Search Results (1,029)

This study proposes a sequential Hybrid GP-ANN architecture based on the concept of autonomous feature engineering for multi-output performance prediction of single-phase induction motors, serving as a high-fidelity surrogate model ready for digital twin integration. The required high-resolution motor dataset (88,375 samples) was generated using a parametric sweep approach with Ansys RMxprt. In the proposed architecture, Genetic Programming (GP) is not positioned as a final predictor but as an analytical filter that discovers hidden physical relationships in raw input data and converts them into super features, thereby structurally eliminating the polynomial explosion risk and scale sensitivity that arise when raw data are modelled directly. The nonlinear physical relationships discovered autonomously by GP are added to the network input matrix as mathematical vectors, breaking the black-box structure of standard artificial neural networks and transforming it into a physics-inspired grey-box model. The nonlinear terms discovered by GP are fed into the network after independent Z-score normalisation to preserve gradient stability. To comprehensively evaluate this framework, its predictive performance is benchmarked against industry-standard machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Results on variation-based unseen test data show that the hybrid model achieves competitive prediction accuracy relative to XGBoost—matching or exceeding it for Output Torque and Input Current, and remaining broadly comparable for Output Power, though XGBoost achieves substantially lower RMSE for Efficiency and Power Factor—while additionally offering transparent mathematical traceability, reducing error rates (RMSE) by 42.1% to 66.1% per parameter compared to the standard ANN. The model achieves an $R^2$ score of 0.9879 for the power factor parameter, where conventional approaches struggle. To rigorously validate the interpretability of this grey-box architecture, a global Permutation Feature Importance (PFI) analysis was conducted, showing that the GP-derived super features collectively account for 61.46% of the decision logic, outweighing the combined contribution of the raw inputs (38.54%). Furthermore, the autonomous feature engineering layer is found to reduce the learning burden on the ANN, allowing it to converge to a substantially lower error floor within the same fixed epoch budget. With an online inference time below 0.02 ms, the proposed architecture offers a robust methodological infrastructure for sustainable motor digital twins through a balanced trade-off between prediction accuracy and computational efficiency. Full article

In response to ecological degradation caused by uncontrolled dust emissions from open-pit mines, this study selected the Hequ open-pit coal mine as the study area and established a monitoring system to collect data on TSP, PM10, PM2.5, and environmental indicators across three zones: the mining pit, the main haul road, and the coal yard. The necessity of zoning was validated using the least significant difference (LSD) method. Pollutant correlations were examined using the individual air quality index (IAQI), Pearson correlation matrix analysis, and grey relational analysis. Univariate models, multiple linear regression (MLR), and principal component analysis–multiple linear regression (PCA–MLR) were applied to quantitatively analyze dust evolution patterns and the influence of environmental factors, with model accuracy verified by the mean relative error (MRE) method. The results showed significant differences in dust concentrations among the three zones. Dust concentrations of all particle sizes in the mining pit and coal yard exceeded the secondary standard limit, whereas those on the haul road only exceeded the primary limit, with pollution intensity ranked as mining pit > coal yard > haul road and PM2.5 identified as the core pollutant in all zones. Linear relationships were significant in univariate models, and multivariate fitting outperformed univariate fitting, with MLR prediction accuracy ranked as coal yard (3.02%) > haul road (9.46%) > mining pit (10.75%). In the mining pit, TSP and PM10 exhibited a strong positive correlation with atmospheric pressure, while PM2.5 showed a strong negative correlation with relative humidity. On the haul road, all particle size fractions displayed strong negative correlations with temperature and wind speed. In the coal yard, only a strong negative correlation with temperature was observed. The PCA–MLR model improved prediction accuracy by 56.63% and 13.41% compared to the direct MLR model. Comprehensive analysis indicates that the atmospheric environment of the Hequ open-pit mine urgently requires proactive restoration measures to optimize the sustainability of the ecological environment. Full article

In Fusion Deposition Modeling-based manufacturing, process parameters affect the mechanical and physical properties of the print. Considering these properties, accurately predicting print quality is essential. This is where machine learning (ML) models for three-dimensional (3D) print quality prediction come to the forefront. In this study, a dataset was prepared under strict operational measurement standards—utilizing the Interquartile Range (IQR) method for data sanitization—encompassing 10 material types, 2 printer types, and 4 printing parameters. Five hundred different sample combinations were prepared and printed in sets of three according to ISO 527-2 Type 4 standard dimensions. Tensile, hardness, and surface roughness tests were applied to the prepared samples. Using this validated dataset, a Hybrid Multi-Material Quality–Ensemble System–Stacking–Gradient Boosting Regressor (HMQ-ES-Stack-GBR) architecture is proposed as a diagnostic framework for multi-output quality prediction. Particularly in terms of quality outputs such as tensile strength, hardness, and surface roughness, while also providing a quantitative analysis of the effect of material type on print quality. Furthermore, a multi-objective optimization pipeline integrating three distinct meta-heuristic algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—was coupled with the framework to systematically derive material-specific optimal processing parameter configurations. Furthermore, the study shows that open-system printers exhibit higher prediction errors than closed-system printers. Reflecting system-induced variability rather than full hardware independence. Although the study is limited to internal validation within the current experimental design and includes material imbalance across filament groups, the findings suggest that the proposed framework provides a promising diagnostic decision-support tool for pre-print quality estimation within the studied dataset. By accurately reflecting rather than physically overcoming manufacturing variability, it supports decision-making processes through pre-print quality estimation, thereby enabling proactive interventions that reduce raw material, time, and energy losses. Full article

Lead-free double-absorber perovskite solar cells offer broad-spectrum absorption and environmental benefits, but their multilayer heterostructure creates computational challenges for conventional design optimization. This study introduces an automated framework integrating SCAPS-1D simulation, multilayer perceptron (MLP) surrogate modeling, metaheuristic algorithms, and reinforcement learning (RL). Using FTO/ZnO/Cs₂TiBr₆/RbGeI₃/CuI/Au cells, the MLP model trained on Latin hypercube sampling data achieved high accuracy (R² > 0.95). The proximal policy optimization (PPO) RL agent converged to 27.41% power conversion efficiency (PCE) in approximately 20 steps. For direct 15-dimensional optimization, simulated annealing and particle swarm optimization reached 98% target PCE with 138 and 111 function evaluations, respectively, while Grey Wolf Optimizer (GWO) yielded the highest average PCE. Transfer learning successfully adapted the pretrained model to a novel FASnI₃/Sb₂S₃ structure, improving the prediction accuracy of PCE, J_SC, and FF. This work systematically optimizes Cs₂TiBr₆/RbGeI₃ solar cells while establishing an efficient, generalizable paradigm for intelligent photovoltaic device design, validation, and material discovery. Full article

Background/Objectives: The mandatory spinal cord stimulation (SCS) trial was established in the era of tonic, paresthesia-dependent stimulation, when pre-implantation outcome prediction was unreliable, validated psychological screening was unavailable, and post-implantation programming options were limited. This narrative review examines whether universal trialing remains justified in the context of contemporary paresthesia-free waveforms, mechanistically informed psychological assessment, and current evidence on the predictive utility of screening trials. The objective was to evaluate the contemporary rationale for selective rather than universal trialing and to propose a structured four-step clinical decision framework integrating contraindication screening, pain phenotype certainty, psychological risk stratification, and centre-level criteria, with the aim of identifying patients in whom a trial is most likely to add prognostic information. Methods: PubMed and the Cochrane Library were searched using predefined search strings. Eligible publications included randomised controlled trials (including the TRIAL-STIM RCT), systematic reviews, registry-based cohort studies, health-economic analyses, and consensus guidelines. Results: The evidence base comprised randomised controlled trials of trial-versus-no-trial strategies, paresthesia-free waveforms, and closed-loop stimulation (including TRIAL-STIM, SENZA-RCT, EVOKE, and ECHO-MAC), together with large registry and cohort studies, systematic reviews, qualitative patient-experience data, and current consensus guidance. Modern paresthesia-free and physiology-guided paradigms reduce several technical functions historically served by trialing, although the extent varies by waveform. The only randomised trial designed specifically to compare trial-first and no-trial strategies (TRIAL-STIM) found no difference in pain outcomes at 6 or 36 months; however, its single healthcare setting and predominantly paresthesia-based waveform mix limit generalisability. Structured pre-implantation psychological evaluation may provide prognostic information that short-duration trialing cannot replicate. At the same time, contemporary guidelines continue to recommend trialing and several clinically relevant arguments for retaining it persist, including patient experiential learning, identification of screening false negatives, and grey-zone decision-making. Conclusions: In carefully selected patients with established neuropathic pain phenotypes and comprehensive pre-implantation evaluation, selective trialing may be clinically reasonable. The proposed four-step framework offers a structured basis for allocating trial stimulation to the patients most likely to benefit from it and should be interpreted as hypothesis-generating pending prospective validation. This review does not advocate abandoning trial stimulation; rather, it argues that its role should be examined within an individualised, phenotype- and risk-stratified pathway. Full article

Selective energy conversion in confined catalytic nanocavities is examined through a coupled reactive–photonic framework. The practical target is a combustor-integrated selective emitter for thermophotovoltaic (TPV) conversion and cascaded thermoelectric (TEG) recovery, in which Pt-coated anodic porous alumina (APA) functions simultaneously as a catalytic reactor, a cavity-modified electromagnetic environment and a heat-routing structure. Visible/near-infrared spectra (380–780 nm) show that Pt-coated APA exhibits a substantially stronger non-grey red-edge depression than a smooth zirconia reference. This observation establishes a spectral contrast in the measured window but is not used to identify an experimental cutoff wavelength, because a finite, open, lossy and array-coupled pore does not exhibit the abrupt edge predicted for an ideal cylindrical waveguide. For the mid-infrared, analytical scaling shows that the principal H₂O and CO₂ bands at 2.7, 4.3, 6.3 and 15.0 µm all lie deep in the evanescent regime relative to the ideal TE₁₁ cutoff wavelength λc ≈ 0.513 µm for a 300 nm pore. A converged finite-difference time-domain benchmark at the CO₂ 4.3 µm band yields a source-local Purcell factor F_p ≈ 0.38, indicating suppression of the total local density of optical states, while aperture flux is more than six orders of magnitude smaller than the near-field power budget. The specific contribution is therefore not the established fact of below-cutoff attenuation, but the co-design and separate quantification of a catalytic nanocavity as a reactive compartment, photonic environment and energy-branching element. The results provide a bounded mechanistic basis for combustor-integrated TPV and hybrid TPV/TEG architectures. Full article

Manufacturing firms increasingly face challenges in inventory allocation due to rising logistics costs, regional demand heterogeneity, and demand fluctuations. To address these issues, this study proposes a demand prediction-based multi-product lateral transshipment location–allocation–inventory (LAI) model for a two-echelon supply chain under a vendor-managed inventory environment. An integrated framework combining an improved grey demand prediction model with the success history-based adaptive differential evolution algorithm (IGDP-SHADE) is developed. The IGDP model is employed to forecast product demand under small-sample and highly volatile conditions, while the SHADE algorithm optimizes facility location, inventory allocation, replenishment, and lateral transshipment decisions with the objective of minimizing total system costs. A case study based on the excavator supply chain of Sany Heavy Industry is conducted to evaluate the proposed approach. The results demonstrate that the IGDP model achieves a mean absolute percentage error (MAPE) of 13.86% and a root mean squared logarithmic error (RMSLE) of 0.1806, outperforming benchmark models including ARIMA and Holt–Winters in forecasting seasonal small-sample demand data. Compared with Gurobi, the proposed IGDP-SHADE algorithm obtains the same optimal solution while reducing computational time by 17.47%. Sensitivity results indicate that introducing the lateral transshipment mechanism reduces total system costs by 2.97% compared with the scenario without transshipment. Furthermore, the optimal number of centralized storage points is identified as six, enabling an effective balance between transportation, inventory holding, and transshipment costs. Full article

The palladium market represents a critical role in supporting key industrial sectors and facilitating the energy transition, as it is widely used in the automotive industry, electronics, chemical manufacturing, and hydrogen energy. These sectors influence a steady demand amid tightening environmental regulations and the development of green technologies. The aim of this study is to assess the structural transformation of the global palladium market through 2030 and to project Russian palladium production for 2026–2028 amid the energy transition by applying economic-mathematical methods, including linear regression, the Grey forecasting model, exponential smoothing, and Autoregressive Integrated Moving Average (ARIMA) time-series modeling. Particular attention is paid to the analysis of factors influencing the dynamics of the global palladium market, including the electrification of transportation, the substitution of palladium with alternative materials, and changes in global supply chains. The simulation results showed that the exponential smoothing model possesses the highest predictive accuracy, enabling it to estimate future palladium production volumes. The market is undergoing a structural transformation: declining demand from the traditional automotive sector is partially offset by the development of new applications in hydrogen energy, electronics, and advanced materials, suggesting that technological improvements can compensate for the loss of conventional demand segments. The key findings are (1) exponential smoothing (R² = 0.9812) outperforms linear regression, Grey model, and ARIMA; (2) Russian palladium production is projected at 74–130 tonnes (2026), 63–141 tonnes (2027), and 54–150 tonnes (2028); and (3) the decline in automotive demand is partially offset by new applications. Our findings confirm the need for Russian producers to adapt their strategies to the structural transformation of global demand, deepen domestic processing, and develop new high-tech applications for palladium to maintain their competitive positions amid the energy transition. Full article

Accurate prediction of tunnel water inflow in water-rich fault zones is important for groundwater control design and construction risk prevention. In this study, a per-linear-meter tunnel water inflow database containing 425 valid samples was established through orthogonal numerical simulations based on a three-dimensional steady-state seepage model with a grouting ring. The input variables included four hydraulic and grouting parameters and two excavation-position descriptors, namely the excavation-position distance and excavation-position category, thereby reflecting both the water-blocking effect of grouting reinforcement and the spatial variation in water inflow as the excavation face approached the fault zone. Considering that the samples were generated from 25 orthogonal simulation cases at different excavation positions, grouped validation was adopted to reduce information leakage at the simulation-case level. Four baseline machine learning models, including SVM, RF, XGBoost, and CatBoost, were evaluated using ten repeated grouped hold-out validations. CatBoost achieved the best overall baseline generalization performance, with an average test R² of 0.6209 ± 0.0405, MAE of 0.1084 ± 0.0079, and RMSE of 0.1555 ± 0.0085. CatBoost was therefore selected for further hyperparameter optimization. Subsequently, random search, Bayesian optimization, the Osprey Optimization Algorithm, and the Grey Wolf Optimizer were compared under the same search space and computational budget. Hyperparameter optimization was conducted only within the training set using grouped cross-validation, and the independent grouped test set was used only for final evaluation. The results showed that the unoptimized CatBoost model achieved the best overall balance between prediction accuracy, stability, and computational efficiency. Although RS-CatBoost slightly improved MAE and MAPE among the optimized models, none of the optimization strategies consistently outperformed the unoptimized CatBoost baseline, indicating that the choice of hyperparameter optimization algorithm played a secondary role under the current dataset and grouped-validation framework. The proposed framework is intended as a preliminary modeling reference under controlled numerical simulation conditions, and its practical engineering reliability requires further validation using field monitoring data or independent benchmark cases. Full article

During software testing phases, fault detection and correction processes are carried out simultaneously. However, the fault correction process is not considered in the grey software reliability growth model (SRGM). To solve this problem, this paper proposed a new grey SRGM framework named the grey SRGM integrating correction process (ICP) that considers both fault detection and correction processes. In the new model framework, the corrected faults are directly incorporated into the grey SRGM, and a one-step iterative calculation method is used to estimate the model. Numerical experiments involving four specific models tested on two real datasets validate the effectiveness of grey ICP-SRGM compared to the original grey model framework. Furthermore, the predictive performance of the new model framework is compared with various other prediction methods, including the Brown exponential smoothing model, Holt exponential smoothing model, autoregressive integrated moving average model, support vector regression model, and feedforward neural network model. Comparative analysis demonstrates that the new model framework exhibits superior adaptability when dealing with small samples and highlights its potential for practical applications where data availability is limited. Full article

This work presents an integrated methodology for modeling, parameter tuning, and experimental emulation of Dual Active Bridge (DAB) converters aimed at detailed simulation, control design, and real-time emulation. This is needed to bridge the gap between theoretical modeling and practical implementation, enabling accurate prediction of converter behavior under realistic operating conditions and facilitating the development of control strategies. The study begins with the derivation of a nonlinear model including parasitic elements and transformer characteristics, enabling accurate representation of the converter’s dynamics across operating conditions. To address deviations caused by component tolerances, the model parameters are calibrated using a multi-algorithm optimization framework based on Particle Swarm Optimization, Grey Wolf Optimizer, Secretary Bird Optimization, and Whale Optimization, where the error between predicted and experimental waveforms is minimized. The comparative analysis allows selecting the most suitable optimization strategy based on statistical analyses. The model is also discretized and implemented on a Hardware-in-the-Loop (HIL) platform based on a high-performance microcontroller, enabling real-time emulation of the converter as a digital twin. Moreover, a control-oriented version of the model is presented and used to design a voltage controller, which is subsequently tested in both the HIL environment and on a real DAB converter prototype. Experimental results report differences between HIL and real prototype below 3.9% for currents and 4.45% for voltages in multiple operation conditions, demonstrating an accurate representation of the real power system. This methodology ensures low errors between theoretical, simulated, and experimental behavior, providing a framework for accurate modeling and controller design of DAB converters. Full article

The development of tight sandstone gas reservoirs in the Ordos Basin is increasingly challenged by complex geological conditions and declining resource quality. Accurate identification of productivity-controlling factors and reliable prediction of fracturing sweet spots are therefore essential for improving reservoir development efficiency. In this study, geological, engineering, and production data from 56 wells in the target area were collected and preprocessed using forward and reverse normalization. Grey Relational Analysis was first used to identify the dominant factors controlling absolute open flow, and the selected variables were then incorporated into Light Gradient Boosting Machine to establish an integrated GRA-LightGBM prediction framework. The results indicate that permeability, average total hydrocarbon content, porosity, brittleness index, fracture toughness, and clay content are the primary productivity-controlling factors in the study area. The proposed GRA-LightGBM model achieved an R² value of 0.9233, indicating strong agreement between predicted and measured AOF values. Comparative experiments with traditional machine learning models and tree-based ensemble models further demonstrated that GRA-LightGBM provides more accurate and stable predictions, with smaller residual fluctuations and better overall performance. Based on the prediction results, the spatial distribution of fracturing sweet spots was visualized using the Petrel platform. This study provides an effective data-driven workflow for dominant factor identification, AOF prediction, and sweet spot delineation, offering technical support for the optimization of hydraulic fracturing and well deployment in tight sandstone gas reservoirs. Full article

Breed and herd are major determinants of milk productivity and efficiency. This study evaluated 1508 lactating cows from three dairy (Holstein, Brown Swiss, Jersey) and three dual-purpose (Simmental, Rendena, Alpine Grey) breeds across 41 multi-breed mountain herds. Data on body size, production, and milk samples were collected. Daily milk yield, milk energy, and cheese yield were quantified, and herds were classified under high (>75 MJ/d) or low (<75 MJ/d) average daily milk energy production. Cheese-making traits were predicted from 508 individual model cheese-making trials. Nine productivity ratios and three efficiency indicators (energy efficiency, economic efficiency, and income over feed costs) were calculated. Data was analyzed using mixed models including herd productivity class, herd within class (random), breed, parity, and days in milk. Differences among breeds were smaller within herds than across herds and were more pronounced for quality and cheese-making traits. Dairy and dual-purpose breeds showed similar overall performance. Jerseys were the least productive in absolute terms but, when adjusted for body size, were as or more efficient than other dairy breeds. Holsteins had the highest milk yield, whereas Brown Swiss showed superior milk quality and cheese yield. Simmental outperformed local dual-purpose breeds in size and production but not in efficiency. Accurate assessment of energy and economic efficiency is essential for breeding and crossbreeding strategies. Full article

To support offline process-parameter screening for Czochralski (CZ) silicon single crystal growth, this paper proposes a surrogate-assisted optimization framework based on an improved Langevin evolutionary algorithm. First, a multi-variable constrained optimization model is established, with the LSA-Transformer-predicted solid–liquid interface deformation used as the objective evaluation and with process-smoothness and physical-feasibility constraints considered. Six key process parameters–heater power, pulling rate, argon flow rate, crystal rotation speed, crucible rotation speed, and magnetic field strength–are selected as decision variables. Second, building on the classical Langevin algorithm, an adaptive inertia weight mechanism, a diversity promoter (DP) operator, and a local escaping operator (LEO) are introduced to improve global exploration and local optima escape in complex search spaces. Verification on 23 classical benchmark functions indicates that the ILEE algorithm shows competitive overall performance and achieves better or comparable results on many functions when compared with particle swarm optimization (PSO), grey wolf optimization (GWO), the original Langevin evolutionary algorithm (LEE), and other baseline algorithms. The proposed framework is then used for offline candidate recipe generation during the crystal equal-diameter growth stage (200 mm, 400 mm, 600 mm, 800 mm, and 1000 mm). The optimized candidate parameter combinations yield lower surrogate-predicted interface deformation under the given LSA-Transformer model and physical constraints. Because these values are not independent CFD or experimental measurements, the results should be interpreted as process-parameter guidance for future physical validation. This work provides a feasible surrogate-assisted offline screening framework for CZ silicon single crystal growth. Full article

To address the issues of limited interpretability and low predictive accuracy in traditional photovoltaic forecasting models, this paper proposes a hybrid forecasting model named HO-Transformer-KAN-PINN. First, Maximal Information Coefficient (MIC) is used to select the key meteorological features: irradiance and temperature. Then, the grey relational analysis combined with cosine similarity is applied to identify similar days. The prediction framework is then constructed. The Transformer-KAN model provides high predictive accuracy and strong interpretability, while embedding physics-informed neural network (PINN) constraints enforces compliance with the underlying physical laws, yielding the Transformer-KAN-PINN framework. Simultaneously, the Hippopotamus Optimization (HO) algorithm is used to optimize the model hyperparameters. Finally, the photovoltaic power combination prediction model of HO-Transformer-KAN-PINN is constructed. This model has achieved excellent results in short-term photovoltaic power forecasting in Yunnan, Gansu, and Australia. Taking winter in Yunnan Province as an example, the forecasting results of this model yield an MAE of 0.3204 MW, an RMSE of 0.4197 MW, a MAPE of 4.9561%, and an R² of 0.9986. Therefore, the hybrid forecasting model proposed in this paper demonstrates a certain degree of advancement and effectiveness. Therefore, it provides reliable technical support for accurate prediction of photovoltaic output. Full article