Search Results (1,448)

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

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

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations. Full article

Strategic competition in health service markets requires managers to make pricing, marketing, and investment decisions under uncertainty, yet educational programs in healthcare management and dietetics lack experiential tools for teaching these competencies within a game-theoretic framework. This study develops and computationally validates SY142-Game-Theory-1, a computational simulation framework that models strategic competition between two asymmetric healthy living centers as a 36-month repeated Prisoner’s Dilemma, integrating demand decomposition, net present value analysis, employee satisfaction dynamics with burnout thresholds, reputation feedback, and stochastic shock events. The simulation produces a valid and distinctly asymmetric Prisoner’s Dilemma structure in which the established provider faces the classical temptation to defect while the new entrant’s rational incentive aligns with cooperation; Axelrod-style tournaments across 22 strategies (96,800 simulations) identify Forgiving Tit-for-Tat as the top-performing strategy; Monte Carlo validation (n = 1000) confirms a statistically significant cooperation premium of 24.1% over Nash equilibrium; and sensitivity analyses across four parameters demonstrate robustness of all qualitative findings. The open-source framework bridges game theory, simulation-based learning, and health service management education, providing a computationally validated foundation for teaching strategic decision-making in competitive healthcare environments, with empirical evaluation of learning outcomes reserved for future work. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

Accurate object detection in agricultural environments remains challenging due to illumination variation, background clutter, partial occlusion, and overlapping fruits. Conventional object detection methods mainly rely on deterministic data augmentation strategies or feature-level refinement, which often exhibit limited robustness under complex field conditions. To address this issue, this paper proposes a Diffusion-Regularized Object Detection (DROD) framework for robust pineapple target detection in agricultural imagery. The proposed framework introduces a mathematically grounded forward diffusion and diffusion-guided representation mechanism directly in the image domain, where stochastic perturbations are generated through forward diffusion and semantically meaningful image representations are learned via diffusion-guided representation. A unified optimization framework and theoretical analyses of perturbation propagation, Lipschitz stability, and training convergence are further established to provide mathematical support for the proposed method. Extensive experiments were conducted on a self-constructed dataset containing 1600 real-world pineapple images collected under practical agricultural conditions. Comparative evaluations involving YOLOv8-s, YOLOv8-l, traditional data augmentation, and the recent JTA:GAN method demonstrate that the proposed DROD framework consistently achieves the best detection performance in terms of Precision, Recall, mAP@0.5, and mAP@0.5:0.95 while maintaining computational complexity and inference speed comparable to the original YOLOv8 architecture. Furthermore, ablation studies, diffusion parameter sensitivity analysis, visualization analysis, and experimental validation under different perturbation levels consistently verify the effectiveness and robustness of the proposed diffusion mechanism. These results demonstrate that diffusion-based regularization provides an effective and computationally efficient solution for robust agricultural object detection and offers a practical framework for intelligent precision agriculture applications. Full article

Relative radiometric normalization (RRN) is fundamental to multi-temporal remote sensing analysis; however, conventional techniques often struggle with nonlinear distortions, outlier contamination, and heterogeneous land-cover conditions. To address these challenges, we propose a diffusion-based probabilistic framework that models radiometric inconsistency as a combination of deterministic residuals and stochastic perturbations. In this framework, the forward process injects structured noise and stochastic perturbations, while the reverse process restores radiometric consistency through a dual-objective variational formulation. At the core of this framework is a spatial–spectral attention residual network (SSARN), which integrates residual learning with dual attention mechanisms to capture cross-band dependencies and multi-scale spatial context. A preprocessing stage guided by the structural similarity index (SSIM) further enhances robustness by automatically selecting stable pseudo-invariant regions for model training. Comprehensive experiments on multi-temporal Sentinel-2 datasets demonstrate that the proposed method consistently outperforms existing approaches, achieving higher accuracy and enhanced spectral fidelity. Moreover, the framework ensures greater consistency of the normalized difference vegetation index (NDVI) and preserves fine-grained textural details, underscoring its potential as a scalable and resilient solution for large-scale RRN in remote sensing applications. Full article

This paper presents a decentralized adaptive generalized-minimum-variance (GMV) control framework for large-scale stochastic nonlinear systems composed of interconnected multi-input multi-output (MIMO) Hammerstein subsystems with unknown time-varying parameters. Each subsystem consists of a coupled multivariable static nonlinearity represented on a known invertible basis, followed by a matrix-polynomial dynamic block affected by colored noise and delayed input–output interconnections. The proposed scheme estimates only identifiable composite Hammerstein parameters through a decentralized recursive extended least-squares algorithm with forgetting, thereby avoiding the non-unique separation of nonlinear and linear gains. A constructive matrix Diophantine identity is established to derive an optimal multi-step predictor, leading to a GMV control law expressed as a multivariable polynomial equation in the current input. Sufficient conditions for real solvability, mean-square boundedness, and near-optimal adaptive tracking are provided using Hadamard–Lévy global-diffeomorphism, minimum-phase, small-gain, persistent-excitation, strict-positive-realness, and convex-projection arguments, and the implemented controller—inexact Newton solver with fallback and persistent dither—is itself covered by the analysis. The analysis further shows that delayed interconnections become measurable and can be exactly compensated, while robustness to basis under-modeling is explicitly quantified. Simulation results on an interconnected two-subsystem MIMO Hammerstein process with coupled cubic nonlinearities, colored noise, delayed interactions, and time-varying parameters—run in the forgetting-factor regime required by the theory, with measured persistent excitation and complete solver diagnostics—demonstrate operational-noise-floor tracking and a $2.3$-fold mean-RMSE reduction relative to the strongest linear-MIMO surrogate, while a channel-wise SISO Hammerstein design fails structurally and a feedback-linearization controller with exactly known nonlinearity offers no advantage. The study further demonstrates scalability on a chain of four subsystems with size-independent per-subsystem computational cost, validates a physically motivated interconnected coupled-tank network with progressive-valve nonlinearities, and confirms agreement between the observed stability limits and the predicted small-gain boundary. Full article

This study applies the Belief Structure TOPSIS (B-TOPSIS) method to analyse cross-country attitudes toward AI-driven workplace practices across the EU27. The proposed approach preserves the full distribution of survey responses, explicitly incorporates uncertainty, and evaluates alternatives based on their distance from ideal and anti-ideal belief structures. Using data from Special Eurobarometer 554, we construct individual B-TOPSIS indexes for eight AI-related workplace applications and an aggregated B-TOPSIS index capturing overall acceptance. The results reveal systematic cross-country differentiation. Activities such as gathering applicant information, allocating work, and processing employee data generally receive moderate acceptance. Safety-focused applications are widely supported, whereas ethically sensitive practices, such as employee monitoring and automated dismissal, face low acceptance. Additionally, sensitivity analysis based on Monte Carlo simulation and stochastic dominance demonstrates that the obtained rankings remain highly stable under alternative assumptions regarding utility functions, confirming the robustness of the proposed framework. A comparison with rankings derived from total positive responses, commonly used in EU reports, shows that although the two approaches are strongly correlated, they are not interchangeable. By retaining the complete response structure, the proposed method captures differences in response intensity that are obscured by conventional summary measures. The findings highlight the multidimensional and conditional nature of workplace AI acceptance in the EU and demonstrate the value of belief-structure-based approach for analysing survey data. Full article

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

Driven by the global low-carbon transition and the rapid expansion of marine energy development, offshore integrated energy systems are emerging as a critical configuration for coupling offshore renewable resources, geothermal and subsurface thermal resources, oil and gas infrastructure, hydrogen pathways, multi-carrier networks, and offshore loads. Unlike onshore integrated energy systems, offshore systems are constrained by resource intermittency, harsh marine environments, platform space and weight limits, long-distance transmission, operation and maintenance accessibility, safety risks, and cross-regional governance mechanisms. Recent studies have advanced offshore wind-to-hydrogen systems, oil and gas platform electrification, offshore energy hubs, platform repurposing, and offshore geothermal utilization. However, these studies remain fragmented in terms of system boundaries, multi-energy coupling mechanisms, engineering constraints, and optimization methods. This paper reviews offshore integrated energy systems from the perspectives of system configuration, key integration technologies, optimization and assessment methods, and future research needs. Offshore integrated energy systems are first classified into offshore renewable-energy-dominated systems, offshore wind–hydrogen systems, oil and gas platform integrated systems, offshore energy hubs and multi-carrier networks, decommissioned-platform repurposing systems, and offshore geothermal and repurposed-well systems. Resource-side, conversion-side, storage-side, network-side, and load-side integration technologies are then summarized. Capacity configuration, operational scheduling, stochastic and robust optimization, multi-objective optimization, energy, exergy, economic, and environmental (4E) assessment, advanced exergy analysis, and energy-hub modelling are further reviewed. Finally, key research gaps are identified, including resource uncertainty, offshore engineering constraints, multi-carrier network coupling, insufficient demonstration data, and policy and economic uncertainty. This review provides a structured reference for the modelling, integration, optimization, and demonstration of offshore integrated energy systems for low-carbon transition. Full article

The new type of multi-airbag hybrid airship is a novel lighter-than-air platform, but its flexible structures pose challenges for accurate modal parameter identification under complex fluid-structure interaction. Traditional methods often fail to capture the dynamic characteristics of such compliant systems. In this paper, a stochastic subspace identification method is proposed to estimate the modal parameters of the three capsule hybrid airship. The method constructs the Hankel matrix using only output response data and extracts the system matrix by singular value decomposition so as to identify the natural frequency and damping coefficient. Moreover, the numerical model of the airship (aspect ratio 2.22) is built, and the simulated response data (first five modes) are used to validate the approach. The results show that the identified frequencies and damping ratios match the theoretical values with a maximum error of 6.35%, demonstrating good accuracy and robustness. The proposed technique can provide a reliable tool for online modal identification of flexible airships, supporting structural health monitoring and vibration control. Full article

The aging of social infrastructure, intensively constructed during periods of rapid economic growth, is a pressing challenge facing modern society. Conventional infrastructure asset management has disproportionately emphasized a “managerial financial perspective,” aiming to maintain physical functions within limited budgets. However, the malfunction of road appurtenances such as tunnel lighting facilities induces severe traffic accidents and chronic congestion, resulting in public health risks for users (physical trauma, psychological stress, and the deterioration of Disability-Adjusted Life Years: DALYs) as well as massive socio-economic losses. The primary novelty of this study lies in bridging the gap between stochastic engineering deterioration models—specifically, discrete-time Markov chain models predicting physical degradation—and socio-economic stakeholder value chains. This study constructs a “Social Life Cycle Cost (LCC) Optimization Model” that directly incorporates these social losses and stakeholder risk disparities into the evaluation function, addressing the limitations of conventional financial-centric LCC models. By conducting robust uncertainty and global sensitivity analyses via large-scale Markov Chain Monte Carlo simulations (number of trials $N={10}^5$), we reveal that a corrective maintenance strategy inheres a critical “fat-tail risk” of stochastically incurring catastrophic social losses. Conversely, preventive intervention at State C minimizes the expected total cost with statistical significance ($p<0.001$) and drastically decouples engineering costs from social risks. This research provides quantitative evidence that early infrastructure intervention functions as an indispensable “social investment” for mitigating public health risks under the specific parameters of the proposed model. Full article

The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics. Full article