Search Results (801)

Corrosion, combined with cyclic loading, is inevitable and becomes a challenging problem, even when inherently corrosion-protected materials have been selected and applied based on established in-house experience. Aero engine mount structures are exposed to dusty and salty environmental conditions during both operational and non-operational periods. It is becoming tough to predict the remaining useful corrosion fatigue life due to the unascertainable material strength degradations under service conditions. As such, a rationalized approach is currently being used to assess their structural integrity, which produces more wastages of the flying parts. This paper presents a novel approach for predicting corrosion fatigue by proposing a random-parameter model in combination with validated experimental data. The two-random-parameter model is employed here with the probability method to determine the time-independent corrosion fatigue life of a magnesium structural casting, which is used heavily in engine front-mount aircraft systems. This is also correlated with experimental data from the literature, validating the proposed stochastic corrosion fatigue model that addresses the technical variances that occur during service to increase optimal mount structure usage using an automated data system. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

Understanding the processes that drive microbial community assembly is a fundamental question in ecology, with important implications for predicting community responses to environmental disturbances. River ecosystems are under growing pressure from human disturbances, jeopardizing their ecological functions. Here, we investigated bacterial and protistan communities along the Yujiang River using environmental DNA metabarcoding. Bacterial communities exhibited significantly greater alpha diversity and broader habitat niches compared to protists. Additionally, a negative correlation was found between alpha diversity and niche breadth for both groups. Protistan communities exhibited significantly higher beta diversity (Bray–Curtis distance) than bacterial communities, with species turnover being the principal factor driving the variations in both communities. Null model results indicated that heterogeneous selection primarily structured bacterial communities, while stochastic processes (drift) mainly governed protist communities. Redundancy analysis and Mantel tests showed significant associations between environmental factors (e.g., temperature and pH) and bacterial community composition. Moreover, the longitude of sampling sites was linked to spatial variations in both bacterial and protistan communities. Further analyses, including distance-decay patterns, variation partitioning, and multiple regression on distance matrices, demonstrated that bacterial communities were driven by both environmental and spatial factors, while protist communities exhibited a stronger response to spatial factors. These results enhance our understanding of microbial community assembly in river ecosystems and provide valuable insights for the conservation and sustainable management of freshwater systems. Full article

Various approaches have been explored to address the path planning problem for mobile robots. However, it remains a significant challenge, particularly in environments where a multi-tasking mobile robot operates alongside stochastically moving humans. This paper focuses on path planning for a mobile robot executing multiple pickup and delivery tasks in an environment shared with humans. To plan a safe path and achieve high task success rate, a Reinforcement Learning (RL)-based double layer controller is proposed in which a double-layer learning algorithm is developed. The high-level layer integrates a Finite-State Automaton (FSA) with RL to perform global strategy learning and task-level decision-making. The low-level layer handles local path planning by incorporating a Markov Decision Process (MDP) that accounts for environmental uncertainties. We verify the proposed double layer algorithm under different configurations and evaluate its performance based on several metrics, including task success rate, reward, etc. The proposed method outperforms conventional RL in terms of reward (+63.1%) and task success rate (+113.0%). The simulation results demonstrate the effectiveness of the proposed algorithm in solving path planning problem with stochastic human uncertainties. Full article

This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing. Full article

Integrated agriculture–aquaculture (IAA), represented by integrated rice–fish farming, offers a sustainable production method that addresses global food issues and ensures food security. Partitioned recirculating renovation based on traditional integrated rice–fish farming is an effective way to facilitate the convenient harvesting of aquatic products and avoid difficulties associated with mechanical operations. To elucidate the impact of partitioned recirculating renovation on the bacterial communities within paddy field ecosystems, we investigated the soil environmental conditions and soil bacterial communities within integrated rice–fish farming, comparing those with and without partitioned recirculating renovations. The findings indicated a significant reduction in the bacterial community richness within paddy soil in the ditch (fish farming area), along with noticeable changes in the relative proportions of the predominant bacterial phyla in both the ditch and the rice cultivation area following the implementation of partitioned recirculating renovation. In both the ditch and the rice cultivation area, partitioned recirculating renovation diminished the edges and nodes in the co-occurrence networks for soil bacterial communities and considerably lowered the robustness index, negatively impacting the stability of bacterial communities in paddy soil. Simultaneously, the partitioned recirculating renovation substantially influenced the bacterial community assembly process, enhancing the relative contributions of stochastic processes such as dispersal limitation, drift, and homogenizing dispersal. In addition, partitioned recirculating renovation significantly altered the soil environmental conditions in both the ditch and the rice cultivation area, with environmental factors being markedly correlated with the soil bacterial community, especially the total nitrogen (TN) and total phosphorus (TP), which emerged as the primary environmental drivers influencing the soil bacterial community. Overall, these results elucidated the ecological impacts of partitioned recirculating renovation on the paddy soil from a microbiomic perspective, providing a microbial basis for optimizing partitioned rice–fish systems. Full article

With the growing deployment of latency-sensitive applications, the Age of Information (AoI) has emerged as a key performance metric for the evaluation of data freshness in networked systems. While prior studies have extensively explored the AoI under centralized scheduling or random-access protocols such as carrier sense multiple access (CSMA) and ALOHA, most assume that sources generate independent information. However, in practical scenarios such as environmental monitoring and visual sensing, information correlation frequently exists among correlated sources, providing new opportunities to enhance network timeliness. In this paper, we propose a novel analytical framework that captures the interplay between CSMA channel contention and spatial information correlation among sources. By leveraging the stochastic hybrid systems (SHS) methodology, we jointly model random backoff behavior, medium access collisions, and correlated updates in a scalable and mathematically tractable manner. We derive closed-form expressions for the average AoI under general correlation structures and further propose a lightweight estimation approach for scenarios where the correlation matrix is partially known or unknown. To our knowledge, this is the first work that integrates correlation-aware modeling into AoI analysis under distributed CSMA protocols. Extensive simulations confirm the accuracy of the theoretical results and demonstrate that exploiting information redundancy can significantly reduce the AoI, particularly under high node densities and constrained sampling budgets. These findings offer practical guidance for the design of efficient and timely data acquisition strategies in dense or energy-constrained Internet of Things (IoT) networks. Full article

This article discusses and compares approaches to the visualization of the danger zone formed as a result of spreading toxic substances during a fire at an industrial enterprise, to create predictive models and scenarios for evacuation and environmental protection measures. The purpose of this study is to analyze the features and conditions for the application of algorithms for predicting the spread of a danger zone, based on the Gauss equation and the probabilistic algorithm of a cellular automaton. The research is also aimed at the analysis of the consequences of a fire at an industrial enterprise, taking into account natural and climatic conditions, the development of the area, and the scale of the fire. The subject of this study is the development of software and algorithmic support for the visualization of the danger zone and analysis of the consequences of a fire, which can be confirmed by comparing a computational experiment and actual measurements of toxic substance concentrations. The main research methods include a Gaussian model and probabilistic, frontal, and empirical cellular automation. The results of the study represent the development of algorithms for a cellular automation model for the visual forecasting of a dangerous zone. They are characterized by taking into consideration the rules for filling the dispersion ellipse, as well as determining the effects of interaction with obstacles, which allows for a more accurate mathematical description of the spread of a cloud of toxic combustion products in densely built-up areas. Since the main problems of the cellular automation approach to modeling the dispersion of pollutants are the problems of speed and numerical diffusion, in this article the frontal cellular automation algorithm with a 16-point neighborhood pattern is used, which takes into account the features of the calculation scheme for finding the shortest path. Software and algorithmic support for an integrated system for the visualization and analysis of fire consequences at an industrial enterprise has been developed; the efficiency of the system has been confirmed by computational analysis and actual measurement. It has been shown that the future development of the visualization of dangerous zones during fires is associated with the integration of the Bayesian approach and stochastic forecasting algorithms based on Markov chains into the simulation model of a dangerous zone for the efficient assessment of uncertainties associated with complex atmospheric processes. Full article

This study presents a stochastic SVIR epidemic model in which disease transmission rates fluctuate randomly over time, driven by independent, mean-reverting processes with multiplicative noise. These dynamics capture environmental variability and behavioral changes affecting disease spread. We derive analytical expressions for the conditional moments of the transmission rates and establish the existence of their stationary distributions under broad conditions. By averaging over these distributions, we define a stationary effective reproduction number that enables a probabilistic classification of outbreak scenarios. Specifically, we estimate the likelihood of disease persistence or extinction based on transmission uncertainty. Sensitivity analyses reveal that the shape and intensity of transmission variability play a decisive role in epidemic outcomes. Monte Carlo simulations validate our theoretical findings, showing strong agreement between empirical distributions and theoretical predictions. Our results underscore how randomness in disease transmission can fundamentally alter epidemic trajectories, offering a robust mathematical framework for risk assessment under uncertainty. Full article

Since power demand varies due to uncertain environmental conditions, a deterministic power control strategy for hybrid electric propulsion ships contains a limitation in securing robust performance. To overcome this limitation, this study applies a stochastic power control strategy based on the augmented operational dataset. This study generated 150 datasets and derived the optimal control strategy set using a dynamic programming algorithm. By synthesizing a set of optimal control strategies, we divided them into a total of 10 bins according to the battery state of charge (SOC) and implemented a probabilistic map for the power distribution ratio according to the demanded power in each bin. Additionally, the memory and SOC correction factor were utilized to prevent frequent changes in power control and ensure that the SOC remains stable. This strategy resulted in a 3% improvement in efficiency compared to the deterministic method. In addition, it can be implemented in a real-time strategy utilizing stochastic maps. Full article

The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt filtering parameters, predict signal reliability, and change the processing mode depending on the level of interference. In conditions of signal loss, noise, and unstable measurement reliability, traditional methods do not provide stable and accurate tracking, especially at the initial stages of radar operation. To address this issue, an intelligent method is proposed that integrates a probabilistic graphical evaluation and review technique (GERT) model, a recursive Kalman filter, and a measurement reliability prediction module based on a long short-term memory (LSTM) neural network. The proposed approach allows for the real-time adaptation of filtering parameters, fusion of local and global trajectory estimates, and dynamic switching between tracking modes depending on the environmental conditions. The dynamic weighting algorithm between model estimates ensures a balance between accuracy and robustness. Simulation experiments confirmed the effectiveness of the method: the root mean square error (RMSE) of coordinate estimation was reduced by 25%; the probability of tracking loss decreased by half (from 0.2 to 0.1); and the accuracy of loss prediction exceeded 85%. The novelty of the approach lies in integrating stochastic modeling, machine learning, and classical filtering into a unified adaptive loop. The proposed system can be adapted to various types of radar and easily scaled to multi-sensor architectures. This makes it suitable for practical implementation in both defense and civilian air object detection systems operating under complex conditions. Full article

The Qinghai–Tibet Plateau is a key region for biodiversity conservation, where alpine grasslands are ecologically important. While previous studies have mainly addressed vegetation, ecosystem processes, and soil microbes, phyllosphere microorganisms are essential for nutrient cycling, plant health, and stress tolerance. However, their communities remain poorly understood compared to those in soil. The relative influence of host identity and environmental conditions on shaping phyllosphere microbial diversity and community assembly remains uncertain. In this study, we characterized phyllosphere bacterial and fungal communities of the phyllosphere at two alpine steppe sites with similar vegetation but climatic conditions: the Qilian Mountains (QLM) and the Qinghai Lake region (LQS). At both sites, Cyanobacteriota and Ascomycota were the predominant bacterial and fungal taxa, respectively. Microbial α-diversity did not differ significantly between the two regions, implying that host-associated mechanisms may stabilize within-site diversity. In contrast, β-diversity exhibited clear spatial differentiation. In QLM, bacterial β-diversity was significantly correlated with mean annual precipitation, while fungal α- and β-diversity were associated with soil nutrient levels (including nitrate, ammonium, available potassium, and phosphorus) and vegetation coverage. At LQS, the β-diversity of both bacterial and fungal communities was strongly influenced by soil electrical conductivity, and fungal communities were further shaped by vegetation cover. Community assembly processes were predominantly stochastic at both sites, although deterministic patterns were more pronounced in QLM. Variability in moisture availability contributed to random bacterial assembly at LQS, while increased environmental heterogeneity promoted deterministic assembly in fungal communities. The elevated diversity of microbes and plants in QLM also reinforced deterministic processes. Overall, our findings support a host–environment interaction hypothesis, indicating that host factors primarily govern α-diversity, while climatic and soil-related variables have stronger effects on β-diversity and microbial assembly dynamics. These insights advance our understanding of how phyllosphere microbial communities may respond to environmental change in alpine ecosystems. Full article

The mechanical reliability of metallic alloys under cyclic loading is crucial for optimizing their microstructure–property relationships. Understanding the statistical behavior of fatigue failure data is essential for designing alloys that endure extreme environmental conditions. This study introduces a generalization of the Student’s t-Birnbaum–Saunders distribution to improve the modeling of fatigue life data, which often exhibit heavy tails and are common in advanced alloy systems. Seven different estimation methods are employed to estimate and compare the parameters of the proposed distribution, providing a comprehensive statistical framework for fatigue failure analysis. The goodness-of-fit of the proposed model and its sub-models, along with the joint relative efficiency of parameter estimates, is assessed using real fatigue data within the maximum likelihood framework. Additionally, the robustness of estimation methods is examined through Monte Carlo simulations across various sample sizes and parameter configurations. The results highlight the effectiveness of the generalized Student’s t-Birnbaum–Saunders distribution in capturing the stochastic nature of fatigue failure in metallic alloys, offering valuable insights for materials design and predictive reliability modeling. These findings align with advancements in computational modeling and simulation, contributing to developing alloys with tailored mechanical properties. Full article