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Australia will use robot boats to find asylum seekers at sea

New Scientist

Australia is deploying a fleet of uncrewed robot boats to patrol its waters and monitor weather and wildlife. They will also flag boats potentially transporting asylum seekers, a plan that has concerned human rights groups. The 5-metre-long vessels, known as Bluebottles after an Australian jellyfish, look like miniature sailing yachts. They use a combination of wind, wave and solar power to maintain a steady 5-knot speed in all conditions. Sydney-based Ocius Technology delivered the prototype in 2017 and Australia's Ministry of Defence has now awarded an AU$5.5 million (£3m) …

Can we predict solar flares?


Flares from the Sun are the strongest explosions in our Solar System. They can cause severe space weather disturbances, posing a hazard to astronauts and technological systems in space and on the ground. Solar flares have an immediate impact in the form of enhanced radiation and energetic particles in as little as 8 min after the start of the event. Reliable prediction methods for flares are needed to provide longer warning times. However, pinning down the flare onset conditions is necessary for reliable predictions and is still a struggle ([ 1 ][1]). On page 587 of this issue, Kusano et al. ([ 2 ][2]) introduce a method to predict and successfully test for large imminent flares. Since their discovery more than 160 years ago by Carrington and Hodgson ([ 3 ][3], [ 4 ][4]), flares have been associated with sunspots on parts of the Sun with strong magnetic fields called active regions. The vast amount of flare energy is stored in complex (nonpotential) active region magnetic fields. The energy is impulsively released by magnetic reconnection, a fundamental plasma physics process that changes the topology of the magnetic field and converts magnetic energy into kinetic energy, thermal energy, and the acceleration of high-energy particles ([ 5 ][5]). Solar flares have been extensively studied for many decades. Now, regular observations are made at various wavelengths from a large number of ground- and space-based observatories. Regular measurements are made of the magnetic field and its vector components in the photosphere, which is considered the “surface” of the Sun. Despite extensive observations, the specific onset conditions and what triggers a flare are not understood. The lack of magnetic field measurements in the corona, where the field reconfiguration causing the sudden energy release in flares takes place, is a major limitation. Analytical and numerical methods are used instead to reconstruct the coronal magnetic field ([ 6 ][6]), model the instability and evolution of the field using magnetohydrodynamics ([ 5 ][5], [ 7 ][7], [ 8 ][8]), and indirectly infer magnetic reconnection signatures from the spatial and thermal distribution of the flaring plasma ([ 9 ][9]). Magnetohydrodynamic models are an important means for understanding the physics of solar eruptions and their onset conditions, but they cannot be used to predict the time of a flare. Current flare prediction schemes are mostly empirical and based on parameterizations of the surface magnetic field, such as the total magnetic flux of active regions, strong field gradients, and so on ([ 10 ][10], [ 11 ][11]). However, the forecast accuracy of empirical flare prediction schemes is still rather low, as measured by metrics like the skill score. Kusano et al. chose a different, physics-based approach for predicting imminent large flares through a critical condition of a magnetohydrodynamic instability that is triggered by magnetic reconnection. The method of Kusano et al. is based on “double-arc instability” ([ 12 ][12]) that allows a stability assessment of the sigmoidal field that is formed by reconnection between two sheared magnetic flux systems. The instability starts with magnetic reconnection on small spatial scales, called “trigger-reconnection,” between the sheared magnetic loops to create a double-arc loop, which contains the magnetic free energy that can be released during a flare. When the instability grows, the double-arc loops move upward, causing further magnetic reconnection that provides a positive feedback to the instability (see the figure). The double-arc instability is initiated when the ratio of the poloidal flux in the double arc to the flux of the stabilizing overlying magnetic field exceeds a limit. Based on this criterion, Kusano et al. determine the critical length-scale for the trigger-reconnection to destabilize the double arc and estimate the energy that can be released. The predictive model was tested on 200 active regions during solar cycle 24 that contained the largest sunspots but which did not produce large flares, in comparison with all seven active regions that produced large flares above class X2. X-class flares are powerful enough to trigger long-lasting radiation storms. Magnetic field vector measurements of the solar photosphere by the Helioseismic and Magnetic Imager ([ 13 ][13]) onboard NASA's Solar Dynamics Observatory were used for data. The authors found that the location and the time evolution of the identified critical regions provide a precursor to large flares, with lead times of 1 to 24 hours. The two large flares for which this scheme did not work were from a very specific large active region (AR12192) present on the Sun in October 2014. AR12192 was the source of numerous large flares, including six X-class flares, but none of them associated with a large cloud of plasma and embedded magnetic field ejected from the Sun known as a coronal mass ejection ([ 14 ][14]). This is a strong exception to the general statistics, because >90% of all large flares are accompanied by coronal mass ejections ([ 15 ][15]). However, the failure of the Kusano et al. prediction scheme for these events is also relevant because it shows that flare prediction methods have specific difficulties for large flares that are not accompanied by coronal mass ejections. This may be related to flare reconnection occurring relatively high in the corona or to strong overlying fields preventing an eruption ([ 2 ][2], [ 14 ][14]). ![Figure][16] Forecasting solar flares A physics-based model helps better estimate where and when large solar flares (shown above) will erupt. CREDIT: N. CARY/ SCIENCE Predicting solar flares is a very challenging task. The physics is complex and covers a large range of spatial scales, and key observables like the coronal magnetic field are lacking. Finally, the potential that flares are inherently stochastic processes cannot be ruled out. Nonetheless, tackling this issue has occurred by working along different paths. The diverse set of approaches will gain enormously from the upcoming observations of the 4-m Daniel K. Inouye Solar Telescope (DKIST), which had first light in December 2019. DKIST will provide improved resolution of the solar magnetic field fine structure and its dynamics and provide measurements of the coronal magnetic field. These key data are vital for a better understanding and probing of the onset and physics of solar flares. 1. [↵][17]1. L. Green, 2. T. Török, 3. B. VrŠnak, 4. W. Manchester IV, 5. A. Veronig , Space Sci. Rev. 214, 46 (2018). [OpenUrl][18] 2. [↵][19]1. K. Kusano et al ., Science 369, 587 (2020). [OpenUrl][20][CrossRef][21] 3. [↵][22]1. R. A. Carrington , Mon. Not. R. Astron. Soc. 20, 13 (1859). [OpenUrl][23][CrossRef][24] 4. [↵][25]1. R. Hodgson , Mon. Not. R. Astron. Soc. 20, 15 (1859). [OpenUrl][26][CrossRef][27] 5. [↵][28]1. E. R. Priest, 2. T. E. Forbes , Astron. Astrophys. Rev. 10, 313 (2002). [OpenUrl][29] 6. [↵][30]1. C. J. Schrijver et al ., Astrophys. J. 675, 1637 (2008). [OpenUrl][31] 7. [↵][32]1. T. Török, 2. B. Kliem , Astrophys. J. 630, L97 (2005). [OpenUrl][33] 8. [↵][34]1. T. Amari, 2. A. Canou, 3. J.-J. Aly , Nature 514, 465 (2014). [OpenUrl][35] 9. [↵][36]1. Y. Su et al ., Nat. Phys. 9, 489 (2013). [OpenUrl][37] 10. [↵][38]1. C. J. Schrijver , Astrophys. J. 655, L117 (2007). [OpenUrl][39] 11. [↵][40]1. K. D. Leka et al ., Astrophys. J. Suppl. Ser. 243, 36 (2019). [OpenUrl][41] 12. [↵][42]1. N. Ishiguro, 2. K. Kusano , Astrophys. J. 843, 101 (2017). [OpenUrl][43][CrossRef][44] 13. [↵][45]1. P. H. Scherrer et al ., Sol. Phys. 275, 207 (2012). [OpenUrl][46][CrossRef][47] 14. [↵][48]1. J. K. Thalmann, 2. Y. Su, 3. M. Temmer, 4. A. M. Veronig , Astrophys. J. 801, L23 (2015). [OpenUrl][49][CrossRef][50] 15. [↵][51]1. S. Yashiro, 2. S. Akiyama, 3. N. Gopalswamy, 4. R. A. Howard , Astrophys. J. 650, L143 (2005). [OpenUrl][52] Acknowledgments: A.M.V. acknowledges the Austrian Science Fund (FWF): P27292-N20 and I4555-N. 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Data Science: The Key Tool Cities Need To Reduce Carbon Emissions


A cyclist passes electric automobiles charging at Ubeeqo SAS electric vehicle charge stations in ... [ ] Paris, France, on Wednesday, May 27, 2020. President Emmanuel Macron's plan includes incentives for the purchase of electric cars, cash-for-clunkers to encourage consumers to trade in older, more polluting cars and subsidies for struggling car-parts makers. In November 2019, the first case of Covid-19 was reported in Wuhan, China. During the early days of the outbreak, local authorities attempted to clamp down on sharing information about the virus, but as the transmission strengthened in the region, the government imposed lockdown measures across China's Hubei province to control the spread of Covid-19. On January 22, Wuhan became the first major city under quarantine, and in the months that followed, many cities followed suit that caused a shock to the global economy.

PV Powershed to charge robotic lawnmowers – IAM Network


Solar Alliance Energy has launched a photovoltaic charging station for robotic lawnmowers. The Powershed system allows users to cut the cord and place a robotic mower anywhere the sun shines, the company said. Solar Alliance developed the design in cooperation with a researcher from the University of Tennessee and a provisional patent application has been filed with the US Patent office. The first Powershed unit has been installed at the University of Tennessee and is currently operating. Solar Alliance said the unit is designed to meet demand through a scalable production model and will initially be offered through commercial distribution partners and direct sales.

Optimising DERs: Artificial intelligence and the modern grid


The optimal integration of distributed energy resources such as solar, battery storage and smart thermostats becomes an ever-more complex and pressing question. Rahul Kar, general manager and VP for New Energy at AutoGrid Systems looks at the role artificial intelligence can play in smarter energy networks. This article first appeared in Volume 23 of Solar Media's quarterly journal, PV Tech Power, in'Storage & Smart Power', the section of the journal contributed by The modern electric grid is an engineering marvel and millions depend on it for reliable and on-demand power supply. The grid is becoming greener with the growing retirement of fossil fuel generation and the penetration of renewable energy, energy storage, electric vehicles (EVs), and a variety of other networked distributed energy resources (DERs).

Breakthrough ML Approach Produces 50X Higher-Resolution Climate Data – IAM Network


Researchers at the US Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) have developed a novel machine learning approach to quickly enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times--an enhancement that has never been achieved before with climate data. The researchers took an alternative approach by using adversarial training, in which the model produces physically realistic details by observing entire fields at a time, providing high-resolution climate data at a much faster rate. This approach will enable scientists to complete renewable energy studies in future climate scenarios faster and with more accuracy. "To be able to enhance the spatial and temporal resolution of climate forecasts hugely impacts not only energy planning, but agriculture, transportation, and so much more," said Ryan King, a senior computational scientist at NREL who specializes in physics-informed deep learning. Recommended AI News: Interlink Electronics Welcomes Aboard Edward Suski As Chief Product Officer King and NREL colleagues Karen Stengel, Andrew Glaws, and Dylan Hettinger authored a new article detailing their approach, titled "Adversarial super-resolution of climatological wind and solar data," which appears in the journal Proceedings of the National Academy of Sciences of the United States …

A New Approach to Lunar Robots

CMU School of Computer Science

The current development of particular robots for NASA represents a methodical shift in how some Lunar or Martian vehicles are designed and how the related components or systems are included to support vehicle operation. Carnegie Mellon University and Pittsburgh-based Astrobotic are working on a lunar robot for NASA's Lunar Surface and Instrumentation and Technology Payload program, or LSITP, that is small, fast, solar-powered and will not be teleoperated nor radiation-hardened, which is quite a change from more risk-adverse prior methods. The more affordable yet dynamic approach of constructing the so-called MoonRanger is a shift from past rovers that were behemoth in size, protected from radiation and very slow, says William "Red" Whittaker, director of Carnegie Mellon University's (CMU's) Field Robotics Center, who is leading the technical development and construction of the MoonRanger. The rover will have fully autonomous operations and will provide high-fidelity 3D maps of the ice fields on the moon's south pole. The robot will be equipped with a special instrument with an optical laser designed to help guide the robot in the dark, as well as measure the ice fields and map the terrain of the pole.

How Having Bigger AI Models Can Have A Detrimental Impact On Environment


The COVID crisis has skyrocketed the applications of artificial intelligence -- from tackling this global pandemic, to being a vital tool in managing various business processes. Despite its benefits, AI has always been scrutinised for its ethical concerns like existing biases and privacy issues. However, this technology also has some significant sustainability issues – it is known to consume a massive amount of energy, creating a negative impact on the environment. As AI technology is getting advanced in predicting weather, understanding human speech, enhancing banking payments, and revolutionising healthcare, the advanced models are not only required to be trained on large datasets, but also require massive computing power to improve its accuracy. Such heavy computing and processing consumes a tremendous amount of energy and emits carbon dioxide, which has become an environmental concern. According to a report, it has been estimated that the power required for training AI models emits approximately 626,000 pounds (284 tonnes) of carbon dioxide, which is comparatively five times the lifetime emissions of the average US car.

IIT Hyderabad uses artificial intelligence to study supply chain network of biofuels - Kashmir Convener


New Delhi, Jul 02: Bio-derived fuels are gaining widespread attention among the scientific community across the world. The work on biofuels is in response to the global call for reducing carbon emissions associated with the use of fossil fuels. In India too, biofuels have caught the imagination of researchers. For instance, researchers of the Indian Institute of Technology (IIT) Hyderabad have started using computational methods to understand the factors and impediments in incorporating biofuels into the fuel sector in India. A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.

IIT Hyderabad Researchers Use Machine Learning Algorithms To Study Supply Chain Network Of Biofuels


IIT Hyderabad Researchers are using computational methods to understand the factors and impediments in incorporating biofuels into the fuel sector in India. This work has been spurred by the increasing need to replace fossil fuels by bio-derived fuels, which, in turn, is driven by the dwindling fossil fuel reserves all over the world, and pollution issues associated with the use of fossil fuels. The model developed by the IIT Hyderabad team has shown that in the area of bioethanol integration into mainstream fuel use, the production cost is the highest (43 per cent) followed by import (25 per cent), transport (17 per cent), infrastructure (15 per cent) and inventory (0.43 per cent) costs. The model has also shown that feed availability to the tune of at least 40 per cent of the capacity is needed to meet the projected demands. A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.