Lifeboat Foundation

Understanding how transport networks, such as river systems, form and evolve is crucial to optimizing their stability and resilience. It turns out that networks are not all alike. Tree-like structures are adequate for transport, while networks containing loops are more damage-resistant. What conditions favor the formation of loops?

Researchers from the Faculty of Physics at the University of Warsaw and the University of Arkansas sought to answer this question. The findings, published in Physical Review Letters, show that networks tend to form stable loop structures when flow fluctuations are appropriately tuned. This finding will allow us to understand the structure of dynamic transport networks better.

Transport networks, like blood vessels or river systems, are essential for many natural and human-made systems. Understanding how these networks form and grow is crucial for optimizing their stability and resilience.

Researchers at the Paul-Drude-Institute for Solid State Electronics (PDI) have observed a novel modulation regime characterized by the emergence of previously unseen “acceleration beats” in a modulated semiconductor-based laser.

As they detail in a paper published today in Nature Communications, the key—and somewhat counterintuitive—feature of this novel regime is the ability to coherently manipulate quantum systems using modulation periods longer than the coherence time, provided that the modulation amplitude is large enough.

Harmonic modulation of light sources, such as lasers, is the cornerstone of many modern and emergent telecommunications technologies. In this regard, two regimes of modulation are well-known: the adiabatic regime and the non-adiabatic regime.

Speech recognition, weather forecasts, smart home applications: Artificial intelligence and the Internet of Things are enhancing our everyday lives. Systems based on reservoir computing are a very promising new field.

The research group led by Prof Dr. Karin Everschor-Sitte at the University of Duisburg-Essen (UDE), is conducting research in this area. They are primarily investigating new possibilities for reservoir computing, for example using magnetic materials.

Now, together with specialists from the field of ferroelectric materials, the team has shown that these systems are also suitable for processing complex data faster and more efficiently. Their results have been published in Nature Reviews Physics.

Non-collinear antiferromagnetic materials, which have a net magnetic moment of nearly zero, yet exhibit significant anomalous transverse transport properties, are considered candidate materials for the next generation of spintronic devices.

The magnetic domain structure of these materials is crucial for information storage. However, magnetic domain imaging for non-collinear antiferromagnetic materials such as Mn₃Sn and Mn₃Ge has always been a significant challenge in this field of research.

Prof. Dazhi Hou’s team from the University of Science and Technology of China, in collaboration with Prof. Yanfeng Guo’s team from ShanghaiTech University, has successfully achieved magnetic domain imaging of Mn₃Sn and Mn₃Ge using the anomalous Ettingshausen effect and lock-in thermography (LIT) technique. They verified the superiority of this innovative method in simultaneously resolving the magnetic domain structure in both in-plane and out-of-plane directions.

Scientists have made a significant breakthrough in creating a new method for transmitting quantum information using particles of light called qudits. These qudits promise a future quantum internet that is both secure and powerful. The study is published in the journal eLight.

Traditionally, quantum information is encoded on qubits, which can exist in a state of 0, 1, or both at the same time (superposition). This quality makes them ideal for complex calculations but limits the amount of data they can carry in communication. Conversely, qudits can encode information in higher dimensions, transmitting more data in a single go.

The new technique harnesses two properties of light—spatial mode and polarization—to create four-dimensional qudits. These qudits are built on a special chip that allows for precise manipulation. This manipulation translates to faster data transfer rates and increased resistance to errors compared to conventional methods.

In an ongoing game of cosmic hide and seek, scientists have a new tool that may give them an edge. Physicists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) have developed a computer program incorporating machine learning that could help identify blobs of plasma in outer space known as plasmoids. In a novel twist, the program has been trained using simulated data.

The program will sift through reams of data gathered by spacecraft in the magnetosphere, the region of outer space strongly affected by Earth’s magnetic field, and flag telltale signs of the elusive blobs. Using this technique, scientists hope to learn more about the processes governing magnetic reconnection, a process that occurs in the magnetosphere and throughout the universe that can damage communications satellites and the electrical grid.

Scientists believe that machine learning could improve plasmoid-finding capability, aid the basic understanding of magnetic reconnection and allow researchers to better prepare for the aftermath of reconnection-caused disturbances.

Is nature really as strange as quantum theory says—or are there simpler explanations? Neutron measurements at TU Wien prove that it doesn’t work without the strange properties of quantum theory.

Can a particle be in two different places at the same time? In quantum physics, it can: Quantum theory allows objects to be in different states at the same time—or more precisely: in a superposition state, combining different observable states. But is this really the case? Perhaps the particle is actually in a very specific state, at a very specific location, but we just don’t know it?

The question of whether the behavior of quantum objects could perhaps be described by a simple, more classical theory has been discussed for decades. In 1985, a way of measuring this was proposed: the so-called “Leggett-Garg inequality.” Any theory that describes our world without the strange superposition states of quantum theory must obey this inequality.

Researchers have developed a new two-photon polymerization technique that uses two lasers to 3D print complex high-resolution structures. The advance could make this 3D printing process less expensive, helping it find wider use in a variety of applications.

Two-photon polymerization is an advanced additive manufacturing technique that traditionally uses femtosecond lasers to polymerize materials in a precise, 3D manner. Although this process works well for making high-resolution microstructures, it isn’t widely used in manufacturing because femtosecond lasers are expensive and increase the cost of printing parts.

“We combined a relatively low-cost laser emitting visible light with a femtosecond laser emitting infrared pulses to reduce the power requirement of the femtosecond laser,” said research team leader Xianfan Xu from Purdue University. “In this way, with a given femtosecond laser power, the printing throughput can be increased, leading to a lower cost for printing individual parts.”

New findings reveal that individuals with an average sense of smell may unknowingly be living with natural gas leaks. According to a peer-reviewed study in the scientific journal Environmental Research Letters, minor leaks can deteriorate indoor air quality by emitting various hazardous pollutants, such as benzene—a carcinogen detected in 97% of natural gas samples throughout North America.

“While these smaller leaks are not large enough to cause gas explosions, hard-to-smell leaks are common,” said lead author and PSE Healthy Energy Scientist Sebastian Rowland. “The fact that they are so small makes them hard to identify and fix, which can lead to a persistent indoor source of benzene and methane.”