Search Results (1,094)

To address the severe challenge of soil salinization, effective soil amelioration methods are urgently needed; however, current research on the microbial mechanisms of the combined application of multiple amendments is insufficient. Therefore, this study aims to investigate the impacts of biochar combined with humic acid (HA) on the physicochemical properties and microbial community structure of saline–alkali soils by a field experiment. The results showed that the co-application treatments significantly improved soil physicochemical properties and increased bacterial community richness; specific effects depended on the biochar feedstock. Notably, the H-MBC treatment was the most effective in reducing soil electrical conductivity (EC) by 44.1%, while the H-SBC treatment most significantly increased soil water content by 80.3%. Stochastic processes influenced the assembly of microbial communities, particularly the co-application group, forming a more complex and stable microbial network. Furthermore, Spearman correlation and random forest analyses revealed EC, nitrate nitrogen, and available phosphorus as the primary variables affecting microbial communities. These findings support the potential of the combined application of biochar and HA for saline–alkali soil amelioration, as this strategy mitigates salt stress and increases nutrient availability, thereby reshaping microbial communities toward states conducive to ecological restoration. Full article

Uncertainty quantification (UQ) is critical for predicting solute transport in heterogeneous porous media, with applications in groundwater management and contaminant remediation. Traditional UQ methods, such as Monte Carlo (MC) simulations, are computationally expensive and impractical for real-time decision-making. This study introduces a novel machine learning framework to address these limitations. We developed a surrogate model for a 2D advection-dispersion solute transport model using a Bayesian Neural Network (BNN). The BNN was trained on a synthetic dataset generated by simulating solute transport across various stochastic permeability and dispersivity fields. Uncertainty was quantified through variational inference, capturing both data-related (aleatoric) and model-related (epistemic) uncertainties. We evaluated the framework’s performance against traditional MC simulations. Our BNN model accurately predicts solute concentration distributions with a mean squared error (MSE) of 9.8 × ${10}^{-5}$, significantly outperforming other machine learning surrogates. The framework successfully quantifies uncertainty, providing calibrated confidence intervals that align closely with the spread of the MC results. The proposed approach achieved a 98.5% reduction in computational time compared to a standard Monte Carlo simulation with 1000 realizations, representing a 65-fold speed-up. A sensitivity analysis revealed that permeability field heterogeneity is the dominant source of uncertainty in plume migration. The developed machine learning framework offers a computationally efficient and robust alternative for quantifying uncertainty in solute transport models. By accurately predicting solute concentrations and their associated uncertainties, our approach can inform risk-based decision-making in environmental and hydrogeological applications. The method shows promise for scaling to more complex, three-dimensional systems. Full article

Against the backdrop of global climate change intensifying seasonal freeze–thaw cycles, deteriorating soil conditions in farmland within seasonal frost zones constrain agricultural sustainability. This study employed an in situ field experiment during seasonal freeze–thaw periods in the black soil zone of Northeast China to investigate the joint regulatory effects of seasonal freeze–thaw processes and tillage practices on multidimensional features of soil bacterial communities. Key results demonstrate that soil bacterial communities possess self-reorganization capacity. α-diversity exhibited cyclical fluctuations: an initial decline followed by a rebound, ultimately approaching pre-freeze–thaw levels. Significant compositional shifts occurred throughout this process, with the frozen period (FP) representing the phase of maximal differentiation. Actinomycetota and Acidobacteriota consistently dominated as the predominant phyla, collectively accounting for 33.4–49% of relative abundance. Bacterial co-occurrence networks underwent dynamic topological restructuring in response to freeze–thaw stress. Period-specific response patterns supported sustained soil ecological functionality. Furthermore, NCM and NST analyses revealed that stochastic processes dominated community assembly during freeze–thaw (NCM R² > 0.75). Tillage practices modulated this stochastic–deterministic balance: no-tillage with straw mulching (NTS) shifted toward determinism (NST = 0.608 ± 0.224) during the thawed period (TP). Across the seasonal freeze–thaw process, soil temperature emerged as the primary driver of temporal community variations, while soil water content governed treatment-specific differences. This work provides a theoretical framework for exploring agricultural soil ecological evolution in seasonal frost zones. Full article

The stochastic and undetermined nature of longwall coal mining results from the complex interaction between geological-mining and technical-organizational factors. This interaction causes variability in key parameters of the production process. This article presents three stochastic models developed on the basis of probability density functions, which describe selected process parameters. These mathematical functions serve as the foundation for effective stochastic models, enabling analysis of complex mining operations. The methodology employed in the study involves empirical data collection, statistical analysis, and stochastic simulation, carried out under both laboratory and field conditions. The results include empirical probability functions for output, delays, and crew-dependent productivity, offering insights into process variability and its impact on performance. Each method is characterized by its theoretical foundations, algorithmic structure, and application areas. The models have been validated through statistical tests and operational field data and can be applied as decision-support tools in both scientific research and industrial management. Given the extensive nature of the described methods, the article provides a comprehensive reference list for readers interested in further exploration and practical implementation in mining engineering. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

To address the complex operating conditions and challenging dynamic characteristics of bearings in the main shaft transmission system of wind turbines, this study investigates a specific wind turbine model. By comprehensively considering factors such as main shaft structure, cumulative damage, and stochastic wind loads, we adopt a modular analysis framework integrating the wind field, aerodynamics, the structural response, and fatigue life prediction to establish a method for predicting the fatigue life of main shaft bearings under stochastic wind conditions. To verify this method, the fixed-end main shaft bearing of a 4.5 MW wind turbine was selected as a case study. The research results show the following: (1) Increases in both average wind speed and turbulence intensity significantly shorten the fatigue life of the bearing. (2) Higher turbulence intensity amplifies the dispersion of the speed and load of rolling elements, thereby increasing the probability of extreme operating conditions and exerting an adverse impact on fatigue life. (3) The average wind speed has a significant influence on the overall fatigue life: within a specific range, the fatigue failure probability of the main bearing increases as the average wind speed decreases. (4) The impact of wind speed fluctuations on the hub center load is much greater than that caused by rotational speed changes. (5) In addition, the modular design method adopted in this study calculates that the fatigue life of the fixed-end bearing is 28.8 years, with an overall error of only 0.8 years compared to the 29.6-year fatigue life obtained using Romax simulation software. This research provides important theoretical references and engineering value for improving the operational reliability of wind turbines and enhancing the accuracy of bearing fatigue life prediction. Full article

Woven composite structures are inherently influenced by uncertainties across multiple scales, ranging from constituent material properties to mesoscale geometric variations. These uncertainties give rise to both spatial autocorrelation and cross-correlation among material parameters, resulting in stochastic strength performance and damage morphology at the macroscopic structural level. This study established a comprehensive multiscale uncertainty quantification framework to systematically propagate uncertainties from the microscale to the macroscale. A novel dual-correlation sampling approach, based on multivariate random field (MRF) theory, was proposed to simultaneously capture spatial autocorrelation and cross-correlation with clear physical interpretability. This method enabled a realistic representation of both inter-specimen variability and intra-specimen heterogeneity of material properties. Experimental validation via in-plane tensile tests demonstrated that the proposed approach accurately predicts not only probabilistic mechanical responses but also discrete damage morphology in woven composite structures. In contrast, traditional independent sampling methods exhibited inherent limitations in representing spatially distributed correlations of material properties, leading to inaccurate predictions of stochastic structural behavior. The findings offered valuable insights into structural reliability assessment and risk management in engineering applications. Full article

We study the regularity properties of the unique solution of a generalized mean-field G-SDE. More precisely, we consider a generalized mean-field G-SDE with a square-integrable random initial condition, establish its first- and second-order Fréchet differentiability in the stochastic initial condition, and specify the G-SDEs of the respective Fréchet derivatives. The first- and second-order Fréchet derivatives are obtained for locally Lipschitz coefficients admitting locally Lipschitz first- and second-order Fréchet derivatives respectively. Our approach heavily relies on the Grönwall inequality, which leverages the Lipschitz continuity of the coefficients. Full article

Understanding the dynamics of fire propagation is essential in improving predictive models and developing effective fire management strategies. This study applies computer vision techniques to complement traditional fire behaviour modelling. We employ the Segment Anything Model to achieve the accurate segmentation of experimental fire videos, enabling the frame-by-frame segmentation of fire perimeters, quantification of the rate of spread in multiple directions, and explicit analysis of slope effects. Our laboratory experiments reveal that the ROS increases exponentially with slope, but with coefficients differing from those prescribed in the Canadian Fire Behaviour Prediction System, reflecting differences in field conditions. Complementary field data from prescribed burns in coniferous fuels (C-7) further demonstrate that slope effects vary under operational conditions, suggesting field-dependent dynamics not fully captured by existing deterministic models. Our experiments show that, even under controlled laboratory conditions, substantial variability in spread rate is observed, underscoring the inherent stochasticity of fire spread. Together, these findings highlight the value of vision-based perimeter extraction in generating precise spread data and reinforce the need for probabilistic modelling approaches that explicitly account for uncertainty and emergent dynamics in fire behaviour. Full article

Hydrothermal vents, widely occurring along middle-ocean ridges and volcanic arcs, have been well-studied in vent-associated microbiology, mineralogy, and geochemistry. However, there are rarely investigations regarding the detailed microbial community in the hydrothermal vent-influenced sediment. To explore hydrothermal activities on microbial diversity at the Carlsberg Ridge in the northwestern Indian Ocean, four sediment cores were sampled from the near-vent fields to distant vent sedimentary fields in the Tianxiu hydrothermal field, and the microbial community compositions were analyzed. The sediment microorganisms closest to the hydrothermal vent were primarily composed of Acidimicrobiia, Gammaproteobacteria, Anaerolineae, and Planctomycetes. The microbial communities at the depth containing extensive signals of hydrothermal activity consisted mainly of Dehalococcoidia, Aerophoria, Anaerolineae, and Gammaproteobacteria. No significant differences in microbial composition were observed between the two weak hydrothermal sediment cores, primarily composed of Nitrososphaeria, Gammaproteobacteria, Alphaproteobacteria, and Acidimicrobiia. Moreover, heterogeneous selection substantially impacted the bacterial community assembly in near-vent sediments other than stochasticity. Multivariate statistical analysis identified that environmental fluctuations accounted for 55.59% of the community variation, with hydrothermal inputs (such as Fe, Pb, Cu, and Zn) being the primary factors shaping the construction of hydrothermal sediment microbial communities. These results enhance understanding of the response of deep-sea sediments to hydrothermal activity. Full article

Shale reservoirs offer significant potential for CO₂ geological sequestration due to their extensive nanopore networks and heterogeneous pore systems. This study comparatively assessed the CO₂ storage potential of the Lower Silurian Longmaxi and Lower Cambrian Qiongzhusi shales through an integrated approach involving organic geochemical analysis, mineralogical characterization through X-ray diffraction (XRD), mercury intrusion capillary pressure (MICP), low-pressure nitrogen and carbon dioxide physisorption, field-emission scanning electron microscopy (FE-SEM), stochastic 3D microstructure reconstruction, multifractal analysis, and three-dimensional succolarity computation. The results demonstrate that mineral assemblages and diagenetic history govern pore preservation: Longmaxi shales, with moderate maturity and shallower burial, retain abundant organic-hosted mesopores, whereas overmature and deeply buried Qiongzhusi shales are strongly compacted and mineralized, reducing pore availability. Multifractal spectra and 3D reconstructions reveal that Longmaxi develops broader singularity spectra and higher succolarity values, reflecting more isotropic meso-/macropore connectivity at the SEM scale, while Qiongzhusi exhibits narrower spectra and lower succolarity, indicating micropore-dominated and anisotropic networks. Longmaxi has nanometer-scale throats (D₅₀ ≈ 10–25 nm) with high CO₂ breakthrough pressures (P₁₀ ≈ 0.57 MPa) and ultra-low RGPZ permeability (mean ≈ 1.5 × 10⁻² nD); Qiongzhusi has micrometer-scale throats (D₅₀ ≈ 1–3 μm), very low breakthrough pressures (P₁₀ ≈ 0.018 MPa), and much higher permeability (mean ≈ 4.63 × 10³ nD). Storage partitioning further differs: Longmaxi’s median total capacity is ≈15.6 kg m⁻³ with adsorption ≈ 93%, whereas Qiongzhusi’s median is ≈12.8 kg m⁻³ with adsorption ≈ 70%. We infer Longmaxi favors secure adsorption-dominated retention but suffers from injectivity limits; Qiongzhusi favors injectivity but requires reliable seals. Full article

High-order linear polarization (LP) modes and vortex beams carrying orbital angular momentum (OAM) are highly useful in various fields. High-order LP modes provide higher thresholds for nonlinear effects, reduced sensitivity to distortions, and better energy extraction in high-power lasers. OAM beams are useful in optical communication, imaging, particle manipulation, and fiber sensing. The ability to switch between these mode outputs enhances system versatility and adaptability, supporting advanced applications both in research and industry. This paper presents the design of a 19 × 1 photonic lantern capable of outputting 19 LP modes and 16 OAM modes with low loss. Using the beam propagation method, we simulated and analyzed the mode evolution process and insertion loss, and we calculated the transmission matrix of the photonic lantern. The results indicate that the designed device can efficiently evolve into these modes with a maximum insertion loss not exceeding 0.07 dB. Furthermore, an adaptive control system was developed by introducing a mode decomposition system at the output and combining it with the Stochastic Parallel Gradient Descent (SPGD) + basin hopping algorithm. Simulation results show that this system can produce desired modes with over 90% mode content, demonstrating promising application prospects in switchable high-order mode systems. Full article

Mathematical modeling is indispensable in oncology for unraveling the interplay between tumor growth, vascular remodeling, and therapeutic resistance. We present a hybrid modeling framework (continuum-discrete) and present its hybrid mathematical formulation as a coupled partial differential equation–agent-based (PDE-ABM) system. It couples reaction–diffusion fields for oxygen, drug, and tumor angiogenic factor (TAF) with discrete vessel agents and stochastic phenotype transitions in tumor cells. Stochastic phenotype switching is handled with an exact Gillespie algorithm (a Monte Carlo method that simulates random phenotype flips and their timing), while moment-closure methods (techniques that approximate higher-order statistical moments to obtain a closed, tractable PDE description) are used to derive mean-field PDE limits that connect microscale randomness to macroscopic dynamics. We provide existence/uniqueness results for the coupled PDE-ABM system, perform numerical analysis of discretization schemes, and derive analytically tractable continuum limits. By linking stochastic microdynamics and deterministic macrodynamics, this hybrid mathematical formulation—i.e., the coupled PDE-ABM system—captures bidirectional feedback between hypoxia-driven angiogenesis and resistance evolution and provides a rigorous foundation for predictive, multiscale oncology models. Full article

As an essential technology for precision airdrop missions, parafoil systems have gained widespread adoption in military and civilian applications due to their superior glide performance and maneuverability compared to conventional parachutes. Addressing the trajectory-tracking control challenges of the parafoil system under significant wind disturbances, characterized by wind uncertainty and system underactuation, this paper proposes a stochastic model predictive control (SMPC) framework based on Markov-based multi-scenario optimization. Traditional deterministic model predictive control (MPC) methods often exhibit excessive conservatism due to reliance on worst-case assumptions and fail to capture the time-varying nature of real-world wind fields. To address these limitations, a high-fidelity dynamic model is developed to accurately characterize aerodynamic coupling effects, overcoming the oversimplifications of conventional three-degree-of-freedom point-mass models. Leveraging Markov state transitions, multiple wind-disturbance scenarios are dynamically generated, effectively overcoming the limitations of independent and identically distributed hypotheses in modeling realistic wind variations. A probabilistic constraint-reconstruction strategy combined with a rolling time-domain covariance update mechanism mitigates uncertainties and enables cooperative optimization of inner-loop attitude stabilization and outer-loop trajectory tracking. The simulation results demonstrate that the SMPC framework achieves superior comprehensive performance compared to deterministic MPC, evidenced by significant reductions in maximum position error, average position error, and control effort variation rate, along with a 94% tracking success rate. By balancing robustness, tracking precision, and computational efficiency, the method provides a theoretical foundation and a promising simulation-validated solution for airdrop missions. Full article

Effective containment of contaminant plumes in heterogeneous aquifers is critically challenged by the inherent uncertainty in hydraulic conductivity (K). Conventional, deterministic optimization approaches for pump-and-treat (P&T) system design often fail when confronted with real-world geological variability. This study proposes a novel robust simulation-optimization framework to design reliable hydraulic containment systems that explicitly account for this subsurface uncertainty. The framework integrates the Karhunen–Loève Expansion (KLE) for efficient stochastic representation of heterogeneous K-fields with a Genetic Algorithm (GA) implemented via the pymoo library, coupled with the MODFLOW groundwater flow model for physics-based performance evaluation. The core innovation lies in a multi-scenario assessment process, where candidate well configurations (locations and pumping rates) are evaluated against an ensemble of K-field realizations generated by KLE. This approach shifts the design objective from optimality under a single scenario to robustness across a spectrum of plausible subsurface conditions. A structured three-step filtering method—based on mean performance, consistency (pass rate), and stability (low variability)—is employed to identify the most reliable solutions. The framework’s effectiveness is demonstrated through a numerical case study. Results confirm that deterministic designs are highly sensitive to the specific K-field realization. In contrast, the robust framework successfully identifies well configurations that maintain a high and stable containment performance across diverse K-field scenarios, effectively mitigating the risk of failure associated with single-scenario designs. Furthermore, the analysis reveals how varying degrees of aquifer heterogeneity influence both the required operational cost and the attainable level of robustness. This systematic approach provides decision-makers with a practical and reliable strategy for designing cost-effective P&T systems that are resilient to geological uncertainty, offering significant advantages over traditional methods for contaminated site remediation. Full article