Search Results (579)

This study investigates seven advanced hybrid composite thermal protection system (TPS) prototypes, featuring an innovative internal air chamber design that reduces heat conduction and enhances overall thermal protection performance. Specimens were manufactured by fused deposition modeling (FDM), an additive manufacturing technique, using a fire-retardant thermoplastic. Selected configurations were reinforced with continuous carbon or glass fibers, coated with ceramic surface layer, or hybridized with carbon fiber reinforced polymer (CFRP) layers or a CFRP laminate disk. To validate performance, a harsh oxy-acetylene torch (OAT) protocol was implemented, deliberately designed to exceed the severity of most reported typical ablative assessments. The exposed surface of each specimen was subjected to direct flame at a 50 mm distance, recording peak temperatures of 1600 ± 50 °C. Two samples of each configuration were tested under 60 and 90 s exposures. Back-face thermal readings at potential payload sites consistently remained below 85 °C, well under the 200 °C maximum standard threshold for TPS applications. Several configurations preserved structural integrity despite the extreme environment. Prototypes 4.1 and 4.2 demonstrate the most favorable performance, maintaining structural integrity and low back-face temperatures despite substantial thickness loss. By contrast, specimen 6.2 exhibited rapid degradation following 60 s of exposure, which served as a rigorous and selective early-stage screening tool for evaluating polymer-based ablative TPS architectures. Full article

In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption. Full article

With the widespread application of multi-view data across various domains, multi-view unsupervised feature selection (MUFS) has achieved remarkable progress in both feature selection (FS) and missing-view completion. However, existing MUFS methods typically rely on centralized servers, which not only fail to meet privacy requirements in distributed settings but also suffer from suboptimal FS quality and poor convergence. To overcome these challenges, we propose a novel federated incomplete MUFS method (Fed-IMUFS), which integrates a fractional Sparsity-Guided Whale Optimization Algorithm (SGWOA) and Tensor Alternating Learning (TAL). Within this federated learning framework, each client performs local optimization in two stages: in the first stage, SGWOA introduces an $L_{2,1}$ proximal projection to enforce row-sparsity in the FS weight matrix, while fractional-order dynamics and fractal-inspired elite kernel injection mechanisms enhance global search ability, yielding a discriminative and stable weight matrix; in the second stage, based on the obtained weight matrix, an alternating optimization framework with tensor decomposition is employed to iteratively complete missing views while simultaneously optimizing low-dimensional representations to preserve cross-view consistency, with the objective function gradually minimized until convergence. During federated training, the server employs an aggregation and distribution strategy driven by normalized mutual information, where clients upload only their local weight matrices and quality indicators, and the server adaptively fuses them into a global FS matrix before distributing it back to clients. This process achieves consistent FS across clients while safeguarding data privacy. Comprehensive evaluations on CEC2022 and several incomplete multi-view datasets confirm that Fed-IMUFS outperforms state-of-the-art methods, delivering stronger global optimization capability, higher-quality feature selection, faster convergence, and more effective handling of missing views. Full article

Wool traits such as fiber diameter, fiber length, and greasy fleece yield are economically significant characteristics in sheep breeding programs. Traditional genome-wide association studies (GWAS) have identified relevant genomic regions but often fail to capture the non-linear and polygenic architecture underlying these traits. In this study, we implemented a two-stage machine learning (ML)-based GWAS framework to dissect the genetic basis of wool traits in Central Anatolian Merino sheep. Phenotypic records were collected from 228 animals, genotyped with the Illumina OvineSNP50 BeadChip. In the first stage, feature selection was conducted using LASSO, Ridge Regression, and Elastic Net, generating a consensus SNP panel per trait. In the second stage, association modeling with Random Forest and Support Vector Regression (SVR) identified the most predictive models (R² up to 0.86). Candidate gene annotation highlighted biologically relevant loci: MTHFD2L and EPGN (folate metabolism and keratinocyte proliferation) for fiber diameter; COL5A2, COL3A1, ITFG1, and ELMO1 (extracellular matrix integrity and actin remodeling) for staple length; and FAP, DPP4, PLCH1, and NPTX1 (extracellular matrix remodeling, proteolysis, and sebaceous gland function) for greasy fleece yield. These findings demonstrate the utility of ML-enhanced GWAS pipelines in identifying biologically meaningful markers and propose novel targets for genomic selection strategies to improve wool quality and yield in indigenous sheep populations. Full article

Stomach ulcers, a common type of gastrointestinal (GI) disease, pose serious health risks if not diagnosed and treated at an early stage. Therefore, in this research, a method is proposed based on two deep learning models for classification and segmentation. The classification model is based on Convolutional Neural Networks (CNN) and incorporates Q-learning to achieve learning stability and decision accuracy through reinforcement-based feedback. In this model, input images are passed through a custom CNN model comprising seven layers, including convolutional, ReLU, max pooling, flattening, and fully connected layers, for feature extraction. Furthermore, the agent selects an action (class) for each input and receives a +1 reward for a correct prediction and −1 for an incorrect one. The Q-table stores a mapping between image features (states) and class predictions (actions), and is updated at each step based on the reward using the Q-learning update rule. This process runs over 1000 episodes and utilizes Q-learning parameters (α = 0.1, γ = 0.6, ϵ = 0.1) to help the agent learn an optimal classification strategy. After training, the agent is evaluated on the test data using only its learned policy. The classified ulcer images are passed to the proposed attention-based U-Net model to segment the lesion regions. The model contains an encoder, a decoder, and attention layers. The encoder block extracts features through pooling and convolution layers, while the decoder block up-samples the features and reconstructs the segmentation map. Similarly, the attention block is used to highlight the important features obtained from the encoder block before passing them to the decoder block, helping the model focus on relevant spatial information. The model is trained using the selected hyperparameters, including an 8-batch size, the Adam optimizer, and 50 epochs. The performance of the models is evaluated on Kvasir, Nerthus, CVC-ClinicDB, and a private POF dataset. The classification framework provides 99.08% accuracy on Kvasir and 100% accuracy on Nerthus. In contrast, the segmentation framework yields 98.09% accuracy on Kvasir, 99.77% accuracy on Nerthus, 98.49% accuracy on CVC-ClinicDB, and 99.13% accuracy on the private dataset. The achieved results are superior to those of previous methods published in this domain. Full article

Accurate and reliable credit risk classification is fundamental to the stability of financial systems and the efficient allocation of capital. However, with the rapid expansion of customer information in both volume and complexity, traditional rule-based or purely statistical approaches have become increasingly inadequate. Motivated by these challenges, this study introduces a domain-constrained stacking ensemble framework that systematically integrates business knowledge with advanced machine learning techniques. First, domain heuristics are embedded at multiple stages of the pipeline: threshold-based outlier removal improves data quality, target variable redefinition ensures consistency with industry practice, and feature discretization with monotonicity verification enhances interpretability. Then, each variable is transformed through Weight-of-Evidence (WOE) encoding and evaluated via Information Value (IV), which enables robust feature selection and effective dimensionality reduction. Next, on this transformed feature space, we train logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and a two-layer stacking ensemble. Finally, the ensemble aggregates cross-validated out-of-fold predictions from LR, RF and XGBoost as meta-features, which are fused by a meta-level logistic regression, thereby capturing both linear and nonlinear relationships while mitigating overfitting. Experimental results across two credit datasets demonstrate that the proposed framework achieves superior predictive performance compared with single models, highlighting its potential as a practical solution for credit risk assessment in real-world financial applications. Full article

Objectives: Endometrial carcinoma is the most common gynaecological cancer in developed countries, with both incidence and mortality rates continuing to rise globally. For women of reproductive age diagnosed with early-stage disease or endometrial intraepithelial neoplasia, fertility-preserving treatment should be considered to maintain the possibility of future childbearing. Effective fertility-sparing management requires a multidisciplinary approach that includes patient education, reduction in risk factors, accurate molecular and histological classification to guide targeted therapies, assisted reproductive technologies to improve early conception rates, and attention to the psycho-sexual well-being of patients to support treatment adherence. Methods: This retrospective cohort study analysed the clinicopathological features and treatment outcomes of thirteen patients who received fertility-preserving therapy between 2018 and 2023. Results: The mean age of the patients (n = 13) was 34.4 years, with a range of 20 to 41 years. The overall treatment response rate was 76.9%, including 69.2% complete and 7.7% partial responses. Stable disease was observed in 15.4% of cases, while progression occurred in 7.7%. Among those who achieved complete remission, in vitro fertilisation (IVF) was initiated in four cases, with two ongoing as of the time of data analysis. In one of the cases, after two unsuccessful assisted reproductive attempts, spontaneous conception occurred, resulting in the birth of a child. Conclusions: Our findings support the feasibility and success of fertility-preserving treatment in carefully selected patients, allowing the preservation of reproductive potential alongside oncological care. Full article

Accurate prediction of fracturing pressure is critical for operational safety and fracturing efficiency in unconventional reservoirs. Traditional physics-based models and existing deep learning architectures often struggle to capture the intense fluctuations and complex temporal dependencies observed in actual fracturing operations. To address these challenges, this paper proposes a multi-source data-driven fracturing pressure prediction model, a model integrating TCN-BiLSTM-Attention Mechanism (Temporal Convolutional Network, Bidirectional Long Short-Term Memory, Attention Mechanism), and introduces a feature selection mechanism for fracture pressure prediction. This model employs TCN to extract multi-scale local fluctuation features, BiLSTM to capture long-term dependencies, and Attention to adaptively adjust feature weights. A two-stage feature selection strategy combining correlation analysis and ablation experiments effectively eliminates redundant features and enhances model robustness. Field data from the Sichuan Basin were used for model validation. Results demonstrate that our method significantly outperforms baseline models (LSTM, BiLSTM, and TCN-BiLSTM) in mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²), particularly under high-fluctuation conditions. When integrated with slope reversal analysis, it achieves sand blockage warnings up to 41 s in advance, offering substantial potential for real-time decision support in fracturing operations. Full article

Rolling element bearings are commonly used in rotating machines. Bearing fault detection and diagnosis play a critical role in machine operations to recognize bearing faults at their early stage and prevent machine performance degradation, improve operation quality, and reduce maintenance costs. Although many fault detection techniques are proposed in the literature for bearing condition monitoring, reliable bearing fault detection remains a challenging task in this research and development field. This study proposes an enhanced Teager–Kaiser (eTK) technique for bearing fault detection and diagnosis. Vibration signals are used for analysis. The eTK technique is novel in two aspects: Firstly, an empirical mode decomposition analysis is suggested to recognize representative intrinsic mode functions (IMFs) with different frequency components. Secondly, an eTK denoising filter is proposed to improve the signal-to-noise ratio of the selected IMF features. The analytical signal spectrum analysis is conducted to identify representative features for bearing fault detection. The effectiveness of the proposed eTK technique is verified by experimental tests corresponding to different bearing conditions. Full article

Background: Diabetes mellitus is a significant primary global health concern that requires accurate diagnosis at an early stage to prevent severe complications. However, accurate prediction remains challenging due to limited, noisy, and imbalanced datasets. This study proposes a novel machine learning framework for improved diabetes prediction, addressing key challenges such as inadequate feature selection, class imbalance, and data preprocessing. Methods: This proposed work systematically evaluates five feature selection algorithms—Recursive Feature Elimination, Grey Wolf Optimizer, Particle Swarm Optimizer, Genetic Algorithm, and Boruta—using cross-validation and SHAP analysis to enhance feature interpretability. Classification is performed using two boosting algorithms: the light gradient boosting machine algorithm (LGBM) and the extreme gradient boosting algorithm (XGBoost). Results: The proposed framework, using the five most important features selected by the Boruta feature selection algorithm, outperformed other configurations with the LightGBM classifier, achieving an accuracy of 85.16%, an F1-score of 85.41%, and a 54.96% reduction in training time. Conclusions: Additionally, we have benchmarked our approach against recent studies and validated its effectiveness on both the Pima Indian Diabetes Dataset and the newly released DiaHealth dataset, demonstrating robust and accurate early diabetes detection across diverse clinical datasets. This approach offers a cost-effective, interpretable, and clinically relevant solution for early diabetes detection by reducing the number of input features, providing transparent feature importance, and achieving high predictive accuracy with efficient model training. Full article

In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power forecasting method for distributed photovoltaics that can identify seasonal characteristics matching weather types, enabling a deeper analysis of complex meteorological changes. First, historical power data is decomposed seasonally using the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Next, each component is reconstructed based on a characteristic similarity approach, and a two-stage feature selection process is applied to identify the most relevant features for reconstruction, addressing the issue of nonlinear variable selection. A CNN-LSTM-KAN model with multi-dimensional spatial representation is then proposed to model different weather types obtained by the K-shape clustering method, enabling the segmentation of weather processes. Finally, the proposed method is applied to a case study of distributed PV users in a certain province for short-term power prediction. The results indicate that, compared to traditional methods, the average RMSE decreases by 8.93%, the average MAE decreases by 4.82%, and the R² increases by 9.17%, demonstrating the effectiveness of the proposed method. Full article

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS³Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article

Background: Microvascular invasion (MVI) is a critical prognostic factor in hepatocellular carcinoma (HCC), but preoperative three-class prediction remains challenging. Radiomics and clinical biomarkers may enable more accurate and individualized assessment. Aim: The aim of this study was to develop and validate a Transformer-based deep learning framework that integrates radiomic and clinical features for direct three-class MVI classification in HCC patients. Methods: This retrospective study included 437 patients with pathologically confirmed hepatocellular carcinoma (HCC) and microvascular invasion (MVI) status from two campuses of a single institution. Patients from Hospital A (n = 305) were randomly divided into training and internal test cohorts, while patients from Hospital B (n = 132) were used as an independent external validation cohort. Radiomic features were extracted from preoperative Gd-BOPTA-enhanced MRI, and clinical laboratory data were collected. A two-stage feature selection strategy, combining univariate statistical testing and recursive feature elimination, was applied. A Transformer-based model was built to classify three MVI categories (M0, M1, M2), and its performance was evaluated in both the internal test cohort and the external validation cohort. Results were compared with those from traditional machine learning models, including Random Forest, Logistic Regression, XGBoost, and LightGBM. Results: On the internal test set (n = 76, Hospital A), the model achieved an accuracy of 0.733 (95% CI: 0.64–0.83), a weighted F1-score of 0.733, and a macro-average AUC of 0.880 (95% CI: 0.807–0.953). The sensitivity and specificity for M1 were 0.56 (95% CI: 0.31–0.78) and 0.86 (95% CI: 0.74–0.94), respectively; for high-risk M2 cases, the sensitivity was 0.73 (95% CI: 0.64–0.81) and the specificity was 0.91 (95% CI: 0.85–0.96). On the external validation set (n = 132, Hospital B), performance remained stable with an accuracy of 0.758, a weighted F1-score of 0.768, and a macro-average AUC of 0.886 (95% CI: 0.833–0.940). Conclusions: This Transformer-based model enables accurate and objective three-class MVI prediction using multi-modal features, supporting individualized surgical planning and improved clinical outcomes. In particular, the ability to preoperatively identify high-risk M2 patients may inform surgical margin design, guide adjuvant therapy strategies, and influence liver transplantation eligibility. Full article

High-resolution remote sensing (HRRS) imagery enables the extraction of cropland information with high levels of detail, especially when combined with the impressive performance of deep convolutional neural networks (DCNNs) in understanding these images. Comprehending the factors influencing DCNNs’ performance in HRRS cropland extraction is of considerable importance for practical agricultural monitoring applications. This study investigates the impact of classifier selection and different training data characteristics on the HRRS cropland classification outcomes. Specifically, Gaofen-1 composite images with 2 m spatial resolution are employed for HRRS cropland extraction, and two county-wide regions with distinct agricultural landscapes in Shandong Province, China, are selected as the study areas. The performance of two deep learning (DL) algorithms (UNet and DeepLabv3+) and a traditional classification algorithm, Object-Based Image Analysis with Random Forest (OBIA-RF), is compared. Additionally, the effects of different band combinations, crop growth stages, and class mislabeling on the classification accuracy are evaluated. The results demonstrated that the UNet and DeepLabv3+ models outperformed OBIA-RF in both simple and complex agricultural landscapes, and were insensitive to the changes in band combinations, indicating their ability to learn abstract features and contextual semantic information for HRRS cropland extraction. Moreover, compared with the DL models, OBIA-RF was more sensitive to changes in the temporal characteristics. The performance of all three models was unaffected when the mislabeling error ratio remained below 5%. Beyond this threshold, the performance of all models decreased, with UNet and DeepLabv3+ showing similar performance decline trends and OBIA-RF suffering a more drastic reduction. Furthermore, the DL models exhibited relatively low sensitivity to the patch size of sample blocks and data augmentation. These findings can facilitate the design of operational implementations for practical applications. Full article

Small and medium-sized enterprises (SMEs) chronically face financing frictions. While supply chain finance (SCF) can help, reliable credit risk assessment in SCF is hindered by redundant features, heterogeneous data sources, small samples, and class imbalance. Using 360 A-share–listed SMEs from 2019–2023, we build a 77-indicator, multidimensional system covering SME and core-firm financials, supply chain stability, and macroeconomic conditions. To reduce dimensionality and remove low-contribution variables, feature selection is performed via a genetic algorithm enhanced LightGBM (GA-LightGBM). To mitigate class imbalance, we employ TabDDPM for data augmentation, yielding consistent improvements in downstream performance. For modeling, we propose a two-stage predictive framework that integrates TabNet-based feature engineering with a stacking ensemble (TabNet-Stacking). In our experiments, TabNet-Stacking outperforms strong machine-learning baselines in accuracy, recall, F1 score, and AUC. Full article