While sorting your plastic recycling might be frustrating, scientists warn that a lack of waste collection could be deadly for millions around the world. Scientists from the University of Leeds have used AI modelling to reveal the 10 countries responsible for the most plastic pollution. Overall, the researchers calculate that 52 million tonnes of uncollected plastic waste entered the environment in 2020, creating a serious health risk for those exposed. India topped the table as the biggest producer of plastic pollution - creating 9.3 million tonnes of waste in a single year - followed by Nigeria and Indonesia. Lead author Dr Costas Velis says: 'This is an urgent global human health issue -- an ongoing crisis: people whose waste is not collected have no option but to dump or burn it.'

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Plastic has imbibed itself as an indispensable part of our day to day activities, becoming a source of problems due to its non-biodegradable nature and cheaper production prices. With these problems, comes the challenge of mitigating and responding to the aftereffects of disposal or the lack of proper disposal which leads to waste concentrating in locations and disturbing ecosystems for both plants and animals. As plastic debris levels continue to rise with the accumulation of waste in garbage patches in landfills and more hazardously in natural water bodies, swift action is necessary to plug or cease this flow. While manual sorting operations and detection can offer a solution, they can be augmented using highly advanced computer imagery linked with robotic appendages for removing wastes. The primary application of focus in this report are the much-discussed Computer Vision and Open Vision which have gained novelty for their light dependence on internet and ability to relay information in remote areas. These applications can be applied to the creation of edge-based mobility devices that can as a counter to the growing problem of plastic debris in oceans and rivers, demanding little connectivity and still offering the same results with reasonably timed maintenance. The principal findings of this project cover the various methods that were tested and deployed to detect waste in images, as well as comparing them against different waste types. The project has been able to produce workable models that can perform on time detection of sampled images using an augmented CNN approach. Latter portions of the project have also achieved a better interpretation of the necessary preprocessing steps required to arrive at the best accuracies, including the best hardware for expanding waste detection studies to larger environments.

Plastic waste entering the riverine harms local ecosystems leading to negative ecological and economic impacts. Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. In this context, efficient and automatized monitoring of mismanaged plastic waste is paramount. To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios. We enable near-real-time tracking of partially submerged plastics by using snapshot Visible-Shortwave Infrared hyperspectral imaging. Our experiments indicate that imaging strategies associated with machine learning classification approaches can lead to high detection accuracy even in challenging scenarios, especially when leveraging hyperspectral data and nonlinear classifiers. All code, data, and models are available online: https://github.com/RIVeR-Lab/hyperspectral_macro_plastic_detection.

A robotic shark hungry for plastic is to snap up waste in the River Thames as part of efforts to tackle water pollution. WasteShark is the first marine robot to take London's river by storm, with the ability to'eat' up to 1,100lbs of waste everyday - equivalent to 22,700 plastic bottles. The electric shark has been released in Canary Wharf where it can travel through 3.1 miles (5km) of water before needing a recharge. It comes at a time when plastic waste has almost doubled globally since 2000, with only nine per cent of this successfully recycled, according to an Organisation for Economic Co-operation and Development report. But Britvic-owned Aqua Libra, which is launching the shark, hope to combat this by recycling the collected rubbish wherever possible.

DeepMind has predicted the structure of almost every protein so far catalogued by science, cracking one of the grand challenges of biology in just 18 months thanks to an artificial intelligence called AlphaFold. Researchers say that the work has already led to advances in combating malaria, antibiotic resistance and plastic waste, and could speed up the discovery of new drugs. Determining the crumpled shapes of proteins based on their sequences of constituent amino acids has been a persistent problem for decades in biology. Some of these amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to accurately determine. UK-based AI company DeepMind first announced it had developed a method to accurately predict the structure of folded proteins in late 2020, and by the middle of it 2021 it had revealed that it had mapped 98.5 per cent of the proteins used within the human body.

You might not notice it, but you've likely adopted artificial intelligence into your daily life. It can be as simple as personalizing your news feeds, searching for products on shopping sites or voice-to-text conversion on smartphones. It can also be applied to more sophisticated tasks like predicting court outcomes in cases involving employment law or used for robotic welding applications. The transformative power of AI is also an economic growth driver, which is why the Canadian government has given the green light to advancing the country's AI strategy. According to a recent announcement from Minister of Innovation, Science and Industry François-Philippe Champagne, more than $443 million in Budget 2021 is designated for the second phase of the pan-Canadian Artificial Intelligence Strategy.

Developed by Minderoo Foundation, the'Global Plastic Watch' tool uses advanced satellite data technology and machine learning to create a near-real-time, high resolution map of plastic pollution. The tool aims to help authorities better manage plastic leakage into the marine environment, and is said to provide the largest ever open source dataset of plastic waste across dozens of countries. Global Plastic Watch uses remote sensing satellite imagery from the European Space Agency and a novel machine learning model created in collaboration with digital product agency Earthrise Media. The tool can determine the size and scale of land-based plastic waste sites, which fuel the growing issue of plastic pollution in the world's rivers and oceans. By using the data, governments, industry and communities can evaluate and monitor the risk of land-based plastic waste sites as well as prioritise investment in solutions, Minderoo Foundation said.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Researchers in Texas have created an enzyme variant that can break down plastics that would typically take hundreds of years to dissolve in a matter of hours or days. The creation by officials at The University of Texas at Austin could solve the problem of how to rid the world of billions of tons of plastic piling up in landfills and polluting natural lands and water. "The possibilities are endless across industries to leverage this leading-edge recycling process," Hal Alper, professor in the McKetta Department of Chemical Engineering at UT Austin said in a statement.

Plastic waste dumped in landfill could be cleared sooner than expected, after engineers developed an enzyme that can break it down in just a few hours. Millions of tons of plastic is left abandoned every year, pilling up in landfills and pollution the land and waterways - typically taking centuries to degrade. A team from the University of Texas in Austin created a new enzyme variant that can supercharge recycling on a large scale, reducing the impact of plastic pollution. The work focusing on PET (polyethylene terephthalate), which is a polymer found in most consumer plastic including bottles, packaging and some textiles. The enzyme was able to complete a'circular process' of breaking down the plastic into smaller parts and chemically putting it back together in as little as 24 hours. They've called it FAST-PETase (functional, active, stable, and tolerant PETase), developed from a natural PETase that allows bacteria to degrade and modify plastic.