Nonlinear Aspects Research Articles

Auditory Temporal Processing Can Predict Reading Measurements in Neurotypical Adults: Evidence for Cascading Effects of Low-Level Auditory Processing on High-Level Cognitive Ability

ABSTRACT Accurate processing of auditory timing information has been proposed as crucial for the development of various complex cognitive abilities, such as literacy. However, empirical validation of this hypothesis has primarily involved using isochronous stimuli, which do not capture the nonlinearity inherent in the perception of timing in speech and music. Additionally, most work has focused on clinical/developmental populations and assessed timing at slower time scales, limiting our knowledge of this relationship. The current study introduces a perturbation stimulus design aimed at overcoming these limitations. We examined the association between reading abilities and behavioral entrainment to perturbation stimuli and applied a novel curve-fitting technique to analyze participants’ entrainment in response to perturbations. We demonstrate that behavioral entrainment during the post-perturbation time window yields superior predictive value across a wide array of reading measurements compared to entrainment to isochronous stimuli (before the perturbation) in a neurotypical adult population. Our perturbation paradigm effectively reveals individual differences in auditory temporal processing among neurotypical adults, offering a novel approach to investigating the link between auditory temporal processing and literacy skills beyond clinical/developmental contexts. Our novel curve-fitting analysis effectively captures the nonlinear aspects of participants’ entrainment when recovering from tempo perturbations.

The Influence of Dopaminergic Medication on Regularity and Determinism of Gait and Balance in Parkinson's Disease: A Pilot Analysis.

Background/Objectives: Understanding how dual-tasking and Parkinson's disease medication affect gait and balance regularity can provide valuable insights to patients, caregivers, and clinicians regarding frailty and fall risk. However, dual-task gait and balance studies in PD most often only employ linear measures to describe movement regularity. Some have used nonlinear techniques to analyze PD performances, but only in the on-medication state. Thus, it is unclear how the nonlinear aspects of gait or standing balance are affected by PD medication. This study aimed to assess how dopaminergic medication influenced the regularity and determinism of joint angle and anterior-posterior (AP) and medial-lateral (ML) center of pressure (COP) path time-series data while single- and dual-tasking in PD. Methods: Sixteen subjects with PD completed single- and dual-task gait and standing balance trials for 3 min off and on dopaminergic medication. Sample entropy and percent determinism were calculated for bilateral hip, knee, and shoulder joints, and the AP and ML COP path. Results: There were no relevant medication X task interactions for either the joint angles series or the balance series. Instead, the results supported independent effects of medication, dual-tasking, or standing with eyes closed. Balance task difficulty (i.e., eyes open vs. eyes closed) was detected by the nonlinear analyses, but the nonlinear measures yielded opposing results such that standing with eyes closed simultaneously yielded less regular and more deterministic signals. Conclusions: When juxtaposed with previous findings, these results suggest that medication-induced functional improvements in people with PD might be accompanied by a shift from lesser to greater signal consistency, and the effects of dual-tasking and standing with eyes closed were mixed. Future studies would benefit from including both linear and nonlinear measures to better describe gait and balance performance and signal complexity in people with PD.

Characterization and linear / nonlinear optical aspects of polyaniline (PANI) films incorporated with Ethylenediamine for low-cost limiting optical technologies

Polymer composite combined film with Polyaniline (PANI) and Ethylenediamine (EDA) were successfully synthesized via the spin coating technique on a glass substrate followed by different thermal annealing degrees and times. Successful incorporation of EDA into the PANI polymer matrix and the effect of the annealing temperature have been demonstrated by XRD, FTIR, FESEM and TEM. Using UV–Vis optical spectroscopy, we determine the absorption coefficient, band edge, and Urbach energy. The effects of different annealing temperatures and times on linear/nonlinear optical characteristics were investigated. The band gap of EDA-doped PANI films is reduced with the different thermal annealing from 3.43 eV to 3.19 eV, while the Urbach tail of the 100 °C/4h film which is 0.492 eV was decreased to 0.469 eV for 150 °C/1h sample. However, the absorbance and optical conductivity were both increased. Besides, the optical limiting effect assesses their potential to mitigate intense light. It was found from the study that for green wavelength 100 °C/4h film decreases the input power by 16.5 % whereas the 150 °C/1h sample shows superior limiting behavior of 99.96 %. It has been found that thermal annealing of the EDA-doped PANI films enhances the synthetic composite's optical characteristics, making the 150 °C/1h sample of EDA-doped PANI films appropriate for utilization in both energy applications and optoelectronics.

A simple model linking radiative–convective instability, convective aggregation and large-scale dynamics

Abstract. A simple model is presented which is designed to analyse the relation between the phenomenon of convective aggregation at small scales and larger-scale variability that results from coupling between dynamics and moisture in the tropical atmosphere. The model is based on single-layer dynamical equations coupled to a moisture equation to represent the dynamical effects of latent heating and radiative heating. The moisture variable q evolves through the effect of horizontal convergence, nonlinear horizontal advection and diffusion. Following previous work, the coupling between moisture and dynamics is included in such a way that a horizontally homogeneous state may be unstable to inhomogeneous disturbances, and, as a result, localised regions evolve towards either dry or moist states, with divergence or convergence respectively in the horizontal flow. The time evolution of the spatial structure of the dry and moist regions is investigated using a combination of theory and numerical simulation. One aspect of the evolution is a spatial coarsening that, if moist regions and dry regions are interpreted as convecting and non-convecting respectively, represents a form of convective aggregation. When the weak temperature gradient (WTG) approximation (i.e. a local balance between heating and convergence) applies, and horizontal advection is neglected, the system reduces to a nonlinear reaction–diffusion equation for q, and the coarsening is a well-known aspect of such systems. When nonlinear advection of moisture is included, the large-scale flow that arises from the spatial pattern of divergence and convergence leads to a distinctly different coarsening process. When thermal and frictional damping and f-plane rotation are included in the dynamics, there is a dynamical length scale Ldyn that sets an upper limit for the spatial coarsening of the moist and dry regions. The f-plane results provide a basis for interpreting the behaviour of the system on an equatorial β plane, where the dynamics implies a displacement in the zonal direction of the divergence relative to q and hence to coherent equatorially confined zonally propagating disturbances, comprising separate moist and dry regions. In many cases the propagation speed and direction depend on the equatorial wave response to the moist heating, with the relative strength of the Rossby wave response to the Kelvin wave response determining whether the propagation is eastward or westward. Within this model, the key overall properties of the propagating disturbances, the spatial scale and the phase speed, depend on nonlinearity in the coupling between moisture and dynamics, and any linear theory for such disturbances therefore has limited usefulness. The model described here, in which the moisture and dynamical fields vary in two spatial dimensions and important aspects of nonlinearity are captured, provides an intermediate model between theoretical models based on linearisation and one spatial dimension and general circulation models (GCMs) or convection-resolving models.

Effect of SiO2 on structural, magnetic, and nonlinear optical behavior of cobalt ferrite nanoparticles

CoFe2O4 and CoFe2O4/SiO2 were synthesized by sol-gel method. The studies carried out using XRD, FT-IR, SEM, TEM, VSM, UV–Vis DRS, and Z-scan techniques. The CoFe2O4 has the inverse cubic spinal structure. TEM and SEM confirmed the less crystallite size of CoFe2O4/SiO2 nanocomposite than CoFe2O4. The results show that two samples have a pure single phase cobalt ferrite with face-centred cubic spinel phase. SiO2 matrix in CoFe2O4 nanocomposite results in reduction particle size and magnetic properties as well as enhancement band gap. The band-gap energy of CoFe2O4 and CoFe2O4/SiO2 calculated 1.76 and 1.92 eV, respectively. Our findings in nonlinear optical aspects suggest that the distinctive characteristics of CoFe2O4/SiO2 present opportunities for advancements in telecommunications and other related fields.

An Ionization-Based Aerosol Sensor and Its Performance Study.

In recent years, with the rapid development of new energy vehicles, the safety issues of lithium-ion batteries have attracted attentions from all sectors of society. Research has found that during the thermal runaway process of lithium-ion batteries, aerosol emissions usually occur earlier than other gases. Accurate and timely measurement of these aerosol concentrations can help to warn the power battery pack fires. However, existing aerosol sensors are unable to meet the requirements of real-time monitoring and high precision. This article proposes an ionization mechanism based aerosol sensor that works at principles of field emission, field charging and gas discharge, and investigates its static and dynamic response characteristics. The sensor is manufactured and assembled using Microelectro Mechanical Systems processing technology. The sensor exhibits superior performances in terms of range, sensitivity, nonlinearity, repeatability, response time, and other aspects. The study provides a new solution for current aerosol detection with great potential for application.

Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

Decision-focused neural adaptive search and diving for optimizing mining complexes

Optimizing industrial mining complexes, from extraction to end-product delivery, presents a significant challenge due to non-linear aspects and uncertainties inherent in mining operations. The two-stage stochastic integer program for optimizing mining complexes under joint supply and demand uncertainties leads to a formulation with tens of millions of variables and non-linear constraints, thereby challenging the computational limits of state-of-the-art solvers. To address this complexity, a novel solution methodology is proposed, integrating context-aware machine learning and optimization for decision-making under uncertainty. This methodology comprises three components: (i) a hyper-heuristic that optimizes the dynamics of mining complexes, modeled as a graph structure, (ii) a neural diving policy that efficiently performs dives into the primal heuristic selection tree, and (iii) a neural adaptive search policy that learns a block sampling function to guide low-level heuristics and restrict the search space. The proposed neural adaptive search policy introduces the first soft (heuristic) branching strategy in mining literature, adapting the learning-to-branch framework to an industrial context. Deployed in an online fashion, the proposed hybrid methodology is shown to optimize some of the most complex case studies, accounting for varying degrees of uncertainty modeling complexity. Theoretical analyses and computational experiments validate the components’ efficacy, adaptability, and robustness, showing substantial reductions in primal suboptimality and decreased execution times, with improved and more robust solutions that yield higher net present values of up to 40%. While primarily grounded in mining, the methodology shows potential for enabling smart, robust decision-making under uncertainty.

Subcortical origin of nonlinear sound encoding in auditory cortex

A major challenge in neuroscience is to understand how neural representations of sensory information are transformed by the network of ascending and descending connections in each sensory system. By recording from neurons at several levels of the auditory pathway, we show that much of the nonlinear encoding of complex sounds in auditory cortex can be explained by transformations in the midbrain and thalamus. Modeling cortical neurons in terms of their inputs across these subcortical populations enables their responses to be predicted with unprecedented accuracy. By contrast, subcortical responses cannot be predicted from descending cortical inputs, indicating that ascending transformations are irreversible, resulting in increasingly lossy, higher-order representations across the auditory pathway. Rather, auditory cortex selectively modulates the nonlinear aspects of thalamic auditory responses and the functional coupling between subcortical neurons without affecting the linear encoding of sound. These findings reveal the fundamental role of subcortical transformations in shaping cortical responses.

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

This article delves into the seasonal variation of solar radiation patterns across Nigeria’s four geo-political zones, exploring their complex, scale-dependent behaviors. By employing chaotic quantifiers, the study characterizes irregular and self-similar patterns, enhancing our understanding of solar radiation variability and heterogeneity. The research uniquely fits meteorological parameters onto a global solar radiation model and focuses on the underexplored nonlinear aspects within tropical regions, specifically in the Nigerian context. Utilizing daily data from ERA INTERIM satellite archives for representative stations, the study employs nonlinear time series analysis methods like recurrence plots, recurrence qualitative analysis, and multifractal spectral analysis to comprehensively explore the unpredictable behaviors observed. The quantifier spectrums play a key role in revealing intricate scaling behaviors and correlation structures among environmental variables, shedding light on patterns often concealed by conventional statistical methods. For instance, we found that solar radiation variability in the northern region increases more significantly during the dry season compared to the wet season, unlike in the southern region. Additionally, the multifractal spectral analysis revealed a higher degree of complexity in solar radiation patterns during transition periods between seasons. The findings reveal a low recurrence quantitative analysis, long right tail, and truncations at both ends of the spectrum. This suggests instability in solar radiation across different seasons and locations. Nonetheless, the results also demonstrate that solar radiation is consistently available throughout the year, which is typical of tropical regions.

A study of thermal and nonlinear radiative aspects in peristalsis of Cu–H2O through compliant wall conduits

A study of thermal and nonlinear radiative aspects in peristalsis of Cu–H2O through compliant wall conduits

MODELING INFLATION DYNAMICS USING THE LOGISTIC MODEL: INSIGHTS AND FINDINGS

This paper examines applying the logistic model, frequently used in biology, to analyze inflation patterns in dynamic economic systems. The primary objective is to simulate and analyze the complex dynamics of inflation, thus providing new insights into the stability of financial institutions. Numerical methods such as Euler's Method, Runge-Kutta Method (RK4), and Adams-Bashforth-Moulton's method were used to simulate inflation patterns by discretizing the logistic equation. The data utilized in this research were obtained from INSTAT, BoA, MoF, and Eurostat, with quarterly results from 1995 to 2023. The simulation results indicated that the RK4 and Adams-Bashforth-Moulton methods yielded more precise and reliable inflation forecasts than Euler's. The logistic model represented the non-linear aspects of inflation dynamics well, emphasizing the necessity of using suitable numerical approaches. The study's findings highlight the effectiveness of the logistic model in economic analysis, specifically in forecasting inflation trends. Enhanced closure approaches have proven their effectiveness in analyzing intricate economic data, providing crucial insights into the stability of inflation, and informing policy formulation. This study utilizes the logistic model to analyze inflation dynamics, offering a unique methodology for comprehending and forecasting inflation in economic systems. An analysis of several closure techniques reveals a novel aspect of financial modeling tools. The findings indicate that incorporating advanced numerical methods can significantly improve the precision of economic models. These findings significantly impact economic research and policy formulation, especially in devising measures to manage inflation and ensure financial stability.

Nonlinear stability of two superimposed electrified dusty fluids of type Rivlin-Ericksen: Non-perturbative approach

The nonlinear instability of a planar interface between two overlapping Rivlin-Ericksen viscoelastic liquids, in the occurrence of fine dust and vertical electric fields throughout permeable media, and the influence of surface tension is studied. The prototype structure is assumed to be a two-dimensional groundwork for the sake of simplicity. The growing approval of several disciplines of practical physics and technology serves as motivation for analyzing this problem. Using viscous potential theory, the mathematical procedure is condensed. To properly control electro convection in liquid crystals, which is widely used in various displays technologies, it was crucial to have a thorough comprehension of stability. Gaining understanding of the behavior of viscoelastic fluids at interfaces was crucial in polymer manufacturing industries. Employing the linear equations of movement with the suitable nonlinear bounder circumstances forms the basis of the considered nonlinear technique. A nonlinear partial differential equation that evaluates the interface movement is the objective of the process. Since linear stability has frequently been investigated, the present situation will be constructed only in the nonlinear sense, where a number of dimensionless physical numerals are achieved. The non-perturbative methodology is used to accomplish the nonlinear standards. The main idea behind the novel performance is to convert an ordinary nonlinear ordinary differential equation into a linear one. The new technique differs from the conventional perturbation methods in that it may be used to exactly and correctly analyze the behavior of the strong nonlinear aspects of the interface displacement. This innovative method is established by using He's frequency formula. Using the nonlinear distinguishing equation, the special situation of the real and the complex coefficients is accomplished. It was observed that the instability zone expands as the electric field, porosity of porous medium, and density ratio of two fluids rise. The stability configuration is enhanced by the viscoelasticity parameter, Bond number, and ratio of kinematic viscoelasticity.

Examination of structural and spectrophotometric optical characteristics of nano-like flower quinolinyl carbonyl pyrazole-1-carbodithioate films: a new trend for optoelectronic applications

The current investigation entailed the synthesis of benzyl 5-amino-4-[(1-ethyl-4-hydroxy-2-oxo-1,2-dihydroquinolin-3-yl)carbonyl]-1H-pyrazole-1-carbodithioate (referred as BAEHQP, compound 2) from 6-ethyl-4,5-dioxo-5,6-dihydro-4H-pyrano[3,2-c]quinoline-3-carbonitrile (compound 1), followed by its characterization in nanocrystalline thin films produced through thermal evaporation in a high vacuum. Computational and experimental methodologies were employed for this purpose. Density functional theory (DFT) calculations using the B3LYP/6-311++G(d,p) basis set were utilized to predict the HOMO-LUMO gap and evaluate the global reactivity descriptors, elucidating the chemical hardness, kinetic stability, and electron-donating capabilities. X-ray diffraction (XRD) analysis verified the presence of a polycrystalline structure in the BAEHQP powder and indicated an amorphous nature in the thin film. Utilizing the Williamson-Hall formula, the mean crystallite size and microstrain were determined to be 63.36 nm and 0.00667, respectively. Scanning electron microscopy corroborated the nanoflower-like structure, and particle size analysis was conducted to determine the size distribution. The optical properties, encompassing both linear and nonlinear aspects, of BAEHQP thin film were investigated using spectrophotometric methods, delineating band transitions and identifying a fundamental direct energy gap of 2.2 eV. The distinct attributes of thin-film absorption and dispersion were outlined, and the unanticipated findings hold potential implications for advancing the industrialization of photodiode device manufacturing.

Landslide Susceptibility Mapping Using Machine Learning: A Case Study of Oregon

Landslide susceptibility assessment, a crucial component of disaster prevention and mitigation, has traditionally relied on geological datasets and commonly used evaluation metrics. However, these metrics, often applied to 2D susceptibility maps, may not capture the full complexity of the issue. In this study, we incorporated spatial distribution data, including a historical landslide inventory, and integrated morphological, geological, hydrological, and land-use features. This approach enabled the construction of predictive models using eight distinct algorithms. The evaluation of classifier performance scores unveiled the superior performance of gradient tree boosting. In contrast, logistic regression struggled to handle the nonlinear aspects of landslide susceptibility analysis. A finding in our research was the consistent disparity in predictive susceptibility maps, even when similar performance scores were achieved across different algorithms. This underscores the necessity of combining evaluation metrics with spatial mapping in landslide susceptibility model assessment. We also highlighted the critical role of digital elevation models in constructing effective models when feature data was limited. Conversely, when abundant information was available, the integration of multi-source data significantly enhanced the precision of landslide susceptibility mapping. Overall, this study provides valuable insights for spatial prioritization in landslide field investigations and enhances our understanding of landslide risk assessment.

The Complex Influence of Information Communication Technology (ICT) on Students’ Performance in Singapore: Evidence From 2018 PISA Data

This study focuses on the effects of negative and non-linear aspects of ICT use and environmental factors on PISA mathematics, science, and reading performance. Data from the 2018 PISA [the OECD’s Programme for International Student Assessment] for 6,297 15-year-old students from Singapore were analyzed. The data analysis used the IDB analyzer to construct a two-step regression model and identified four distinct patterns of how ICT factors affect PISA performance. The “U” shape pattern includes perceived ICT competence which has a significant negative linear coefficient and a positive quadratic coefficient. The “∩” shape pattern includes ICT resources, use of ICT outside of school (for schoolwork activities), ICT use outside of school (leisure) and interest in ICT. This group consists of either a significant positive linear coefficient or a non-significant linear coefficient with a significant negative quadratic coefficient. The strictly negative pattern includes the use of ICT at school in general and ICT as a topic in social interaction. This group consists of only a significant negative linear coefficient. The strictly positive pattern includes perceived autonomy related to ICT use which only has a significant positive linear coefficient. Overall, the impact of ICT factors on the three subjects was similar, with minor differences. Given the complexity of how ICT factors could negatively affect performance, policymakers will need to fine-tune ICT practices to ensure students receive the maximum benefits with optimal ICT resources.

Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM).

This article addresses the evolving landscape of data advertising within network-based new media, seeking to mitigate the accuracy limitations prevalent in traditional film and television advertising evaluations. To overcome these challenges, a novel data-driven nonlinear dynamic neural network planning approach is proposed. Its primary objective is to augment the real-time evaluation precision and accuracy of film and television advertising in the dynamic interactive realm of network media. The methodology primarily revolves around formulating a design model for visual advertising in film and television, customized for the dynamic interactive milieu of network media. Leveraging DeepFM+long short-term memory (LSTM) modules in deep learning neural networks, the article embarks on constructing a comprehensive information statistics and data interest model derived from two public datasets. It further engages in feature engineering for visual advertising, crafting self-learning association rules that guide the data-driven design process and system flow. The article concludes by benchmarking the proposed visual neural network model against other models, using F1 and root mean square error (RMSE) metrics for evaluation. The findings affirm that the proposed model, capable of handling dynamic interactions among images, visual text, and more, excels in capturing nonlinear and feature-mining aspects. It exhibits commendable robustness and generalization capabilities within various contexts.

Discuss the effect of the Third Organizational Efficiency Theory on Society and Economic growth via Corruption axes frustration by a non-linear model Corruption aspects benefits)

Discuss the effect of the Third Organizational Efficiency Theory on Society and Economic growth via Corruption axes frustration by a non-linear model Corruption aspects benefits)

Nonlinear aspects of stochastic particle acceleration

Nonlinear aspects of stochastic particle acceleration

Modeling and Numerical Computation of the Longitudinal Non-Linear Dynamics of High-Speed Elevators

High-speed elevator systems comprise numerous components, and vibration issues are prevalent. The evident non-linear behavior resulting from changes in the wire rope length adds complexity to the investigation of elevator dynamics issues. This paper investigates dynamics modeling and numerical solution methods for longitudinal vibrations in a typical high-speed elevator system. The primary contributions of this paper include constructing a dynamics model for high-speed elevators using a substructure dynamics modeling approach. This model incorporates Newton’s law and the Lagrange equation to comprehensively represent the dynamics of the elevator car, car frame, traction system, and tension system. Additionally, a non-linear dynamics model of the steel wire rope is developed using the centralized mass method. This paper also presents an algorithm to solve the time-domain dynamics based on the variable-step-length Runge–Kutta method. Furthermore, it investigates the non-linear dynamics of elevators considering variations in the elevator’s intrinsic frequency and different elevator control strategies, focusing on the response characteristics of high-speed elevator dynamics. The findings of this thesis hold significant importance in the field of high-speed elevator dynamics. They aid in the design and debugging of high-speed elevator systems and serve as a foundation for future research into the non-linear aspects of elevators.