Influence of disorder on generation and probability of extreme events in Salerno lattices.

Phys Rev E

Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11000 Belgrade, Serbia.

Published: March 2017

Extreme events (EEs) in nonlinear and/or disordered one-dimensional photonic lattice systems described by the Salerno model with on-site disorder are studied. The goal is to explain particular properties of these phenomena, essentially related to localization of light in the presence of nonlinear and/or nonlocal couplings in the considered systems. Combining statistical and nonlinear dynamical methods and measures developed in the framework of the theory of localization phenomena in disordered and nonlinear systems, particularities of EEs are qualitatively clarified. Findings presented here indicate that the best environment for EEs' creation are disordered near-integrable Salerno lattices. In addition, it is been shown that the leading role in the generation and dynamical properties of EEs in the considered model is played by modulation instability, i.e., by nonlinearities in the system, although EEs can be induced in linear lattices with on-site disorder too.

Download full-text PDF

Source
http://dx.doi.org/10.1103/PhysRevE.95.032212DOI Listing

Publication Analysis

Top Keywords

extreme events
8
salerno lattices
8
nonlinear and/or
8
on-site disorder
8
influence disorder
4
disorder generation
4
generation probability
4
probability extreme
4
events salerno
4
lattices extreme
4

Similar Publications

Extending from Adaptation to Resilience Pathways: Perspectives from the Conceptual Framework to Key Insights.

Environ Manage

January 2025

TECNALIA Research & Innovation, Basque Research and Technology Alliance (BRTA), Energy, climate, and urban transition, Parque Tecnológico de Bizkaia, Derio, Spain.

The extent and timescale of climate change impacts remain uncertain, including global temperature increase, sea level rise, and more frequent and intense extreme events. Uncertainties are compounded by cascading effects. Nevertheless, decision-makers must take action.

View Article and Find Full Text PDF

Bacterial and fungal diversity and species interactions inversely affect ecosystem functions under drought in a semi-arid grassland.

Microbiol Res

January 2025

Key Laboratory of Vegetation Ecology, Ministry of Education, Institute of Grassland Science, Northeast Normal University, Changchun, China; State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, China. Electronic address:

Extreme climatic events, such as drought, can significantly alter belowground microbial diversity and species interactions, leading to unknown consequences for ecosystem functioning. Here, we simulated a drought gradient by removing 30 %, 50 %, and 70 % of precipitation in a semi-arid grassland over five years. We assessed the effects of drought on bacterial and fungal diversity, as well as on their species interactions.

View Article and Find Full Text PDF

Climate change is making extreme heat events more frequent and intense. This negatively impacts many aspects of society, including organised sport. As the world's most watched sporting event, the FIFA World Cup commands particular attention around the threat of extreme heat.

View Article and Find Full Text PDF

Rigid reinforced concrete (RC) frames are generally adopted as stiff elements to make the building structures resistant to seismic forces. However, a method has yet to be fully sought to provide earthquake resistance through optimizing beam and column performance in a rigid frame. Due to its high corrosion resistance, the integration of CFRP offers an opportunity to reduce frequent repairs and increase durability.

View Article and Find Full Text PDF

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!