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Motion capture is guiding the next generation of extraterrestrial robots

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“How do we build robots that can optimally explore space?” is the core question behind Dr Frances Zhu’s research at the University of Hawai’i. One part of the answer is, “with motion capture”.

“It is my hope that my research contributes to the way extraterrestrial robots move and make decisions on other planets,” explains Zhu (main picture), an assistant researcher and deputy director at the University’s Hawai‘i Institute of Geophysics and Planetology.

That research is in its early stages, but NASA has seen the value in it and awarded Zhu an EPSCoR grant by the name “Autonomous Rover Operations for Planetary Surface Exploration using Machine Learning Algorithms”. [Read more…] about Motion capture is guiding the next generation of extraterrestrial robots

Farmers help create ‘Virtual safe space’ to save bumblebees

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Solutions to help pollinators can be tested using a “virtual safe space” tool created by scientists at the University of Exeter in collaboration with farmers and land managers.

Bee-Steward is a decision-support tool which provides a computer simulation of bumblebee colony survival in a given landscape.

The tool lets researchers, farmers, policymakers and other interested parties test different land management techniques to find out which ones and where could be most beneficial for bees. [Read more…] about Farmers help create ‘Virtual safe space’ to save bumblebees

Deep learning model classifies brain tumors with single MRI scan

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A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan, according to a study published in Radiology: Artificial Intelligence.

“This is the first study to address the most common intracranial tumors and to directly determine the tumor class or the absence of tumor from a 3D MRI volume,” said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, Ph.D., and Daniel Marcus, Ph.D., in Mallinckrodt Institute of Radiology’s Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri. [Read more…] about Deep learning model classifies brain tumors with single MRI scan

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Stanford researchers develop a better way to track methane in the skies

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When Stanford University graduate student Jeff Rutherford began his doctorate in 2018, the amount of methane entering the atmosphere from oil and gas extraction operations – mostly due to fracking – had become a major matter of contention. Tracking this harmful greenhouse gas falls to the Environmental Protection Agency.

To help in their accounting, the EPA uses computer models that take a “bottom-up” approach, counting the total number of well heads, storage tanks, miles of pipeline and other sources of methane, declaring an average annual release per component, and totaling everything up. They call it an “inventory.”

The only problem is that other organizations taking a “top-down” approach – using satellite imaging or atmospheric measurement to calculate the actual total methane emissions – were saying that the EPA was missing the mark by as much as half. [Read more…] about Stanford researchers develop a better way to track methane in the skies

Artificial intelligence breakthrough gives longer advance warning of ozone issues

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Ozone levels in the earth’s troposphere (the lowest level of our atmosphere) can now be forecasted with accuracy up to two weeks in advance, a remarkable improvement over current systems that can accurately predict ozone levels only three days ahead.

The new artificial intelligence system developed in the University of Houston’s Air Quality Forecasting and Modeling Lab could lead to improved ways to control high ozone problems and even contribute to solutions for climate change issues.

“This was very challenging. Nobody had done this previously. I believe we are the first to try to forecast surface ozone levels two weeks in advance,” said Yunsoo Choi, professor of atmospheric chemistry and AI deep learning at UH’s College of Natural Sciences and Mathematics. The findings are published online in the scientific journal, Scientific Reports-Nature. [Read more…] about Artificial intelligence breakthrough gives longer advance warning of ozone issues

Cities ‘must become car-free to survive’

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Cities must become fully car-free in order to be liveable in the future, according to the UCL experts behind a new modelling report looking at urban car use.

The experts have called for a shift in collective behaviour to reduce the number of private cars in cities. Globally, the number of cars produced is increasing faster than the population; 80m cars were produced in 2019, while the population increased by 78m.

The researchers said future city planning must include a focus on reducing dependence on cars, promoting fewer and shorter trips and encouraging walking and cycling as primary modes of local transport. [Read more…] about Cities ‘must become car-free to survive’

Modeling a circular economy for electronic waste

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Think about how many different pieces of technology the average household has purchased in the last decade. Phones, TVs, computers, tablets, and game consoles don’t last forever, and repairing them is difficult and often as expensive as simply buying a replacement.

Electronics are integral to modern society, but electronic waste (e-waste) presents a complex and growing challenge in the path toward a circular economy – a more sustainable economic system that focuses on recycling materials and minimizing waste.

Adding to the global waste challenge is the prevalence of dishonest recycling practices by companies who claim to be recycling electronics but actually dispose of them by other means, such as in landfills or shipping the waste to other countries. [Read more…] about Modeling a circular economy for electronic waste

RUDN mathematician builds a COVID-19 spread model – it shows how vaccination affects pandemic

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RUDN University mathematicians built a model of COVID-19 spreading based on two regression models. The mathematicians divided the countries into three groups, depending on the spreading rate and on the climatic conditions, and found a suitable mathematical approximation for each of them.

Based on the model, the mathematicians predicted the subsequent waves. The forecast was accurate in countries where mass vaccination was not introduced. The results are published in Mathematics.

The epidemy spreading rate within the country depends, among other things, on the climatic conditions: temperature, humidity, winds. For example, in the cold season, dry air dries the nasal mucus out which acts as a first line of defence to the virus. Therefore, a person becomes infected faster. High temperature, on the contrary, prevents the virus from surviving. [Read more…] about RUDN mathematician builds a COVID-19 spread model – it shows how vaccination affects pandemic

UEFA Euro forecast: France will be European Champion

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On Friday, 11 June, Europe’s men’s football teams started the European Championship – a year later than planned. The favourite this time is France with a probability of winning of 14.8 per cent.

This is what an international team of researchers consisting of Andreas Groll and Franziska Popp (both TU Dortmund, Germany), Gunther Schauberger (TU Munich, Germany), Christophe Ley and Hans Van Eetvelde (both Ghent University, Belgium), Achim Zeileis (University of Innsbruck, Austria) and Lars Hvattum (Molde University College, Norway) has shown with the help of machine learning.

Their forecast combines several statistical models for the teams’ strengths with information about the team structure (such as market value, number of Champions League players, club match performance of individual players) as well as socio-economic factors of the country of origin (population and gross domestic product). [Read more…] about UEFA Euro forecast: France will be European Champion

Machine learning ‘accurately predicts cardiac arrest risk’

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A branch of artificial intelligence (AI), called machine learning, can accurately predict the risk of an out of hospital cardiac arrest – when the heart suddenly stops beating – using a combination of timing and weather data, finds research published online in the journal Heart.

Machine learning is the study of computer algorithms, and based on the idea that systems can learn from data and identify patterns to inform decisions with minimal intervention.

The risk of a cardiac arrest was highest on Sundays, Mondays, public holidays and when temperatures dropped sharply within or between days, the findings show. [Read more…] about Machine learning ‘accurately predicts cardiac arrest risk’

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