Search Results (3,738)

To address the difficulty of accurately capturing the dynamic patterns of carbon emission time series—characterized by nonlinearity, non-stationarity, and complex fluctuations—this paper proposes a carbon emission prediction model based on an elite-guided collaborative social spider learning optimization algorithm (EGC-SSLO) integrated with a Long short-term memory (LSTM) network. First, considering the limitations of the standard stochastic social learning optimization (SSLO) algorithm in complex high-dimensional optimization problems, such as insufficient elite information guidance, weak local exploitation in the later stages, and a tendency to become trapped in local optima, three complementary improvement strategies are introduced. The adaptive elite mean-guided search strategy enhances the search directionality by incorporating the cooperative information of the best individual and the elite mean. The worst-individual hybrid Cauchy–Lévy search mechanism achieves a dynamic balance between early-stage global exploration and late-stage local exploitation through long-range Lévy flights and fine-grained Cauchy perturbations. The quadratic directional exploitation strategy further refines the search trajectory of candidate solutions, thereby improving convergence accuracy. These three strategies significantly enhance the optimization performance without increasing the time complexity order of the algorithm. Experimental results on the CEC2017 (30-dimensional), CEC2020 (20-dimensional), and CEC2022 (20-dimensional) benchmark suites demonstrate that EGC-SSLO consistently outperforms classical algorithms such as PSO, GWO, and HHO, as well as their improved variants, in terms of convergence accuracy, convergence speed, and robustness. Furthermore, the Wilcoxon rank-sum test and Friedman test confirm that the observed improvements are statistically significant. Finally, an EGC-SSLO-LSTM carbon emission prediction model is constructed and applied to daily carbon emission data in China from 2019 to 2025 for empirical analysis. The experimental findings show that the EGC-SSLO-LSTM model markedly outperforms both the standard LSTM and SSLO-LSTM approaches across key evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²). In particular, the MAE is decreased by 39.9% and 4.64% compared with the two benchmark models, respectively, which highlights the strong effectiveness and practical potential of the proposed method in real-world carbon emission forecasting applications. Full article

Global ocean models at non-eddying resolutions currently used for subseasonal to seasonal to decadal (S2S2D) prediction suffer from a severe deficit in internal variability. In ensemble data assimilation (DA), this can lead to under-dispersed ensembles that require inflation schemes. However, inflation corrections do not persist into the forecast phase, causing ensemble spread to collapse at longer lead times. This study evaluates an alternative approach: addressing the variability deficit directly within the model physics using a “Backscatter Package” (BackPack) consisting of the stochastic Stanley, stochastic GM+E, and Leith+E backscatter parameterizations. Implemented within a global MOM6/CESM framework at nominal $2/3°$ resolution using the DART ensemble DA package, the BackPack’s impacts are compared against cutting-edge adaptive inflation. The results demonstrate that the BackPack substantially increases internal variability and ensemble spread, successfully lowering the amount of required inflation. While reductions in ensemble-mean state errors are modest, the BackPack significantly improves ensemble calibration, assessed using a novel spread–error calibration ratio metric. Although this study only addresses the data assimilation phase, we expect that physics-based BackPack schemes may provide a physically sustainable pathway to maintain spread during the subsequent forecast phase. Full article

Let $(X,\mathfrak{A},P)$, $(Y,\mathfrak{B},Q)$ be two arbitrary probability spaces and $\mathbb{P}:=\{(\mathfrak{A},P_y):y\in Y\}$ be a regular conditional probability (rcp) on $\mathfrak{A}$ with respect to Q. Denote by R the skew product of P and Q determined by $\mathbb{P}$ on the product $\sigma$-algebra $\mathfrak{A}\otimes \mathfrak{B}$ and by $\widehat{R}$ its completion. I prove that if $(X,\mathfrak{A},P)$ is separable in the Fréchet–Nikodým pseudo-metric, then the stochastic process $\{{\xi }_y:y\in Y\}$ has an equivalent measurable modification if and only if it is measurable with respect to a certain particular $\sigma$-algebra larger than $\mathfrak{A}\otimes \mathfrak{B}$. The theorem is a strong generalization of two earlier results of the author and coauthors, where it was only proved that a suitable class of liftings transfer a measurable process into a measurable process. It is known that not every process possesses an equivalent measurable modification. My approach is essentially different from the earlier trials. It reverts to an earlier paper of Talagrand, who proved the existence of an equivalent separable modification of a measurable process (in case of $R=P\times Q$), provided Y is endowed with a separable pseudo-metric. Full article

Based on the high relevance of climate change issues and the extent of their investigation at global and regional levels, this study aims to quantify current changes in average annual air temperature, relative humidity, and precipitation within the Chu River watershed, and to identify patterns of their spatiotemporal variability under conditions of intensifying global warming. The study is based on long-term observational data from 18 meteorological stations collected during the period 1940–2024 under diverse physical and geographical conditions across the basin. The analysis of climatic dynamics within the Chu River basin catchment during the period 1940–2024, conventionally divided into four physiographic-altitudinal zones (high-mountain, mid-mountain, low-mountain, and plain regions), and conducted using a linear trend assessment approach with the application of statistical criteria, revealed the presence of multidirectional trends in climatic variables. It was established that the spatial and temporal variability of the trend coefficients for mean annual air temperature ranged from −2.50 to 2.45, for mean annual relative humidity from 0.93 to 1.03, and for annual atmospheric precipitation from 0.59 to 1.42. The identified positive trend in mean annual air temperature, occurring simultaneously with negative trends in relative humidity and atmospheric precipitation, is characterized not only by a stochastic component but also by a pronounced deterministic component manifested in the form of persistent positive and negative trends. These observed patterns are associated with the complex nature of climatic responses occurring in the northern part of the Tien Shan mountain system and along the eastern periphery of the Turan Lowland. Full article

We propose a decentralized strategy for coordinating the bidirectional charging and discharging of battery electric vehicles (BEVs) in renewable-powered parking lots. The framework combines mean-field games (MFGs) and model predictive control (MPC) to address the coupled stochastic dynamics induced by uncertain renewable generation and random vehicle arrivals and departures. Solar and wind power fluctuations are modeled using autoregressive moving-average (ARMA) processes, while the time-varying vehicle population is represented through finite Poisson processes. The coordination problem is formulated as a large-scale game, where an aggregator designs individual cost functions to maximize available energy utilization while promoting fairness through near-equal states of charge (SOCs) at departure. Scalability is achieved through MFG theory, ensuring convergence and stability even under highly volatile generation and fluctuating agent populations. Numerical simulations validate the proposed strategy against two straightforward algorithms: capacity-ordered saturation allocation (COSA) and capacity-ordered fair allocation (COFA). These centralized approaches achieve high target fulfillment in static, low-intensity environments, where available energy accommodates a stable fleet without exceeding power limits. However, their efficacy degrades significantly in dynamic, high-intensity environments, where the interplay of volatile generation, continuous fleet turnover, and strict power constraints strains the system. In contrast, the proposed MFG-MPC framework provides a decentralized response that elegantly navigates the trade-offs between energy availability, demand stochasticity, and power limits. Ultimately, this approach ensures robust energy utilization while safeguarding vehicle equity, confirming its strong suitability for real-time deployment. Full article

This study aims to quantify and compare the reliability of two alternative power system architectures: a conventional configuration based on traditional elements and a future envisioned architecture, where multi-port power converters operate as network nodes. The objective is to evaluate how these two approaches perform relative to each other in terms of reliability, and to determine whether the emerging converter-based structure represents an improvement or a drawback compared with the conventional design. To simplify the analysis and the comparison results, the analysis is presented for a double-fed power system. Both systems were modeled using two-state components characterized by constant failure and re- pair rates. Reliability assessment was carried out using a continuous-time Markov chain (CTMC) approach to derive the key adequacy indices. To validate the analytical results, a non-sequential Monte Carlo Simulation (MCS) was also performed, allowing a direct comparison between stochastic sampling and analytical modeling. The results show that the transformer-based configuration achieves a reliability of 0.9972 compared with 0.9960 for the power-router-based configuration, while also exhibiting lower LOLE and EENS, indicating a modest reliability advantage for the conventional architecture under the adopted assumptions. Full article

In general dynamic reliability problems, the input excitation often exhibits non-Gaussian characteristics. In practice, both the parameters of the excitation power spectral density (PSD) model and the structural parameters may exhibit randomness. Considering the uncertainty in both sources is therefore essential for a rational dynamic reliability analysis. The point-estimation method (PEM) is widely used in structural reliability analysis. When combined with Hermite-type quadrature for dynamic reliability evaluation, most existing PEM-based approaches employ fixed integration points, thereby neglecting the functional relationships among them. To achieve higher computational efficiency while maintaining reasonable accuracy, in this paper, a dynamic reliability approach for stochastic structural systems with random PSD parameters is developed by combining the PEM with adaptive Bayesian quadrature (ABQ). First, the moment spectra of structural responses are derived while simultaneously accounting for the randomness of PSD model parameters and structural parameters. Subsequently, an unconditional reliability point-estimation method is developed for the case of one random variable and then extended to multivariate cases. Finally, the effectiveness and computational efficiency of the proposed method are validated through the dynamic reliability analysis of a six-story structure. Full article

Cyber–Physical Systems (CPS), including smart grids and industrial control networks, must maintain secure and stable operations despite increasingly adaptive cyber threats. Existing moving target defense (MTD) approaches often rely on fixed reconfiguration strategies or flat learning architectures that fail to scale and do not explicitly ensure operational resilience under real-time constraints. This study proposes a resilience-oriented hierarchical multi-agent reinforcement learning (MARL) framework for adaptive MTD in CPS environments. The attacker–defender interaction is modeled as a partially observable stochastic game, enabling defenders to learn adaptive strategies with incomplete information. The proposed architecture consists of three layers: a strategic MARL layer that optimizes high-level defense parameters, a distributed k-winner-take-all coordination layer for low-latency defender selection, and a robust execution layer based on sliding-mode control to preserve physical system stability during reconfiguration. By decoupling strategic adaptation from real-time control, the framework improves scalability and supports resource-aware defense through selective agent activation. Extensive simulations with up to 50 defender agents demonstrate that the proposed approach achieves a defense success rate of 92.4%, reduces the response time by 15% compared with the random MTD, and lowers the energy consumption by 34% on average (up to 52% at N = 50) relative to the flat MARL. These results indicate that hierarchical MARL can significantly enhance CPS resilience by enabling adaptive, efficient, and operationally safe defenses against dynamic cyber-attacks. The proposed framework is particularly suitable for edge-enabled CPS environments with strict, real-time, and safety constraints. Full article

Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, including both its reliability in the long-term perspective and deployment costs. In addition, the sophistication of up-to-date industrial machinery increases the number of stochastic disruptions that affect the final control quality. Thus, the Control Theory satisfies the need for a novel, robust strategy by proposing the Active Disturbance Rejection Control (ADRC) algorithm. It stands out with great dynamic performance and versatility. It has been widely tested in a variety of different industrial applications, including aviation, autonomous and unmanned vehicles, marine robots, automotive solutions, renewable energy, and power systems. Many of the above-mentioned applications use electric drive units. This paper elaborates on the review of the current state-of-the-art in the field of electric drive control with the ADRC strategy employed. Then, the ADRC designs regarding multi-mass drive trains are reviewed with emphasis on the speed control issue. This paper evaluates its variants and control approaches depending on the application purpose. Moreover, an exemplary dynamic properties analysis is performed to verify the default effectiveness of the algorithm. Then, the summary section is followed by an indication of possible future research directions. Full article

This study investigates the possibilities for recently listed Romanian Information and Communications Technology (ICT) firms to select optimal sets of financial strategies under adverse macroeconomic conditions, with high and persistent inflation, high volatility, high cost of financing and liquidity constraints that influence investment decisions and financial resilience. Using a stochastic investment model with the Tobin’s Q factor in a dynamic framework equipped with a generalized Wiener process, this study offers an intuitive approach for simultaneously assessing corporate market value under financial constraints and formulating optimal decisions based on liquidity management. In this research, the numerical simulations in Python for a set of 16 scenarios resulting from combining technological and macroeconomic variables were performed, with a series of parameters held constant. The results highlight the decisive role of financial restrictions and macroeconomic volatility in shaping the investment behavior, as well as the importance of adjusting the timing of investments together with liquidity mechanisms capable of improving financial resilience. The main contribution of this study is the simplicity with which one can assess the impact of the risk-free rate and volatility on the main parameters of the set of strategies (earnings dynamics, liquidity risk, cost of capital, liquidation value, opportunity cost associated with holding cash) and to assess the integrated perspective on financial resilience. This procedure is simple and scalable and can also represent a practical tool to support management and investment decisions in volatile and turbulent conditions. Full article

The inherent brittleness of cementitious materials presents a fundamental limitation for advanced structural applications. While bio-inspired Bouligand architectures have demonstrated remarkable damage tolerance in natural composites, their systematic translation to brittle inorganic binders via 3D concrete printing (3DCP)—and the development of high-fidelity meso-scale models to quantitatively map the resulting strength–toughness design space—remains underexplored. This study aims to decouple the intrinsic topological toughening potential of helicoidal Bouligand architectures from the stochastic defects inherent to additive manufacturing, through a meso-scale finite element (FE) framework. To physically validate the model, a nano-clay-assisted rheological strategy was utilized to enable the support-free fabrication of these helicoidal prototypes. Computationally, a meso-scale FE framework integrating the concrete damaged plasticity (CDP) model with three-dimensional cohesive zone elements was developed to explicitly resolve inter- and intra-layer interfacial crack kinematics. Coupled physical compression tests and numerical simulations indicate that the 15° Bouligand architecture achieves a computationally predicted 16.3-fold increase in volumetric energy absorption (experimentally: 13.7-fold) compared to the 0° unidirectional baseline, with a modest ~11% reduction in compressive strength (from ~33.0 MPa to ~29.5 MPa in simulations; ~12% experimentally). Furthermore, numerical parametric studies across the complete pitch-angle design space reveal an optimal topological window at 15–30°, wherein the competing effects of crack deflection and structural integrity are balanced. Imperfection sensitivity analysis demonstrates that the topological toughening mechanism is relatively robust: even with a 30% reduction in inter-filament bonding strength, the work of fracture remains 12.4 times higher than that of the 0° control. These findings suggest that spatial toolpath programming offers a viable, geometry-driven strategy for developing damage-tolerant cementitious composites, complementing conventional material-level reinforcement approaches. Full article

Banks must balance deposit stability, loan expansion, and regulatory compliance while operating under liquidity constraints and financial risks. This study presents a mathematical model to examine the dynamics of bank deposits and loans under the influence of liquidity mechanisms and regulatory policies. The model proceeds in three stages: a deterministic nonlinear model, a dynamic optimal control model, and a stochastic model. Under the deterministic model, deposit withdrawals are liquidity-dependent, leading to a feedback mechanism in which liquidity improves deposit stability while financing loan growth. The theoretical results demonstrate the model’s positive and bounded solutions and show the existence and local stability of equilibria. Several parameters are based on regulatory policies or calibrated from Indonesian banking data, while the unknown parameters are estimated using the particle swarm optimization (PSO) algorithm. The results show that the proposed model is capable of fitting and predicting the data and has slightly lower mean absolute percentage errors for in-sample and out-of-sample compared with the benchmark model, and achieves comparable directional forecasting performance based on the index of directionality. Sensitivity analysis shows that the capital adequacy ratio supports lending, whereas an increased reserve requirement limits lending. An optimal control approach is developed by considering the reserve and capital requirements as time-varying policy variables. By applying Pontryagin’s maximum principle, we establish the necessary conditions for optimality. Numerical experiments demonstrate that the optimal control regulation enhances financial ratios, particularly the loan-to-deposit and liquidity ratios, at a reasonable cost. Finally, the stochastic model accounts for random variations in withdrawals and credit risks. Simulation-based observations reveal that although the system becomes more volatile, the mean dynamics are close to the deterministic case. Our framework offers a data-based and analytically tractable approach for studying the dynamics of banking variables and the effects of regulatory policies. The proposed model provides a mathematical tool for assessing the long-term effects of regulatory policies on banking performance and can assist bank managers and regulators in designing strategies that balance lending activity and liquidity resilience. Full article

This paper investigates the stabilization of unstable third-order Multiple-Input Multiple-Output (MIMO) systems whose interaction structure is described by a doubly stochastic combined matrix, also known as the Relative Gain Array (RGA). Starting from systems with negative Niederlinski index, we derive necessary and sufficient conditions under which stability can be recovered through diagonal perturbations while preserving the doubly stochastic structure of the combined matrix. By exploiting the canonical representation of matrices associated with a prescribed combined matrix and the invariance properties under diagonal equivalence, the problem is reduced to a structured parametric form that allows a complete algebraic characterization. Special attention is given to perturbations involving the (1, 1) entry and one additional diagonal entry, leading to explicit bounds on the perturbation parameters that guarantee stabilization. The results extend previous papers on diagonal perturbations of combined matrices and provide a constructive method for stabilizing MIMO systems without altering their interaction pattern. Numerical examples illustrate the applicability of the proposed approach. Full article

The scheduling of crude oil operations in marine refineries is a complex combinatorial problem, exacerbated by stochastic disruptions like vessel delays and port congestion. Traditional deterministic and expected-value approaches fail to mitigate high-impact tail events, causing severe demurrage and production bottlenecks. To address this, we propose a novel CVaR-Driven Reactive GRASP Simheuristic. This framework hybridizes GRASP with Monte Carlo simulation, embedding Conditional Value-at-Risk (CVaR) into the adaptive memory to actively steer the search away from catastrophic logistical gridlocks. Overcoming standard “unlimited port capacity” assumptions, the model endogenously calculates demurrage dynamics and introduces an automated Failure Taxonomy for explainable insights. Evaluated on a 30-day industrial case study, representing a standard short-term operational scheduling horizon, under baseline conditions and severe dynamic disruptions (vessel delays, unit maintenance), the diagnostic reveals that over 80% of scheduling failures stem from endogenous port congestion rather than internal dead-ends. Furthermore, a comprehensive ablation study mathematically validates the superiority of the CVaR-driven memory over standard expected-cost optimization in preventing catastrophic tail-risk scenarios. Results demonstrate that this CVaR-driven approach effectively absorbs stochastic shocks, prevents stockouts, and minimizes worst-case costs, generating highly robust schedules in under three minutes. Ultimately, it provides a robust, risk-aware Decision Support System (DSS) for supply chain and operations managers. Full article

Simulation optimization has been used to analyze construction operations and support planning decisions under uncertainty. It enables the identification of effective planning strategies throughout a project’s lifecycle. However, the use of stochastic simulation to evaluate alternative strategies results in higher computational demands and the generation of inferior solutions within the resulting optimal solutions. This study examines the feasibility of overcoming these issues by implementing variance reduction techniques into a discrete-event simulation optimization framework. Three variance reduction techniques are evaluated in a case study: Common Random Numbers, Antithetic Variates, and a combined application of both. While these techniques are well established in simulation, their impact on the optimization performance of construction problems has not been fully explored. The results show that VRT not only reduces the computational effort required to evaluate planning strategies but also provides better planning strategies. Among the evaluated techniques, the combined approach demonstrates the best improvements. Overall, the study highlights that variance reduction techniques can make simulation optimization frameworks more practical and reliable for complex construction projects. Full article