Nonlinear Factors Research Articles

Adhesive nonlinearity analysis of longitudinal guided wave using circumference pasted piezoelectric array based nonlinear shear stress lag model

The adhesive layer between an ultrasonic transducer and a circular tube can generate a nonlinear signal during guided wave excitation and reception, which is called adhesive nonlinearity (AN). It may override the damage-related signals and result in false detection if not adequately evaluated and mitigated. This study investigated the AN of the guided wave excitation model composed of a piezoelectric array in a circular tube structure. The classical shear stress lag model was extended to the circumference pasted piezoelectric array-based nonlinear shear stress lag model to investigate the coupling properties of the AN and to evaluate the AN by comparing it with other nonlinear factors within a circular tube structure. On this basis, the nonlinear shear stress was combined with the normal mode expansion to establish a frequency tuning model for the AN, which allowed the effect of the AN to be minimized by adjusting the half-wavelength of the guided wave to match the length of the actuator. Finite-element analyses and experiments validated the tuning characteristics of the AN mentioned above. This work was used to mitigate the effect of the AN on the nonlinear guided wave during thermal damage evaluation.

Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

Improved Honey Badger Algorithm and Its Application to K-Means Clustering

As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering center of mass. In this paper, an improved honey badger optimization algorithm is proposed: (1) The population is initialized using sin chaos to make the population uniformly distributed. (2) The density factor is improved to enhance the optimization accuracy of the population. (3) A nonlinear inertia weight factor is introduced to prevent honey badger individuals from relying on the behavior of past individuals during position updating. (4) To improve the diversity of solutions, random opposition learning is performed on the optimal individuals. The improved algorithm outperforms the comparison algorithm in terms of performance through experiments on 23 benchmark test functions. Finally, in this paper, the improved algorithm is applied to K-means clustering and experiments are conducted on three data sets from the UCI data set. The results show that the improved honey badger optimized K-means algorithm improves the clustering effect over the traditional K-means algorithm.

The contribution of general intelligence to cognitive performance across the lifespan: A differentiation analysis of the wechsler tests.

Human cognitive abilities exhibit positive interrelationships that can be represented by a latent general intelligence factor (g). Differentiation hypotheses propose that there are systematic interindividual differences in the strength of g, specifically along the dimensions of ability level (ability differentiation) and age (age differentiation). Despite the potential implications for cognitive theory and assessment, the available evidence on the matter is inconclusive. We present comprehensive analyses of differentiation effects across the lifespan, drawing on the meta-analytic integration of nonlinear factor analyses with German standardization samples (N = 4,129) of the most widely used intelligence tests worldwide (i.e., the Wechsler tests). Results support ability differentiation at all ages, with particularly large effect sizes in young adults, and suggest a complex pattern of age differentiation and dedifferentiation across the lifespan. These findings challenge the uniformity of g, highlighting the need to account for differentiation effects in cognitive theories and assessment. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Measurement Invariance of the First Years Inventory (FYIv3.1) Across Age and Sex for Early Detection of Autism in a Community Sample of Infants.

The use of parent-report screeners for early detection of autism is time- and cost-efficient in clinical settings but their utility may vary by respondent characteristics. This study aimed to examine the degree to which infants' age and sex impacted parental reports of early behavioral signs of autism captured by the First Years Inventory Version 3.1 (FYIv3.1). The current sample included 6,454 caregivers of infants aged 6 to 16 months recruited through the North Carolina vital records. Using moderated nonlinear factor analysis for each of the seven FYIv3.1, we identified differential item functioning in small to medium effect sizes across 18 out of 69 items, with the majority of biases associated with infants' age (e.g., object mouthing, walking, pretend, and imitation), while sex-related biases were minimal. This indicates that differential scoring algorithms by infants' age and more closely spaced monitoring may be needed for these constructs for more accurate identification of autism in infancy.

A Self-Formed Ag Nanostructure Based Neuromorphic Device Performing Arithmetic Computation and Area Integration: Influence of Presynaptic Pulsing Scheme on Mathematical Precision.

A material equivalent of a biosynapse is the key to neuromorphic architecture. Here we report a self-forming labyrinthine Ag nanostructure activated with a few pulses of 0.5 V, width and interval set at 50 ms, at current compliance (ICC) of 400 nA, serving as the active material for a highly stable device with programmable volatility. Both the conductance (G) and its retention time (tr) in the potentiated state are found to vary linearly with the pulse number for pulses of positive and negative polarities, with the nonlinearity factors being noticeably small, ∼0.03 for G during potentiation and ∼0.08 during depression. This was tested for over 200 days, and the results were highly reproducible. Relying on the high linearity, arithmetic operations involving counting of positive and negative integers were realized using pulses of both polarities, often by mixing them in the feeding sequence. The observed outcomes based on G and independently from tr are highly accurate, with deviations being typically less than ∼1.5% from the expected results. Notably, the way the pulse polarities are mixed is found to have an influence, with random sequences producing relatively larger deviations in integer estimation. However, deviations decreased with higher ICC values, which promoted stronger filament formation in the percolation networks. Besides, the G and tr values were found to vary with the pulse amplitude as well, which enabled the calculation of the area under a curve. Further, the device exhibited a simulation-based image classification accuracy of 94.95%, close to the ideal value (96.05%). Simulations utilizing the finite element method have showcased the uniqueness of the labyrinthine morphology, giving rise to field intensification along potential percolative paths.

Rotational motion control of small deformation flexible rotating structure considering multiple nonlinear factors based on nonlinear disturbance observer

Rotational motion control of small deformation flexible rotating structure considering multiple nonlinear factors based on nonlinear disturbance observer

Secure and Efficient PAPR Reduction in OFDM Using PTS Based Nonlinear Variation Convergence Factor with Spotted Hyena Optimization

Secure and Efficient PAPR Reduction in OFDM Using PTS Based Nonlinear Variation Convergence Factor with Spotted Hyena Optimization

Advanced Dynamic Thermal Vibration of Thick Composited FGM Cylindrical Shells with Fully Homogeneous Equation by Using TSDT and Nonlinear Varied Shear Coefficient

A numerical method using advanced nonlinear shear is used to study the thermal vibration of functionally graded material (FGM) thick circular cylindrical shells. The third-order shear deformation theory (TSDT) of displacements is applied and the equations are derived of the motion of cylindrical shells and the expression of the advanced nonlinear varied shear factor. The expressions of stiffness of thick composited two-layer FGM circular cylindrical shells with sinusoidal rising temperature are applied. The partial differential equation (PDE) in dynamic equilibrium of thick FGM circular cylindrical shells is derived with respect to shear rotations and displacements under terms of thermal–mechanical loads and density inertia terms. Important parametric effects of the advanced nonlinear varied shear factor, power law index, and temperature on the stress and displacement of thick FGM circular cylindrical shells are studied. Additionally, the advanced nonlinear varied shear factor effect is included and studied for a vibrating frequency using a fully homogeneous equation.

Scheduling optimization of ship plane block flow line considering dual resource constraints

A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved Grey Wolf Optimization Algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building.

Time-optimal trajectory planning for a six-degree-of-freedom manipulator: a method integrating RRT and chaotic PSO

This study proposes an innovative algorithm based on a hybrid optimization strategy, integrating the rapidly-exploring random tree (RRT) and an improved particle swarm optimization (PSO) method to address the time-optimal trajectory planning problem for six-degree-of-freedom robotic arms. The proposed approach emphasizes obstacle avoidance and motion smoothness. RRT is utilized within a dynamic three-dimensional environment to rapidly generate an initial collision-free path. Subsequently, improved PSO enhances global search performance by introducing a multi-source chaotic mapping-based population initialization strategy, dynamically adjusted inertia weights, and nonlinear learning factors. These enhancements effectively mitigate the limitations of traditional PSO methods, which are prone to premature convergence in complex optimization problems. Furthermore, the proposed 3-5-3 polynomial interpolation method significantly smooths the trajectory, reducing fluctuations in velocity and acceleration, and thereby improving the precision and energy efficiency of trajectory planning. Experimental results demonstrate that the proposed algorithm outperforms existing methods, such as the improved RRT and improved non-dominated sorting genetic algorithm, across multiple metrics. Notable achievements include a reduction of total motion time by approximately 21%, improved stability in robotic arm motion, and enhanced adaptability to dynamic environments. Particularly, the method achieves superior trajectory diversity and uniformity through joint optimization of multiple chaotic sources, overcoming the inherent limitations of single chaotic mappings. This research expands the application scenarios of RRT and PSO and provides a novel solution for intelligent control and real-time planning of high-degree-of-freedom robotic arms.

Analysis of time-of-flight secondary ion mass spectrometry data of human skin treated with diclofenac using sparse autoencoder.

Methods that facilitate molecular identification and imaging are required to evaluate drug penetration into tissues. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), which has high spatial resolution and allows 3D distribution imaging of organic materials, is suitable for this purpose. However, the complexity of ToF-SIMS data, which includes nonlinear factors, makes interpretation challenging. Therefore, in this study, ToF-SIMS data of a stratum corneum treated with diclofenac were analyzed using machine learning to enable the evaluation of drug distribution. Diclofenac-related mass peaks were identified using autoencoder results, and the degree of penetration was evaluated across 2-20th stripped tapes. In addition, the permeation pathway was clarified by comparing the secondary ion images of phosphatidylethanolamine (PhEA; a marker of the inside of the cell); cholesterol, which is abundant in cell membranes; and diclofenac. Based on the biomolecule-related ion images showing the penetration pathway of diclofenac applied to the skin, diclofenac penetrates both the extracellular space and inside cells.

Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review

The increasing demand for sustainable energy has spurred interest in biofuels as a renewable alternative to fossil fuels. Biomass gasification and pyrolysis are two prominent thermochemical conversion processes for biofuel production. While these processes are effective, they are often influenced by complex, nonlinear, and uncertain factors, making optimization and prediction challenging. This study highlights the application of fuzzy neural networks (FNNs)—a hybrid approach that integrates the strengths of fuzzy logic and neural networks—as a novel tool to address these challenges. Unlike traditional optimization methods, FNNs offer enhanced adaptability and accuracy in modeling nonlinear systems, making them uniquely suited for biomass conversion processes. This review not only highlights the ability of FNNs to optimize and predict the performance of gasification and pyrolysis processes but also identifies their role in advancing decision-making frameworks. Key challenges, benefits, and future research opportunities are also explored, showcasing the transformative potential of FNNs in biofuel production.

Supramolecular Assembly Enhanced Linear and Nonlinear Chiroptical Properties of Chiral Manganese Halides.

Chiral hybrid organic-inorganic metal halides (HOMHs) hold greatpromise in broad applications ranging from ferroelectrics, spintronics to nonlinear optics, owing to their broken inversion symmetry and tunable chiroptoelectronic properties. Typically, chiral HOMHs are constructed by chiral organic cations and metal anion polyhedra, with the latter regarded as optoelectronic active units. However, the primary design approaches are largely constrained to regulation of general components within structural formula. Herein, supramolecular approaches have been taken for the functionalization of chiral enantiomers by anchoring chiral cations with crown ether hosting molecules. Chiral HOMHs of R-/S-(18-crown-6@ClMBA)2MnBr4 have been thus obtained with boosted linear and nonlinear chiroptical properties. The self-assembled cations lead to enhanced structural rigidity, which promote near-unity green light emission and strong circularly polarized luminescence with a high asymmetry factor, along with high efficiency second-order nonlinear optical responses. In particular, these chiral HOMH single crystals demonstrate a sensitive discrimination for circularly polarized laser in the near-infrared region with the nonlinear opticalasymmetry factor (gSHG-CD) as high as 1.8. This work highlights the contribution of supramolecular assembly in improving chiroptical performances, offering valuable insights for the design of new chiral HOMH materials with promising application potentials as linear and nonlinear CPL emitters and detectors.

Supramolecular Assembly Enhanced Linear and Nonlinear Chiroptical Properties of Chiral Manganese Halides

Chiral hybrid organic–inorganic metal halides (HOMHs) hold great promise in broad applications ranging from ferroelectrics, spintronics to nonlinear optics, owing to their broken inversion symmetry and tunable chiroptoelectronic properties. Typically, chiral HOMHs are constructed by chiral organic cations and metal anion polyhedra, with the latter regarded as optoelectronic active units. However, the primary design approaches are largely constrained to regulation of general components within structural formula. Herein, supramolecular approaches have been taken for the functionalization of chiral enantiomers by anchoring chiral cations with crown ether hosting molecules. Chiral HOMHs of R‐/S‐(18‐crown‐6@ClMBA)2MnBr4 have been thus obtained with boosted linear and nonlinear chiroptical properties. The self‐assembled cations lead to enhanced structural rigidity, which promote near‐unity green light emission and strong circularly polarized luminescence with a high asymmetry factor, along with high efficiency second‐order nonlinear optical responses. In particular, these chiral HOMH single crystals demonstrate a sensitive discrimination for circularly polarized laser in the near‐infrared region with the nonlinear optical asymmetry factor (gSHG‐CD) as high as 1.8. This work highlights the contribution of supramolecular assembly in improving chiroptical performances, offering valuable insights for the design of new chiral HOMH materials with promising application potentials as linear and nonlinear CPL emitters and detectors.

Data-driven motion estimation for cable-driven end-effectors through parallel 1D-convolution and recurrent neural networks with attention

Minimally invasive surgery requires the inclusion of cable-driven manipulators, due to the advantages in terms of lower inertia, compact design, and remote actuation. However, the nonlinear factors underlying in a cable-driven system bring a challenge for the motion control of the end-effectors of a surgical robot. Further, conventional data-driven approaches for cable-driven systems fail to jointly consider local and temporally sequential information in control signals. To this end, we propose a motion-estimation approach for cable-driven end-effectors, using 1D-convolution and recurrent neural networks with attention, taking into account local and sequential information in modelling control series. Then, we perform cross-domain experiments on our Hefei University of Technology’s Cable-Driven End-effector’s Motion (HFUT-CDEM) dataset, with the experimental results showing that the proposed approach achieves better performance, compared with conventional regression approaches for sequential-data modelling.

Enhanced Synaptic Memory Window and Linearity in Planar In2Se3 Ferroelectric Junctions.

A synaptic memristor using 2D ferroelectric junctions is a promising candidate for future neuromorphic computing with ultra-low power consumption, parallel computing, and adaptive scalable computing technologies. However, its utilization is restricted due to the limited operational voltage memory window and low on/off current (ION/OFF) ratio of the memristor devices. Here, it is demonstrated that synaptic operations of 2D In2Se3 ferroelectric junctions in a planar memristor architecture can reach a voltage memory window as high as 16V (±8 V) and ION/OFF ratio of 108, significantly higher than the current literature values. The power consumption is 10-5W at the on state, demonstrating low power usage while maintaining a large ION/OFF ratio of 108 compared to other ferroelectric devices. Moreover, the developed ferroelectric junction mimicked synaptic plasticity through pulses in the pre-synapse. The nonlinearity factors are obtained 1.25 for LTP, -0.25 for LTD, respectively. The single-layer perceptron (SLP) and convolutional neural network (CNN) on-chip training results in an accuracy of up to 90%, compared to the 91% in an ideal synapse device. Furthermore, the incorporation of a 3nm thick SiO2 interface between the α-In2Se3 and the Au electrode resulted in ultrahigh performance among other 2D ferroelectric junction devices to date.

A Novel Slime Mould Multiverse Algorithm for Global Optimization and Mechanical Engineering Design Problems

The slime mould optimization algorithm (SMA) is one of the well-established optimization algorithms with a superior performance in a variety of real-life optimization problems. The SMA has certain limitations that reduce the diversity and accuracy of solutions, raising the risk of premature convergence with an inadequate balance between its exploitation and exploration phases. In this study, a novel hybrid slime mould multi-verse algorithm (SMMVA) is proposed to improve the performance of SMA algorithm. The SMA and multi-verse optimization (MVO) algorithm hybrid is introduced while updating variation parameter through novel nonlinear convergence factor. The proposed algorithm balances the ability of the SMA algorithm to explore and exploit, boosts the global exploration capability and improves the accuracy, stability, and convergence speed. The performance of SMMVA algorithm is compared with 16 well-established and recently-published metaheuristic algorithms on 23 standard benchmark functions, CEC2017, CEC2022 test functions, five engineering design problems, and five UCI repository datasets. The statistical tests such as Friedman’s test, box plot comparison and Wilcoxon rank sum test are employed to verify the SMMVA’s stability and statistical superiority. The algorithm was tested on total 64 benchmark functions, achieving an overall success rate of 68.75% across 30 runs compared to the other counterparts. The results for the feature selection problem show that the proposed algorithm with k-nearest neighbour (KNN) classifier obtained more informative features with higher accuracy values. Thus, the proposed SMMVA algorithm is proven to perform excellent performance in solving optimization problems with better solution accuracy and promising prospect.

Dynamic Response of Electromechanical Coupled Motor Gear System with Gear Tooth Crack

The motor gear system (MGS) is recognized for its potential in enhancing transmission efficiency and optimizing space utilization. However, the system is subjected to challenges, notably the occurrence of abnormal vibrations. These issues stem from the dynamic interaction between the motor and gears, the presence of nonlinear factors in gear system, and the impact of gear faults, all of which contribute to complex vibration patterns. Traditional dynamic models have been found to be inadequate in effectively addressing the complexities associated with electromechanical coupling problems in MGS. To address these limitations, a comprehensive analysis approach is proposed in this paper, which is grounded in the development of an electromechanical coupling model. This method involves establishing a coupled dynamic model of the motor and gear system, integrating numerical simulations, and experimental validations to thoroughly analyze the vibration characteristics of the system. Through this multifaceted methodology, a detailed analysis of the system’s vibration characteristics is conducted. The results indicate that internal excitations from tooth root cracks not only directly affect dynamic characteristics of the gear transmission system (GTS) but also indirectly influence dynamic behavior of the motor, which offers valuable insights into modeling integrated MGS and provides significant solutions for fault diagnosis within these systems.

Building a simpler moderated nonlinear factor analysis model with Markov Chain Monte Carlo estimation.

Moderated nonlinear factor analysis (MNLFA) has emerged as an important and flexible data analysis tool, particularly in integrative data analysis setting and psychometric studies of measurement invariance and differential item functioning. Substantive applications abound in the literature and span a broad range of disciplines. MNLFA unifies item response theory, multiple group, and multiple indicator multiple cause modeling traditions, and it extends these frameworks by conceptualizing latent variable heterogeneity as a source of differential item functioning. The purpose of this article was to illustrate a flexible Markov chain Monte Carlo-based approach to MNLFA that offers statistical and practical enhancements to likelihood-based estimation while remaining plug and play with established analytic practices. Among other things, these enhancements include (a) missing data handling functionality for incomplete moderators, (b) multiply imputed factor score estimates that integrate into existing multiple imputation inferential methods, (c) support for common data types, including normal/continuous, nonnormal/continuous, binary, ordinal, multicategorical nominal, count, and two-part constructions for floor and ceiling effects, (d) novel residual diagnostics for identifying potential sources of differential item function, (e) manifest-by-latent variable interaction effects that replace complex moderation function constraints, and (f) integration with familiar regression modeling strategies, including graphical diagnostics. A real data analysis example using the Blimp software application illustrates these features. (PsycInfo Database Record (c) 2024 APA, all rights reserved).