Computers Math

Researchers at the University of Tsukuba have created a new carbon-based electrical device, π-ion gel transistors (PIGTs), by using an ionic gel made of a conductive polymer. This work may lead to cheaper and more reliable flexible printable electronics.
Organic conductors, which are carbon-based polymers that can carry electrical currents, have the potential to radically change the way electronic devices are manufactured. These conductors have properties that can be tuned via chemical modification and may be easily printed as circuits. Compared with current silicon solar panels and transistors, systems based on organic conductors could be flexible and easier to install. However, their electrical conductivity can be drastically reduced if the conjugated polymer chains become disordered because of incorrect processing, which greatly limits their ability to compete with existing technologies.
Now, a team of researchers led by the University of Tsukuba have formulated a novel method for preserving the electrical properties of organic conductors by forming an “ion gel.” In this case, the solvent around the poly(para-phenyleneethynylene) (PPE) chains was replaced with an ionic liquid, which then turned into a gel. Using confocal fluorescent microscopy and scanning electron microscopy, the researchers were able to verify the morphology of the organic conductor.
“We showed that the internal structure of our π-ion gel is a nanofiber network of PPE, which is very good at reliably conducting electricity” says author Professor Yohei Yamamoto.
In addition to acting as wires for delocalized electrons, the polymer chains direct the flow of mobile ions, which can help move charge-carriers to the carbon rings. This allows current to flow through the entire volume of the device. The resulting transistor can switch on and off in response to voltage changes in less than 20 microseconds — which is faster than any previous device of this type.
“We plan to use this advance in supramolecular chemistry and organic electronics to design a whole arrange of flexible electronic devices,” explains Professor Yamamoto. The fast response time and high conductivity open the way for flexible sensors that enjoy the ease of fabrication associated with organic conductors, without sacrificing speed or performance.

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Materials provided by University of Tsukuba. Note: Content may be edited for style and length. More

A model based solely on the past 40 years of weather events uses 7,000 times less computer power than today’s weather forecasting tools. An A.I.-powered model could someday provide more accurate forecasts for rain, snow and other weather events. More

New research offers clues to what goes on inside the minds of machines as they learn to see. Instead of attempting to account for a neural network’s decision-making on a post hoc basis, their method shows how the network learns along the way, by revealing how much the network calls to mind different concepts to help decipher what it sees as the image travels through successive layers. More

Neuroscientists have found reading computer code does not rely on the regions of the brain involved in language processing. Instead, it activates the ‘multiple demand network,’ which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles. More

Scientists often look to nature for cues when designing robots – some robots mimic human hands while others simulate the actions of octopus arms or inchworms. Now, researchers have designed a new soft robotic gripper that draws inspiration from an unusual source: pole beans. More

Methods currently used around the world for predicting the development of COVID-19 and other pandemics fail to report precisely on the best and worst case scenarios. Newly developed prediction method for epidemics, published in Nature Physics, solve this problem.
“It is about understanding best and worst-case scenarios — and the fact that worst case is one of the most important things to keep track of when navigating through pandemics — regardless whether it be in Denmark, the EU, the USA or the WHO. If you are only presented with an average estimate for the development of an epidemic — not knowing how bad it possible can get, then it is difficult to act politically,” says Professor Sune Lehmann, one of four authors of the article Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles just published in Nature Physics.
Researchers Jonas L. Juul, Kaare Græsbøll, Lasse Engbo Christiansen and Sune Lehmann, all from DTU Compute, act as advisors to the National Board of Health in Denmark during the corona crisis. And partly based on their own experience as advisors, they have become aware that the existing methods of projecting the development of epidemics such as COVID-19 have a problem in describing the extremes possibilities of the expected development.
Epidemics are unpredictable
“Disease outbreaks are fundamentally stochastic processes. The same disease introduced in the same population can infect a large number of people or disappear quickly without having a particular prevalence. It depends in part on coincidences,” explains postdoc Jonas L. Juul.
It is precisely the unpredictability of epidemics which makes it so difficult to make the right decisions everywhere in society when it hits. How many beds and respirators will there be a need for? And how much can we reduce this demand by enforcing restrictions?

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However, the general unpredictability is just one of many problems in estimating the development of an epidemic.
“It is not just the unpredictable nature of epidemics that makes it difficult to predict their course — it is also our lack of knowledge about the disease’s characteristics and prevalence in society at any given time. Just to give a few concrete examples of this: there is typically no one who has any idea exactly when an outbreak has started, how many infected we have in an area on any given day, or in which regions the epidemic is getting a foothold right now. The only thing we know for sure is that when the health authorities discover an outbreak, it has been going on for a while, “says Sune Lehmann.
The common way to deal with the lack of information, almost everywhere in the world, is to model many scenarios based on e.g. different numbers of unknown infections and starting times and then summarize by looking at each day separately and assessing the ‘middle’ predictions as the most likely outcomes of the day. If most input parameters give infection numbers of less than 4000 on Christmas Day, more than 4000 new infected are subsequently assessed to be unlikely.
The ‘day-based’ way of making these predictions is used all over the world, and although the link between the development of an epidemic and specific dates is useful in some contexts, it systematically excludes data on how bad or mild the epidemic will be.
If all projections e.g. predict that the epidemic will peak at 4000 infected in one day, but none of the curves shows it on the same day, then on a given day it will be an extreme and therefore not included in any estimate.
“We, therefore, suggest making the summary ‘curve-based’: Instead of assessing which infection rates are probable or unlikely on individual days, we should look at one entire simulation at a time. Is the entire simulated infection curve probable or not? And based on that you can make a summary of the most likely curves for the development of the epidemic, “says Jonas L. Juul.
“By looking at entire prediction curves instead of individual days, you will get a more realistic estimate of how bad the epidemic can become. It is especially useful if you are trying to avoid the hospital system being overloaded,” concludes Sune Lehmann.

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Materials provided by Technical University of Denmark. Original written by Jesper Spangsmark Nielsen. Note: Content may be edited for style and length. More

Researchers in Japan and Italy are embracing chaos and nonlinear physics to create insectlike gaits for tiny robots — complete with a locomotion controller to provide a brain-machine interface.
Biology and physics are permeated by universal phenomena fundamentally grounded in nonlinear physics, and it inspired the researchers’ work.
In the journal Chaos, from AIP Publishing, the group describes using the Rössler system, a system of three nonlinear differential equations, as a building block for central pattern generators (CPGs) to control the gait of a robotic insect.
“The universal nature of underlying phenomena allowed us to demonstrate that locomotion can be achieved via elementary combinations of Rössler systems, which represent a cornerstone in the history of chaotic systems,” said Ludovico Minati, of Tokyo Institute of Technology and the University of Trento.
Phenomena related to synchronization allow the group to create very simple networks that generate complex rhythmic patterns.
“These networks, CPGs, are the basis of legged locomotion everywhere within nature,” he said.

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The researchers started with a minimalistic network in which each instance is associated with one leg. Changing the gait or creating a new one can be accomplished by simply making small changes to the coupling and associated delays.
In other words, irregularity can be added by making individual systems or the entire network more chaotic. For nonlinear systems, a change of output is not proportional to a change of input.
This work shows that the Rössler system, beyond its many interesting and intricate properties, “can also be successfully used as a substrate to construct a bioinspired locomotion controller for an insect robot,” Minati said.
Their controller is built with an electroencephalogram to enable a brain-computer interface.
“Neuroelectrical activity from a person is recorded and nonlinear concepts of phase synchronization are used to extract a pattern,” said Minati. “This pattern is then used as a basis to influence the dynamics of the Rössler systems, which generate the walking pattern for the insect robot.”
The researchers tap into the fundamental ideas of nonlinear dynamics twice.
“First, we use them to decode biological activity, then in the opposite direction to generate bioinspired activity,” he said.
The key implication of this work is that it “demonstrates the generality of nonlinear dynamic concepts such as the ability of the Rössler system, which is often studied in an abstract scenario,” Minati said, “but is used here as a basis to generate biologically plausible patterns.”

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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

New 2D materials have the potential to transform technologies, but they’re expensive and difficult to synthesize. Researchers used computer modeling to predict the properties of 2D materials that haven’t yet been made in real life. These highly-accurate predictions show the possibility of materials whose properties could be ‘tuned’ to make them more efficient than existing materials in particular applications. A separate paper demonstrated a way to integrate these materials into real electronic devices. More