Nonlinear Processes Research Articles

Harmonic‐Assisted Super‐Resolution Rotational Measurement

AbstractEnhancing rotational measurement resolution and broadening the detectable spectral range are two critical and unresolved matters within the realm of motion perception. The rotational Doppler effect (RDE) is combined with the harmonic generation process to create a rotational measurement scheme that offers flexible detection wavelength conversion, exponential improvement of measurement resolution, and real‐time display of detection results. In the experiments, a cascaded second harmonic generation process is employed to attain a fourfold enhancement in rotational resolution and demonstrate how low‐cost silicon‐based detectors can be used for real‐time detection of infrared objects. This scheme employs a Gaussian beam within the nonlinear process to achieve high conversion efficiency, thereby enabling potential for subsequent cascade amplification. Additionally, it is fully compatible with existing RDE schemes, allowing for co‐amplification of rotational resolution at both the front‐end and back‐end. This research could offer a more precise and cost‐effective method for remote sensing detection.

A failure-dependence related stochastic crack growth modeling approach of competing cracking mode

Competing crack-induced fatigue fracture has been a typical failure mode especially in the very high cycle fatigue regime. However, the stochastic crack growth modeling related to failure-dependence has not been fully investigated. Here, a failure-dependence related stochastic crack growth modeling approach for competing cracking mode is proposed to address this issue. Firstly, copulas are used to model the dependent competing relationships among multiple fatigue fracture modes. The reliability analysis of multiple fatigue fracture modes is conducted from a copula perspective. Besides, the fatigue crack growth is modeled based on a nonlinear Wiener process, with unit-to-unit variability addressed by introducing random effects. Marginal reliability expressions are derived based on the Wiener process. Furthermore, a two-stage Bayesian inference method based on Hamiltonian Monte Carlo sampling is proposed to estimate model parameters. A Monte Carlo simulation study is conducted to validate the accuracy and robustness of the proposed inference method. Finally, the effectiveness of the proposed approach is proven through a real case study. It turns out that the ignorance of the competing relationships among multiple cracking modes leads to an underestimation of overall reliability. The accuracy of the model can be further improved with random effects considered.

Efficient prediction framework for large-scale nonlinear petrochemical process based on feature selection and temporal-attention LSTM: Applied to fluid catalytic cracking

Data-driven modeling plays a vital role in petrochemical industry, especially fluid catalytic cracking (FCC). However, the long operational cycles, large-scale measurements, multivariate data, and intricate temporal correlations in FCC units may lead to the problem of low prediction accuracy when only use a single time series data-driven modeling neural network such as long short-term memory (LSTM) network. To address these challenges, an effective prediction framework is proposed that integrates LSTM network with extreme gradient boosting (XGBOOST)-based feature selection and temporal-attention (TA) mechanisms. XGBOOST is applied to filter features related to the predictive variables in order to eliminate redundant variables. TA mechanisms within an LSTM network is used to capture the relevant historical time steps of the current moment. The results of our efficient prediction framework, applied to FCC process and three additional petrochemical processes, have proven to be superior to other methods.

Efficient Second-Harmonic Generation in Adapted-Width Waveguides Based on Periodically Poled Thin-Film Lithium Niobate.

Frequency conversion process based on periodically poled thin-film lithium niobate (PPTFLN) has been widely recognized as an important component for quantum information and photonic signal processing. Benefiting from the tight confinement of optical modes, the normalized conversion efficiency (NCE) of nanophotonic waveguides is improved by orders of magnitude compared to their bulk counterparts. However, the power conversion efficiency of these devices is limited by inherent nanoscale inhomogeneity of thin-film lithium niobate (TFLN), leading to undesirable phase errors. In this paper, we theoretically present a novel approach to solve this problem. Based on dispersion engineering, we aim at adjusting the waveguide structure, making local waveguide width adjustment at positions of different thicknesses, thus eliminating the phase errors. The adapted waveguide width design is applied for etched and loaded waveguides based on PPTFLN, achieving the ultrahigh power conversion efficiency of second harmonic generation (SHG) up to 2.1 × 104%W-1 and 6936%W-1, respectively, which surpasses the power conversion efficiency of other related works. Our approach just needs standard periodic poling with a single period, significantly reducing the complexity of electrode fabrication and the difficulty of poling, and allows for the placing of multiple waveguides, without individual poling designs for each waveguide. With the advantages of simplicity, high production, and meeting current micro-nano fabrication technology, our work may open a new way for achieving highly efficient second-order nonlinear optical processes based on PPTFLN.

Phase-field finite element modelling of creep crack growth in martensitic steels

Creep fracture presents a major concern for structural materials and critical components operating at elevated temperatures, thus requiring effective computational models. This study presents a phase-field framework for modelling creep crack growth and fracture behaviour of modern high-temperature materials such as Creep Strength Enhanced Ferritic (CSEF) martensitic steels. The model was formulated using the thermodynamic principles of the variational phase field theory of fracture and considering some physical aspects of creep fracture. Within the modelling framework, a dissipation potential dependent on creep damage is introduced to capture the effect of creep cavitation on the fracture energy and the creep crack growth resistance of the solid in a phenomenological manner. An elastoplastic power-law creep model is coupled to the phase-field formulations to account for the non-linear deformation processes ahead of the crack tip due to inelastic deformations, as well as their contribution to fracture at high temperatures. The capability of the proposed model is assessed against experimental data from compact tension (CT) creep tests conducted on P91 and P92 steels. Good agreement was obtained between the FE-predicted creep crack growth behaviour and the experimental measurements, showcasing the model’s feasibility. Further, numerical experiments were conducted using the proposed model to elucidate some key aspects influencing the fracture behaviour of martensitic steels. The proposed computational framework not only demonstrated good capability but was also able to offer improved mechanistic insights into the influence of material tendency to develop creep cavities on crack growth behaviour. This work contributes valuable insights into understanding the fracture process of CSEF steels at elevated temperatures and further demystifies the role of creep ductility.

Research on Citrus Fruit Freshness Detection Based on Near-Infrared Spectroscopy

The study developed a novel method for evaluating the freshness of citrus fruits by integrating near-infrared spectroscopy with the non-linear data processing capabilities of a BP neural network. This approach utilizes specific wavelength analysis to distinguish between fresh and non-fresh fruits effectively. Advanced pre-processing techniques are employed to remove spectral anomalies, enhancing the network’s ability to accurately identify crucial quality indicators like sugar content. Concurrently, an experiment utilizing a mathematical computing software -based BP neural network optimized the number of hidden layer nodes, identifying 61 as optimal. This configuration achieves impressive indicators, including a mean square error of 0.0025665 and a root mean square error of 49.8214. More than 1000 training iterations were performed on 100 citrus samples, and the learning rate was 80%. The model demonstrated a high accuracy rate of 97.6275%, confirming its precision and reliability in assessing citrus freshness. This synergy between advanced neural network processing and spectroscopic techniques marks a significant advancement in agricultural quality assessment, setting new standards for speed and efficiency in data processing.

Signatures of exciton-exciton annihilation in 2DES spectra including up to six-wave mixing processes.

Two-dimensional electronic spectroscopy (2DES) is a powerful spectroscopic tool that allows us to study the dynamics of excited states. Exciton-exciton annihilation is at least a fifth order process, which corresponds to intrachromophoric internal conversion from the double-excited high-energy chromophoric state into the single-excited state of the same chromophore. At high excitation intensities, this effect becomes apparent in standard 2DES and can be inspected via high order nK1⃗-nK2⃗+K3⃗ nonlinear processes. We calculate 2DES based on K1⃗-K2⃗+K3⃗ and 2K1⃗-2K2⃗+K3⃗ wave mixing processes to reveal exciton-exciton annihilation (EEA) induced exciton symmetry breaking, which occurs at high excitation intensities. We present the general theory that captures all these processes for bosonic and paulionic quasiparticles in a unified way and demonstrate that the NEEs can be easily utilized for highly nonlinear two-dimensional spectra calculations by employing phase cycling for separating various phase matching conditions. The approach predicts various excitonic third- to fifth-order features; however, due to high excitation intensities, contributions of different order processes become comparable and overlap, i.e., the signals no longer can be associated with well-defined order-to-the-field contributions. In addition, EEA leads to breaking of the exciton symmetries, thus enabling population of dark excitons. Such effects are due to the local nature of the EEA process.

Indian Ocean Dipole (IOD) forecasts based on convolutional neural network with sea level pressure precursor

Abstract Forecasting the Indian Ocean Dipole (IOD) is crucial because of its significant impact on regional and global climates. While traditional dynamic and empirical models suffer from systematic errors due to nonlinear processes, convolutional neural networks (CNN) are nonlinear in nature and have demonstrated remarkable El Niño Southern Oscillation (ENSO) and IOD forecasting skills based on oceanic predictors, particularly sea surface temperature and heat content. However, it is difficult to measure heat content and easily introduces uncertainties, prompting the need to explore atmospheric predictors for IOD forecasts. Based on sensitivity prediction experiments, we identified the sea level pressure (SLP) signal as a crucial predictor, which forecasts IOD at a 7 month lead. In addition, the CNN model improves monthly forecasting accuracy while reducing errors by 13.43%. Utilizing the heatmap analysis, we elucidated that the multi-seasonal predictability of the IOD primarily originates from mid-latitude climate variability. Besides ENSO signals in the Pacific Ocean, our study highlights the significant impact of remote climate forcing in the South Indian Ocean, tropical North Indian Ocean, and Northwest Pacific Ocean on IOD forecasts. By introducing the SLP precursor and extratropical zones into IOD forecasts, our study offers fresh insights into the underlying dynamics of IOD evolution.

Enhanced Ultrasound Image Formation with Computationally Efficient Cross-Angular Delay Multiply and Sum Beamforming.

Ultrasound imaging is a valuable clinical tool. It is commonly achieved using the delay and sum beamformer algorithm, which takes the signals received by an array of sensors and generates an image estimating the spatial distribution of the signal sources. This algorithm, while computationally efficient, has limited resolution and suffers from high side lobes. Nonlinear processing has proven to be an effective way to enhance the image quality produced by beamforming in a computationally efficient manner. In this work, we describe a new beamforming algorithm called Cross-Angular Delay Multiply and Sum, which takes advantage of nonlinear compounding to enhance contrast and resolution. This is then implemented with a mathematical reformulation to produce images with tighter point spread functions and enhanced contrast at a low computational cost. We tested this new algorithm over a range of in vitro and in vivo scenarios for both conventional B-Mode and amplitude modulation imaging, and for two types of ultrasound contrast agents, demonstrating its potential for clinical settings.

A novel finite-difference converged ENO scheme for steady-state simulations of Euler equations

The high-order shock-capturing scheme is critical for compressible fluid simulations, in particular for cases where strong shockwaves are present. However, for the steady-state solution of Euler equations, the high-order shock-capturing schemes usually suffer from the difficulty that the numerical residual hangs at a relatively high level instead of converging to machine zero. In this paper, a new paradigm of finite-difference converged ENO (CENO) scheme for steady-state simulations of Euler equations is proposed. Different from the existing methods dedicating to eliminating the slight post-shock oscillation, the unsteady effect of shock-induced numerical instability on the nonlinear weighting process as well as the resultant variation of the numerical formula for the spatially fixed cell interface flux at different time instants are demonstrated to be the core contributors to the non-convergence of high-order shock-capturing schemes. Unlike classical ENO/WENO/TENO schemes, which generate the final reconstruction based only on spatial information, evolutionary information is also exploited by a novel converged stencil selection strategy in the present scheme. With this strategy, the unsteady effect of shock-induced numerical instability on the nonlinear weighting process is incorporated and eliminated by a tailored adaptive scale-separation algorithm, while shock-capturing capabilities are not sacrificed. A set of well-established and newly designed challenging benchmark simulations involving strong shockwaves, contact discontinuities and rarefaction waves demonstrates that the new fifth-order CENO5 scheme features superior convergence properties and achieves the essentially non-oscillation property better when compared to the robust TENO5 scheme and the state-of-the-art WENO-type schemes designed for steady-state problems.

Physiologically based in vitro – in vivo correlation of modified release oral formulations with non-linear intestinal absorption: A case study using mirabegron

Establishing an in vitro – in vivo correlation (IVIVC) for oral modified release (MR) formulations would make it possible to substitute an in vitro dissolution test for human bioequivalence (BE) studies when changing the formulation or manufacturing methods. However, the number of IVIVC applications and approvals are reportedly low. One of the main reasons for failure to obtain IVIVCs using conventional methodologies may be the lack of consideration of the dissolution and absorption mechanisms of drugs in the physiological environment. In particular, it is difficult to obtain IVIVC using conventional methodologies for drugs with non-linear absorption processes. Therefore, the aim of the present study was to develop a physiologically based biopharmaceutics model (PBBM) that enables Level A IVIVCs for mirabegron MR formulations with non-linear absorption characteristics.Using human pharmacokinetic (PK) data for immediate-release formulations of mirabegron, the luminal drug concentration-dependent membrane permeation coefficient was calculated through curve fitting. The membrane permeation coefficient data were then applied to the human PK data of the MR formulations to estimate the in vivo dissolution rate by curve fitting. It was assumed that in vivo dissolution could be described using a zero-order rate equation. Furthermore, a Levy plot was generated using the estimated in vivo dissolution rate and the in vitro dissolution rate obtained from the literature. Finally, the dissolution rate of the MR formulations from the Levy plot was applied to the PBBM to predict the oral PK of the mirabegron MR formulations.This PB-IVIVC approach successfully generated linear Levy plots with slopes of almost 1.0 for MR formulations with different dose strengths and dissolution rates. The Cmax values of the MR formulations were accurately predicted using this approach, whereas the prediction errors for AUC exceeded the Level A IVIVC criteria. This can be attributed to the incomplete description of colonic absorption in the current PBBM.

Enhanced high harmonic efficiency through phonon-assisted photodoping effect

High-harmonic generation (HHG) has emerged as a central technique in attosecond science and strong-field physics, providing a tool for investigating ultrafast dynamics. However, the microscopic mechanism of HHG in solids is still under debate, and it is unclear how it is modified in the ubiquitous presence of phonons. Here we theoretically investigate the role of collectively coherent vibrations in HHG in a wide range of solids (e.g., hBN, graphite, 2H-MoS2, and diamond). We predict that phonon-assisted high harmonic yields can be significantly enhanced, compared to the phonon-free case – up to a factor of ~20 for a transverse optical phonon in bulk hBN. We also show that the emitted harmonics strongly depend on the character of the pumped vibrational modes. Through state-of-the-art ab initio calculations, we elucidate the physical origin of the HHG yield enhancement – phonon-assisted photoinduced carrier doping, which plays a paramount role in both intraband and interband electron dynamics. Our research illuminates a clear pathway toward comprehending phonon-mediated nonlinear optical processes within materials, offering a powerful tool to deliberately engineer and govern solid-state high harmonics.

In Situ Generation of Planetary Waves in the Mesosphere by Zonally Asymmetric Gravity Wave Drag: A Revisit

Abstract This study investigates the in situ generation of planetary waves (PWs) by zonally asymmetric gravity wave drag (GWD) in the mesosphere using a fully nonlinear general circulation model extending to the lower thermosphere. To isolate the effects of GWD, we establish a highly idealized but efficient framework that excludes stationary PWs propagating from the troposphere and in situ PWs generated by barotropic and baroclinic instabilities. The GWD is prescribed in a zonally sinusoidal form with a zonal wavenumber (ZWN) of either 1 or 2 in the lower mesosphere of the Northern Hemisphere midlatitude. Our idealized simulations clearly show that zonally asymmetric GWD generates PWs by serving as a nonconservative source Z′ of linearized disturbance quasigeostrophic potential vorticity q′. While Z′ initially amplifies PWs through enhancing q′ tendency, the subsequent zonal advection of q′ gradually balances with Z′, thereby attaining steady-state PWs. The GWD-induced PWs predominantly have the same ZWN as the applied GWD with minor contributions from higher ZWN components attributed to nonlinear processes. The amplitude of the induced PWs increases in proportion with the magnitude of the peak GWD, while it decreases in proportion to the square of ZWN. Moreover, the amplitude of PWs increases as the meridional range of GWD expands and as GWD shifts toward lower latitudes. These PWs deposit substantial positive Eliassen–Palm flux divergence (EPFD) of ∼30 m s−1 day−1 at their origin and negative EPFD of 5–10 m s−1 day−1 during propagation. In addition, the in situ PWs exhibit interhemispheric propagation following westerlies that extend into the Southern Hemisphere.

Models and methods for RZ-signals distinction in non-Gaussian noise for information-measurement systems

Abstract Polynomial Decision Rules (DR) for the problems of discrete RZ (Return-to-Zero) signal distinction in asymmetric-excess non-Gaussian noise are proposed. A new approach is proposed, which is based on the use of a modified moment quality criterion of statistical hypothesis testing and the application of higher-order statistics to describe the characteristics of non-Gaussian noise. Simulation of the DR with different parameters of the signal and noise was carried out. It is shown that taking into account the coefficients of skewness and kurtosis of the non-Gaussian noise the efficiency of signal distinction increases with non-linear processing DR compared to known results, which are optimal for the Gaussian noise model. The conducted studies demonstrate a reduction in false decisions in the processing of RZ signals when considering the coefficients of skewness and kurtosis of non-Gaussian noise. Such an increase in efficiency can exceed twofold, depending on the noise parameters. It is shown that the efficiency of the proposed approach is much higher for small SNR (Signal-to-Noise Ratio) values, for example, less than 1.

Integrating Temporal and Feedforward Models for Solar Energy Prediction: LSTM and ANN Hybrid Approaches

The increasing reliance on renewable energy, particularly solar power, necessitates accurate models for predicting energy output to optimize storage and distribution systems. Traditional methods such as Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANNs) offer unique strengths in forecasting photovoltaic (PV) system outputs. LSTM excels in capturing temporal dependencies in time-series data, while ANNs effectively model nonlinear relationships between variables. This study aims to develop and evaluate a hybrid LSTM-ANN model for improving the accuracy of PV energy output predictions, focusing on voltage, power, and irradiance. Using data collected from a solar-powered greenhouse in Talang Kemang, Indonesia, the model was trained and validated. The hybrid model demonstrated significant improvements in prediction accuracy. For voltage, the model achieved a Mean Absolute Error (MAE) of 0.1016 and a Root Mean Squared Error (RMSE) of 0.1417, while irradiance predictions resulted in an MAE of 0.0895 and RMSE of 0.1149. Power predictions also yielded strong results, with an MAE of 0.1506 and RMSE of 0.1954. These results highlight the hybrid LSTM-ANN model's effectiveness in combining temporal and nonlinear data processing capabilities, leading to superior accuracy in predicting PV system outputs. This approach can enhance the reliability of energy forecasting models, enabling better integration of solar power into electrical grids. The model holds promise for broader applications in renewable energy systems, improving their efficiency and sustainability

Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics

Music, a universal medium that effortlessly transcends the confines of language and culture, serves as a vessel for the distinctive expression of a composer's ingenuity, particularly palpable through the elaborate symphony of melodies, harmonies, and rhythms. This phenomenon is acutely observable in the realm of Turkish Classical Music, where the identification of individual composers poses a formidable challenge due to a confluence of diverse stylistic expressions and sophisticated techniques. Shaped by centuries of cultural interchanges, this genre is celebrated for its convoluted rhythmic frameworks and deep melodic modes, often exhibiting fractal characteristics that compound the complexity of composer classification based on mere audio signals. In response to these complexities, this study introduces an advanced analytical paradigm that amalgamates Multi-resolution analysis, spectral entropy assessments, and a spectrum of multidimensional chaotic and statistical descriptors. By invoking chaos theory, the research delineates distinct patterns and features inherent to musical compositions, subsequently deploying these discoveries for composer categorization. Employing a model fusion-based strategy, the approach utilizes esteemed base estimators for section-level probabilistic determinations, subsequently amalgamated at the song level through a Long Short-Term Memory (LSTM) neural network model to classify a corpus of 380 compositions from 15 distinct composers. The results of this study not only highlight the efficacy of chaos-based approaches in Musical Information Retrieval but also provide a nuanced understanding of the unique characteristics of Turkish Classical Music, thus advancing the boundaries of how musicological data is scrutinized and conceptualized within scholarly discourse.

Flexibility assessment and aggregation of alkaline electrolyzers considering dynamic process constraints for energy management of renewable power-to-hydrogen systems

In the energy management of renewable power-to-hydrogen (ReP2H) systems, quantifying the flexibility of alkaline electrolyzers (AELs) to respond to fast load-tracking commands and maintain energy balance across various timescales is crucial. However, the flexibility of AELs is subject to electrochemical, and dynamic heat and mass transfer constraints, making them very different, both physically and mathematically, from conventional power system flexibility resources such as energy storage (ES) and electrical vehicles (EVs). Additionally, in large hydrogen plants (HPs), aggregating the flexibility of multiple AELs is necessary. Despite extensive flexibility-related research on power and integrated energy systems, work on the electrolyzers is limited. To address these gaps, this paper presents a flexibility assessment method for AELs. Considering comprehensive nonlinear dynamic process constraints, we establish a flexibility metric based on the maximal/minimal energy an AEL can provide within varying time windows. This metric is additive, facilitating the aggregation of multiple AELs within seconds. By comparing it with the energy demanded by regulatory commands, the feasibility of load-tracking control can be assessed without time-domain simulation. In cases of infeasibility, the cause and required compensation can also be interpreted. Case studies validate the proposed method, and key factors impacting the flexibility of AELs are quantitatively analyzed.

Hierarchical stacked spatiotemporal self-attention network for sea surface temperature forecasting

Sea surface temperature (SST) is a highly complex spatiotemporal variable, which stems from its susceptibility to non-linear dynamical processes and substantial spatiotemporal variability. In particular, accurately forecasting small-scale SST is a formidable challenge due to the compounded effects of diverse physical processes spanning across various scales. In this study, we employ deep learning methods to mine the ocean’s evolutionary patterns, as the ocean’s dynamic mechanisms are inherently embedded in spatiotemporal data. We propose a hierarchical stacked spatiotemporal self-attention mechanism (HSSSA) network architecture. The hierarchical stacked encoder–decoder architecture provides the capability for feature fusion and extraction at different scales. The spatial self-attention and temporal self-attention modules simultaneously focus on information from different spatial locations and time steps, allowing the exploration of spatiotemporal patterns in the complex dynamics of the ocean. The experiments are conducted on a high-resolution East China Sea dataset (1/10°×1/10°) to demonstrate the forecast performance of the proposed model for refined ocean variables. The 15-day forecasts indicate that the HSSSA method outperforms the EOF-ARIMA and CNN-Transformer methods.

Fast vs. slow rhythms: balancing human resource change and corporate strategic development in tourism firms

ABSTRACT Due to the dynamic and complex environment in which tourism firms operate, it can be challenging to align the pace of human resource change with corporate strategic development. In this study, we introduce strategic rhythm theory to explore the antecedents of strategic human resource management from a temporal perspective. We construct an innovative theoretical framework categorising strategic rhythms into market and non-market types to elucidate how these rhythms influence the evolutionary process of strategic human resource management within tourism firms. Based on a longitudinal case study, our findings indicate that aligning strategic development with human resource change is a non-linear, sequential, and dynamic process. Both non-market and market strategic rhythms exhibit an entrainment mechanism, achieving an intermittent fit between strategic development and human resource change through optimisation and complementary functions. Furthermore, this study deconstructs the process by which strategic rhythm drives strategic human resources, summarising three action logics: strategic follow-up, strategic leadership, and strategic lag. It also identifies the states of rhythmic systems into three stages: arrhythmia, polyrhythmic coordination, and eurhythmia. This study extends the scope of strategic human resource management by showing how tourism firms can achieve strategic fit, offering practical value to help them leverage strengths to thrive.

Optimization of process parameters in laser cladding multi channel forming using MVBM-NSGA-II method

ABSTRACT Laser cladding multi-channel forming is a complex, nonlinear process characterized by multi-parameter coupling. To determine the technology parameters, an optimization method of multivariate black-box prediction model (MVBM) matching non-dominated sequential genetic algorithm (NSGA-II) was constructed. Multi-channel LC orthogonal experiments were designed. The MVBM was established with process technology parameters as inputs and characteristic parameters reflecting the quality (surface hardness, dilution rate) as responses. The set of optimal solutions for technology parameters by the NSGA-II. The experimental results showed that, under the optimum parameters, the average hardness of the optimized increased by 6.1%, and the dilution rate was reduced by 49.8%. The dilution rate was maintained within the optimal range. The study’s findings can provide theoretical support for optimizing LC multi-objective technology parameters and improving coating quality control.