Nonlinear Response Research Articles

Linear and Nonlinear Dielectric Response of Intrinsically Disordered Proteins.

Linear and nonlinear dielectric responses of solutions of intrinsically disordered proteins (IDPs) were analyzed by combining molecular dynamics simulations with formal theories. A large increment of the linear dielectric function over that of the solvent is found and related to large dipole moments of IDPs. The nonlinear dielectric effect (NDE) of the IDP far exceeds that of the bulk electrolyte, offering a route to interrogate protein conformational and rotational statistics and dynamics. Conformational flexibility of the IDP makes the dipole moment statistics consistent with the gamma/log-normal distributions and contributes to the NDE through the dipole moment's non-Gaussian parameter. The intrinsic non-Gaussian parameter of the dipole moment combines with the protein osmotic compressibility in the nonlinear dielectric susceptibility when dipolar correlations are screened by the electrolyte. The NDE is dominated by dipolar correlations when electrolyte screening is reduced.

Symmetry breaking in 2D materials for optimizing second-harmonic generation

Abstract Second-harmonic generation (SHG) is the generation of 2ω (or half wavelength) light from incident light with frequency ω as a nonlinear optical response of the material. Three-dimensional (3D) SHG materials are widely investigated for developing laser technology to obtain shorter wavelengths in photolithography fabrication of semiconductor devices and the medical sciences, such as for imaging techniques that do not use fluorescent materials. However, to obtain the optimized SHG intensity, the 3D material is required to have no spatial-inversion symmetry (or non-centrosymmetry) and special crystal structure (or so-called phase-matched condition). Recently, engineering symmetry breaking of thin two-dimensional (2D) materials whose 3D structure has the inversion symmetry can offer a breakthrough to enhance the SHG intensity without requiring the phase-matched condition. Over the past decade, many 2D SHG materials have been synthesized to have broken inversion symmetry by stacking heterostructures, twisted moire structures, dislocated nanoplates, spiral nanosheets, antiferromagnetic order, and strain. In this review, we focus on the recent progress in breaking inversion and rotational symmetries in out-of-plane and/or in-plane directions. The theoretical calculations and experimental setup are briefly introduced for the non-linear optical response of the 2D materials. We also present our perspectives on how these can optimize the SHG of the 2D materials.

Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm to solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold algorithm is proposed, which is AHCSMA, to solve these problems efficiently. Specifically, there are three innovations in this paper: (i) new self-adaptive Cauchy mutation operator is developed to improve the mutation ability of the population; (ii) the crossover rate balance mechanism is proposed to make up for the neglected relationship between individuals and crossover rates. Then the differential vector information between the dominant individual and other individuals in the population is highly utilized to increase the evolution speed of the algorithm; (iii) self-adaptive restart hybrid opposition learning is designed to alleviate the situation where the algorithm falls into local optimality. To verify the competitive of AHCSMA, UAV path planning problems, engineering problems, nonlinear parameter extraction of photovoltaic model problems and parameter identification problems of highly nonlinear infinite impulse response are used to test the ability of the AHCSMA, accumulation more than 50 algorithms are used as comparison algorithms, and results report that AHCSMA is extremely competitive and performs better when optimizing these real-life problems.

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Effect of oxygen defects on ferroelectric oxides: Correlation between linear and nonlinear dielectric responses

Oxygen defects are introduced into PbZr0.5Ti0.5O3 films and their impact on ferroelectric behavior, linear dielectric response (LDR), and nonlinear dielectric response (NDR) is studied. Apart from a notable decrease in both polarization and LDR permittivity, the frequency spectra of LDR exhibit a distinctive loss peak. The peak position varies with temperature and oxygen defect concentration. NDR parameters, including the Rayleigh coefficient (α1), the slope (α2) correlating the imaginary part of permittivity with the ac electric field, and the slope (k) between the real and the imaginary parts of permittivity, demonstrate diverse behaviors in response to temperature and oxygen defect concentration. The similarity in the frequency spectra of tanδ and 1/k reveals the correlation between LDR and NDR induced by the presence of oxygen defects. These observations are attributed to the behavior of the single and composite defects associated with oxygen vacancies.

Self-centering of steel braced frames equipped with Fe-SMA TADAS dampers

Abstract This study proposes a novel triangular added damping and stiffness (TADAS) damper that uses the shape memory effect (SME) of iron-based shape memory alloy (Fe-SMA) to provide a self-centering system to recover the initial shape of a structure after significant inelastic deformation induced by nonlinear response of fused elements, without the need for difficult and expensive replacement procedures of conventional TADAS systems. Unlike most studies which consider simplified uniaxial behavior of Fe-SMAs, the present non-linear finite element simulations cover the full 3D material non-linearity of Fe-SMA component based on the SMA constitutive law to capture both flexural and shear behavior in various coupled thermomechanical loadings, including the mechanical loading/unloading, the heating, and the final cooling (as the recovering process). Simulations performed on a one-bay steel frame for different drift ratios reveal that although the dissipation energy of the new device is at most 10% less than the ordinary one, it enjoys the self-centering property to recover the initial shape of the frame before loading, showing that the proposed damper is an effective alternative to ordinary TADAS yielding dampers to achieve the self-centering characteristics.

State Identification via Symbolic Time Series Analysis for Reinforcement Learning Control

Abstract This technical brief makes use of the concept of symbolic time series analysis (STSA) for identifying discrete states from the non-linear time response of a chaotic dynamical for model-free reinforcement learning (RL) control. Along this line, a projection-based method is adopted to construct probabilistic finite state automata (PFSA) for identification of the current state (i.e., operational regime) of the Lorenz system; and a (Q-map-based and model-free) RL control strategy is formulated to reach the target state from the (identified) current state. A synergistic combination of PFSA-based state identification and RL control is demonstrated by simulation of a numeric model of the Lorenz system, which yields very satisfactory performance to reach the target states from the current states in real time.

Optical non-linearities and applications of ZnS phosphors

Optical non-linearities play a crucial role in enabling efficient and ultrafast switching applications that are essential for next-generation photonic devices. ZnS phosphor material produces the best results in terms of increased luminescence quantum yield when doped with certain impurities. Nevertheless, the investigation of the third-order non-linear optical susceptibility of the phosphor materials can be exploited for various switching applications. In this regard, we review the recent advancements in the investigation of non-linear optical properties of ZnS phosphors, where the knowledge of absorption and refraction is utilized in various optical and detector applications. Furthermore, the review highlights strategies employed to enhance the non-linear optical response of phosphor materials as well as a general discussion of an attosecond optical switching scheme which can be used to fabricate devices with petahertz speeds. Consequently, we provide a solution to the unsolved problem of the significant extension of optical limiting applications to switching applications by developing design strategies to manipulate conventional ZnS phosphor material. The potential challenges and future prospects of utilizing phosphor materials for switching applications are also addressed. The strategies for manipulating ZnS phosphor can be generalized for a broad range of other materials by minimizing linear and non-linear losses, while enhancing the values of the non-linear refractive index coefficient. We propose that the figure-of-merit of ZnS material can be enhanced by using a suitable combination of pump and probe wavelength values, which can be useful for optical switching applications.

Static and dynamic response of pyramidal lattice sandwich plate with composite face sheets reinforced by graphene platelets

For the pursuit of lightweight structures with excellent mechanical performance, sandwich structures with lightweight cores and strengthened face sheets have become popular in the advanced engineering fields in recent years. In this paper, the nonlinear static and dynamic responses of the sandwich plate constructed with the aluminum pyramidal lattice core and functionally graded graphene platelets reinforced composite (FG-GPLRC) face layers are analyzed. The partial differential equations of the pyramidal lattice sandwich plate considering the geometric nonlinearity are achieved based on the Reddy’s third order shear deformation theory (TSDT), which are solved by the combination of finite element model, Raphson’s method and Newmark numerical integration method. Four different types of the impact loads including the step loading, triangular loading, half-sine loading and exponential loading with the same peak value and duration time are investigated. The influences of the thickness-to-truss radium ratio and the angle of the truss core, as well as the weight fraction and distribution patterns of graphene platelets (GPLs) over the face sheets under different impact loads and different boundary conditions are analyzed. According to the numerical results, it is concluded that the sandwich plate with higher weight fraction of GPLs, lower thickness-to-truss radium ratio, and higher angle of the truss core can exhibit better performance in both static and dynamic loading conditions. The FG-X distribution of GPLs presents the best dynamic performance as compared to the other GPL distributions. The combination of lightweight truss core with the advanced GPLRC face sheets is instructive for potential applications and further research.

Investigating the Z-scan technique for quantifying circulating cell-free DNA (ccfDNA) extracted from blood plasma as a potential biomarker for various cancers.

Abstract
The Z-scan technique is a nonlinear optical method that has found applications in characterizing various materials, particularly those exhibiting nonlinear optical response (NLOR). This study applies the continuous wave (CW) Z-scan technique to examine the NLOR in terms of the nonlinear optical phase shifts (ΔΦ_0) exhibited by the ccfDNA extracted from blood plasma samples collected from a group constituting 30 cancer-diagnosed patients and another group constituting 30 non-diagnosed individuals. The cancer group exhibited significantly higher ΔΦ_0vs. incident power slopes compared to the non-cancer group (0.34 vs 0.12) providing a clear distinction between the two groups. The receiver operating characteristic (ROC) curve analysis of the results indicates a clear separation between cancer and non-cancer groups, along with a 94% accuracy rate of the data. The Z-scan results are corroborated by spectrophotometric analysis, revealing a consistent trend in the concentration values of ccfDNA samples extracted from both cancerous and non-cancerous samples, measuring 3.24 and 1.41 respectively. Additionally, more sensitive fluorometric analyses of the respective samples demonstrate significantly higher concentrations of ccfDNA in the cancer group, further affirming the correlation with the Z-scan results. The study suggests that the Z-scan technique holds promise as an effective method for cancer detection, potentially contributing to improved oncology diagnosis and prognosis in the future.&#xD.

Nonlinear inductive response of pinned superconducting vortices in artificial pinning sites

Nonlinear inductive response of pinned superconducting vortices in artificial pinning sites

Seismic design of concrete structures for damage control

Recent earthquakes have demonstrated that code-conforming modern (i.e. post-1970s) reinforced concrete (RC) buildings can satisfy life safety performance objectives. However, the accumulated earthquake damage in these modern buildings raised concerns about their performance in future events, contributing to widespread demolition and long-term closure of damaged buildings. The economic and environmental impacts associated with the demolition and long-term closure of modern buildings led to societal demands for improved design procedures to limit damage and shorten recovery time after earthquakes. To address societal demands, this study proposes a damage-control-oriented seismic design approach that targets functional recovery by ensuring structural component demands do not exceed the damage-control limit state (DLS) under design-level events. Herein, DLS is defined as the post-earthquake state beyond which the strength and deformation capacity of a structural component is compromised, and its performance in a future event cannot be relied upon without safety-critical repair. This study proposes a methodology to determine component deformation limits for the design of structures for damage control. Using the developed methodology, we propose component rotation limits for RC beams, columns, and walls. The seismic performance and capability of buildings designed using the proposed design approach to satisfy recovery-based performance objectives is demonstrated through nonlinear response history and recovery analyses (using the ATC-138 methodology) of four archetype frame buildings, designed per New Zealand standards to different beam deformation limits. The analyses show that building codes can achieve functional recovery using the proposed component deformation limits without the need for sophisticated recovery analyses.

Assessing Profit Shifting Using Country-by-Country Reports: A Nonlinear Response to Tax-Rate Differentials

Assessing Profit Shifting Using Country-by-Country Reports: A Nonlinear Response to Tax-Rate Differentials

Iodinated lutetium (III) phthalocyanine: Synthesis, photochemical and nonlinear optical properties

New octaiodo-substituted lutetium (III) phthalocyanine was prepared using a six-step synthetic procedure starting from commercially available o-xylene. The amide synthesis and dehydration step were optimized. The influence of the nature of peripheral halogen atoms on optical and photochemical properties was studied. For the complex under study, the possibility of participation in photoreactions of types I and II with the formation of two different forms of reactive oxygen species was investigated: superoxide anion radical and singlet oxygen respectively. Competition between two photoreactions was demonstrated. The Z-scan method revealed a pronounced nonlinear optical response of the iodinated complex, caused by the action of heavy atoms of peripheral iodine groups.

Nonlinear mechanical response analysis and convolutional neural network enabled diagnosis of single-span rotor bearing system

The wide application of rotating machinery has boosted the development of electricity and aviation, however, long-term operation can lead to a variety of faults. The use of different measures to deal with corresponding malfunctions is the key to generating benefits, so it is significant to carry out the fault diagnosis of rotating machinery. In this work, a test bench for single-span rotor bearings was established, three faults, including spindle bending, spindle crack without end loading and spindle crack with end loading, are experimental analyzed with basic mechanical response. Moreover, a diagnosis is performed using a convolutional neural network, according to the differences in mechanical responses of the three faults obtained from experiments. For three faults, the change in the properties of spindle itself results in different axis trajectories and spectra. Compared with spindle bending fault, spindle crack fault not only cause 1×, 2×, 3× frequency component excitation, also 4×, 5× frequency component excitation. Additionally, the classification accuracy of the training set and the test set under machine learning for the three types of working conditions is 100%. This indicates that the network can significantly identify signal features so as to make effective fault classification.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Development of a novel fractional magneto-viscoelastic dynamic model for an adaptive beam featuring functional composite magnetoactive elastomers: Simulations and experimental studies

Recently, the rapid emergence of functional composite magnetoactive elastomers with integrated hard magnetic particles (known as hard magnetoactive elastomers) has attracted significant interest in fields such as soft robotics and material science. It is of paramount importance to develop an efficient model that can accurately predict the nonlinear dynamic behavior of adaptive structures for their practical application and the synthesis of effective control strategies. In this study, a novel nonlinear fractional magneto-viscoelastic model of an adaptive cantilevered beam featuring hard magnetoactive elastomers has been developed. This model effectively characterizes the beam’s nonlinear and rate-dependent response behavior under magnetic stimuli of varying frequencies and magnitudes. Considering the fractional Kelvin-Voigt energy dissipation model and large deformation nonlinearity, the governing equations for the cantilevered hard-magnetic soft beam were derived using the Hamilton principle. The finite difference method, combined with the Galerkin modal decomposition scheme, was subsequently utilized to discretize the time and space domains, respectively. A hardware-in-the-loop experimental framework was finally designed to experimentally investigate the beam’s nonlinear response behavior and validate the simulation results. The simulated nonlinear quasi-static responses of the beam, subjected to magnetic loading up to 30 mT, exhibited excellent agreement with the experimental data collected. Moreover, the simulated dynamic responses of the beam, subjected to various amplitudes of the applied magnetic field and magnetic frequencies up to 2 Hz, were thoroughly investigated. These included time-histories, phase-plane, and hysteretic responses, all demonstrating strong alignment with the experimental observations.

Vortex waves in fluid–structure interaction with high Froude number and a damped structure

In this paper, we study the non-linear dynamic response generated as a result of a fluid–structure interaction between a flexible structure and a flowing fluid, when the structure is subjected to non-linear excitations. In first place, the use of semi-discrete approximations allowed us to show that the motion of a flexible structure coupled with a surrounding fluid flowing could be modelled and analysed via a coupled Complex Cubic Ginzburg–Landau equations (CCGLEs). Through the obtained CCGLEs, we were able to show that modulational instability (MI) is the main mechanism responsible for the generation of vortex shedding. Moreover, we showed that the stability of continuous wave depends on the coupling parameters between the fluid and the structure. Secondly, using a mathematical method, namely the G’/G expansion method, we found that vortex wave trains could be generated as cylindrical waves. These results are highly significant from a theoretical point of view and could be a plus to explain the process of generation of Kármán Vortex as a consequence of unstable coupling between two continuous wave in the fluid–structure system. Moreover, considering the industrial interest, such as floating wind turbines, this work aims to provide an additional understanding of the interactions between a flexible body and a surrounding flow.

A new method for rapidly capturing the strength and full nonlinear response of partially interacting steel–concrete composite beams

A semi-analytical procedure is presented for predicting the complete flexural response of partially interacting steel–concrete composite beams up to failure. The governing equation of the Euler–Bernoulli beam theory is solved wherein concrete, steel and the shear connectors joining the concrete slab to the steel beam are assumed to have nonlinear stress-deformation relationships. The adopted constitutive relationship for the connectors allows for partial or full composite action. The solution is applicable to beams and one-way slabs subjected to concentrated or uniform load and/or their combination. The governing equation is numerically solved by satisfying the equilibrium and compatibility requirements along the member. For the reinforced concrete part of the composite beam, a nonlinear moment–curvature relationship is developed that accounts for concrete nonlinearity in compression and for cracking and tension-stiffening in tension as well as for steel reinforcement nonlinearity. The steel profile is assumed to have a bilinear elasto–plastic strain-hardening moment–curvature relationship. Comparison of the proposed model results with the corresponding experimental load–deflection curves and interfacial shear–slip curves of several beams tested by others shows good agreement. The relative simplicity, efficiency and easy application of the present solution make it possible to accurately predict the failure load, interfacial slip and full nonlinear response of partially interacting composite beams.

Nanoarchitectonics of an acetogenin-enriched nanosystem mediated by an aqueous extract ofAnnona cherimolaMill with anti-inflammatory and proapoptotic activity against HepG2 cell line.

For the first time, this study shows the nanoarchitectonic process to obtain an acetogenin-enriched nanosystem (AuNPs-Ac) using an aqueous extract from Annona cherimola Mill (ACM) composed of gold nanoparticles embedded in an organic matrix that acts as stabilizing agent and presents anti-inflammatory activity and cytotoxical effect against HepG2 cell line, promoting apoptosis. The synthesis of AuNPs-Ac was confirmed by X-ray diffraction analysis, showing metallic gold as the only phase, and the scanning transmission microscope showed an organic cap covering the AuNPs-Ac. Fourier-transformed infrared suggests that the organic cap comprises a combination of different annonaceous acetogenins, alkaloids, and phenols by the presence of bands corresponding to aromatic rings and hydroxyl groups. High-Performance Liquid Chromatography has demonstrated the presence of annonacin, a potent acetogenin, in the extract of ACM. An in vitro anti-inflammatory activity of the extract of ACM and the AuNPs-Ac was performed using the albumin denaturation method, showing a nonlinear response, which is better than sodium diclofenac salt in a wide range of concentrations that goes from 200 to 400 µg/mL with both samples. The viability assay was studied using trypan blue, treating IMR90 and HepG2 at different concentrations of AuNPs-Ac. The results defined a median lethal dose of 800 µg/mL against HepG2 through apoptosis according to the ratio of caspase-cleaved 9/alpha-tubulin evaluated. It was also demonstrated that the nanosystem presents a higher cytotoxic effect on the HepG2 cell line than in IMR90, suggesting a targeted mechanism. In addition, the nanosystem performs better than using only the extract of ACM in the anti-inflammatory or antiproliferative test, attributed to their higher surface area.