Search Results (82)

Breaking barriers to sustainable jobs and promoting inclusive employment are key goals of the 2030 Agenda, with SDG8 Target 8.5 aiming to achieve decent work for all, including persons with disabilities (PWDs). This paper contributes to the scholarly debate by empirically examining how various regulatory areas, including credit market regulation, labor market regulation, business regulation, and the freedom to compete, influence the informal employment of PWDs in 15 countries between 2007 and 2022. The empirical investigation is conducted for the entire population with disabilities, as well as for adults and youth with disabilities. The analysis employs a dynamic labor demand function estimated through the two-step system GMM method to account for adjustment costs within the labor market. In addition, the Feasible Generalized Least Squares method is employed to assess the robustness of the results. The findings reveal significant heterogeneity in the effects of regulation on the informal employment of PWDs, with substantial differences between adults and youth. At the aggregate level, greater flexibility in most regulatory areas reduces informal employment of PWDs, except for labor market regulation. Upon examining age cohorts, the outcomes for adults exhibit similarities to the aggregate analysis. In contrast, more flexible regulations increase informal employment among young people with disabilities, except for business regulations, which exert negative impacts, and credit market regulations, which demonstrate no significant effects. This study recommends that policymakers support formal business development for PWDs and implement anti-discrimination laws. For youth with disabilities, targeted initiatives, including financial inclusion and wage subsidies, are essential to convert regulatory flexibility into formal employment opportunities. Full article

Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing chain and adversely affect displacement estimation accuracy, particularly in the case of a small number of SAR datasets. This study presents a unified data fusion framework designed to enhance the detection of gross errors in multi-source InSAR observations, incorporating a robust Least Squares Adjustment (LSA) methodology. The proposed framework develops a comprehensive mathematical model that integrates the fusion of multi-source InSAR data with robust LSA analysis, thereby establishing a theoretical foundation for the integration of heterogeneous datasets. Then, a systematic, reliability-driven data fusion workflow with robust LSA is developed, which synergistically combines Multi-Temporal InSAR (MT-InSAR) processing, homonymous Persistent Scatterer (PS) set generation, and iterative Baarda’s data snooping based on statistical hypothesis testing. This workflow facilitates the concurrent localization of gross errors and optimization of displacement parameters within the fusion process. Finally, the framework is rigorously evaluated using datasets from Radarsat-2 and two Sentinel-1 acquisition campaigns over the Tianjin Binhai New Area, China. Experimental results indicate that gross errors were successfully identified and removed from 11.1% of the homonymous PS sets. Following the robust LSA application, vertical displacement estimates exhibited a Root Mean Square Error (RMSE) of 5.7 mm/yr when compared to high-precision leveling data. Furthermore, a localized analysis incorporating both leveling validation and time series comparison was conducted in the Airport Economic Zone, revealing a substantial 42.5% improvement in accuracy compared to traditional Ordinary Least Squares (OLS) methodologies. Reliability assessments further demonstrate that the integration of multiple InSAR datasets significantly enhances both internal and external reliability metrics compared to single-source analyses. This study underscores the efficacy of the proposed framework in mitigating errors induced by phase unwrapping inaccuracies, thereby enhancing the robustness and credibility of InSAR-derived displacement measurements. Full article

To address low training efficiency and poor reconstruction in traditional K Singular Value Decomposition (KSVD) for compressive sensing of shock wave signals, this study proposes an improved algorithm, DH-KSVD, integrating dynamic pruning and hybrid coding. The dynamic pruning mechanism eliminates redundant atoms according to their contributions and adaptive thresholds, while incorporating residual features to enhance dictionary compactness and training efficiency. The hybrid sparse constraint integrates the sparsity of ${\ell }_0$-Orthogonal Matching Pursuit (OMP) with the noise robustness of ${\ell }_1$-Least Absolute Shrinkage and Selection Operator (LASSO), dynamically adjusting their relative weights to enhance both coding quality and reconstruction stability. Experiments on typical shock wave datasets show that, compared with Discrete Cosine Transform (DCT), KSVD, and feature-based segmented dictionary methods (termed CC-KSVD), DH-KSVD reduces average training time by 46.4%, 31%, and 13.7%, respectively. At a Compression Ratio (CR) of 0.7, the Root Mean Square Error (RMSE) decreases by 67.1%, 65.7%, and 36.2%, while the Peak Signal-to-Noise Ratio (PSNR) increases by 35.5%, 39.8%, and 11.8%, respectively. The proposed algorithm markedly improves training efficiency and achieves lower RMSE and higher PSNR under high compression ratios, providing an effective solution for compressing long-duration, transient shock wave signals. Full article

Background: In an era of unprecedented technology adoption in healthcare, it is imperative to understand and predict factors influencing users’ perspective. This study employs a risk-integrated technology acceptance model aiming to identify the determinants of the intention to use mobile health applications among patients with chronic diseases in Romania. Methods: A face-to-face survey method was used to collect research data from 207 subjects, and the partial least squares structural equation modeling approach was employed for data analysis. Results: The behavioral intention to use mobile health applications (INT) was influenced positively by the perceived ease of use (PEOU, f² = 0.358, β = 0.500, p < 0.001) and perceived usefulness (PU, f² = 0.271, β = 0.678, p < 0.001). Another core predictor, with a negative effect on the intention to use, was the user’s perceived risk of using the technology (RISK, f² = 0.239, β = −0.321, p < 0.001), in turn influenced by the perceived degree of cyber-insecurity (CYBER, f² = 0.492, β = 0.639, p < 0.001). Digital self-efficacy (DSE) was identified as an external determinant with strong positive influence on PEOU (f² = 0.486, β = 0.610, p < 0.001). The model shows strong performance, reflected in a high Tenenhaus goodness-of-fit index (0.770) and solid explanatory power for the outcome variable (adjusted R² = 0.718). Conclusions: This study validates an extended risk-integrated technology acceptance model, offering robust insights into the determinants of mobile health application adoption among chronic patients in Romania. The findings provide actionable guidance for designing targeted interventions and healthcare policies to enhance technology adoption in this population. Full article

Target tracking in typical radar applications faces critical challenges in complex environments, including nonlinear dynamics, non-Gaussian noise, and sensor outliers. Current robustness-enhanced approaches remain constrained by empirical kernel tuning and computational trade-offs, failing to achieve balanced noise suppression and real-time efficiency. To address these limitations, this paper proposes the variational Bayesian-based maximum correntropy criterion cubature Kalman filter (VBMCC-CKF), which integrates variational Bayesian inference with CKF to establish a fully adaptive robust filtering framework for nonlinear systems. The core innovation lies in constructing a joint estimation framework of state and kernel size, where the kernel size is modeled as an inverse-gamma distributed random variable. Leveraging the conjugate properties of Gaussian-inverse gamma distributions, the method synchronously optimizes the state posterior distribution and kernel size parameters via variational Bayesian inference, eliminating reliance on manual empirical adjustments inherent to conventional correntropy-based filters. Simulation confirms the robust performance of the VBMCC-CKF framework in both single and multi-target tracking under non-Gaussian noise conditions. For the single-target case, it achieves a reduction in trajectory average root mean square error (Avg-RMSE) by at least 14.33% compared to benchmark methods while maintaining real-time computational efficiency. Integrated with multi-Bernoulli filtering, the method achieves a 40% lower optimal subpattern assignment (OSPA) distance even under 10-fold covariance mutations, accompanied by superior hit rates (HRs) and minimal trajectory position RMSEs in cluttered environments. These results substantiate its precision and adaptability for dynamic tracking scenarios. Full article

For active control of impulsive noise, the conventional filtered-x least mean square (FxLMS) algorithm has poor noise reduction performance. To address this issue, this paper designs a robust cost function by embedding the cost function of the FxLMS algorithm into the framework of the adjustable Softsign function, thereby designing a robust Softsign-FxLMS (SFxLMS) algorithm for ANC systems. Furthermore, the parameter λ of the SFxLMS algorithm significantly influences its robustness and convergence speed. Therefore, a variable λ-parameter SFxLMS (VSFxLMS) algorithm is designed to improve the performance of the ANC system. Simulation studies indicate that the proposed SFxLMS algorithm and VSFxLMS algorithm exhibit stronger robustness, faster convergence rates, and better tracking performance compared to several robust FxLMS algorithms. Moreover, the symmetric properties of the proposed Softsign function contribute to balanced error suppression in both positive and negative directions, enhancing the robustness and stability of the ANC system under asymmetric impulsive noise conditions. Full article

Penalized regression estimators have become widely adopted alternatives to ordinary least squares while analyzing collinear data, despite introducing some bias. However, existing penalized methods lack universal superiority across diverse data conditions. To address this limitation, we propose a novel adaptive ridge estimator that automatically adjusts its penalty structure based on key data characteristics: (1) the degree of predictor collinearity, (2) error variance, and (3) model dimensionality. Through comprehensive Monte Carlo simulations and real-world applications, we evaluate the estimator’s performance using mean squared error (MSE) as our primary criterion. Our results demonstrate that the proposed method consistently outperforms existing approaches across all considered scenarios, with particularly strong performance in challenging high-collinearity settings. The real-data applications further confirm the estimator’s practical utility and robustness. Full article

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s range and avert thermal runaway due to improper charging methods. In this study, an adaptive SOC estimation methodology was developed using parameter identification with forgetting factor recursive least squares (FFRLS). These parameters are then incorporated into a dual adaptive extended Kalman filter (DAEKF) for SOC estimation under varying load conditions. DAEKF is used to dynamically adjust the covariance matrices for process and measurement noises, significantly enhancing the filter’s adaptability and precision. The integration of FFRLS and DAEKF enables a robust SOC estimation of electric vehicles, featuring rapid computation speeds, high accuracy, and excellent adaptability, positioning them as ideal candidates for enhancements in battery management system technology. Full article

Background/Objectives: Traditional models like ordinary least squares (OLS) struggle to capture non-linear relationships in children’s subjective well-being (SWB), which is associated with physical activity. This study evaluated machine learning (ML) for predicting SWB, focusing on sports participation, and explored theoretical prediction limits using a global dataset. It addresses a gap in understanding complex patterns across diverse cultural contexts. Methods: We analyzed 128,184 records from the ISCWeB survey (ages 6–14, 35 countries), with self-reported data on sports frequency, emotional states, and family support. To ensure cross-country generalizability, we used GroupKFold CV (grouped by country) and leave-one-country-out (LOCO) validation, yielding mean R² = 0.45 ± 0.05, confirming robustness beyond cultural patterns, SHAP for interpretability, and bootstrapping for error estimation. No pre-registration was required for this secondary analysis. Results: XGBoost and LightGBM outperformed OLS, achieving R² up to 0.504 in restricted datasets (sensitivity excluding affective leakage: R² = 0.35), with sports-related variables (e.g., exercise frequency) associated positively with SWB predictions (SHAP values: +0.15–0.25; incremental ΔR² = 0.06 over demographics/family/school base). Using test–retest reliability from literature (r = 0.74), the estimated irreducible RMSE reached 0.941; XGBoost achieved RMSE = 1.323, approaching the predictability bound with 68.1% of explainable variance captured (after noise adjustment). Partial dependence plots showed linear associations with exercise without satiation and slight age decline. Conclusions: ML improves SWB prediction in children, highlighting associations with sports participation, and approaches predictable variance bounds. These findings suggest potential for data-driven tools to identify patterns, such as through physical literacy pathways, informing physical activity interventions. However, longitudinal studies are needed to explore causality and address cultural biases in self-reports. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

This study presents an adaptive integrated chassis control strategy for enhancing vehicle stability under different road conditions, specifically through the real-time estimation of tire cornering stiffness. A hierarchical control architecture is developed, combining active front steering (AFS) and direct yaw moment control (DYC). A recursive regularized weighted least squares algorithm is designed to estimate tire cornering stiffness from measurable vehicle states, eliminating the need for additional tire sensors. Leveraging this estimation, an adaptive sliding mode controller (ASMC) is proposed in the upper layer, where a novel self-tuning mechanism adjusts control parameters based on tire saturation levels and cornering stiffness variation trends. The lower-layer controller employs a weighted least squares allocation method to distribute control efforts while respecting physical and friction constraints. Co-simulations using MATLAB 2018a/Simulink and CarSim validate the effectiveness of the proposed framework under both high- and low-friction scenarios. Compared with conventional ASMC and DYC strategies, the proposed controller exhibits improved robustness, reduced sideslip, and enhanced trajectory tracking performance. The results demonstrate the significance of the real-time integration of tire dynamics into chassis control in improving vehicle handling and stability. Full article

To address the performance degradation in model predictive control (MPC) under vehicle state uncertainties caused by external disturbances (e.g., crosswinds and tire cornering stiffness variations) and rigid constraint conflicts, we propose a robust MPC framework with adaptive weight adjustment and dynamic constraint relaxation. Traditional MPC methods often suffer from infeasibility or deteriorated tracking accuracies when handling model mismatches and disturbances. To overcome these limitations, three key innovations are introduced: a three-degree-of-freedom vehicle dynamic model integrated with recursive least squares-based online estimation of tire slip stiffness for real-time lateral force compensation; an adaptive weight adjustment mechanism that dynamically balances control energy consumption and tracking accuracy by tuning cost function weights based on real-time state errors; and a dynamic constraint relaxation strategy using slack variables with variable penalty terms to resolve infeasibility while suppressing excessive constraint violations. The proposed method is validated via ROS (noetic)–MATLAB2023 co-simulations under crosswind disturbances (0–3 m/s) and varying road conditions. The results show that the improved algorithm achieves a 13% faster response time (5.2 s vs. 6 s control cycles), a 15% higher minimum speed during cornering (2.98 m/s vs. 2.51 m/s), a 32% narrower lateral velocity fluctuation range ([−0.11, 0.22] m/s vs. [−0.19, 0.22] m/s), and reduced yaw rate oscillations ([−1.8, 2.8] rad/s vs. [−2.8, 2.5] rad/s) compared with a traditional fixed-weight MPC algorithm. These improvements lead to significant enhancements in trajectory tracking accuracy, dynamic response, and disturbance rejection, ensuring both safety and efficiency in autonomous vehicle control under complex uncertainties. The framework provides a practical solution for real-time applications in intelligent transportation systems. Full article

Fractional cointegration has been extensively examined in time series analysis, but its extension to heterogeneous panel data with unobserved heterogeneity and cross-sectional dependence remains underdeveloped. This paper develops a robust framework for testing fractional cointegration in heterogeneous panel data, where unobserved heterogeneity, cross-sectional dependence, and persistent shocks complicate traditional approaches. We propose the Bayesian Tapered Narrowband Least Squares (BTNBLS) estimator, which addresses three critical challenges: (1) spectral leakage in long-memory processes, mitigated via tapered periodograms; (2) precision loss in fractional parameter estimation, resolved through narrowband least squares; and (3) unobserved heterogeneity in cointegrating vectors (${\theta }_i$) and memory parameters ($\nu ,\delta$), modeled via hierarchical Bayesian priors. Monte Carlo simulations demonstrate that BTNBLS outperforms conventional estimators (OLS, NBLS, TNBLS), achieving minimal bias (0.041–0.256), near-nominal coverage probabilities (0.87–0.94), and robust control of Type 1 errors (0.01–0.07) under high cross-sectional dependence ($\rho =0.8$), while the Bayesian Chen–Hurvich test attains near-perfect power (up to 1.00) in finite samples. Applied to Purchasing Power Parity (PPP) in 18 fragile Sub-Saharan African economies, BTNBLS reveals statistically significant fractional cointegration between exchange rates and food price ratios in 15 countries ($p<0.05$), with a pooled estimate ($\widehat{\theta }=0.33$, $p<0.001$) indicating moderate but resilient long-run equilibrium adjustment. These results underscore the importance of Bayesian shrinkage and spectral tapering in panel cointegration analysis, offering policymakers a reliable tool to assess persistence of shocks in institutionally fragmented markets. Full article

In remote conductivity control for water–fertilizer integration systems, challenges such as long-distance nonlinearities and variable parameters can lead to fertilization inaccuracies, including over-irrigation and uneven distribution, affecting both productivity and environmental sustainability. To mitigate these issues, this study proposes a variable-parameter sliding mode control (VSMC) strategy, combined with an adaptive observer based on Recursive Least Squares (RLS) to estimate system inertia and load torque in real time. This allows for dynamic adjustment of the sliding surface parameters, ensuring robust control even under varying operating conditions. Two parameter derivation approaches—analytical modeling and data-driven fitting—are evaluated. Field tests demonstrate that VSMC outperforms the Proportional–Integral (PI) and conventional sliding mode control (SMC) methods in maintaining target electrical conductivity (EC) levels. Specifically, for a target EC of 1.4 mS/cm, VSMC stabilizes the system to within 1.18–1.60 mS/cm in 95 s, with a 14.3% overshoot, well within agronomic tolerance. In regional irrigation trials, VSMC significantly improves fertilizer uniformity, reducing the standard deviation of potassium nitrate distribution from 2.14 (PI) to 0.59. The simulation and experimental results validate the effectiveness and robustness of the proposed method, highlighting its potential to enhance agronomic efficiency and reduce environmental impact. Full article