Nonlinear Techniques Research Articles

Does the ionic distribution in the electrical double layer modify second harmonic scattering?

Surface-specific nonlinear optical techniques are ideally suited to investigate the complex structure of aqueous interfaces. For colloidal particles dispersed in aqueous solutions, interfacial properties can be retrieved with angle-resolved second harmonic scattering (AR-SHS). The mathematical framework of AR-SHS does not require a priori knowledge on the electrostatic distribution in the first few nanometers close to the interface, therefore allowing us to formulate a molecular-level description of the electrical double layer (EDL) based on the experimental data. However, farther away from the interface, an analytical form of the electrostatic potential decay is necessary to account for the distance dependence of the surface electrostatic field propagating into the solution. This requirement is especially important at low ionic strengths, where the electrostatic field is not efficiently screened by counterions. Here, we examine to what extent the analytical form of the electrostatic potential decay impacts the AR-SHS data analysis. We analyze the effect of different functions on the scattering form factors, on the integrated AR-SHS signal intensity, and on the surface parameters extracted from fitting the AR-SHS data. We find that the trends of the surface parameters remain similar regardless of the chosen function, demonstrating the robustness of our approach to establish a molecular-level picture of the EDL. At ionic strengths <10-4M for 100-nm diameter particles, a functional form that physically represents counterions packed more densely in the vicinity of the surface than in the case of the Poisson-Boltzmann distribution has the largest impact, resulting in an overestimation of the obtained surface potential.

Intellectual capital and firm risk: evidence from an emerging economy

Purpose Intellectual capital (IC) plays a pivotal role in determining corporate risk profiles in the contemporary knowledge era. Consequently, this study aims to analyze the impact of IC on firm risk (FR) among the manufacturing companies listed on the Pakistan Stock Exchange (PSX). Design/methodology/approach The authors have adopted the modified value-added intellectual model which combines human capital efficiency, structural capital efficiency, efficiency of capital employed and relational capital efficiency. FR has been used as the dependent variable, measured as the standard deviation of the daily stock prices. The study has used panel data from a sample of 40 manufacturing companies listed in the KSE-100 Index from 2015 to 2021. Findings The results suggest that IC has a significant impact on the FR of manufacturing companies listed on the benchmark index of PSX. Moreover, this relationship is direct; thus, an increase in IC would also increase FR measured by the change in stock prices. Research limitations/implications The current study has only used linear techniques. Future researchers may consider investigating the impact of IC at varying levels of FR using nonlinear techniques. Practical implications This study provides corporate managers and policymakers valuable insight into the need to strike a balance between investment in IC and their FR, particularly in an emerging market context. Originality/value IC is frequently associated with firm performance. However, the relationship between IC and FR has generally been underexplored. This study adds to the strand of limited IC literature by investigating the impact of a modified IC model on FR in an emerging economy.

Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques - a short review including new research.

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or "label-free" imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques with an emphasis on FPM and advanced X-ray microscopies. We next demonstrate with our own results computational imaging through Fourier ptychographic microscopy (FPM) and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are reported. X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are also presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.

Quantifying the Impact of Natural Resources on Sustainable Growth in Developing Economies: Evidence from Panel Nonlinear ARDL Technique

Quantifying the Impact of Natural Resources on Sustainable Growth in Developing Economies: Evidence from Panel Nonlinear ARDL Technique

Enhancing the trustworthiness of chaos and synchronization of chaotic satellite model: a practice of discrete fractional-order approaches

Accurate development of satellite maneuvers necessitates a broad orbital dynamical system and efficient nonlinear control techniques. For achieving the intended formation, a framework of a discrete fractional difference satellite model is constructed by the use of commensurate and non-commensurate orders for the control and synchronization of fractional-order chaotic satellite system. The efficacy of the suggested framework is evaluated employing a numerical simulation of the concerning dynamic systems of motion while taking into account multiple considerations such as Lyapunov exponent research, phase images and bifurcation schematics. With the aid of discrete nabla operators, we monitor the qualitative behavioural patterns of satellite systems in order to provide justification for the structure’s chaos. We acquire the fixed points of the proposed trajectory. At each fixed point, we calculate the eigenvalue of the satellite system’s Jacobian matrix and check for zones of instability. The outcomes exhibit a wide range of multifaceted behaviours resulting from the interaction with various fractional-orders in the offered system. Additionally, the sample entropy evaluation is employed in the research to determine complexities and endorse the existence of chaos. To maintain stability and synchronize the system, nonlinear controllers are additionally provided. The study highlights the technique’s vulnerability to fractional-order factors, resulting in exclusive, changing trends and equilibrium frameworks. Because of its diverse and convoluted behaviour, the satellite chaotic model is an intriguing and crucial subject for research.

The Role of Public Assistance in Improving Food Security During Covid-19 in Togo

This paper provides insights into the potential implication of public assistance on food insecurity in the Togolese population during the Covid-19 period. The Oaxaca–Blinder Non-Linear decomposition technique and binary logit model were applied to Data of Round 8 of Afrobarometer of Togo. The empirical results reveal that women were more food insecure in Togo during the Covid-19 period. This particular vulnerability of women was explained by the difference in economic resources, place of residence, and response measures put in place by the State. However, the public assistance received has helped to reduce the gaps observed in food insecurity. The study reveals the importance of public assistance measures, especially those based on gender, during shocks that may occur in an economy.

Asymmetric Effect of Shadow Economy on Environmental Pollution in Egypt: Evidence from Bootstrap NARDL Technique

This study examines the asymmetric effect of shadow economy (SE) on environmental pollution in Egypt during the 1970 and 2022 period. Using the bootstrap nonlinear autoregressive distributed lag (NARDL) bounds-testing approach, the study presents evidence of nonlinear cointegrating relationship between environmental degradation (carbon emission) and shadow economic activities (alongside globalisation, urbanisation, size of economic activity, industrial growth). In addition, the results demonstrate that the impact of shadow economy on environmental pollution (Env) is nonlinear, with the positive shock in shadow economy promoting environmental degradation and negative shocks promoting environmental quality, both in the short- and long-runs. However, the study discovered that the magnitude of the impact of the SE on Env is larger in the short-run. This is further validated by the dynamic nonlinear ARDL simulations technique which demonstrates that the short-run effect of the SE on Env is large. Additionally, the results suggest that income growth, urbanisation, and industrial growth are important drivers of environmental pollution. Therefore, the study recommends the adoption, and most importantly implementation, of policies and strategies geared towards reducing the shadow economy, and consequently environmental pollution.

Emission line predictions for mock galaxy catalogues: a new differentiable and empirical mapping from DESI

Abstract We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including Hα, Hβ, [O II], and [O III]) from a galaxy’s rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman’s rank correlation coefficient ρs &amp;gt; 0.87 between predictions and observations for most lines. Using a non-linear dimensionality reduction technique (UMAP), we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterise and account for biases in the spectroscopic training sets used for training and calibration of photo-z’s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.

Robustness and Interpretability of Machine Learning Models in Financial Forecasting

Purpose: The practice of financial forecasting is an essential component of contemporary financial management and the process of making decisions on investments. As a result of the complexity and volatility of financial markets, traditional financial forecasting techniques often fail to adequately capture the situation. Methodology/ Findings: One of the most effective methods for improving the precision and effectiveness of financial forecasting is the use of machine learning techniques. Through an examination of its potential, limitations, and future possibilities. They take linear and nonlinear possibilities into account in their study. Our focus is on linear methods, namely penalized regressions, and ensemble of models. The study considers a range of nonlinear techniques, including tree-based methods like boosted trees and random forests, and deep and shallow neural networks in both feed-forward and recurrent forms. They also think about ensemble and hybrid models, which combine features from several kinds of alternatives. Implications to Theory, Practice and Policy: A brief overview of the tests used to measure outstanding predictive ability is provided. In the last part of this article, they focus on the possible applications of machine learning in the fields of economics and finance, and we provide an example that makes use of high-frequency financial data (Benti, Chaka, and Semie, 2023).

Near-term distributed quantum computation using mean-field corrections and auxiliary qubits

Distributed quantum computation is often proposed to increase the scalability of quantum hardware, as it reduces cooperative noise and requisite connectivity by sharing quantum information between distant quantum devices. However, such exchange of quantum information itself poses unique engineering challenges, requiring high gate fidelity and costly non-local operations. To mitigate this, we propose near-term distributed quantum computing, focusing on approximate approaches that involve limited information transfer and conservative entanglement production. We first devise an approximate distributed computing scheme for the time evolution of quantum systems split across any combination of classical and quantum devices. Our procedure harnesses mean-field corrections and auxiliary qubits to link two or more devices classically, optimally encoding the auxiliary qubits to both minimize short-time evolution error and extend the approximate scheme’s performance to longer evolution times. We then expand the scheme to include limited quantum information transfer through selective qubit shuffling or teleportation, broadening our method’s applicability and boosting its performance. Finally, we build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms. To characterize our technique, we introduce a non-linear perturbation theory that discerns the critical role of our mean-field corrections in optimization and may be suitable for analyzing other non-linear quantum techniques. This fragmented pre-training is remarkably successful, reducing algorithmic error by orders of magnitude while requiring fewer iterations.

A New Methodology in Evaluating Nonlinear Eelectrohydrodynamic Azimuthal Stability between Two Dusty Viscous Fluids

The objective of the current paper is to scrutinize the azimuthal stability of a circular interface between two viscous fluids across porous medium in the presence of fine dust. Dusty fluid models are employed to comprehend the dynamics of aerosols in the atmosphere, a crucial aspect in the investigation of air quality, climate change, and the dissemination of pollutants. Furthermore, the comprehension and management of azimuthal nonlinear instabilities are essential in diverse engineering applications, where turbulence is a prominent factor. This includes aerospace engineering, where it is necessary for developing more efficient aircraft, as well as fluid dynamics, where it is critical for optimizing industrial processes. The system is subjected to the influence of a uniform electric field (EF) that is directed in the azimuthal direction. Electrospray trustees utilized in spaceship propulsion systems make use of the principles of Electrohydrodynamics (EHD). These representatives employ the process of ionization on a liquid propellant and utilize EFs to accelerate the resulting ions, so producing thrust. Assuming a simplified situation, the prototype is assumed to have a two-dimensional structure. Both fluids are presumed as rotating. An examination of rotating fluids is crucial for comprehending the dynamics of the atmosphere and oceans. Obtaining this knowledge is essential for accurately predicting weather, simulating climate patterns, and investigating oceanic currents. The mathematical approach is simplified by considering the viscous potential theory (VPT). The nonlinear technique relies on the use of linear fundamental partial differential equations (PDEs) and the application of the appropriate nonlinear boundary conditions (BCs). This method produces a nonlinear PDE that controls the displacement of the interface. By disregarding the nonlinear elements, a dispersion equation that is linear in nature is obtained. The stability criteria are graphed using several dimensionless physical quantities. The non-perturbative analysis (NPA) achieves nonlinear stability. As well-known, the primary objective of NPA is to convert a linear ordinary differential equation (ODE) into a linear equation. The NPA distinguishes itself from conventional perturbation methods by its ability to accurately analyze the dynamics of highly nonlinear oscillators. This novel methodology is developed utilizing the He's frequency formula (HFF) for the purpose of designing the displacement of the interface. The computational computations demonstrated that the NPA is efficacious, encouraging, and powerful. The linear and nonlinear techniques demonstrate that the effects of various dimensionless physical parameters remain constant. It is found that the Weber number We and Capillary number Cabecome zero as crucial consequences. The Ohnesorge number, Oh which represents the ratio of viscosity and the mass concentration in dusty particles, has a destabilizing effect.

Planar curved path following controller for a small fixed-wing unmanned aerial vehicle with constrained parameters optimized by nonlinear model predictive control

Path following presents a pivotal challenge within the realm of small fixed-wing unmanned aerial vehicles. Firstly, a Lyapunov-stable path guidance law was formulated to follow specific planar curved paths. To ensure differentiability of the guidance law, a modified, smooth saturation function was derived. Secondly, an analysis was conducted to ascertain the interrelationship between control parameters and input constraints, thereby identifying the relevant parameter domains. Thirdly, the nonlinear model predictive control technique was harnessed to optimize both guidance law parameters, enhancing the unmanned aerial vehicle’s capacity to achieve optimal performance in both straight-line and circular path following, hereafter referred to as PFC_NMPC. By leveraging Lyapunov stability arguments for switched systems, the stability of the corresponding nonlinear switched system was guaranteed. In this study, square and circular paths were generated to assess the path-following control of a simulated fixed-wing unmanned aerial vehicle. The performance of various guidance laws, including those with fixed parameters (PFC), those with parameters tuned using fuzzy logic (PFC_FL), PFC_NMPC, vector field, and pure pursuit with line-of-sight, was compared. Notably, the proposed PFC_NMPC method exhibited the ability to expedite the unmanned aerial vehicle’s convergence to the desired path while maximizing the effective flight path length.

Does your job shape your experience or interpretation of workplace telepressure? Exploring measurement invariance across occupational characteristics

Workplace telepressure is a psychological experience related to work-related messages. Research on measurement invariance for telepressure is scarce, especially with respect to occupations. This paper used a moderated nonlinear factor analysis technique to examine how occupational characteristics predict telepressure experiences and differential ratings across two studies with full-time workers. Email-related behaviors and demands predicted factor means and variances of telepressure, but there were no consistent results for job control and time pressure. Invariance testing at the item-level showed that occupational characteristics did not moderate most item parameter estimates; that is, employees interpreted workplace telepressure items equivalently regardless of occupational context.

Visualization of slit defect by scanning nonlinear airborne ultrasound source technique using compressed sensing

A high-speed, non-contact non-destructive testing method using a scanning airborne ultrasound source and a nonlinear harmonic method has been developed for plate-like structures. The testing time depends on the scanning speed and the number of measurement points. To solve this problem, we propose using compressed sensing with this method. In this paper, we investigated visualizing Lamb wave propagation using the proposed compressed sensing method. In addition, we detected a slit defect by using the Lamb wave propagation image. Our results demonstrated that compressed sensing could be used to reduce the testing time.

Analysis of Nonlinear Narrative via the Lens of the Coherence Theory of Language

The objective of this study is to examine the utilization of nonlinear narrative approaches in literature and investigate the potential optimization of comprehension of intricate plots through the application of linguistics-related knowledge. This study has determined, based on an examination of pertinent literature, that the use of non-linear narrative techniques can augment the appeal and intricacy of a literary piece, while also intensifying the level of reading challenge for its audience. This research use qualitative research methodologies to examine the utilization of non-linear narrative approaches, with a focus on comprehending these strategies through the analysis of textual data. The findings indicate that the principles of linguistic theory can be effectively employed in the analysis of literary texts as well as in the interpretation of films and television productions. This discovery has the potential to enhance the applicability of linguistics, thereby bridging a portion of the existing disparity between linguistic theory and real-world applications.

Monitoring fatigue cracks in riveted plates using a sideband intensity based nonlinear ultrasonic technique

Aluminum structures are routinely used in aircraft due to their lightweight and corrosion resistance properties. Multi-layered aluminum plates are generally joined by rivets forming regions which are prone to fatigue crack formation in an aircraft. Therefore, the detection and monitoring of fatigue cracks at rivet joints in aluminum structures are crucial for ensuring flight safety. In this study, piezoelectric sensors were utilized to generate and detect Lamb waves on aluminum plates with rivet joints to investigate the feasibility of a newly developed Sideband Peak Count (SPC) technique for detecting fatigue cracks around these joints. To overcome the limitations of existing SPC-I (Sideband Peak Count – Index) and SPI (Sideband Peak Intensity) techniques in capturing harmonic and modulating wave frequencies due to material nonlinearity, a comprehensive index, the Sideband Intensity Index (SII) is introduced. Comparative analysis with existing SPC-I and SPI techniques confirm the effectiveness of the SII technique. This investigation shows that the SII technique significantly improves the detection capability of initial fatigue cracks around rivet joints on aluminum plates. This study offers a more efficient method for detecting critical fatigue cracks in rivet joints.

Characterization of Buried Interfaces of Silicone Materials in Situ to Understand Their Fouling-Release, Antifouling, and Adhesion Properties.

Poly(dimethylsiloxane) (PDMS) has numerous excellent properties and is extensively used as the main component of many silicone products in a variety of research fields and practical applications such as biomedical materials, aviation, construction, electronic devices, and automobiles. Interfacial structures of PDMS and other components in silicone systems are important for such research and applications. It is difficult to probe interfacial molecular structures of buried solid-liquid and solid-solid interfaces of silicone materials due to the lack of appropriate analytical tools. In this feature article, we presented our research on elucidating the molecular structures of PDMS as well as other additives in silicone samples at buried interfaces in situ at the molecular level using a nonlinear optical spectroscopic technique, sum frequency generation (SFG) vibrational spectroscopy. SFG was applied to study various PDMS surfaces in liquid environments to understand their fouling-release and antifouling activities. SFG has also been used to study buried solid-solid interfaces between silicone adhesives and polymers, elucidating the molecular adhesion mechanisms. Our SFG studies provide important knowledge on interfacial structure-function relationships of silicone materials, helping the design and development of silicone materials with improved properties through optimization of silicone interfacial structures.

Characterizing Lossy Dielectric Materials in Shock Physics by Millimeter-Wave Interferometry Using One-Dimensional Convolutional Neural Networks and Nonlinear Optimization

When a dielectric material undergoes mechanical impact, it generates a shock wave, causing changes in its refractive index. Recent demonstrations have proven that the modified refractive index can be determined remotely using a millimeter-wave interferometer. However, these demonstrations are based on the resolution of an inverse electromagnetic problem, which assumes that the losses in the material are negligible. This restrictive assumption is overcome in this article, in which a new approach is proposed to solve the inverse electromagnetic problem in lossy and shocked dielectric materials. Our methodology combines a one-dimensional convolutional neural network architecture, namely Inverse problem of Lossless or Lossy Shocked Wavefront Network (ILSW-Net), with a nonlinear optimization technique based on the Nelder–Mead algorithm to estimate losses within dielectric materials under a mechanical impact. Experimental results for both simulated and real signals show that our method can successfully predict the velocities and the refractive index while accurately estimating the shock wavefront.

The symmetric and asymmetric effect of financial development on ecological footprint in South Africa: ARDL and NARDL approach

Introduction: The literature on the finance–emission nexus offers conflicting conclusions. This study resolves this inconsistency by investigating the symmetric and asymmetric effect of financial development on ecological footprint in South Africa, using the Environmental Kuznets Curve framework as a guide. Given the coexistence of ecological deficits and world-class financial development systems in South Africa, it is essential to explore and evaluate potential solutions to mitigating these deficits. Our empirical analysis contributes to the body of literature on the impact of financial development and ecological footprint by using a comprehensive measure of financial development and disaggregates it into its sub-indices to provide a nuanced analysis.Method: This study employs the linear auto regressive distribution lag and nonlinear auto regressive distribution lag techniques to explore the complex interactions of financial development and ecological footprint.Results and Discussion: The findings of this research indicate that financial markets and institutions seem to have varying effects on the ecological footprint. Financial market indices promote environmental quality, while financial institutions exacerbate environmental quality. These results call for policymakers to craft a watertight process that will encourage both financial markets and institutions to allocate capital to projects that are pro-environmental.

Peri-ultrasound modeling to investigate the performance of different nonlinear ultrasonic techniques for damage monitoring in plate structures

Abstract Peri-ultrasound modeling which is based on nonlocal peridynamics is found and proven to be effective for modeling nonlinear waves propagating and interacting with damages in structures. This work presents the peri-ultrasound modeling to investigate the performance of three commonly used nonlinear ultrasonic (NLU) techniques – wave mixing, higher harmonic generation (HHG) and sideband peak count-index (or SPC-I) for monitoring damages (or cracks) in three-dimensional (3-D) plate structures. Cracks can be defined as “thin cracks” and “thick cracks” according to the horizon size mentioned in peridynamics. Peri-ultrasound modeling results reveal that the SPC-I results are consistent with other reported numerical modeling and experimental results available in the literature. However, the modulation indicator (MI) from wave mixing model only shows consistent trends for thin cracks but not for thick cracks and its reliability is affected by the initial excitation bandwidth. The relative acoustic nonlinearity factor β from HHG technique shows consistent trends for thick cracks but not for thin cracks. It can be concluded from the obtained parametric analysis results that the SPC-I technique is more robust and reliable for monitoring damages in engineering structures.