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Category: mathematics

Artificial womb technology for extremely preterm infants — jasmijn kok, juno perinatal healthcare.

Every year, 800000 babies are born extremely preterm (defined as less than 28 weeks of age) worldwide. These infants are usually transferred to an air-based neonatal intensive care unit to support their heart and lung development. Exposure to air, however, leads to many complications, because the lungs are not fully developed yet.

An artificial uterus, or artificial womb, is a device that would allow for extra-corporeal pregnancy, by supporting the growth of a fetus outside the body of an organism that would normally carry the fetus to term.

Juno Perinatal Healthcare (https://www.junoperinatalhealthcare.com/) is a fascinating Dutch neonatal healthcare start-up which has a mission of developing a novel, alternative environment, similar to the mother’s womb, where extremely premature babies could be transferred, where the lungs remain filled with fluid and the umbilical cord will be attached to an artificial placenta to improve their organ development and ease the transition to newborn life.

Juno Perinatal Healthcare is a companion project to a interdisciplinary consortium known as the Perinatal Life Support (PLS) Project (https://perinatallifesupport.eu/), a consortium of three European universities, Aachen, Milan and Eindhoven, to establish the first ex-vivo fetal maturation system for clinical use.

The PLS project, coordinated by the Eindhoven University of Technology brings together world-leading experts in obstetrics, neonatology, industrial design, mathematical modelling, ex-vivo organ support, and non-invasive fetal monitoring.

The PLS consortium is led by professor Frans van de Vosse and Professor Dr Guid Oei.

In 2020, the spin off Juno Perinatal Healthcare was set up by engineers Jasmijn Kok and Lyla Kok.

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Training neural networks to perform tasks, such as recognizing images or navigating self-driving cars, could one day require less computing power and hardware thanks to a new artificial neuron device developed by researchers at the University of California San Diego. The device can run neural network computations using 100 to 1000 times less energy and area than existing CMOS-based hardware.

Researchers report their work in a paper published recently in Nature Nanotechnology.

Neural networks are a series of connected layers of artificial neurons, where the output of one layer provides the input to the next. Generating that input is done by applying a mathematical calculation called a non-linear activation function. This is a critical part of running a neural network. But applying this function requires a lot of computing power and circuitry because it involves transferring data back and forth between two separate units – the memory and an external processor.

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The now-familiar sight of traditional propeller wind turbines could be replaced in the future with wind farms containing more compact and efficient vertical turbines.

New research from Oxford Brookes University has found that the vertical turbine design is far more efficient than traditional turbines in large-scale wind farms, and when set in pairs the vertical turbines increase each other’s performance by up to 15%.

A research team from the School of Engineering, Computing and Mathematics (ECM) at Oxford Brookes led by Professor Iakovos Tzanakis conducted an in-depth study using more than 11500 hours of computer simulation to show that wind farms can perform more efficiently by substituting the traditional propeller-type Horizontal Axis Wind Turbines (HAWTs), for compact Vertical Axis Wind Turbines (VAWTs).

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Hebrew University Researcher Introduces New Approach to Three-Body Problem, Predicts its Outcome Statistics.

The “three-body problem,” the term coined for predicting the motion of three gravitating bodies in space, is essential for understanding a variety of astrophysical processes as well as a large class of mechanical problems, and has occupied some of the world’s best physicists, astronomers and mathematicians for over three centuries. Their attempts have led to the discovery of several important fields of science; yet its solution remained a mystery.

At the end of the 17th century, Sir Isaac Newton succeeded in explaining the motion of the planets around the sun through a law of universal gravitation. He also sought to explain the motion of the moon. Since both the earth and the sun determine the motion of the moon, Newton became interested in the problem of predicting the motion of three bodies moving in space under the influence of their mutual gravitational attraction (see illustration to the right), a problem that later became known as “the three-body problem.”

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The growth of space businesses makes this “the most exciting time” to be involved in the industry, but one CEO says private and government organizations must do more to tap the next generation of U.S. workers.

“I do think there’s opportunities for everybody to participate in the excitement … [and] it’s a great opportunity for the government to really lean in on looking for those public-private partnerships,” Steve Isakowitz, CEO of The Aerospace Corporation and former president of Virgin Galactic, told attendees of the America’s Future Series Space Innovation Summit. The event ran on April 6 and 7.

“We need to do more and expand the candidate pool — we’ve got to make sure that all of America has the benefit of being part of the STEM, K-12, opportunities that are out there,” he added, referring to the academic discipline that includes science, tech, engineering and math.

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Composite membrane origami has been an efficient and effective method for constructing transformable mechanisms while considerably simplifying their design, fabrication, and assembly; however, its limited load-bearing capability has restricted its application potential. With respect to wheel design, membrane origami offers unique benefits compared with its conventional counterparts, such as simple fabrication, high weight-to-payload ratio, and large shape variation, enabling softness and flexibility in a kinematic mechanism that neutralizes joint distortion and absorbs shocks from the ground. Here, we report a transformable wheel based on membrane origami capable of bearing more than a 10-kilonewton load. To achieve a high payload, we adopt a thick membrane as an essential element and introduce a wireframe design rule for thick membrane accommodation. An increase in the thickness can cause a geometric conflict for the facet and the membrane, but the excessive strain energy accumulation is unique to the thickness increase of the membrane. Thus, the design rules for accommodating membrane thickness aim to address both geometric and physical characteristics, and these rules are applied to basic origami patterns to obtain the desired wheel shapes and transformation. The capability of the resulting wheel applied to a passenger vehicle and validated through a field test. Our study shows that membrane origami can be used for high-payload applications.

Origami has been a rich source of inspiration for art, education, and mathematics, and it has proven to be an efficient and effective method for realizing transformable structures in nature (1–3) and artificial systems (4–8). Composite membrane origami, the design technique based on the laminar composition of flexible membranes with rigid facet constraints, opens a new field for robotics by the transition from component assembly to lamination, which considerably simplifies design, fabrication, and assembly. This transition simplifies and speeds up fabrication and enables reaching size scales that were difficult to access before (9, 10). In addition, membrane origami provides a versatile shape-changing ability that has been exploited in various applications (11–15), and its applicability has been extended by additional design dimensions obtained from material characteristics such as softness and stretchability (16–19).

Beyond the aforementioned benefits, origami has been an effective design tool for constructing a high payload-to-weight structure, such as a honeycomb panel, by markedly increasing the buckling strength using unique geometric configurations (20, 21). Combining this feature with reconfigurability, various stiffness transition mechanisms have also been introduced (22–24). The rigidity of components is another important factor to secure high load capacity and closely related to the thickness. Origami design is, traditionally, a matter of organizing fold lines under fundamental and ideal assumptions—zero facet thickness and zero fold line width (25–27). However, in response to growing interest in origami-inspired applications that require load-bearing capability, various thickness accommodation methods have been introduced (28–30).

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Classical hydrodynamics laws can be very useful for describing the behavior of systems composed of many particles (i.e., many-body systems) after they reach a local state of equilibrium. These laws are expressed by so-called hydrodynamical equations, a set of mathematical equations that describe the movement of water or other fluids.

Researchers at Oak Ridge National Laboratory and University of California, Berkeley (UC Berkeley) have recently carried out a study exploring the hydrodynamics of a quantum Heisenberg spin-1/2 chain. Their paper, published in Nature Physics, shows that the spin dynamics of a 1D Heisenberg antiferromagnet (i.e., KCuF3) could be effectively described by a dynamical exponent aligned with the so-called Kardar-Parisi-Zhang universality class.

“Joel Moore and I have known each other for many years and we both have an interest in quantum magnets as a place where we can explore and test new ideas in physics; my interests are experimental and Joel’s are theoretical,” Alan Tennant, one of the researchers who carried out the study, told Phys.org. “For a long time, we have both been interested in temperature in quantum systems, an area where a number of really new insights have come along recently, but we had not worked together on any projects.”

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Artificial microswimmers that can replicate the complex behavior of active matter are often designed to mimic the self-propulsion of microscopic living organisms. However, compared with their living counterparts, artificial microswimmers have a limited ability to adapt to environmental signals or to retain a physical memory to yield optimized emergent behavior. Different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to Brownian motion, which randomizes their position and propulsion direction. Here, we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a simple standard navigation problem under the inevitable influence of Brownian motion at these length scales. We show that, with external control, collective learning is possible. Concerning the learning under noise, we find that noise decreases the learning speed, modifies the optimal behavior, and also increases the strength of the decisions made. As a consequence of time delay in the feedback loop controlling the particles, an optimum velocity, reminiscent of optimal run-and-tumble times of bacteria, is found for the system, which is conjectured to be a universal property of systems exhibiting delayed response in a noisy environment.

Living organisms adapt their behavior according to their environment to achieve a particular goal. Information about the state of the environment is sensed, processed, and encoded in biochemical processes in the organism to provide appropriate actions or properties. These learning or adaptive processes occur within the lifetime of a generation, over multiple generations, or over evolutionarily relevant time scales. They lead to specific behaviors of individuals and collectives. Swarms of fish or flocks of birds have developed collective strategies adapted to the existence of predators (1), and collective hunting may represent a more efficient foraging tactic (2). Birds learn how to use convective air flows (3). Sperm have evolved complex swimming patterns to explore chemical gradients in chemotaxis (4), and bacteria express specific shapes to follow gravity (5).

Inspired by these optimization processes, learning strategies that reduce the complexity of the physical and chemical processes in living matter to a mathematical procedure have been developed. Many of these learning strategies have been implemented into robotic systems (7–9). One particular framework is reinforcement learning (RL), in which an agent gains experience by interacting with its environment (10). The value of this experience relates to rewards (or penalties) connected to the states that the agent can occupy. The learning process then maximizes the cumulative reward for a chain of actions to obtain the so-called policy. This policy advises the agent which action to take. Recent computational studies, for example, reveal that RL can provide optimal strategies for the navigation of active particles through flows (11–13), the swarming of robots (14–16), the soaring of birds , or the development of collective motion (17).

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