A deep neural network framework for real-time on-site estimation of acceleration response spectra of seismic ground motions

Various earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground-shaking intensities, and/or potential consequences) about ongoing seismic events for real-time/near real-time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified empirical models to estimate the source parameters and intensity measures of interest. Given the recent boost in computational resources, data-driven methods/models are now widely accepted as effective alternatives for EEW. This study introduces a highly accurate deep-learning-based computational framework named ROSERS (i.e., Real-time On-Site Estimation of Response Spectra) to estimate the acceleration response spectrum (Sa(T)) of the expected on-site ground-motion waveforms using early non-damage-causing early p-waves and site characteristics. The framework is trained using a carefully selected extensive database of recorded ground motions. Due to the well-known correlation of Sa(T) with structures’ seismic response and resulting damage/losses, rapid and accurate knowledge of expected on-site Sa(T) values is highly beneficial to various end-users to make well-informed real-time and near-real-time decisions. The framework is thoroughly assessed and investigated through multiple statistical tests under three historical earthquake events. These analyses demonstrate that the overall framework leads to excellent prediction power and, on average, has an accuracy above 85% for hazard-consistent early-warning trigger classification

Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme

Fragility Analysis of Deteriorating Bridge Components Subjected to Simulated Ground-Motion Sequences

This study assesses the impact of corrosion deterioration on the seismic performance of bridge components during a sequence of ground motions. Specifically, a simplified methodology is proposed to derive state-dependent fragility relationships for bridge components (i.e., relationships that explicitly depend on the damage state achieved by the component during a first shock) subjected to chloride-induced corrosion deterioration and simulated ground-motion sequences. Specifically, vector-valued probabilistic seismic demand models are derived for various corrosion levels. Those models relate the dissipated hysteretic energy in the sequence to a deformation-based engineering demand parameter induced by the first shock and a ground-motion intensity measure of the second shock, calibrated via sequential cloud-based time-history analyses. For each corrosion level, fragility relationships are first derived for a single ground motion; state-dependent fragility relationships are then derived by considering the additional damage induced by a second ground motion within the simulated sequence (structure-specific damage states are considered). Finally, continuous functional models are developed from the analysis results to assemble fragility relationships at a given corrosion level. The results demonstrate the significant impact of environmental deterioration in seismic-prone regions, emphasising the necessity of accounting for deteriorating effects in current practice

A recurrent-neural-network-based generalized ground-motion model for the Chilean subduction seismic environment

This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and intraslab subduction earthquakes recorded in Chile. A total of ∼7000 ground-motion records from ∼1700 events are used to train the proposed GGMM. Unlike common ground-motion models (GMMs), which generally consider individual ground-motion intensity measures such as peak ground acceleration and spectral accelerations at given structural periods, the proposed GGMM is based on a data-driven framework that coherently uses recurrent neural networks (RNNs) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (denoted as IM). The IM vector includes geometric mean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration (denoted as Iageom, PGVgeom, PGAgeom, and D5-95geom, respectively), and RotD50 spectral accelerations at 31 periods between 0.05 and 5 s for a 5 % damped oscillator (denoted as Sa(T)). The inputs to the GGMM include six causal seismic source and site parameters, including fault slab mechanism, moment magnitude, closest rupture distance, Joyne-Boore distance, soil shear-wave velocity, and hypocentral depth. The statistical evaluation of the proposed GGMM shows high prediction power with R2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, the GGMM is carefully compared against two state-of-the-art Chilean GMMs, showing that the proposed GGMM leads to better goodness of fit for all periods of Sa(T) compared to the two considered GMMs (on average 0.2 higher R2). Finally, the GGMM is implemented to select hazard-consistent ground motions for nonlinear time history analysis of a sophisticated finite-element model of a 20-story steel special moment-resisting frame. Results of this analysis are statistically compared against those for hazard-consistent ground motions selected based on the conditional mean spectrum (CMS) approach. In general, it is observed that the drift demands computed using the two approaches cannot be considered statistically similar and the GGMM leads to higher demands

A Deep Learning based Generalized Ground Motion Model for the Chilean Subduction Seismic Environment

This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and inslab subduction earthquakes recorded in Chile. A total of ~7000 ground-motion records from ~1700 events are used to train the GGMM. Unlike common ground-motion models (GMM), which generally consider individual ground-motion intensity measures such as spectral acceleration at a given period, the proposed GGMM is a data-driven framework that coherently uses recurrent neural networks (RNN) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (IM). The IM vector includes geomean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration, and RotD50 spectral accelerations at 32 periods between 0.05 to 5 seconds (denoted as Sa(T)). The inputs to the GMM include six causal seismic source and site parameters. The statistical evaluation of the proposed GGMM shows that the proposed framework results in high prediction power with coefficient of determination R2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, it is observed that the proposed GGMM leads to better goodness of fit for all periods of Sa(T) compared to two state-of-the-art Chilean GMMs (on average 0.2 higher R2)

Prevalence of chronic pain in the UK : a systematic review and meta-analysis of population studies

Acknowledgements The authors are grateful for the input of Professor Blair Smith (University of Dundee): his counsel early in the project, and his advice and comments regarding the search strategy; and Professor Danielle van der Windt (Keele University) for helpful advice and comments. Funding The British Pain Society provided financial assistance to AF with the costs of this project. PC was partly supported by an Arthritis Research UK Primary Care Centre grant (reference: 18139).Peer reviewedPublisher PD

Helminth infection in coldwater fishes of Suru river Ladakh, Jammu and Kashmir, India

A helminthological survey of coldwater fishes of Ladakh was carried out from November 2007 to April 2009 (18 months). A total of 93 fishes belonging to two species viz., Schizothorax plagiostomus and Diptychus maculatus were collected and examined from different collection sites of Suru river, Kargil. A total of 2 helminth species viz., Neoechinorhynchus yalei Datta, 1936 and Rhabdochona himalayai Fotedar & Dhar, 1977 belonging to two helminth groups, i-e. Acanthocephala and Nematode were reported. It was found that out of 93 hosts examined, 31 were found infected with 43 parasites recovered in total, with an overall prevalence, mean intensity and abundance of 33.33%, 1.38 and 0.46 respectively. Distribution of helminth infection and its relation with sex and size of host was analysed. The helminth infection showed no significant relationship with sex of hosts however it showed mostly significant relation to size of host

Frozen shuffle update for an asymmetric exclusion process on a ring

We introduce a new rule of motion for a totally asymmetric exclusion process (TASEP) representing pedestrian traffic on a lattice. Its characteristic feature is that the positions of the pedestrians, modeled as hard-core particles, are updated in a fixed predefined order, determined by a phase attached to each of them. We investigate this model analytically and by Monte Carlo simulation on a one-dimensional lattice with periodic boundary conditions. At a critical value of the particle density a transition occurs from a phase with `free flow' to one with `jammed flow'. We are able to analytically predict the current-density diagram for the infinite system and to find the scaling function that describes the finite size rounding at the transition point.Comment: 16 page

Stability analysis of surface ion traps

Motivated by recent developments in ion trap design and fabrication, we investigate the stability of ion motion in asymmetrical, planar versions of the classic Paul trap. The equations of motion of an ion in such a trap are generally coupled due to a nonzero relative angle $\theta$ between the principal axes of RF and DC fields, invalidating the assumptions behind the standard stability analysis for symmetric Paul traps. We obtain stability diagrams for the coupled system for various values of $\theta$, generalizing the standard $q$-$a$ stability diagrams. We use multi-scale perturbation theory to obtain approximate formulas for the boundaries of the primary stability region and obtain some of the stability boundaries independently by using the method of infinite determinants. We cross-check the consistency of the results of these methods. Our results show that while the primary stability region is quite robust to changes in $\theta$, a secondary stability region is highly variable, joining the primary stability region at the special case of $\theta=45^{\circ}$, which results in a significantly enlarged stability region for this particular angle. We conclude that while the stability diagrams for classical, symmetric Paul traps are not entirely accurate for asymmetric surface traps (or for other types of traps with a relative angle between the RF and DC axes), they are safe in the sense that operating conditions deemed stable according to standard stability plots are in fact stable for asymmetric traps, as well. By ignoring the coupling in the equations, one only underestimates the size of the primary stability region