Computers Math

Colloidal particles have become increasingly important for research as vehicles of biochemical agents. In future, it will be possible to study their behaviour much more efficiently than before by placing them on a magnetised chip. A research team from the University of Bayreuth reports on these new findings in the journal Nature Communications. The scientists have discovered that colloidal rods can be moved on a chip quickly, precisely, and in different directions, almost like chess pieces. A pre-programmed magnetic field even enables these controlled movements to occur simultaneously.
For the recently published study, the research team, led by Prof. Dr. Thomas Fischer, Professor of Experimental Physics at the University of Bayreuth, worked closely with partners at the University of Poznán and the University of Kassel. To begin with, individual spherical colloidal particles constituted the building blocks for rods of different lengths. These particles were assembled in such a way as to allow the rods to move in different directions on a magnetised chip like upright chess figures — as if by magic, but in fact determined by the characteristics of the magnetic field.
In a further step, the scientists succeeded in eliciting individual movements in various directions simultaneously. The critical factor here was the “programming” of the magnetic field with the aid of a mathematical code, which in encrypted form, outlines all the movements to be performed by the figures. When these movements are carried out simultaneously, they take up to one tenth of the time needed if they are carried out one after the other like the moves on a chessboard.
“The simultaneity of differently directed movements makes research into colloidal particles and their dynamics much more efficient,” says Adrian Ernst, doctoral student in the Bayreuth research team and co-author of the publication. “Miniaturised laboratories on small chips measuring just a few centimetres in size are being used more and more in basic physics research to gain insights into the properties and dynamics of materials. Our new research results reinforce this trend. Because colloidal particles are in many cases very well suited as vehicles for active substances, our research results could be of particular benefit to biomedicine and biotechnology,” says Mahla Mirzaee-Kakhki, first author and Bayreuth doctoral student.

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Before autonomous vehicles participate in road traffic, they must demonstrate conclusively that they do not pose a danger to others. New software developed at the Technical University of Munich (TUM) prevents accidents by predicting different variants of a traffic situation every millisecond.
A car approaches an intersection. Another vehicle jets out of the cross street, but it is not yet clear whether it will turn right or left. At the same time, a pedestrian steps into the lane directly in front of the car, and there is a cyclist on the other side of the street. People with road traffic experience will in general assess the movements of other traffic participants correctly.
“These kinds of situations present an enormous challenge for autonomous vehicles controlled by computer programs,” explains Matthias Althoff, Professor of Cyber-Physical Systems at TUM. “But autonomous driving will only gain acceptance of the general public if you can ensure that the vehicles will not endanger other road users — no matter how confusing the traffic situation.”
Algorithms that peer into the future
The ultimate goal when developing software for autonomous vehicles is to ensure that they will not cause accidents. Althoff, who is a member of the Munich School of Robotics and Machine Intelligence at TUM, and his team have now developed a software module that permanently analyzes and predicts events while driving. Vehicle sensor data are recorded and evaluated every millisecond. The software can calculate all possible movements for every traffic participant — provided they adhere to the road traffic regulations — allowing the system to look three to six seconds into the future.
Based on these future scenarios, the system determines a variety of movement options for the vehicle. At the same time, the program calculates potential emergency maneuvers in which the vehicle can be moved out of harm’s way by accelerating or braking without endangering others. The autonomous vehicle may only follow routes that are free of foreseeable collisions and for which an emergency maneuver option has been identified.
Streamlined models for swift calculations
This kind of detailed traffic situation forecasting was previously considered too time-consuming and thus impractical. But now, the Munich research team has shown not only the theoretical viability of real-time data analysis with simultaneous simulation of future traffic events: They have also demonstrated that it delivers reliable results.
The quick calculations are made possible by simplified dynamic models. So-called reachability analysis is used to calculate potential future positions a car or a pedestrian might assume. When all characteristics of the road users are taken into account, the calculations become prohibitively time-consuming. That is why Althoff and his team work with simplified models. These are superior to the real ones in terms of their range of motion — yet, mathematically easier to handle. This enhanced freedom of movement allows the models to depict a larger number of possible positions but includes the subset of positions expected for actual road users.
Real traffic data for a virtual test environment
For their evaluation, the computer scientists created a virtual model based on real data they had collected during test drives with an autonomous vehicle in Munich. This allowed them to craft a test environment that closely reflects everyday traffic scenarios. “Using the simulations, we were able to establish that the safety module does not lead to any loss of performance in terms of driving behavior, the predictive calculations are correct, accidents are prevented, and in emergency situations the vehicle is demonstrably brought to a safe stop,” Althoff sums up.
The computer scientist emphasizes that the new security software could simplify the development of autonomous vehicles because it can be combined with all standard motion control programs.

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Materials provided by Technical University of Munich (TUM). Note: Content may be edited for style and length. More

Machine learning can help public health officials identify children most at risk of lead poisoning, enabling them to concentrate their limited resources on preventing poisonings rather than remediating homes only after a child suffers elevated blood lead levels, a new study shows.
Rayid Ghani, Distinguished Career Professor in Carnegie Mellon University’s Machine Learning Department and Heinz College of Information Systems and Public Policy, said the Chicago Department of Public Health (CDPH) has implemented an intervention program based on the new machine learning model and Chicago hospitals are in the midst of doing the same. Other cities also are considering replicating the program to address lead poisoning, which remains a significant environmental health issue in the United States.
In a study published today in the journal JAMA Network Open, Ghani and colleagues at the University of Chicago and CDPH report that their machine learning model is about twice as accurate in identifying children at high risk than previous, simpler models, and equitably identifies children regardless of their race or ethnicity.
Elevated blood lead levels can cause irreversible neurological damage in children, including developmental delays and irritability. Lead-based paint in older housing is the typical source of lead poisoning. Yet the standard public health practice has been to wait until children are identified with elevated lead levels and then fix their living conditions.
“Remediation can help other children who will live there, but it doesn’t help the child who has already been injured,” said Ghani, who was a leader of the study while on the faculty of the University of Chicago. “Prevention is the only way to deal with this problem. The question becomes: Can we be proactive in allocating limited inspection and remediation resources?”
Early attempts to devise predictive computer models based on factors such as housing, economic status, race and geography met with only limited success, Ghani said. By contrast, the machine learning model his team devised is more complicated and takes into account more factors, including 2.5 million surveillance blood tests, 70,000 public health lead investigations, 2 million building permits and violations, as well as age, size and condition of housing, and sociodemographic data from the U.S. Census.
This more sophisticated approach correctly identified the children at highest risk of lead poisoning 15.5% of the time — about twice the rate of previous predictive models. That’s a significant improvement, Ghani said. Of course, most health departments currently aren’t identifying any of these children proactively, he added.
The study also showed that the machine learning model identified these high-risk children equitably. That’s a problem with the current system, where Black and Hispanic children are less likely to be tested for blood lead levels than are white children, Ghani said.
In addition to Ghani, the research team included Eric Potash and Joe Walsh of the University of Chicago Harris School of Public Policy; Emile Jorgensen, Nik Prachand and Raed Manour of CDPH; and Corland Lohff of the Southern Nevada Health District. The Robert Wood Johnson Foundation supported this research.

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Materials provided by Carnegie Mellon University. Original written by Byron Spice. Note: Content may be edited for style and length. More

Optical signals produced by laser sources are extensively used in fiber-optic communications, which work by pulsing information packaged as light through cables, even at great distances, from a transmitter to a receiver. Through this technology it is possible to transmit telephone conversations, internet messages and cable television images. The great advantage of this technology over electrical signal transmission is its bandwidth — namely, the amount of information that can be broadcast.
New research from a collaboration between Michigan Technological University and Argonne National Laboratory further improves optical signal processing, which could lead to the fabrication of even smaller fiber-optic devices.
The article, unveiling an unexpected mechanism in optical nonreciprocity — developed by the research group of Miguel Levy, professor of physics at Michigan Tech — has been published in the journal Optica. “Boosting Optical Nonreciprocity: Surface Reconstruction in Iron Garnets” explains the quantum and crystallographic origins of a novel surface effect in nonreciprocal optics that improves the processing of optical signals.
An optical component called the magneto-optic isolator appears ubiquitously in these optical circuits. Its function is to protect the laser source — the place where light is generated before transmission — from unwanted light that might be reflected back from downstream. Any such light entering the laser cavity endangers the transmitted signal because it creates the optical equivalent of noise.
“Optical isolators work on a very simple principle: light going in the forward direction is allowed through; light going in the backwards direction is stopped,” Levy said. “This appears to violate a physical principle called time-reversal symmetry. The laws of physics say that if you reverse the direction of time — if you travel backwards in time — you end up exactly where you started. Therefore, the light going back should end up inside the laser.”
But the light doesn’t. Isolators achieve this feat by being magnetized. North and south magnetic poles in the device do not switch places for light coming back.
“So forward and backward directions actually look different to the traveling light. This phenomenon is called optical nonreciprocity,” Levy said.
Optical isolators need to be miniaturized for on-chip integration into optical circuits, a process similar to the integration of transistors into computer chips. But that integration requires the development of materials technologies that can produce more efficient optical isolators than presently available.
Recent work by Levy’s research group has demonstrated an order-of-magnitude improvement in the physical effect responsible for isolator operation. This finding, observable in nanoscale iron garnet films, opens up the possibility of much tinier devices. New materials technology development of this effect hinges on understanding its quantum basis.
The research group’s findings provide precisely this type of understanding. This work was done in collaboration with physics graduate student Sushree Dash, Applied Chemical and Morphological Analysis Laboratory staff engineer Pinaki Mukherjee and Argonne National Laboratory staff scientists Daniel Haskel and Richard Rosenberg.
The Optica article explains the role of the surface in the electronic transitions responsible for the observed enhanced magneto-optic response. These were observed with the help of Argonne’s Advanced Photon Source. Mapping the surface reconstruction underlying these effects was made possible through the state-of-the-art scanning transmission electron microscope acquired by Michigan Tech two years ago.
The new understanding of magneto-optic response provides a powerful tool for the further development of improved materials technologies to advance the integration of nonreciprocal devices in optical circuits.

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Materials provided by Michigan Technological University. Note: Content may be edited for style and length. More

An Australian collaboration has reviewed the fundamental theories underpinning the quantum anomalous Hall effect (QAHE).
QAHE is one of the most fascinating and important recent discoveries in condensed-matter physics.
It is key to the function of emerging ‘quantum’ materials, which offer potential for ultra-low energy electronics.
QAHE causes the flow of zero-resistance electrical current along the edges of a material.
QAHE IN TOPOLOGICAL MATERIALS: KEY TO LOW-ENERGY ELECTRONICS
Topological insulators, recognised by the Nobel Prize in Physics in 2016, are based on a quantum effect known as the quantum anomalous Hall effect (QAHE).

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“Topological insulators conduct electricity only along their edges, where one-way ‘edge paths’ conducts electrons without the scattering that causes dissipation and heat in conventional materials,” explains lead author Muhammad Nadeem.
QAHE was first proposed by 2016 Nobel-recipient Prof Duncan Haldane (Manchester) in the 1980s, but it subsequently proved challenging to realize QAHE in real materials. Magnetic-doped topological insulators and spin-gapless semiconductors are the two best candidates for QAHE.
It’s an area of great interest for technologists,” explains Xiaolin Wang. “They are interested in using this significant reduction in resistance to significantly reduce the power consumption in electronic devices.”
“We hope this study will shed light on the fundamental theoretical perspectives of quantum anomalous Hall materials,” says co-author Prof Michael Fuhrer (Monash University), who is Director of FLEET.
THE STUDY
The collaborative, theoretical study concentrates on these two mechanisms:
large spin-orbit coupling (interaction between electrons’ movement and their spin)
strong intrinsic magnetization (ferromagnetism)
The study was supported by the Australian Research Council (Centres of Excellence and Future Fellowship projects).

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Materials provided by ARC Centre of Excellence in Future Low-Energy Electronics Technologies. Note: Content may be edited for style and length. More

Biosensors that are wearable on human skin or safely used inside the body are increasingly prevalent for both medical applications and everyday health monitoring. Finding the right materials to bind the sensors together and adhere them to surfaces is also an important part of making this technology better. A recent study from Binghamton University, State University of New York offers one possible solution, especially for skin applications.
Matthew S. Brown, a fourth-year PhD student with Assistant Professor Ahyeon Koh’s lab in the Department of Biomedical Engineering, served as the lead author for “Electronic?ECM: A Permeable Microporous Elastomer for an Advanced Bio-Integrated Continuous Sensing Platform,” published in the journal Advanced Materials Technology.
The study utilizes polydimethylsiloxane (PDMS), a silicone material popular for use in biosensors because of its biocompatibility and soft mechanics. It’s generally utilized as a solid film, nonporous material, which can lead to problems in sensor breathability and sweat evaporation.
“In athletic monitoring, if you have a device on your skin, sweat can build up under that device,” Brown said. “That can cause inflammation and also inaccuracies in continuous monitoring applications.
“For instance, one experiment with electrocardiogram (ECG) analysis showed that the porous PDMS allowed for the evaporation of sweat during exercise, capable of maintaining a high-resolution signal. The nonporous PDMS did not provide the ability for the sweat to readily evaporate, leading to a lower signal resolution after exercise.
The team created a porous PDMS material through electrospinning, a production method that makes nanofibers through the use of electric force.

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During mechanical testing, the researchers found that this new material acted like the collagen and elastic fibers of the human epidermis. The material was also capable of acting as a dry adhesive for the electronics to strongly laminate on the skin, for adhesive-free monitoring. Biocompatibility and viability testing also showed better results after seven days of use, compared to the nonporous PDMS film.
“You can use this in a wide variety of applications where you need fluids to passively transfer through the material — such as sweat — to readily evaporate through the device,” Brown said.
Because the material’s permeable structure is capable of biofluid, small-molecule and gas diffusion, it can be integrated with soft biological tissue such as skin, neural and cardiac tissue with reduced inflammation at the application site.
Among the applications that Brown sees are electronics for healing long-term, chronic wounds; breathable electronics for oxygen and carbon dioxide respiratory monitoring; devices that integrate human cells within implantable electronic devices; and real-time, in-vitro chemical and biological monitoring.
Koh — whose recent projects include sweat-assisted battery power and biomonitoring — described the porous PDMS study as “a cornerstone of my research.”
“My lab is very interested in developing a biointegrated sensing system beyond wearable electronics,” she said. “At the moment, technologies have advanced to develop durable and flexible devices over the past 10 years. But we always want to go even further, to create sensors that can be used in more nonvisible systems that aren’t just on the skin.
“Koh also sees the possibilities for this porous PDMS material in another line of research she is pursuing with Associate Professor Seokheun Choi from the Department of Electrical and Computer Engineering. She and Choi are combining their strengths to create stretchable papers for soft bioelectronics, enabling us to monitor physiological statuses.

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Materials provided by Binghamton University. Original written by Chris Kocher. Note: Content may be edited for style and length. More

Solar cells have come a long way, but inexpensive, thin film solar cells are still far behind more expensive, crystalline solar cells in efficiency. Now, a team of researchers suggests that using two thin films of different materials may be the way to go to create affordable, thin film cells with about 34% efficiency.
“Ten years ago I knew very little about solar cells, but it became clear to me they were very important,” said Akhlesh Lakhtakia, Evan Pugh University Professor and Charles Godfrey Binder Professor of Engineering Science and Mechanics, Penn State.
Investigating the field, he found that researchers approached solar cells from two sides, the optical side — looking on how the sun’s light is collected — and the electrical side — looking at how the collected sunlight is converted into electricity. Optical researchers strive to optimize light capture, while electrical researchers strive to optimize conversion to electricity, both sides simplifying the other.
“I decided to create a model in which both electrical and optical aspects will be treated equally,” said Lakhtakia. “We needed to increase actual efficiency, because if the efficiency of a cell is less than 30% it isn’t going to make a difference.” The researchers report their results in a recent issue of Applied Physics Letters.
Lakhtakia is a theoretician. He does not make thin films in a laboratory, but creates mathematical models to test the possibilities of configurations and materials so that others can test the results. The problem, he said, was that the mathematical structure of optimizing the optical and the electrical are very different.
Solar cells appear to be simple devices, he explained. A clear top layer allows sunlight to fall on an energy conversion layer. The material chosen to convert the energy, absorbs the light and produces streams of negatively charged electrons and positively charged holes moving in opposite directions. The differently charged particles get transferred to a top contact layer and a bottom contact layer that channel the electricity out of the cell for use. The amount of energy a cell can produce depends on the amount of sunlight collected and the ability of the conversion layer. Different materials react to and convert different wavelengths of light.
“I realized that to increase efficiency we had to absorb more light,” said Lakhtakia. “To do that we had to make the absorbent layer nonhomogeneous in a special way.”
That special way was to use two different absorbent materials in two different thin films. The researchers chose commercially available CIGS — copper indium gallium diselenide — and CZTSSe — copper zinc tin sulfur selenide — for the layers. By itself, CIGS’s efficiency is about 20% and CZTSSe’s is about 11%.
These two materials work in a solar cell because the structure of both materials is the same. They have roughly the same lattice structure, so they can be grown one on top of the other, and they absorb different frequencies of the spectrum so they should increase efficiency, according to Lakhtakia.
“It was amazing,” said Lakhtakia. “Together they produced a solar cell with 34% efficiency. This creates a new solar cell architecture — layer upon layer. Others who can actually make solar cells can find other formulations of layers and perhaps do better.”
According to the researchers, the next step is to create these experimentally and see what the options are to get the final, best answers.

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Materials provided by Penn State. Original written by A’ndrea Elyse Messer. Note: Content may be edited for style and length. More

Army research has extended the state-of-the-art in autonomy by providing a more complete picture of how actions and nonverbal signals contribute to promoting cooperation. Researchers suggested guidelines for designing autonomous machines such as robots, self-driving cars, drones and personal assistants that will effectively collaborate with Soldiers.
Dr. Celso de Melo, computer scientist with the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory at CCDC ARL West in Playa Vista, California, in collaboration with Dr. Kazunori Teradafrom Gifu University in Japan, recently published a paper in Scientific Reports where they show that emotion expressions can shape cooperation.
Autonomous machines that act on people’s behalf are poised to become pervasive in society, de Melo said; however, for these machines to succeed and be adopted, it is essential that people are able to trust and cooperate with them.
“Human cooperation is paradoxical,” de Melo said. “An individual is better off being a free rider, while everyone else cooperates; however, if everyone thought like that, cooperation would never happen. Yet, humans often cooperate. This research aims to understand the mechanisms that promote cooperation with a particular focus on the influence of strategy and signaling.”
Strategy defines how individuals act in one-shot or repeated interaction. For instance, tit-for-tat is a simple strategy that specifies that the individual should act as his/her counterpart acted in the previous interaction.
Signaling refers to communication that may occur between individuals, which could be verbal (e.g., natural language conversation) and nonverbal (e.g., emotion expressions).

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This research effort, which supports the Next Generation Combat Vehicle Army Modernization Priority and the Army Priority Research Area for Autonomy, aims to apply this insight in the development of intelligent autonomous systems that promote cooperation with Soldiers and successfully operate in hybrid teams to accomplish a mission.
“We show that emotion expressions can shape cooperation,” de Melo said. “For instance, smiling after mutual cooperation encourages more cooperation; however, smiling after exploiting others — which is the most profitable outcome for the self — hinders cooperation.”
The effect of emotion expressions is moderated by strategy, he said. People will only process and be influenced by emotion expressions if the counterpart’s actions are insufficient to reveal the counterpart’s intentions.
For example, when the counterpart acts very competitively, people simply ignore-and even mistrust-the counterpart’s emotion displays.
“Our research provides novel insight into the combined effects of strategy and emotion expressions on cooperation,” de Melo said. “It has important practical application for the design of autonomous systems, suggesting that a proper combination of action and emotion displays can maximize cooperation from Soldiers. Emotion expression in these systems could be implemented in a variety of ways, including via text, voice, and nonverbally through (virtual or robotic) bodies.”
According to de Melo, the team is very optimistic that future Soldiers will benefit from research such as this as it sheds light on the mechanisms of cooperation.
“This insight will be critical for the development of socially intelligent autonomous machines, capable of acting and communicating nonverbally with the Soldier,” he said. “As an Army researcher, I am excited to contribute to this research as I believe it has the potential to greatly enhance human-agent teaming in the Army of the future.”
The next steps for this research include pursuing further understanding of the role of nonverbal signaling and strategy in promoting cooperation and identifying creative ways to apply this insight on a variety of autonomous systems that have different affordances for acting and communicating with the Soldier. More