Search Results (388)

Different methodologies have been developed for the analysis and study of dynamical systems, including both theoretical models and natural systems. Examples span a wide range of applications, such as astronomy, financial and economic time series, biophysical systems, physiological phenomena, and Earth sciences, including seismicity and climatic processes. The study of these complex systems is commonly based on the analysis of the signals they generate, using mathematical tools to extract relevant information. A broad spectrum of mathematical disciplines converges in this context, including stochastic, probability and statistical theory, entropic and informational measures, fractal and multifractal analysis, natural time analysis, modeling of non-linearity and recurrence methods, generalized entropies, non-extensive systems, machine learning, and high-dimensional and multivariate complexity. Research in this area is largely focused on the characterization of complex systems, providing indicators of determinism or stochasticity, distinguishing between regularity, chaos, and noise, and identifying topological as well as disorder-regularity features. In addition, short- and long-term forecasting, together with the identification of short- and long-range correlations, play a central role in such characterization. To address these objectives, numerous mathematical tools have been developed for the analysis of time series and point processes, each designed to capture specific signal properties. In this work, many of the most important tools used in time series analysis are compiled and reviewed, highlighting their main characteristics and the different types of complex systems to which they have been applied. Full article

This study introduces a computational method for designing realistic, geometrically controlled three-dimensional (3-D) micromodels of porous media to investigate fluid flow in hydrocarbon reservoirs. The methodology utilizes a virtual framework of cubes where an arbitrary, continuous 3-D pore network is generated via two-dimensional (2-D) sketches. A key strength of this deterministic, cube-by-cube approach is the ability to independently control porosity and permeability by adjusting channel size and connectivity, facilitating the systematic study of spatial heterogeneity. Six digital models were developed with porosities ranging from 18.4% to 44.4%. Unlike traditional stochastic algorithms, this explicit geometric control enabled the accurate extraction of pore volume distributions and the establishment of a robust power-law relationship between localized porosity and specific surface area. Statistical analysis confirmed a linear correlation between porosity and pore dimensions. While focusing on design and validation, these models are 3-D printable and provide exact boundary conditions for CFD simulations. Single-phase simulations confirmed the capability to decouple absolute permeability from porosity. Consequently, this framework bridges the gap between numerical simulations and physical laboratory experiments to optimize Enhanced Oil Recovery (EOR) processes. Full article

The freeze–thaw cycles lead to cumulative damage and gradual strength deterioration in concrete, which cannot be accurately represented by traditional empirical models. To address this issue, shale-based lightweight structural concrete (LSC) specimens with four strength grades (LSC20-LSC50) were subjected to basic mechanical performance and freeze–thaw cycle experiments. The study investigated the patterns of mass loss, relative dynamic elastic modulus, and loss of compressive strength in LSC subjected to varying numbers of freeze–thaw cycles. Furthermore, the correlation between the dynamic modulus of elasticity and compressive strength was examined. A Gamma process-based stochastic degradation model for the compressive strength of LSC was then developed. The results show that the compressive strength degradation of LSC under freeze–thaw cycles follows a monotonically increasing trend that gradually stabilizes, with low-strength LSC deteriorating faster than high-strength LSC. After 200 cycles, the compressive strength degradation of LSC30 and LSC50 was only 32.66% and 29.79% of that of their corresponding ordinary concretes (C30 and C50). The proposed Gamma process model showed high fitting accuracy for all strength grades of LSC (R² > 0.96, RMSE < 0.25 MPa, MAPE < 11%). The research results provide a scientific basis for the structural design of concrete in cold regions. Full article

Under dynamic loading conditions, the output voltage of proton exchange membrane fuel cells (PEMFCs) exhibits nonlinear degradation characterized by non-Gaussian fluctuations, abrupt changes, and long-range temporal dependence, which are difficult to model using conventional short-correlation or remaining useful life (RUL) prediction approaches. To capture both historical dependency and stochastic jump behavior, this study proposes a SHAP-driven mechanism–data fusion fractional stochastic degradation model based on fractional Brownian motion (fBm) and fractional Poisson process (fPp) for degradation trend forecasting. A terminal voltage mechanism model considering activation, ohmic, and concentration polarization losses is first established, and SHapley Additive exPlanations (SHAP) analysis is employed to quantify the contributions of multi-source operational variables and enhance interpretability. The Hurst exponent is then used to verify long-range dependence and jump characteristics in the voltage sequence. Subsequently, fBm is integrated with a fPp to construct a unified stochastic degradation framework capable of jointly describing continuous decay and discrete abrupt variations, enabling multi-step probabilistic prediction with confidence intervals. Validation on the publicly available FCLAB FC1 and FC2 datasets shows that the proposed model achieves superior overall performance under both steady and dynamic conditions, with MAPE/RMSE/R² of 0.027%/0.00178/0.9895 and 0.056%/0.00259/0.9896, respectively, outperforming fBm, Wiener, WTD-RS-LSTM, and CNN-LSTM methods. The proposed approach provides accurate and interpretable degradation forecasting for PEMFC health management and maintenance decision support. Full article

Pricing multi-peril agricultural insurance under compound climate hazards demands a framework that captures stochastic dependence among heterogeneous perils, accommodates non-stationary loss dynamics, and supports adaptive policy optimisation. We demonstrate that backward stochastic differential equations, combined with copula dependence, recurrent neural networks, and reinforcement learning, provide a unifying language for this task; the contribution lies in their principled integration. The dynamic premium is the unique adapted solution of a BSDE whose driver encodes compound-risk dependence through a Student-t copula, forward loss dynamics through a jump-diffusion process, and a green-finance adjustment through an optimal control variable. Within this framework we derive three progressive results by adapting standard BSDE theory to the compound-dependence and policy-control setting. First, existence and uniqueness hold under Lipschitz and square-integrability conditions. Second, a comparison theorem guarantees that a larger correlation matrix yields higher premiums; the degrees-of-freedom effect enters separately through the risk-loading magnitude. Third, the Euler discretisation converges at a rate of one half of the time-step size, with copula estimation, LSTM conditional expectation approximation, and Q-learning HJB solution as sequential components. Applied to eleven Zhejiang cities (2014–2023, $N \times T=110$), in this illustrative application the framework reduces premium variance by 43.5 percent (bootstrap 95% CI: $[38.2\%,48.7\%]$) while maintaining actuarial adequacy with a mean loss ratio of 0.678, though the modest sample size warrants caution in generalising these findings. Each component contributes statistically significant improvements confirmed by the Friedman test at the 0.1 percent significance level. Full article

Incorporating plant-based additives was a promising approach for modulating the microbial ecosystems of fermentation starters. This study investigated how adding Kuding tea (20% wt/wt) influenced the assembly and succession of fungal communities during Jiang-flavored Daqu production, compared to traditional wheat-based Daqu. Using amplicon sequencing of the ITS1 region and integrated measurements of endogenous factors, we analyzed community dynamics across a 40-day fermentation period. Results showed that tea addition significantly increased fungal diversity and altered succession trajectories. Community assembly shifted from stochastic towards deterministic processes, with homogeneous selection increasing from 0.47 in wheat-based Daqu to 0.62 in tea-added Daqu. Temporal species accumulation was stronger (STR exponent z: 0.565 vs. 0.436), while compositional turnover slowed (TDR slope w: −0.539 vs. −0.626). Random forest models revealed tea-specific fungal drivers and stronger correlations with endogenous factors (e.g., reducing sugar and moisture). We concluded that Kuding tea appears to function predominantly as an environmental filter that enhanced deterministic selection, stabilized community succession, and restructured the key microbial–physicochemical relationships, providing a potential strategy for steering Daqu fermentation. Full article

Microbial volatile organic compounds (VOCs) mediate rhizosphere plant-microbe interactions, yet their integrated effects on plant microbiome assembly and host transcriptional regulation remain unresolved. Here we address this gap by investigating how two common VOCs, acetoin (AC) and 2,3-butanediol (BD), influence growth, rhizosphere communities, and root gene expression in the medicinal plant Pseudostellaria heterophylla using a split-pot system. Bacterial and fungal communities were monitored across three developmental stages via amplicon sequencing, alongside root transcriptome profiling during tuber enlargement. Contrasting with widely reported growth-promoting effects of microbial VOCs, both compounds significantly reduced tuber number and biomass. Bacterial communities remained taxonomically stable, shaped primarily by species replacement, with modest VOC responses but clear shifts across developmental stages. Fungal communities exhibited marked compositional restructuring and greater treatment sensitivity, particularly under BD. Neutral community modeling indicated predominantly stochastic bacterial assembly, while fungal assembly—especially under BD—showed stronger influence of deterministic processes. BD associated with broader transcriptional reprogramming than AC, including downregulation of photosynthesis, specialized metabolism, and defense pathways. Cross-omics network analysis revealed discriminant genera (e.g., Granulicella, Harposporium) that correlated strongly with host genes involved in stress response, development, and epigenetic regulation, with fungal taxa showing tighter associations with host expression than bacteria. Together, these findings establish a mechanistic framework for how microbial VOCs shape rhizosphere communities and host responses, with implications for microbiome-based strategies in medicinal plant cultivation. Full article

Guaranteed annuity options (GAOs) allow policyholders to convert accumulated funds into life annuities at maturity at a guaranteed minimum rate. Thus, insurers are exposed to both investment and longevity risks. Accurate valuation of these long-term, survival-contingent contracts is essential for solvency assessment and risk management. Many existing approaches assume independence between interest rate and mortality risks. This paper develops a computationally efficient pricing framework for GAOs that jointly models interest and mortality rates as correlated stochastic processes with regime-switching dynamics governed by a finite-state continuous-time Markov chain. Model parameters are estimated using U.S. interest rates and cohort mortality data via quasi-maximum likelihood estimation. A semi-analytic valuation formula is derived based on the joint distribution of the underlying processes. Numerical results show that incorporating correlation and regime-switching materially increases GAO prices relative to conventional one-state models. The proposed semi-analytic approach delivers substantial computational advantages over standard Monte Carlo simulations. Sensitivity analysis further identifies the parameters most relevant for long-horizon pricing and solvency considerations. This highlights the practical relevance of the framework for managing longevity-linked guarantees under economic and demographic uncertainty. Full article

Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (R_max) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R_max evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and R_max escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric R_max,num = (d_initial + d_secondary)/2 achieved 11–19% average error against the experimental R_max,exp, with complementary valley depth (R_v) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold R_max increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (R_pc), and overlap geometry to surface topography, enabling process optimization (I·t_on < 60 A·s maintains R_max < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound R_max predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article

Unmanned Aerial Vehicle (UAV)-assisted edge computing supports latency-sensitive applications by offloading computational tasks to ground-based servers. However, determining optimal resource allocation under strict latency constraints and stochastic channel conditions remains challenging. This paper addresses the joint computation partitioning and power allocation problem for UAV-assisted edge computing systems. We formulate the problem as a Constrained Markov Decision Process (CMDP) that explicitly models latency constraints, rather than relying on implicit reward shaping. To solve this CMDP, we propose Constrained Soft Actor–Critic (C-SAC), a deep reinforcement learning algorithm that combines maximum-entropy policy optimization with Lagrangian dual methods. C-SAC employs a dedicated constraint critic network to estimate long-term constraint violations and an adaptive Lagrange multiplier that automatically balances energy efficiency against latency satisfaction without manual tuning. Extensive experiments demonstrate that C-SAC achieves an $18.9\%$ constraint violation rate. This represents a 60.6-percentage-point improvement compared to unconstrained Soft Actor–Critic, with $79.5\%$, and a 22.4-percentage-point improvement over deterministic TD3-Lagrangian, achieving $41.3\%$. The learned policies exhibit strong channel-adaptive behavior with a correlation coefficient of $-0.894$ between the local computation ratio and channel quality, despite the absence of explicit channel modeling in the reward function. Ablation studies confirm that both adaptive mechanisms are essential, while sensitivity analyses show that C-SAC maintains robust performance with violation rates varying by less than 2 percentage points even as channel variability triples. These results establish constrained reinforcement learning as an effective approach for reliable UAV edge computing under stringent quality-of-service requirements. Full article

Enzyme performance parameters, including the turnover number and specificity constant, exhibit remarkable diversity due to biological evolution and natural selection. In some bacterial and human enzymes, catalytic efficiencies approach fundamental physical limits, underscoring the importance of physical constraints on enzymatic function. A deeper understanding of these constraints, particularly in far-from-equilibrium irreversible processes, is therefore essential for rational enzyme engineering. Such constraints are most naturally addressed within the frameworks of nanothermodynamics and stochastic thermodynamics, which remain relatively unfamiliar to much of the molecular biology community. Recent theoretical and experimental advances indicate that classical enzyme kinetic parameters are not independent, but are systematically linked to energetic dissipation. In particular, enzymes appear to occupy a characteristic dissipation plane defined by entropy production, reflecting the coupled influence of thermodynamic principles and evolutionary selection. In this review, we synthesize evidence across diverse enzyme families demonstrating correlated increases in housekeeping dissipation, evolutionary divergence, and enzymatic performance. Together, these findings support dissipation as a physically grounded parameter that connects enzyme kinetics, biological evolution, and nonequilibrium thermodynamics. Full article

The manufacturing systems face growing demands due to the instability of the market, the demanding sustainability policies, and the high rate of old equipment, but traditional planning structures are mostly fixed and deterministic, leading to the inefficiency of joint optimization of operational stability and environmental sustainability in unpredictable situations. This research proposed and empirically tested an artificial-intelligence-based adaptive planning platform, which combines a physics-based Digital Twin (DT) and a Pareto-conditioned Multi-Objective Proximal Policy Optimization (MO-PPO) algorithm to be able to co-optimize reliability and sustainability indicators in real-time. The platform reinvents manufacturing planning as a Constrained Multi-Objective Markov Decision Process (CMDP), optimizing an Overall Equipment Effectiveness (OEE) and energy carbon intensity as well as material waste, and strongly adhering to operational restrictions. The study utilizes a four-layer cyber–physical architecture, which includes an edge-based data acquisition layer, a high-fidelity stochastic simulation engine that is calibrated via Bayesian inference, a graph attention network-based state-encoding layer, and a closed-loop execution loop that runs with 60 s long planning cycles. In this study, a statistically significant enhancement was shown in 10,000 stochastic simulation experiments and a 12-week industrial pilot deployment: 96.8% schedule performance, 84.7% OEE, 16.5% cut in specific energy usage (2.38 kWh/kg), 17.1% reduction in material-waste rate (6.8%), and 21.4% enhancement in carbon effectiveness, outperforming all baseline strategies (p = 0.001). The analysis showed that there was a surprising synergistic correlation between waste minimization and OEE enhancement (r = −0.73), and 34.1% of overall OEE improvement could be explained by sustainability strategies. This study provides a robust framework for adaptive, resilient, and eco-friendly manufacturing processes in line with Industry 5.0 ideologies. Full article

Establishing mixed conifer–broadleaf forests enhances soil multi-nutrient cycling (SMC), yet the underlying mechanisms, particularly the role of rhizosphere microbial communities, remain poorly understood. This study investigated how bacterial and fungal communities in the rhizosphere soil of Larix olgensis drive SMC in both pure and mixed plantations with Fraxinus mandshurica, elucidating the microbial pathways for nutrient supply in mixed stands. Our results indicated that SMC in the L. olgensis rhizosphere soil was significantly greater in mixed stands (0.43) than in pure stands (−0.51). Tree species mixing significantly enhanced microbial diversity, increased the stochasticity of community assembly, and reduced dispersal limitation. Cross-kingdom (bacteria–fungi) co-occurrence networks in mixed stands showed a 19.7% increase in positive correlations, indicating stronger microbial cooperation. Random forest analysis identified microbial diversity, network complexity, and bacterial assembly processes as the main predictors of SMC. Structural equation modeling indicated that microbial diversity indirectly promoted SMC via increased network complexity, while bacterial assembly processes directly influenced SMC. These findings demonstrate that mixed conifer–broadleaf plantations improve soil microbial functioning and nutrient cycling by modifying microbial diversity, assembly processes, and interaction networks. Full article

A Monte Carlo based fractal prediction model is proposed to describe the evolution of surface morphology during the running-in process. The model accounts for the random and fractal characteristics of worn surfaces. The Weierstrass–Mandelbrot function is employed to simulate rough surfaces and establish the correlation between fractal dimension and surface roughness. By integrating traditional sliding wear models with surface effect functions, a unified prediction framework is developed. Experiments are conducted to obtain worn surface parameters and calculate fractal dimensions at different running-in stages. Model parameters are optimized by minimizing the variance between experimental and predicted results. Monte Carlo simulations are then introduced to represent the stochastic nature of the friction system, thereby improving prediction accuracy and objectivity. The proposed model reveals locally random yet globally convergent patterns, which are consistent with experimental observations. It effectively captures the stochastic evolution of surface morphology and provides a reliable approach for predicting worn surface behavior during running-in. Full article

Transport emissions are a major source of urban polycyclic aromatic hydrocarbons (PAHs), posing risks to human health. While plant leaves and their epiphytic microbes contribute to PAH degradation, how plant traits and environmental factors affect this process remains unclear. This study examined 20 tree species in Beijing’s traffic corridors to explore PAH enrichment on leaves and the structure of phyllospheric bacterial communities. Results show that leaf area, morphology, and sampling height significantly influenced bacterial community assembly. Normalized Stochasticity Ratio (NST) analysis indicated that deterministic processes dominate on medium-sized leaves (11.8–40.1 cm²), simple leaves, and those below 2.3 m or above 3 m in height, whereas stochastic factors prevail on nano leaves, compound leaves, and leaves at low-position (<2.3 m). Although low-molecular-weight PAHs (2–4 rings) were predominant in leaves, Mantel tests revealed significant positive correlations between bacterial communities and high molecular weight PAHs (4–6 rings), such as benz(a)anthracene, benzo[e]pyrene, and picene. Spearman analysis identified 10 dominant bacterial taxa with PAH degradation potential, including Kocuria rosea, Serratia symbiotica, Massilia sp. WG5, and seven unclassified species from Hymenobacter, Sphingomonas, Roseomonas, Curtobacterium, and Deinococcus. Functional Annotation of Prokaryotic Taxa(FAPROTAX) prediction further associated 14 species across six genera, including Acinetobacter, Nocardioides, Gordonia, Rhodococcus, Clostridium_sensu_stricto_18, and Geobacter, with PAH degradation function. This work clarifies the composition and function of phyllospheric PAH-degrading bacteria in an urban traffic environment, offering a theoretical basis for enhancing degradation via bacterial consortia, biosurfactants, and optimized plant selection. Full article