Search Results (707)

Complex optimization tasks in data-driven prediction and engineering applications often involve nonlinear, multimodal, and ill-conditioned objective functions. This study proposes an Enhanced Moss Growth Optimization algorithm (EMGO), an improved variant of the baseline MGO framework, to enhance exploratory step-size control and local covariance exploitation. EMGO incorporates two key algorithmic augmentations: a budget-adaptive jump regulation mechanism that balances global dispersal and fine-grained refinement, and a shrinkage-regularized covariance-guided sampling operator with relative eigenvalue flooring to exploit correlation structures among elite individuals without rank deficiency. The proposed algorithm is evaluated on the CEC2017 benchmark suite across 50 and 100 dimensions with 29 test functions, 30 independent runs, and a budget of $3\times {10}^5$ function evaluations per run, compared against ten state-of-the-art optimizers including CMA-ES, L-SHADE, SBO, and baseline MGO. Nonparametric Friedman ranking, Holm-adjusted Wilcoxon signed-rank tests, and runtime-matched analyses demonstrate that EMGO achieves highly competitive performance across high-dimensional landscapes. Furthermore, EMGO is applied to tune support vector regression (SVR) hyperparameters for effluent suspended solid (SS) prediction using the UCI Water Treatment Plant dataset under an expanding-window rolling-origin cross-validation scheme. EMGO-SVR achieves superior predictive accuracy ($\mathrm{RMSE}=5.58\pm 0.64$, $\mathrm{MAE}=3.97\pm 0.46$, $R^2=0.889\pm 0.028$), outperforming standard SVR, tree-based ensembles, and Bayesian optimization baselines. SHAP-based feature importance analysis confirms the physical and process consistency of the model predictions. Full article

Against the background of increasing styling convergence in the new energy vehicle (NEV) market, rear-end styling has gradually become a key visual interface for communicating brand identity, shaping product differentiation, and eliciting users’ Kansei cognition. However, existing Kansei design studies on automotive styling have mainly focused on whole-vehicle forms or front-face morphology, while systematic modeling methods for local rear-end styling remain limited. Under small-sample conditions, the nonlinear mapping between the Kansei semantic space and styling parameters also faces the risk of overfitting. To address these issues, this study proposes a data-driven Kansei Engineering (KE) framework integrating non-negative matrix factorization (NMF), grey relational analysis (GRA), and deep feedforward neural network (DFNN), aiming to achieve a continuous translation from Kansei need identification to parametric scheme generation for rear-end styling. First, the original seven-dimensional Kansei evaluations were aggregated into three latent Kansei dimensions through the non-negative low-rank decomposition of NMF. Second, GRA was used to screen key morphological features and reduce modeling complexity at the feature level. Third, DFNN and random forest (RF) were constructed as prediction models, and DFNN showed better average test RMSE and R² than RF. Finally, the optimal codes predicted by the DFNN were transformed into design schemes constrained by morphological coding, and their consistency in expressing the target Kansei images was verified, thereby establishing an engineering constraint-oriented and interpretable decoding pathway distinct from free-association-based Kansei design. The ablation experiment indicates that the performance advantage of the proposed framework does not arise solely from DFNN, but from the mathematical coupling among NMF-based semantic aggregation, GRA-based feature screening, and DFNN-based nonlinear mapping. This framework reformulates Kansei design as a hierarchical decomposition and modeling process, establishing a data-driven decision-support tool jointly driven by mathematical algorithms and artificial intelligence for NEV rear-end styling design. Full article

Cardiac electrophysiology (EP) is inherently data-centric, spanning brief 12-lead electrocardiograms (ECGs), high-density electroanatomic maps, and continuous device-based monitoring. This data volume can strain provider workflows while creating an opportunity for artificial intelligence (AI). Machine learning (ML) and its deep learning subfield extract clinically actionable patterns from complex electrical signals. This narrative review summarizes contemporary AI applications across the major domains of EP. In arrhythmia detection, deep neural networks classify rhythms at a level comparable to cardiologists on internal test sets, identify occult atrial fibrillation (AF) from a normal sinus-rhythm ECG, and, through consumer wearables, extend screening to ambulatory populations. In catheter ablation, an AI algorithm that adjudicates intracardiac electrogram dispersion improved single-procedure freedom from AF in a randomized trial of persistent AF, and ML models help predict arrhythmia recurrence; we distinguish these from adjacent non-AI technologies, such as computed-tomography integration and three-dimensional mapping, that reduce fluoroscopy but are not themselves AI. In cardiac implantable electronic devices (CIEDs), AI-based filtering lowers false-positive alert burden, and multi-parametric algorithms provide earlier prediction of heart-failure decompensation. ML models may refine patient selection for cardiac resynchronization therapy (CRT) and, using late-gadolinium-enhancement cardiac magnetic resonance, may sharpen arrhythmic-risk and implantable cardioverter-defibrillator (ICD) decision-making. AI-enhanced ECG broadens the standard ECG into a low-cost screening tool for channelopathies, dyskalemias, and ventricular dysfunction. Important barriers remain, including limited external validation, incomplete explainability, and a scarcity of prospective outcome trials. Full article

The digital transformation of manufacturing enterprises necessitates more efficient engineering change management through data-driven decision support and process optimization. Although process mining has advanced considerably in recent years, most existing studies focus on descriptive or predictive process analysis, while prescriptive optimization integrated with sustainability considerations remains insufficiently explored. This study aims to develop and evaluate a prescriptive optimization model for Engineering Change Management based on process mining methods and Environmental, Social, and Governance considerations. The proposed model combines rule-based case routing, resource profiling, and risk-oriented alerts and is evaluated using a real-world event log from a manufacturing and engineering company. The methodological framework integrates process discovery using the Inductive Miner algorithm, conformance checking, performance and bottleneck analysis, deterministic rule-based case classification, resource profiling, and prescriptive analytics. The proposed model was evaluated through retrospective validation on a test dataset using both parametric and non-parametric statistical methods. The retrospective analysis estimated a 36.8% reduction in average active case processing time, from 6.31 to 3.98 h, while maintaining the modeled satisfaction level (p < 0.001). These results represent model-based estimates rather than effects observed after prospective implementation. The proposed approach extends existing process mining applications by combining prescriptive analytics with sustainability-oriented process governance. It provides a basis for integration into Product Lifecycle Management, Enterprise Resource Planning, and Engineering Change Management systems as a decision-support module, with the potential to improve operational efficiency and support sustainable manufacturing practices. Full article

Integrated-light spectral energy distribution modeling is widely used to infer the star-formation and assembly histories of galaxies that cannot be resolved into individual stars. However, existing approaches are often discussed primarily in terms of particular fitting codes, star-formation-history parameterizations, or inference algorithms. In this Review, we formulate the recovery of galaxy evolution histories from integrated spectral energy distributions as a unified inverse problem. We separate the physical spectral-generation operator from the observational operator and examine the resulting information loss through non-identifiability, singular-value structure, null directions, effective resolution, regularization, and model discrepancy. We then classify parametric and nonparametric star-formation histories, PCA, MOPED, VESPA, non-negative matrix factorization, deep learning, and simulation-based inference within a common five-component framework consisting of the representation space, forward operator, physical or statistical constraints, inference method, and uncertainty assessment. On this basis, we introduce information-driven adaptive representation as a general design principle in which the complexity of the recovered history is matched to the information supported by the observations. Finally, we extend the framework from star-formation histories to coupled galaxy-evolution states involving chemical enrichment, dust evolution, interstellar-medium conditions, and radiative transfer, and outline a three-layer research program linking controlled mock experiments, inverse-problem theory, and physical forward modeling. Full article

This study aimed to identify socioenvironmental vulnerability profiles among Mexican households and analyze their association with health expenditure. Data from 86,102 households included in the 2024 National Household Income and Expenditure Survey were analyzed. Socioenvironmental profiles were constructed using housing, basic services, sanitation, household energy, socioeconomic stratum, and overcrowding through factor analysis of mixed data and k-means. Internal validation, stability analyses, algorithm comparisons, and sensitivity analyses supported a three-profile solution representing low, intermediate, and high vulnerability. Health expenditure was examined using survey-weighted descriptive estimates, exploratory nonparametric comparisons, and a survey-adjusted two-part model. The intermediate vulnerability profile showed higher odds of reporting health expenditure than the low vulnerability profile (OR = 1.129, 95% CI: 1.048 to 1.217, p = 0.002), whereas the high vulnerability profile showed no significant difference. Among households with positive expenditure, high vulnerability was associated with lower logarithmic expenditure ($\beta =-0.341$, 95% CI: $-0.490$ to $-0.192$, p < $0.001$), whereas the intermediate profile was not significantly different in the main model. The association for high vulnerability remained significant in sensitivity analyses, while results for the intermediate profile were more sensitive to model specification and income adjustment. Despite several statistically significant associations, effect sizes in the exploratory comparisons were small and the regression models explained a limited proportion of the variability in health expenditure. The findings therefore indicate modest associations between socioenvironmental vulnerability and health expenditure, with household economic resources and other unmeasured health-related factors likely contributing to the observed differences. Full article

This paper addresses the problem of formulating generalized, non-extensive information-theoretic measures on finite non-distributive algebraic structures equipped with Riečan states, with particular emphasis on product Sheffer stroke basic algebras. Our approach formalizes finite summations, admissible partitions, refinement relations, and Sheffer stroke joint refinement candidates by using the primitive Sheffer stroke operation, with partition and marginalization properties imposed under the stated product and admissibility assumptions. By leveraging the state-theoretic properties of Riečan states, we construct baseline Shannon and logical entropies alongside algorithmic procedures for their computational evaluation. As the main result, we introduce and analytically characterize a parametric Tsallis entropy functional over these basic algebras. We prove its fundamental properties, including bounding inequalities, state concavity, monotonicity under refinement, subadditivity (for $\alpha >1$), conditional chain-type identities under the relevant joint refinement marginalization assumptions, and exact analytical convergence to the classical Shannon limit as the entropic index $\alpha \to 1$. Furthermore, under a state-dependent statistical independence condition, we show that the joint Tsallis entropy satisfies a pseudo-additive relation. By defining the Tsallis mutual information and the associated pseudo-additive residual, we isolate the deviation of a joint Sheffer stroke refinement from the factorized model determined by its marginal Riečan-state distributions. This residual is intended as a state-dependent algebraic indicator of deviations from the factorized Tsallis pseudo-additive model; it is not claimed to be an operational contextuality witness, a contextuality inequality, an entanglement measure, or a physical implementation criterion. Full article

The first step in the analysis of the contact mechanics and leakage prediction of metallic seals applied to nuclear reactor pressure vessels is the proper characterization of the surface topography. At present, two approaches are used for this characterization. On the one hand, there are non-parametric techniques such as HPD and PSD, which retain all the characteristics of the surfaces that have been measured, but the results are high dimensional; hence, they cannot be analyzed analytically. The other type is parametric fractal methods, where the parameters used are fractal dimension D and characteristic scale G, where the analytical derivations can be made; however, this leads to systematic deviations in the mechanical response due to some idealized assumptions, like isotropy, Gaussian distribution, and infinite self-similarity. In this article, we propose an equivalent fractal parameter inversion model (EFPIM) that does not rely on geometric fitting; instead, it fits the mechanical contact behavior of a physical surface. This inversion procedure reduces three errors simultaneously. Thus, the EFPIM does not use D and G as the geometrical fitting variables but rather redefines them as mechanically equivalent ones, the purpose of which is to minimize the difference between the W-M fractal surface and the real measured surface. To address the problem of constrained inversion, we adopt a genetic algorithm with BFGS. To prove its effectiveness, we carried out experiments on C-ring seal surfaces and found that the deviation in the contact area was reduced by an order of magnitude in comparison to traditional structure-function extraction, and the deviation in the approach and the maximum pressure were less than 2%. Moreover, the equivalent parameters shift systematically away from their geometric counterparts in the direction that compensates for the dominant non-ideal deficit of the W-M surface; when both parameters are free, the equivalent fractal dimension decreases, while the equivalent characteristic scale increases, compensating for the absent non-Gaussian deep valleys of ideal W-M surfaces. Existing models of analytical contact and leakage may be directly implemented using equivalent parameters and with accuracy comparable to that of FFT-based simulations, with the modest cost of the offline computations. Full article

Balanced joint adaptive progressive Type-II censoring (B-JAPC) is developed for two independent Lomax populations with a common shape parameter and population-specific scale parameters. Classical and Bayesian inference methods are constructed for the model parameters, survival functions, and hazard rates. To address the exponential scale–shape ridge where standard maximum likelihood estimates often fail to converge, a constrained maximum likelihood estimator (CMLE) with parametric bootstrap confidence intervals is established. A partially conjugate Bayesian framework under a Beta–Gamma prior is also implemented via a Metropolis-within-Gibbs algorithm. Monte Carlo simulations demonstrate that the proposed adaptive design substantially reduces the mean test duration compared to non-adaptive schemes while maintaining high inferential accuracy. The methodology is successfully applied to randomized cloud-seeding rainfall data, confirming its practical utility and quantifying the sensitivity of lifetime inference to shape regularization. Full article

Aiming at the difficulty of extracting fault features from nonlinear and non-stationary vibration signals of planetary gearboxes, a planetary gearbox fault diagnosis method based on Refined Composite Multiscale Fuzzy Entropy (RCMFE), Parametric-t-distributed Stochastic Neighbor Embedding (P-t-SNE), and Artificial Jellyfish Search Algorithm Optimized Support Vector Machine (JS-SVM) is proposed. Firstly, RCMFE is used to calculate and combine the feature vectors of the original fault signals of the planetary gearbox to construct the original high-dimensional fault feature set. Secondly, Parametric-t-SNE (P-t-SNE) based on a deep feedforward neural network is employed to reduce the dimensionality of the high-dimensional features, thereby extracting sensitive low-dimensional features and achieving out-of-sample mapping. Finally, the low-dimensional features are inputted into the JS-SVM for the identification of fault types. The experimental results of planetary gearbox fault diagnosis show that the proposed method can accurately identify common faults in planetary gearboxes, demonstrating promising application prospects. Full article

This paper presents the mathematical modeling, numerical implementation, and experimental validation of a decoupling control algorithm for a five-port multiport modular active bridge (MMAB) converter in DC microgrid applications. Based on an analytically derived generalized state-space framework of the MMAB topology, a matrix-based method for suppressing non-linear mutual cross-couplings among individual ports is proposed. The study addresses parametric uncertainties within the system matrix caused by parasitic bus inductances; by formulating a linear system of equations solved via the numerical least-squares method, the equivalent parameter identification error was reduced from over 18% to a valid threshold. The decoupling performance and dynamic responsiveness of the closed-loop system were experimentally verified on a dual-core TMS320F28379D digital signal processor. The experimental results demonstrate that the proposed algorithm effectively isolates transient step-load perturbations, maintaining voltage stability on adjacent undisturbed ports within a strict deviation of less than $+0.51\%$ and achieving a recovery time below 5 ms. Furthermore, the real-time execution of the online Jacobian matrix inversion via the Newton–Raphson method confirms the computational feasibility and convergence of the iterative approach under tight sampling periods. The obtained results provide a robust, experimentally validated foundation for advanced algebraic and numerical control strategies in high-stability multiport power conversion systems. Full article

Urban battery swapping station (BSS) planning is difficult because site opening, service-module allocation, user assignment, battery degradation pressure, travel burden, and congestion are tightly coupled. A plan that minimizes investment alone may create long queues, whereas a plan that only reduces waiting can overbuild costly and underused capacity. This study formulates the urban BSS siting–sizing problem as an operation-aware mixed-integer nonlinear model and evaluates feasible plans with payoff-table global-criterion normalization. To search this rugged planning space, we propose a Stable Portfolio Hyper-Heuristic (SPHH) that combines Greedy construction, BO-guided large-neighborhood search, simulated annealing, and optimized annealing with reheating, followed by feasible-incumbent preservation and non-worsening post-processing. The formal campaign contains 18 synthetic cases, 10 independent repeats, and five algorithms, yielding 900 optimization records. Across the 18 cases, SPHH produced the lowest mean GC score and reduced the mean GC value on average relative to the baseline algorithms. Nonparametric Friedman and Holm-adjusted Wilcoxon tests confirmed statistically significant differences among methods, although the advantage over the strongest baseline was not universal. These results indicate that SPHH is most useful as an offline planning selector that improves recommendation stability when additional computation is acceptable, rather than as a universally faster optimizer. Full article

Accurate prediction of input–output relationships in natural gas exploration is essential for improving exploration efficiency and optimizing investment allocation. However, this task is severely hindered by data sparsity and strong nonlinear characteristics inherent in oil and gas exploration systems, rendering conventional statistical methods and single machine learning models ineffective. This study develops a novel integrated framework combining data augmentation, nonlinear feature engineering, and ensemble learning to achieve accurate and interpretable prediction of exploration input–output matching under limited data constraints. Taking four core exploration indicators—including the number of exploration wells, total drilling depth, reserve abundance, and proven reserves—as input variables, adaptive prediction models were constructed for seven typical hydrocarbon basin exploration systems. To ensure comprehensive algorithmic exploration, nine advanced algorithms, including mainstream ensemble methods (RandomForest), few-shot neural networks (FewShot_NN), and kernel-based regressions, were systematically benchmarked. Furthermore, SHapley Additive exPlanations (SHAPs) was adopted to enhance model interpretability, and non-parametric Wilcoxon signed-rank tests were introduced to rigorously validate statistical significance. The results demonstrate that the optimal predictive pathway varies across different geological systems. Specifically, RandomForest and GBDT exhibit superior performance in systems with moderate heterogeneity (e.g., Jialingjiang and Changxing–Feixianguan Formations), whereas FewShot_NN and Kernel Ridge achieve the highest accuracy under extreme data sparsity and volatility (e.g., Xujiahe Formation and Lower Permian). The established framework yields a coefficient of determination (${\mathrm{R}}^2$) greater than 0.96 for the majority of study cases, with overall absolute percentage errors heavily minimized. SHAP analysis further verifies that drilling depth and reserve abundance are the dominant controlling factors. This data-driven framework provides a robust and interpretable technical tool for the intelligent management of energy resources. Full article

Accurate binarization of phase-detection probe signals (gas vs. liquid) is necessary for the estimation of local void fraction, interfacial velocity, and bubble statistics in gas–liquid flows, particularly in nuclear thermal–hydraulic experiments. Classical threshold-based methods—single or double level—perform well on clean laboratory signals but degrade under realistic industrial conditions where noise, baseline drift, and clustered (slug-like) events challenge fixed rules. This work investigates whether deep learning (DL) models trained exclusively on synthetic data can deliver robust, generalizable binarization on real probe measurements. We (i) build a parametric generator of realistic time series from bubbly pulse templates, extended to clusters/slug patterns and perturbed with controlled noise, drift, and oscillatory baselines; (ii) train four lightweight DL architectures—one-dimensional U-Net (UNET-1D), Temporal Convolutional Network (TCN), a minimal one-dimensional Convolutional Neural Network (CNN-1D), and a Bidirectional Long-Short Memory network (BiLSTM)—only on synthetic signals; and (iii) evaluate them against classical threshold methods using event-level and sample-level metrics. On synthetic signal evaluation, UNET-1D and TCN achieve near-perfect event detection and sub-millisecond onset errors. On real bubbly and slug flow sensor data, classical threshold-based methods remain highly competitive on clean sensor signals, while DL models retain advantages under non-stationary baselines and clustered events, yielding accurate void and timing with no hand-tuned assumptions. Results support DL as a practical, data-driven complement to fixed algorithms, particularly in noisy or drift-dominated measuring conditions typical of nuclear thermal–hydraulic loops and safety-relevant test facilities. Full article

The transition from continuous swarm intelligence algorithms to discrete combinatorial domains remains a critical challenge in bio-inspired computing. Traditional binarization techniques frequently induce premature convergence in highly constrained landscapes. This paper presents a chaotic discretization framework that replaces the classical behavior of the two-step binarization technique to regulate the balance between exploration and exploitation. The proposal systematically integrates three leading continuous metaheuristics in the literature, with twenty-four binarization configurations, across three distinct NP-hard problem archetypes: capacity-constrained (0–1 Knapsack), sparse (Set Covering), and mathematically degenerate flat landscapes (Unicost Set Covering). Nonparametric statistical tests confirm that chaotic discretization acts as a powerful regulator in the landscape (p < 0.05). Empirical evidence shows that the highest-performing chaotic mapping is heavily influenced by the specific landscape morphology evaluated: the 0–1 Knapsack Problem is statistically optimized by the Circle map under standard rules; the Set Covering Problem achieves optimal median performance with the Tent map under elitist formulations, although severe matrix constraints ultimately force statistical ties; and the Unicost Set Covering Problem utilizes the nonlinear sequences of the sinusoidal map under complementary operators to break convergence stagnation. Full article