Blockchain Papers

Szabolcs Vitus, Ferenc Járai-Szabó

Synchronization and swarming are canonical manifestations of self-organization, observable across scales from cellular processes to animal flocks. This study investigates the collective dynamics of a novel agent-based model where individuals exhibit both spatial mobility and internal, two-mode stochastic oscillatory states. By introducing a local, distance-dependent coupling between the agents' spatial configuration and their internal state transitions, we establish a mutual feedback loop that drives complex pattern formation. Through large-scale numerical simulations, we identify seven distinct morphological configurations, ranging from stationary \textit{Filled-disk} states to highly disordered \textit{Intense-motion} regimes. By performing a rigorous quantitative analysis of the rotational energy and radial dispersion, we transcend simple morphological classification and demonstrate that the system organizes into discrete, quantized topological attractors. We derive a macroscopic scaling law, $Ω\propto r^{-1/2}$, which proves that the emerging rotating states are not rigid-body rotations, but rather composite differential vortex structures characterized by spontaneous chiral symmetry breaking. Our results suggest that these stable, quantized dynamical states are fundamental features of systems governed by bidirectional spatial-phase feedback, offering a robust framework for designing autonomous, decentralized robotic swarms.

Ayush M. Patil, Atharv S. Pakmode, Rishi Jain, Rupali D. Rode · 5 authors

Swarm robotics is a broad area of study that looks at the collective behavior of many autonomous robots to complete challenging tasks. Swarm robotics attempts to take advantage of the power of decentralization and self-organization to achieve adaptability, scalability, and robustness in robotic systems by taking inspiration from the collective behavior of natural swarms, such as ants, bees, and schools of fish.

Mingqi Chen, Guangjie Song, Bin Ren, Lin Cai · 6 authors

No abstract is available for this record.

In Ho Kim, Subi Choi, Jieun Lee, Jiyoung Jung · 11 authors

Artificial muscles are indispensable components for next-generation robotics capable of mimicking sophisticated movements of living systems. However, an optimal combination of actuation parameters, including strain, stress, energy density and high mechanical strength, is required for their practical applications. Here we report mammalian-skeletal-muscle-inspired single fibres and bundles with large and strong contractive actuation. The use of exfoliated graphene fillers within a uniaxial liquid crystalline matrix enables photothermal actuation with large work capacity and rapid response. Moreover, the reversible percolation of graphene fillers induced by the thermodynamic conformational transition of mesoscale structures can be in situ monitored by electrical switching. Such a dynamic percolation behaviour effectively strengthens the mechanical properties of the actuator fibres, particularly in the contracted actuation state, enabling mammalian-muscle-like reliable reversible actuation. Taking advantage of a mechanically compliant fibre structure, smart actuators are readily integrated into strong bundles as well as high-power soft robotics with light-driven remote control.