The satellite imagery provides detailed information about where the earthquakes occurred, how big the surface deformation was, and where the earthquakes occurred relative to population centers, typically within two to three days of the earthquake. This information was then incorporated into a set of operational response guides managed by the USGS National Earthquake Information Center (NEIC) that is distributed to decision makers, search and rescue operations, and other groups.

In the paper published online June 6 in the journal Remote Sensing, the researchers determined that the satellite imagery gathered from each earthquake provided new information, which improved the analysis of its impact.

“This, in turn, led to more accurate estimates of the numbers of fatalities and economic losses that are critical to more accurately determine in the days and weeks following devastating earthquakes,” says Bill Barnhart, assistant professor in the Department of Earth and Environmental Sciences at the UI and a lead author on the study.

Mainstays in determining an earthquake’s impact are ground-based seismometers that measure seismic activity around the world. But these instruments are not located everywhere, which can lead to incomplete information about the effects of some earthquakes in the critical time immediately after they occur. Moreover, some quakes are more complex and can’t be measured precisely by seismometers alone.

Increasingly, earthquake specialists are turning to geodetic methods — the math-based study of changes in the Earth’s shape — that use satellites and other instruments to complement data gathered by seismometers.

“While this is not yet a fully operational system, we are working with the USGS to make operational earthquake response with satellite imagery a systematic component of the NEIC’s global earthquake monitoring and response efforts,” Barnhart says.

One example is the work done by Emma Mankin, a UI senior and geoscience major who will graduate in December. Mankin processed radar imagery, or interferograms, from a 6.9 magnitude quake that struck Indonesia in August 2018. She then used this imagery to produce a model of the earthquake and where it was located. The USGS used this model directly to update its predictions of ground shaking and earthquake impact that were incorporated into its disaster-response systems.

“Emma’s rapid work on the Indonesia earthquake directly contributed to the operational analysis of a global earthquake,” Barnhart says. “Her contributions improved earthquake impact estimates for that event and helped to further demonstrate that these satellite approaches can provide actionable information that benefits society.”

Contributing authors include Gavin Hayes and David Wald from the USGS National Earthquake Information Center.

The USGS funded the research.

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Materials provided by University of Iowa. Original written by Richard C. Lewis. Note: Content may be edited for style and length.

“Animals are not passive, and there’s plenty of evidence that some of them will seek out shade to regulate their body temperature,” said Rice University ecologist Volker Rudolf, co-author of a study in Global Change Biology that examined both the behavior and habitats of 39 species. “The big question, for ecologists, is whether we can create a predictive framework that uses what we already know about species’ behaviors and habitats to predict whether this behavior might buffer them from rapid climate change and potentially rescue them from otherwise going extinct.”

Rudolf said he and his colleagues, including study co-lead authors Samuel Fey of Reed College and David Vasseur of Yale University, set out to create such a framework, in part because a number of high-profile studies have ignored behavior in making predictions about the possible impacts of climate warming.

Scientists have examined the impact of temperature on the fitness of many species. To control for external factors, almost all such tests are performed in a laboratory, where temperature can be increased while all other factors remain constant, said Rudolf, a professor in Rice’s Department of BioSciences. The overall health, or fitness, of individuals often falls off as temperatures increase, especially in insects and other ectothermic animals whose bodies don’t self-regulate temperature.

“Behavior allows for self-regulation of body temperature, even in some cases where physiology doesn’t,” Rudolf said. “So ignoring this behavior means you’re probably making wrong predictions.”

But behavior only goes so far. If a creature lives where there is no shade or other means to cool off, it’s obviously not an option. It may also be impractical, especially if getting to the shady spot requires an enormous expenditure of energy.

To examine the consequences of this behavior across a wide range of animals, the scientists created a mathematical framework that accounts for variability in microclimate in the habitat of a species to estimate the cost-benefit trade-off an individual faces when expending energy to seek shade. The framework was first verified on tests with the southern rock agama, a lizard native to South Africa, and was later applied to a database of 38 insect species from Europe, Asia, North America, Africa and Australia.

Using International Panel on Climate Change temperature projections for the year 2050, the researchers found that 19 of the insects were likely to be negatively impacted by warming temperatures. They further found that behavior would likely mitigate the ill effects of warming for 17 of the 19 species, including six that were predicted to experience “behavioral rescue,” a situation where behavioral adaptation keeps the species from going extinct. In fact, warmer temperatures, in conjunction with behavioral adaptation, were predicted to increase fitness for 10 species.

“With climate change already increasing temperatures worldwide, it is important to find ways to forecast how this will affect individual species as well as tightly couple ecological communities,” Rudolf said.

In addition to looking at the direct effects of temperature on one species, he said it will be important for ecologists to consider how rising temperatures will affect a species’ predators, competitors and food resources, including prey. Where looking at temperature alone might suggest a species will do better or worse, those predictions could change based on the impacts on closely coupled species.

Additional co-authors include Karla Alujević and Susana Clusella-Trullas of Stellenbosch University in South Africa; Kristy Kroeker of the University of California, Santa Cruz; Michael Logan of the University of Nevada, Reno, and the Smithsonian Tropical Research Institution in Panama; Mary O’Connor of the University of British Columbia; John DeLong of the University of Nebraska; Scott Peacor of Michigan State University; Rebecca Selden of Rutgers University; and Andy Sih of the University of California, Davis. The research was supported by CapeNature and initiated by conversations at the 2016 Gordon Research Conference on Predator-Prey Interactions.

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Materials provided by Rice University. Note: Content may be edited for style and length.

“We used materials informatics — the application of the methods of data science to materials problems — to predict a class of materials that have superior mechanical properties,” said primary author Jeffrey M. Rickman, professor of materials science and engineering and physics and Class of ’61 Professor at Lehigh University.

Researchers also used experimental tools, such as electron microscopy, to gain insight into the physical mechanisms that led to the observed behavior in the class of materials known as high-entropy alloys (HEAs). High-entropy alloys contain many different elements that, when combined, may result in systems having beneficial and sometimes unexpected thermal and mechanical properties. For that reason, they are currently the subject of intense research.

“We thought that the techniques that we have developed would be useful in identifying promising HEAs,” Rickman said. “However, we found alloys that had hardness values that exceeded our initial expectations. Their hardness values are about a factor of 2 better than other, more typical high-entropy alloys and other relatively hard binary alloys.”

All seven authors are from Lehigh University, including Rickman; Helen M. Chan, New Jersey Zinc Professor of materials science and engineering; Martin P. Harmer, Alcoa Foundation Professor of materials science and engineering; Joshua Smeltzer, graduate student in materials science and engineering; Christopher Marvel, postdoctoral research associate in materials science and engineering; Ankit Roy, graduate student in mechanical engineering and mechanics; and Ganesh Balasubramanian, assistant professor of mechanical engineering and mechanics.

Rise of High-Entropy Alloys and Data Analysis

The field of high-entropy, or multi-principal element, alloys has recently seen exponential growth. These systems represent a paradigm shift in alloy development, as some exhibit new structures and superior mechanical properties, as well as enhanced oxidation resistance and magnetic properties, relative to conventional alloys. However, identifying promising HEAs has presented a daunting challenge, given the vast palette of possible elements and combinations that could exist.

Researchers have sought a way to identify the element combinations and compositions that lead to high-strength, high-hardness alloys and other desirable qualities, which are a relatively small subset of the large number of potential HSAs that could be created.

In recent years, materials informatics, the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. The relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, and polymer dielectrics.

“Creation of large data sets in materials science, in particular, is transforming the way research is done in the field by providing opportunities to identify complex relationships and to extract information that will enable new discoveries and catalyze materials design,” Rickman said. The tools of data science, including multivariate statistics, machine learning, dimensional reduction and data visualization, have already led to the identification of structure-property-processing relationships, screening of promising alloys and correlation of microstructure with processing parameters.

Lehigh University’s research contributes to the field of materials informatics by demonstrating that this suite of tools is extremely useful for identifying promising materials from among myriad possibilities. “These tools can be used in a variety of contexts to narrow large experimental parameter spaces to accelerate the search for new materials,” Rickman said.

New Method Combines Complementary Tools

Lehigh University researchers combined two complementary tools to employ a supervised learning strategy for the efficient screening of high-entropy alloys and to identify promising HEAs: (1) a canonical-correlation analysis and (2) a genetic algorithm with a canonical-correlation analysis-inspired fitness function.

They implemented this procedure using a database for which mechanical property information exists and highlighting new alloys with high hardnesses. The methodology was validated by comparing predicted hardnesses with alloys fabricated in a laboratory using arc-melting, identifying alloys with very high measured hardnesses.

“The methods employed here involved a novel combination of existing methods adapted to the high-entropy alloy problem,” Rickman said. “In addition, these methods may be generalized to discover, for example, alloys having other desirable properties. We believe that our approach, which relies on data science and experimental characterization, has the potential to change the way researchers discover such systems going forward.”

The research was funded by the Office of Naval Research with support from Lehigh University’s Nano/Human Interface Initiative.

Despite the apparent opportunities presented by a lot of options, the need to choose creates a “paralyzing paradox,” according to Thomas Saltsman, a graduate student in the UB Department of Psychology and co-author of the study with Mark Seery, an associate professor of psychology at UB.

“You want to make a good choice, but feel like you can’t,” says Saltsman. “This combination of perceiving high stakes and low ability may contribute to a deep-seated fear that one will inevitably make the wrong choice, which could stifle the decision-making process.”

To manage the seemingly unmanageable, Saltsman says to consider the relative importance of the choice at hand.

“Choosing the wrong menu item for dinner or what to binge-watch is not going to define you as a person,” he says. “It may also be helpful to enter high-choice situations with a few clear guidelines of what you want from your desired option. Doing so may not only help scale down the number of possible choices, by eliminating options that do not meet your guidelines, but may also bolster confidence and trust in your ability to find a choice that meets your needs.”

The findings are published in the journal Biological Psychology.

Previous research clearly establishes how choice overload is associated with negative outcomes, but this research looks specifically at two understudied motivational factors of decision-making: how valuable is the decision to someone and to what extent do people view themselves as capable of making a good choice.

Having choices seems like an appealing situation that speaks to freedom and autonomy. But the emerging digital realities manifest in forums like online shopping and entertainment can be overwhelming.

Searching online for a spring jacket can return thousands of hits. One streaming service claims to offer more than 7,000 titles, while online dating services can enroll millions of subscribers.

All of those choices seems like a great idea, according to Seery. Until you’re actually the one having to choose.

“We love having these choices, but when we’re actually faced with having to choose from among those countless options, the whole process goes south,” says Seery. “Research shows that, after the fact, people often regret their decision in these cases, but what our research suggests is that this kind of turn — the inherent paradox of liking choices and then being troubled by choices — happens almost immediately.

“That transition is fascinating.”

For the research, the team had nearly 500 participants across three different experiments, two of which used psychophysiological measures.

“We had participants reading through what were fictional dating profiles and asked them to consider their ideal partner,” says Saltsman. “Because we used psychophysiological measures, we wanted people faced with a choice that demanded consideration and had them actively engaged.”

Those measures include heart rate and how hard the heart is pumping. When people care more about a decision, Seery says, their heart rate increases and beats harder. Other measures, like how much blood the heart is pumping and the degree to which blood vessels dilate, indicate levels of confidence.

The results showed that when faced with a large number of profiles to choose from rather than a small number, participants’ hearts and blood vessels revealed that they experienced making their choice as being both more important and more overwhelming. This occurred during the deliberation process.

Although additional work is needed, this study can help us understand the relationship between choice overload and negative outcomes.

“Examining people’s experiences in the moment may ultimately help us better understand those negative downstream choice overload outcomes and how to prevent them,” says Saltsman.