Search Results (1,434)

Conceptual design is a critical phase in the development of propulsion plants. Its aim is to explore multidimensional design spaces to provide guidelines for the detailed design phase. This exploration is necessarily broad in scope and shallow in fidelity. Lately, developments in computing hardware have allowed the use of simplified computational fluid dynamics (CFD) models for conceptual design purposes, thereby increasing significantly the amount of data to be processed and generalised. In this context, the present work benchmarks two specific surrogate-model implementations for the conceptual design of a rocket-type pulse detonation engine: a tensor-based method based on high-order singular value decomposition (HOSVD) and a fully connected feed-forward neural network (NN). Two complementary comparisons were considered: HOSVD versus NN, using the same factorial databases to assess the effect of the surrogate method; and factorial versus low-discrepancy sampling, using the same NN architecture to assess the effect of the database distribution. The benchmark, which involved five input architecture parameters and five output operation parameters, was performed for both the direct analysis problem (outputs obtained from inputs) and the inverse design problem (inputs obtained from outputs). Three different situations were considered in the comparison—dimensionally balanced, overdetermined, and underdetermined—to simulate conditions that typically arise in these design phases. Three databases of different sizes were generated for the comparison. The results provide a case-specific assessment of the performance of the two implementations under the conditions considered and may provide useful guidance on the application of these data-analysis approaches in conceptual design. Full article

Accurate and robust state-of-health (SOH) estimation is essential for efficient battery management systems (BMS), especially in practical scenarios suffering from severe cell-to-cell inconsistencies and data distribution shifts. Although prevailing data-driven estimation methods can achieve satisfactory accuracy within specific domains, their generalization capability degrades drastically when applied to unseen battery cells. To fill this research gap, this paper develops an integrated physics-guided and domain-aware SOH estimation framework. The proposed framework combines redundancy-aware feature screening, cycle-aware soft covariance alignment specifically designed for physics-based health indicators, as well as monotonicity-constrained LightGBM regression embedded with split conformal uncertainty quantification. Experimental validations are conducted on the NASA B0005, B0006 and B0007 battery datasets under the leave-one-cell-out (LOCO) cross-cell evaluation strategy. Comparative results reveal that the presented method achieves prominent performance improvement on the most difficult B6 domain, where the root mean square error (RMSE) is reduced from 0.1068 to 0.0845 with a decline rate of 20.9%, and the coefficient of determination ($R^2$) rises from 0.2464 to 0.5286, while maintaining stable estimation accuracy on less challenging target domains. In terms of overall performance, the average RMSE across all tested cells drops from 0.0499 to 0.0425, corresponding to a 14.8% reduction, indicating improved cross-cell performance across the investigated cells. Furthermore, the adaptive alignment mechanism can be dynamically activated only when necessary, effectively avoiding redundant distortion of the original feature distribution. The research findings indicate that the integrated framework can alleviate cross-cell battery degradation discrepancies under the investigated conditions. The proposed strategy demonstrates promising potential for improving cross-cell SOH estimation under the investigated laboratory conditions; however, the uncertainty intervals are not fully calibrated under severe domain shift, and broader validation on larger and more heterogeneous battery datasets is required before practical deployment claims. Full article

Fuel design based on distillation fractions is crucial for advancing fuel development and optimizing combustion systems. However, the chemical diversity and broad boiling-point distribution of full-range fuels pose significant challenges for kinetic modeling. In this study, a volatility-aware, multi-component kinetic mechanism was developed for simulating the combustion of various distillation fractions from an FRF in diesel engines. The mechanism comprises 261 species and 860 reactions. Unlike conventional surrogate mechanisms designed primarily for a single fuel or narrow distillation range, the proposed framework simultaneously represents the major hydrocarbon classes, ignition quality, and boiling-point distribution of FRF. The surrogate palette includes n-pentane, n-heptane, n-decane, n-dodecane, n-hexadecane, heptamethylnonane, 1-methylnaphthalene, iso-octane, methylcyclohexane, decalin, toluene, tetralin, and 1,2,4-trimethylbenzene. These components were selected to reproduce the molecular structures, ignition characteristics, and distillation behavior of the target fuel fractions. The mechanism was further refined through targeted replacement of the toluene sub-mechanism using updated hydrogen-abstraction and benzyl-radical oxidation reactions, followed by a fuel-oriented five-stage reduction strategy involving reaction-pathway-based pruning, DRGEP reduction, isomer lumping, sensitivity/ROP refinement, and targeted rate optimization. The resulting mechanism provides reasonable predictions of ignition delay, laminar flame speed, and species profiles for pure components, surrogate fuels, and real gasoline, jet, and diesel fuels. Coupled with a three-dimensional CFD model, the reduced mechanism also reproduces the main combustion phasing, pressure-rise process, and peak in-cylinder pressure of a diesel engine at 500 and 800 r/min over the investigated intake-temperature range. Although discrepancies remain in the low-temperature/negative-temperature-coefficient regime and in the quantitative prediction of the peak apparent heat-release rate, the mechanism provides a unified and practical framework for linking FRF distillation characteristics with chemical reactivity and engine-level combustion behavior. It therefore offers a foundation for designing tailored fuels from distillation fractions for operation in extreme environments. Full article

Multi-source cross-domain hyperspectral image (HSI) classification is challenged by heterogeneous sensor configurations, scene-dependent distribution shifts, and limited labeled target data, which hinder effective knowledge transfer across multiple scenes. Motivated by progressive feature correction, we propose a three-stage Residual Feature Discrepancy Refinement (RFD) framework for collaborative representation learning across heterogeneous HSI domains. RFD formulates this correction as a deterministic, discrepancy-conditioned residual refinement process. First, domain-specific encoders project four source domains and the target domain, which may have unequal spectral dimensions and label spaces, into a common-dimensional feature space. Adaptive severity and domain weighting uses first- and second-order feature discrepancies to estimate source-specific conditioning coordinates and collaborative contribution weights. A shared discrepancy-conditioned residual refiner then performs multi-step feature refinement to reduce domain-dependent statistical deviations. Finally, an exponential-moving-average historical prototype memory stabilizes target adaptation, followed by cosine 1-nearest-neighbor classification. Across ten randomized runs, RFD achieves mean overall accuracies of 94.65%, 94.87%, and 96.95% on NC12, Salinas, and WHU-Hi-LongKou, respectively, and obtains the highest mean overall accuracy, average accuracy, and $\kappa$ among the evaluated unified-protocol methods. Full article

This paper presents a 3-bit distributed step attenuator for Ka-band applications in a 100 nm GaN HEMT process. Designed for a 0 to −7 dB range across 28–38 GHz, the fabricated device provides 0 to −10.9 dB with a measured insertion loss of 0.94–1.11 dB, return losses better than 10 dB, and an RMS phase error of 4.15° at 38 GHz. Every bit was realized 40 to 58 percent above its design target at both design frequencies, a deviation that simulation did not reproduce. To identify its origin, the sensitivity of the attenuation to the tuning resistor is quantified for each cell. A sweep across 5–20 Ω gives 6.5 mdB/Ω for the 1 dB cell, rising to 42.6 mdB/Ω for the 4 dB cell. In the 1 dB cell the measured step cannot be reproduced at any resistor value, which excludes resistor variation and locates the discrepancy in the switch branch. The branch on-resistance is estimated at about 81 Ω per device, placing the tuning resistors at 0.11 to 0.20 of the branch impedance. The analysis yields a design guideline: step-size margin must be secured through device sizing rather than resistor selection, with the largest margin allocated to the cells of smallest attenuation. Full article

Glycosylation is a critical determinant of the efficacy, stability, and pharmacological behavior of therapeutic proteins. R27T, an engineered variant of interferon-β1a, contains two N-glycosylation sites (Asn25 and Asn80), increasing its structural complexity and analytical requirements. In this study, we performed comprehensive total and site-specific glycan profiling of R27T using complementary analytical approaches. For total glycan analysis, the released N-glycans were fluorescently labeled with procainamide, providing enhanced sensitivity and broader glycan coverage compared with conventional 2-aminobenzamide labeling. Site-specific glycan profiling was performed by liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based peptide mapping. Protease digestion conditions were optimized to improve recovery of site-specific glycopeptides, with chymotrypsin identified as the most effective enzyme for resolving glycopeptides from individual glycosylation sites. Total glycan distributions reconstructed from peptide-mapping data were compared with fluorescence-based glycan profiling, showing that total and site-specific glycan data can be effectively combined. Minor discrepancies were observed depending on glycan structure, mainly due to differences in ionization efficiency. Distinct glycan distributions were observed between the two N-glycosylation sites of R27T. Molecular modeling further suggested that the additional glycan at Asn25 may enhance structural stability and receptor-binding affinity. These results demonstrate an integrative strategy for accurate glycan characterization in multi-site glycoproteins relevant to biotherapeutic development. Full article

In unmanned aerial vehicle inspection of transmission lines, insulators often exhibit arbitrary orientations and elongated shapes and are frequently embedded in complex backgrounds. Horizontal bounding boxes tend to include substantial redundant regions. Meanwhile, existing oriented object detection methods still suffer from angular discontinuities at periodic boundaries, insufficient geometric supervision for rotated bounding boxes, and difficulties in lightweight deployment. To address these issues, this paper proposes a lightweight oriented object detection model, termed R-YOLOv8-PSGH, which integrates dual-frequency phase-shift encoding and Gaussian geometric supervision. Based on a lightweight R-YOLOv8 architecture, a rotated detection head is developed to decouple the predictions of object categories, bounding-box locations, and orientation angles. To improve the periodic continuity of angle representations and strengthen the geometric constraints on rotated bounding boxes, a dual-frequency phase-shift angle encoding strategy and a Gaussian geometric localization loss are designed. Specifically, the complementary relationship between periodic signals with periods of 180°and 90° is exploited to map orientation angles into continuous phase responses, thereby improving the stability of orientation prediction. Moreover, the spatial structure of each rotated bounding box is modeled as a two-dimensional Gaussian distribution, and overlap consistency, center distance, and shape discrepancy are jointly optimized. In this manner, the orientation representation and bounding-box-level geometric supervision are collaboratively enhanced. Experimental results demonstrate that the proposed method improves the detection accuracy and localization stability of rotated objects while maintaining favorable lightweight deployment capability, providing a new solution for lightweight object detection in complex scenarios. Full article

The Ouenza open-pit mine is one of Algeria’s major iron ore producers, where efficient blasting and fragmentation control are essential to maintaining stable, productive mining operations. The primary objective of this study is to quantitatively assess the agreement between predicted and measured rock fragmentation at the Ouenza open-pit mine. The research methodology includes a comprehensive analysis of blasting parameters. Field experiments used four blasts, all executed with the same blasting plans on the same working face to ensure comparability. The rock fragmentation was precisely measured and analyzed using the advanced image-processing software Wip-Frag to determine the particle-size distribution. The Kuz–Ram model predicted the fragment size distribution using the same blasting design implemented in the field, and the predicted fragmentation was quantitatively evaluated against Wip-Frag measurements using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The statistical evaluation yielded an R² of 0.9095, an RMSE of 9.71%, and an MAE of 6.54%, indicating good overall agreement between the predicted and measured fragmentation, despite noticeable deviations in the coarse-fragment size range. The observed discrepancies were mainly associated with potential inaccuracies in the field implementation of the designed blast pattern, limited compliance with blasting procedures, and the natural heterogeneity of the rock mass. The results show that comparing Wip-Frag measurements with Kuz–Ram predictions is an effective way to identify discrepancies between predicted and actual fragmentation and to determine the field factors driving these differences. These findings provide practical insights into blast implementation and fragmentation control under actual mining conditions. Full article

Distance measures in random permutation set (RPS) theory are crucial for characterizing inconsistency among permutation-based information distributions. However, existing RPS discrepancy measures do not explicitly distinguish ordering conflicts according to their positional importance under propensity semantics. To address this issue, this paper proposes a new RPS distance, termed MEOWA-KTC, by combining maximum-entropy-based ordered weighted averaging (MEOWA) weights with Kendall’s tau coefficient (KTC). Specifically, MEOWA-KTC constructs a top-weighted similarity between permutation events by using KTC to evaluate the ordinal consistency of corresponding sub-permutations and MEOWA weights controlled by an adjustable orness parameter to emphasize discrepancies at leading positions. Additionally, a spectral correction is applied to ensure that the proposed distance satisfies the metric axioms. Numerical examples and ablation results demonstrate the positional sensitivity of the proposed distance and the respective contributions of MEOWA weighting and KTC. Based on this distance, a fusion model is further developed to derive source support degrees and fusion weights from pairwise RPS distances. In the threat-assessment application, the proposed method produces stable decisions and generally larger decision margins than the benchmark methods. Monte Carlo experiments further demonstrate its robustness to mass-distribution and permutation-order noise. Full article

Rockbursts occur frequently in deep hard-rock tunnels, posing a major challenge to the safe and efficient construction of underground engineering. Engineering practice shows that in addition to high in-situ stress and hard brittle lithology, widely distributed structural planes in surrounding rock also significantly modify rockburst failure modes and intensity. This review systematically investigates structural-plane-controlled rockburst phenomena in deep hard-rock tunnels, based on 16 published field cases and more than 40 laboratory studies. First, we summarize the influence mechanisms of structural planes on tunnel rockbursts at the engineering scale through statistical analysis of case data. We then integrate existing experimental findings to analyze how the geometric and physical properties of structural planes alter rockburst behavior, from four perspectives: location (concealed/exposed), attitude (dip angle, strike, length), filling state, and multi-plane combination. We further synthesize multi-physical field response characteristics (acoustic emission, infrared thermal radiation, and surface strain field) from laboratory tests, and compare crack propagation and energy evolution patterns dominated by structural planes. The scale dependence of structural plane effects is discussed, highlighting consistencies and discrepancies between laboratory-scale mechanisms and field-scale engineering phenomena. Finally, we analyze rockburst mechanisms under the coupled action of structural planes and dynamic disturbances, and propose targeted engineering control strategies for different structural plane conditions. The purpose of this review is to integrate a set of analysis frameworks to establish the relationship between structural plane characteristics (location, attitude, filling state, and multi-plane combination) and multi-physical field responses, fracture evolution and energy evolution laws, as well as engineering-scale rockburst behavior. It is noteworthy that the engineering cases compiled in this review predominantly originate from deep hard-rock tunnels in China. The universality of the impact of structural planes on rockbursts still needs to be further verified by combining cases from different structural settings and engineering backgrounds. Full article

Foundation models for Earth observation are commonly transferred to downstream tasks without explicit attention to the preprocessing mismatch between pretraining and target distributions. This study isolates preprocessing mismatch as a controlled experimental factor in transferring the Hydro multispectral foundation model, a Swin Transformer V2 Base encoder pretrained with SimMIM on Sentinel-2 Level-2A water-body imagery, to the Marine Debris and Oil Spill (MADOS) marine pollution benchmark dataset, processed through ACOLITE Rayleigh reflectance and providing 11 of the 12 spectral bands used during pretraining. The two datasets are therefore produced by different atmospheric correction algorithms under different reflectance conventions, and the resulting per-band statistical discrepancy is quantified as the starting point of the analysis. Three preprocessing dimensions are then systematically varied while all other settings are held constant: input normalisation, spectral band adaptation for the missing B09, and encoder transfer mode. From this, four findings emerge. Normalisation mismatch between training and inference is the single largest source of performance degradation, reducing the mean Intersection over Union (mIoU) by 0.458, more than seven times the largest radiometric perturbation tested. A zero-parameter Frobenius-matched column crop of the patch embedding adapts the 12-band pretrained encoder to the 11-band target, at least as effectively as any learnt linear or nonlinear adapter, at a lower cross-seed variance. Under limited target supervision (1433 training patches against an 87.9 million-parameter encoder), freezing the encoder outperforms both fine-tuning in full and random initialisation training from scratch. The gains of partial unfreezing are attributable to augmented training (very simple copy–paste (VSCP) augmentation, exponential moving average (EMA), and test-time augmentation (TTA)) rather than to encoder adaptation. With matched preprocessing, the frozen encoder reaches 0.600 mIoU and matches the published MariNeXt baseline within seed variability. Mechanistic analysis via band-occlusion attribution and feature-space separability shows that input normalisation determines which spectral bands the encoder relies upon, with the magnitude of the shift correlated to the per-band gap between the source and target distributions. Operationally, preprocessing alignment, rather than architectural modification, carries most of the practical effort in transferring a multispectral foundation model to marine surface segmentation. These results are established for a single encoder–benchmark pair under limited target supervision. The mechanism they identify is more portable than the magnitude reported. A frozen encoder’s representations remain bound to the normalisation statistics of its pretraining dataset, so any transfer that departs from these statistics at inference is predicted to degrade sharply in proportion to the per-band distance between the two distributions. Full article

Industry 5.0 requires resilient, adaptive, and trustworthy manufacturing systems capable of maintaining reliable diagnostic performance across heterogeneous industrial environments. However, data-driven fault diagnosis models often experience substantial performance degradation when transferred across machines, operating conditions, and data acquisition platforms because of domain distribution shifts. This study proposes an explainable domain-adaptive CNN–Transformer framework for unsupervised bidirectional cross-domain bearing fault diagnosis using the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets. The framework integrates one-dimensional convolutional layers for extracting local high-frequency vibration patterns, Transformer encoders for modelling long-range temporal dependencies, and Maximum Mean Discrepancy (MMD)-based feature-distribution alignment for learning transferable domain-invariant representations. Under the Unsupervised Domain Adaptation (UDA) protocol, the source domain supplies labelled samples for classification learning, whereas the target domain contributes unlabelled features only for MMD-based alignment; target labels are withheld from training and model selection and are used only for final evaluation. Conventional 1D-CNN and bidirectional long short-term memory baselines achieve over 90% accuracy in-domain but fall to 82.14% and 79.88%, respectively, for CWRU→PU, corresponding to domain-drop magnitudes of 12.07 and 12.99 percentage points. The proposed framework achieves 98.63% and 96.82% in-domain accuracy on CWRU and PU, respectively, and 92.46% for CWRU→PU and 94.18% for PU→CWRU, with domain-drop magnitudes of 6.17 and 2.64 percentage points. Ablation results confirm the complementary contributions of convolutional feature extraction, Transformer-based temporal modelling, and domain alignment. Furthermore, attention, saliency, and feature-importance analyses show that the model focuses on fault-relevant vibration regions and informative diagnostic characteristics, including kurtosis, root-mean-square (RMS), and crest factor, improving prediction transparency. These findings support accurate, transferable, and interpretable vibration-based condition monitoring across heterogeneous bearing datasets. Full article

Classical Munk-type added-mass modeling remains a fundamental tool in airship flight dynamics because the displaced fluid mass is comparable to the vehicle mass and contributes directly to the generalized inertia. This paper investigates the applicability limits of classical Munk-type modeling for slender airships and develops a geometry-sensitive generalized framework based on actual hull contour information. The proposed formulation preserves consistency with the classical ellipsoidal limit while introducing discrepancy measures derived from the sectional area distribution and its longitudinal moment. The model is organized into a strict potential-flow added mass layer and a separate correction layer so that geometry-sensitive inertial effects are not conflated with non-potential aerodynamic corrections. Representative hulls are used to quantify the structural error of equivalent-ellipsoid approximations, and the Lotte airship is selected for a regularized literature-based assessment using published static and quasi-static aerodynamic coefficient curves. For the Lotte assessment case, the results show that the geometry-sensitive regularized model improves the representation of body-fixed normal force and pitching moment coefficient trends relative to the corresponding classical ellipsoidal baseline, with the clearest improvement in the pitching moment response. The analysis further indicates that the discrepancy associated with the second axial area moment is more influential for the moment response than the maximum thickness location parameter. Full article

Cross-condition aero-engine gas-path fault diagnosis remains challenging because gas-path parameters exhibit heterogeneous transferability, hidden feature distributions shift substantially across operating conditions, and the class structure of the unlabeled target domain is often unstable. To address these issues, this study proposes a Transferability-Guided Residual Attention Domain Adaptation Network (TG-RADAN) for unsupervised cross-condition aero-engine gas-path fault diagnosis. First, a Transferability Index (TI) is constructed by jointly considering source-domain fault discriminability and cross-domain distribution stability. Based on the TI, a learnable feature-gating mechanism is introduced to adaptively reweight gas-path parameters and suppress operating-condition-sensitive features. Second, a residual-attention encoder with domain-specific batch normalization is developed to enhance fault-discriminative representations while mitigating hidden-layer statistical discrepancies between domains. Third, conditional adversarial domain adaptation, high-confidence pseudo-label prototype alignment, and target entropy minimization are jointly employed to improve class-conditional alignment and preserve the target-domain structure. Experiments on 12 cross-condition transfer tasks demonstrate that TG-RADAN achieves average accuracies of 94.43% and 94.41% on the two groups of transfer tasks, respectively, outperforming the conventional CDAN baseline by 11.94% and 12.91%. Ablation results show that the complete model improves upon the model without TI-guided gating by 2.69% and 3.20%, respectively. These findings indicate that TG-RADAN can effectively improve the transferability, robustness, and interpretability of unsupervised cross-condition aero-engine gas-path fault diagnosis. Full article

Non-small cell lung cancer (NSCLC) represents most lung cancer diagnoses worldwide and remains a leading cause of cancer-related mortality. The advent of precision oncology has transformed the therapeutic landscape of NSCLC through the identification of druggable genetic alterations, enabling the use of targeted therapies with significant clinical benefit. However, the reported prevalence of these alterations varies widely across studies and populations, raising important questions about the underlying determinants of such discrepancies. In this context, molecular epidemiology provides a framework to understand the distribution of genomic alterations and their interplay with demographic, clinical, and methodological factors. This narrative review examines the spectrum of druggable genetic alterations in NSCLC and critically analyzes the sources of variability in their reported frequencies. We discuss geographic and ethnic differences, particularly between East Asian, European, and North American populations, as well as the influence of smoking status, environmental exposures, histologic subtypes, and sex-related factors. Furthermore, we explore how disease stage and sample source may contribute to heterogeneity in molecular profiles. A substantial focus is placed on methodological sources of discrepancy, including differences in molecular testing platforms, analytical sensitivity and limits of detection, tissue versus liquid biopsy approaches, tumor heterogeneity, and gene panel design. Finally, emerging trends in the field, such as the use of ultra-large genomic datasets, real-world evidence, multi-omics integration, and artificial intelligence, alongside ongoing efforts toward standardization of molecular testing, are discussed. Full article