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Understanding the complex drivers of water yield is essential for ensuring basin water resource security, yet existing linear approaches often overlook the critical nonlinear effects arising from factor interactions. Previous studies combining the InVEST model with attribution methods have typically treated climate and land use as independent factors, failing to quantify their interactive effects beyond additive assumptions. This study addresses this gap by introducing a coupled framework that explicitly isolates and quantifies nonlinear climate–land interactions through scenario-based residual decomposition and spatial interaction detection. Focusing on the Minjiang River Basin, this study first applies a locally calibrated InVEST model to analyze the spatiotemporal patterns of water yield from 2000 to 2023. Through scenario analysis and the Geographical Detector method, we decoupled the contributions of climatic factors, land use, and their interactions. The results show significant spatiotemporal heterogeneity in water yield, averaging 1053.59 mm, with a spatial pattern aligned closely with precipitation. Climatic factors dominated the changes (average contribution 93.43%), while the direct contribution of land use was minimal (−1.56%). Importantly, a significant nonlinear interaction effect was identified (average 8.13%), with the interplay between precipitation and forest land proportion showing the strongest explanatory power for spatial differentiation (q-statistic up to 96.4%). These findings highlight the necessity of an integrated climate-land regulatory strategy that enhances climate resilience and optimizes key land uses to promote sustainable water management, providing a methodological framework for analyzing complex hydrological drivers. Full article

(This article belongs to the Special Issue Advances in Management of Hydrology, Water Resources and Ecosystem)

33 pages, 4848 KB

Open AccessArticle

Machine Learning-Guided Design and Performance Prediction of Multidimensional Magnetic MXene-Based Nanocomposites for High-Efficiency Microwave Absorption

by Tiancai Zhang, Yi Yang and Tao Hong

Magnetochemistry 2026, 12(3), 37; https://doi.org/10.3390/magnetochemistry12030037 - 11 Mar 2026

Abstract

MXene-based microwave absorbers have received extensive attention owing to their high electrical conductivity, abundant interfacial polarization sites, and tunable surface terminations. However, the structure–property relationship of MXene composites remains highly nonlinear, and the design of high-efficiency absorbers still relies heavily on trial-and-error experiments. Herein, multidimensional magnetic components, including zero-dimensional (0D) Fe₃O₄ nanoparticles, one-dimensional (1D) Fe₃O₄/Co₃O₄ nanowires, and two-dimensional (2D) Fe₃O₄-based heterostructures, were rationally integrated with Fe/MXene and Fe/Co/MXene nanosheets to engineer synergistic dielectric and magnetic losses. Comprehensive electromagnetic characterization and loss mechanism analysis reveal that the structural dimensionality strongly impacts impedance matching and attenuation capability. To further enable predictive and data-driven optimization, a machine learning framework was established to correlate the microstructure, component ratio, thickness, and electromagnetic parameters with the microwave absorption performance (e.g., minimum reflection loss (RL_min), effective absorption bandwidth (EAB)). The optimized multidimensional composite achieves an RL_min of −56.4 dB at 10.2 GHz with an EAB of 8.4 GHz (9.6–18.0 GHz) at a thin matching thickness of 1.8 mm. The machine learning model demonstrates excellent accuracy (R² = 0.947) and enables the inverse design of absorber geometries to target specific operational frequencies. This work provides a generalizable paradigm for the intelligent design of MXene-based microwave absorbers and opens up broader opportunities for the AI-accelerated discovery of advanced electromagnetic functional materials. Full article

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30 pages, 3938 KB

Open AccessArticle

Camera Pose Revisited

by Władysław Skarbek, Michał Salamonowicz and Michał Król

Appl. Sci. 2026, 16(6), 2690; https://doi.org/10.3390/app16062690 - 11 Mar 2026

Abstract

Estimating the position and orientation of a camera with respect to an observed scene remains a fundamental problem in computer vision, particularly in calibration procedures and multi-sensor vision systems. This paper revisits the planar Perspective–n–Point (PnP) problem with emphasis on rotation representation, initialization strategy, and optimization behavior. We propose the PnP-ProCay78 algorithm, which combines analytical elimination of translation via quadratic reconstruction error with nonlinear least-squares minimization of projection residuals in Cayley parameter space. A deterministic initialization scheme based on canonical directions of the reconstruction matrix eliminates the need for spectral search over the full solution space. Experimental evaluation on heterogeneous datasets acquired from high-resolution RGB cameras and low-resolution thermal cameras demonstrates that the proposed method achieves reprojection accuracy comparable to state-of-the-art OpenCV implementations such as SQPnP and IPPE. Convergence analysis in Cayley space reveals stable and rapidly contracting optimization trajectories, with consistent behavior across sensors of significantly different resolution and noise characteristics. The results indicate that a carefully chosen rotation parameterization combined with a transparent optimization framework can yield competitive numerical performance while maintaining geometric interpretability and structural simplicity. Full article

(This article belongs to the Special Issue RGB-IR Vision for 3D Scene Analysis and Thermal Assessment)

20 pages, 2657 KB

Open AccessArticle

Markov Transition Fields-Based Dual-Modal Fusion Method on Transient Stability Assessment for Power Systems

by Min Yan, Qian Chen, Zhihua Huang, Beiqi Qian, Lei Zhang, Yifan Ding and Zehua Su

Energies 2026, 19(6), 1417; https://doi.org/10.3390/en19061417 - 11 Mar 2026

Abstract

There is an extremely urgent need to develop a transient stability assessment method for new power systems with greater rapidity and higher accuracy due to the increased complexity and difficulty caused by massive nonlinear power electronics-dominated generation and loads. In recent years, computing power has increased significantly, meaning that artificial intelligence (AI) algorithms have develop rapidly, and large-scale AI models have become available. Among them, deep learning (DL) algorithms have received more attention due to their inherent advantages, on which assessment strategy and methods are based, but these algorithms are still not sufficiently applicable. Therefore, a Markov Transition Field (MTF)-based dual-modal fusion method for transient stability assessment of power systems is proposed in this paper. First, the influence and effect on transient stability assessment by the fusion of both image modality and time series modality are studied. Then, for enhancing key features, the strategy to convert the time series modality into image modality by MTF is established, which allows the features to be described at multiple time scales and the feature correlation between different time points to be strengthened. Thus, features from image modality and time series modality are extracted, respectively, by Convolutional Neural Networks (CNNs), and gated recurrent units are adopted; the extracted features are further fused by a concatenation fusion method. It is demonstrated by the simulation results that the accuracy of the transient stability assessment is improved effectively by the aforementioned fusion method. Full article

(This article belongs to the Special Issue Advanced in Modeling, Analysis and Control of Microgrids)

29 pages, 5046 KB

Open AccessArticle

Integrating Reliable Value into the Process Modeling of High-Speed Railway Timetabling with Redundancy Allocation

by Huizhang Xu, Wei Xiao, Jiaming Fan, Angyang Chen, Xin Qi and Tianze Gao

Mathematics 2026, 14(6), 954; https://doi.org/10.3390/math14060954 - 11 Mar 2026

Abstract

As the development of High-Speed Railways (HSRs) shifts from scale expansion to quality and efficiency, high-density timetables face increasing challenges regarding operational stability. Traditional capacity metrics often prioritize volume over service quality, neglecting the economic and service implications of delays. To reconcile theoretical capacity with practical reliability, this paper proposes a novel Reliable Value (RV)-oriented framework for HSR timetabling. We construct a Reserve Capacity Incremental Heuristic Optimization Framework that employs a synergetic integrated stochastic optimization strategy. This methodology treats reserve capacity as a systematically varied analytical parameter rather than a static constant, integrating redundancy layout planning with dynamic recovery adjustments under stochastic delay scenarios. The RV metric quantitatively combines efficiency (Expected Running Time) and robustness (Indirect Capacity Loss). A case study on the Beijing–Shanghai high-speed railway corridor demonstrates a non-linear relationship between reserve capacity allocation and system value. The results identify an optimal saturation interval of 5 to 14 min, where the marginal gains in reliability maximize the overall system value without excessively compromising operational efficiency. These findings provide theoretical support for transitioning from static capacity planning to proactive, value-based resilience engineering through optimized redundancy allocation. Full article

(This article belongs to the Section D2: Operations Research and Fuzzy Decision Making)

22 pages, 5180 KB

Open AccessArticle

A Design-Oriented Exponential Model for Partial Stirrup Replacement with Steel Fibers in Reinforced Concrete Beam–Column Joints

by Mehmet Alper Çankaya

Buildings 2026, 16(6), 1117; https://doi.org/10.3390/buildings16061117 - 11 Mar 2026

Abstract

Reinforcement congestion in reinforced concrete (RC) beam–column joints creates constructability difficulties and may compromise seismic performance due to inadequate consolidation and confinement. Although fiber-reinforced concrete (FRC) has been widely investigated as an alternative to dense transverse reinforcement, current seismic codes (e.g., ACI 318-19, TBEC-2018) do not provide explicit provisions to quantify the interaction between steel fiber dosage and joint shear demand. This study examines the feasibility of partial stirrup replacement through a hybrid confinement strategy that preserves minimum transverse reinforcement for bar stability while using steel fibers to compensate for joint shear demand. Two large-scale exterior beam–column assemblies were tested under quasi-static reversed cyclic loading: a code-compliant reference specimen and a hybrid specimen incorporating minimum stirrups with 0.5% hooked-end steel fibers. The hybrid specimen exhibited improved stiffness retention and energy dissipation without brittle joint shear failure. A validated nonlinear finite element model (VecTor2) was used to conduct a parametric investigation covering beam reinforcement ratios of 1.3–1.5% and fiber volume fractions of 0.5–1.2%. Results demonstrate a consistent non-linear interaction between beam-induced joint shear demand and fiber contribution. This interaction is formulated through a demand-based exponential relationship that links required steel fiber dosage to joint shear demand while preserving minimum transverse reinforcement for longitudinal bar stability. The proposed model provides a design-compatible framework for hybrid fiber-stirrup confinement in seismic design practice. Full article

(This article belongs to the Section Building Structures)

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21 pages, 5692 KB

Open AccessArticle

Integrating Computer Vision and GIS for Large-Scale Morphological Mapping and Driving Force Analysis of Vernacular Courtyard Dwellings

by Lihua Liang, Xianda Li, Shutong Liu, Zhenhao Guo, Shuo Tang and Baohua Wen

Buildings 2026, 16(6), 1118; https://doi.org/10.3390/buildings16061118 - 11 Mar 2026

Abstract

This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwellings. Focusing on Liangshuaixiu dwellings in Wu’an, southern Hebei, we trained an HRNetV2 semantic segmentation model on high-resolution satellite imagery to identify and extract contours for 134,280 courtyard spaces. Core morphological parameters (area, orientation) were calculated and analyzed using GIS spatial statistics and the geographic detector model. The results show that (1) the computer vision pipeline achieved efficient recognition with satisfactory accuracy (~10% mean error); (2) spatial autocorrelation and hotspot analysis revealed distinct regional patterns, including a west–east increase in average courtyard area; and (3) geographic detector analysis demonstrated that courtyard morphology is shaped by complex interactions between natural and socio-economic factors. While average area and orientation were primarily governed by climate (air pressure, wind, temperature) and topography (elevation), diversity and internal variation were strongly influenced by nonlinear interactions, particularly between natural factors (e.g., wind–aspect) and between natural and human factors (e.g., population–climate). This work provides a scalable, data-driven framework for the quantitative spatial analysis of vernacular architectural heritage, advancing the understanding of building morphology as an outcome of coupled human–environment systems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

24 pages, 11793 KB

Open AccessArticle

Visual Servoing Sliding Mode Control with Vibration Model Compensation for Trajectory Tracking in a 2-DOF Ball Balancer System

by Mohammed Abdeldjalil Djehaf, Ahmed Hamet Sidi and Youcef Islam Djilani Kobibi

Vibration 2026, 9(1), 19; https://doi.org/10.3390/vibration9010019 - 11 Mar 2026

Abstract

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%. Full article

(This article belongs to the Special Issue Vibration Damping)

15 pages, 296 KB

Open AccessArticle

Existence, Optimal Control, and Numerical Analysis of a Caputo Fractional Model for Oxygen Saturation Regulation

by Nawal Alharbi

Symmetry 2026, 18(3), 482; https://doi.org/10.3390/sym18030482 - 11 Mar 2026

Abstract

Fractional-order models are widely recognized for their ability to capture memory and hereditary effects in biological and physiological systems. In this paper, we develop and analyze a Caputo fractional-order dynamical model for the regulation of blood oxygen saturation (SpO

_{2}

) under bounded [...] Read more.

_{2}

) under bounded control inputs. The model incorporates nonlinear saturation mechanisms and auxiliary state variables to represent delayed oxygen transport and adaptation effects. By reformulating the system as an operator equation in a suitable Banach space, sufficient conditions for existence and uniqueness of solutions are established using fixed-point theory. An optimal control problem is then formulated to steer oxygen saturation toward a prescribed safe target level, and the existence of an optimal control is proved via compactness arguments and the direct method of the calculus of variations. Numerical simulations are provided to illustrate the theoretical findings and to demonstrate the impact of the fractional order on transient oxygen saturation dynamics, including comparison with the classical integer-order case. The results show that fractional modeling offers a mathematically rigorous and physiologically interpretable framework for describing delayed oxygenation responses and achieving stable regulation under bounded control constraints. Full article

(This article belongs to the Special Issue Symmetry in Fixed Point Theory and Optimization: Computations and Applications)

26 pages, 321 KB

Open AccessArticle

Weakly Singular Wendroff-Type Integral Inequalities of Multiple Variables with Multiple Nonlinear Terms and Their Applications

by Yongsheng Li and Zizun Li

Mathematics 2026, 14(6), 944; https://doi.org/10.3390/math14060944 - 11 Mar 2026

Abstract

This paper systematically studies a class of weakly singular Wendroff-type integral inequalities with multiple variables and multiple nonlinear terms. We establish explicit bounds for the unknown functions by utilizing the method of characteristic functions and mathematical induction. These results generalize and improve existing inequalities found in the literature. Furthermore, we apply them to fractional partial differential equations to study the uniqueness, boundedness, and continuous dependence of solutions. Even in the presence of singularities, the proposed method proves effective. An application example is provided to illustrate the validity of the main results. Full article

(This article belongs to the Special Issue Advances in Nonlinear Differential Equations with Applications)

18 pages, 14896 KB

Open AccessArticle

Data-Driven Modeling and Classification of Brain Blood-Flow Pathologies

by Irem Topal, Alexander Cherevko, Yuriy Bugai, Maxim Shishlenin, Jean Barbier, Deniz Eroglu, Édgar Roldán and Roman Belousov

AI 2026, 7(3), 105; https://doi.org/10.3390/ai7030105 - 11 Mar 2026

Abstract

Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine-learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving a balanced accuracy of 74%. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions. Full article

(This article belongs to the Topic Mathematical Applications and Computational Intelligence in Medicine and Biology)

22 pages, 6785 KB

Open AccessArticle

Nonlinear Robust Excitation Controller Design for Synchronous Generators Using Improved Slime Mould Algorithm

by Liyang Zhang, Xia Li, Zhuoli Song, Yinghe Sun and Yidong Zou

Energies 2026, 19(6), 1414; https://doi.org/10.3390/en19061414 - 11 Mar 2026

Abstract

This paper proposes a nonlinear robust H_∞ excitation controller based on an improved slime mould optimization algorithm (ISMA) to enhance the stability and anti-disturbance performance of synchronous generators (SGs) in power systems. First, a nonlinear dynamic model of the excitation system (ES) is established based on the electromechanical coupling mechanism of SGs, and it is transformed into an equivalent linear state-space form through feedback linearization. Subsequently, a controller design framework with linear matrix inequality (LMI) constraints satisfying H_∞ performance indicators is constructed, and ISMA is utilized to optimize the key design parameters, thereby balancing dynamic response and control robustness. Simulation results demonstrate that, compared with traditional excitation control strategies, the proposed method exhibits superior comprehensive performance in terms of transient response speed, steady-state regulation accuracy, and robust performance under parameter perturbations and disturbance conditions. The research results can provide a technical reference for achieving safe and stable operation of SGs in power grids. Full article

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26 pages, 1154 KB

Open AccessArticle

Digital Economy and Urban–Rural Integration: Threshold Effects and Regional Heterogeneity in China

by Haoyu Niu, Jianluan Guo and Huijing Luo

Sustainability 2026, 18(6), 2739; https://doi.org/10.3390/su18062739 - 11 Mar 2026

Abstract

Persistent urban–rural disparities remain a major challenge to inclusive and sustainable development in many developing economies. The digital economy is widely viewed as a potential mechanism for alleviating such inequalities, yet empirical evidence for its effectiveness across heterogeneous regional conditions remains limited. Using panel data from 30 Chinese provinces over the period 2013–2022, this study constructs composite indices of digital economy development and urban–rural integration based on the entropy weight method. Fixed-effects models, panel threshold regressions, and moderation analyses are employed to examine average and nonlinear effects, while instrumental variable approaches, including a Bartik-type instrument, are used to address potential endogeneity. The results indicate that the digital economy significantly promotes urban–rural integration, but the effect is highly conditional rather than uniform. Specifically, the positive impact becomes substantially stronger only after rationalization of industrial structure and education levels exceed critical threshold values. Government support and financial development further amplify this effect, and pronounced regional heterogeneity is observed, with stronger effects in eastern regions and clear late-mover advantages in western regions. These findings highlight the conditions under which digital transformation can effectively support inclusive urban–rural integration and offer policy-relevant insights for developing economies. Full article

(This article belongs to the Section Sustainable Urban and Rural Development)

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31 pages, 7238 KB

Open AccessArticle

Multimodal Fault Diagnosis of Rolling Bearings Based on GRU–ResNet–CBAM

by Kunbo Xu, Jingyang Zhang, Dongjun Liu, Chaoge Wang, Ran Wang and Funa Zhou

Machines 2026, 14(3), 318; https://doi.org/10.3390/machines14030318 - 11 Mar 2026

Abstract

Rolling bearings exhibit nonlinear and non-stationary fault signals under complex working conditions, rendering single-modal representation insufficient for accurate diagnosis. To address this limitation, this paper proposes a novel parallel multimodal fusion fault diagnosis model based on a Gated Recurrent Unit (GRU), a Residual Network (ResNet), and a Convolutional Block Attention Module (CBAM). First, a systematic multimodal representation selection framework is introduced, identifying the Markov Transition Field (MTF) as the optimal two-dimensional (2D) image modality due to its superior texture clarity and noise resistance compared to other methods. Second, parallel dual-branch architecture is designed to simultaneously process heterogeneous data. The 1D-GRU branch captures long-range temporal dependencies directly from raw vibration signals, while the 2D ResNet-CBAM branch extracts deep spatial features from the MTF images, adaptively focusing on key fault regions. These heterogeneous features are then fused through concatenation to retain complementary diagnostic information. Experimental validation on the Case Western Reserve University (CWRU) dataset demonstrates that the proposed model achieves a 99.57% accuracy in a 10-classification task. Furthermore, it exhibits significant parameter efficiency and outstanding robustness, with the accuracy decreasing by no more than 1.2% under noise interference and cross-load scenarios, comprehensively outperforming existing single-modal and advanced fusion methods. Full article

(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)

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35 pages, 1554 KB

Open AccessReview

Multiscale Rheological Properties of Pavement Asphalt: A State-of-the-Art Review

by Qiqi Zhan, Zuoyang Cheng, Xuejuan Cao, Qing Liu, Ying Yuan, Lihong He and Junfeng Gao

Coatings 2026, 16(3), 355; https://doi.org/10.3390/coatings16030355 - 11 Mar 2026

Abstract

Asphalt rheological properties are fundamental to pavement performance, yet their accurate assessment requires multi-scale characterization due to asphalt’s inherent complexity. This article reviews the connections between asphalt rheology across chemical, microstructural, and macro-mechanical scales, employing a methodological analysis of supramolecular and colloidal models for micro-scale behavior and dynamic shear rheometry for macro-scale properties. Current research confirms asphalt as a complex multiphase continuum, where micro-scale rheology is explained by intermolecular interactions and colloidal structures, while macro-scale analysis successfully characterizes linear viscoelasticity through established empirical and mechanical models. However, the study identifies critical gaps: nonlinear viscoelastic characterization under large-amplitude oscillatory shear (LAOS) remains underdeveloped, and fundamental issues like directly probing molecular interactions and the origin of microstructures like the “bee structure” are unresolved. The primary conclusion is that a comprehensive understanding of asphalt rheology hinges on future research that integrates experimental and simulation data across these scales to bridge the gaps between chemical composition, microstructure, and macroscopic performance. Full article

(This article belongs to the Special Issue Advances in Pavement Materials and Civil Engineering)

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