, who served on the government task force that investigated the 2003 outage. “The problem is that this doesn’t reflect the physics of how the power grid operates.”

Critical order

Havlin and his colleagues focused on idealized scenarios. They found that randomly structured networks — such as social networks — degrade slowly as nodes are removed, which in the real world might mean there is time to diagnose and address a problem before a system collapses. By contrast, the connections of orderly lattice structures have more critical nodes, which increase the instability. The problem is that such orderly networks are always operating near an indefinable edge, Havlin says. To reduce that risk, he recommends adding a small number of longer transmission lines that provide short cuts to different parts of the grid.

Benjamin Carreras, a physicist at Oak Ridge National Laboratory in Tennessee who has conducted similar work2, says that network theory can be useful for providing insight into electric grids but must be complemented with more complex models that attempt to represent both the physical realities and the responsiveness of the modern electric grid. Although in some cases adding long lines can benefit the overall stability of an electric system, Carreras’ work suggests that in certain circumstances such an approach allows problems to propagate even farther.

“More connections may stabilize some processes, by, for instance, increasing the number of paths to generators, but also may destabilize others,” Carreras says. “One cannot make generic statements on this topic.”

Although local outages caused by falling trees knocking down distribution lines are common, large-scale failures within the core transmission lines rarely occur on a modern electric grid. Before 2003, the last major blackout in the United States had been on the west coast in 1996, and more recently an outage has struck in the San Diego area.

Dagle says that the 2003 blackout stemmed from a combination of bad vegetation management — the first three lines tripped after sagging into trees but were all within their load rating — and a series of monitoring and communications breakdowns. Vegetation requirements have since been standardized, and a new generation of sensors is providing grid operators with more information about what is happening across the grid at any given moment.

“Many more utilities have much more data,” Dagle says. “The next phase of our voyage is to make better use of that data.”

Source: Nature

US behavioural research studies skew positive.

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Scientists speculate ‘US effect’ is a result of publish-or-perish mentality.

US behavioural researchers have been handed a dubious distinction — they are more likely than their colleagues in other parts of the world to exaggerate findings, according to a study published today.

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The research highlights the importance of unconscious biases that might affect research integrity, says Brian Martinson, a social scientist at the HealthPartners Institute for Education and Research in Minneapolis, Minnesota, who was not involved with the study.

“The take-home here is that the ‘bad guy/good guy’ narrative — the idea that we only need to worry about the monsters out there who are making up data — is naive,” Martinson says.



The study, published in Proceedings of the National Academy of Sciences1, was conducted by Daniele Fanelli, an evolutionary biologist at the University of Edinburgh, UK, and John Ioannidis, a physician at Stanford University in California. The pair examined 82 meta-analyses in genetics and psychiatry that collectively combined results from 1,174 individual studies. The researchers compared meta-analyses of studies based on non-behavioural parameters, such as physiological measurements, to those based on behavioural parameters, such as progression of dementia or depression.



The researchers then determined how well the strength of an observed result or effect reported in a given study agreed with that of the meta-analysis in which the study was included. They found that, worldwide, behavioural studies were more likely than non-behavioural studies to report ‘extreme effects’ — findings that deviated from the overall effects reported by the meta-analyses.
 And US-based behavioural researchers were more likely than behavioural researchers elsewhere to report extreme effects that deviated in favour of their starting hypotheses.



 “We might call this a ‘US effect,’” Fanelli says. “Researchers in the United States tend to report, on average, slightly stronger results than researchers based elsewhere.”

This ‘US effect’ did not occur in non-behavioral research, and studies with both behavioural and non-behavioural components exhibited slightly less of the effect than purely behavioural research. Fanelli and Ioannidis interpret this finding to mean that US researchers are more likely to report strong effects, and that this tendency is more likely to show up in behavioural research, because researchers in these fields have more flexibility to make different methodological choices that produce more diverse results.

The study looked at a larger volume of research than has been examined in previous studies on bias in behavioural research, says Brian Nosek, a psychologist at the University of Virginia in Charlottesville. However, he and other researchers say that this study shows only a correlation, so it does not prove that being a behavioural researcher or working in the United States causes the more extreme results. Behavioural studies may report more extreme outcomes because they examine more diverse conditions, researchers argue.

“One cannot straightforwardly conclude that the predictors are causes of the outcomes,” Nosek says. “To do an experimental test, we would need random assignment to biological or behavioural research and to US or non-US locations.”

Fanelli says that the new paper shows that behavioral research outcomes are more variable than in another fields – genetics — which has tighter methodological standards. A key question raised by this study, Fanelli says, is why such differences lead more often towards favourable extreme results in the United States.

“Whatever methodological choices are made, those made by researchers in the United States tend to yield subtly stronger supports for whatever hypothesis they test,” Fanelli says.

Fanelli and Ioannidis do not explain why that might be. They found that the ‘small-study effect’, in which overall results are biased towards positive, extreme findings because negative findings from small studies are not published, did not explain their results.



“It has to be because of methodological choices made before the study is submitted,” Fanelli says, possibly under pressure from the ‘publish or perish’ mentality that takes hold when career progress depends on high-profile publications.

Zubin Master, a bioethicist at Albany Medical College in New York, finds this explanation credible. “The current economic climate may further add to the pressure on researchers to publish in high-profile journals in order to enhance their chances of securing research funds,” he says.

But how to verify that possibility is a bigger question.

“The value of this study is not to say that this phenomenon is hugely worse in the United States, or in this field of science compared to that one,” Martinson says. “But the fact that you can show it raises the question of what it means.”

Source: Nature