Aurelia Butler

Can you trust the map on your smartphone, or the satellite image on your computer screen?
So far, yes, but it may only be a matter of time until the growing problem of “deep fakes” converges with geographical information science (GIS). Researchers such as Associate Professor of Geography Chengbin Deng are doing what they can to get ahead of the problem.
Deng and four colleagues — Bo Zhao and Yifan Sun at the University of Washington, and Shaozeng Zhang and Chunxue Xu at Oregon State University — co-authored a recent article in Cartography and Geographic Information Science that explores the problem. In “Deep fake geography? When geospatial data encounter Artificial Intelligence,” they explore how false satellite images could potentially be constructed and detected. News of the research has been picked up by countries around the world, including China, Japan, Germany and France.
“Honestly, we probably are the first to recognize this potential issue,” Deng said.
Geographic information science (GIS) underlays a whole host of applications, from national defense to autonomous cars, a technology that’s currently under development. Artificial intelligence has made a positive impact on the discipline through the development of Geospatial Artificial Intelligence (GeoAI), which uses machine learning — or artificial intelligence (AI) — to extract and analyze geospatial data. But these same methods could potentially be used to fabricate GPS signals, fake locational information on social media posts, fabricate photographs of geographic environments and more.
In short, the same technology that can change the face of an individual in a photo or video can also be used to make fake images of all types, including maps and satellite images. More

Researchers in Penn State’s College of Agricultural Sciences have developed a robotic mechanism for mushroom picking and trimming and demonstrated its effectiveness for the automated harvesting of button mushrooms.
In a new study, the prototype, which is designed to be integrated with a machine vision system, showed that it is capable of both picking and trimming mushrooms growing in a shelf system.
The research is consequential, according to lead author Long He, assistant professor of agricultural and biological engineering, because the mushroom industry has been facing labor shortages and rising labor costs. Mechanical or robotic picking can help alleviate those problems.
“The mushroom industry in Pennsylvania is producing about two-thirds of the mushrooms grown nationwide, and the growers here are having a difficult time finding laborers to handle the harvesting, which is a very labor intensive and difficult job,” said He. “The industry is facing some challenges, so an automated system for harvesting like the one we are working on would be a big help.”
The button mushroom — Agaricus bisporus — is an important agricultural commodity. A total of 891 million pounds of button mushrooms valued at $1.13 billion were consumed in the U.S. from 2017 to 2018. Of this production, 91% were for the fresh market, according to the U.S. Department of Agriculture, and were picked by hand, one by one, to ensure product quality, shelf life and appearance. Labor costs for mushroom harvesting account for 15% to 30% of the production value, He pointed out.
Developing a device to effectively harvest mushrooms was a complex endeavor, explained He. In hand-picking, a picker first locates a mature mushroom and detaches it with one hand, typically using three fingers. A knife, in the picker’s other hand, is then used to remove the stipe end. Sometimes the picker waits until there are two or three mushrooms in hand and cuts them one by one. Finally, the mushroom is placed in a collection box. A robotic mechanism had to achieve an equivalent picking process.
The researchers designed a robotic mushroom-picking mechanism that included a picking “end-effector” based on a bending motion, a “4-degree-of-freedom positioning” end-effector for moving the picking end-effector, a mushroom stipe-trimming end-effector, and an electro-pneumatic control system. They fabricated a laboratory-scale prototype to validate the performance of the mechanism.
The research team used a suction cup mechanism to latch onto mushrooms and conducted bruise tests on the mushroom caps to analyze the influence of air pressure and acting time of the suction cup.
The test results, recently published in Transactions of the American Society of Agricultural and Biological Engineers, showed that the picking end-effector was successfully positioned to the target locations and its success rate was 90% at first pick, increasing to 94.2% after second pick.
The trimming end-effector achieved a success rate of 97% overall. The bruise tests indicated that the air pressure was the main factor affecting the bruise level, compared to the suction-cup acting time, and an optimized suction cup may help to alleviate the bruise damage, the researchers noted. The laboratory test results indicated that the developed picking mechanism has potential to be implemented in automatic mushroom harvesting.
Button mushrooms for the study were grown in tubs at Penn State’s Mushroom Research Center on the University Park campus. Fabrication and experiments were conducted at the Fruit Research and Extension Center in Biglerville. A total of 70 picking tests were conducted to evaluate the robotic picking mechanism. The working pressures of the pneumatic system and the suction cup were set at 80 and 25 pounds per square inch, respectively.
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Materials provided by Penn State. Original written by Jeff Mulhollem. Note: Content may be edited for style and length. More

Simulators have long been used for training surgeons and surgical teams, but traditional simulator platforms typically have a built-in limitation: they often simulate one or a limited number of conditions that require performance of isolated tasks, such as placing an intravenous catheter, instead of simulating and providing opportunities for feedback on the performance of multiple interventions that a trauma victim may require at the same time. To overcome this limitation, the Advanced Modular Manikin (AMM), an innovative simulation platform that allows integration of other simulation devices, was developed and field testing was conducted, with support from the Department of Defense (DoD).
The DoD subcontracted with the American College of Surgeons (ACS) Division of Education to conduct field testing of the AMM. The results have been published online in advance of print by the Journal of the American College of Surgeons. Robert M. Sweet, MD, FACS, MAMSE, of the department of surgery at the University of Washington, served as principal investigator (PI) of the DoD contract to build the AMM. Ajit K. Sachdeva, MD, FACS, FRCSC, FSACME, MAMSE, Director, Division of Education, American College of Surgeons, served as the PI for the subcontract to conduct field testing.
The investigators reported that members of trauma teams at a testing site preferred the integrated AMM platform including a “peripheral” simulator over the “peripheral” simulator alone, in terms of realism, physiologic responses, and feedback they receive on the multiple and overlapping interventions they perform on a simulated trauma patient. Corresponding study author Dimitrios Stefanidis, MD, PhD, FACS, FASMBS, FSSH, of the department of surgery at Indiana University School of Medicine, Indianapolis, described the AMM as “more of a platform rather than a manikin.”
The DoD supported development of the AMM through a contract with the University of Minnesota and the University of Washington. The goal was to create an open-source simulation platform that permits integration of a number of simulators, known as “peripherals,” into a singular, comprehensive training platform. A Steering Committee composed of leaders and staff of the ACS Division of Education and the Research and Development Committee of the ACS-Accredited Education Institutes, along with leaders from the Development Team of the AMM Project created the model for field testing the AMM.
“The AMM platform, along with the ‘peripherals,’ can help to address the need for more robust simulators that focus on open procedures and interprofessional teamwork,” Dr. Sachdeva explained. “The ability to integrate the anatomic and physiologic elements of the simulation is an important advance. The experience with the trauma scenario may readily be extended to other surgical procedures and settings.”
Corresponding author Dr. Stefanidis explained that with most traditional simulators, instructors have to manipulate vital signs to respond to specific actions of the learner. He pointed out that the AMM promotes “a learner experience that is more based on the actual physiology of what’s happening to the patient.” The AMM platform allows different members of the trauma team to perform different tasks concurrently — one inserts a breathing tube, another starts an intravenous line, another performs a splenectomy. “All of these interventions impact the physiology,” he said.
The researchers evaluated team experience ratings of 14 trauma teams consisting of 42 individual members who performed tasks on the integrated AMM platform and the standalone “peripheral” simulator. Team experience ratings were higher for the integrated AMM platform as compared with the standalone “peripheral” simulator. Among the team members, surgeons and first responders rated their experience significantly higher than anesthesiologists, who noted higher workload ratings. In focus groups, the team members said they preferred the AMM platform because of its increased realism, and for the way it responded physiologically to their actions and the feedback it provided.
Dr. Stefanidis explained how the AMM can potentially aid in training trauma teams. “Trauma requires exemplary teamwork,” he said. “When we see patients who are injured, there are typically multiple providers who take care of them simultaneously — trauma surgeons, emergency room physicians, anesthesiologists, orthopedic surgeons, neurosurgeons, nurses, respiratory therapists, etc. So, it’s extremely important to also be able to train these teams in a low-stress simulation environment, such as by using the AMM, where they can hone their skills, individually and as a team, and perform at their best when faced with the very high-stress clinical environment.”
The AMM platform offers other benefits for improving the training and proficiency of trauma teams, said the field study PI, Dr. Sachdeva. “Specific training models could be standardized and the situation made increasingly complex in this safe simulation environment,” he said. More

University of South Florida researchers recently developed a novel approach to mitigating electromigration in nanoscale electronic interconnects that are ubiquitous in state-of-the-art integrated circuits. This was achieved by coating copper metal interconnects with hexagonal boron nitride (hBN), an atomically-thin insulating two-dimensional (2D) material that shares a similar structure as the “wonder material” graphene.
Electromigration is the phenomenon in which an electrical current passing through a conductor causes the atomic-scale erosion of the material, eventually resulting in device failure. Conventional semiconductor technology addresses this challenge by using a barrier or liner material, but this takes up precious space on the wafer that could otherwise be used to pack in more transistors. USF mechanical engineering Assistant Professor Michael Cai Wang’s approach accomplishes this same goal, but with the thinnest possible materials in the world, two-dimensional (2D) materials.
“This work introduces new opportunities for research into the interfacial interactions between metals and ångström-scale 2D materials. Improving electronic and semiconductor device performance is just one result of this research. The findings from this study opens up new possibilities that can help advance future manufacturing of semiconductors and integrated circuits,” Wang said. “Our novel encapsulation strategy using single-layer hBN as the barrier material enables further scaling of device density and the progression of Moore’s Law.” For reference, a nanometer is 1/60,000 of the thickness of human hair, and an ångström is one-tenth of a nanometer. Manipulating 2D materials of such thinness requires extreme precision and meticulous handling.
In their recent study published in the journal Advanced Electronic Materials, copper interconnects passivated with a monolayer hBN via a back-end-of-line (BEOL) compatible approach exhibited more than 2500% longer device lifetime and more than 20% higher current density than otherwise identical control devices. This improvement, coupled with the ångström-thinness of hBN compared to conventional barrier/liner materials, allows for further densification of integrated circuits. These findings will help advance device efficiency and decrease energy consumption.
“With the growing demand for electric vehicles and autonomous driving, the demand for more efficient computing has grown exponentially. The promise of higher integrated circuits density and efficiency will enable development of better ASICs (application-specific integrated circuits) tailored to these emerging clean energy needs.” explained Yunjo Jeong, an alumnus from Wang’s group and first author of the study.
An average modern car has hundreds of microelectronic components, and the significance of these tiny but critical components has been especially highlighted through the recent global chip shortage. Making the design and manufacturing of these integrated circuits more efficient will be key to mitigating possible future disruptions to the supply chain. Wang and his students are now investigating ways to speed up their process to the fab scale.
“Our findings are not limited only to electrical interconnects in semiconductor research. The fact that we were able to achieve such a drastic interconnect device improvement implies that 2D materials can also be applied to a variety of other scenarios.” Wang added.
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Materials provided by University of South Florida (USF Innovation). Note: Content may be edited for style and length. More

Quantum computing and quantum sensing have the potential to be vastly more powerful than their classical counterparts. Not only could a fully realized quantum computer take just seconds to solve equations that would take a classical computer thousands of years, but it could have incalculable impacts on areas ranging from biomedical imaging to autonomous driving.
However, the technology isn’t quite there yet.
In fact, despite widespread theories about the far-reaching impact of quantum technologies, very few researchers have been able to demonstrate, using the technology available now, that quantum methods have an advantage over their classical counterparts.
In a paper published on June 1 in the journal Physical Review X, University of Arizona researchers experimentally show that quantum has an advantage over classical computing systems.
“Demonstrating a quantum advantage is a long-sought-after goal in the community, and very few experiments have been able to show it,” said paper co-author Zheshen Zhang, assistant professor of materials science and engineering, principal investigator of the UArizona Quantum Information and Materials Group and one of the paper’s authors. “We are seeking to demonstrate how we can leverage the quantum technology that already exists to benefit real-world applications.”
How (and When) Quantum Works
Quantum computing and other quantum processes rely on tiny, powerful units of information called qubits. The classical computers we use today work with units of information called bits, which exist as either 0s or 1s, but qubits are capable of existing in both states at the same time. This duality makes them both powerful and fragile. The delicate qubits are prone to collapse without warning, making a process called error correction — which addresses such problems as they happen — very important. More

From the biggest bridges to the smallest medical implants, sensors are everywhere, and for good reason: The ability to sense and monitor changes before they become problems can be both cost-saving and life-saving.
To better address these potential threats, the Intelligent Structural Monitoring and Response Testing (iSMaRT) Lab at the University of Pittsburgh Swanson School of Engineering has designed a new class of materials that are both sensing mediums and nanogenerators, and are poised to revolutionize the multifunctional material technology big and small.
The research, recently published in Nano Energy, describes a new metamaterial system that acts as its own sensor, recording and relaying important information about the pressure and stresses on its structure. The so-called “self-aware metamaterial” generates its own power and can be used for a wide array of sensing and monitoring applications.
The most innovative facet of the work is its scalability: the same design works at both nanoscale and megascale simply by tailoring the design geometry.
“There is no doubt that the next generation materials need to be multifunctional, adaptive and tunable.” said Amir Alavi, assistant professor of civil and environmental engineering and bioengineering, who leads the iSMaRT Lab. “You can’t achieve these features with natural materials alone — you need hybrid or composite material systems in which each constituent layer offers its own functionality. The self-aware metamaterial systems that we’ve invented can offer these characteristics by fusing advanced metamaterial and energy harvesting technologies at multiscale, whether it’s a medical stent, shock absorber or an airplane wing.”
While nearly all of the existing self-sensing materials are composites that rely on different forms of carbon fibers as sensing modules, this new concept offers a completely different, yet efficient, approach to creating sensor and nanogenerator material systems. The proposed concept relies on performance-tailored design and assembly of material microstructures. More

The electronic structure of metallic materials determines the behavior of electron transport. Magnetic Weyl semimetals have a unique topological electronic structure — the electron’s motion is dynamically linked to its spin. These Weyl semimetals have come to be the most exciting quantum materials that allow for dissipationless transport, low power operation, and exotic topological fields that can accelerate the motion of the electrons in new directions. The compounds Co3Sn2S2 and Co2MnGa [1-4], recently discovered by the Felser group, have shown some of the most prominent effects due to a set of two topological bands.
Researchers at the Max Planck Institute for Chemical Physics of Solids in Dresden, the University of South Florida in the USA, and co-workers have discovered a new mechanism in magnetic compounds that couples multiple topological bands. The coupling can significantly enhance the effects of quantum phenomena. One such effect is the anomalous Hall effect that arises with spontaneous symmetry breaking time-reversal fields that cause a transverse acceleration to electron currents. The effects observed and predicted in single crystals of Co3Sn2S2 and Co2MnGa display a sizable increase compared to conventional magnets.
In the current publication, we explored the compounds XPt3, where we predicted an anomalous Hall effect nearly twice the size of the previous compounds. The large effect is due to sets of entangled topological bands with the same chirality that synergistically accelerates charged particles. Interestingly, the chirality of the bands couple to the magnetization direction and determine the direction of the acceleration of the charged particles. This chirality can be altered by chemical substitution. Our theoretical results of CrPt3 show the maximum effect, where MnPt3 significantly reduced the effect due to the change in the order of the chiral bands.
Advanced thins films of the CrPt3 were grown at the Max Planck Institute. We found in various films a pristine anomalous Hall effect, robust against disorder and variation of temperature. The result is a strong indication that the topological character dominates even at finite temperatures. The results show to be near twice as large as any intrinsic effect measured in thin films. The advantage of thin films is the ease of integration into quantum devices with an interplay of other freedoms, such as charge, spin, and heat. XPt3 films show possible utilization for Hall sensors, charge-to-spin conversion in electronic devices, and charge-to-heat conversion in thermoelectric devices with such a strong response.
[1] Enke Liu et al., Nat. Phys. 14, 1125 (2018).
[2] Kaustuv Manna et al., Phys. Rev. X 8, 041045 (2018).
[3] D. F. Liu, et al. Science 365, 1282-1285 (2019).
[4] Noam Morali et al. Science 365, 1286-1291 (2019).
[5] Anastasios Markou et al., Commun. Phys. 4, 104 (2021).
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Firefighting is a race against time. Exactly how much time? For firefighters, that part is often unclear. Building fires can turn from bad to deadly in an instant, and the warning signs are frequently difficult to discern amid the mayhem of an inferno.
Seeking to remove this major blind spot, researchers at the National Institute of Standards and Technology (NIST) have developed P-Flash, or the Prediction Model for Flashover. The artificial-intelligence-powered tool was designed to predict and warn of a deadly phenomenon in burning buildings known as flashover, when flammable materials in a room ignite almost simultaneously, producing a blaze only limited in size by available oxygen. The tool’s predictions are based on temperature data from a building’s heat detectors, and, remarkably, it is designed to operate even after heat detectors begin to fail, making do with the remaining devices.
The team tested P-Flash’s ability to predict imminent flashovers in over a thousand simulated fires and more than a dozen real-world fires. Research, just published in the Proceedings of the AAAI Conference on Artificial Intelligence, suggests the model shows promise in anticipating simulated flashovers and shows how real-world data helped the researchers identify an unmodeled physical phenomenon that if addressed could improve the tool’s forecasting in actual fires. With further development, P-Flash could enhance the ability of firefighters to hone their real-time tactics, helping them save building occupants as well as themselves.
Flashovers are so dangerous in part because it’s challenging to see them coming. There are indicators to watch, such as increasingly intense heat or flames rolling across the ceiling. However, these signs can be easy to miss in many situations, such as when a firefighter is searching for trapped victims with heavy equipment in tow and smoke obscuring the view. And from the outside, as firefighters approach a scene, the conditions inside are even less clear.
“I don’t think the fire service has many tools technology-wise that predict flashover at the scene,” said NIST researcher Christopher Brown, who also serves as a volunteer firefighter. “Our biggest tool is just observation, and that can be very deceiving. Things look one way on the outside, and when you get inside, it could be quite different.”
Computer models that predict flashover based on temperature are not entirely new, but until now, they have relied on constant streams of temperature data, which are obtainable in a lab but not guaranteed during a real fire. More