One step closer to bomb-sniffing cyborg locusts

If you want to enhance a locust to be used as a bomb-sniffing bug, there are a few technical challenges that need solving before sending it into the field.

Is there some way to direct the locust — to tell it where to go to do its sniffing? And because the locusts can’t speak (yet), is there a way to read the brain of these cyborg bugs to know what they’re smelling?

For that matter, can locusts even smell explosives?

Yes and yes to the first two questions. Previous research from Washington University in St. Louis has demonstrated both the ability to control the locusts and the ability to read their brains, so to speak, to discern what it is they are smelling. And now, thanks to new research from the McKelvey School of Engineering, the third question has been settled.

The answer, again: ‘yes.’

In a pre-proof published online Aug. 6 in the journal Biosensors and Bioelectronics: X, researchers showed how they were able to hijack a locust’s olfactory system to both detect and discriminate between different explosive scents — all within a few hundred milliseconds of exposure.

They were also able to optimize a previously developed biorobotic sensing system that could detect the locusts’ firing neurons and convey that information in a way that told researchers about the smells the locusts were sensing.

“We didn’t know if they’d be able to smell or pinpoint the explosives because they don’t have any meaningful ecological significance,” said Barani Raman, professor of biomedical engineering. “It was possible that they didn’t care about any of the cues that were meaningful to us in this particular case.”

Previous work in Raman’s lab led to the discovery that the locust olfactory system could be decoded as an ‘or-of-ands’ logical operation. This allowed researchers to determine what a locust was smelling in different contexts.

With this knowledge, the researchers were able to look for similar patterns when they exposed locusts to vapors from TNT, DNT, RDX, PETN and ammonium nitrate — a chemically diverse set of explosives. “Most surprisingly,” Raman said, “we could clearly see the neurons responded differently to TNT and DNT, as well as these other explosive chemical vapors.”

With that crucial piece of data, Raman said, “We were ready to get to work. We were optimized.”

Now they knew that the locusts could detect and discriminate between different explosives, but in order to seek out a bomb, a locust would have to know from which direction the odor emanated. Enter the “odor box and locust mobile.”

“You know when you’re close to the coffee shop, the coffee smell is stronger, and when you’re farther away, you smell it less? That’s what we were looking at,” Raman said. The explosive vapors were injected via a hole in the box where the locust sat in a tiny vehicle. As the locust was driven around and sniffed different concentrations of vapors, researchers studied its odor-related brain activity.

The signals in the bugs’ brains reflected those differences in vapor concentration.

The next step was to optimize the system for transmitting the locusts’ brain activity. The team, which included Shantanu Chakrabartty, the Clifford W. Murphy Professor in the Preston M. Green Department of Electrical & Systems Engineering, and Srikanth Singamaneni, the Lilyan & E. Lisle Hughes Professor in the Department of Mechanical Engineering & Materials Science, focused the breadth of their expertise on the tiny locust.

In order to do the least harm to the locusts, and to keep them stable in order to accurately record their neural activity, the team came up with a new surgical procedure to attach electrodes that didn’t hinder the locusts’ movement. With their new instrumentation in place, the neuronal activity of a locust exposed to an explosive smell was resolved into a discernible odor-specific pattern within 500 milliseconds.

“Now we can implant the electrodes, seal the locust and transport them to mobile environments,” Raman said. One day, that environment might be one in which Homeland Security is searching for explosives.

The idea isn’t as strange as it might first sound, Raman said.

“This is not that different from in the old days, when coal miners used canaries,” he said. “People use pigs for finding truffles. It’s a similar approach — using a biological organism — this is just a bit more sophisticated.”

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Turning faces into thermostats: Autonomous HVAC system could provide more comfort with less energy

As lockdown requirements ease, COVID-19 is changing the way we use indoor spaces. That presents challenges for those who manage those spaces, from homes to offices and factories.

Not least among these challenges is heating and cooling, which is the largest consumer of energy in American homes and commercial buildings. There’s a need for smarter, more flexible climate control that keeps us comfortable without heating and cooling entire empty buildings.

Now, a group of researchers at the University of Michigan has developed a solution that could provide more efficient, more personalized comfort, completely doing away with the wall-mounted thermostats we’re accustomed to. Human Embodied Autonomous Thermostat, or “HEAT,” is detailed in a study published in the July 2020 issue of Building and Environment.

The system pairs thermal cameras with three-dimensional video cameras to measure whether occupants are hot or cold by tracking their facial temperature. It then feeds the temperature data to a predictive model, which compares it with information about occupants’ thermal preferences.

Finally, the system determines the temperature that will keep the largest number of occupants comfortable with minimum energy expenditure. The new study shows how the system can effectively and efficiently maintain the comfort of 10 occupants in a lab setting.

“COVID presents a variety of new climate control challenges, as buildings are occupied less consistently and people struggle to stay comfortable while wearing masks and other protective gear,” said project principal investigator and study co-author Carol Menassa, associate professor of civil and environmental engineering.

“HEAT could provide an unobtrusive way to maximize comfort while using less energy. The key innovation here is that we’re able to measure comfort without requiring users to wear any detection devices and without the need for a separate camera for each occupant.”

HEAT works a bit like today’s internet-enabled learning thermostats. When it’s newly installed, occupants teach the system about their preferences by periodically giving it feedback from their smartphones on a three-point scale: “too hot,” “too cold” or “comfortable.” After a few days, HEAT learns their preferences and operates independently.

The research team is working with power company Southern Power to begin testing HEAT in its Alabama offices, where test cameras will be mounted on tripods in the corners of rooms. Menassa explains that cameras would be placed less obtrusively in a permanent installation. The cameras collect temperature data without identifying individuals, and all footage is deleted immediately after processing, usually within a few seconds.

A second test, also with Southern Power, will place the system in an Alabama community of newly constructed smart homes. The team estimates that they could have a residential system on the market within the next five years.

Facial temperature is a good predictor of comfort, Menassa said. When we’re too hot, the blood vessels expand to radiate additional heat, raising facial temperature; when we’re too cold, they constrict, cooling the face. While earlier iterations of the system also used body temperature to predict comfort, they required users to wear wristbands that measured body temperature directly, and to provide frequent feedback about their comfort level.

“The cameras we’re using are common and inexpensive, and the model works very well in a residential context,” said study co-author Vineet Kamat, U-M professor of civil and environmental engineering, and electrical engineering and computer science. “Internet-enabled thermostats that detect you and learn from you have sort of built a platform for the next phase, where there’s no visible thermostat at all.”

HEAT’s predictive model was built by U-M industrial operations and engineering associate professor Eunshin Byon, who is also an author on the study. She believes that tweaks to the model could make the system useful in applications beyond homes and offices — in hospitals, for example, where care providers struggle to stay comfortable under masks and other protective equipment.

“The COVID-19 pandemic requires nurses and other hospital workers to wear a lot of protective gear, and they’ve struggled to stay comfortable in the fast-faced hospital environment,” Byon said. “The HEAT system could be adapted to help them stay comfortable by adjusting room temperature or even by signaling to them when they need to take a break.”

In partnership with the U-M school of nursing, Menassa’s research group has already conducted a pilot study that explored how the system can be used to provide personalized thermal comfort for nurses working in healthcare environments such as chemotherapy administration units.

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3D Printing Industry

South African researchers identify solutions for challenges in using bioinks to 3D print tissue constructs 

Researchers from the University of Witwatersrand have assessed the challenges of using hydrogel-based bio-inks for 3D printing tissues, and made recommendations to enhance the applications of the technology.  The scientists found that although it’s safe and efficiable to 3D print tissues, bioprinting has limitations based on the cost, integrity, and strength of biomaterials used in […]

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Author: Paul Hanaphy

3D Printing Industry

Queensland University researchers challenge accuracy of FDM 3D printed medical models 

Researchers from the Queensland University of Technology have released a study that challenges the Fused Deposition Modelling (FDM) 3D printing process behind the production of anatomical medical reconstructions. 3D printed models can be important tools for doctors, in diagnosing and treating patients, or educating and training future surgeons. In addition, medical models often serve as […]

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Author: Paul Hanaphy


From China to the South Pole: Joining forces to solve the neutrino mass puzzle

Among the most exciting challenges in modern physics is the identification of the neutrino mass ordering. Physicists from the Cluster of Excellence PRISMA+ at Johannes Gutenberg University Mainz (JGU) play a leading role in a new study that indicates that the puzzle of neutrino mass ordering may finally be solved in the next few years. This will be thanks to the combined performance of two new neutrino experiments that are in the pipeline — the Upgrade of the IceCube experiment at the South Pole and the Jiangmen Underground Neutrino Observatory (JUNO) in China. They will soon give the physicists access to much more sensitive and complementary data on the neutrino mass ordering.

Neutrinos are the chameleons among elementary particles

Neutrinos are produced by natural sources — in the interior of the sun or other astronomical objects, for example — but also in vast quantities by nuclear power plants. However, they can pass through normal matter — such as the human body — practically unhindered without leaving a trace of their presence. This means that extremely complex methods requiring the use of massive detectors are needed to observe the occasional rare reactions in which these ‘ghost particles’ are involved.

Neutrinos come in three different types: electron, muon and tau neutrinos. They can change from one type to another, a phenomenon that scientists call ‘neutrino oscillation’. It is possible to determine the mass of the particles from observations of the oscillation patterns. For years now, physicists have been trying to establish which of the three neutrinos is the lightest and which is the heaviest. Prof. Michael Wurm, a physicist at the PRISMA+ Cluster of Excellence and the Institute of Physics at JGU, who is playing an instrumental role in setting up the JUNO experiment in China, explains: “We believe that answering this question will contribute significantly towards enabling us to gather long-term data on the violation of matter-antimatter symmetry in the neutrino sector. Then, using this data, we hope to find out once and for all why matter and anti-matter did not completely annihilate each other after the Big Bang.”

Global cooperation pays off

Both large-scale experiments use very different and complementary methods in order to solve the puzzle of the neutrino mass ordering. “An obvious approach is to combine the expected results of both experiments,” points out Prof. Sebastian Böser, also from the PRISMA+ Cluster of Excellence and the Institute of Physics at JGU, who researches neutrinos and is a major contributor to the IceCube experiment.

No sooner said than done. In the current issue of the journal Physical Review D, researchers from the IceCube and the JUNO collaboration have published a combined analysis of their experiments. For this, the authors simulated the predicted experimental data as a function of the measuring time for each experiment. The results vary depending on whether the neutrino masses are in their normal or reversed (inverted) order. Next, the physicists carried out a statistical test, in which they applied a combined analysis to the simulated results of both experiments. This revealed the degree of sensitivity with which both experiments combined could predict the correct order, or rather rule out the wrong order. As the observed oscillation patterns in JUNO and IceCube depend on the actual neutrino mass ordering in a way specific to each experiment, the combined test has a discriminating power significantly higher than the individual experimental results. The combination will thus permit to definitively rule out the incorrect neutrino mass ordering within a measuring period of three to seven years.

“In this case, the whole really is more than the sum of its parts,” concludes Sebastian Böser. “Here we have clear evidence of the effectiveness of a complementary experimental approach when it comes to solving the remaining neutrino puzzles.” “No experiment could achieve this by itself, whether it’s the IceCube Upgrade, JUNO or any of the others currently running,” adds Michael Wurm. “Moreover it just shows what neutrino physicists here in Mainz can achieve by working together.”

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IEEE Spectrum

Your Guide to Sprinting Across the 5G Finish Line First.

5G introduces test challenges related to massive MIMO, mmWave frequencies, and over-the-air (OTA) test. Successfully overcoming these challenges is the only way to reach 5G commercialization before the competition.

In Keysight’s latest eBook, Making 5G Work, you will learn:

  • 5 strategies to accelerate 5G designs
  • 4 insights to ace conformance testing
  • 3 ways to speed up carrier acceptance test for your device
  • 4 techniques that reduce 5G manufacturing test times and costs


This ‘lemon’ could help machine learning create better drugs

One of the challenges in using machine learning for drug development is to create a process for the computer to extract needed information from a pool of data points. Drug scientists must pull biological data and train the software to understand how a typical human body will interact with the combinations that come together to form a medication.

Purdue University drug discovery researchers have created a new framework for mining data for training machine learning models. The framework, called Lemon, helps drug researchers better mine the Protein Data Base (PDB) — a comprehensive resource with more than 140,000 biomolecular structures and with new ones being released every week. The work is published in the Oct. 15 edition of Bioinformatics.

“PDB is an essential tool for the drug discovery community,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science who works with other researchers in the Purdue Institute for Drug Discovery and led the team that created Lemon. “The problem is that it can take an enormous amount of time to sort through all the accumulated data. Machine learning can help, but you still need a strong framework from which the computer can quickly analyze data to help in the creation of safe and effective drugs.”

The Lemon software platform is a fast C++11 library with Python bindings that mines the PDB within minutes. Loading all traditional mmCIF files in the PDB takes about 290 minutes, but Lemon does this in about six minutes when applying a simple workflow on an 8-core machine. Lemon allows the user to write custom functions, include it as part of their software suite, and develop custom functions in a standard manner to generate unique benchmarking datasets for the entire scientific community.

“Experimental structures deposited in PDB have resulted in several advances for structural and computational biology scientific and education communities that help advance drug development and other areas,” said Jonathan Fine, a PhD student in chemistry who worked with Chopra to develop the platform. “We created Lemon as a one-stop-shop to quickly mine the entire data bank and pull out the useful biological information that is key for developing drugs.”

Lemon got its name as it was originally designed to create benchmarking sets for drug design software and identify the lemons, biomolecular interactions that cannot be modeled well, in the PDB.

The software development work is the latest project involving health innovations from Chopra and his team. Lemon is freely available on GitHub at Detailed documentation is available at

Chopra also worked with the Purdue Research Foundation Office of Technology Commercialization to patent other innovations from his lab.

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IEEE Spectrum

Envisioning the Future of Urban Transportation

Growing urbanization around the globe is creating increasingly difficult challenges in areas of transportation and energy, but engineers at the University of Maryland (UMD) think there are solutions in the promise of electric vertical takeoff and landing (eVTOL) aircraft.

Just a decade ago, the idea of air taxies and cityscapes equipped with “verti-port” stations may have seemed like the latest science fiction, but with the technical advances and commercial success of electric vehicles, eyes are turning to the sky to see how similar ideas of electric power and propulsion could create a new generation of lightweight air vehicles capable of moving people quietly, safely, and efficiently in dense urban environments.

“eVTOL has many advantages over traditional helicopters,” explains Anubhav Datta, an associate professor in UMD’s A. James Clark School of Engineering. “They don’t cause the pollution of traditional engines, have no engine noise, require fewer mechanical parts, and depending on the design could be easier to fly and more responsive to autonomy.”

While eVTOL technology is in its infancy, Datta has been involved from the start. He published the first peer-reviewed journal article demonstrating the viability of eVTOL by presenting conceptual designs for three all-electric options for a manned ultralight utility helicopter, and anticipating growth of the field. Since then, he has been instrumental in spearheading efforts to expand basic research in eVTOL, create pools of technical knowledge, and develop multidisciplinary education and outreach programs.

At Maryland, Datta and his graduate students are pursuing several projects addressing some of the principal barriers that prevent eVTOL from becoming a day-to-day reality.

One major barrier to eVTOL success is developing lightweight on-board electrical energy storage systems that would allow these aircraft to fly for longer periods with adequate reserves. According to Datta, lithium-ion batteries built for consumer electronics and automobiles are too low-energy to be a long-term solution. Batteries must be built to meet VTOL requirements or alternative sources of power explored, such as the work of Ph.D. student Emily Fisler, who is trying to quantify these requirements and explore more advanced chemistries for future batteries.

Datta, along with students Wanyi Ng and Mrinal Patil, are also exploring the application of hydrogen in fuel cells as a renewable and clean energy source. Hydrogen gas can store four to five times as much energy as current batteries, but the high power fuel stacks are heavy—so Datta’s team is looking at ways to maximize the energy benefits of hydrogen by using supplemental batteries to boost output during high-power loads, such as takeoff and landing.

Since 2017, UMD’s Department of Aerospace Engineering has won two multi-year research tasks on eVTOL funded jointly by the U.S. Army, NASA, and the U.S. Navy. As part of this work, Ph.D. student Brent Mills has built a unique hybrid-electric engine—capable of powering a scaled-down 50-pound VTOL aircraft to test and acquire data on electro-aero-mechanical behavior of the engine. Aircraft designers of the future can use this data in conceptualizing and building vehicles.

A key advantage of electric drives is that they do not require heavy, interconnecting mechanical shafts to drive more than one rotor. While multiple rotors are less efficient, they make an aircraft more stable and maneuverable, which could possibly reduce training times for future pilots and make them safer to operate in urban environments. In addition, being easier to control makes them more receptive to autonomous operation.

According to Datta, one critical advantage to UMD’s eVTOL research is the university’s historic Glenn L. Martin Wind Tunnel. Constructed in 1949, it is one of only a few tunnels its size on a university campus. This facility enables them to acquire truth-data from direct observation that is critical to the safe design of advanced rotorcraft, yet far beyond what the best computational tools can predict. Special-purpose rigs are needed to carry out model tests in the tunnel.

One such rig is the Maryland Tiltrotor Rig (MTR). Designed to study the aeromechanics of advanced prop-rotors and wing combinations, the MTR has a direct electric drive on the pylon so that data collected in the course of the project can also be applied to eVTOL. The MTR can test up to 4.75-foot diameter Mach-scaled rotors, features interchangeable blades and hubs turning at 2,500 revolutions per minute, and has interchangeable spars that can change the wing behavior.

“It is the only test rig of its kind on a university campus,” says Datta, “and the Ph.D. students who developed it are laying the foundations of the future of tiltrotor and eVTOL research at Maryland for the next decade.”

Datta was a member of the American Helicopter Society’s (AHS) inaugural eVTOL workshop in 2014, chaired the NASA Aeronautics Research Institute’s (NARI) Transformative Vertical Flight working group on intra-city Urban Air Mobility in 2016, and led the AHS in establishing eVTOL as a distinct technical discipline by founding the eVTOL Technical Committee in 2019. Chaired by Datta, this committee includes technical leaders from across industry, government, and several UMD alumni who have become leaders in the field of rotorcraft.

As part of these efforts, Datta, with support from the Vertical Flight Society (VFS, formerly AHS) and NASA, created the first formal education course in eVTOL now taught annually at the VFS Forum and the American Institute of Aeronautics and Astronautics (AIAA) Aviation forum.

Datta believes that the promise of better utilization of airspace through eVTOL advancements could bring about more energy efficient transportation solutions, but there is a lot of research and expertise that still needs to be developed to propel this new field forward.  

“Through research efforts here at Maryland, we are not just building the future of eVTOL,” Datta says, “but we are providing opportunities for students to become the next generation of engineers that will have the knowledge and hands-on expertise to go out and be major contributors to that field.”


Cesium vapor aids in the search for dark matter

The hunt for dark matter is one of the most exciting challenges facing fundamental physics in the 21st century. Researchers have long known that it must exist, as many astrophysical observations would otherwise be impossible to explain. For example, stars rotate much faster in galaxies than they would if only ‘normal’ matter existed.

In total, the matter we can see only accounts for, at the most, 20 percent of the total matter in the universe — meaning that a remarkable 80 percent is dark matter. “There’s an elephant in the room but we just can’t see it,” said Professor Dmitry Budker, a researcher at the PRISMA+ Cluster of Excellence of Johannes Gutenberg University Mainz (JGU) and the Helmholtz Institute Mainz (HIM), explaining the problem he and many of his colleagues worldwide are contending with.

Dark matter could consist of extremely light particles

But so far no one knows what dark matter is made of. Scientists in the field are considering and researching a whole range of possible particles that might theoretically qualify as candidates. Among these are extremely lightweight bosonic particles, currently considered to be one of the most promising prospects. “These can also be regarded as a classical field oscillating at a specific frequency. But we can’t yet put a figure on this — and therefore the mass of the particles,” explained Budker. “Our basic assumption is that this dark matter field is coupled to visible matter and has an extremely subtle influence on certain atomic properties that would normally be constant.”

Budker and his team in Mainz have now developed a new method which they describe in the current issue of the leading specialist journal Physical Review Letters. It employs atomic spectroscopy and involves the use of cesium atom vapor. Only on exposure to laser light of a very specific wavelength do these atoms become excited. The conjecture is that minute changes in the corresponding observed wavelength would indicate coupling of the cesium vapor to a dark matter particle field.

“In principle, our work is based on a particular theoretical model, the hypotheses of which we are experimentally testing,” added the paper’s principal author, Dr. Dionysis Antypas. “In this case, the concept underlying our work is the relaxion model developed by our colleagues and co-authors at the Weizmann Institute in Israel.” According to the relaxion theory, there must be a region in the vicinity of large masses such as the Earth in which the density of dark matter is greater, making the coupling effects easier to observe and detect.

Previously inaccessible frequency range searched

With their new technique, the scientists have now accessed a hitherto unexplored frequency range in which, as postulated in relaxion theory, the effects of certain forms of dark matter on the atomic properties of cesium should be relatively easy to spot. The results also allow the researchers to formulate new restrictions as to what the nature of dark matter is likely to be. Dmitry Budker likens this meticulous search to the hunt for a tiger in a desert. “In the frequency range that we’ve explored in our current work, we still have not pinpointed dark matter. But at least, now that we’ve searched in this range, we know we don’t have to do it again.” The researchers still don’t know where dark matter — the tiger in his metaphor — is lurking, but they now know where it is not. “We just keep on targeting in more closely on the part of the desert where the tiger is most likely to be. And, at some point, we will catch him,” maintained Budker with confidence.

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Electronic glove offers ‘humanlike’ features for prosthetic hand users

People with hand amputations experience difficult daily life challenges, often leading to lifelong use of a prosthetic hands and services.

An electronic glove, or e-glove, developed by Purdue University researchers can be worn over a prosthetic hand to provide humanlike softness, warmth, appearance and sensory perception, such as the ability to sense pressure, temperature and hydration. The technology is published in the Aug. 30 edition of NPG Asia Materials.

While a conventional prosthetic hand helps restore mobility, the new e-glove advances the technology by offering the realistic human hand-like features in daily activities and life roles, with the potential to improve their mental health and wellbeing by helping them more naturally integrate into social contexts.

The e-glove uses thin, flexible electronic sensors and miniaturized silicon-based circuit chips on the commercially available nitrile glove. The e-glove is connected to a specially designed wristwatch, allowing for real-time display of sensory data and remote transmission to the user for post-data processing.

Chi Hwan Lee, an assistant professor in Purdue’s College of Engineering, in collaboration with other researchers at Purdue, the University of Georgia and the University of Texas, worked on the development of the e-glove technology.

“We developed a novel concept of the soft-packaged, sensor-instrumented e-glove built on a commercial nitrile glove, allowing it to seamlessly fit on arbitrary hand shapes,” Lee said. “The e-glove is configured with a stretchable form of multimodal sensors to collect various information such as pressure, temperature, humidity and electrophysiological biosignals, while simultaneously providing realistic human hand-like softness, appearance and even warmth.”

Lee and his team hope that the appearance and capabilities of the e-glove will improve the well-being of prosthetic hand users by allowing them to feel more comfortable in social contexts. The glove is available in different skin tone colors, has lifelike fingerprints and artificial fingernails.

“The prospective end user could be any prosthetic hand users who have felt uncomfortable wearing current prosthetic hands, especially in many social contexts,” Lee said.

The fabrication process of the e-glove is cost-effective and manufacturable in high volume, making it an affordable option for users unlike other emerging technologies with mind, voice and muscle control embedded within the prosthetic at a high cost. Additionally, these emerging technologies do not provide the humanlike features that the e-glove provides.

Lee and Min Ku Kim, an engineering doctoral student at Purdue and a co-author on the paper, have worked to patent the technology with the Purdue Research Foundation Office of Technology Commercialization. The team is seeking partners to collaborate in clinical trials or experts in the prosthetics field to validate the use of the e-glove and to continue optimizing the design of the glove.

A video about the technology is available at

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