Scientists in Japan have combined two computational models to extract more data on steel alloys from a single test, with implications for the discovery of new materials.

A new approach uses data from one type of test on small metal alloy samples to extract enough information for building databases that can be used to predict the properties and potentials of new materials. The details were published in the journal Science and Technology of Advanced Materials: Methods.

The test is called instrumented indentation. It involves driving an indenter tip into a material to probe some of its properties, such as hardness and elastic stiffness. Scientists have been using the data extracted from instrumented indentation to estimate the stress-strain curve of materials using computational simulations. This curve, and the data it provides, are important for understanding a material's properties. That data is also used for building massive materials databases, which can be used, in conjunction with artificial intelligence, for predicting new materials.

A problem scientists face is that this approach for estimating material properties is limited when it comes to materials called 'high work-hardening alloys': metal alloys, like steel, that are strengthened through physical processes like rolling and forging. Only so much information can be estimated from the curve of these materials. To get the necessary additional information needed to determine their properties, more experiments would need to be done, which costs time, effort, and money.

Ta-Te Chen of the University of Tsukuba and Ikumu Watanabe of the National Institute for Materials Science in Japan have developed a new computational approach to extract that additional information from instrumented indentation tests on work-hardening alloys.

"Our approach builds on an already-existing model, making it ready for use in industry. It is also applicable to existing data, including hardness," says Watanabe.

The approach involves combining the results from two computational models, the power-law and linear hardening models, which produce their own individual stress-plastic strain curves from information gathered from indentation tests. Combining the data from both curves provides the extra data that, when added to the original stress-strain curve, shows a more holistic picture of the work-hardening alloys' properties.

The scientists validated their approach by using it on high-work-hardening stainless steel.

We have extended this approach to also evaluate mechanical properties at elevated temperatures, which can contribute to the development of high-temperature alloys," says Chen.

Pioneering artificial intelligence (AI) which automatically diagnoses lung diseases – such as tuberculosis and pneumonia – could ease winter pressures on hospitals, the University of the West of Scotland researchers believe.

Tuberculosis and pneumonia – potentially serious infections which mainly affect the lungs –often require a combination of different diagnostic tests,– such as CT scans, blood tests, X-rays, and ultrasounds. These tests can be expensive, with often long waiting times for results.

Developed by UWS, the revolutionary technology – created to quickly detect Covid-19 from X-ray images – has been proven to automatically identify a range of different lung diseases in a matter of minutes, with around 98 percent accuracy.

UWS researcher Professor Naeem Ramzan said: “Systems such as this could prove to be crucial for busy medical teams worldwide.”
It is hoped that the technology can be used to help relieve strain on pressured hospital departments through the quick and accurate detection of disease – freeing up radiographers continuously in high demand; reducing waiting times for test results, and creating efficiencies within the testing process.

Professor Ramzan, Director of the Affective and Human Computing for SMART Environments Research Centre at UWS, led the development of the technology, along with UWS Ph.D. students Gabriel Okolo and Dr. Stamos Katsigiannis.

Professor Ramzan added: “There is no doubt that hospital departments across the globe are under pressure and the outbreak of Covid-19 exacerbated this, adding further strain to pressured departments and staff. There is a real need for technology that can help ease some of these pressures and detect a range of different diseases quickly and accurately, helping free up valuable staff time.

“X-ray imaging is a relatively cheap and accessible diagnostic tool that already assists in the diagnosis of various conditions, including pneumonia, tuberculosis, and Covid-19. Recent advances in AI have made automated diagnosis using chest X-ray scans a very real prospect in medical settings.”

The state-of-the-art technique utilizes X-ray technology, comparing scans to a database of thousands of images from patients with pneumonia, tuberculosis, and covid. It then uses a process known as a deep convolutional neural network – an algorithm typically used to analyze visual imagery – to make a diagnosis.

During an extensive testing phase, the technique proved to be 98 percent accurate.

Professor Milan Radosavljevic, UWS’s Vice-Principal of Research, Innovation, and Engagement, said: “Hospitals around the world are under sustained stress. This can be seen throughout the UK, as our fantastic NHS continues to undergo immense pressure, with hard-pressed medical staff bearing the brunt.

“I am excited about the potential of this innovative technology, which could help streamline diagnostic processes and reduce strain on staff.
“It’s another example of purposeful, impactful research at UWS, as we strive to find solutions to global challenges.”

Researchers at UWS are now exploring the suitability of the technology in detecting other diseases using X-ray images, such as cancer.

A signature in the X-ray light emitted by a highly magnetized dead star known as a magnetar suggests the star has a solid surface with no atmosphere, according to a new study by an international collaboration co-led by UCL researchers.

The study, published in the academic journal Science, uses data from a NASA satellite, the Imaging X-ray Polarimetry Explorer (IXPE), which was launched last December. The satellite, a collaboration between NASA and the Italian Space Agency, provides a new way of looking at X-ray light in space by measuring its polarisation – the direction of the light waves’ wiggle. 

The team looked at IXPE’s observation of magnetar 4U 0142+61, located in the Cassiopeia constellation, about 13,000 light years away from Earth. This was the first time polarised X-ray light from a magnetar had been observed.

Magnetars are neutron stars – very dense remnant cores of massive stars that have exploded as supernovae at the ends of their lives. Unlike other neutron stars, they have an immense magnetic field – the most powerful in the universe. They emit bright X-rays and show erratic periods of activity, with the emission of bursts and flares which can release in just one second an amount of energy millions of times greater than our Sun emits in one year. They are believed to be powered by their ultra-powerful magnetic fields, 100 to 1,000 times stronger than standard neutron stars.

The research team found a much lower proportion of polarised light than would be expected if the X-rays passed through an atmosphere. (Polarised light is light where the wiggle is all in the same direction – that is, the electric fields vibrate only in one way. An atmosphere acts as a filter, selecting only one polarisation state of the light.)

The team also found that for particles of light at higher energies, the angle of polarization – the wiggle – flipped by exactly 90 degrees compared to light at lower energies, following what theoretical models would predict if the star had a solid crust surrounded by an external magnetosphere filled with electric currents.

Co-lead author Professor Silvia Zane (UCL Mullard Space Science Laboratory), a member of the IXPE science team, said: “This was completely unexpected. I was convinced there would be an atmosphere. The star’s gas has reached a tipping point and become solid in a similar way that water might turn to ice. This is a result of the star’s incredibly strong magnetic field.

“But, like with water, the temperature is also a factor – a hotter gas will require a stronger magnetic field to become solid.

“A next step is to observe hotter neutron stars with a similar magnetic field, to investigate how the interplay between temperature and magnetic field affects the properties of the star’s surface.”

Lead author Dr. Roberto Taverna, from the University of Padova, said: “The most exciting feature we could observe is the change in polarisation direction with energy, with the polarisation angle swinging by exactly 90 degrees.

“This is in agreement with what theoretical models predict and confirms that magnetars are indeed endowed with ultra-strong magnetic fields.”

Quantum theory predicts that light propagating in a strongly magnetized environment is polarised in two directions, parallel and perpendicular to the magnetic field. The amount and direction of the observed polarisation bear the imprint of the magnetic field structure and the physical state of matter in the vicinity of the neutron star, providing information inaccessible otherwise.

At high energies, photons (particles of light) polarised perpendicularly to the magnetic field are expected to dominate, resulting in the observed 90-degree polarisation swing.

Professor Roberto Turolla, from the University of Padova, who is also an honorary professor at the UCL Mullard Space Science Laboratory, said: “The polarisation at low energies is telling us that the magnetic field is likely so strong to turn the atmosphere around the star into a solid or a liquid, a phenomenon known as magnetic condensation.”

The solid crust of the star is thought to be composed of a lattice of ions, held together by the magnetic field. The atoms would not be spherical but elongated in the direction of the magnetic field.

It is still a subject of debate whether or not magnetars and other neutron stars have atmospheres. However, the new paper is the first observation of a neutron star where a solid crust is a reliable explanation.

Professor Jeremy Heyl of the University of British Columbia (UBC) added: “It is also worth noting that including quantum electrodynamics effects, as we did in our theoretical modeling, gives results compatible with the IXPE observation. Nevertheless, we are also investigating alternative models to explain the IXPE data, for which proper numerical simulations are still lacking.”

Researchers have developed new “intelligent compaction” technology, which integrates into a road roller and can assess in real-time the quality of road base compaction. Improved road construction can reduce potholes and maintenance costs, and lead to safer, more resilient roads.

Months of heavy rain and floods have highlighted the importance of road quality, with poor construction leading to potholes and road subsidence. This not only causes tire blowouts and structural damage to cars and trucks but also increases the chance of serious accidents.

The innovative machine-learning technique, which processes data from a sensor attached to a construction roller, was developed by a research team from the University of Technology Sydney. The study was led by Associate Professor Behzad Fatahi, head of geotechnical and transport engineering, together with Professor Hadi Kahbbaz, Dr. Di Wu, and Ph.D. student Zhengheng Xu.

“We have developed an advanced computer model that incorporates machine learning and big data from construction sites to predict the stiffness of compacted soil with a high degree of accuracy in a fraction of a second, so roller operators can make adjustments,” said Associate Professor Fatahi.

Roads are made up of three or more layers, which are rolled and compacted. The subgrade layer is usually soil, followed by natural materials such as crushed rock, and then asphalt or concrete on top. The variable nature of soil and moisture conditions can result in under or over-compacted material.

“Like Goldilocks, the compaction needs to be ‘just right to provide the correct structural integrity and strength. Over-compaction can break down the material and change its composition, and under-compaction can lead to uneven settlement,” said Associate Professor Fatahi.

“A well-compacted multi-layer road base provides a stable foundation and increases the capacity of a road to bear heavy loads. Trucks can weigh up to 40 tonnes, so a poor quality base can quickly lead to cracks and weak spots in the asphalt surface.”

The research, recently published in the academic journal Engineering Structures, suggests the application of this technology could help build longer-lasting roads that can better withstand severe weather conditions.

The team is now looking to test the new technology onsite for various ground and roller conditions for road, railway and dam construction projects, and explore techniques to measure the density and moisture content of the compacted soil in real time during construction.