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Surface faulting earthquakes are known to cluster in time from historical and palaeoseismic studies, but the mechanism(s) responsible for clustering, such as fault interaction, strain-storage, and evolving dynamic topography, are poorly quantified, and hence not well understood. We present a quantified replication of observed earthquake clustering in central Italy. Six active normal faults are studied using Cl cosmogenic dating, revealing out-of-phase periods of high or low surface slip-rate on neighboring structures that we interpret as earthquake clusters and anticlusters. Our calculations link stress transfer caused by slip averaged over clusters and anti-clusters on coupled fault/shear-zone structures to viscous flow laws. We show that (1) differential stress fluctuates during fault/shear-zone interactions, and (2) these fluctuations are of sufficient magnitude to produce changes in strain-rate on viscous shear zones that explain slip-rate changes on their overlying brittle faults. These results suggest that fault/shear-zone interactions are a plausible explanation for clustering, opening the path towards process-led seismic hazard assessments.
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http://dx.doi.org/10.1038/s41467-022-34821-5 | DOI Listing |
This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling.
View Article and Find Full Text PDFPLoS One
September 2025
School of Geological Engineering, Institute of Disaster Prevention, Langfang 065201, China.
Bedrock fault dislocations significantly influence the rupture instability of rock and soil slopes adjacent to fault zones. Understanding the dynamic processes, kinematic characteristics, and genesis mechanisms of landslides induced by strong seismic fault dislocations is crucial for advancing the theoretical framework of landslide studies. This paper presents a representative experiment simulating the emergence of seismic faults (internal rupture belts within the soil mass) at the shoulders and toes of slopes due to bedrock fault dislocations.
View Article and Find Full Text PDFSmall
September 2025
Institute of New Energy Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300072, China.
Copper (Cu) catalysts with abundant defects are pivotal for converting CO into valuable multi-carbon products. However, the practical application of Cu catalysts is challenged by the thermodynamic instability of the defects, often leading to surface reconstruction during catalytic processes. Here, it is found that particle size and COO-containing intermediates are key factors driving reconstruction, as the defect stability is size-dependent and can be amplified by leveraging the highly reactive intermediates as the initial reactant.
View Article and Find Full Text PDFNat Commun
September 2025
Freie Universität Berlin, Berlin, Germany.
Quantum low-density parity-check codes are a promising candidate for fault-tolerant quantum computing with considerably reduced overhead compared to the surface code. However, the lack of a practical decoding algorithm remains a barrier to their implementation. In this work, we introduce localized statistics decoding, a reliability-guided inversion decoder that is highly parallelizable and applicable to arbitrary quantum low-density parity-check codes.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Solar photovoltaic (PV) systems, especially in dusty and high-temperature regions, suffer performance degradation due to dust accumulation, surface heating, and delayed maintenance. This study proposes an AI-integrated autonomous robotic system combining real-time monitoring, predictive analytics, and intelligent cleaning for enhanced solar panel performance. We developed a hybrid system that integrates CNN-LSTM-based fault detection, Reinforcement Learning (DQN)-driven robotic cleaning, and Edge AI analytics for low-latency decision-making.
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