Nonlinear Properties Research Articles

Development of a Prandtl-Ishlinskii hysteresis model for a large capacity magnetorheological fluid damper

Magnetorheological (MR) fluid (MRF) dampers, serving as fail-safe semi-active devices, exhibit nonlinear hysteresis characteristics, emphasizing the necessity for accurate modeling to formulate effective control strategies in smart systems. This paper introduces a novel stop operator-based Prandtl-Ishlinskii (PI) model, featuring a reduced parameter set (seven), designed to estimate the nonlinear hysteresis properties of a large-scale bypass MRF damper with variable stiffness capabilities under varying applied current. With only seven parameters, the model realizes current, displacement, and rate dependencies. The force-displacement and force-velocity responses of the designed MRF damper were experimentally characterized under broad ranges of applied current (0–2 A), excitation frequency (0.5–4 Hz), and displacement amplitude (1–2.5 mm). A training dataset was subsequently used to develop a novel field-dependent modified PI model, incorporating multiple hysteresis operators with and without a friction element. The proposed model accurately predicted the MRF damper behavior within the training dataset, and its validity was assessed against data from diverse experimental conditions. The PI model with friction element generally outperformed the model without friction when frequency exceeds 0.5 Hz, demonstrating its ability to characterize nonlinear hysteresis force-displacement and force-velocity properties of the MRF damper under the ranges of applied current and excitations considered with reasonable accuracy. Experimental data were also estimated by the Bouc–Wen model, and compared with those obtained via the formulated PI model, affirming the overall superiority of the proposed PI models considering the computational cost, and total number of parameters. Leveraging the simplicity, minimal parameter requirements, and analytic invertibility of PI models, the proposed PI model is considered a superior choice for modeling and subsequently controlling smart structures employing MRF dampers.

Localized Phonon Transport Study of GaN/SiO2 Core/shell Nanowires under Thermal-Stress Coupling.

In order to comprehensively explore the intricate mechanisms of thermo-mechanical interactions, this study employs the molecular dynamics method to investigate the influence of heat flux density, shell thickness and length, as well as stress on the radial interface phonon transport in GaN/SiO2 core/shell nanowire. Additionally, the surface eigenmode decomposition method is employed to analyze the interface phonon dispersion curves. The investigation reveals that with increasing heat flux density, internal thermal stresses intensify, leading to a complex distribution of thermal stresses within the system. Under the influence of thermal stress, the nonlinear acoustic properties interact with phonon scattering, resulting in the pronounced localization of interface phonons. Compressive stress causes an upshift in low-frequency phonons, while tensile stress induces a downward shift in the high-frequency optical branches at the interface. The localized phonon vibrations at the SiO2/GaN interface under nonuniform stress are identified as the primary cause for the abundant presence of nondispersive phonon modes at the radial interface. By elucidating the subtle interplay between lattice vibrations and stress fields, this study offers a novel and profound understanding of thermo-mechanical coupling effects, thereby providing innovative theoretical foundations for the design and performance management of thermoelectric devices.

Nonlinear Elastic Bottlebrush Polymer Hydrogels Modulate Actomyosin Mediated Protrusion Formation in Mesenchymal Stromal Cells.

The nonlinear elasticity of many tissue-specific extracellular matrices is difficult to recapitulate without the use of fibrous architectures, which couple strain-stiffening with stress relaxation. Herein, bottlebrush polymers are synthesized and crosslinked to form poly(ethylene glycol)-based hydrogels and used to study how strain-stiffening behavior affects human mesenchymal stromal cells (hMSCs). By tailoring the bottlebrush polymer length, the critical stress associated with the onset of network stiffening is systematically varied, and a unique protrusion-rich hMSC morphology emerges only at critical stresses within a biologically accessible stress regime. Local cell-matrix interactions are quantified using 3D traction force microscopy and small molecule inhibitors are used to identify cellular machinery that plays a critical role in hMSC mechanosensing of the engineered, strain-stiffening microenvironment. Collectively, this study demonstrates how covalently crosslinked bottlebrush polymer hydrogels can recapitulate strain-stiffening biomechanical cues at biologically relevant stresses and be used to probe how nonlinear elastic matrix properties regulate cellular processes.

A Nonlinear Photochromic Reaction Based on Sensitizer-Free Triplet-Triplet Annihilation in a Perylene-Substituted Rhodamine Spirolactam.

Nonlinear photochromic reactions that work with weak incoherent light are important for molecular operations with high spatial resolution and multiple photofunctions based on single molecules. However, nonlinear photochromic compounds generally require complex molecular design, restricting accessibility in various fields. Herein, we report nonlinear photochromic properties in a perylene-substituted rhodamine spirolactam derivative (Rh-Pe), which is synthesized from rhodamine B in facile procedures. Direct excitation of Rh-Pe produces the triplet excited state via the charge-transfer (CT) state. The triplet excited state causes triplet-triplet annihilation to bring the generation of the intensely colored ring-open form with nonlinear behavior. Furthermore, green- and red-light-induced photochromism was achieved in Rh-Pe using triplet sensitizers, although Rh-Pe can be directly excited only by ultraviolet and blue light. Our findings are expected to contribute to the development of photofunctional materials showing nonlinear behavior and low-energy light responsivity.

A Nonlinear Photochromic Reaction Based on Sensitizer‐Free Triplet–Triplet Annihilation in a Perylene‐Substituted Rhodamine Spirolactam

AbstractNonlinear photochromic reactions that work with weak incoherent light are important for molecular operations with high spatial resolution and multiple photofunctions based on single molecules. However, nonlinear photochromic compounds generally require complex molecular design, restricting accessibility in various fields. Herein, we report nonlinear photochromic properties in a perylene‐substituted rhodamine spirolactam derivative (Rh−Pe), which is synthesized from rhodamine B in facile procedures. Direct excitation of Rh−Pe produces the triplet excited state via the charge‐transfer (CT) state. The triplet excited state causes triplet–triplet annihilation to bring the generation of the intensely colored ring‐open form with nonlinear behavior. Furthermore, green‐ and red‐light‐induced photochromism was achieved in Rh−Pe using triplet sensitizers, although Rh−Pe can be directly excited only by ultraviolet and blue light. Our findings are expected to contribute to the development of photofunctional materials showing nonlinear behavior and low‐energy light responsivity.

Silicon nanocrystal slab optical waveguide by multi-energy ion implantation: linear and nonlinear optical properties

Silicon nanocrystals (Si-nc) have gained attention in optics and photonics research areas in recent years due to their advanced optical nonlinear properties. However, a nonlinear integrated optical platform that allows the fabrication of photonic devices with different Si-nc composition is yet under investigation. In this work, we experimentally demonstrated a nonlinear optical waveguide based on ion implantation that offers advantages in terms of material composition, low propagation losses, and enhanced nonlinear optical properties, such as a high nonlinear refractive index. In this work we present the fabrication of Si-nc containing waveguiding structures, and a study of their linear and nonlinear optical properties. Our nonlinear integrated optical platform presents a two-photon absorption coefficient β = 3.17x10-6 cm·W-1 and a nonlinear refractive index n2 = 1.19x10-10 cm2·W-1, which shows potential applications in all-optical signal processing devices, such as single-photon sources.

Consensus for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme

This paper studies the leader-follower consensus problem for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme. This adaptive mechanism allows parameters in triggereded mechanism to change automatically compared to fixed ones in traditional strategies. The paper introduces two distinct approaches to address the issue of saturation. The first approach is sector bounded condition, while the second relies on convex hull representation. Compared with the former one, the latter one can reduce the conservatism of controller design effectively. Furthermore, it is assumed that the nonlinear function adheres to incremental quadratic limitations, thereby offering a more comprehensive depiction of its nonlinear properties. Finally, the validity of the proposed approaches is demonstrated by one numerical example in the background of mechanical systems.

Enhancing the nonlinear rheological property and digestibility of mung bean flour gels using controlled microwave treatments: Effect of starch debranching and protein denaturation

In this study, we examined the possibility of using industrial microwave processing to enhance the gelling properties and reduce the starch digestibility of mung bean flour (MBF). MBF (12.6 % moisture) was microwaved at a power of 6 W/g to different final temperatures (100–130 °C), and then its structural and functional properties were characterized. The microwave treatment had little impact on the crystalline structure or amylose content of the starch, but it roughened the starch granule surfaces and decreased the short-range ordered structure and degree of branching. In addition, the extent of mung bean protein denaturation caused by the microwave treatment depended on the final temperature. Slightly denaturing the proteins (100 °C) did not affect the nature of the gels (protein phase dispersed in a starch phase) but the gel network became more compact. Moderately denaturing the proteins (110–120 °C) led to more compact and homogeneous starch-protein double network gels. Excessive protein denaturation (130 °C) caused the gel structure to become more heterogeneous. As a result, the facilitated tangles between starch chains by more linear starch molecules after debranching, and the protein network produced by moderate protein denaturation led to the formation of stronger gel and the improvement of plasticity during large deformation (large amplitude oscillatory shear-LAOS). Starch recrystallization, lipid complexion, and protein network retard starch digestion in the MBF gels. In conclusion, an industrial microwave treatment improved the gelling and digestive properties of MBF, and Lissajous curve has good adaptability in characterizing the viscoelasticity of gels under large deformations.

Multiscale modeling for the impact behavior of 3D angle-interlock woven composites

In this paper, a multiscale modeling framework predicting the impact response of three-dimensional angle-interlock woven composites (3DAWCs) is presented. Multiscale modeling is performed sequentially at the mirco, meso, and macroscales. A microscopic representative volume element (RVE) model is established to characterize the yarn's nonlinear mechanical properties and damage behavior at different strain rates. At the mesoscale, a novel subcell-based discretization scheme is proposed to discretize 3DAWCs, and developing six representative subcells (RSCs) that bridge the microscale and macroscale effective properties. Based on it, a ballistic impact model involving six kinds of homogeneous material bricks is established. Furthermore, elastic-viscoplastic damage constitutive models are developed for the epoxy matrix and the yarn, respectively, to enhance the predicted accuracy of the multiscale model. Ballistic impact tests of 3DAWCs are conducted, and X-ray micro-computed tomography is employed to evaluate the internal damage of the 3DAWC target after impact. Validation work is conducted by comparing the experimental and numerical results of the ballistic impact of the 3DAWC target, which indicates the effectiveness and efficiency of the developed multiscale framework in the impact damage prediction of 3DAWCs.

Origami-inspire quasi-zero stiffness structure for flexible low-frequency vibration isolation

As an emerging technology, origami exhibits rich nonlinear static and dynamic properties. However, simulating creases remains a key challenge hindering practice application, especially for volumetric (deformable) origami. Inspired by the bistable behavior and axial-rotational coupling mechanism of triangulated cylindrical origami (TCO), this paper proposes a novel combined truss-spring structure with additional inertial mechanism (TCTS-RIM) for low-frequency vibration isolation. Unlike pure TCO, the TCTS-RIM uses elastic linkages to simulate all creases and modifies crease positions, enhancing physical realization in engineering applications and offering tunable stiffness and inertia on demand. Static analysis shows the modified truss-spring structure in TCTS-RIM has superior quasi-linear negative stiffness compared to pure TCO, which enables a very wide quasi-zero stiffness (QZS) range via linear spring compensation. The wide QZS range for different loads can be realized by setting appropriate structural parameters. From system dynamic equations, nonlinear inertia, nonlinear quadratic force, and nonlinear damping due to RIM are identified and discussed. The Alternating frequency–time harmonic balance method is used to obtain the displacement transmissibility, and its parametric influencing investigations verify that the proposed TCTS-RIM has excellent low-frequency vibration isolation performance and rich tunability. Comparative discussions show the wide QZS range significantly reduces the resonance frequency, broadens the isolation frequency band, and yields superior dynamic performance versus pure TCO isolators. Moreover, equivalent mass, anti-resonance and softening behavior induced by the rotational inertia mechanism further enhance low-frequency vibration isolation performance. An experimental prototype is built, and experimental results validate the correctness of the theoretical analyses. This work presents a simple mechanical implementation of volumetric origami, which may progress the engineering applications of origami and provide a viable approach to low-frequency vibration control.

An improved continuum model for hypersonic thermal nonequilibrium flow in the near-continuum regime

In this work, the rarefied Couette flow of diatomic gases with thermal nonequilibrium effects is investigated by the direct simulation Monte Carlo (DSMC) method, and a macroscopic computational model is developed to consider the local rarefaction effects for diatomic gases in the near-continuum regime. The nonlinear transport properties of the diatomic gases are studied, indicating that effective viscosity and effective translational thermal conductivity in the shear nonequilibrium state are affected by translational nonequilibrium effects, which obey the same laws for both monatomic and diatomic gases. The transport coefficients of internal energy modes are affected by both translational nonequilibrium and internal energy relaxation, therefore, the effective rotational and vibrational thermal conductivities are related to the effective viscosity through a modified Eucken relation that accounts for internal energy relaxation. Conclusively, effective constitutive relations are newly established as a function of the shear nonequilibrium parameter and the modified Eucken factors for thermal nonequilibrium flows, and these are integrated into the macroscopic two-temperature model. Subsequently, it is assessed in the simulation of hypersonic flows over flat plates and cylinders at various Knudsen numbers. The results show that the surface shear stress and heat flux obtained by the proposed model agree well with the DSMC results, indicating significantly improved performance compared to the conventional Navier–Stokes two-temperature model for hypersonic flows in the near-continuum regime.

Bilinear stiffness and bimodular Poisson's ratio in cylindrical sinusoidal lattices through topology morphing

Bilinear elastic behaviour allows structural designs to respond in either a stiff or compliant manner depending on the load. Here a cylindrical sinusoidal lattice structure is described that stiffens beyond a certain load. When subjected to axial compression, the lattice can undergo a topological transformation by forming contact connections. This topology change involves a transition from rectangular-like unit cells to kagome-like unit cells, associated with an approximately fourfold increase in stiffness. The lattice exhibits negative Poisson's ratio with a step-change from ≈−0.66 to ≈−0.23 prior to and during contact formation, respectively. After contact formation, it displays a nonlinear Poisson's ratio behaviour. The mechanics underpinning these behaviours are analysed using a combination of experiments and numerical modelling. A comparison with similar planar lattices reveals the effect of the global topology of the lattice (e.g. planar, cylindrical) on the unit cell-level topology morphing. The proposed topology-morphing cylindrical sinusoidal lattice introduces new design possibilities in the application-rich context of tubular structures with nonlinear mechanical properties.

Nonlinear dynamic analysis of aircraft CFRP sandwich wings under explosive blast loading: Introducing SVM-DNN algorithm to predict dynamical information

The aircraft wings are a crucial component, particularly during mechanical shock excitation. Thus, for the first time, an inventive adaptable model to simulate the nonlinear dynamics of the wings under explosive blast loading at the moment of the collision is described in this article. It is crucial to increase the structure's stability because of explosive blast loading. The mentioned structure is made of two composite face sheets and a carbon fiber-reinforced polymer (CFRP). Functionally graded (FG) graphene origami (GOri)-enabled auxetic metallic metamaterial (GOEAM) is introduced to improve the stability of this kind of applicable structure, especially in air maneuvers. The analysis is conducted utilizing Reddy's third-order shear deformation of Reddy theory (TSDRT), incorporating Hamilton's principle and Von Kármán nonlinear geometric assumptions. The governing and boundary equations are accurately computed at the domain and boundary edges of the doubly curved structure. The coupled mesh-free radial point interpolation method (MRPIM) and Newton-Raphson method are applied for numerically simulating the current doubly curved structure by employing Newmark's temporal integration method with a constant-average acceleration approach. The present findings are cross-checked against the machine learning method and open-source results from the literature. Based on physical data, machine learning integrates supervised machine learning to predict the nonlinear vibration of the system. In this case, nonlinear transient bending properties are found using hybrid machine learning methods and solutions. The findings demonstrate the significance of GOEAM for nonlinear vibrations of the composite system. The current study's conclusions could be used as a benchmark and useful recommendations for future structural design techniques with enhanced features.

A micromechanical scheme with nonlinear concentration functions by physics-guided neural network

The mechanical behavior of heterogeneous materials has been reported to be significantly influenced by the nonlinear properties of both the matrix and interface. However, the micromechanical homogenization methods for predicting the effective properties are challenged in nonlinear problems due to the difficulties in solving the analytical form of the concentration tensors. In this study, we develop a nonlinear micromechanical scheme for heterogeneous materials with complex interfacial behaviors, where the key component, namely nonlinear concentration functions, is determined by the devised physics-guided neural network. In particular, the nonlinear Mori–Tanaka method (NMT) implemented within this new micromechanical scheme yields accurate solutions to axisymmetric nonlinear homogenization problems considering the effects of finite deformation, loading conditions, volume fraction, etc. Furthermore, the NMT is equivalent to the linear Mori–Tanaka method in the condition of the small deformation. Notably, this neural-network-based micromechanical scheme shows good generalization for different types of nonlinear interfaces, while the corresponding approach for generating training data via the finite element method (FEM) is cost-effective. This theoretical framework introduces a novel approach to nonlinear physical modeling, namely, not by the direct regression from the dataset but by deeply embedding neural networks in physical laws.

Influence of real gas properties on aerodynamic stability of a SCO2 centrifugal compressor

The safe operation of closed-Bryton-cycle system is hindered by the nonlinear real gas properties in a supercritical carbon dioxide (SCO2) centrifugal compressor for waste heat recovery in a powertrain system. This paper aims to understand the influence of real gas properties on the aerodynamic stability of a shrouded SCO2 centrifugal compressor designed for waste heat recovery. Firstly, the numerical method was calibrated and validated using experimental results from the Sandia SCO2 centrifugal compressor. Next, based on the numerical method, the stability performance of an inhouse-design shrouded SCO2 compressor was discussed through a direct comparison with the identical compressor using air. The results showed that the performance of the SCO2 compressor was significantly different from that of the air compressor. Particularly, the impeller was the most unstable component featuring a notable recirculating region near the shroud at the leading edge. Further analysis is carried out to understand the discrepancies in the stability performance between the two compressors with different fluids. It is revealed that the boundary layer on the SCO2 impeller suction surface thickens at a faster rate, leading to stronger flow separation. Meanwhile, the stronger accumulation of low-momentum secondary flow near the SCO2 impeller outlet enhances the ‘wake’ structure near the shroud of suction surface at the impeller tailing edge, resulting in considerable backflow. The different behaviors of boundary layer were attributed to pressure gradient normal to the suction surface. Specifically, the pressure gradient on the suction surface for SCO2 impeller is orders of magnitude higher than that of the air impeller. The stronger gradient weakens momentum exchange in the boundary layer, thus increasing the thickness of boundary layer more rapidly along the streamwise direction. Moreover, the boundary layer is pushed towards the shroud of suction surface by the strong pressure gradient, resulting in the evident accumulation of secondary flow nearby. At the meantime, the low-momentum flow near the impeller outlet reduced the inlet flow velocity of the diffuser, causing more recirculation at the top of the vaneless diffuser in all circumferential directions, thus worsening its instability.

Electrocardiogram-based Emotion Recognition using Convolutional Neural Networks

Introduction: Electrocardiogram-based facial emotion identification has been a widely spread field in the last few decades. Due to its non-linearity, non-stationary and noisy properties, it is a very difficult job to create a framework that is capable of recognizing emotions with a high recognition rate. background: Electrocardiogram based facial emotion identification has been widely spread field in last some decades. Due to its non-linearity, non-stationary and noisy properties, it is very difficult job to create a framework that is capable of recognizing emotions with high recognition rate. Method: In this work, we introduce a new framework for facial emotion detection based on feature creation using a topographic representation of ECG signal properties. The feature map is created using deep learning techniques, and further, extricated features are then used for classification techniques to detect facial emotion recognition. Result: The recognition results are achieved on two publicly available facial expression datasets, i.e., Ascertain and Dreamer. We illustrated the usefulness of our framework by comparing results with other existing methods. Conclusion: The recognition results prove that the introduced framework can enhance the identifying rate on various given datasets.

Integrated Cost Prediction for Distribution Network Engineering Based on the Novel Multi-objective Coati Algorithm and Laguerre Polynomials

To achieve precise and stable cost prediction for distribution network engineering, a novel multi-objective coati optimization algorithm (MOCOA) is proposed, and based on this algorithm, a new multi-objective integrated prediction model based on Laguerre polynomials (LNN-Adaboost) is developed. Firstly, utilizing the nonlinear approximation properties of Laguerre orthogonal polynomials, a Laguerre neural network is constructed. Secondly, targeting prediction accuracy and stability, the Laguerre neural network is optimized using the multi-objective coati algorithm. Finally, ensemble learning Adaboost is introduced to correct the prediction errors of the model, achieving automatic allocation and recombination of error weights. By comparing with three mainstream multi-objective optimization algorithms on eight test problems, the effectiveness of the proposed algorithm is verified. Taking the cost data of the Ningxia distribution network engineering as the research object, the proposed model MOCOA-LNN-Adaboost is compared with several mainstream models. The results demonstrate that the proposed prediction model exhibits higher prediction accuracy and better prediction stability. Compared to MOCOA-ELM-Adaboost, the proposed prediction model shows an increase of 9.64% in R2, 2.65% in IA, and a decrease of 53.62% in RMSE, and 61.87% in SDEX..

Simulating the LOcal Web (SLOW) II. Properties of local galaxy clusters

This is the second paper in a series presenting the results from a 500 $h^ $Mpc large constrained simulation of the local Universe (SLOW). The initial conditions for this cosmological hydro-dynamical simulation are based on peculiar velocities derived from the CosmicFlows-2 catalog. The simulation follows cooling, star formation, and the evolution of super-massive black holes. This allows one to directly predict observable properties of the intracluster medium (ICM) within galaxy clusters, including X-ray luminosity, temperatures, and the Compton-y signal. Comparing the properties of observed galaxy clusters within the local Universe with the properties of their simulated counterparts enables us to assess the effectiveness of the initial condition constraints in accurately replicating the mildly nonlinear properties of the largest, collapsed objects within the simulation. Based on the combination of several, publicly available surveys we compiled a sample of galaxy clusters within the local Universe, of which we were able to cross-identify 46 of them with an associated counterpart within the SLOW simulation. We then derived the probability of the cross identification based on mass, X-ray luminosity, temperature, and Compton-y by comparing it to a random selection. Our set of 46 cross-identified local Universe clusters contains the 13 most massive clusters from the Planck SZ catalog as well as 70<!PCT!> of clusters with $M_ $ larger than $2 $ M$_ odot $. Compared to previous constrained simulations of the local volume, we found in SLOW a much larger amount of replicated galaxy clusters, where their simulation-based mass prediction falls within the uncertainties of the observational mass estimates. Comparing the median observed and simulated masses of our cross-identified sample allows us to independently deduce a hydrostatic mass bias of $(1-b) approx0.87$. The SLOW constrained simulation of the local Universe faithfully reproduces numerous fundamental characteristics of a sizable number of galaxy clusters within our local neighborhood, opening a new avenue for studying the formation and evolution of a large set of individual galaxy clusters as well as testing our understanding of physical processes governing the ICM.

Exploring the disruptions of the neurophysiological organization in Alzheimer’s disease: An integrative approach

Background and Objective:Alzheimer’s disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. Methods:To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. Results:Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann–Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. Conclusion:This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.

Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm

Precisely forecasting the price of crude oil is challenging due to its fundamental properties of nonlinearity, volatility, and stochasticity. This paper introduces a novel hybrid model, namely, the KV-MFSCBA-G model, within the decomposition–integration paradigm. It combines the mixed-frequency convolutional neural network–bidirectional long short-term memory network-attention mechanism (MFCBA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. The MFCBA and GARCH models are employed to respectively forecast the low-frequency and high-frequency components decomposed through variational mode decomposition optimized by Kullback–Leibler divergence (KL-VMD). The classification of these components is performed using the fuzzy entropy (FE) algorithm. Therefore, this model can fully exploit the advantages of deep learning networks in fitting nonlinearities and traditional econometric models in capturing volatilities. Furthermore, the intelligent optimization algorithm and the low-frequency economic variable are introduced to improve forecasting performance. Specifically, the sparrow search algorithm (SSA) is employed to determine the optimal parameter combination of the MFCBA model, which is incorporated with monthly global economic conditions (GECON) data. The empirical findings of West Texas Intermediate (WTI) and Brent crude oil indicate that the proposed approach outperforms other models in evaluation indicators and statistical tests and has good robustness. This model can assist investors and market regulators in making decisions.