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Parameter estimation of proton exchange membrane fuel cells using enhanced Fourier transform optimizer with adaptive learning mechanisms
Abstract The seven unknown parameters in the Amphlett semi-empirical proton exchange membrane fuel cell (PEMFC) model are critical to accurate performance prediction, system design, and degradation diagnosis. Gradient-based methods are often unreliable in this context because the sum-of-squared-errors (SSE) objective is highly nonlinear and multimodal, while existing population-based approaches tend to exhibit substantial run-to-run variability. To address these challenges, this paper proposes the Enhanced Fourier Transform Optimizer (EFTO), a hybrid adaptive algorithm that combines a success-history differential evolution branch with elite-guided mutation and a covariance-adaptive sampling branch, dynamically balanced through an online branch-selection mechanism. On the IEEE CEC 2017 benchmark suite (28 functions, D = 50, 30 independent runs), EFTO achieved a Friedman rank of one with an average rank of 1.607 and a Wilcoxon p ≤ 1.17 × 10⁻ 3 against six competing algorithms. A supplementary comparison with three recognized state-of-the-art solvers found no statistically significant performance differences relative to two of the three solvers ( p > 0.05), while EFTO recorded the lowest average runtime of 8.653 s. When applied to three commercially validated PEMFC stacks, EFTO achieved machine-precision reproducibility, with a standard deviation below 10⁻ 14 V 2 across 30 independent runs and best SSE values of 2.0656 V 2 (NedStack PS6), 1.0564 V 2 (AVISTA SR-12), and 0.8139 V 2 (Ballard Mark V). These results were statistically confirmed by Wilcoxon signed-rank tests (R⁺ = 465, p = 1.73 × 10⁻⁶). Component wise ablation showed that the covariance-adaptive sampling mechanism was the most influential component, with removal of the covariance-adaptive sampling mechanism causing a 98.7% degradation in SSE ( p = 1.09 × 10⁻ 24 ). Stack-specific sensitivity analysis further demonstrated that the identified parameters remain physically consistent with the electrochemical operating regime of each stack, establishing EFTO as a competitive and consistent tool for PEMFC parameters identification on the evaluated benchmark configurations.
Interstitial Nitrogen Tuning of Magnetocrystalline Anisotropy and High-Frequency Electromagnetic Properties in Nd-Based 3:29-Type Intermetallics
Revealing hidden nonlinear soliton dynamics, multistable regimes, and chaotic transitions in regularized long-wave equations under complex external forcing
Theoretical study on N(sp2)+–C(sp2)–H···O hydrogen bonds in the context of drug discovery
Tricationic Xanthylium-Catalyzed Oxyamination of Activated Alkenes Using <i>O</i> -Benzoylhydroxylamines As Bifunctional Reagents
Coagulation-assisted removal of polystyrene microplastics from aqueous solution using amine-functionalized multi-walled carbon nanotubes with okra extract
Mechanism of O <sub>2</sub> /NO-Promoted Oxidative C–C Bond Cleavage in Linear Alkanes
A privacy-preserving rule fusion approach for uncertainty-aware decision-making in posture detection
Morphodynamic effects of porosity and tailwater depth on scour mitigation by gabion grade control structures
Metaheuristic optimized hybrid machine learning framework for predicting soil compaction parameters
Population-scale network embeddings expose educational divides in network structure related to right-wing populist voting
Abstract Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations—embeddings—that automatically capture an individual’s position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed that differences in educational ties and attainment corresponded to distinct network structures associated with right-wing populist voting. Our study contributes methodologically by demonstrating how population-scale network embeddings can be made interpretable, and substantively by linking structural network differences in education to right-wing populist voting.
Predicting CO2 injection profiles in heterogeneous reservoirs using a physics-aware deep learning framework
A hybrid attention-based spatio-temporal model for deepfake video detection
Molecular dynamics investigation of interfacial energy and mechanical behavior in braid-reinforced hollow fiber membranes
Abstract This study presents a molecular dynamics investigation of braid-reinforced hollow fiber membranes to elucidate the interfacial and mechanical behaviors of polymeric composites composed of cellulose acetate (CA) and polyacrylonitrile (PAN). The analysis focuses on three representative configurations, homogeneous (CA/CA-II), semi-heterogeneous (PAN|CA/CA-I), and heterogeneous hybrid (CA/PAN-III), to evaluate their interfacial energies, adhesion mechanisms, and tensile responses. The calculated interfacial energies of − 6.32077, − 5.69262, and − 4.71113 mJ/m 2 for CA/CA-II, PAN|CA/CA-I, and CA/PAN-III, respectively, reveal that chemical homogeneity promotes stronger interfacial bonding, whereas polarity mismatches between functional groups (–OH, –OCOCH 3 , and –CN) weaken adhesion and increase diffusivity at the interface. Mechanical testing through MD tensile simulations further demonstrates that the CA/PAN-III composite exhibits pronounced stress fluctuations and higher local interfacial activity. At the same time, the CA/CA-II system maintains the highest cohesive stability and elastic modulus due to structural uniformity. The CA/PAN-III hybrid achieves an optimal balance between flexibility and strength, indicating its suitability for water treatment membranes that require both mechanical resilience and interfacial durability. These findings provide molecular-level insight into how polymer compatibility governs the performance of braid-reinforced hollow fiber membranes and offer valuable guidelines for designing next-generation high-strength composite membranes.