Computers Math

Along with the use of face masks, social distancing in public remains one of the most practiced front-line defenses against the spread of COVID-19. However, flows of pedestrians, including those practicing the 6-foot rule for distancing, are dynamic and characterized by nuances not always carefully considered in the context of everyday, public spaces.
In Physics of Fluids, by AIP Publishing, researchers from Carnegie Mellon University examine the dynamics of social distancing practices through the lens of particle-based flow simulations. The study models social distance as the distance at which particles, representing pedestrians, repel fellow particles.
“Even at modest pedestrian density levels, a strong preference for 6 feet of social distance can cause large-scale pedestrian ‘traffic jams’ that take a long time to clear up,” said Gerald J. Wang, of Carnegie Mellon University. “This is pretty evident to all of us who have engaged in that ‘awkward dance of social distance’ in a grocery store aisle during the past 18 months, but it has important implications for how we set occupancy thresholds as workplaces, campuses, and entertainment venues return to pre-pandemic densities.”
Motivated by the pandemic, the researchers shed light on the relationship between social distancing and pedestrian flow dynamics in corridors by illustrating how adherence to social distancing protocols affects two-way pedestrian movement in a shared space. The results add to a significant body of recent work around the effects of various factors on pedestrian counterflows and focuses on the characterization of jamming phenomena in relatively narrow corridors, a topic of current interest.
“Dense pedestrian flows plus social distancing recommendations is a recipe for a lot of frustration,” said Wang. “I mean this both in the physics sense of the word ‘frustration,’ with low particle mobilities because a bunch of ‘stuff’ is seemingly in their way, and in the everyday sense of the word ‘frustration,’ with people feeling flustered because, well, a bunch of ‘stuff’ is seemingly in their way!”
Wang noted public health messaging should be aligned with realistic, achievable behavior, adding that “strict adherence to social distancing — a la ‘the 6-foot rule’ — is simply not a practical recommendation in pedestrian flows at densities that are typical of large, shared venues.”
Though conceptually easy to digest, the findings underscore the complications of applying a “one-size-fits-all” policy recommendation to a public sphere characterized by nuanced pedestrian flow dynamics.
“Particle-based flow simulation, powered by high-performance computing, has enormous potential to rapidly explore a broad range of pedestrian flow problems, both during the pandemic and beyond,” said co-author Kelby B. Kramer.
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The COVID-19 pandemic has led to more than 218 million infections and over 4.5 million deaths as of Sept. 3, 2021. Nonpharmaceutical interventions (NPIs), such as case isolation, quarantining contacts, and the complete lockdown of entire countries, were implemented in an effort to contain the pandemic. But these NPIs often come at the expense of economic disruption, harm to social and mental well-being, and costly administration costs to ensure compliance.
Given the slow rollout of vaccination programs worldwide and the rise of several mutations of the coronavirus, the use of these types of interventions will continue for some time. In Chaos, by AIP Publishing, researchers in China use a data-driven agent-based model to identify new and sustainable NPIs to contain outbreaks while minimizing the economic and social costs.
“Based on the proposed model, we proposed targeted interventions, which can contain the outbreak with minimal disruption of society. This is of particular importance in cities like Hong Kong, whose economy relies on international trade,” said author Qingpeng Zhang.
The researchers built a data-driven mobility model to simulate COVID-19 spreading in Hong Kong by combining synthetic population, human behavior patterns, and a viral transmission model. This model generated 7.55 million agents to describe the infectious state and movement for each Hong Kong resident.
Since mobile phone data is difficult to obtain in most countries, the researchers calibrated their model with open-source data, so it could be easily extended to the modeling of other metropolises with various demographic and human mobility patterns.
“With the agent-based model, we can simulate very detailed scenarios in Hong Kong, and based on these simulations, we are able to propose targeted interventions in only a small portion of the city instead of city-level NPIs,” said Zhang.
The researchers found that by controlling a small percentage (top 1%-2%) of grids in Hong Kong, the virus could be largely contained. While such interventions are not as effective as citywide NPIs and compulsory COVID-19 testing, such targeted control has the benefit of a much smaller disruption of society.
The proposed model leading to the targeted interventions has the potential to guide current citywide NPIs to achieve a balance between lowering the risk and preserving human mobility and economy of the city.
“Our findings also apply to other major cities in the world, such as Beijing, New York, London, and Toyko, as COVID-19 is likely to be around indefinitely, and we have to learn how to live with it,” said Zhang.
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A team of physicists from Germany and Sweden working with first author Jens Christian Grauer from Heinrich Heine University Düsseldorf (HHU) has examined a special system of colloidal particles that they activated using laser light. The researchers discovered that self-propelling droplets, which they have named ‘droploids’, formed which contain the particles as an internal motor. They describe these droploids in more detail in the latest edition of the journal Nature Communications.
According to an age-old saying, the whole is often more than the sum of its parts. After all, a sandwich made of bread, lettuce and mayonnaise tastes better than its individual components. A team of physicists from HHU, TU Darmstadt and Sweden’s University of Gothenburg has determined that this adage is also true in the realm of physics, and that combining individual parts can create something with entirely new properties.
The research project involved combining different atoms and larger particles and studying the effects they have on each other. It is ultimately a typical example of what the matter that surrounds us is composed of. The researchers extended this general principle of combination to include additional feedback processes, thus creating new kinds of dynamic structures referred to as ‘positive feedback loops’.
Specifically, they combined two different types of colloid particles — in a water-lutidine heat bath. They irradiated the bath with lasers, and the light from the lasers brought the liquid near the particles to the critical point. The fluctuations are particularly strong at this point, allowing droplet-like structures to form that in turn surround the particles.
Inside the droplets, the two types of colloid particles heat up to different temperatures. This results in effective forces that contradict Newton’s fundamental law of motion (actio = reactio) to propel the droplets forwards. This means that the colloid particles induce the formation of droplets that encapsulate the colloids and are in turn propelled by the particles. This feedback loop results in novel superstructures with a self-organised colloidal motor. The researchers adopted the term ‘droploids’, a portmanteau of the words ‘droplets’ and ‘colloids’, to describe these superstructures.
The research team combined theoretical and experimental approaches, with the system modelling performed in Düsseldorf and Darmstadt, while the colleagues in Gothenburg verified the findings using real-life experiments, thus confirming the theoretical models.
Prof. Dr. Hartmut Löwen, Head of the Institute of Theoretical Physics II at HHU, had this to say: “It’s important here that the process can be controlled entirely by laser illumination. This makes it possible to steer the system externally so that it is flexible for different applications.”
Prof. Dr. Benno Liebchen, leader of the “Theory of Soft Matter” working group at TU Darmstadt, explained the actual use of the droploids as follows: “Besides justifying a novel concept for micromotors, the droploids and the non-reciprocal interactions involved could serve as important ingredients for generating future biomimetic materials.”
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A research collaboration between Associate Professor MISHIMA Tomokazu (Kobe University Graduate School of Maritime Sciences) and Associate Professor LAI Ching-Ming (National Chung Hsing University, Taiwan) has successfully developed a new power controller system for wireless power transfer. The developed system is highly precise and efficient, and the circuitry is simpler than existing systems. This technical proposal will effectively reduce the amount of circuit components in wireless power transfer devices, as well as their cost and weight. These research results were given online advanced publication in the international scientific journal ‘IEEE Journal of Emerging and Selected Topics in Industrial Electronics’ on August 6, 2021.
Wireless transfer systems are used to transfer electric energy in a contactless manner to the batteries inside electrified vehicles, such as automated guided vehicles in factories, electric cars, and ships. Consequently, wireless transfer systems have been gathering much attention from various fields in terms of improving the convenience of electrical energy utilization and the advancement of clean energy. In a wireless transfer system, contactless power transfer occurs between the transferring (Tx) coils and the receiving (Rx) coils. However, a large amount of the transferred power is lost if the distance (gap) between the two coils increases and they are no longer in their optimum position. To prevent power losses and reduced efficiency resulting from these occurrences, it is necessary to control electrical parameters, such as the frequency of Tx and Rx coils’ currents, in accordance with the battery capacity. Consequently, the structures of power conversion and controller devices have become more complex.
Research Findings
To tackle the technical issue mentioned above, Associate Professor Mishima et al. have developed a novel control strategy that applies resonant frequency tracking and load impedance regulation to a high frequency inverter in the Tx side. Resonant frequency tracking automatically adjusts the operation of the high frequency inverter via the phase difference between the current and voltage of the Tx coils in a highly efficient manner. In addition, applying delta sigma transformation (a technique for processing electrical signals) into the pulse density modulation of the high frequency inverter eliminates the need for a complicated extra controller in the Rx side. In this way, the researchers developed a novel, practical and cost-effective power control scheme that enables a wireless power transfer system to be operated with high precision and efficiency from the Tx side.
Further Research
The researchers have successfully simplified the structure of the power conversion circuitry in the Rx side as well as the logical scheme of the power controller. This development and its experimental verification demonstrate that it is possible to reduce the number of components, which will contribute towards the implementation of highly reliable and cost-effective wireless transfer systems. For example, this technology could be especially beneficial for electric cars, drones and other such vehicles for which a light weight and compact size are important. Furthermore, the research results could also be applied to biomedical wireless power transfers for implantable medical devices such as pacemakers.
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Blood transfusions save lives, yet the precious fluid is in desperately short supply, not just in the U.S. but around the globe. But what if transfusions don’t always require blood?
A new mathematical model of the body’s interacting physiological and biochemical processes — including blood vessel expansion, blood thickening and flow-rate changes in response to the transfusion of red blood cells — shows that patients with anemia, or blood with low oxygen levels, can be effectively treated with transfusions of blood substitutes that are more readily available.
The research, co-authored by scientists at Stanford University and the University of California, San Diego (UCSD), was published on Oct. 14 in the Journal of Applied Physiology.
Using a different fluid could also eliminate a harmful consequence of blood transfusion: Blood use has been observed to lower lifespan by 6 percent per unit transfused per decade because of its adverse side effects.
“Instead of real blood, we can use a substitute that can lower the costs and eliminate blood transfusion’s negative effects,” said lead study author Weiyu Li, a PhD student in energy resources engineering at Stanford’s School of Earth, Energy & Environmental Sciences (Stanford Earth).
Transfusion is a common procedure for transferring blood components directly to anemic patients’ circulation. Red blood cells are uniquely equipped to perform the function of carrying oxygen, which is why they are used for transfusions for patients experiencing anemia. But the process of obtaining, storing and delivering the correct, sanitary blood type for each patient is also intensive and costly. Moreover, the supply of blood that is available falls far short of the demand: The global deficit across all countries without enough supply totals about 100 million units of blood per year. More

A special artificial intelligence (AI)-based computer algorithm created by Mount Sinai researchers was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure.
“We showed that deep-learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data,” said Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for Digital Health at Mount Sinai, and a senior author of the study published in the Journal of the American College of Cardiology: Cardiovascular Imaging. “Ordinarily, diagnosing these type of heart conditions requires expensive and time-consuming procedures. We hope that this algorithm will enable quicker diagnosis of heart failure.”
The study was led by Akhil Vaid, MD, a postdoctoral scholar who works in both the Glicksberg lab and one led by Girish N. Nadkarni, MD, MPH, CPH, Associate Professor of Medicine at the Icahn School of Medicine at Mount Sinai, Chief of the Division of Data-Driven and Digital Medicine (D3M), and a senior author of the study.
Affecting about 6.2 million Americans, heart failure, or congestive heart failure, occurs when the heart pumps less blood than the body normally needs. For years doctors have relied heavily on an imaging technique called an echocardiogram to assess whether a patient may be experiencing heart failure. While helpful, echocardiograms can be labor-intensive procedures that are only offered at select hospitals.
However, recent breakthroughs in artificial intelligence suggest that electrocardiograms — a widely used electrical recording device — could be a fast and readily available alternative in these cases. For instance, many studies have shown how a “deep-learning” algorithm can detect weakness in the heart’s left ventricle, which pushes freshly oxygenated blood out to the rest of the body. In this study, the researchers described the development of an algorithm that not only assessed the strength of the left ventricle but also the right ventricle, which takes deoxygenated blood streaming in from the body and pumps it to the lungs.
“Although appealing, traditionally it has been challenging for physicians to use ECGs to diagnose heart failure. This is partly because there is no established diagnostic criteria for these assessments and because some changes in ECG readouts are simply too subtle for the human eye to detect,” said Dr. Nadkarni. “This study represents an exciting step forward in finding information hidden within the ECG data which can lead to better screening and treatment paradigms using a relatively simple and widely available test.”
Typically, an electrocardiogram involves a two-step process. Wire leads are taped to different parts of a patient’s chest and within minutes a specially designed, portable machine prints out a series of squiggly lines, or waveforms, representing the heart’s electrical activity. These machines can be found in most hospitals and ambulances throughout the United States and require minimal training to operate. More

Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as “barren plateaus” has limited the application of these neural networks for large data sets, new research overcomes that Achilles heel with a rigorous proof that guarantees scalability.
“The way you construct a quantum neural network can lead to a barren plateau — or not,” said Marco Cerezo, coauthor of the paper titled “Absence of Barren Plateaus in Quantum Convolutional Neural Networks,” published today by a Los Alamos National Laboratory team in Physical Review X. Cerezo is a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos. “We proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters.”
As an artificial intelligence (AI) methodology, quantum convolutional neural networks are inspired by the visual cortex. As such, they involve a series of convolutional layers, or filters, interleaved with pooling layers that reduce the dimension of the data while keeping important features of a data set.
These neural networks can be used to solve a range of problems, from image recognition to materials discovery. Overcoming barren plateaus is key to extracting the full potential of quantum computers in AI applications and demonstrating their superiority over classical computers.
Until now, Cerezo said, researchers in quantum machine learning analyzed how to mitigate the effects of barren plateaus, but they lacked a theoretical basis for avoiding it altogether. The Los Alamos work shows how some quantum neural networks are, in fact, immune to barren plateaus.
“With this guarantee in hand, researchers will now be able to sift through quantum-computer data about quantum systems and use that information for studying material properties or discovering new materials, among other applications,” said Patrick Coles, a quantum physicist at Los Alamos and a coauthor of the paper. More

As a robotics engineer, Yasemin Ozkan-Aydin, assistant professor of electrical engineering at the University of Notre Dame, gets her inspiration from biological systems. The collective behaviors of ants, honeybees and birds to solve problems and overcome obstacles is something researchers have developed in aerial and underwater robotics. Developing small-scale swarm robots with the capability to traverse complex terrain, however, comes with a unique set of challenges.
In research published in Science Robotics, Ozkan-Aydin presents how she was able to build multi-legged robots capable of maneuvering in challenging environments and accomplishing difficult tasks collectively, mimicking their natural-world counterparts.
“Legged robots can navigate challenging environments such as rough terrain and tight spaces, and the use of limbs offers effective body support, enables rapid maneuverability and facilitates obstacle crossing,” Ozkan-Aydin said. “However, legged robots face unique mobility challenges in terrestrial environments, which results in reduced locomotor performance.”
For the study, Ozkan-Aydin said, she hypothesized that a physical connection between individual robots could enhance the mobility of a terrestrial legged collective system. Individual robots performed simple or small tasks such as moving over a smooth surface or carrying a light object, but if the task was beyond the capability of the single unit, the robots physically connected to each other to form a larger multi-legged system and collectively overcome issues.
“When ants collect or transport objects, if one comes upon an obstacle, the group works collectively to overcome that obstacle. If there’s a gap in the path, for example, they will form a bridge so the other ants can travel across — and that is the inspiration for this study,” she said. “Through robotics we’re able to gain a better understanding of the dynamics and collective behaviors of these biological systems and explore how we might be able to use this kind of technology in the future.”
Using a 3D printer, Ozkan-Aydin built four-legged robots measuring 15 to 20 centimeters, or roughly 6 to 8 inches, in length. Each was equipped with a lithium polymer battery, microcontroller and three sensors — a light sensor at the front and two magnetic touch sensors at the front and back, allowing the robots to connect to one another. Four flexible legs reduced the need for additional sensors and parts and gave the robots a level of mechanical intelligence, which helped when interacting with rough or uneven terrain. More