Nonlinear Factors Research Articles

Establishing measurement equivalence of a youth-reported parental monitoring measure across sex, race/ethnicity, and intersectional identity.

Parental monitoring is a robust family-level predictor of youth well-being. Identification of variations by gender and/or race/ethnicity in parental monitoring has important implications for tailoring parenting practices. However, valid comparisons can only be conducted if cross-subpopulation measurement equivalence is established. Although measurement equivalence testing is widely used, it rarely (a) assesses intersectional identity (i.e., identity reflecting multiple factors such as race/ethnicity and gender) or (b) involves generating scores adjusted for nonequivalence. This is the first known study to do both with a parental monitoring measure. Measurement equivalence by sex (proxy for gender), race/ethnicity, and intersectional identity (sex by race/ethnicity) was assessed in the five-item Parental Monitoring Questionnaire administered to middle-school-aged Black, Latinx, and White girls and boys. Data were drawn from the second follow-up of the Adolescent Brain Cognitive Development Study (n = 9,082; 47.5% female, 52.5% male; 15.5% Black, 22.9% Latinx, 61.6% White). Moderated nonlinear factor analysis was used to identify group differences in item-level (intercepts and loadings) and factor-level (mean and variance) parameter estimates for a latent parental monitoring variable and subsequently to generate factor scores accounting for measurement nonequivalence. Intercepts or loadings for four items differed by sex, race/ethnicity, and/or intersectional identity. Factor mean and variance differed by race/ethnicity. Comparisons across the six groups using adjusted (factor) scores differed substantially from comparisons using unadjusted scores, underscoring the impact of systematic measurement bias on the valid assessment of parental monitoring in girls and boys who identify with these racial/ethnic groups. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Research on the impact of the digital economy on carbon emissions based on the dual perspectives of carbon emission reduction and carbon efficiency

China’s digital economy is currently thriving, with the “dual carbon” targets representing a significant pursuit of economic development. The role of the digital economy in achieving these targets warrants detailed discussion. Using urban panel data from China spanning 2011 to 2021, this paper empirically examines the impact of the digital economy on urban carbon emissions. The findings reveal several key points: Firstly, the digital economy significantly reduces urban carbon emissions and enhances efficiency, a conclusion that remains valid after a series of robustness checks. Secondly, there is a notable structural effect, with different dimensions of the digital economy exhibiting varying impacts on urban emission reduction and efficiency enhancement. Thirdly, green economic efficiency and green technological innovation are crucial mechanisms through which the digital economy facilitates urban carbon emission reduction and efficiency improvement, operating through green development and innovation channels. Fourthly, industrial structure upgrading is a key nonlinear factor reducing the digital economy’s impact on carbon reduction, while the digital economy’s effect on urban carbon efficiency shows a U-shaped pattern. Lastly, the impact of the digital economy on urban carbon emissions displays significant heterogeneity across cities of different locations, tiers, and characteristics.

Nonlinear forecasting with many predictors using mixed data sampling kernel ridge regression models

Abstract Policy institutes such as central banks need accurate forecasts of key measures of economic activity to design stabilization policies that reduce the severity of economic fluctuations. Therefore, this paper develops a kernel ridge regression estimator in a mixed data sampling framework. Kernel ridge regression can handle many predictors with a nonlinear relationship to the target variable. Consequently, it has potential to improve the currently used principal component-based methods when the economic data follow a nonlinear factor structure. In a Monte Carlo study, we show that the kernel ridge regression approach is superior in terms of mean square error and is more robust than principal component-based methods to different nonlinear data generating processes. By using a dataset consisting of 24 economic indicators, we forecast Swedish gross domestic production. The results confirm the superiority of the kernel ridge regression approach. Therefore, we suggest that policy institutes consider the use of kernel-based approaches when forecasting key measures of economic activity.

Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas.

An Improved Whale Optimization Algorithm for the Integrated Scheduling of Automated Guided Vehicles and Yard Cranes

With the rapid development of global trade, the cargo throughput of automated container terminals (ACTs) has increased significantly. To meet the demands of large-scale, high-intensity, and high-efficiency ACT operations, the seamless integration of various terminal facilities has become crucial, particularly the collaboration between yard cranes (YCs) and automated guided vehicles (AGVs). Therefore, an integrated scheduling problem for YCs and AGVs (YAAISP) is proposed and formulated in this paper, considering stacking containers and bidirectional transport of AGVs. As the YAAISP is an NP-hard problem, an Improved Whale Optimization Algorithm (IWOA) is proposed in which a reverse learning strategy is used for the population to enhance population diversity; a random difference variation strategy is employed to improve individual exploration capabilities; and a nonlinear convergence factor alongside an adaptive weighting mechanism to dynamically balance global exploration and local exploitation. For container tasks of size 100, the objective function value (OFV) of the IWOA was reduced by 9.25% compared to the standard Whale Optimization Algorithm. Comparisons with other algorithms, such as the Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer, showed an OFV reduction of 9.61% to 11.75%. This validates the superiority of the proposed method.

P0306 Application of 5 different machine-learning approaches to develop an artificial intelligence pathway to predict IBD-associated fatigue: Analysis of 1200 data points in 531 IBD patients.

Abstract Background Fatigue is commonly identified by IBD patients as one of the most important issues that affect their wellbeing1. Despite advanced immune therapies, 50% of patients experienced significant fatigue. The central mechanisms beyond gut inflammation that drives fatigue are not known. Methods We utilized 5 different machine-learning (ML) approaches in a combined prospective and cross-sectional multi-center cohort2 in UK (2020-24) to develop models and identify factors that can predict IBD-associated fatigue. Our ML-modelling involved 531 IBD (260 UC, 246 CD, 25 IBDU) patients, ~100 clinical variables (including CUCQ32-patient reported outcomes, seasonality, BMI, drug treatments, disease phenotype and all hospital-based blood/stool tests) and 1200 data points over time. ML-approaches were gradient boosting, random forests, logistic regression, support vector machines and deep neural networks. We incorporated various methods to identify the relative importance of clinical factors and our ML-performance. We trained our ML-fatigue on overall IBD and on patients in remission. Results Using an IBD CUCQ32 dataset involving 2500 patient responses (Figure 1), we used patient-reported data of significant fatigue experienced in 10 out of 14 days as IBD-fatiguehigh (Figure 1). Through train-validate-test steps, we found that traditional statistics and machine learning performed similarly (Random Forest AUC of 0.75; logistic regression AUC of 0.74 using R). AdaBoost, XGBoost, random forest and logistic regression models performed similarly while support vector classifiers and neural networks performed poorly (Table 1). SHapley Additive exPlanations (SHAP) analysis of the models3, based on game theory, revealed that each model prioritises different variables - the top 3 factors for XGBoost were has_active_symptoms, weight and platelets; whereas logistic regression ranked seasonality variables (autumn and winter) higher. The analysis indicates the potential of ML to integrate non-linear factors in a way that traditional statistics do not capture well. ML performance to predict IBD-fatiguehigh in patients in biochemical remission (CRP<5 and calprotectin <250) was poor, with AUCs of 0.61 for logistic regression and 0.66 for random forest. Conclusion We provide a comprehensive ML-pathway to predict IBD-associated fatigue. Despite integration of multiple clinical factors and use of interpretable ML-approaches, prediction of fatigue in IBD patients, remains suboptimal. Our data suggests a large ‘hidden’ pathobiological component and current work is in progress to integrate deep molecular data and build a clinical-scientific AI model to improve understanding of IBD-associated fatigue.

Multi-UAV cooperative task assignment based on multi-strategy improved DBO

Effective task assignment technology plays a pivotal role in optimizing Unmanned Aerial Vehicles operations during the collaboration of multiple Unmanned Aerial Vehicles (multi-UAV) in combat scenarios. Therefore, aiming at the cooperative task assignment of multi-UAV, this paper takes the value and time window of the ground target into consideration, takes the total fuel consumption, execution time, and execution cost of all tasks completed by multi-UAV as the objective functions, constructs a multi-objective multi-task assignment mathematical model, and proposes a multi-strategy improved Dung Beetle Optimizer (MIDBO) to solve the model. The MIDBO employs Sinusoidal chaotic mapping to generate the initial population, enhancing population diversity. Additionally, it integrates the nonlinear convergence factor and spiral search factor to augment the exploration capabilities of the rolling dung beetles. Moreover, by incorporating the subtraction average strategy, the algorithm bolsters the prowess of the foraging dung beetles, leading to improved algorithmic performance and attaining high-quality solutions. The experimental results show that the multi-UAV collaborative task assignment based on the MIDBO can enhance the global optimization ability and assign the optimal task sequence to multi-UAV.

Height Control Method for Air Suspension Systems Based on Model-Free Adaptive Control and Improved Genetic Algorithm

<div>This article presents a height control method for air suspension systems, which are influenced by strong nonlinearity and multiple coupling factors, based on model-free adaptive control (MFAC) using full-form dynamic linearization (FFDL). To address the impact of different damping coefficients of the shock absorber on the height control effect, an improved genetic algorithm is employed to globally optimize the relevant parameters involved in the design of the control law, thereby enhancing the height control performance. The precision of modeling the air suspension system has a direct impact on the simulation of both static and dynamic vehicle models, as well as the accuracy of height control. In this article, an equivalent thermodynamic model of the air suspension system is established based on the principle of energy conservation for height control research. Considering the nonlinearity of the air suspension system and the need to make additional assumptions before modeling, a MFAC method using FFDL is adopted for controller design. Traditional height control methods do not consider the impact of changes in the shock absorber damping coefficient on the height control effect. For different damping coefficients, the body height tracking error is large when using the same height control law initialization parameters. Therefore, an improved genetic algorithm is employed to globally optimize the MFAC parameters under different damping states. The effectiveness of the thermodynamic model of the air suspension system and the MFAC method for height control, with parameters tuned using the improved genetic algorithm, was validated through MATLAB/Simulink simulations.</div>

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.

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

АСИМПТОТИКА ПЕРЕХІДНИХ ІМОВІРНОСТЕЙ НАПІВМАРКОВСЬКОГО ПРОЦЕСУ

The paper considers a semi-Markov process with a nonlinear normalization factor. The asymptotics of transitional probabilities for the semi-Markov process are found. The functions of the multidimensional renewal equation are determined. Keywords: semi-Markov process, Markov chain, renewal function, transitional probabilities.

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.