Search Results (307)

Soil erosion assessment in Southeast Europe’s mountainous regions often lacks systematic field validation, limiting confidence in model-based predictions. This study integrates the Erosion Potential Model (EPM) with remote sensing and field verification across 26,570 km² in the Federation of Bosnia and Herzegovina (FBiH) and Brčko District (BD). We developed a two-stage framework: initial GIS-based assessment using digital elevation models, soil maps, climate data, CORINE Land Cover, and Landsat imagery, followed by field calibration at 190 representative sites. Spectral indices (NDVI, BSI) provided dynamic corrections for vegetation cover and visible erosion features. Field validation significantly improved model performance; the erosion coefficient increased from Z = 0.21 to Z = 0.24, while discriminatory power improved AUC from 0.82 to 0.85, with corresponding gains in overall accuracy from 0.78 to 0.84 and F1-score from 0.78 to 0.85. The field-validated model estimated mean annual sediment production of 546.60 m³·km⁻²·year⁻¹, with total erosion material production of 14,074,940.2 m³·year⁻¹. Field calibration revealed substantial spatial redistribution, with medium-to-excessive erosion categories expanding by 30.37%, affecting 1319.12 km² requiring priority intervention. The Kappa coefficient (0.81) confirms high classification reliability. This field-validated framework enables evidence-based identification of degradation hotspots and provides actionable guidance for soil conservation planning in geomorphologically heterogeneous, data-limited regions. Full article

For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments. Full article

This study addresses the high-risk scenario of dual-sided deep foundation pit construction adjacent to operational metro lines, a complex urban underground engineering context with significant safety implications. A multi-level dynamic safety risk assessment model is proposed by integrating the Analytic Hierarchy Process (AHP) with a risk matrix. Existing approaches generally lack the capability to dynamically incorporate spatiotemporal variations and real-time construction management information, limiting their applicability under complex working conditions. To overcome these limitations, the Tianjin Shouchuang Beiyunhe Metro Complex project is adopted as a case study to develop a concise and efficient risk assessment framework. The framework introduces spatiotemporal effect and safety management coefficients to dynamically adjust risk values and conducts risk identification and integrated evaluation across four dimensions—geology, environment, design, and construction—using 25 indicators. The model enables quantitative, real-time identification and dynamic control of safety risks during metro foundation pit construction. The assessment results indicate that the overall project risk is classified as Level I (highest), with the western pit exhibiting slightly higher risk. Targeted mitigation measures include the use of diaphragm walls with internal buttresses and grouting reinforcement. Compared with conventional methods, the proposed model demonstrates significant advantages in adapting to dynamic construction conditions, enhancing engineering applicability, and strengthening early-warning capability. These improvements provide a scientific, practical, and scalable technical solution for the accurate identification of critical risks and proactive safety management in complex metro foundation pit projects. Full article

Ensuring DC bus voltage stability is a key enabler for the sustainable development of photovoltaic-storage DC microgrids (PV–storage DC MGs), which are regarded as critical infrastructure for high-penetration renewable energy utilization. However, the inherent randomness of PV power generation seriously threatens this stability. This paper proposes a novel model predictive control (MPC) scheme for the energy storage system (ESS) to mitigate voltage fluctuations and enhance system stability. To improve the model precision, a forgetting-factor-augmented recursive least squares (RLS) algorithm is employed for online identification and correction of the estimated equivalent impedance between the ESS and the DC bus. Rigorous Lyapunov stability analysis is performed to obtain the sufficient stability conditions and quantitative tuning rules for the weighting coefficients, which transforms the qualitative parameter selection into a theoretical constrained optimization. The state of charge (SOC) of the ESS is set as a security constraint to avoid excessive charge/discharge and extend battery service life. A distinguished advantage of the proposed strategy is that it generates ESS power commands solely based on local measurements, eliminating the dependence on external communication and improving system reliability. Simulation results on MATLAB R2021b/Simulink and hardware-in-the-loop experiments based on RT-Lab and DSP demonstrate that the proposed MPC method significantly reduces the DC bus voltage deviation, accelerates the dynamic recovery process, and maintains stable ESS operation under both normal PV fluctuations and sudden PV outage conditions. Full article

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions. Full article

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems. Full article

Digital trade rules have proliferated rapidly, yet the literature still treats institutional environments and firm behavior in a comparative-static manner, overlooking the feedback loops and stock-like accumulation dynamics through which regulatory openness shapes firm capabilities over time. Drawing on general systems theory and system dynamics, this paper models the digital trade rule regime as an “institutional system” and the overseas subsidiary network of digital MNEs as an “enterprise system,” linked through three capability stocks (market, production, knowledge), cross-subsystem coupling, absorptive capacity modulation, and five internal feedback loops. We derive a reduced-form dynamic panel equation mapping structural parameters onto estimable coefficients, and test its static counterpart using data on 6850 subsidiaries of UNCTAD’s top 100 digital MNEs (2000–2024) matched with the TAPED database. Three findings emerge. First, institutional openness—measured by rule depth and breadth—exerts a positive causal effect on subsidiary ROA, surviving IV estimation and multiple robustness checks. Second, the effect transmits through market expansion, production efficiency, and knowledge accumulation channels, confirmed by Baron–Kenny mediation with Sobel tests. Third, the New Digital Economy (NDE) module displays point estimates 4–8 times larger than other modules, and joint Wald tests reject coefficient equality, providing qualified support for Meadows’ leverage-point hierarchy. Our contribution lies in bridging system dynamics modeling with econometric causal identification, and in unifying transaction cost theory, the OLI paradigm, and the knowledge-based view within a single open-system framework. Full article

Background: Accurate estimation of the postmortem interval (PMI), the time elapsed between death and body discovery, is a critical challenge in forensic science due to the complex interplay of factors affecting decomposition. Traditional methods based on macroscopic changes often lack precision, especially in later postmortem stages. Methods: This study aimed to develop a novel PMI estimation framework by integrating the dynamics of endogenous small non-coding RNAs (sncRNAs) and exogenous bacterial-derived small RNAs (sRNAs) using sRNA transcriptomics and machine learning. Results: Cardiac RNA degradation strongly correlated with PMI, with a random forest (RF) model achieving high accuracy (coefficient of determination (R²) = 0.939, mean absolute error (MAE) = 2.987 h). Employing PANDORA-seq, we profiled temporal changes in sncRNAs (miRNAs, tsRNAs and piRNAs) in postmortem cardiac tissue within 30 h in a mouse model, while simultaneously assessing RNA integrity (RIN) across eight organs. PANDORA-seq revealed stable sncRNA landscapes with specific dynamic shifts, leading to the identification of seven novel biomarkers (four tsRNAs, three piRNAs) for PMI prediction (R² = 0.760, MAE = 158.990 min). Bacterial-derived sRNAs, predominantly from Staphylococcus aureus, were upregulated at 30 h postmortem, suggesting complementary biomarker potential. Bioinformatics analysis indicated that host miRNAs may target bacterial mRNAs, hinting at cross-kingdom interactions. Conclusion: These findings highlight the potential of integrated endogenous and exogenous sRNA analysis in PMI estimation, providing a high-precision, rapid diagnostic tool and revealing complex postmortem molecular processes. Full article

This study investigates the dynamic and causal effects of climate stress and Artificial Intelligence-enabled agricultural monitoring on cultivated land quality, productivity, and sustainability in Saudi Arabia. Using a balanced panel of region–crop observations covering 13 administrative regions and six major crops over the period 2010–2024, the analysis integrates high-resolution climate variables with remote sensing-based indicators, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index, Net Primary Productivity, Water-Use Efficiency, and crop water productivity. A comprehensive econometric framework combining the System Generalized Method of Moments, Difference-in-Differences, and event-study approaches is employed to address persistence, endogeneity, and causal identification. The results show that water availability—captured by soil moisture and precipitation—significantly enhances cultivated land outcomes (coefficients ≈ 0.05–0.11), while heat stress and wind speed exert strong negative effects (coefficients ≈ −0.04 to −0.12), highlighting the vulnerability of arid agricultural systems. Artificial Intelligence-enabled monitoring and smart irrigation adoption consistently improve land quality and productivity, with the largest gains observed in water-use efficiency and crop water productivity. Artificial Intelligence adoption increases water-use efficiency and crop water productivity by approximately 8–10%, while heat stress reduces vegetation indicators by about 9–12%. Event-study evidence confirms that these effects emerge after adoption and persist over time, supporting a causal interpretation. Overall, the findings demonstrate that AI technologies mitigate climate stress primarily through improved water management and adaptive decision-making. The study provides policy-relevant insights aligned with Saudi Vision 2030, emphasizing digital agriculture as a key instrument for sustainable cultivated land governance, climate adaptation, and food security in water-scarce environments. Full article

Accurate prediction of contact temperature in disconnectors is critical for early fault detection. However, purely physics-based models face difficulties in parameter identification, while purely data-driven models often suffer from error accumulation in long-term forecasting. To address these challenges, this paper proposes a novel framework named Hybrid Physics-Informed Long Short-Term Memory (Hybrid-PI-LSTM). Firstly, this paper mathematically formulates the transient heat transfer process as a constrained optimization problem governed by a nonlinear ordinary differential equation (ODE), embedding physical laws into the loss function as a regularization term to promote dynamic consistency. Secondly, to address the inverse problem of parameter drift caused by environmental changes, an Adaptive Parameter Updating (APU) mechanism is introduced. This algorithm utilizes a gradient-based iterative approach to dynamically estimate equivalent physical coefficients (e.g., heat capacity) from observational residuals during inference. Finally, numerical experiments on a real-world dataset demonstrate that the proposed framework significantly outperforms baseline models. Specifically, it achieves a Root Mean Squared Error (RMSE) of 0.283 at a 720-step forecasting horizon, reducing the prediction error by over 35% compared to static-parameter physical models. The results indicate that the proposed adaptive constraint mechanism contributes to enhanced long-term numerical stability and physics-guided parameter tracking. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

Potato is one of the major staple crops, and precise identification of potato planting areas is crucial for yield monitoring and future planting planning. However, during the later growth stages of potatoes, vegetation index identification tends to saturate, and the spectral differences between potatoes and other crops become minimal, resulting in difficulties in extraction and reduced accuracy. This study focuses on Huize County, a typical potato-producing region in Yunnan Province, China. Based on Landsat-8 OLI imagery from the 2020 winter cropping season, a novel optimized enhanced vegetation index (ONDVI) was developed and applied within a vegetation index recombination algorithm (NEG) for regional potato identification. The study first addresses the saturation issue of the Normalized Difference Vegetation Index (NDVI) under high vegetation coverage conditions through three steps: selecting the red, near-infrared, and green bands; reconstructing the saturation structure of the SR_B5 band; and re-defining the weighted band difference. ONDVI was nonlinearly combined with the enhanced vegetation index (EVI) and the Green Chlorophyll Vegetation Index (GCVI) to develop the vegetation index recombination algorithm (NEG), enhancing spectral differentiation among crops and improving the integrated characterization of potato canopy structure, chlorophyll content, and biomass dynamics. Finally, under a supervised classification framework, the random forest classifier was combined with manually labeled training samples to compare the classification performance of seven combination algorithms using the ONDVI, EVI, and GCVI indices. The results show that the NEG classification algorithm exhibits the best performance (Kappa coefficient = 0.9833; overall accuracy = 98.69%), with ONDVI contributing the most to the classification features in the NEG algorithm. The NEG combination algorithm fully leverages the advantages of the ONDVI, EVI, and GCVI indices, demonstrating high application potential for potato classification. Full article

Accurate modelling of hydrodynamic damping remains a critical challenge in the dynamic analysis of floating offshore wind turbines (FOWTs), particularly when motion coupling between degrees of freedom is significant. This study addresses the limitations of conventional single-degree-of-freedom damping identification techniques by proposing a novel multi-degree-of-freedom identification procedure capable of including off-diagonal coupling terms in the estimation of both linear and quadratic damping matrices. The aim is to assess whether viscous cross-coupling effects can be explicitly identified within a multi-degree-of-freedom lumped-parameter framework and to evaluate their impact on motion prediction. The methodology employs a hybrid optimisation approach, combining a genetic algorithm with a gradient-based solver. The procedure is applied to a taut-leg moored semi-submersible floating platform, focusing on surge–pitch coupling and using both experimental wave-basin data and high-fidelity CFD free-decay simulations. The results show that diagonal damping coefficients can be robustly identified even under coupled free-decay conditions, whereas the inclusion of off-diagonal viscous terms does not significantly improve the reconstruction of free-decay responses. Moreover, the simultaneous calibration of the added mass matrix enabled by the proposed procedure further improves agreement with the reference data. Although the findings highlight limited identifiability of viscous cross-coupling effects from free-decay tests, this paper provides a flexible tool for more advanced damping identification in operational and extreme conditions. Full article

Under the background of the “dual-carbon” goals and the ecological ecological-civilization-construction strategy, improving the synergistic efficiency of pollution reduction and carbon abatement is a key to promoting green high-quality development. Based on a panel of 30 provincial-level regions in China for 2012–2022, this paper evaluates the impact of digital financial inclusion on the synergistic efficiency of pollution reduction and carbon abatement. First, using a global-frontier directional-distance function (DDF), we characterize the improvement space of “desirable-output expansion—simultaneous contraction of pollution and carbon emissions” under given input constraints, and construct a synergistic efficiency indicator (eff_main). Second, we present a correlation benchmark within a two-way fixed-effects (TWFE) framework and use lead/lag (placebo) tests to probe potential endogeneity; we further construct a Bartik (shift–share) instrumental variable and employ Two-Stage Least Squares (2SLS) to strengthen causal identification. The results show that in TWFE regressions, digital financial inclusion (dif100) is positively and significantly correlated with synergistic efficiency, with a coefficient of 0.113 (i.e., an increase of 100 index points in the digital financial inclusion index is associated with an average increase of 0.113 in eff_main), but a significant lead effect is present, so this result should be interpreted as correlational only; 2SLS estimates indicate a robust positive causal effect of digital financial inclusion on synergistic efficiency, with a baseline coefficient of 0.405, rising to 0.501 under lagged specifications—exhibiting a dynamic feature of “gradual release in subsequent years.” The study suggests that developing digital financial inclusion helps raise regions’ comprehensive green-transition performance and sustainable development capacity; policy implications include accelerating the closing of digital infrastructure gaps, improving green-finance institutions and performance constraints, and guiding funds more effectively toward energy-saving, emission reduction and low-carbon technology areas. Full article

The development and validation of a comprehensive five-phase methodology for creating a functional digital twin of complex educational infrastructures are presented, implemented through the IPN Hidalgo campus as a case study. Unlike conventional approaches that focus on isolated aspects of digital twin development, this integrated methodology systematically addresses the complete lifecycle from physical characterization to operational synchronization. The implementation resulted in an interactive digital twin integrating 15 buildings and over 200 network components, deployed across multiple platforms, including: desktop, mobile, and mixed reality devices. The validation results demonstrated a 30% reduction in fault identification time for technical teams and 85% user satisfaction regarding interface intuitiveness, with instrument reliability confirmed by a Cronbach’s alpha coefficient of 0.78. The methodological framework establishes a reproducible standard for developing educational digital twins that combine geometric accuracy with dynamic operational capabilities, offering significant advantages over fragmented approaches reported in the literature. Furthermore, the digital twin serves as a foundational platform for future integration of Internet of Things (IoT) sensors and predictive analytics, aligning with emerging trends in educational infrastructure management through immersive technologies. Full article