Search Results (7,633)

During the high-temperature deposition of tungsten thin films on alumina ceramic substrates, the inherent mismatch in thermal expansion coefficients frequently triggers interfacial delamination, where uncontrollable factors in stochastic surface topographies can exacerbate localized stress concentrations. To resolve these interfacial failures, the enhancement of interfacial adhesion through a deterministic surface microgroove design is identified as the general objective of the present research. Within this framework, the establishment of a robust quantitative mapping between the transverse scratching offset distances and the resultant periodic microgeometry is first pursued as a specific experimental objective. This methodological approach effectively transforms the stochastic nature of the substrate into deterministic geometric configurations. Second, a specific numerical objective is fulfilled by evaluating the interfacial stress redistribution and damage evolution utilizing refined thermomechanical coupled simulations based on the cohesive zone model. The integrated findings demonstrate that optimizing the microgroove spacing effectively governs the morphological transition and broadens stress diffusion pathways to mitigate thermal mismatch effects. Specifically, the structural optimization at a spacing of 28.8 μm facilitates an approximately 31.8% reduction in the maximum interfacial stress and a 10% decrease in the average film stress compared to the 13.6 μm spacing. Finally, this research clarifies the underlying mechanisms of stress buffering and provides a rigorous engineering methodology for the structural design of reliable high-performance ceramic–metal interfaces in extreme environments. Full article

This paper provides a comprehensive analysis of the Optimal Reactive Power Dispatch (ORPD) problem, focusing on its mathematical formulations and the methodologies employed to solve it. This paper systematically categorizes the problem into single-objective and multi-objective formulations, as well as single-period and multi-period models, and addresses both single-area and multi-area operational frameworks. It explores a broad range of optimization techniques used to tackle the ORPD problem, including classical optimization methods, metaheuristic algorithms, and hybrid approaches. Additionally, this paper discusses the incorporation of uncertainty in ORPD models, highlighting methods to account for the stochastic nature of power systems. A critical assessment of the current literature identifies existing knowledge gaps and outlines promising future research directions. This paper aims to provide researchers with a thorough understanding of the ORPD problem, offering insights into emerging trends and areas for further exploration. Full article

The stochastic and intermittent characteristics of renewable energy pose significant challenges to energy utilization and power system stability. The reversible solid oxide cell (RSOC), as an emerging multi-energy conversion technology, exhibits high efficiency in both electrolysis and power generation modes, offering a promising solution to renewable energy integration and energy supply issues. However, RSOC performance degrades over time, and its average efficiency decay rate directly influences capacity investment decisions and day-ahead scheduling strategies. To address this, a comprehensive energy system model considering RSOC capacity is developed, with a detailed representation of each subsystem. A bi-level optimization framework is then proposed, where the upper level minimizes system investment and operation costs, and the lower level optimizes day-ahead scheduling costs. The model explicitly accounts for RSOC efficiency degradation and lifetime attenuation. Particle swarm optimization is applied to determine the optimal capacity configuration. Case studies demonstrate that the proposed model enhances system economics, promotes multi-energy complementarity, and prolongs RSOC lifetime, providing theoretical and technical support for the planning and operation of integrated energy systems with RSOC. Full article

Decentralized microgrids have been proven to enable socioeconomic growth in developing countries. However, they are long-lasting investments whose profitability is highly uncertain due to unstable local socioeconomic contexts, which may delay the breakeven point, if ever reachable. Over the long term, capacity expansion and non-linear degradation of components also arise. Moreover, policymakers and developers are increasingly focusing on environmental and social considerations, raising the complexity of project development. Accordingly, multi-year planning has been simplified by addressing single challenges independently. In this paper, we propose a comprehensive procedure to efficiently solve stochastic multi-year problems for off-grid microgrids in developing countries, including capacity expansion and the non-linear degradation of battery and renewable assets. The novel procedure combines the efficient A-AUGMECON2 methodology for multi-objective formulation, the iterative decomposition of the non-linearities of the battery, and the inclusion of a two-step capacity expansion. A case study developed for Soroti, Uganda shows that the proposed model is suitable for planning purposes, with savings even beyond 20%. The Pareto frontier highlights the trade-offs among the net present cost, total emissions, and land use, which can support policy and business decision-making under uncertainty. The methodology renders these complex modeling challenges solvable and is scalable to energy system applications. Full article

This paper reviews recent work on dynamic virtual power plants (DVPPs) using an Energy–Information–Market framework. It addresses the important problem of how DVPPs can support low-inertia power system operation and feeder-level stability under high renewable penetration. First, system-level studies on low-inertia operation and frequency control are used to frame quantitative requirements on rate of change of frequency, nadir, and quasi-steady-state limits. Second, energy-layer models are surveyed, including participation-factor-based DVPP controllers, grid-forming architectures, model-free frequency regulation, and robust frequency-constrained scheduling for allocating virtual inertia and fast frequency response (FFR) across distributed energy resource fleets. Third, information-layer and market-layer models are reviewed, covering stochastic and robust bidding, distribution locational marginal price-based clearing, peer-to-peer and community markets, privacy-preserving coordination, and emerging governance and cybersecurity schemes for DVPP participation. Across these strands, much of the literature remains centred on steady-state active and reactive power dispatch, with dynamic security enforced as constraints rather than formulated as verifiable and tradable services. This review identifies gaps in dynamic metrics and benchmarks, forecasting of available inertia and FFR capacity, market-physics co-design, multi-aggregator interaction, and experimentally validated DVPP implementations. These findings suggest that DVPPs can “sell stability” at the feeder level only through co-designed control, information, and market mechanisms and outline a research roadmap for this purpose. Full article

The mean annual change in aboveground biomass (ΔAGB) is a pivotal indicator for assessing forest carbon cycle dynamics. This study analyzed 791 independent Abies Mill. forest patches across China to elucidate their driving mechanisms by integrating abiotic, anthropogenic, and biotic factors. We employed a spatially explicit framework, including spatial error regression and structural equation modeling (SEM), to account for significant spatial autocorrelation (Moran’s I = 0.375, p < 0.001). Our results show that abiotic factors predominantly dictate ΔAGB, with soil fertility (pH and Total Nitrogen), elevation (DEM), and soil physical properties (Coarse Fragments and Thickness) explaining the majority of deterministic variance. This relatively low explanatory variance (marginal R² = 0.09) likely reflects the high environmental stochasticity inherent in alpine ecosystems. Specifically, soil fertility exerted the strongest positive influence (Std. Estimate = 0.33), while elevation and soil physical constraints were the primary limiting factors. Biotic factors (Stand Age, Height, and Tree Cover) played a subordinate role, contributing only a marginal 2% gain in explained variance (increasing marginal R² from 0.07 to 0.09). Path analysis revealed an “environmental filtering” hierarchy where abiotic factors shape stand structure, which in turn has limited impact on growth dynamics. These findings underscore that carbon management in alpine forests should prioritize habitat quality conservation over simple biotic structural manipulation. Full article

Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations. This study proposes a “Frugal AI” distributed multi-GPU framework that optimizes hyperparameters via a stochastic simplex-based search coupled with five-fold cross-validation. Utilizing three low-cost NVIDIA GTX 1050 Ti GPUs, the framework performs parallel candidate exploration with an asynchronous model-level exchange mechanism to escape local optima without the overhead of gradient synchronization. Seven CNN backbones—VGG16, VGG19, GoogLeNet, MobileNetV2, ResNet18, ResNet50, and ResNet101—were evaluated within YOLOv2 and Faster R-CNN detectors. To address memory constraints (4 GB VRAM), YOLOv2 was selected for extensive benchmarking. Performance was measured using a harmonic precision–recall-based cost metric to strictly penalize imbalanced outcomes. Experimental results demonstrate that under identical wall-clock time budgets, the proposed framework achieves an average 1.38% reduction in aggregated cost across all models, with the highly sensitive VGG19 backbone showing a 4.00% improvement. Benchmarking against Bayesian optimization, genetic algorithms, and random search confirms that our method achieves superior optimization quality with statistical significance ($p < 0.05$). Under a rigorous IoU = 0.75 threshold, the optimized models consistently yielded F1-scores 0.8444 $\pm$ 0.0346. Ablation studies further validate that the collaborative model exchange is essential for accelerating convergence in rugged loss landscapes. This research offers a practical, scalable, and cost-efficient solution for deploying robust AI surveillance in resource-constrained smart city infrastructure. Full article

While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding—an AI-assisted development and analysis methodology that allows for the `encoding’ and interpretation of the dynamic `vibe’ or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed–Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures. Full article

This paper develops a probabilistic Available Transfer Capability (ATC) framework that quantifies export headroom for renewables across transmission-distribution interfaces under time-varying uncertainty. Static transmission reliability margins can unnecessarily curtail exports. A dynamic transmission reliability margin (TRM) is embedded within ATC using rolling window statistics and adaptive confidence factor scheduling to release capacity in calm periods and tighten margins during volatile transitions. Uncertainty is modeled as net nodal power imbalance variability from load and renewable deviations, together with stochastic thermal limit fluctuations. Correlated multivariate scenarios are generated via Latin Hypercube Sampling with Iman-Conover correlation preservation and propagated through full AC power flow analysis. Validation on the IEEE 39-bus system and New Zealand’s HVDC inter-island corridor recovers 93.31 MW of usable transfer capacity on the IEEE system relative to the pooled Monte Carlo P95 constant-margin baseline, with 78.11 MW attributable to rolling window volatility tracking and 15.20 MW to adaptive confidence factor scheduling, and 59.51 MW (+7.6%) on the New Zealand corridor relative to the corresponding pooled Monte Carlo P95 baseline, with the gain arising primarily from rolling window volatility tracking. Relative to a 95% one-sided reliability target, achieved coverage is 93.9% for IEEE and 91.8% for New Zealand, translating into increased export headroom and reduced curtailment. Full article

Background: Global water scarcity, intensified by climate change, demands optimization of irrigation systems consuming 70% of freshwater resources. Despite significant investments in modernizing irrigation infrastructure from open channels to pressurized networks, performance often falls below expectations. Objective: This study develops an integrated diagnostic and simulation framework for evaluating and improving large-scale pressurized irrigation systems by adapting the Mapping System and Services for Pressurized Irrigation (MASSPRES) methodology. Methods: The framework integrates three components: (1) demand flow dynamics determination using stochastic modelling; (2) hydraulic performance simulation incorporating multiple flow regimes; and (3) performance analysis using relative pressure deficit and reliability indicators. The methodology combines deterministic soil water balance calculations with stochastic farmer behaviour modelling. Results: Application to the Sinistra Ofanto irrigation scheme revealed localized pressure deficits during peak demand periods. The rehabilitation strategy restored full hydraulic feasibility of the network, increasing the proportion of hydraulically satisfied operating configurations from 62% to 100% under peak demand conditions and ensuring adequate pressure at all 317 hydrants across the system. Conclusions: The methodology provides robust decision support for cost-effective rehabilitation, ensuring reliable water delivery while promoting water-energy efficiency. Full article

A novel flexible single-parameter polynomial distribution is presented in this study. The forms of hazard rate and density functions are examined. Additionally, exact formulas for a number of numerical characteristics of distributions are obtained. Stochastic ordering, the moment technique, the maximum likelihood, and a Bayesian analysis of this novel distribution based on type II censored data are used to derive the extreme order statistics. We construct Bayes estimators and the associated posterior risks using a variety of loss functions, such as the generalized quadratic, entropy, and Linex functions. Since tractable analytical formulations of these estimators are unattainable, we suggest using a simulation technique based on Markov chain Monte-Carlo (MCMC) to examine their performance. Furthermore, we construct maximum likelihood estimators given initial values for the model’s parameters. Additionally, we use integrated mean square error and Pitman’s proximity criteria to compare their performance with that of the Bayesian estimators. Lastly, we apply the new family to many real-world datasets to show its versatility, and we model cancer survival data using this new distribution to explain our methodology. Full article

Tourist congestion and load imbalance during peak periods pose critical challenges to the safe operation and experience assurance of large scenic areas. To address the limitations of traditional management approaches in capturing the dynamic and stochastic nature of tourist flows, this study develops a dynamic visitor guidance modeling and analysis framework based on a Timed Petri Net. The proposed model provides a formal representation of tourist movements, scenic spot load evolution, and guidance decision mechanisms within a scenic area. Under unified parameter settings and controlled random conditions, multiple visitor guidance strategies with different information coverage scopes are designed, and minute-level simulation experiments are conducted using the Huangshan Scenic Area as a case study. The simulation results show that, compared with unguided tourist flows, the proposed strategies significantly reduce average load levels, alleviate spatial load imbalance, and enhance TS. Using mean–standard deviation analysis, distributional analysis, and dynamic evolution analysis, differences among guidance strategies in terms of load control, visitor experience, and operational stability are systematically evaluated. Furthermore, a quantitative relationship model between tourist satisfaction and scenic area load is constructed, revealing a consistent inverted-U pattern. Robustness tests under multiple random seeds indicate that the main conclusions are not sensitive to specific stochastic realizations. Overall, the simulation results suggest that dynamic visitor guidance may improve load control, visitor experience, and system stability by optimizing the spatiotemporal distribution of tourist flows, thereby providing simulation-based quantitative insights for peak-period management in large scenic areas. Full article

This review presents a comprehensive study of adaptive control techniques for nonlinear systems influenced by complex nonlinearities and system faults. Nonlinear systems are categorized into general, stochastic, and switched classes, with a focus on their modeling and control challenges. Common nonlinearities such as input saturation, dead-zone, and backlash-like hysteresis, along with actuator and sensor faults, are examined due to their critical impact on system performance. Fuzzy logic systems and neural networks are explored as effective function approximators capable of handling system uncertainties and complex dynamics. Their design methodologies, advantages, and implementation issues are discussed in detail. The review also highlights recent developments in fault-tolerant adaptive control using these intelligent approximators. Finally, the paper outlines open challenges and future research directions, including the integration of adaptive learning frameworks with real-time control and enhanced fault detection strategies for practical nonlinear systems. Full article

Temperature and nutrient availability are pivotal drivers of coastal phytoplankton dynamics; however, how they regulate the interplay between community assembly and ecological network stability remains less explored. In this study, we integrated 18S rRNA high-throughput sequencing with molecular ecological network analysis and the iCAMP model to investigate the seasonal succession and driving mechanisms of phytoplankton in a coastal region (Qiongdong) of the South China Sea. Our results suggest that water temperature is a key factor influencing community succession. However, rather than following a linear response to temperature rise, the molecular ecological network exhibited a significant network contraction in spring, characterized by minimized complexity and peak vulnerability. This structural shift coincided with a transition in nutrient limitation (from phosphorus to nitrogen) induced by spring upwelling. Assembly process analysis revealed that while stochastic processes dominated overall community construction, a notable increase in dispersal limitation occurred in spring. The intensification of dispersal limitation driven by changes in the nutritional structure may be the main cause of network simplification and reduced stability. In conclusion, our findings highlight that while temperature affects the seasonal replacement of phytoplankton species, nutrient-induced shifts in assembly mechanisms degrade ecological network integrity in coastal environments. Full article

Microbial communities are the fundamental determinants of Nongxiang Daqu quality. In this study, we systematically investigated the assembly and succession mechanisms of microbial communities during Nongxiang Daqu fermentation. Our findings reveal that this ecological succession is primarily driven by deterministic processes, encompassing dynamic environmental variables and interspecific microbial interactions. Significant stage-specific temporal variations in the community structure were observed, and biomarkers identified via a random forest model further corroborated these dynamic successional patterns. Both the neutral community model and Modified Stochasticity Ratio (MST) tests demonstrated that community assembly is dominated by deterministic processes, the influence of which intensifies as fermentation progresses. Notably, the fungal community exhibited a more pronounced response to these deterministic environmental selections than the bacterial community. Furthermore, co-occurrence network analysis, Mantel tests, and redundancy analysis (RDA) collectively illustrated that microbial interactions and environmental factors—specifically temperature, humidity, oxygen, carbon dioxide, and acidity—synergistically regulate this succession. Crucially, the rates of change in these environmental parameters directly dictated the pace of microbial turnover. Among these, oxygen and acidity had the greatest influence: oxygen accounted for 17.32% and 29.05% of the effects on fungi and bacteria, respectively, while acidity accounted for 16.77% and 25.23%, respectively. Time-series forecasting indicated that community structural assembly and stabilization predominantly conclude within the initial 30 days of fermentation. Ultimately, this study uncovers the ecological driving forces shaping the Nongxiang Daqu microbiome, providing a vital theoretical foundation for the targeted regulation of Daqu microecology and the enhancement of product quality. Full article