Browse Articles

Under compression, fluid-filled cylindrical shells, such as soda cans, exhibit localized axisymmetric corrugations which appear sequentially but are evenly spaced once the surface is fully decorated. Here, the authors demonstrate how the pattern formation process underpinning this buckling phenomenon depends on material nonlinearities, offering insights that could inform the design of resilient cylindrical structures.

Sensitivity to small changes is essential for organisms to make timely and reliable decisions, but operating near criticality also amplifies fluctuations and hinders information accumulation. Here, the authors show that when information is integrated over a finite time, as in biological readouts, the optimal sensitivity for a given integration time is achieved away from criticality, approaching criticality only as longer integration becomes available.

Network dismantling, crucial for optimizing systems like immunization and rumor control, faces challenges in integrating higher-order structures to identify key nodes. Here, the authors introduce a Higher-order Graph Neural Network framework, demonstrating superior efficiency and resilience in dismantling networks by accurately targeting minimal nodes, impacting fields from ecology to cybersecurity.

The superconducting diode effect describes non-reciprocal transport of the superconducting current and there is focus on how to engineer and apply this property practically. Here, the authors utilise a heterostructure to implement a dual-mode superconducting diode effect, where dissipationless current flows along a single direction, that can be activated by both out-of-plane and in-plane magnetic field. The dual modes share similar diode efficiency but requires different magnitude of magnetic field to be operated.

The concept of distance in graphs and hypergraphs faces challenges when extended to weighted hypergraphs due to potential inconsistencies. The authors propose a well-defined distance measure for weighted hypergraphs and demonstrate its applicability on real-world datasets, showing that the use of the measure may help to avoid the information loss typically arising when standard approaches are used.

Collective failure poses a significant challenge to sustaining cooperation in various systems. Here, the authors demonstrate that adaptively raising collective targets after success or increasing rewards after failure effectively maintains cooperation across different risk levels, offering a dynamic governance strategy with broad implications for enhancing collaborative efforts.

Criticality and percolation in dynamical systems are widely studied, yet whether they can emerge from purely deterministic interactions and control parameters remains unclear. Here, the authors reveal deterministic critical points associated with percolation and self-organized criticality in the logistic Game of Life, advancing the understanding of emergent scale invariance in deterministic systems.

Shortcuts to adiabaticity are essential for rapid state evolution, and their true power lies in restoring adiabaticity against nonadiabatic transitions. Here, the authors employ an enhanced framework that restores adiabaticity in coupled elastic waveguides by simultaneously optimizing the parameter-space path and velocity, enabling efficient wave control in compact devices.

The electrical generation of orbital angular momentum in materials has attracted significant attention due to its fundamental importance and technological potential. Here, the authors demonstrate current-induced orbital accumulation in pristine and naturally oxidized Cu films using magneto-optical Kerr effect measurements.

Long Gamma Ray Bursts (GRBs) are linked to the core collapse of massive stars, and in many cases Type Ic supernovae. Here, the authors identify that the time-evolving emission line in GRB 221009A is consistent with Doppler-boosted 56Ni decay, providing insights into supernova nucleosynthesis and relativistic jet dynamics.

Relaxor ferroelectrics (RFEs) are widely used for their excellent electrical properties rooted in polar nano regions (PNRs), yet how PNRs’ collective dynamic behavior impacts material performance is poorly understood. Taking KBT RFEs as a model, the authors identify unique PNR mesostructures, reveal their Turing instability origin, and clarify jamming effects as key to boosting their electrical properties.

The authors review advancements in underground dark matter experiments, highlighting the need for enhanced detector sensitivity to unravel dark matter’s fundamental properties, with implications for astrophysics and cosmology.

Synthetic self-propelled particles often emulate the dynamics of microorganisms but are typically limited to a single mode of active Brownian motion. Here, the authors introduce a method to encode diverse motion types into active Brownian particles, revealing how individual propulsion modes shape the emergent organization of active matter systems.

Microfluidics experiments provide insights into transport and chemical processes in porous media, yet measuring evolving concentration profiles remains challenging. Here, the authors introduce a physics-based machine learning toolbox that integrates the non-intrusive reduced basis method, U-Net, and Convolutional Autoencoder to efficiently predict concentration profiles, enabling real-time analysis and tuning of experiments on the fly.

Accurate atomic data are crucial for plasma diagnostics and various scientific applications, yet current methods for determining fine structure energy levels are labor-intensive and inefficient. The authors introduce a graph reinforcement learning approach to automate this process, achieving significant accuracy and efficiency improvements, potentially transforming atomic spectroscopy and related fields.

Accurately sensing atmospheric turbulence is vital for optical communications, yet standard methods often obscure the spatial nature of these distortions. Via numerical simulations, the authors show that two-dimensional orbital angular momentum spectroscopy resolves turbulence across radial dimensions, enhancing the accuracy of environmental sensing.

Catalytic majorization describes when one state can be transformed into another with the help of an auxiliary “catalyst”, but deciding this normally requires checking infinitely many inequalities. The authors derive a finite set of sufficient conditions that guarantee such catalytic transformations and extend these results to thermal processes.

Non-equilibrium Rydberg gases exhibit unique many-body phases arising from the interplay of coherent interactions and dissipation, yet controlling their temporal order remains challenging. This work demonstrates injection locking of a Rydberg dissipative time crystal using a radio-frequency electric field, achieving full synchronization and revealing dynamical behavior relevant to precision sensing and quantum metrology.

Chiral active Brownian particles convert stored or environmental energy into both self-propulsion and autonomous rotation, driving systems far from equilibrium. Here, the authors combine experiments and theory to reveal a nonmonotonic diffusion enhancement in chiral active Brownian particles confined within an annular channel, driven by obstacle interactions, offering insights into nonequilibrium transport in biologically relevant settings.