The Green Revolution during the 1950s and 1960s remarkably drove up the global food production around the world, saving a billion people from starvation. The revolution led to the adoption of new technologies like high-yielding varieties (HYVs) of cereals, chemical fertilizers and agro-chemicals, better irrigation and mechanization of cultivation methods. India followed suite and adopted the use of hybrid seeds, machine, fertilisers and pesticides. While these practices solved the food shortage problem, they created some problems too in terms of excessive use of fertilisers and pesticides, depletion of ground-water, soil degradation etc. These problems were exacerbated by lack of training to use modern technology and awareness about the correct usage of chemicals etc.
Researchers at the University of Liverpool have built an intelligent, mobile, robotic scientist that can solve a range of research problems. The robot seen here can work almost 24-7, carrying out experiments by itself. The automated scientist – the first of its kind – can make its own decisions about which chemistry experiments to perform next, and has already discovered a new catalyst. With humanoid dimensions, and working in a standard laboratory, it uses instruments much like a human does. Unlike a real person, however, this 400 kg robot has infinite patience, and works for 21.5 hours each day, pausing only to recharge its battery.
Over the past 30 years, the use of glass and carbon-fiber reinforced composites in aerospace and other high-performance applications has soared along with the broad industrial adoption of composite materials. Key to the strength and versatility of these hybrid, layered materials in high-performance applications is the orientation of fibers in each layer. Recent innovations in additive manufacturing (3-D printing) have made it possible to finetune this factor, thanks to the ability to include within the CAD file discrete printer-head orientation instructions for each layer of the component being printed, thereby optimizing strength, flexibility, and durability for specific uses of the part. These 3-D-printing toolpaths (a series of coordinated locations a tool will follow) in CAD file instructions are therefore a valuable trade secret for the manufacturers. However, a team of researchers from NYU Tandon School of Engineering led by Nikhil Gupta, a professor in the Department of Mechanical and Aerospace Engineering showed that these toolpaths are also easy to reproduce--and therefore steal--with machine learning (ML) tools applied to the microstructures of the part obtained by a CT scan.
Technology is supposed to have a positive effect on humanity. That was the initial vision, correct? But for some reason this artificial intelligence hype has become a controversy and the new space race all in one. On one hand, Elon Musk, CEO of Tesla, says he's taking a cautious approach to the emerging technology. Musk says it's the most serious threat to the survival of the human race .
The wood species classification is an essential field of investigation that can help to combat illegal logging, then providing the timber certification and allowing the application of correct timber taxing. Today, the wood classification relies on highly qualified professionals that analyze texture patterns on timber sections. However, these professionals are scarce, costly, and subject to failure. Therefore, the automation of this task using computational methods is promising. Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In this study, we introduced the biggest data set of wood timber microscope images to the date, with 281 species, having three types of timber sections: transverse, radial, and tangential.
As COVID-19 continues to affect millions of lives and livelihoods, it is delivering perhaps the most significant shock to industries--from education to healthcare to food supply--in almost a century. Mineral processing companies also have to grapple with profound uncertainty and volatility. Before COVID-19, some were already taking steps to build their capabilities to cope with fluctuations inherent in commodities markets. But recent events triggering challenges in workforce availability, supply chains, and demand created a need for higher levels of operational resilience in a short period of time. Here is where recent advances in artificial intelligence (AI) helped.
Footage has emerged of one of Boston Dynamics' robotic dogs patrolling a SpaceX test site in the US. The video allegedly shows SpaceX using the $75,000 (£60,000) robotic dog to inspect the aftermath of its test site in Boca Chica, Texas. SpaceX had just been conducting a cryogenic pressure test on the Starship SN7 dome tank prototype, according to Tesmanian. SN7 was filled with sub-cooled liquid nitrogen and it was intentionally pressurised to its capacity before it burst and collapsed on its side. The stainless-steel commercial spacecraft, once operational, will be capable of transporting passengers on long-duration voyages to the Moon and Mars. But until the launch vehicle is ready, Elon Musk's company appears to be employing a little help from a trusty robotic companion.
Lawrence Livermore National Laboratory (LLNL) scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Technological progress in materials science applications spanning electronic, biomedical, alternate energy, electrolyte, catalyst design and beyond is often hindered by a lack of understanding of complex relationships between the underlying material microstructure and device performance. But AI-driven data analytics provide opportunities that can accelerate materials design and optimization by elucidating processing-performance correlations in a mathematically tractable way. However, to reliably train large networks one needs data from tens of thousands of samples, which, unfortunately is often prohibitive in new systems and new applications due to the cost of sample-preparation and data collection.
Land-use change by humans, particularly forest loss, is influencing Earth's biodiversity through time. To assess the influence of forest loss on population and biodiversity change, Daskalova et al. integrated data from more than 6000 time series of species' abundance, richness, and composition in ecological assemblages around the world. Forest loss leads to both positive and negative responses of populations and biodiversity, and the temporal lags in population and biodiversity change after forest loss can extend up to half a century. This analysis has consequences for projections of human impact, ongoing conservation, and assessments of biodiversity change.
The construction industry contributes to 39% of global carbon emissions while aviation contributes to only 2% which means we need to look for alternative building materials if we are to make a big impact on the climate crisis soon. We've seen buildings being made using mushrooms, bricks made from recycled plastic and sand waste, organic concrete, and now are seeing another innovative solution – a floating 3D printed house! Prvok is the name of this project and it will be the first 3D printed house in the Czech Republic built by Michal Trpak, a sculptor, and Stavebni Sporitelna Ceske Sporitelny who is a notable member of the Erste building society. The house is designed to float and only takes 48 hours to build! Not only is that seven times faster than traditional houses, but it also reduces construction costs by 50%.