Nonlinear Behavior Research Articles

EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.

In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients.

Non-linear behavior of castellated beams consisting hexagonal and diamond openings using CFRP stiffeners

Non-linear behavior of castellated beams consisting hexagonal and diamond openings using CFRP stiffeners

A reliable hybrid autoregressive integrated moving average and deep reinforcement machine learning strategy for resiliency enhancement in microgrid

Machine learning algorithms are crucial for energy transition and climate change system resilience. This paper presents the essential issue of improved microgrid resilience after catastrophes, with a focus on the Root Mean Square Error (RMSE) assessment metric. By using a Hybrid Autoregressive Integrated Moving Average- Deep Reinforcement Learning (HARIMA-DRL) based machine learning microgrid restoration method for post-disaster power recovery, resilience in the operation aspect is enhanced. The local power grid's resilience during a simulated disaster is examined using a quantitative resilience measure that is based on two significant quality functions. When the hybrid HARIMA-DRL model final resilience value is 3.07 %, 7.78 %, and 11.51 % higher for 5000, 8000, and 10000 training datasets, as compared to Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) and Gauss Reinforcement Memory (GRM). Simulation findings comparing the proposed HARIMA-DRL method to CNN-LSTM and GRM models trained on separate datasets show that the HARIMA-DRL enhances grid instantaneous and average resilience by 3.38 % over T1 and 4.96 % over T2. Flexible kernel settings and smooth nonlinear prediction behaviour allow the proposed HARIMA-DRL approach to provide the highest resilience enhancement and lowest prediction error.

Selective online model updating in hybrid simulation of a full‐scale steel moment frame

AbstractThis study presents the implementation of an online model updating algorithm within a full‐scale hybrid simulation (HS) of a six story, four bay steel moment frame. The experimental substructure consists of a cruciform subassembly generating critical data on the nonlinear behavior of the first story column and two beams with reduced beam sections (RBSs) on each side. The updating algorithm focuses on the modeling parameters of plastic hinge elements representing the RBSs in the numerical model. A smooth plasticity model is utilized for beam plastic hinges with updating parameters identified from on‐line experimental data through a modified version of the unscented Kalman filter. The HS shows that the numerical beam hinges based on simple hysteretic model with updated parameters are able to capture the characteristic behavior observed in experiments. Due to fracture of beam flanges is observed in the experiments, a selective updating concept is proposed to allow for updating multiple numerical components accounting for asymmetric behavior and variability in the response. The selective updating method is validated through virtual HSs that are better able to identify and isolate the effects of fracture and other behavioral characteristics. The combination of results from physical and virtual tests highlights the benefits of model updating on the local and overall system‐level response.

Do bromine and surface-active substances influence the coastal atmospheric particle growth?

New particle formation (NPF) is considered a major source of aerosol particles and cloud condensation nuclei (CCN); however, our understanding of NPF and the subsequent particle growth mechanisms in coastal areas remains limited. This study provides evidence of frequent NPF events followed by particle growth in the middle Adriatic Sea during the summer months at the coastal station of Rogoznica in Croatia. To our knowledge, this is the first study to report such events in this region. Our research aims to improve the understanding of NPF by investigating particle growth through detailed physicochemical characterization and event classification. We used a combination of online measurements and offline particle collection, followed by a thorough chemical analysis. Our results suggest the role of bromine in the particle growth process and provide evidence for its involvement in combination with organic compounds. In addition, we demonstrated the significant influence of surface-active substances (SAS) on particle growth. NPF and particle growth events have been observed in air masses originating from the Adriatic Sea, which can serve as an important source of volatile organic compounds (VOC). Our study shows an intricate interplay between bromine, organic carbon (OC), and SAS in atmospheric particle growth, contributing to a better understanding of coastal NPF processes. In this context, we also introduced a new approach using the semi-empirical 1st derivative method to determine the growth rate for each time point that is not sensitive to the nonlinear behavior of the particle growth over time. We observed that during NPF and particle growth event days, the OC concentration measured in the ultrafine mode particle fraction was higher compared to non-event days. Moreover, in contrast to non-event days, bromine compounds were detected in the ultrafine mode atmospheric particle fraction on nearly all NPF and particle growth event days. Regarding sulfuric acid, the measured sulfate concentration in the ultrafine mode atmospheric particle fraction on both NPF event and non-event days showed no significant differences. This suggests that sulfuric acid may not be the primary factor influencing the appearance of NPF and the particle growth process in the coastal region of Rogoznica.

Analysis of nonlinear bending behavior of nano-switches considering surface effects

Nano-switch structures are important control elements in nanoelectromechanical systems and have potential applications in future nanodevices. This paper analyzes the effects of surface effects, geometric nonlinearity, electrostatic forces, and intermolecular forces on the nonlinear bending behavior and adhesion stability of nano-switches. Based on the Von Karman geometric nonlinearity theory, four types of boundary conditions for the nano-switch structure were specifically calculated. The results show that surface effects have a significant impact on the nonlinear bending and adhesion stability of nano-switches. Surface effects increase the adhesion voltage of the nano-switch and decrease its adhesion displacement, and as the size of the nano-switch structure increases, the impact of surface effects decreases. A comparative analysis of the linear theory and the nonlinear theory results shows that the adhesion voltage predicted by the linear theory is smaller than that predicted by the nonlinear theory. The effect of geometric nonlinearity increases as the size of the nano-switch structure increases, as the distance between the electrodes increases, and as the aspect ratio of the nano-switch structure increases. These findings provide theoretical support and reference for the design and use of future nanodevices and nanoelectromechanical systems.

Nonlinear vibrations of a ballistic vehicle with friction joints subjected to vibroacoustic excitation - experiments, modeling and simulations

This study presents the experiments, modeling and numerical simulations of an industrial aeronautical structure subjected to vibroacoustic excitation. In flight, especially during atmospheric re-entry, an aeronautical structure is exposed to major pressure fluctuations on its external surface. These fluctuations are described as a dynamic excitation which generates structural vibrations of the outer surface and of the components inside the aeronautical structure. Due to some of these internal components such as joints, the assembly may exhibit a nonlinear vibration response. The simulation of these nonlinear vibrations requires reliable modeling of the wall pressure fluctuations and a nonlinear vibration simulation method adapted to the nonlinear modeling of the structure. In this study, a modeling and simulation method is developed to compute the nonlinear vibration response of an industrial assembly to such a surface, random, correlated dynamic excitation. More specifically, numerical simulations are performed by proposing an extension of the well-known Harmonic Balance Method for nonlinear mechanical systems subjected to complex vibroacoustic excitation. The method is validated using a dedicated ground experiment. A metallic industrial assembly representing a ballistic vehicle and including friction joints is used. This structure is subjected to controlled vibroacoustic excitation: diffuse acoustic loading in a reverberant chamber. The structure exhibits a nonlinear vibration response due to friction. The finite element model is validated through an experimental modal analysis with an electrodynamic shaker. The vibroacoustic modeling of the excitation is then validated through a test-simulation comparison using diffuse acoustic field testing at low excitation level. Then the global nonlinear simulation process is validated using diffuse acoustic field testing at increasing excitation levels. Test and simulation results exhibit the same nonlinear behavior: increase of dissipation and softening effect at the main resonances. This work thus represents a new step towards the use of nonlinear vibration simulation methods with real industrial structures and real-life loading.

The effect of interfacial imperfection on the nonlinear dynamic behavior of composite laminates under low-velocity impact

The effect of interfacial imperfection on the nonlinear dynamic behavior of composite laminates under low-velocity impact

Large displacement model for the nonlinear behaviour of the face plate component

This paper deals with the face plate component in steel joints and addresses the full characterization of the nonlinear behaviour of the face plate component. An equivalent beam strip model is proposed that tackles the connection of a beam to the column web of open I-sections in a minor axis joint or the face of tubular columns, covering endplate or fin plate joint typologies. Closed-form analytical solutions are obtained for the elastic large displacement and the elastic-plastic large displacement behaviour of the equivalent beam strip. Criteria for the establishment of the design resistance using the continuous strength method are also proposed. It was concluded that the model is easy to apply, was validated against a large parametric study using finite element beam models, leads to accurate solutions and demonstrates the need to consider membrane effects in design.

Nonlinear viscoelasticity of Melaleuca alternifolia essential oil Pickering emulsion stabilized with cellulose nanofibrils

Pickering emulsions are suitable technological approaches to stabilize the bioactive compounds of essential oils (EOs) with economic interests for the pharmaceutical, cosmetic and food industries using atoxic and renewable stabilizers, such as cellulose nanostructures. The Melaleuca alternifolia essential oil (MaEO) is recognized for its pronounced antimicrobial performance, which has attracted your interest in developing eco-friendly antiseptic products. In this contribution, we report the nonlinear viscoelasticity behavior of MaEO-in-water Pickering emulsions stabilized by cellulose nanofibrils (CNF) using Large Amplitude Oscillatory Shear (LAOS) tests. The rheological characterizations by Fourier Transform (FT) rheology spectroscopy and Lissajous plots enabled the correlation of the viscous and elastic behaviors with the physical processes involving the deformation of the emulsion. Also, the internal structure of the emulsion was elucidated by fluorescence optical microscopy. Several findings relative to the effects of the shear deformation amplitude over the nonlinear viscoelasticity of the emulsified system are reported here, which are essential to the industrial processes used to develop technological products.

Inverse Scheme to Locally Determine Nonlinear Magnetic Material Properties: Numerical Case Study

We are interested in the determination of the local nonlinear magnetic material behaviour in electrical steel sheets due to cutting and punching effects. For this purpose, the inverse problem has to be solved, where the objective function, which penalises the difference between the measured and the simulated magnetic flux density, has to be minimised under a constraint defined according to the corresponding partial differential equation model. We use the adjoint method to efficiently obtain the gradients of the objective function with respect to the material parameters. The optimisation algorithm is low-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS), the forward and adjoint formulations are solved using the finite element (FE) method and the ill-posedness is handled via Tikhonov regularisation, in combination with the discrepancy principle. Realistic numerical case studies show promising results.

Recursive demodulated synchro spline-kernelled chirplet extracting transform: a useful tool for non-linear behavior estimation of non-stationary signal and application to wind turbine fault detection

Non-linear behavior is widespread in many kinds of signals from nature and engineering fields. Although the high energy-concentration level of various advanced time-frequency (TF) analysis (TFA) techniques currently developed ensure a fine representation of non-linear behavior of time-varying component (TVC) of the signal, it is far from sufficient to solely consider the single aspect of energy-concentration level, because the actual signal composition is always more complicated, especially for some thorny difficulties such as strong noise interference and the early weak TVC, etc., these negative factors bring significant challenges to reveal the non-linear behavior of TVC of practical signals. A new TFA method aimed at this issue, called recursive demodulated synchro spline-kernelled chirplet extracting transform (RDSSCET), is proposed in this paper. The proposed RDSSCET is developed on the frame of synchro spline-kernelled chirplet extracting transform (SSCET) and a newly designed external-internal nested double iteration mechanism, which effectively addresses the limitation of SSCET in handling multicomponent signals while also exhibiting superior high energy concentration and noise robustness. As such, the proposed RDSSCET can yield a more favorable outcome when revealing the non-linear behavior of TVC, particularly for weak TVC with strong noise interference. Comparison analysis results in numerical simulations verified the performance of RDSSCET. Its effectiveness in real applications is fully tested via two real-world sound signals and a practical case of wind turbine fault detection.

Low-cycle fatigue simulation of ductile materials using elasto-plastic gradient damage approach

This work presents a novel computational strategy based on gradient enhanced damage and cyclic yielding with nonlinear mixed hardening behaviour for simulating low-cycle fatigue of ductile materials. A new yield function is introduced which accommodates the cyclically evolving state variables during mixed hardening and the damage induced during the application of repetitive loads. The resulting constitutive relations are derived in an incremental form. The non-local strains facilitate the computation of incremental damage in the material. A finite element scheme is developed from the governing physics through full coupling between damage and cyclic-elastoplastic deformations. In order to obtain an accurate computational response, a stress-return mapping scheme is formulated for the damage-dependent yield function. The developed numerical strategy is investigated for specimens made of various ductile materials exhibiting elasto-plastic behaviour under low-cycle fatigue loads. The stress-strain hysteresis and cyclic softening computed by the present numerical framework agree well with experimental data available in literature. The low-cycle fatigue crack-growth results are also accurately captured for various fatigue loading conditions.

A kinetic Monte Carlo approach for Boolean logic functionality in gold nanoparticle networks

Nanoparticles interconnected by insulating organic molecules exhibit nonlinear switching behavior at low temperatures. By assembling these switches into a network and manipulating charge transport dynamics through surrounding electrodes, the network can be reconfigurably functionalized to act as any Boolean logic gate. This work introduces a kinetic Monte Carlo-based simulation tool, applying established principles of single electronics to model charge transport dynamics in nanoparticle networks. We functionalize nanoparticle networks as Boolean logic gates and assess their quality using a fitness function. Based on the definition of fitness, we derive new metrics to quantify essential nonlinear properties of the network, including negative differential resistance and nonlinear separability. These nonlinear properties are crucial not only for functionalizing the network as Boolean logic gates but also when our networks are functionalized for brain-inspired computing applications in the future. We address fundamental questions about the dependence of fitness and nonlinear properties on system size, number of surrounding electrodes, and electrode positioning. We assert the overall benefit of having more electrodes, with proximity to the network’s output being pivotal for functionality and nonlinearity. Additionally, we demonstrate an optimal system size and argue for breaking symmetry in electrode positioning to favor nonlinear properties.

Linear and nonlinear axial behaviour of internal replacement pipe systems for pipeline rehabilitation

Internal replacement pipe (IRP) is an innovative trenchless technology for the rehabilitation of legacy steel and cast-iron pipelines. This advanced IRP system must be properly designed so that it can safely and effectively restore the service of damaged pipelines. This paper studies the axial behaviour of IRP systems for repairing pipelines with circumferential host-pipe discontinuities under seasonal and extreme levels of temperature change. Analytical solutions along with a total of 270 linear and nonlinear finite element (FE) simulations, validated against experimental results and available closed-form solutions, were used for a parametric study on the effects of geometrical and material properties on axial stresses and deformations of IRP systems subjected to temperature change. Effects of the internal pressure, material and geometric nonlinearities, and different modes of IRP-host pipe unbonding were also investigated. An analytical model was developed for the prediction of temperature change induced loading and response of the IRP system. Using the results obtained from a comprehensive FE-based parametric study, three modification factors were extracted for the application to the developed analytical model in order to accurately predict the maximum axial stress of IRP and the opening of a circumferential host-pipe discontinuity subjected to various levels of temperature change.

Unravelling the effect of molecular interactions on macroscale properties in Sustainion anion exchange membrane (AEM) under hydrated conditions using MD simulations

Sustainion is an anion exchange water electrolyzer membrane that has exhibited a scalable performance. In this work, we present a molecular model for the Sustainion membrane incorporating functionalization of the polymer in order to better mimic experimental conditions. Here in, we present a comprehensive exploration of its structural and transport properties like density, diffusivity and conductivity at various operating hydration levels using molecular dynamics simulations. The density exhibits a non-monotonic trend while the diffusivity showcases a non-linear behaviour with hydration. Furthermore, diffusion exhibits an Arrhenius-like dependence on temperature with the activation energies exhibiting a non-monotonic relationship akin to the density. It is concluded that the afore-mentioned properties of the Sustainion membrane are due to the counteracting influence of the enhancement in coordination number, and a reduction in the potentials of mean force of the different atomic pairs in the system. The effect of added salt on Sustainion properties is also determined and collated with experiments. Based on comparison with experiment, we deduce that vehicular diffusion is the predominant mechanism in the diffusion of the chloride ion, while it contributes approximately 15% to the diffusivity of the hydroxide ion.

Nonlinear oscillatory rheology of aqueous suspensions of cellulose nanocrystals and nanofibrils

In this work, the nonlinear rheological behavior of aqueous suspensions composed of two typical nanocellulose [rod-like cellulose nanocrystals (CNCs) and filamentous cellulose nanofibrils (CNFs)] was examined and compared by using various large-amplitude oscillatory shear (LAOS) analysis methods, such as Fourier-transform rheology, stress decomposition, Chebyshev polynomials, and the sequence of physical processes. From our analysis, the nonlinear rheological parameters of higher harmonics, dissipation ratio, strain hardening ratio, shear thickening ratio, transient modulus, and cage modulus were obtained and quantitatively analyzed. CNCs tend to assemble to form anisotropic structures in an aqueous medium while the CNFs are entangled to form gels. The CNF suspensions demonstrated a significant viscous modulus overshoot and had stronger yield stresses, but the yield of CNC suspensions was more ductile. In the case of low concentrations, the CNF suspensions demonstrated stronger intracycle shear thickening behavior in medium-amplitude oscillatory shear region and lower dissipation ratios at small strain amplitudes. Although both nanocellulose suspensions revealed the existence of four intracycle rheological transition processes (viscoplastic deformation, structural recovery, early-stage yielding, and late-stage yielding), the CNF suspensions exhibited a stronger structural recovery ability. Larger strain amplitudes did not invariably result in a broader range of intracycle rheological transitions, which are also affected by the excitation frequency. The application of the various LAOS analysis methods provided valuable intracycle nonlinear rheological insights into nanocellulose suspensions, which are of great importance for enhancing their industrial perspectives.

[formula omitted] anomaly effect on crystallization and mechanical properties of soda-lime silicate glasses

In the present investigation, the effect of B2O3 is investigated on the theoretical mechanical properties and crystallization kinetics of (64-x)SiO2 - xB2O3 - 16Na2O - 12CaO - 2Al2O3 - 6MgO (x = 2, 4, and 6 mol%) glasses. To understand the mechanical properties elastic moduli are calculated using two theoretical models i.e., Makishima–Mackenzie and Rocherulle. Whereas, in crystallization kinetics, activation energies (Eg, Ec, and Ep) are computed using two distinct methods, i.e., the Kissinger model and the Augis and Bennett model. The replacement of SiO2 by B2O3 enhances the strengthening of the glass network which makes the B6-S58 glass network more rigid. The B6-S58 and B2-S62 glasses have the highest and lowest activation energy (Eg) for crystallization, respectively. B4-S60 glass shows non-linear behavior in characteristics temperature as well as in activation energy due to boron anomaly. The present finding could be helpful in designing windshield automobile glasses.

Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete

Background Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article’s incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database.

Neural network survivability approach of a wave energy converter considering uncertainties in the prediction of future knowledge

To tune the wave energy converter (WEC) controller parameters such as damping to reduce the line force during extreme wave conditions, future knowledge of the line force is required. To achieve this, the incoming wave and system state should be predicted for a few seconds in the future. It is rather an arduous task to predict the future knowledge of waves and the system’s dynamic when dealing with breaking and steep waves, and the system is subject to various nonlinear forces. The classical model-based control strategies often rely on linear assumptions to estimate the WEC dynamics for the sake of simplicity. Unlike the model-based, the data-driven approaches are free from modeling errors and the algorithms are trained over the true and noisy data to predict non-linear system behaviors. Using data-driven approaches, we are able to model nonlinear dynamics. However, new questions emerge on the accuracy of the future wave and system state predictions, and how this uncertainty propagates into the final prediction of the line force. As incorrect damping may lead to excessive line force and detrimental damage to the system, these are the knowledge gaps that need to be addressed. The main purpose of this paper is to answer these questions through a survivability strategy for wave energy converters by providing a realistic perspective on the implementation of the neural network approaches by accounting for the errors in the input data. For this purpose, a series of neural networks is designed that first predicts the surface elevation for 0.36 s ahead, i.e. corresponding to 2 s in the full-scale WEC. This future knowledge of the wave elevation is then used to predict the system state (i.e. power take-off (PTO) translator position) for the same prediction horizon based on the PTO damping. This information is then fed to a convolutional neural network (CNN) that predicts the peak line force 0.36 s ahead. This paper also evaluates the predictive capabilities of neural network (NN) models, comparing them to classical autoregressive (AR) and Kalman filter methods. While the AR model exhibits slightly higher accuracy in fixed PTO system configurations, it falters in providing real-time predictions when system parameters undergo variations. In contrast, the NN prediction model excels by establishing a robust relationship between system configuration and output signals. Especially noteworthy is its ability to outperform classical methods with minimal training on a selective set of system configurations. Then, the sensitivity of the peak line force prediction to the uncertainties in the input data from NN models and the prediction horizon is analyzed. The neural network models are trained over the experimental data subjected to extreme sea states for a point absorber wave energy converter. The results suggest that the accuracy of the surface elevation prediction has an insignificant direct effect on the peak force prediction model. However, these uncertainties are reflected in the PTO translator position prediction, and the model is considerably sensitive to the accuracy of this prediction. This sensitivity nonetheless is less notable for higher PTO damping values.