Search Results (509)

Due to the significant nonlinear characteristics of the magnetic bearing, it is difficult to establish an accurate mathematical model, and it is susceptible to external disturbances. Traditional control methods struggle to meet the control requirements. Active disturbance rejection control (ADRC) does not rely on accurate models and has outstanding anti-interference ability. In order to improve the anti-disturbance ability and control stability of the system, a cascaded linear–nonlinear active disturbance rejection control method (CL-NLADRC) based on the improved artificial jellyfish algorithm is proposed and applied to the magnetic levitation ball system. Firstly, the mathematical model of the magnetic levitation ball system is established, and based on this model, a cascaded linear–nonlinear extended state observer is constructed to estimate and compensate for the system state, thereby enhancing the dynamic response capability of the system. Subsequently, the tangent spiral motion and the lens reversal learning strategy are introduced to improve the artificial jellyfish algorithm to further improve the global optimization performance of the algorithm. Finally, the improved artificial jellyfish algorithm is used to optimize the CL-NLADRC controller parameters. The simulation and experimental results show that compared with the traditional LADRC and PID controllers, the proposed CL-NLADRC has a significant improvement in the steady-state error, response speed, and anti-disturbance performance of the system. Among them, the root mean square error decreased by 14% and 47%, respectively, which verified the effectiveness and stability of the method in the magnetic levitation ball system. Full article

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to correct positional errors. However, the propagation characteristics of underwater acoustic signals are susceptible to environmental disturbances, often resulting in non-Gaussian, heavy-tailed distributions of ranging noise. Additionally, the strong nonlinearity of the system and the limited observability of measurement information further constrain positioning accuracy. To tackle these issues, this paper innovatively proposes a Factor Graph-based Adaptive Cooperative Positioning Algorithm (FGAWSP) suitable for heavy-tailed noise environments. The method begins by constructing a factor graph model for UUV cooperative positioning to intuitively represent the probabilistic dependencies between system states and observed variables. Subsequently, a novel factor graph estimation mechanism integrating adaptive weights with the product algorithm is designed. By conducting online assessment of residual information, this mechanism dynamically adjusts the fusion weights of different measurements, thereby achieving robust handling of anomalous range values. Experimental results demonstrate that the proposed method reduces positioning errors by 22.31% compared to the traditional algorithm, validating the effectiveness of our approach. Full article

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

Recently, there has been a rapid increase in the demand for magnetic levitation systems. Since they are utilized in many levitation-based systems, one such application is in magnetic levitated (Maglev) trains. Moreover, these systems are complicated to control due to their nonlinear characteristics, susceptibility to external disturbances, and model uncertainties. This article proposes an enhanced integral sliding mode control (ISMC) strategy with a robust optimal framework designed for electromagnetic levitation systems (EMLSs). Traditional sliding mode control (SMC) often suffers from a high-frequency phenomenon in the input, thereby necessitating the development of a more robust controller. This requirement is addressed through the implementation of a comprehensive integral robust optimal sliding mode control strategy. The proposed controller effectively mitigates the chattering phenomenon while simultaneously enhancing the system’s robustness against uncertainties. The robust optimal approach is specifically designed to handle the matched uncertainties inherent in the system dynamics, thereby facilitating an appropriate feedback control mechanism. The Hamilton–Jacobi–Bellman (HJB) equation is used to achieve the robust control design. This feedback control is integrated with the ISMC to execute the desired control action effectively. The simulation results highlight the effectiveness of the proposed control scheme, presenting a comparative analysis of performance indices, including integral time absolute error (ITAE), integral absolute error (IAE), integral squared error (ISE), and integral time squared error (ITSE). These indices collectively underscore the robustness of the control design. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

Bitumen content is a critical factor influencing the long-term performance and durability of asphalt pavements. This study evaluates how different binder percentages affect the mechanical behaviour of asphalt mixtures. Mixtures containing 4.7%, 5.1% and 5.5% binder were tested through an extensive experimental program that included Marshall stability and flow, semi-circular bending, PAV aging, wheel rutting, dynamic modulus, creep compliance and fatigue resistance, supported by finite element simulations. To model the nonlinear viscoplastic and damage behaviour, a Perzyna-type viscoplastic formulation and Lemaitre’s isotropic damage model were applied. Model parameters were further refined using Bayesian estimation, based on 10,000 samples generated with a Markov Chain Monte Carlo procedure employing the Metropolis–Hastings algorithm. The findings indicate that mixtures with 4.7% binder content develop fatigue damage earlier, while increasing the binder above 5.1% leads to greater rutting susceptibility and higher creep compliance, as seen in the 5.5% mixture. Among the three, the 5.1% binder content delivered the best overall performance, reducing plastic strain-related damage by 40% compared with the 4.7% mixture and by 27% compared with the 5.5% mixture. Full article

This study introduces a nonlinear fractional bi-susceptible model for COVID-19 using the Atangana–Baleanu derivative in Caputo sense (ABC). The fractional framework captures nonlocal effects and temporal decay, offering a realistic presentation of persistent infection cycles and delayed recovery. Within this setting, we investigate multiple transmission modes, determine the major risk factors, and analyze the long-term dynamics of the disease. Analytical results are obtained at equilibrium states, and fundamental properties of the model are validated. Numerical simulations based on the Toufik–Atangana method further endorse the theoretical results and emphasize the effectiveness of the ABC derivative. Bifurcation analysis illustrates that adjusting time-invariant treatment and awareness efforts can accelerate pandemic control. Sensitivity analysis identifies the most significant parameters, which are used to construct an optimal control problem to determine effective disease control strategies. The numerical results reveal that the proposed control interventions minimize both infection levels and associated costs. Overall, this research work demonstrates the modeling strength of the ABC derivative by integrating fractional calculus, bifurcation theory, and optimal control for efficient epidemic management. Full article

Forest fire susceptibility prediction is essential for effective management, yet considerable uncertainty persists under future climate change, especially in climate-sensitive plateau regions. This study integrated MODIS fire data with climatic, topographic, vegetation, and anthropogenic variables to construct an Extreme Gradient Boosting (XGBoost) model for the Yunnan Plateau, a region highly prone to forest fires. Compared with Support Vector Machine and Random Forest models, XGBoost showed superior ability to capture nonlinear relationships and delivered the best performance, achieving an AUC of 0.907 and an overall accuracy of 0.831. The trained model was applied to climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 to assess future fire susceptibility. Results indicated that high-susceptibility periods primarily occur in winter and spring, driven by minimum temperature, average temperature, and precipitation. High-susceptibility areas are concentrated in dry-hot valleys and mountain basins with elevated temperatures and dense human activity. Under future climate scenarios, both the probability and spatial extent of forest fires are projected to increase, with a marked expansion after 2050, especially under SSP5-8.5. Although the XGBoost model demonstrates strong generalizability for plateau regions, uncertainties remain due to static vegetation, coarse anthropogenic data, and differences among climate models. Full article

Flood hazards in arid oil-producing regions result from both natural hydrological processes and terrain changes due to land subsidence. In the Yibal field in northern Oman, long-term hydrocarbon extraction has caused measurable ground deformation, altering surface gradients and drainage patterns. This study presents a deformation-adjusted flood hazard assessment by integrating a 2013 photogrammetric DEM with a 2023 subsidence-corrected DEM derived from multi-temporal PS-InSAR observations (RADARSAT-2 and TerraSAR-X). Key hydrological indicators—including slope, drainage networks, Height Above Nearest Drainage (HAND), floodplain depth, Curve Number, and extreme precipitation from the wettest monthly rainfall in a 10-year archive—were recalculated for both years. Flood hazard maps for 2013 and 2023 were generated using an AHP-based multi-criteria framework across five hydrologically motivated scenarios. Results indicate that while the total area of high- and very-high-hazard zones changed only slightly in most scenarios (within ±6%), these zones shifted into subsidence-affected depressions, reflecting deformation-driven redistribution of flood-prone areas. Low-hazard zones grew most significantly, especially in Scenarios S2–S4, with increases of 160–320% compared to 2013, while moderate-hazard areas showed smaller but consistent growth. Floodplain-dominated conditions (S5) produced the most pronounced nonlinear response, with a substantial increase in very low hazard and localized concentration of very high hazard in areas of deepest subsidence. Geomorphic analysis using the Geomorphic Flood Index (GFI) shows deepening of flow pathways and expansion of geomorphic depressions between 2013 and 2023, supporting the modeled redistribution of hazards. These findings demonstrate that even moderate subsidence can significantly alter hydrological susceptibility and underscore the importance of incorporating deformation-adjusted terrain modeling into flood hazard assessments in petroleum fields and other subsidence-prone areas. Full article

This study presents a validated numerical investigation into the seismic liquefaction potential of fine-grained reclaimed sediments commonly encountered in coastal, containment, and reclamation projects. Fine-grained reclaimed sediments pose a particular challenge for seismic liquefaction assessment due to their low permeability, high fines content, and complex cyclic response under earthquake loading. A fully coupled, nonlinear finite element model was developed using the Pressure-Dependent Multi-Yield (PDMY) constitutive framework, calibrated against laboratory Cyclic Direct Simple Shear (CDSS) tests and verified using in situ Cone Penetration Tests with pore pressure measurement (CPTu). The model effectively captured the dynamic response of saturated sediments, including excess pore pressure generation, cyclic mobility, and post-liquefaction behavior, under three earthquake ground motions: Livermore, Chi-Chi, and Loma Prieta. Results showed that near-surface layers (0–2.3 m) experienced full liquefaction within two to three cycles, with excess pore pressure ratios (R_u) approaching 1.0 and peak pressures closely matching laboratory data with less than 10% deviation. The numerical approach revealed that traditional CPT-based cyclic resistance methods underestimated liquefaction susceptibility in intermediate layers due to limitations in accounting for pore pressure redistribution, evolving permeability, and seismic amplification effects. In contrast, the finite element model captured progressive strength degradation, revealing strength gain in deeper layers due to consolidation, while upper zones remained vulnerable due to low confinement and resonance effects. A critical threshold of R_u ≈ 0.8 was identified as the onset of rapid shear strength loss. The findings confirm the advantage of advanced numerical modeling over empirical methods in capturing the complex cyclic behavior of reclaimed sediments and support the adoption of performance-based seismic design for such geotechnically sensitive environments. Full article

The transition to the 4th Industrial Revolution (4IR) in the electroplating industry necessitates intelligent, real-time monitoring systems to replace traditional, time-consuming offline analysis. In this study, we developed a cost-effective, automated measurement system for hexavalent chromium (Cr(VI)) in plating wastewater using an Arduino-based RGB sensor. Unlike conventional single-variable approaches, we conducted a comprehensive feature sensitivity analysis on multi-sensor data (including pH, ORP, and EC). While electrochemical sensors were found to be susceptible to pH interference, the analysis identified that the Red and Green optical channels are the most critical indicators due to the distinct chromatic characteristics of Cr(VI). Specifically, the combination of these two channels effectively functions as a dual-variable sensing mechanism, compensating for potential interferences. To optimize prediction accuracy, a systematic machine learning strategy was employed. While the Convolutional Neural Network (CNN) achieved the highest classification accuracy of 89% for initial screening, a polynomial regression algorithm was ultimately implemented to model the non-linear relationship between sensor outputs and concentration. The derived regression model achieved an excellent determination coefficient (R² = 0.997), effectively compensating for optical saturation effects at high concentrations. Furthermore, by integrating this sensing model with the chemical stoichiometry of the reduction process, the proposed system enables the precise, automated dosing of reducing agents. This capability facilitates the establishment of a “Digital Twin” for wastewater treatment, offering a practical ICT (Information and Communication Technology)-based solution for autonomous process control and strict environmental compliance. Full article

Lately the nonlinear optical third-harmonic generation (THG) microscopy is starting to emerge as a laboratory standard for label-free studies in biological samples. In this study, the THG signals produced from corn starch granules are investigated. In particular, the polarization-dependent THG (P-THG) signals emerging from the outer layer (shell) of the starch granules are compared with the P-THG signals originating from their inner portion (core). By rotating the linear polarization of the excitation beam, two distinct P-THG modulation patterns are revealed within single granules, corresponding to their shells and to their structurally different cores. These patterns are analyzed using a theoretical framework that describes THG from an orthorhombic crystal symmetry, characteristic of corn starch. This allows us to extract point-by-point in the granules the ratios of the ${\chi }^{\left(3\right)}$ susceptibility tensor elements and the average molecular orientations. Then, the anisotropy ratio (AR = ${\chi }_{xxxx}^{\left(3\right)}$/${\chi }_{yyyy}^{\left(3\right)}$) is defined and used as a quantitative descriptor of the local molecular arrangements. Our results show that the shells and cores exhibit distinct AR values, probing the anisotropy in the molecular arrangements between the two regions. This study establishes P-THG as a powerful contrast mechanism for probing structural anisotropy in biological samples beyond conventional THG intensity-only microscopy. Full article

The objective of the article is the assessment of the sensitivity of the equilibrium path of structures susceptible to loss of stability due to node snap-through. Increasingly, architectural designs feature organic forms and structures with complex geometry, which makes it possible to create very large, column-free spaces. These developments lead to the design of structures with ever smaller rise while simultaneously increasing their span. Due to the parameters of structures of this type, it is justified to analyze such systems with consideration of nonlinear effects. The Newton–Raphson algorithm was used to determine the limit points on the equilibrium path, employing both load control and displacement control. To accurately determine the position of the limit point on the equilibrium path, the step length was defined adaptively. Four dome space trusses were analyzed. Individual load sets were differentiated in terms of load location. The developed algorithm was implemented in a proprietary finite element method program created in the Matlab R2025b software. The analysis showed that, despite significant differences in the critical force (P_ult), the global response of the structure at the moment of snap-through remains similar. The displacement of the apex node at the moment of snap-through remains similar across most of the proposed load sets. In the case of low-rise structures, a significant increase in the sensitivity of the load location to the critical load value and in the displacement of the apex node at the moment of snap-through was observed. Full article

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

Micro milling of metallic materials presents unique dynamic challenges due to highly nonlinear cutting forces and the susceptibility to self-excited vibrations (chatter). This paper presents a novel mathematical model for chatter prediction in micro milling, based on an enhanced formulation of cutting forces that includes the frictional interaction between the tool’s flank face and the machined surface. The proposed approach enables accurate simulation of the cutting process and prediction of the limiting depth of cut, beyond which chatter occurs. Experimental validation was performed using pneumatic spindle and micro end mills, with chatter detection based on surface inspection via digital microscopy. A strong correlation was observed between the simulated and experimentally determined limiting depths of cut, confirming the model’s predictive capability. This research offers a new methodology for modelling cutting forces and improves the ability to predict chatter in micro milling processes, contributing to the optimization of machining parameters across a wide range of materials. Full article