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Bayer Crop Science posted on LinkedIn

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Knowledge is power, but our capacity to learn is even more important. The same is true for machine learning. See how #artificialintelligence is constantly...


Machine-learning helps sort out massive materials' databases

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Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which can measure up to 7,800 m2 in a single gram of material. As a result, MOFs are extremely versatile and find multiple uses: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples. Because of their popularity, material scientists have been rapidly developing, synthesizing, studying, and cataloguing MOFs. Currently, there are over 90,000 MOFs published, and the number grows every day.


Spectroscopy and Chemometrics News Weekly #37, 2020

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Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry" LINK "Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches" LINK "NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley" LINK "Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy" LINK "Multi-task deep learning of near infrared spectra for improved grain quality trait predictions" LINK "Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri'Ya') Using Vis/NIR Online Half-transmittance Technique" LINK "Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and ...


Artificial intelligence helps researchers up-cycle waste carbon - Express Computer

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Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources. "Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage," says Professor Ted Sargent, one of the senior authors on a new paper published today in Nature.


AIoT: Why it has been labelled as the catalyst to IoT Strategy

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As you may already know, IoT connects a vast array of portable devices, home appliances, wearables, and other electronics/machines over a network. Connected devices can signal their environment and be remotely monitored, controlled, and maintained. While all this works well on paper, there is a catch (and a rather obvious one). Round the clock monitoring naturally leads to a never-ending influx of complex data. For instance, a car manufacturing company may want to monitor everything from tire pressure to fuel performance in order to push the boundaries of future models.


Catalyst of change: Bringing artificial intelligence to the forefront - The Financial Express

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Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.


Catalyst of change: Bringing artificial intelligence to the forefront

#artificialintelligence

Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.


10 Ways Artificial Intelligence Improves Turfgrass Management

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Turfgrass research and management comprises a specialized discipline that has evolved to a state of elegance. It takes significant training and mentorship to hone the craft. The practice is becoming highly competitive and data oriented. Leading local practitioners find themselves stretched thin during the busy season. So, it is no surprise that superintendents, product managers, landscapers, and environmental scientists are turning to Artificial Intelligence (AI) to fine tune insights, spend less time walking the grounds, and multiply their expertise.


Tiny Robo-beetle is powered by liquid methanol-fuelled 'muscles'

Daily Mail - Science & tech

A tiny robotic beetle that can crawl, climb slopes, carry different loads and has'muscles' powered by a liquid methanol fuel has been developed by researchers. Roboticists from California developed the bug-sized'RoBeetle' -- which weighs in at less than 1/100th of an ounce -- to explore new means of propelling tiny machines. It is hoped that the design will inspire a new breed of small-scale robots that can perform simple tasks without the need for external controls or bulky components. A tiny robotic beetle that can crawl, climb slopes, carry different loads and has'muscles' powered by a liquid methanol fuel has been developed by researchers. When building robots of the scale of the RoBeetle, batteries become relatively inefficient at storing energy, especially when compared to the amount that can be stored in animal fat -- the biological equivalent of a fuel tank.


Artificial intelligence sheds light on membrane performance

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Membrane separations have long been recognized as energy-efficient processes with a rapidly growing market. In particular, organic solvent nanofiltration (OSN) technology has shown considerable potential when applied to various industries, such as petrochemicals, pharmaceuticals and natural products. The energy consumed by these industries accounts for 10 to 15 percent of the world's entire energy consumption. Nevertheless, difficulties in predicting the separation performance of OSN membranes have hindered smooth transition from lab discovery to industry implementation. Predicting the performance of membranes is a challenging task because of the complex nature of solvent, solute and membrane interactions.