Search Results (461)

Operational ensemble numerical weather prediction models are typically underspread near the land surface, with the Australian Bureau of Meteorology’s (BoM) global system being a typical example. In this study, land surface fraction values, representing the estimated proportions of various land cover types, are perturbed with the aim of increasing the ensemble spread at the surface. The perturbations are achieved by multiplying the existing land surface fraction estimates by spatially correlated random error structures that represent the uncertainties in these estimates. The methodology was trialed over a 75-day period during the Australian summer of 2017–2018 when both perturbed and unperturbed forecasting cycling experiments were run. The results showed that land surface fraction perturbations increased surface temperature, sensible heat flux, and latent heat flux ensemble spread significantly, especially in the tropics and over the Australian region. The screen-level temperature ensemble spread also increased, albeit by a relatively smaller magnitude compared to the surface temperature ensemble spread. Root-mean square error values—as measured relative to reanalysis data—were also found to be smaller in the perturbed runs, leading to significantly improved spread-to-skill ratio values. Full article

This study proposes an equipment-level framework for quantifying and grading tower-crane construction safety resilience that addresses three persistent gaps in construction safety research: subjective weighting, static scoring, and weak uncertainty treatment. The Entropy Weight Method (EWM) with Monte Carlo Simulation (MCS) is integrated to convert five objective indicators (fatalities, serious injuries, economic losses, accident-severity factor, and accident frequency) into (i) data-driven weights and (ii) interval-valued resilience estimates (mean and 95% CI). A quintile scheme yields an interpretable five-tier scale from Very Weak to Very Strong. On a multi-source dataset of 696 accidents, casualties and severity dominate the entropy weights and effectively separate resilience tiers. The MCS intervals are stable and decision-oriented. Using the obtained tiers as labels, a Random-Forest classifier achieves superior Accuracy and Macro-F1, demonstrating that the grading is predictable and thus operational for early warning. Two lightweight proxies were further introduced, the Management Behavior Index (MBI) and the Recovery Difficulty Index (RDI), to incorporate management/behavioral signals and recovery burden; both couple with the EWM-MCS score at small weights, smooth zero-event cases, and highlight priority risks. Sensitivity checks on binning rules, simulation budgets, perturbation magnitudes, and coupling coefficients confirm robustness. The proposed framework generates interconnected output metrics, including the mean value, confidence interval, risk tier, and result interpretability. Furthermore, it exhibits high portability and can be readily adapted to other types of critical construction equipment as well as online assessment workflows. Full article

In freshwater ecosystems, cross-trophic interactions among biological communities underpin ecosystem stability and functionality. In arid and semi-arid rivers, however, hydrological fluctuations, invasive species, and other perturbations exacerbate the complexity of biological processes. To systematically assess the community structure of fish, eukaryotic plankton, and prokaryotic microorganism in the Irtysh River basin, this study employed environmental DNA (eDNA) metabarcoding for monitoring. High-throughput sequencing of taxa within the study area was conducted via eDNA metabarcoding, coupled with random forest and linear mixed models to dissect the effects of community structure. The eDNA approach effectively unraveled spatial patterns of biodiversity and identified taxon-specific diversity hotspots: invasive fish exerted a facilitative effect on algae and suppressed the richness of protozoa, fungi, and heterotrophic microorganisms, yet had minimal impact on the dominant structure of autotrophic microorganisms. These findings provide a scientific basis for basin-scale ecological management, emphasizing the necessity of balancing habitat preservation and invasive-species control to safeguard ecosystem functionality. Full article

A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space, which can easily lead to gradient vanishing and premature convergence. Compared with traditional heuristic algorithms relying on a population-based parallel search, such as GA, GWO, DE, etc., the Besiege and Conquer Algorithm (BCA) employs a one-spot update strategy that provides a certain level of global optimization capability but exhibits clear limitations in search flexibility. Specifically, it lacks fast detection, fast adaptation, and fast convergence. First, the fixed sinusoidal amplitude limits the accuracy of fast detection in complex regions. Second, the combination of a random location and fixed perturbation range limits the fast adaptation of global convergence. Finally, the lack of a hierarchical adjustment under a single parameter (BCB) hinders the dynamic transition from exploration to exploitation, resulting in slow convergence. To address these limitations, this paper proposes a Flexible Besiege and Conquer Algorithm (FBCA), which improves search flexibility and convergence capability through three new mechanisms: (1) the sine-guided soft asymmetric Gaussian perturbation mechanism enhances local micro-exploration, thereby achieving a fast detection response near the global optimum; (2) the exponentially modulated spiral perturbation mechanism adopts an exponential spiral factor for fast adaptation of global convergence; and (3) the nonlinear cognitive coefficient-driven velocity update mechanism improves the convergence performance, realizing a more balanced exploration–exploitation process. In the IEEE CEC 2017 benchmark function test, FBCA ranked first in the comprehensive comparison with 12 state-of-the-art algorithms, with a win rate of 62% over BCA in 100-dimensional problems. It also achieved the best performance in six MLP optimization problems, showing excellent convergence accuracy and robustness, proving its excellent global optimization ability in complex nonlinear MLP optimization training. It demonstrates its application value and potential in optimizing neural networks and deep learning models. Full article

Bearings are among the most failure-prone components in rotating systems, making early fault detection crucial in industrial applications. While recent publications have focused on this issue, challenges remain, particularly in dealing with heavy-tailed or non-cyclic impulsive noise in recorded signals. Such noise poses significant challenges for classical fault selectors like kurtosis-based methods. Moreover, many deep-learning approaches struggle in these environments, as they often assume Gaussian or stationary noise and rely on large labeled datasets that are rarely available in practice. To address this, we propose a robust sparse non-negative matrix factorization (NMF) method based on the maximum-correntropy criterion, which is known for its robustness in the presence of heavy-tailed noise. This methodology is applied to identify fault frequency bands in the spectrogram of the signal. The effectiveness of the approach is validated using simulated fault signals under both Gaussian and heavy-tailed noise conditions through Monte Carlo simulations. A statistical efficiency analysis confirms robustness to random perturbations. Additionally, three real datasets are used to evaluate the performance of the proposed method. Results from both simulations and real-world data demonstrate the effectiveness of the proposed approach. Full article

Plant disease recognition is a critical technology for ensuring food security and advancing precision agriculture. However, challenges such as class imbalance, heterogeneous image quality, and limited model interpretability remain unresolved. In this study, we propose a Synergistic Dual-Augmentation and Class-Aware Hybrid (SDA-CAH) model designed to achieve robust and interpretable recognition of plant diseases. Our approach introduces two innovative augmentation strategies: (1) an optimized MixUp method that dynamically integrates class-specific features to enhance the representation of minority classes; and (2) a customized augmentation pipeline that combines geometric transformations with photometric perturbations to strengthen the model’s resilience against image variability. To address class imbalance, we further design a class-aware hybrid sampling mechanism that incorporates weighted random sampling, effectively improving the learning of underrepresented categories and optimizing feature distribution. Additionally, a Grad-CAM–based visualization module is integrated to explicitly localize lesion regions, thereby enhancing the transparency and trustworthiness of the predictions. We evaluate SDA-CAH on the PlantVillage dataset using a pretrained EfficientNet-B0 as the backbone network. Systematic experiments demonstrate that our model achieves 99.95% accuracy, 99.89% F1-score, and 99.89% recall, outperforming several strong baselines, including an optimized Xception (99.42% accuracy, 99.39% F1-score, 99.41% recall), standard EfficientNet-B0 (99.35%, 99.32%, 99.33%), and MobileNetV2 (95.77%, 94.52%, 94.77%). For practical deployment, we developed a web-based diagnostic system that integrates automated recognition with treatment recommendations, offering user-friendly access for farmers. Experimental evaluations indicate that SDA-CAH outperforms existing approaches in predictive accuracy and simultaneously defines a new paradigm for interpretable and scalable plant disease recognition, paving the way for next-generation precision agriculture. Full article

Recently, Aydiner considered dark matter (DM) and dark energy (DE) as two open, non-equilibrium thermodynamic systems, which have heat changes and particle number changes but have no volume changes. These systems are described by nonlinear coupled equations for the description of mutual and self-interactions and satisfy the energy conservation of thermodynamics. Based on this idea, two three-dimensional (3D) models and a four-dimensional (4D) model are produced. Due to the conservation of the energy–momentum tensor of the sum of the DM and DE energy densities, the continuity equations of both energy densities are also included together in these 3D and 4D thermodynamic models. For the parameters satisfying some conditions, one of the 3D models has two marginal stable non-hyperbolic equilibrium points with a negative real root and a pair of conjugate purely imaginary roots. The marginal stability is highly sensitive to nonlinear terms and parameter noise. Another of the 3D models has unstable saddle-focus equilibrium points, which have a negative real root corresponding to a 1D stable manifold and two conjugate complex roots with positive real parts corresponding to a 2D manifold of unstable spiral. At these equilibria, no energy exchange occurs between the two energy densities, and both energy components reach equilibrium. When some perturbations from the nonlinear terms or parameter noise are given, the DM and DE energy densities are far from equilibrium and continue to exchange each other until they reach equilibrium. The energy exchanges between them may exhibit chaotic behavior like chaotic attractors. However, hyperchaos is not easily found. The 4D model also has unstable saddle-focus equilibrium points and can allow for the onset of chaotic attractors and hyperchaos. In fact, the chaotic dynamics of the 3D and 4D models are caused because of the coupled interactions of particle and thermodynamic systems between DM and DE. Under both the self-interactions and the mutual interactions, the energy exchanges are far from and close to the equilibrium. These interactions cause the energy exchanges to become random, irregular and unpredictable. Full article

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments. Full article

This work verifies the interactions between fiscal and monetary policies in Brazil, involving real GDP, the Interest index, Inflation index, real Exchange rate, and actual public debt, using empirical data from January 1998 to December 2018 to calibrate the model. In the analyses, we employ macrodynamic deterministic and stochastic models of differential equations to examine the interconnection of the endogenous variables and the stability of Brazilian economic policy. In the stochastic model, we introduced stochastic perturbations in the uncontrollable coefficients and additive random walks affecting the endogenous variables. Shocks imposed on the structured dynamic model showed that stochastic innovations propagate more strongly in the monetary variables: inflation, interest rates, and exchange rates. We have also established forecasts for endogenous variables from January 2019 to December 2026 and conducted backtest analyses using the empirical data observed for the endogenous variables from January 2019 to December 2023. The forecast estimations were demonstrated to be satisfactory. Full article

Uncertainty from heterogeneous degradation paths, limited experimental samples, and exogenous perturbations often complicates accelerated lifetime modeling and prediction. To confront these intertwined challenges, a Wiener process-based robust framework is developed. The proposed approach incorporates random-effect structures to capture unit-to-unit variability, adopts interval-based inference to reflect sampling limitations, and employs a hybrid estimator, combining Huber-type loss with a Metropolis–Hastings algorithm, to suppress the influence of external disturbances. In addition, a quantitative stress–parameter linkage is established under the accelerated factor principle, supporting consistent transfer from accelerated testing to normal conditions. Validation on contact stress relaxation data of connectors confirms that this methodology achieves more stable parameter inference and improves the reliability of lifetime predictions. Full article

Join size estimation plays a crucial role in query optimization, correlation computing, and dataset discovery. A recent study, LDPJoinSketch, has explored the application of local differential privacy (LDP) to protect the privacy of two data sources when estimating their join size. However, the utility of LDPJoinSketch remains unsatisfactory due to the significant noise introduced by perturbation under LDP. In contrast, the shuffle model of differential privacy (SDP) can offer higher utility than LDP, as it introduces randomness based on both shuffling and perturbation. Nevertheless, existing research on SDP primarily focuses on basic statistical tasks, such as frequency estimation and binary summation. There is a paucity of studies addressing queries that involve join aggregation of two private data sources. In this paper, we investigate the problem of private join size estimation in the context of the shuffle model. First, drawing inspiration from the success of sketches in summarizing data under LDP, we propose a sketch-based join size estimation algorithm, SDPJoinSketch, under SDP, which demonstrates greater utility than LDPJoinSketch. We present theoretical proofs of the privacy amplification and utility of our method. Second, we consider separating high- and low-frequency items to reduce the hash-collision error of the sketch and propose an enhanced method called SDPJoinSketch+. Unlike LDPJoinSketch, we utilize secure encryption techniques to preserve frequency properties rather than perturbing them, further enhancing utility. Extensive experiments on both real-world and synthetic datasets validate the superior utility of our methods. Full article

The complex configuration of the internal flow field in aero-engines leads to limitations on sensor installation positions, and how to accurately identify the disturbances of the installation influence parameters under this constraint has long been a significant challenge. To address this issue, this study proposes an optimization algorithm to identify the optimal sensor layout. This is achieved by employing mutually distinct integer encoding, which ensures the uniqueness of each sensor position and prevents duplication. More importantly, an objective evaluation system incorporating tracking error, sensor comprehensiveness, and spatial coverage is integrated into the fitness function design, thereby overcoming the one-sidedness and limitations of single-indicator evaluation. Building upon this foundation, a sensor optimization scheme is proposed for identifying installation influence parameters. This scheme integrates the rapid search capability of the Tabu Bee Colony Random Variation Dimension Algorithm with the global optimization capability of an Improved Genetic Random Variation Dimension Algorithm, resulting in a Tabu Bee Colony–Improved Genetic Random Variation Dimension Optimization Algorithm (TBC-IG-RVDOA). For each installation influence parameter, different perturbation conditions were established, and the selected optimal sensor combination was then validated using the Extended Kalman Filter (EKF). Experimental studies show that, under all perturbation scenarios, the TBC-IG-RVDOA demonstrates strong convergence, high computational efficiency, and fitness function values consistently exceeding 0.92, thereby accurately capturing the changes in each installation influence parameter. Full article

This paper addresses the finite-time stability of a class of fractional Itô–Doob stochastic systems with time delays. Novel stability criteria are established using a combination of Gronwall-type, Hölder’s, and Burkholder–Davis–Gundy (BDG) inequalities, thereby generalizing classical integer-order stability theory to the fractional domain. Furthermore, the analysis uniquely integrates stochastic perturbations and time delays, providing a comprehensive framework for systems exhibiting both memory and randomness. The effectiveness of the proposed approach is demonstrated through a numerical example of a three-dimensional stochastic delayed system with fractional dynamics. Full article

This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inherent random disturbances in such missions, while also needing to balance ultra-high attitude precision, fuel efficiency, and compliance with engineering constraints. To tackle this, a convex optimization-based attitude control strategy integrating covariance control and free terminal time optimization is proposed. Specifically, a stochastic attitude dynamics model is first established to explicitly incorporate the aforementioned random disturbances. Subsequently, an objective function is designed to simultaneously minimize terminal state error and fuel consumption, with three key constraints (covariance constraints, pointing constraints, and torque saturation constraints) integrated into the convex optimization framework. Furthermore, to resolve non-convex terms in chance constraints, this study employs a hierarchical convexification method that combines Schur’s complementary theorem, second-order cone relaxation, and Taylor expansion techniques. This approach ensures lossless relaxation, renders the optimization problem computationally tractable without sacrificing solution accuracy, and overcomes the shortcomings of traditional convexification methods in handling chance constraints. Finally, numerical simulations demonstrate that the proposed method adheres to engineering constraints while maintaining spacecraft attitude errors below 1 μrad under environmental uncertainties. This study provides a convex optimization solution for laser link acquisition in space gravitational wave detection missions considering uncertainty conditions, and its framework can be extended to the optimal design of other stochastically uncertain systems. Full article

With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high–efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two–stage framework with high computational complexity and frequently struggle to balance compression performance with generalization ability. To address these issues, an end–to–end lossy compression method is proposed in this paper. The approach integrates an enhanced belief propagation algorithm with a multi–layer perceptron neural network, aiming to introduce a novel joint optimization architecture described as “encoding–structured encoding–decoding”. In addition, a quantization module incorporating random perturbation and the straight–through estimator is designed to address the non–differentiability in the quantization process. Simulation results demonstrate that the proposed system significantly improves compression performance while offering superior generalization and reconstruction quality. Furthermore, the designed neural architecture is both simple and efficient, reducing system complexity and enhancing feasibility for practical deployment. Full article