Search Results (29,981)

This paper studies a novel nonlinear fractional-order financial stress model involving Atangana–Baleanu–Caputo (ABC) operators. It focuses on memory effects that are both constant and variable. The novelty of the proposed framework lies in combining multiple interconnected channels of systemic stress into one fractional dynamical model and looks at how they change over time and how they respond to sustained external perturbations. Theoretically, we prove well-posedness results and study the equilibrium structure and stability of the given model. On the computational side, we use numerical simulations of the individual stress components and an aggregate systemic stress index to look into short-term dynamics under different memory regimes. We also include a shock-response analysis to show how memory effects change the way stress builds up, relaxes, and spreads when forced. The sensitivity analysis shows that systemic stress is amplified by the forcing and interaction parameters and reduced by the damping parameters. These findings demonstrate that the model provides a new and effective tool for studying systemic financial instability in a fractional setting. Full article

In robotic grinding of complex curved surfaces, the low stiffness of serial robots causes tool tip deflection and degrades surface quality. The axial symmetry of grinding discs introduces a free rotational parameter at each waypoint, converting a standard 6-DOF robot into a functionally redundant system. However, this redundancy has not been systematically exploited for stiffness optimization along the trajectory. This paper proposes a hierarchical dynamic programming framework to optimize the redundancy angle sequence over the entire grinding trajectory. A kinematic transformation parameterizes the flange target by the redundancy angle, enabling enumeration of feasible candidate configurations over a discretized grid. A composite stiffness index that accounts for the normal, feed, and cross-feed grinding force components is formulated at the contact point. Hierarchical constraint filtering removes configurations that violate posture, singularity, velocity, acceleration, and stiffness constraints. The Viterbi algorithm then recovers the minimum-cost path that balances stiffness performance and joint motion smoothness. Finally, a post-processing step based on a cubic smoothing spline generates $C^2$-continuous joint trajectories. Simulations on a UR5 robot grinding a curved surface evaluate the proposed framework against fixed-angle, greedy, and flange-stiffness baselines. The proposed method improves the mean composite stiffness by 31.7% and 17.9% over the fixed-angle and flange-stiffness baselines, respectively, and reduces the maximum joint jump by two orders of magnitude compared with the greedy strategy. Experimental validation on a UR5 robot confirms that the smoothed trajectory is accurately tracked while the stiffness threshold is preserved. A multi-trajectory analysis further shows that the stiffness threshold is maintained across all grinding trajectories. These results demonstrate the effectiveness of the proposed framework for redundancy optimization in robotic grinding with tool spin symmetry. Full article

Let $U,V\subset \mathbb{H}$ be bounded $C^1$ domains, and let f be quaternion-valued on $U\times V$. We study the mixed Cauchy–Fueter system $D_x^{L}f=0$ and $f{D}_y^R=0$ on product domains by iterating the classical one-variable Borel–Pompeiu formulas in an order consistent with quaternionic multiplication. Under closure regularity on $\overline{U}\times \overline{V}$, we prove an iterated representation formula and show that, in the biregular case, the boundary contribution reduces to the distinguished boundary $\partial U\times \partial V$. This leads to a distinguished boundary transform, ${\mathcal{T}}_{U,V}$, on continuous boundary data. We prove that ${\mathcal{T}}_{U,V}$ maps $C(\partial U\times \partial V;\mathbb{H})$ into $C^{\infty }(U\times V;\mathbb{H})$, establish compact subset estimates for mixed real derivatives, and derive a local approximation theorem within the transform range by finite sums of separated one-variable Cauchy transforms. The analysis is restricted to this representation framework. In particular, the paper does not address a general solvability theory for the mixed inhomogeneous system and does not characterize the full range of ${\mathcal{T}}_{U,V}$. Full article

Machine learning interatomic potentials (MLIPs) are typically developed for globally ordered homogeneous systems (GOHomS), which exhibit only minor local deviations from equilibrium configurations. Consequently, most existing MLIPs trained on GOHomS often perform inadequately when applied to locally ordered heterogeneous systems (LOHetS), e.g., substitutional alloying elements in multicomponent alloys. To describe doping alloy systems, we develop a fine-tuned MLIP based on the MACE foundation model, specifically tailored for Mo-based dilute alloys containing one or two out of 20 substitutional elements: Cr, Fe, Mn, Nb, Re, Ta, Ti, V, W, Y, Zr, Al, Zn, Cu, Ag, Au, Hg, Co, Ni, and Hf. The model is built on more than 7000 equilibrium and non-equilibrium structures derived from first-principles density functional theory (DFT) calculations. The optimized large-scale fine-tuned model attains state-of-the-art accuracy, with a mean absolute error (MAE) and root-mean-square error (RMSE) of 2.27 meV/atom and 3.79 meV/atom for energy predictions, and 13.83 meV/Å and 24.26 meV/Å for force predictions, respectively. Systematic evaluation under different data-splitting protocols shows that unknown element extrapolation remains challenging under strict dopant hold-out, whereas substantially improved accuracy can be achieved in partial-exposure transfer settings. The fine-tuned models reduce the MAE by approximately 7–10 times compared to models trained from scratch, and by 10–20 times relative to zero-shot foundation models. This performance gain remains consistent across varying dataset sizes (equilibrium vs. non-equilibrium structures) and model scales. Our work illustrates the efficacy of transfer learning from globally ordered homogeneous systems to locally ordered heterogeneous multicomponent alloy environments. However, direct transfer to entirely unknown elements remains challenging, especially when proxy embeddings are employed without fine-tuning. Thus, to achieve high accuracy without incurring additional cost, it is essential to include unknown elements in the training dataset while minimizing the number of configurations containing known elements. Moreover, the current findings are primarily validated for dilute Mo-based alloy systems. Extending this approach to more compositionally complex alloy spaces may necessitate additional data and further fine-tuning. Full article

The optimal design of long-span structures is hindered by the combination of prohibitively high computational costs and the limited physical consistency of purely data-driven surrogates. To address this challenge, this study proposes a multi-stage automated design framework that shifts the workflow from repeated per-task solving to reusable digital asset creation. First, a large-scale surrogate-optimized dataset containing 100,000 design samples is generated by embedding a high-speed MLP emulator into a Genetic Algorithm (GA). The core innovation lies in training a physics-regularized neural design generator. By incorporating a reduced-order total potential energy term derived from the principle of minimum potential energy as a regularization constraint, the network learns the mapping from external design conditions to validated near-optimal internal parameter combinations while suppressing mechanically unfavorable configurations associated with low stiffness. This mechanism improves mechanical admissibility, particularly in data-sparse regions. Validation results show that the generator achieves millisecond-level candidate generation and reduces the prediction error to 31% of that of conventional models under sparse-data conditions. In a like-for-like case study with identical external input parameters, the generated candidate design achieves a 21.1% reduction in total steel consumption. The proposed framework is therefore best understood as a rapid preliminary design tool for producing weight-efficient and mechanically admissible candidate schemes, which can then be subjected to subsequent high-fidelity analysis and code-based verification. Full article

Bidirectional power converters generate significant heat losses during high-frequency operation, posing a severe challenge to the performance of heat dissipation systems. Traditional thermal design methods often struggle to balance multiple objectives, such as cooling efficiency, cost, weight, and size, thereby limiting the reliability and safety of the system. To address these challenges, this paper proposes a novel Multi-Objective Exponential Distribution Optimizer algorithm based on the Exponential Distribution Optimizer. Subsequently, key design variables of the heat dissipation system are selected. Next, the Optimal Latin Hypercube Sampling method is employed to generate sample points, and a second-order response surface surrogate model for the heat pipe radiator’s volume and temperature is developed. Lastly, by integrating elite non-dominated sorting, crowding distance mechanisms, and an information feedback mechanism, the multi-objective challenge is decomposed into subproblems, thereby enhancing optimization efficiency. Through comparative simulation experiments on benchmark functions, the Wilcoxon signed-rank test results for the MOEDO algorithm on the majority of the three metrics are denoted as ‘+’, indicating statistically significant advantages over the compared algorithms, thereby demonstrating its superior performance in addressing multi-objective optimization problems. The study further conducts simulation verification of the heat pipe heat dissipation system before and after optimization using ANSYS Icepak. The simulation results demonstrate that, compared with the conventional design, the maximum Insulated Gate Bipolar Transistor (IGBT) temperature is reduced by 17.12% and the heat sink volume is reduced by 14.61%. Full article

Graphene possesses exceptional mechanical strength, high electrical conductivity, and a stable lattice structure, making it an ideal material for sensors in advanced manufacturing. However, these sensors face stability challenges due to complex electromagnetic interference (EMI) environments generated by electrical equipment. Therefore, investigating the influence of EMI on sensor performance is of significant importance. In this study, simulations were performed to analyze electrical parameter perturbations of intrinsic graphene films under EMI conditions. The Magnetic Fields, Solid Mechanics, and Electrostatics modules in COMSOL Multiphysics were employed to construct a coupled model of a three-phase power transformer and a graphene-based pressure sensor. The results indicate that EMI can induce baseline drift on the order of ~5% full scale (FS) in the graphene current density, accompanied by degradation in signal-to-noise ratio (SNR) exceeding ~15 dB under typical simulation conditions. Graphene in direct contact with metal electrodes shows enhanced sensitivity to EMI, with more pronounced noise amplification due to interfacial coupling effects. In contrast, cavity-suspended graphene configurations exhibit relatively improved robustness, suggesting that suspended membrane architectures can mitigate EMI by reducing parasitic coupling and enhancing mechanical isolation. Compared with previous studies, this work highlights the role of multiphysics coupling and membrane suspension in influencing EMI-induced perturbations, providing theoretical guidance for the design of graphene-based sensors in power system and industrial Internet of Things (IoT) applications. Full article

This study addresses the optimal scheduling of a proton exchange membrane fuel cell (PEMFC)-based building combined cooling, heating, and power (CCHP) system, aiming to improve operational efficiency and flexibility under coupled electricity–thermal–cooling demands and load uncertainty. A physics-informed scheduling environment was developed using component models and multi-energy balance constraints, including a PEMFC with waste-heat recovery, a lithium bromide absorption chiller, a reversible heat pump with condenser heat recovery to thermal storage, a battery energy storage system, and a hot-water thermal storage tank. The dispatch problem was formulated as a Markov decision process and solved using deep reinforcement learning with TD3; performance was evaluated on typical summer and winter days, and robustness was tested by generating 100 scenarios with 30% demand perturbations. The results show that TD3 learns coordinated multi-energy dispatch patterns consistent with seasonal operation and reduces hydrogen consumption relative to a rule-based strategy under uncertainty while requiring millisecond-level inference time. Dynamic programming achieved slightly lower hydrogen consumption but incurred orders-of-magnitude higher computation time. Overall, TD3 provides a practical trade-off between near-optimal performance, robustness, and real-time applicability for PEMFC-based building CCHP scheduling. Full article

Surface defect inspection of metallic lock components remains challenging due to strong specular reflections, low-contrast defect patterns, and geometric variability, which limit the consistency of manual inspection and conventional automated optical inspection (AOI) systems. This study presents an integrated visual inspection framework that combines controlled image acquisition with deep learning-based semantic segmentation to enable reliable and repeatable defect detection. A standardized rotational fixture with ring illumination was developed to stabilize imaging geometry, reduce reflection variability, and support consistent multi-view acquisition. A region-of-interest (ROI) masking strategy was further applied to suppress background interference and isolate the effective inspection region. At the algorithmic level, a Transformer-enhanced U-Net (TransU-Net) architecture was employed to jointly model local spatial features and global contextual dependencies, thereby improving boundary delineation and the detection of irregular surface anomalies. In addition, a boundary-aware weighted evaluation scheme was introduced to provide a more robust and application-relevant assessment by accounting for annotation uncertainty near defect edges. Experimental results demonstrate that the proposed method achieved an F1-score of 85.15%, with an average inference time of 0.3357 s per image for model prediction. Considering additional processes such as multi-view image acquisition, mechanical rotation, and preprocessing, the overall system-level inspection time is expected to be on the order of seconds per component in practical deployment. Full article

Network function virtualization (NFV) enables flexible service deployment by implementing network functions as software, with service function chains (SFCs) linking virtual network functions (VNFs) in a specific order to deliver end-to-end services. However, ensuring SFC resilience against large-scale disasters that can disrupt entire disaster zones (DZs) remains a significant challenge. In this paper, we study the multipath disaster-resilient SFC deployment problem, aiming to minimize the total bandwidth and computing resource overhead by jointly optimizing VNF placement, multipath routing, and protection mechanisms, subject to DZ-disjoint constraints. We formulate this problem as a Mixed-Integer Nonlinear Programming (MINLP) model and prove it to be NP-hard. To solve it efficiently, we propose a two-stage adaptive deployment approach; the first stage employs heuristic rules to generate a set of candidate paths satisfying DZ-disjoint constraints, and the second stage leverages deep reinforcement learning to intelligently place VNFs along these candidate paths, approximating the global optimum. Simulation results on real network topologies demonstrate that, compared to traditional dedicated protection strategies and a state-of-the-art exact algorithm, the proposed approach reduces resource overhead by up to 20% while effectively guaranteeing SFC disaster resilience, exhibiting good scalability and online deployment potential. Full article

Constructing rapid aerodynamic shape optimization frameworks based on high-fidelity reduced-order models (ROMs) has become a mainstream solution for alleviating the excessive computational cost of full-order simulation-based design, especially for complex configurations with high-dimensional design spaces. In this study, we propose the concept of transfer optimization, where a low-fidelity, decoupled auxiliary submodule is used to guide the high-fidelity optimization of the full complex system. Building upon our previously proposed reduced-order-model based framework for efficient aerodynamic shape design, a transfer optimization framework is further developed to improve the efficiency of aerodynamic shape design for complex multi-component configurations. A novel auxiliary submodule method is introduced to address the “curse of dimensionality” in sampling over high-dimensional design parameter spaces. By reducing system complexity, this method significantly lowers the cost of individual samples. Based on the transfer optimization assumption, perturbation-based sampling around the low-fidelity solution overcomes the limitations of traditional data augmentation approaches. Moreover, the auxiliary submodule optimization results are used to construct a physically meaningful initial configuration, further accelerating convergence. The framework is validated on two transonic aerodynamic optimization test cases using the three-dimensional undeflected Common Research Model (uCRM) wing–body–tail configuration (with a wing aspect ratio of 9), with and without horizontal tail deflection. Results show that the proposed framework achieves accuracy comparable to full-order optimization while reducing computational cost by up to 69.8%. Compared to traditional ROM-based frameworks, efficiency is further improved by 18.5% and 24.1% in Case 1 and Case 2, respectively. Full article

Inorganic scale formation in oil wells is a major flow assurance challenge, causing production losses, increased intervention costs and reduced operational efficiency. In Brazil, recent discoveries in pre-salt reservoirs have increased the relevance of calcium carbonate (CaCO${}_3$) scaling under high-pressure and high-temperature (HPHT) conditions. Experimental data representative of petroleum environments under such conditions, particularly regarding the influence of CO${}_2$ and flow conditions, remain limited. In this study, a compact pressurized experimental unit was designed and constructed to investigate the dynamic formation, deposition and adhesion of CaCO${}_3$ under conditions close to those encountered in oil production systems. A dedicated experimental methodology was developed to promote controlled mixing of aqueous sodium bicarbonate (NaHCO${}_3$) and calcium chloride (CaCl${}_2$) solutions and CO${}_2$ injection, enabling precise control of pressure, temperature and flow regime. The effects of turbulent flow, expressed by different Reynolds numbers, on the deposited CaCO${}_3$ mass and its adhesion to the substrate were systematically evaluated under controlled conditions of 40 °C and a pressure drop of 15 bar was imposed in the control valve in order to promote the flash of CO${}_2$ and CaCO${}_3$ precipitation. Complementary characterization analyses were performed to assess crystal morphology and adhesion detachment strength. The results provide new experimental insights into CaCO${}_3$ scaling mechanisms under CO${}_2$-rich flowing conditions, contributing to improved understanding of scale adhesion and the development of mitigation strategies for flow assurance in oil and gas operations. Full article

As power-electronic-interfaced renewable generation displaces synchronous machines, modern power systems face coupled day-ahead challenges: net-load variability demands peak shaving, while declining inertia necessitates explicit frequency-regulation scheduling. In sequential security-constrained unit commitment (SCUC) and Security-Constrained Economic Dispatch (SCED), the reserve procured in SCUC may lose deliverability after redispatch because the same storage bandwidth is reassigned to energy service. This paper proposes a two-stage day-ahead framework that addresses both challenges for low-inertia systems with high inverter-based resource (IBR) penetration. Stage I embeds Rate-of-Change of Frequency (RoCoF), frequency nadir, and quasi-steady-state (QSS) constraints in SCUC, with a piecewise-linear outer approximation for the non-convex nadir limit. Stage II strictly inherits the SCUC commitment and reserve reservation, and it applies bandwidth deduction to prevent peak-shaving redispatch from consuming committed frequency reserve. A technology-aware partition further assigns fast-response Lithium Iron Phosphate (LFP) batteries to sub-second frequency support and long-duration Vanadium Redox Flow Batteries (VRFBs) to energy shifting. Evaluated under the adopted reduced-order frequency-response framework and disturbance representation, tests on a modified IEEE 39-bus system under an extreme-wind scenario demonstrate that explicit frequency constraints eliminate all post-contingency violations, the inheritance mechanism closes a 23.85 MW reserve gap after redispatch, and heterogeneous storage partitioning preserves essentially the same disturbance sensitivity while increasing the peak-shaving ratio to 45.85%, lowering the day-ahead cost to CNY $10.483\times {10}^6$ and reducing the average system price to 209.33 CNY/MWh. Full article

Flapping-wing drones (FWDs), owing to their compact size and operation in cluttered and unsteady airflow environments, encounter significant aerodynamic and stability challenges. Studies of avian flight reveal that falcons and other raptors actively deflect their covert feathers to mitigate gusts and maintain stable flight. Drawing inspiration from this mechanism, this study presents a peregrine falcon-inspired Active Flow Control Unit (AFCU) integrated with a Deep Deterministic Policy Gradient (DDPG)-based deep reinforcement learning (DRL) controller for real-time disturbance attenuation. The AFCU employs mechanical covert feathers (MCFs) that actuate to dissipate gust loads during high wind conditions. A reduced-order bond graph model that encapsulates the nonlinear interaction between the primary wing and the feather-based active flow control surfaces is created which is used as a dynamic training environment for the DDPG agent. Utilizing closed-loop interactions, the successfully obtained learned policy produces optimal actuator forces to reduce feather-displacement error and aerodynamic load variations. The designed controller stabilizes the internally unstable open-loop AFCU, attaining near-zero steady-state error and settling times under 1.6 s for gust magnitudes ranging from 12.5 to 20 m/s. Simulations further illustrate a reduction of up to 50% in gust-induced loads compared to traditional approaches. This integration of bio-inspired design with learning-based active flow control offers a viable avenue for the development of highly adaptive and gust-resilient flapping-wing aerial systems. Full article

Softmax is a step in transformer computation during which the internal buffer size grows rapidly because of the use of the exponential function. Softmax is a fundamental yet computationally expensive operation in vision transformer attention, posing significant challenges for deployment on resource-constrained FPGAs (Field Programmable Gate Arrays). Computational precision demands grow at the softmax stage in the attention pipeline mainly because of the use of the exponential function in the softmax computation. This paper proposes a low-precision softmax approximation that combines a truncated Maclaurin-series exponential with input-range clamping to enable efficient hardware realization without sacrificing reconstruction quality. By bounding extreme attention scores that contribute negligibly to final outputs, the proposed method mitigates the instability of low-order polynomial approximations while preserving their hardware efficiency. The approach is first validated in software using SwinIR (Image restoration using the SWIN Transformer) super resolution to ensure reconstruction fidelity and is then analyzed for FPGA deployment. SWINIR is a multi-stage version of other transformers like Deit and Vit, making it a preferred option for testing the reconstruction fidelity of the change for transformers. Experimental results demonstrate that the proposed fourth-order clamped approximation achieves near-reference performance, incurring only 0.15 dB PSNR and 0.0059 SSIM degradation on SwinIR-M, while significantly reducing precision and memory requirements. For the large-sized SWINIR model (SWINIR-L), a PSNR increase with a less than 0.01 SSIM loss is observed, further highlighting the insignificance of extreme values as model size gets bigger. A Horner-form reformulation further improves hardware efficiency by limiting intermediate precision growth. Overall, this work presents a reconstruction-aware and hardware-friendly softmax reformulation that enables practical deployment of vision transformers on small FPGA platforms. This work also uses this contribution to improve the performance of the ViTA accelerator design. We also add bias initialization and a PE loop bound runtime variable to the existing ViTA accelerator design. Full article