Search Results (3,765)

This study systematically compared the structural, functional, pathogenic, and assembly-mechanism characteristics of bacterial communities between urban and rural rivers across China, based on integrated water quality data from 421 sampling sites and 16S rRNA gene sequences from 475 sampling sites. The results revealed that urban rivers had significantly higher nutrient concentrations and bacterial α-diversity, along with lower β-diversity. Urban rivers were enriched with organic matter-degrading phyla such as Chloroflexi and Acidobacteriota and might exhibit more complex co-occurrence networks (average degree: 85.41). In contrast, rural rivers were enriched with phyla including Firmicutes and Cyanobacteria, as well as genera such as Exiguobacterium and Limnohabitans, and might display higher network modularity (modularity: 0.59) and greater spatial heterogeneity in community composition. Functional prediction indicated stronger carbon-cycling potential in urban rivers, whereas nitrogen-cycling functions did not differ between the two river types. Regarding pathogen composition, urban rivers contained a higher number of pathogen species than rural rivers. It was suggested that stochastic processes dominated community assembly in both systems; however, heterogeneous selection contributed more strongly in urban rivers (14.7%). Overall, this work elucidated systematic differences in bacterial community structure, function, pathogen profile, and assembly mechanisms between urban and rural rivers, offering a scientific foundation for differentiated watershed management. Full article

To investigate the underlying mechanisms of talent mobility in higher-education institutions influenced by factors such as the development environment, macroeconomic policies, and evaluation mechanisms, this paper proposes a nonlinear stochastic differential equation (SDE) dynamical model that incorporates time delays and multiplicative noise. We analyze the dynamic processes of talent mobility under varying conditions regarding the number of nodes, policy implementation cycles, and noise intensity. First, we employ central manifold theory and stochastic averaging methods to reduce the system to a one-dimensional averaged $It\widehat{o}$ equation. Subsequently, with $\tau$ as a parameter, we conduct an in-depth study of the system’s stochastic bifurcation behavior using the corresponding Fok–Planck–Kolmogorov equations. Finally, we validate the theoretical conclusions through numerical simulations. The results indicate that the number of nodes, policy delay, and noise intensity all have significant effects on system stability; an increasing delay induces random P-bifurcation in the system, and when $N\le 3$ and $N>3$, the system exhibits distinctly different steady-state behaviors. We also found that excessively high noise intensity disrupts system stability, whereas moderate noise intensity has a positive effect on stability. This study not only provides theoretical insights into the dynamic evolution mechanisms of talent mobility in regional universities but also offers valuable guidance for universities in formulating talent recruitment and evaluation policies. The methodology employed in this study opens up a promising avenue for analyzing complex dynamic problems in the field of sociology. Full article

In this paper, we focus on online stochastic optimization problems in which random parameters follow time-varying distributions. In each round t, a decision is obtained from solving the current optimization problem. Then samples are drawn from distributions which are updated after obtaining the decision. The objective and constraint are updated in this process, and the updated problem is used to obtain the next decision. To solve the online stochastic optimization problem, we propose a model-based stochastic augmented Lagrangian method, which is referred to as the MSALM. In each round, we construct model functions for the sample objective and constraint functions based on their properties, which reduce computational complexity. The step size is designed in a dynamic way and decreases as t increases to accelerate convergence. Due to the setting of the online stochastic problem, we use stochastic dynamic regret and constraint violation to measure the performance of our algorithm. Under certain assumptions, we prove that our algorithm’s stochastic dynamic regret and constraint violation have a sublinear bound in terms of the total number of slots T. We design simulation experiments to verify the efficiency of our online algorithm. Its performance is evaluated on a range of information and system engineering problems, including adaptive filtering, online logistic regression, time-varying smart grid energy dispatch, online network resource allocation, and path planning. In addition, in the context of the path planning problem, we integrate our algorithm with supervised learning to demonstrate its enhanced capabilities. The experimental results validate the performance of our new algorithm in practical applications. Full article

This manuscript introduces a new class of count time series models, referred to as the minification integer-valued Split-BREAK (MIN–SB) process. The proposed framework extends the Split-BREAK modeling philosophy to the integer-valued setting and provides a flexible mechanism for capturing rare events, zero inflation, and structural regime changes frequently observed in safety-related data. The main stochastic properties of the MIN–SB process are derived, including stationarity conditions, explicit moment structure, and correlation dynamics. A key theoretical result reveals an implicit hidden Markov structure underlying the observable process, providing a structural explanation for zero clustering observed in rare-event count processes. Parameter estimation is developed using a simulated method of moments (SMM) approach based on zero-related statistics, and the asymptotic properties of the resulting estimators are established. A Monte Carlo simulation study demonstrates favorable finite-sample performance of the proposed estimation procedure. The practical usefulness of the model is illustrated through an empirical application to time series of injuries and fatalities caused by fire accidents in Serbia. The results show that the MIN–SB specification provides a flexible and accurate framework for modeling zero-inflated count processes arising in fire safety dynamics. Full article

This paper introduces the Touchard process, a flexible two-parameter stochastic framework for modeling count data that depart from the classical Poisson assumptions. In contrast to standard Poisson processes, the proposed model allows for both nonstationary and dependent increments, enabling the representation of overdispersion, underdispersion, and temporal dependence within a unified structure. The main contribution lies in extending weighted Poisson models to a stochastic-process setting through recursively defined transition probabilities associated with Touchard marginal distributions. We derive key theoretical properties, including admissibility conditions and a recursive formulation for the transition probabilities, and propose an efficient simulation algorithm. Maximum likelihood estimation is developed for parameter inference, and a likelihood ratio framework is used for model comparison. An empirical application to motor vehicle crash data illustrates the ability of the model to capture dynamic patterns that are not adequately described by classical Poisson-based approaches. Full article

We perform stochastic simulations of the Hodgkin–Huxley and Morris–Lecar models with different numbers of ion channels in order to describe the effects of periodic electrical driving on spike rates and the regularity of spiking in a single neuron. For stochastic modeling, we use an efficient method that reduces the piecewise-deterministic Markov process of the membrane potential evolution to an ordinary differential equation between random opening and closing events. To characterize a regular component in the resulting voltage time series, we adopt a Wiener order parameter based on the autocorrelation function. We show that the effect of ion channel stochasticity on the spike rate is stronger at lower external force frequencies. The regular component of neural activity exhibits resonant-like behavior as a function of the driving frequency, with a maximum in the beta range. Full article

We consider the problem of time-series forecasting via statistical reconstruction of the coefficients of the Itô representation of the underlying stochastic process $X\left(t\right)$. The reconstructed coefficients are obtained using techniques that account for their dependence on the current value of the process. We augmented the basic linear autoregressive model with the estimated Itô drift coefficients: 1st order $\widehat{a}(t,X_t)$ and 2nd order $\widehat{\widehat{a}}(t,\widehat{a}(t,X_t))$ that can be treated as the 1st and 2nd quasi-derivatives of the original time series that is assumed to be a realization of $X\left(t\right)$. The predictive techniques used in this paper are based on a kind of statistical analog of the Taylor expansion for the time series. The proposed predictive algorithms demonstrate higher accuracy as compared to other autoregressive algorithms applied to forecasting a big set of time series. Full article

Engineering systems generally exhibit continuous state degradation in practical operation, and reliability evaluation is often challenged by data scarcity and uncertain component weights. Existing studies on weighted k-out-of-n systems mainly focus on deterministic or discrete-state models, while rarely addressing reliability modeling and component importance assessment for continuous-state systems with uncertain information under data shortage, which constitutes a clear research gap. This paper first defines the connotation of uncertain continuous state as the continuous degradation process of system performance affected by ambiguous parameter information and insufficient historical data. On this basis, a reliability modeling framework is established for a weighted k-out-of-n system with uncertain continuous states. Adopting stochastic reliability theory, this paper comparatively investigates system reliability under constant component weights and uncertain variable component weights, and further adopts the Birnbaum importance measure to quantify component importance under variable weight uncertainty. To reduce computational complexity and improve solution efficiency, a binary search-based numerical algorithm is developed to solve the established model. A distributed solar power generation system is employed as a practical case to validate the feasibility and applicability of the proposed model and algorithm. The presented approach effectively fills the limitation of existing discrete and deterministic models, and provides a novel theoretical reference for reliability analysis and key component identification of continuous degradation engineering systems. Full article

With the increasing integration of photovoltaic (PV) generation, short-term scheduling of hydro–PV–battery energy storage systems (HPBS) faces growing challenges due to the stochastic variability of PV output, the temporal coupling of hydropower operation, and the accumulation of deviations during the real-time execution of day-ahead schedules. This paper proposes a bi-level coordinated scheduling framework that integrates day-ahead mixed-integer quadratic programming (MIQP) with intraday Soft Actor–Critic (SAC)-based correction. In the upper layer, MIQP generates a 24 h baseline schedule subject to unit output limits, mutually exclusive charging/discharging logic, and operational constraints. In the lower layer, SAC performs bounded real-time residual correction for hydropower and battery storage around the MIQP baseline, while a deviation-triggered replanning mechanism forms a closed-loop process of planning, execution, correction, and replanning. Comparative experiments under the tested setting show that SAC achieves better overall performance than Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Typical-day evaluations under dry-, normal-, and wet-season conditions show that, in the selected case studies, the proposed MIQP + SAC framework achieves better performance than standalone MIQP and MIQP-Replan, which refers to a deviation-triggered MIQP re-optimization strategy, in load tracking, PV curtailment reduction, and hydro-storage coordination. These results indicate the effectiveness of the proposed framework for short-term HPBS scheduling under representative operating conditions. Full article

Accurate prediction of steady-state relative permeability via pore-scale modeling is fundamental to understanding multiphase flow processes in diverse engineering applications. However, the stochastic nature of the initial fluid distribution (IFD) in simulations is frequently overlooked, creating uncertainties that may obscure the physical influence of critical parameters on transport behavior. In this study, a color-gradient lattice Boltzmann method was employed to conduct extensive steady-state simulations across two porous media of varying geometric complexity. The investigation focused on evaluating three representative IFD patterns across different capillary numbers (Ca) and viscosity ratios (M). By introducing the coefficient of variation (CV) and distribution interval overlap analysis, the IFD-induced uncertainty was systematically quantified. The results demonstrate that the IFD is a primary source of statistical variance in relative permeability, exhibiting a strong nonlinear coupling with Ca, M, and structural complexity. CV analysis reveals that uncertainty peaks within specific saturation windows, which shift according to the pore geometry. Specifically, the peak uncertainty window for total relative permeability shifts from S_w$\in$ [0.5, 0.7] in the simple model to S_w$\in$ [0.3, 0.5] in the heterogeneous model. Notably, the wetting phase exhibits pronounced instability in the low-saturation regime, with the wetting-phase CV reaching its maximum at S_w = 0.3 in the simple model. At low Ca conditions, IFD-induced errors can entirely mask the physical sensitivity of relative permeability to Ca and M within certain saturation intervals. Furthermore, variations in initial configurations lead to divergent evolutions of the fluid-fluid interfacial area relative to wetting saturation, highlighting the role of microscopic topological memory in governing flow behavior. This research provides a quantitative foundation for IFD sensitivity in pore-scale modeling and proposes the integration of a CV-based uncertainty framework into macro-scale models to enhance the robustness and reliability of multiphase flow predictions. Full article

Reliable forecasting of seismic activity trends is essential for regional seismic hazard analysis. Based on earthquake catalogs from 1500 to 2026, this study investigates the spatiotemporal evolution of seismic activity in the North-South Seismic Belt using a hybrid framework that integrates Wiener filtering and artificial neural networks. Seismic activity is modeled as a discrete-time stochastic process, and a time series of earthquakes with magnitudes ≥ 6.0 is constructed. Wiener filtering is applied to establish an optimal linear relationship between input and output under the minimum mean square error criterion, and multi-origin extrapolation is employed to predict earthquakes with magnitudes ≥ 7.0 over the next century. The results reveal several stable peaks or peak clusters that agree well with historical strong earthquakes, with prediction errors generally within approximately three years. Sensitivity analyses indicate that longer time series (∼500 years) and higher threshold magnitudes (≥6.0) enhance prediction stability, although the method shows limitations in spatial prediction. To address this issue, a 16–8–4 artificial neural network model is developed, and seismic sequence features are extracted using a sliding time window approach to perform both temporal and spatial forecasting. The artificial neural network achieves high accuracy in temporal prediction (maximum error ≈ 0.5) and outperforms Wiener filtering in spatial prediction, capturing the migration characteristics of seismic activity. The results further suggest that earthquakes with magnitudes ≥ 7.0 are more likely to occur within the latitude range of 30.5–33.0° N in the near future. Full article

The reliable joining of thermoplastic composites is a critical requirement in modern manufacturing, where achieving zero-leakage joints is essential. For this application, ultrasonic welding is a highly efficient technology. Traditionally, standard heuristic methods and static experimental designs are used to optimize machine parameters. However, the process exhibits high stochastic variability due to complex, nonlinear thermomechanical interactions, which significantly influence the final seal quality and the reliability of the entire production system. This paper presents a practical prediction-optimization framework using a hybrid stacking ensemble neural network to process welding data. To improve the accuracy and stability of the manufacturing process, the predictive model is integrated with a Monte Carlo simulation. Evaluation showed that the proposed framework achieved the best performance among the evaluated benchmark models, with a coefficient of determination R² = 0.8523 and a mean absolute error MAE = 0.7224. The proposed framework identifies candidate optimized machine parameters in a simulation-based workflow and defines stable operating conditions for subsequent experimental validation, providing a bounded data-driven approach for minimizing leakage in ultrasonic welding. Full article

Soil nematodes are integral to soil micro-food webs and serve as sensitive bioindicators of soil ecological condition. However, how forest vegetation and soil properties interact to shape nematode community assembly, network structure, and functional stability remains inadequately understood. Using 18S rRNA gene amplicon sequencing coupled with phylogenetic null modeling, we examined soil nematode communities across four forest types along a succession gradient. Although taxonomic diversity (e.g., Shannon and Pielou indices) differed significantly among forest types, network topology and stochastic assembly processes were more closely associated with community restructuring and co-occurrence patterns. This suggests that, while diversity is not irrelevant, network- and assembly-based metrics provide complementary and often more sensitive indicators of nematode community responses to forest type. Co-occurrence network analysis revealed that mixed forests fostered more complex and potentially stable networks, whereas plantations formed dense but potentially vulnerable networks. Assembly processes were not dominated by strong deterministic selection (|βNTI| ≤ 2 for most comparisons), a pattern consistent with undominated processes (e.g., ecological drift, weak environmental filtering). Dispersal limitation played a negligible role in this system. Partial Least Square Path Modeling identified spatial factors and key soil properties (e.g., pH, electrical conductivity, soil water content, and organic carbon) as primary drivers of community structure. Our findings indicate that assessing soil food web health should integrate network analysis and stochasticity metrics rather than rely solely on taxonomic diversity. For sustainable forest management, mixed-species stands are preferable to monoculture plantations, and maintaining soil physicochemical heterogeneity is critical for community stability. Full article

This featured volume is dedicated to the memory of Professor Joe Gani (1924–2016) and Professor Chris Heyde (1939–2008), two outstanding scholars whose research, intellectual leadership, and mentorship had a lasting influence on applied probability, mathematical statistics, stochastic processes, actuarial science, and financial risk analysis [...] Full article

The crushing failure of sea ice is a critical design issue for polar offshore structures and ship structures because ice-induced loads may generate pronounced local damage and dynamic responses. Accurately modelling this process remains challenging because ice crushing involves localized fragmentation, crack propagation, rubble accumulation, and repeated contact release. This paper presents a controlled numerical sensitivity study of level-ice crushing against a vertical structure using a coupled LS-DYNA framework that combines the Structured Arbitrary Lagrangian–Eulerian (S-ALE) formulation with the Cohesive Element Method (CEM). The study focuses on a benchmark-scale indentation configuration and examines how mesh topology, mesh size, and imposed indentation velocity affect the predicted fracture morphology and load-time histories. The results show that random triangular meshes better reproduce stochastic fragmentation and lateral flaking than regular triangular or quadrilateral meshes, while finer meshes reduce excessive load oscillations and provide more stable force histories. The velocity study indicates a transition from gradual crushing and fragment retention at lower velocities to more rapid brittle chipping and stronger dynamic fluctuations at higher velocities. A benchmark-level comparison with published ice-indentation simulations shows that the predicted peak line load is of the same order of magnitude as reference results. The proposed framework is therefore useful for investigating numerical sensitivities and failure-mode trends in ice-crushing simulations, although final design-load application requires further calibration and formal mesh-independence assessment. Full article