Analysis by researchers led by the University of Warwick has revealed that these substorms manifest as global-scale electrical current systems associated with the spectacular aurora, reaching across over a third of the globe at high latitudes.

New research which involves the University of Warwick, John Hopkins University — Applied Physics Laboratory, University of Bergen and Cranfield University, and published today (23 March) in the journal Nature Communications processes data on disturbances in the Earth’s magnetic field from over a hundred magnetometers in the Northern hemisphere using a new technique that enables them to find ‘like-minded friends’.

Magnetometers register changes in the Earth’s magnetic field. When charged particles from our Sun bombard the Earth’s magnetic field, it stores up energy like a battery. Eventually, this energy is released leading to large-scale electrical currents in the ionosphere which generate disturbances of magnetic fields on the ground. At extremes, this can disrupt power lines, electronic and communications systems and technologies such as GPS.

Using historical data from the SuperMAG collaboration of magnetometers, the researchers applied algorithms from network science to find correlations between magnetometer signals during 41 known substorms that occurred between 1997-2001. These use the same principles that allow a social networking site to recommend new friends, or to push relevant advertisements to you as you browse the internet.

Magnetometers detecting coherent signals were linked into communities, regardless of where they were located on the globe. As time progressed, they saw each substorm develop from many smaller communities into a single large correlated system or community at its peak. This led the authors to conclude that substorms are one coherent current system which extends over most of the nightside high latitude globe, rather than a number of individual small and disjointed current systems.

Dr Lauren Orr, who led the research as part of her PhD at the University of Warwick Department of Physics and is now based at Lancaster University, said: “We used a well-established method within network science called community detection and applied it to a space weather problem. The idea is that if you have lots of little subgroups within a big group, it can pick out the subgroups.

“We applied this to space weather to pick out groups within magnetometer stations on the Earth. From that, we were trying to find out whether there was one large current system or lots of separate individual current systems.

“This is a good way of letting the data tell us what’s going on, instead of trying to fit observations to what we think is occurring.”

Some recent work has suggested that auroral substorms are composed of a number of smaller electrical current systems and remain so throughout their lifecycle. This new research demonstrates that while the substorm begins as lots of smaller disturbances, it quite rapidly becomes a large system over the course of around ten minutes. The lack of correlation in its early stages may also suggest that there is no single mechanism at play in how these substorms evolve.

The results have implications for models designed to predict space weather. Space weather was included in the UK National Risk Register in 2012 and updated in 2017 with a recommendation for more investment in forecasting.

Co-author Professor Sandra Chapman adds: “Our research introduces a whole new methodology for looking at this data. We’ve gone from a data poor to a data rich era in space plasma physics and space weather, so we need new tools. It’s a first to show that you can take one of these tools to our field and get a really important result out of it. We’ve had to learn a lot to be able to do that, but in doing so it opens up a new window into the data.”

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The neurons in our brains are connected with each other, forming small functional units called neural circuits. A neuron that is connected to another one via a synapsis can transmit information to the second neuron by sending a signal. This, in turn, might prompt the second neuron to transmit a signal to other neurons in the neural circuit. If that happens, the first neuron is likely an excitatory neuron: one that prompts other neurons to fire. But neurons with the exact opposite task are equally important to the functionality of our brain: inhibitory neurons, which make it less likely that the neurons they are connected to send a signal to others.

The interplay of excitation and inhibition is crucial for normal functionality of neural networks. Its dysregulation has been linked to many neurological and psychiatric disorders, including epilepsy, Alzheimer’s disease, and autism spectrum disorders.

From cell cultures in the lab…

Interestingly, the share of inhibitory neurons among all neurons in various brain structures (like the neocortex or the hippocampus) remains fixed throughout the lifetime of an individual at 15 to 30 percent. “This prompted our curiosity: how important is this particular proportion?,” recalls Anna Levina, a researcher at Tübingen University and the Max Planck Institute for Biological Cybernetics. “Can neural circuits with a different proportion of excitatory and inhibitory neurons still function normally?” Her collaborators from the Weizmann Institute of Science in Rehovot (Israel) designed a novel experiment that would allow to answer these questions. They grew cultures that contained different, even extreme ratios of excitatory and inhibitory neurons.

The scientists then measured the activity of these artificially designed brain tissues. “We were surprised that networks with various ratios of excitatory and inhibitory neurons remained active, even when these ratios were very far from natural conditions,” explains Levina’s PhD student Oleg Vinogradov. “Their activity does not change dramatically, as long as the share of inhibitory neurons stays somewhere in the range of 10 to 90 percent.” It seems that the neural structures have a way of compensating for their unusual composition to remain stable and functional.

…to a theoretical understanding

So naturally the researchers asked next: what mechanism allow the brain tissue to adjust to these different conditions? The researchers theorized that the networks adapt by adjusting the number of connections: If there few inhibitory neurons, they have to take on a bigger role by building more synapses with the other neurons. Conversely, if the share of inhibitory neurons is large, the excitatory neurons have to make up for this by establishing more connections.

The theoretical model of the Tübingen scientists can explain the experimental findings of their colleagues in Rehovot and uncover the mechanisms helping to maintain stable dynamics in the brain. The results provide a clearer picture of how excitation/inhibition balance is preserved and where it fails in living neural networks. In the longer term, they might be useful for the emergent field of precision medicine: induced pluripotent stem cell derived neural cultures could be used to find mechanisms of neuropsychiatric disorders and novel medications.

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With the recent rush of small investors into so-called meme stocks and reemerging interest in cryptocurrencies talk of market instability, rising volatility, and bursting bubbles is surging. However, “traditional economic theories cannot foresee events like the US subprime mortgage collapse of 2007” according to study author Areejit Samal. He and his colleagues from more than ten mathematics, physics, economics, and complex systems focused institutions around the globe have made a great stride in characterizing stock market instability.

Their paper abstracts the complexity of the financial market into a network of stocks and employs geometry-inspired network measures to gauge market fragility and financial dynamics. They analyzed and contrasted the stock market networks for the USA S&P500 and the Japanese Nikkei-225 indices for a 32-year period (1985-2016) and for the first time were able to show that several discrete Ricci curvatures are excellent indicators of market instabilities. The work was recently published in the Royal Society Open Science journal and allows analysts to distinguish between ‘business-as-usual’ periods and times of fragility like bubbles or market crashes.

The network created by connecting stocks with highly correlated prices and trading volumes forms the structural basis of their work. The researchers then employ four discrete curvatures, developed by the director of Max Planck Institute for Mathematics in the Sciences Jürgen Jost and his coworkers, to study the changes in the structure of stock market networks over time. Their comparisons to other market stability metrics have shown that their four notions of curvature serve as generic indicators of market instability.

One curvature candidate, the Forman-Ricci curvature (FRE), has a particularly high correlation with traditional financial indicators and can accurately capture market fear (volatility) and fragility (risk). Their study confirms that in normal trading periods the market is very fragmented, whereas in times of bubbles and impending market crashes correlations between stocks become more uniform and highly interconnected. The FRE is sensitive to both sector-driven and global market fluctuations and whereas common indicators like the returns remain inconspicuous, network curvatures expose these dynamics and reach extreme values during a bubble. Thus, the FRE can capture the interdependencies within and between sectors that facilitate the spreading of perturbations and increase the danger of market crashes.

Max Planck Institute for Mathematics in the Sciences director Jürgen Jost summarizes the struggle of analyzing market fragility: “there are no easy definitions of a market crash or bubble and merely monitoring established market indices or log-returns does not suffice, but our methodology offers a powerful tool for continuously scanning market risk and thus the health of the financial system.” The insights gained by this study can help decision-makers to better understand systemic risk and identify tipping points, which can potentially forecast coming financial crises or possibly even avoid them altogether.

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Healthy forests play a vital role in providing a clean, stable water supply, says eco-hydrologist Dr. Adam Wei.

Acting as natural reservoirs, forests in watersheds release and purify water by slowing erosion and delaying its release into streams. But forests are changing — in part because of human activity — and that’s having an impact on forests’ interaction with hydrological processes.

Dr. Wei, Forest Renewal BC’s chair of watershed research and management, is a professor of earth, environmental and geographic sciences in the Irving K. Barber Faculty of Science, and study co-author.

He says activities like logging, deforestation, creating new forests on previously bare land, agriculture and urbanization are changing the landscape of forests worldwide.

“The notion that humans have left enormous, often negative, footprints on the natural world isn’t new,” he says. “It’s why the term Anthropocene was created, to describe these phenomena. But now we need to acknowledge where we’re at and figure out a way to fix what’s broken.”

While humans bear much of the blame, they aren’t the only culprits.

Natural disturbances like insect infestations and wildfires are also contributing to the swift transformation of forests, leading Dr. Wei to examine current forest-water research and management practices. His goal is to identify the gaps and propose a new approach that reflects numerous variables and their interactions that may be at play at any given watershed.

He points to an example in the study to illustrate the need for a new perspective.

“We were looking at the impacts of deforestation on annual streamflow — and though we were able to draw the conclusion that deforestation increased it, the variations between studies were large, with increases between less than one per cent to nearly 600 per cent,” he explains.

Dr. Wei saw similar variations when he researched the ‘why.’

“We concluded this was due to when water in the soil and on plants evaporates due to a loss of forest cover,” explains Wei. “But the amount lost ranged from less than two per cent to 100 per cent — that’s a huge difference that can be attributed to scale, type and severity of forest disturbance, as well as climate and location of watershed properties. There are so many variables that need to be taken into account, and not doing so can result in contradictory research conclusions.”

To limit disparities, Dr. Wei says future research and watershed management approaches need to be systematic, include key contributing factors and a broad spectrum of response variables related to hydrological services.

He also suggests new tools like machine learning and climatic eco-hydrological modelling should be utilized.

“Implementing a systematic approach to all forest-water research will reduce the likelihood of procuring misleading assessment, which in turn will give us a better chance to solve some of the problems we’ve created,” says Dr. Wei.

This study, published in Science, was conducted by Dr. Wei, and his then-graduate student Dr. Mingfang Zhang, with support from the China National Science Foundation.