Search Results (29,380)

In the present work, an alternative methodology was developed for the rapid prediction of pressure distributions over wings of low-speed aircraft. A hybrid neural network architecture, named “MARTHA” (Model for Airloads Reconstruction using a Trained Hybrid Architecture), was presented, which is composed of a Multilayer Perceptron and the decoder of an Autoencoder. Three compact representation models—Principal Component Analysis (PCA), Autoencoder (AE), and Variational Autoencoder (VAE)—were systematically evaluated to determine the optimal dimensionality reduction architecture; the AE was selected based on its superior reconstruction accuracy and training stability. The main feature of MARTHA is that it provides predictions of the differential pressure coefficient field in the form of monochrome images, where the pixel intensity directly represents the normalized pressure value. One of the main objectives of developing MARTHA was to create a rapid surrogate model that can approximate vortex lattice method (VLM) simulations in preliminary design and optimization tasks, particularly when thousands of wing configurations need to be evaluated. The key feature of the proposed model is its ability to predict the pressure distribution for trapezoidal wings of various geometries 10¹–10⁴ times faster than numerical models, while maintaining accuracy (R² = 0.9998). The data obtained are presented in a convenient format for their further use in CAE systems of strength analysis. To assess the practical utility of the proposed model, implementation cases were carried out using the finite element software ANSYS 18.2 for three wing configurations not present in the training dataset. The pressure fields predicted by MARTHA were mapped onto the wing meshes, and linear static structural analyses were performed. The obtained Von Mises stress distributions showed good agreement with the corresponding distributions obtained using numerical models. Full article

Noise mitigation on urban rail transit lines in service is often constrained by the need to maintain uninterrupted operation and avoid large-scale demolition or reconstruction. Using Kunming Metro Line 4 as an engineering case study, this study combined onboard field measurements with data-driven analysis to characterize passenger-compartment noise and identify its principal determinants. A green noise-mitigation framework integrating source control, transmission-path control, and green operational management was developed, and its benefits were quantified across four dimensions: acoustic-environment improvement, human-health protection, low-carbon energy savings, and enhanced operation and maintenance efficiency. Passenger-compartment noise levels predominantly ranged from 78 to 82 dB(A). SHapley Additive exPlanations (SHAP) analysis identified train operating speed and track gradient as the two dominant factors, with relative importance values of 35.03% and 27.56%, respectively. Engineering implementation of the framework reduced peak noise levels in curved sections by 3–8 dB(A). The results further showed that noise-pollution mitigation and carbon-emission reduction share common sources and intervention pathways. The scenario-based assessment indicated that, under the specified parameter assumptions, the relevant measures could collectively achieve an estimated annual carbon emission reduction of approximately 3353.20 tCO₂. These findings provide a practical and scientific basis for green noise mitigation and coordinated pollution and carbon reduction on urban rail transit lines in service. Full article

The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories. Full article

Intrinsically self-sensing cementitious composites offer a promising basis for continuous structural health monitoring. Their electrical response, however, is strongly affected by reversible moisture change and freeze–thaw exposure. This study presents a computational proof-of-concept for the AI-driven design of a federated digital-twin architecture for damage identification and adaptive sensing in sustainable self-sensing cementitious infrastructure. The framework is developed and evaluated entirely in software on a physics-based synthetic testbed. At its present maturity, it is therefore a digital-twin precursor rather than an operational digital twin: it has no calibrated physical counterpart and no live, two-way data coupling, and no experimental validation is claimed. A transparent, physics-based signal generator produces fractional-change-in-resistance signals for twelve virtual CNT/biochar-functionalized LC3 and geopolymer specimens. Each passes through four progressive damage stages interleaved with wet–dry and freeze–thaw conditioning. The framework integrates a CNN-LSTM damage classifier, unsupervised domain adaptation, federated learning, reinforcement-learning-based active sensing, and quantum-inspired aggregation optimization. On three unseen virtual specimens (654 evaluation windows), the CNN-LSTM achieved 70.3% four-stage accuracy (95% Wilson confidence interval 66.7–73.7%) and a macro-F1 score of 0.650, with per-specimen accuracy ranging from 63.8% to 77.1%. It reached 85.2% (95% CI 82.3–87.7%) for the damaged-versus-undamaged decision and reduced environment-induced false alarms by 74.7% (95% CI 61.7–83.4%) relative to a calibrated threshold detector. Federated averaging was less accurate and less stable than centralized training; the 5.2 percentage-point gain from quantum-inspired aggregation lies within the resolution of the evaluation set and is not established as a real improvement. The active-sensing controller reduced measurement cost by 98.9% but detected only four of 27 damage-progression events. All sensing data are synthetic, and every interval reported here is recomputed from the evaluation counts already reported rather than obtained from additional experiments. The results therefore establish algorithmic feasibility only and identify the components requiring refinement before experimental validation. Full article

This study proposes a surrogate model, Graph-Structured Physics-Informed Deep Operator Network (GS-PI-DeepONet), to simulate two-dimensional hydrodynamics in a tidal river network. The model was coupled with Graph Convolutional Networks (GCNs) for spatial feature extraction and Long Short-Term Memory (LSTM) network for temporal prediction, with 2D shallow-water equations (2D SWEs) embedded as physical constraints. To handle complex river network topologies, a mapping mechanism was proposed to transform discrete irregular boundaries into differentiable neural network constraints. A dynamic weighting strategy was developed to improve model training efficiency. GS-PI-DeepONet was applied to a river network within the Pearl River Basin in Zhuhai. Trained on high-fidelity Delft3D data, it achieved precise flow field reconstruction and millisecond-level extrapolation predictions, outperforming traditional data-driven models. The model can be a valuable tool for real-time hydrodynamic simulations and flood management strategies in tidal river networks. Full article

Background: Portal vein tumor thrombus (PVTT) marks advanced hepatocellular carcinoma (HCC) and carries a dismal prognosis. Survival varies widely even within this stage, yet simple tools for individualized risk stratification remain scarce. Methods: We retrospectively enrolled 134 HCC patients with PVTT and randomly divided them into a training set (n = 94) and a validation set (n = 40). Clinical predictors were selected by variance inflation factor screening and backward elimination Cox regression. A radiomics score (Rad-score) was constructed from portal-venous phase computed tomography (CT) images using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Three Cox models were built: a clinical model, an imaging model based solely on the Rad-score, and a combined model integrating both. Discrimination was assessed by C-index and time-dependent area under the curve (AUC). Calibration was examined with bootstrap-based calibration curves. Decision curve analysis evaluated net benefit. A nomogram was developed from the combined model. Results: Four clinical variables (alpha-fetoprotein (AFP), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and alkaline phosphatase (ALP)) and two CT texture features (GLRLM_SRHGE and GLZLM_SZHGE) were retained as independent predictors. The combined model gave the highest C-index in both the training set (0.843) and the internal validation set (0.815). Its 1-year AUC reached 0.953 and 0.947 in the two sets. Calibration slopes ranged from 1.044 to 1.291 across time points, indicating a tendency toward mild overdispersion; nevertheless, decision curve analysis confirmed net benefit across clinically relevant thresholds. The combined model offered greater net benefit than either single-domain model across a 0–50% threshold range. A nomogram incorporating all five predictors was generated for individualized 12- and 24-month survival prediction. Conclusions: A combined model integrating routine laboratory variables and a CT-based radiomics score improved survival prediction over clinical or imaging models alone. The corresponding nomogram uses inputs from a basic blood panel and a single portal-venous phase CT, suggesting its potential as a low-cost prognostic stratification tool for HCC patients with PVTT, although external validation in prospective multicenter cohorts is required before clinical implementation. Full article

Super-large aggregate asphalt mixtures (SLAM-50) are expected to improve rutting resistance while reducing asphalt demand, but the quantitative relationships between volumetric parameters and key performance indicators remain unclear, limiting performance-oriented mixture design. This study experimentally evaluated the volumetric properties and key performance indices of SLAM-50 across four aggregate gradations and seven asphalt contents. The measured performance indices included compressive strength, splitting strength, compressive resilient modulus, fracture energy, and dynamic stability. With increasing asphalt content, the air voids (VV) decreased, the voids in mineral aggregate (VMA) decreased initially and then increased, and the voids filled with asphalt (VFA) increased monotonically. All performance indices exhibited a non-monotonic trend, increasing first and then decreasing, with a balanced overall performance at 3.0–3.2% asphalt content. Linear regression models showed limited predictive capability (R² = 0.5944–0.8845). To address this gap, a Backpropagation (BP) neural network was developed using asphalt content, volumetric parameters, mixture density, and gradation type as inputs, and the measured performance indices as outputs. This framework enables simultaneous multi-output prediction and captures the nonlinear, coupled relationships among variables. The model achieved R² > 0.91 for both training and testing datasets, demonstrating its ability to accurately predict SLAM-50 performance. These findings provide a practical, data-driven basis for performance-oriented mixture design and optimization, addressing the current scientific gap and offering guidance for engineering practice. Full article

Kneeosteoarthritis (KOA) is a common degenerative joint disease that causes pain, stiffness, and impaired mobility. Automated Kellgren–Lawrence (KL) grading from knee X-ray images facilitates efficient screening and follow-up assessment. However, models trained on a single-source dataset frequently suffer performance degradation when applied to external cohorts, due to heterogeneities in image quality, acquisition protocols, class distributions, and annotation patterns. Furthermore, conventional domain adaptation approaches typically treat all source samples uniformly, making them vulnerable to negative transfer induced by ambiguous or distributionally divergent instances. To overcome these limitations, the present study develops a supervised contrastive domain adaptation framework designed for robust KOA severity grading under domain shift. The framework incorporates two task-specific modules: (1) a source-domain sample screening module that dynamically allocates class-wise quotas based on transferability and identifies high-value source samples by evaluating target intra-class affinity, inter-class separability, and source-class compactness; and (2) a target-balanced ordinal contrastive learning module that aligns the screened source samples with target features and imposes stronger constraints on negative pairs with larger KL-grade distances. The framework was evaluated bidirectionally on KneeKL (8260 images) and MedicalExpert-I (1650 images), two public knee radiograph datasets for KOA grading. With ResNet-18, it achieved a Quadratic Weighted Kappa (QWK) of 0.8557 for KneeKL-to-MedicalExpert-I transfer, exceeding source-only training and direct source–target merging by 0.2652 and 0.0468, respectively. Comparisons with representative existing methods and multiple experimental analyses further validate the competitiveness of the proposed framework. Full article

Foam concrete is a lightweight material characterized by low density and moderate mechanical strength, which can be combined with aluminum alloys to form a novel type of column. Such composite members enable rapid assembly, disassembly, and functional reconfiguration in prefabricated structures. However, research on the axial compressive performance of this new column system remains limited. This study investigates the axial behavior of aluminum alloy-foam concrete short columns through a combination of numerical simulation, theoretical analysis, and machine learning prediction enhanced by the SHAP (SHapley Additive exPlanations) interpretability method. A three-dimensional finite element model was developed in ABAQUS to examine the effects of frame thickness, foam concrete strength, and section dimension on load-bearing capacity. The results indicate that the column sectional dimensions have a significant influence on the axial compressive capacity. The foam concrete strength and frame thickness have relatively smaller effects. In addition, the frame thickness can effectively restrain lateral deformation and delay buckling. Based on the confinement mechanism, polynomial fitting, Mander’s model, and a composite column formulation were proposed for axial capacity prediction. Furthermore, eleven machine learning models were trained on 64 simulation datasets 64 independent computational experiments, among which the Gradient Boosting Decision Tree (GBDT) demonstrated the best performance (R² = 0.9984, MAE = 5.97, RMSE = 7.51). SHAP analysis further revealed the relative contributions of key features, showing that section dimension is the most influential parameter, followed by foam concrete strength, while frame thickness contributes the least. These findings not only enhance the theoretical understanding of the load-transfer mechanism of columns but also provide reliable predictive models and analytical formulations for their application in lightweight prefabricated structures. Full article

Interpreting multispectral remote sensing data for hydrothermal alteration mapping remains challenging in complex mountainous metallogenic belts because dense pixel-level field labels are difficult to obtain and weak spectral responses are affected by lithological background, vegetation, snow/ice cover, and topographic shadow. This study proposes a principal component analysis (PCA)-guided weakly supervised workflow for mapping hydroxyl- and iron-oxide-related spectral anomalies in the Bulong–Maidan–Tuoyun gold–copper metallogenic belt, southwestern Tianshan, China, using Landsat 8 Operational Land Imager (OLI) imagery. PCA was used as a spectral prior to generate PCA-derived positive spectral anomaly samples for model training. A Residual-ECA Alteration Information Extraction (REA-AIE) model was developed to refine PCA-derived anomalies by learning local spectral–spatial features from multispectral image patches. Under the PCA-constrained random sample-level evaluation, REA-AIE achieved F1 scores of 95.90% and 97.09% for hydroxyl- and iron-oxide-related spectral anomalies, respectively; these values indicate agreement with PCA-derived pseudo-labels rather than spatially independent estimates of mapping performance. Petrography-constrained site-level assessment showed that REA-AIE-predicted spectral anomalies occurred within 90 m of 43 of the 53 altered sites, corresponding to a site-level recall of 81.13% and supporting their consistency with field-based geological evidence. Full article

Background: Convolutional neural networks (CNNs) perform well in histopathological classification of oral squamous cell carcinoma (OSCC), but their robustness across distinct tissue domains remains insufficiently studied. This study assessed whether a CNN trained only on primary oral and oropharyngeal squamous cell carcinoma images remained transferable to an independent metastatic lymph node histopathology dataset. Methods: Three public datasets containing 14,760 OSCC/OPSCC and 8530 normal oral mucosa images were combined for model development. An ImageNet-pretrained EfficientNetB0 backbone was used as a fixed feature extractor with a task-specific binary classification head. Performance was first assessed on an independent internal testing subset and subsequently evaluated on 20,000 H&E-stained normal and metastatic lymph node patches from a separate public dataset. Results: The model achieved an internal testing accuracy of 91.48%, with 92.95% sensitivity, 88.92% specificity, 93.56% precision, a 93.25% F1-score, and a Youden’s J index of 0.819. External validation resulted in a substantial decrease in overall classification performance, with an accuracy of 56.91%, specificity of 33.32%, precision of 48.71%, F1-score of 63.37%, balanced accuracy of 61.99%, MCC of 0.279, and a Youden’s J index of 0.240. Nevertheless, sensitivity for metastatic tissue remained high at 90.66%, indicating a markedly asymmetric external error profile characterized predominantly by false-positive classifications. Conclusions: The marked performance decrease during external validation demonstrates the limitations of direct cross-domain transfer between substantially different histopathological environments. However, the preserved sensitivity suggests that some discriminative information remained transferable beyond the development domain. These findings support partial rather than universal cross-domain generalization and emphasize the importance of independent out-of-distribution evaluation when assessing deep learning robustness in computational pathology. Full article

Full three-dimensional gear-contact analysis resolves localized tensor fields that simplified models cannot recover, but repeated nonlinear finite element (FE) solutions are costly. This study develops a multi-fidelity physics-informed graph surrogate combining a coarse learning graph, peak-sensitive KDTree projection, gated message passing, and a regularized least-squares finite-difference equilibrium residual. The stress-prior-conditioned benchmark uses a coarse prior derived from the same high-fidelity FE field and therefore is not label-free. Across five random seeds on the 750-case test split, it yields NMSE = (9.1 ± 0.4) × 10⁻⁵, R² = 0.985 ± 0.001, and peak-stress error = 2.5 ± 0.2%. A geometry-only gate provides a preliminary label-free result, with 4.1% peak-stress error for seed 42; its complete multi-seed metrics were not retained. One conditioned forward pass requires 42 ms, excluding preprocessing and prior construction, and peak training memory is 47.6 GB on the reported hardware. Maximum projection outperforms distance-weighted averaging at one fixed graph resolution. All targets are simulated, so the method is presented as a numerical FE surrogate rather than an experimentally validated digital-twin replacement. Full article

(1) Background: Preconception care (PCC) is an important component of preventive reproductive healthcare and public health because modifiable risk factors affecting maternal, neonatal, and longer-term health may be present before conception. Despite broad recognition of its potential value, substantial uncertainty remains regarding how PCC should be organised, financed, and implemented across different healthcare systems. (2) Methods: This structured narrative review synthesised evidence on healthcare- provider models, financing, affordability, willingness to pay (WTP), and barriers and facilitators affecting PCC implementation. A structured search of PubMed, Scopus, and Web of Science Core Collection covered publications from January 2010 to March 2026 and was updated on 14 July 2026. Original research studies, systematic and scoping reviews, narrative reviews, clinical guidelines, policy reports, and relevant methodological or health-economic publications were considered. A structured qualitative appraisal was used to interpret evidence according to study design or evidence type, directness to PCC, healthcare-system context, methodological limitations, and consistency across sources. A total of 61 publications were retained in the structured evidence synthesis. (3) Results: No single PCC delivery model was consistently supported as universally applicable. Across heterogeneous settings, the evidence more consistently supported broad principles including accessibility, continuity of care, multidisciplinary coordination, clear professional roles, financial accessibility, and integration with existing healthcare pathways. Implementation was constrained by insufficient training, consultation-time limitations, unclear professional responsibilities, fragmented service organisation, inadequate reimbursement, and socioeconomic inequalities. Evidence on WTP, specific financing arrangements, and digital delivery models was more limited and context-dependent. (4) Conclusions: The available evidence supports PCC as an important preventive reproductive-health strategy, but the optimal organisational and financing models remain uncertain and are likely to depend on healthcare-system context. Integrated and multidisciplinary approaches appear promising, although specific provider configurations, financing mechanisms, and digital interventions require further comparative and longitudinal evaluation. The author-developed hypothetical conceptual model proposed in this review should therefore be interpreted as a hypothesis-generating synthesis of the available evidence rather than as an empirically validated clinical pathway or causal model. Full article

Convolutional neural networks (CNNs) are widely used for biomedical image classification, yet it remains unclear under which conditions training on reduced subsets of available data can provide reliable guidance during model development, how much training data is required to achieve stable and comparable performance across CNN architectures, and whether increasing the training set size always leads to improved generalization or can sometimes result in degraded performance. We study this question across four biomedical datasets (breast mammography, pediatric chest X-ray, brain tumor MRI, and skin lesion dermoscopy) using the full grid of 39 CNN architectures (1–3 convolutional layers, 16/32/64 filters) from our companion architectural study, training each configuration from scratch on seven proportions of the training data (5%, 10%, 20%, 40%, 60%, 80%, and 100%) over 5 independent runs per configuration, with the test set held at a fixed size across all sample-size conditions to ensure a like-for-like comparison of generalization performance. The analysis investigates three complementary aspects of sample-size sensitivity: the stabilization of the training–test generalization gap as training-set size increases, the reliability of architecture rankings obtained from reduced training fractions as a proxy for the full-dataset ranking, and the monotonicity of test performance with respect to training-set size. Taken together, the results point to a rough four-band pattern of reliability across the sampled fractions—unstable below $20\%$ of the training set, of uncertain overfitting status between $20\%$ and $60\%$, comparatively stable between $60\%$ and $80\%$, and potentially counterproductive beyond $80\%$—while showing that this pattern is itself dataset-dependent and offers no guarantee on architecture ranking, arguing against reduced-fraction screening as a reliable shortcut for CNN architecture selection in biomedical imaging. All code and datasets are publicly released for reproducibility. Full article

Machine learning (ML) algorithms can be used to extract clinically meaningful information from movement data captured by inertial measurement units (IMUs), but many human activity recognition (HAR) pipelines are developed on large laboratory datasets that may not reflect small, heterogeneous, real-world samples. The purpose of this study is to systematically compare the accuracy of multiple ML models, feature sets (both simple and expanded), class balancing strategies, null and transition period handling techniques, and sensor configurations for recognizing a set of four everyday activities extracted from IMU time series data from an age-diverse population. Six ML classifiers were trained and tested: multilayer perceptron, random forest, k-nearest neighbors, logistic regressor, CatBoost, and gaussian naive bayes. These approaches were used in a pipeline with differing sampling techniques including the synthetic minority oversampling technique or random undersampling, and feature handling steps including principal component analysis or a Select-From-Model metatransformer. Additionally, two deep learning methods, DeepConvLSTM and ResGCNN, were trained and tested. Accuracy, precision, recall, and area under the receiver operating characteristic curve were compared to a dummy classifier as a benchmark approximation to chance performance. All pipelines performed better than the dummy classifier, with model accuracy ranging between 0.427 and 0.644. This study demonstrated the ability of several ML algorithms to properly recognize a set of functional activities using limited IMU data from both children and adults. Full article