Local Wave Research Articles

Characterizing the Spatial Distribution of Mixing and Transport in the Northern Middle Atmosphere During Winter

AbstractA three‐dimensional winter (DJF) climatology of Lagrangian diffusivity characterizing eddy mixing and transport in the northern middle atmosphere is presented. To emphasize aspects other than zonal averages, we use the theory of Lagrangian diffusivity (κyy) hitherto not applied in stratospheric contexts to our knowledge. Our formulation of Lagrangian diffusivity requires the calculation of parcel trajectories, which is made on isentropic surfaces. A Lagrangian descriptor is used to estimate the boundary of the stratospheric polar vortex (SPV). To characterize quasi‐geostrophic motions and their influence on the SPV we apply the wave activity flux (W) and Local Wave Activity . Our data set is the ERA5 reanalysis for the period 1979–2013. Results for κyy show important zonal asymmetries. In the lower and middle stratosphere, κyy is highest at midlatitudes, particularly around the prime meridian. This location is surrounded by manifolds associated with hyperbolic trajectories emanating from the outer SPV boundary. κyy is also high within the SPV, and near the locations where the SPV boundary is open. Zonal asymmetries are also clear in W at midlatitudes. The larger values of are at high latitudes and upstream of the opening of the vortex boundary. The role of quasi‐geostrophic waves on the south‐north shift of the midlatitude westerlies is highlighted. In particular, the waves contribute to open the SPV boundary at around 90W. The interannual variability of κyy is explored by contrasting winters with positive‐negative Northern Annular Mode index, and Sudden Stratospheric Warmings of displacement‐split type.

Discovery of dissipative microwave photonic soliton molecules in dual-bandpass optoelectronic oscillator

Optoelectronic oscillators (OEOs), which have attracted extensive studies in the past decades, are high quality-factor optoelectronic feedback loops for generating various ultra-pure microwave signals. In essence, OEOs are also dissipative nonlinear systems with multiple timescale characteristics and abundant nonlinearities, which open the possibilities for exploring localized dissipative solitary waves. In this paper, we demonstrate a new-class temporal dissipative soliton, i.e., dissipative microwave photonic soliton molecule (DMPSM), in a dual-bandpass OEO. Both the numerical simulation and experiment are conducted to reveal the physical mechanism of DMPSM generation and to evaluate the characteristics of the generated DMPSM sequences. Unlike optical soliton molecules in mode-locked lasers, the formation of DMPSMs arises from the combined action of multiple timescale coupling, nonlinear bistability, and time-delayed feedback in the OEO cavity, where the soliton interval and number in a DMPSM can be well-controlled through varying the multiple timescale variables in the OEO cavity, and the repetition frequency of the DMPSMs can be tuned through changing that of the initially injected perturbation signal. Meanwhile, the generated DMPSM sequence performs with high stability and excellent coherence, which shows enormous application potentials in pulse radar detection, dense microwave comb generation, and neuromorphology.

On possibility of anomalous microwave power absorption and gyrotron frequency sub-harmonics emission in X2-mode ECRH experiments at the TCV tokamak

In this paper, we analyze theoretically the low-threshold parametric decay instability (PDI) that can be excited at the tokamak à configuration variable (TCV tokamak, Lausanne, Switzerland) during X2 electron cyclotron resonance heating experiments producing a two-dimensionally localized upper hybrid (UH) wave and a daughter extraordinary wave running from the decay layer outward to the plasma edge. The primary instability is then saturated due to the cascade of secondary decays into two-dimensionally localized UH and ion Bernstein waves. The level of plasma microwave emission in the frequency range substantially below half the pump wave frequency and of anomalous power losses of the pump are predicted.

Soliton groups and extreme wave occurrence in simulated directional sea waves

The evolution of nonlinear wave groups that can be associated with long-lived soliton-type structures is analyzed, based on the data of numerical simulation of irregular deep-water gravity waves with spectra typical to the ocean and different directional spreading. A procedure of the windowed Inverse Scattering Transform, which reveals wave sequences related to envelope solitons of the nonlinear Schrödinger equation, is proposed and applied to the simulated two-dimensional surfaces. The soliton content of waves with different directional spreading is studied in order to estimate its dynamical role, including characteristic lifetimes. Statistical features of the solitonic part of the water surface are analyzed and compared with the wave field on average. It is shown that intense wave patterns that persist for tens of wave periods can emerge in stochastic fields of relatively long-crested waves. They correspond to regions of locally enhanced on average waves with reduced kurtosis. This eventually leads to realization of locally extreme wave conditions compared to the general background. Although intense soliton-like groups may be detected in short-crested irregular waves as well, they possess much shorter lateral sizes, quickly disperse, and do not influence the local statistical wave properties.

Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function

Objective. This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data. Approach. We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and the R wave information branch to analyze R wave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network’s learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network’s ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification. Main results. The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63 % , 93.62 % , 99.81 % , and 92.98 % , respectively, and for Fusion beat were 99.76 % , 85.56 % , 99.87 % , and 84.16 % , respectively. Under the inter-patient paradigm, these metrics were 96.56 % , 89.16 % , 96.84 % , and 51.99 % for Supraventricular ectopic beat, and 96.10 % , 77.06 % , 96.25 % , and 13.92 % for Fusion beat, respectively. Significance. This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.

Rational localized wave patterns in the form of Schur polynomials for the (2 + 1)-dimensional Bogoyavlenskii–Kadomtsev–Petviashvili-I equation in fluid dynamics

Rational localized wave patterns in the form of Schur polynomials for the (2 <b>+</b> 1)-dimensional Bogoyavlenskii–Kadomtsev–Petviashvili-I equation in fluid dynamics

Use of a gas-operated ventilator as a noninvasive bridging respiratory therapy in critically Ill COVID-19 patients in a middle-income country.

During the COVID-19 pandemic, there was a notable undersupply of respiratory support devices, especially in low- and middle-income countries. As a result, many hospitals turned to alternative respiratory therapies, including the use of gas-operated ventilators (GOV). The aim of this study was to describe the use of GOV as a noninvasive bridging respiratory therapy in critically ill COVID-19 patients and to compare clinical outcomes achieved with this device to conventional respiratory therapies. Retrospective cohort analysis of critically ill COVID-19 patients during the first local wave of the pandemic. The final analysis included 204 patients grouped according to the type of respiratory therapy received in the first 24h, as follows: conventional oxygen therapy (COT), n = 28 (14%); GOV, n = 72 (35%); noninvasive ventilation (NIV), n = 49 (24%); invasive mechanical ventilation (IMV), n = 55 (27%). In 72, GOV served as noninvasive bridging respiratory therapy in 42 (58%) of these patients. In the other 30 patients (42%), 20 (28%) presented clinical improvement and were discharged; 10 (14%) died. In the COT and GOV groups, 68% and 39%, respectively, progressed to intubation (P ≤ 0.001). Clinical outcomes in the GOV and NIV groups were similar (no statistically significant differences). GOV was successfully used as a noninvasive bridging respiratory therapy in more than half of patients. Clinical outcomes in the GOV group were comparable to those of the NIV group. These findings support the use of GOV as an emergency, noninvasive bridging respiratory therapy in medical crises when alternative approaches to the standard of care may be justifiable.

Ensemble hindcasting of winds and waves for the coastal and oceanic region of Southern Brazil

When hindcasting wave fields of storm events with wave models, the quality of the results strongly depends on several factors such as the computational grid resolution and the accuracy of the atmospheric forcing. In an effort to minimize the uncertainties involved in this process, three ocean wave and surface wind ensemble hindcast systems were established using the Simulating WAves Nearshore (SWAN) model and Weather Research and Forecasting Model (WRF) with atmospheric data from ERA5 Ensemble of Data Assimilation (EDA) as well as deterministic high-resolution systems. We established three ensemble systems to tackle this: SWN-ERA5EDA using ERA5-EDA global reanalyses winds, SWN-WRFERA5 employing WRF downscaling of ERA5-EDA, and SWN-WRFPPar incorporating WRF multi-physics runs for dynamical downscaling. This study focuses on extreme events in southern Brazil during an austral winter, highlighting the importance of increasing the resolution of ocean wave and surface wind data to provide more accurate and reliable forecasts for coastal and marine activities. Our analyses revealed that atmospheric downscaling performed with WRF not only increased the ensemble spread by significant amounts but also enhanced the sharpness of the wave ensemble hindcast compared with those based solely on the ERA5 EDA. Specifically, for the Rio Grande buoy location, the significant wave height (Hs) from the SWN-WRFERA5 system showed an increase of 0.5 over SWN-ERA5, and Hs from the SWN-WRFPPar system increased by 0.6. Additionally, the wave peak period (Tp) for both SWN-WRFERA5 and SWN-WRFPPar systems experienced an increase of 1.2 compared to SWN-ERA5. Additionally, the ensemble produced with the WRF multi-physics approach captured peaks in the significant wave height registered by the buoy that were not reproduced by other ensemble systems, demonstrating an improvement in predictive accuracy, despite presenting a smaller correlation between spread and strong localized wave variations. Besides quantifying the hindcast error, the methodology presented in this work also offers a way to generate alternative and improved representations of past extreme events. This approach significantly contributes to our ability to sample recent climatic conditions and expand the dataset for statistical analyses, which is especially valuable for ocean and coastal engineering projects. This study underscores the critical role of enhancing computational and methodological approaches in wave modeling for better understanding and mitigating the impacts of extreme weather events on coastal and oceanic regions.

Intravascular lithotripsy in peripheral lesions with severe calcification and its use in TAVI procedure - a meta-analysis.

Background: Heavily calcified peripheral artery lesions increase the risk of vascular complications, constituting a severe challenge for the operator during catheter-based cardiovascular interventions. Intravascular Lithotripsy (IVL) technology disrupts subendothelial calcification by using localized pulsative sonic pressure waves and represents a promising technique for plaque modification in patients with severe calcification in peripheral arteries. Purpose: Our aim was to systematically review and summarize available data regarding the safety and efficacy of IVL in preparing severely calcified peripheral arteries and its use in Transcatheter Aortic Valve Implantation (TAVI). Patients and methods: This study was conducted according to the PRISMA guidelines. We systematically searched PubMed, SCOPUS, and Cochrane databases from their inception to February 23, 2023, for studies assessing the characteristics and outcomes of patients undergoing IVL in the peripheral vasculature. The diameter of the vessel lumen before and after IVL was estimated. The occurrence of peri-procedural complications was assessed using a random-effects model. Results: 20 studies with a total of 1,223 patients with heavily calcified peripheral lesions were analysed. The mean age of the cohort was 70.6 ± 17.4 years. Successful IVL delivery achieved in 100% (95% CI: 100%-100%, I2 = 0%), with an increase in the luminal diameter (SMD: 4.66, 95% CI: 3.41-5.92, I2 = 90.8%) and reduction in diameter stenosis (SMD: -4.15, 95% CI: -4.75 to -3.55, I2 = 92.8%), and a concomitant low rate of complications. The procedure was free from dissection in 97% (95% CI: 91%-100%, I2 = 81.4%) while dissections of any type (A, B, C, or D) were observed in 6% (95% CI: 2%-10%, I2 = 85.3%) of the patients. Several rare cases of abrupt closure, no-reflow phenomenon, perforation, thrombus formation, and distal embolization were recorded. Finally, the subgroup analysis of patients who underwent a TAVI with IVL assistance presented successful implantation in 100% (95% CI: 100%-100%, I2 = 0%) of the cases, with only 4% (95% CI: 0%-12%, I2 = 68.96%) presenting dissections of any sort. Conclusions: IVL seems to be an effective and safe technique for modifying severely calcified lesions in peripheral arteries and it is a promising modality in TAVI settings. Future prospective studies are needed to validate our results.

Nonlinear wave localization in an acoustic metamaterial with attached masses through one element of main chain

The nonlinear strain solitary waves in a metamaterial mass-in-mass lattice model with a di-atomic symmetry is studied when attached masses tare located through one element of main chain are studied in the continuum limit. The reasonable variables are introduced to account for strains. An asymptotic procedure is developed to reduce coupled continuum equations to a single equation for longitudinal strains. A correspondence with the classic model with a di-atomic symmetry is established. An improvement of the model is suggested to account for a switch-on/off of the parts of the metamaterial model arising due to the studied kind of symmetry. It allows us to change the behavior of the localized nonlinear strain waves. This improvement is important for description of a durability of the metamaterial. Also it helps to understand new heat processes, like thermal diode, in complex symmetric lattices.

Applying artificial neural network to zero-crossing wave parameters for the wave spectrum

This paper presents a neural network model to estimate wave spectra from wave parameters by the zero-crossing analysis, maximum, significant, and average wave heights and their corresponding wave periods. The present model was trained with the wave data in the JES (Japan/East Sea), and then applied to wave measurements in the Yellow Sea and the South Sea for its feasibility at other seas. According to the present study, the predicted wave spectra well agree with the measurements, even the wave data not in model training. This comparison shows that the accuracy of the present model is comparable to that of JONSWAP wave spectrum. Furthermore, the present model was employed to the wave hindcasting to see its applicability, while JONSWAP wave spectrum also being applied. The significant wave heights from both numerical simulations well agree with the measured ones. However, the average RMSE of significant wave period with the present neural network was 7.08 % smaller than the other, and its Pearson's correlation coefficient is 6.33 % larger on average. From this, it can be seen that the present model was well applied to the wave hindcasting without considering the local wave measurements.

Soliton, breather and rogue wave solutions of the higher-order modified Gerdjikov–Ivanov equation

In this paper, we investigate the dynamics of localized waves for the higher-order modified Gerdjikov–Ivanov equation. Based on Lax pair, the Nth-fold Darboux transformation is constructed. The soliton, breather and rogue wave solutions are obtained and presented graphically. Moreover, the dynamic properties of the above localized waves are analyzed. Specially, the interactions between solitons and bound states are achieved. In addition, the rational W-shaped soliton is derived by the degeneration of breather solutions.

Abnormal apparent supershear rupture with discontinuous jumping propagation during the 2023 Türkiye M7.8 earthquake

Earthquake ruptures along a single fault or along a connected system of faults are generally assumed to progress continuously. However, our analysis of the 2023 M7.8 Türkiye earthquake, using finite-fault joint source inversion, uncovered the occurrence of discontinuous rupture jumps. The main fault area adjacent to the splay fault where the earthquake started, and the deeper portion of the northeastern main fault segment exhibited triggered slip before the main rupture front arrived. Through seismic centroid analysis and finite-fault inversion, we estimated apparent rupture speeds within these slip patches reach approximately 6.0 km s-1, exceeding local S-wave velocity. The dynamic triggering mechanism induced the jumping rupture in these areas, resulting in an apparent rupture velocity surpassing the local shear wave velocity. These findings demonstrate the importance of dynamic triggering in adjacent fault systems during large earthquakes, influencing the extent and complexity of rupture propagation.

Darboux transformation-based LPNN generating novel localized wave solutions

Darboux transformation method is one of the most essential and important methods for solving localized wave solutions of integrable systems. In this work, we introduce the core idea of Darboux transformation of integrable systems into the Lax pairs informed neural networks (LPNNs), which we proposed earlier. By fully utilizing the data-driven solutions, spectral parameter and spectral function obtained from LPNNs, we present the novel Darboux transformation-based LPNN (DT-LPNN). The notable feature of DT-LPNN lies in its ability to solve data-driven localized wave solutions and spectral problems with high precision, and it also can employ Darboux transformation formulas of integrable systems and non-trivial seed solutions to discover novel localized wave solutions that were previously unobserved and unreported. The numerical results indicate that, by utilizing the single-soliton solutions as the non-trivial seed solutions, we obtain novel localized wave solutions for the Kraenkel–Manna–Merle (KMM) system by employing DT-LPNN, in which solution u changes from original bright single-soliton on zero background wave to new dark single-soliton dynamic behavior on a variable non-zero background wave. Moreover, by treating the two-soliton solutions as the non-trivial seed solutions, DT-LPNN generates novel localized wave solutions for the KMM system that exhibit completely different dynamic behaviors from prior two-soliton solutions. DT-LPNN combines the Darboux transformation theory of integrable systems with deep neural networks, offering a new approach for generating novel localized wave solutions using non-trivial seed solutions.

Tropical cyclone signatures in SAR ocean radial Doppler Velocity

Ocean surface radial Doppler Velocity (DV) signatures of Tropical Cyclones (TC) at moderate incidence angles are analyzed using a semi-empirical DV model. This model, originally named KaDOP, is based on the physics of Ka-band Doppler radar backscattering from the sea surface and represents the DV as a sum of components due to surface currents, long surface waves, Bragg scattering, and wave breaking. The latter two components were modified in this study to fit the model to C-band observations at strong winds. This modified model, referred to as a TC-DOP, requires several environmental input parameters, including TC winds and translation velocity, waves, and currents. For known winds and TC translation velocity, the surface currents are simulated using a model of the upper ocean response to TC passage. Waves are modeled using a self-similar description for the energy-containing TC-generated local wind waves and emanating swell. Given known winds, waves, and currents, the DV is calculated by the TC-DOP independently at each grid point. Calculations are performed for scanner-like observations that detect only the radial (cross-track) DV component. It is shown that at storm-strength winds and radar incidence angle, θ=45°, the main contribution to the total DV is provided by the surface current and wave breaking DV components (∼1 m s−1). The latter component has strong azimuth asymmetry and maximizes in the upwind sector. Next in magnitude is the contribution from wind waves (∼0.5 m s−1), followed by Bragg waves (∼0.2 m s−1), while the contribution from swell is less important. Comparisons of the TC-DOP calculations with the C-band Sentinel-1A SAR radial DV data show qualitatively good agreement. All observed radial velocity images in TC storm areas have a dipole-like feature similar to that predicted by the simulations, with a zero DV area roughly aligned with the cross-wind direction. The TC-DOP model prediction is found generally consistent with calculations utilizing an empirical geophysical model function (known as the CDOP). The residual differences are attributed to specific features of radar scattering mechanisms in different radar wavelength bands (Ka- vs C-band), as well as possible shortcomings of the Ka-band Modulation Transfer Function (MTF) originally derived from platform-based measurements.

PINNslope: Seismic data interpolation and local slope estimation with physics informed neural networks

Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high-quality velocity models and seismic images. Building on the idea of describing seismic wavefields as a superposition of local plane waves, we propose to interpolate seismic data by using a physics informed neural network (PINN). In the proposed framework, two feed-forward neural networks are jointly trained using the local plane wave differential equation as well as the available data as two terms in the objective function: a primary network assisted by positional encoding is tasked with reconstructing the seismic data, whereas an auxiliary, smaller network estimates the associated local slopes. Results on synthetic and field data validate the effectiveness of the proposed method in handling aliased (coarsely sampled) data and data with large gaps. Our method compares favorably against a classic least-squares inversion approach regularized by the local plane-wave equation as well as a PINN-based approach with a single network and precomputed local slopes. We find that introducing a second network to estimate the local slopes, whereas at the same time interpolating the aliased data enhances the overall reconstruction capabilities and convergence behavior of the primary network. Moreover, an additional positional encoding layer embedded as the first layer of the wavefield network confers to the network the ability to converge faster, improving the accuracy of the data term.

Soliton waves with optical solutions to the three-component coupled nonlinear Schrödinger equation

This study uses the modified Sardar sub-equation method to find novel soliton solutions to the nonlinear three-component coupled nonlinear Schrödinger equation (NLSE), which is used for pulse propagation in nonlinear optical fibers. Multi-component NLSE equations are widely used because they can represent a wide range of complex observable systems and more dynamic patterns of localized wave solutions. The optical solutions proposed in this study are novel and can be described using hyperbolic, trigonometric, and exponential functions. These solutions are categorized as bright, dark, singular, combo bright-singular, and periodic solutions. Some solutions’ dynamic behaviors are demonstrated by selecting appropriate physical parameter values. The results and computational analysis indicate that the techniques provided are simple, effective, and adaptable. They can be applied to a variety of nonlinear evolution equations, whether stable or unstable, and can be used in fields such as mathematics, mathematical physics, and applied sciences.

Data-driven met-ocean model for offshore wind energy applications

In recent years, the global transition towards green energy, driven by environmental concerns and increasing electricity demands, has remarkably reshaped the energy landscape. The transformative potential of marine wind energy is particularly critical in securing a sustainable energy future. To achieve this objective, it is essential to have an accurate understanding of wind dynamics and their interactions with ocean waves for the proper design and operation of offshore wind turbines (OWTs). The accuracy of met-ocean models depends critically on their ability to correctly capture sea-surface drag over the multiscale ocean surface—a quantity typically not directly resolved in numerical models and challenging to acquire using either field or laboratory measurements. Although skin friction drag contributes considerably to the total wind stress, especially at moderate wind speeds, it is notoriously challenging to predict using physics-based approaches. The current work introduces a novel approach based on a convolutional neural network (CNN) model to predict the spatial distributions of skin friction drag over wind-generated surface waves using wave profiles, local wave slopes, local wave phases, and the scaled wind speed. The CNN model is trained using a set of high-resolution laboratory measurements of air-side velocity fields and their respective surface viscous stresses obtained over a range of wind-wave conditions. The results demonstrate the capability of our model to accurately estimate both the instantaneous and area-aggregate viscous stresses for unseen wind-wave regimes. The proposed CNN-based wall-layer model offers a viable pathway for estimating the local and averaged skin friction drag in met-ocean simulations.

A damage localization technique using wave front shapes in composite laminates without knowing the velocity profile

Composite laminates are widely used in various fields, but their structures are prone to cracks and damage. Due to the difference in angles of the instantaneous direction of the wave front propagation and the direction of the energy flow in an anisotropic material, the use of Lamb waves for damage localization in composite laminates is a challenging task. Establishing the wave front shape equation can overcome the difficulty of damage localization caused by anisotropy, but this usually requires a priori knowledge of the acoustic velocity distribution of the laminates, which is not convenient for efficient damage localization. In this paper, a damage localization method based on wave front shapes for composite laminates without any knowledge of the velocity profile is presented. Numerical simulation and experimental results show that the proposed method works. This method shows good damage localization accuracy and has broad application prospects in non-destructive testing for plate structures with strong anisotropy.

Anomalous diffusion, prethermalization, and particle binding in an interacting flat band system

We study the broadening of initially localized wave packets in a quasi one-dimensional diamond ladder with interacting, spinless fermions. The lattice possesses a flat band causing localization. We place special focus on the transition away from the flat band many-body localized case by adding very weak dispersion. By doing so, we allow propagation of the wave packet on significantly different timescales which causes anomalous diffusion. Due to the temporal separation of dynamic processes, an interaction-induced, prethermal equilibrium becomes apparent. A physical picture of light and heavy modes for this prethermal behavior can be obtained within Born–Oppenheimer approximation via basis transformation of the original Hamiltonian. This reveals a detachment between light, symmetric and heavy, anti-symmetric particle species. We show that the prethermal state is characterized by heavy particles binding together mediated by the light particles.