Search Results (3,380)

This paper explores the feasibility of early-season crop classification based on Sentinel-2-time series using the TimeSen2Crop dataset (≈1 million pixels, 16 crops). The aim of the study was to evaluate the spectral-phenological separability of crops during the season and compare the performance of classical tabular algorithms, deep sequence models, and a seasonally oriented hybrid stacking scheme. Based on multispectral observations, a feature set was formed from 9 optical channels and 13 vegetation indices for 30 dates. F-criteria were calculated, confirming a sharp increase in interclass separability during the active vegetative growth phase and substantiating three time series truncation scenarios (early, early + mid-season, and full season). Random Forest (macro-F1: 0.46/0.74/0.75) was used as the base tabular model. LSTM, BiLSTM, GRU, 1D-CNN, and Transformer were trained in parallel, with Transformer showing the best results among the deep architectures (0.42/0.68/0.78). The main contribution of the work is a hybrid multi-layer stacking scheme combining heterogeneous base algorithms and OOF meta-features, which provides the highest quality (0.51/0.83/0.86) in all scenarios. The obtained results confirm the effectiveness of phenology-oriented selection of time windows, informative indices, and hybrid ensemble learning for improving the accuracy of early-season crop monitoring. Full article

Cardiac fibrosis is a hallmark of cardiac aging and a major contributor to development of heart failure. However, therapeutic strategies that specifically target cardiac fibrosis remain limited. In this study, we demonstrate that small-molecule compound ML216 exerts protective effects against aging-associated or β-adrenoceptor agonist isoproterenol-induced cardiac fibrosis in vitro or in vivo. Mechanistically, ML216 inhibits transforming growth factor-β1 (TGF-β1) signaling by reducing TGF-β1 protein levels, thereby attenuating Mothers against decapentaplegic homolog (SMAD) phosphorylation and downstream induction of connective tissue growth factor (CTGF). This leads to a marked suppression of fibrotic genes Col1a1, Cnn2, and Acta2, ultimately resulting in reduced fibrosis. Additionally, the inhibition of the TGF-β1 pathway alleviates cardiomyocytes apoptosis, which may further limit inflammatory responses and contributes to the overall attenuation of cardiac fibrosis. Collectively, these findings demonstrate that ML216 mitigates cardiac fibrosis through the inhibition of TGF-β1 pathway-mediated fibrotic signaling and apoptosis, highlighting its potential as a therapeutic candidate for the treatment of cardiac fibrosis. Full article

Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across tasks, modalities, and validation designs. The goal of this study was to critically analyze deep learning architecture families applied to oral rehabilitation and to provide task-driven selection guidance supported by an evidence table reporting dataset characteristics, validation strategy, and performance metrics. A focused narrative review was conducted using transparent, database-specific search criteria (final n = 10 included studies), emphasizing implant planning (cone–beam computed tomography [CBCT]-based segmentation), prosthodontic design (intraoral scan [IOS]/mesh inputs), and peri-implant diagnosis (periapical/panoramic radiographs). Evidence certainty for each clinical task was assessed using GRADE-informed ratings (High/Moderate/Low/Very Low). Extracted variables included clinical task, imaging modality, dataset size, architecture, validation strategy (internal vs. internal + external), split level, ground truth protocol, and performance metrics. A structured computational and hardware feasibility analysis was conducted for each architecture family to support real-world deployment planning. Encoder–decoder networks (U-Net/nnU-Net) dominate CBCT segmentation for implant planning, while detection architectures (Faster R-CNN, YOLO) support implant localization and peri-implant assessment on radiographs. Generative models (3D GANs, transformer-based point-to-mesh networks) enable crown design from three-dimensional scans. Hybrid CNN–Transformer architectures show promise for multimodal CBCT–IOS fusion, though direct evidence from the included studies remains limited to a single study. External validation remains uncommon yet essential given the risk of domain shift. In conclusion, architecture selection should be anchored to task geometry (2D vs. 3D), artifact burden, and required clinical output type. Reporting standards should prioritize dataset transparency, validation rigor, multi-center external testing, and uncertainty-aware outputs. Full article

Acute Lymphoblastic Leukemia (ALL) is a highly prevalent hematological malignancy, especially in children, for whom precise and prompt subtype identification is essential to establish suitable treatment protocols. Current deep learning-based computer-aided diagnosis (CAD) methods for identifying ALL are hindered by numerous drawbacks, such as a dependence on solely spatial feature depictions, elevated feature dimensions, computationally extensive deep learning architectures, inadequate multi-layer feature utilization, and poor interpretability. This paper introduces LUMINA-Net, a custom, lightweight, and interpretable deep learning CAD for the automated identification and subtype diagnosis of ALL using microscopic blood smear pictures. LUMINA-Net makes four principal contributions: first, it integrates a self-attention module within a lightweight custom Convolution Neural Network (CNN) to effectively capture long-range spatial relationships across clinically pertinent cytological patterns while preserving a compact design. Second, it employs a Discrete Wavelet Transform (DWT)-based wavelet pooling layer that decreases feature dimensions by up to 96.875% while enhancing the obtained depictions with spatial-spectral information. Third, it utilizes a multi-layer feature fusion strategy that combines wavelet-pooled features from two deep layers with a third fully connected layer to create a discriminating multi-scale feature vector. Fourth, it incorporates Gradient-weighted Class Activation Mapping as a dedicated explainability process to furnish clinicians with apparent visual explanations for each classification decision. Withoit the need for image enhancement or segmentation preprocessing, LUMINA-Net outperforms the competing state-of-the-art methods on the same dataset, achieving a peak accuracy of 99.51%, specificity of 99.84%, and sensitivity of 99.51% on the publicly available Kaggle ALL dataset. This demonstrates that LUMINA-Net has the potential to be a dependable, effective, and clinically interpretable CAD tool for ALL diagnosis. Full article

Accurate diagnosis of wheat leaf diseases in real farming conditions requires models that are not only highly accurate but also computationally efficient and interpretable for practical deployment on edge devices. We propose GateProtoNet (GPN), a two-stage, compute-aware, and explainable framework for multi-class leaf disease recognition. Stage-1 performs ultra-light healthy-versus-diseased screening, enabling early exit for healthy samples and substantially reducing average expected inference cost. For diseased samples, Stage-2 applies a novel hybrid backbone featuring a frequency-factorized Discrete Wavelet Transform (DWT) stem, parallel micro-lesion convolutional encoding for fine texture patterns, and a linear token mixer for global context modeling. A cross-gated fusion module adaptively integrates local and global evidence with minimal computational overhead. To ensure trustworthy predictions, GPN introduces a prototype evidence head that performs classification via similarity to learned class prototypes, providing human-interpretable explanations, along with a faithfulness constraint that enforces explanation reliability by measuring confidence degradation under salient region removal. Rigorous evaluation on four publicly available wheat and cotton leaf disease datasets demonstrate that GateProtoNet achieves 99.2% classification accuracy, 99.1% macro-F1 score, and 99.3% AUC, significantly outperforming existing CNN, transformer, and hybrid baselines while requiring substantially fewer parameters and FLOPs. The two-stage inference strategy reduces average computational cost by avoiding full model execution on healthy leaves, enabling real-time, on-device diagnosis for resource-constrained agricultural environments. Full article

By 2024, global photovoltaic (PV) capacity exceeded 2000 GW, corresponding with a decline in levelized costs of approximately 90% since 2010. Artificial intelligence (AI) and machine learning (ML) are enabling novel approaches to solar energy system design and implementation. This survey offers a detailed evaluation of AI/ML methodologies utilized across the solar energy value chain, with a focus on solar irradiance forecasting, maximum power point tracking (MPPT), fault identification, and the expeditious discovery of system materials. The distinction between AI as the broader paradigm and ML as its data-driven subset is drawn and maintained throughout. The primary results cite forecasting improvements via deep learning architectures (LSTM, CNN, Transformer) of 10–40% over traditional methods, while hybrid numerical weather prediction and deep learning models achieve mean absolute error reductions of 15–25%. Reinforcement learning-based MPPT achieves tracking efficiencies in excess of 99% under partial shading, CNN-based fault classification reaches accuracies above 95%, and ML-based screening of materials accelerates perovskite optimization by a factor of 5–10×. Promising paradigms such as explainable AI, federated learning, digital twins, and physics-informed neural networks are evaluated alongside technical, economic, and regulatory constraints. This survey provides a consolidated reference and practical roadmap for the advancement of AI-driven solar energy technologies. Full article

Accurate prediction of the air quality index (AQI) is crucial for understanding urban pollution dynamics and protecting public health. This study proposes a dual-branch fusion framework (CL-XGB-Season) to address seasonal heterogeneity in AQI prediction by integrating temporal dynamic features and static patterns. The CNN-LSTM branch captures short-term temporal fluctuations, while a seasonally split XGBoost branch fits long-term static patterns via independent submodels for spring, summer, autumn, and winter. SHAP-based interpretability analysis revealed the dominant drivers across different seasons: the “temperature × O₃” interaction feature plays a key role in summer, characterizing the ozone formation mechanism dominated by photochemical reactions under conditions of high temperature and strong solar radiation; whereas the PM_2.5/PM₁₀ ratio is crucial in winter (where pollution is primarily driven by pollutant accumulation). The dual-branch fusion framework was validated using hourly resolution data from Chongqing for the 2020–2025 period. Results indicate that the framework achieved a prediction accuracy of 0.197 root mean square error (nRMSE) and 0.9611 coefficient of determination (R²) on the test set, outperforming eight ablation variants and five baseline models (ARIMA, Transformer, etc.) in comparative experiments. Ablation studies confirm the necessity of dual branches and seasonal modeling, with the full model reducing nRMSE by 19–63% versus single-model variants. This framework maintains stable seasonal performance and provides actionable insights for targeted air quality management. Full article

Speech deception detection often suffers from overly narrow feature representations and limited robustness under changing recording conditions. To address these limitations, we propose MFF-Net, a multi-feature fusion network with three modules. The first module uses a Gated-BiLSTM with attention to model sequences of acoustic-prosodic (ACO) and nonlinear dynamic (NLD) features. The second module uses a hybrid Transformer–CNN branch on Mel spectrograms, with a Levenberg–Marquardt (LM) refinement stage for the Mel-branch classifier. The third module fuses the two branches via cross-attention before a softmax decision. On the English Real-Life Trial Deception Database (RTDD), MFF-Net achieves 79.22% accuracy and 77.69% F1. On our Chinese Lie Dataset (CLD), it reaches 78.29% accuracy and 79.13% F1, outperforming strong baselines. Together with speaker-aware evaluation and clarified training objectives, the results support MFF-Net as a reproducible approach for deception detection across complementary corpora. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

Photocatalytic anti-corrosion materials are an emerging intelligent protective material that has been widely used in marine and offshore engineering in recent years. However, its failure mechanism under multi-factor coupling is complex, and it is difficult for traditional methods to achieve accurate life prediction and mechanism analysis. This article takes submarine pipelines as the research object and designs an innovative multi-source environmental data-driven method combined with deep learning (DL), aiming to establish an intelligent prediction model for the failure of the material. This article first systematically collects the multi-source heterogeneous data of materials during service; on this basis, this article constructs a hybrid DL model. Firstly, a multi-scale multimodal image feature fusion network (MMFCT) based on the combination of convolutional neural network (CNN) and Transformer is adopted to automatically extract corrosion features from microscopic images and capture the dynamic correlation between environmental temporal data and performance degradation; then, the Sparrow Search Algorithm (SSA) was constructed to optimize the BP neural network (BPNN) model for predicting the ultimate bearing capacity of submarine corroded pipelines. Simulation experiments show that the proposed method achieves accurate prediction of material remaining life and key performance degradation paths. The corrosion recognition precision reaches 94.7%, and the bearing capacity prediction error remains below 3.1%. Full article

Accurate and rapid transient voltage stability assessment is crucial for the safe and stable operation of new energy bases in desert and grassland regions. Existing deep learning methods fail to adequately capture the high-dimensional dynamic coupling features of transient voltage signals in large-scale renewable energy bases with UHVDC transmission, and suffer from poor performance under class-imbalanced sample conditions. This paper proposes a transient voltage stability assessment method utilizing continuous wavelet transform (CWT) time–frequency images and a deep residual network (ResNet-50). CWT with the Morlet wavelet basis converts voltage time-series signals into multi-scale time–frequency images to simultaneously capture temporal and frequency-domain transient features. An improved focal loss (FL) function is introduced to dynamically adjust category weights based on actual sample distribution, enhancing model robustness under extreme class imbalance. The proposed method is validated on a modified IEEE 39-bus system incorporating the Qishao UHVDC line and wind/photovoltaic integration in Northwest China, using 1490 simulation samples under diverse fault scenarios. Results demonstrate that the proposed CWT-ResNet achieves 98.88% accuracy, 94.74% precision, 100% recall, and 97.29% F1-score, outperforming SVM, 1D-CNN, and 1D-ResNet baselines. Under 5 dB noise conditions, the method maintains over 90% accuracy, demonstrating strong noise robustness. Full article

Tight sandstone gas development is largely governed by mineral composition and micromechanical heterogeneity. This study proposes a cross-scale method integrating these two factors to characterize macroscopic sandstone heterogeneity. First, a CNN–Transformer model was trained on thin-section images to identify mineral types and contents. Second, probability density functions of Young’s modulus for each mineral were derived from nanoindentation data, and stochastic sampling was used to assign mechanical properties to mineral grains in an FDEM-GBM uniaxial compression model. Finally, numerical results validated against experiments show that the random spatial distribution of micromechanical parameters leads to a normal distribution of the macroscopic Young’s modulus. Decreasing high-strength mineral content reduces the mean Young’s modulus while increasing its standard deviation, indicating greater mechanical heterogeneity, with cracks preferentially propagating in low-strength minerals. Mineral composition and content are the primary controls on macroscopic behavior, while micromechanical heterogeneity plays a secondary role. A brittleness index integrating mineral composition and multi-scale Young’s modulus distribution is proposed, providing a theoretical basis for evaluating heterogeneity and fracability in tight sandstone reservoirs. Full article

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is crucial for brain tumor diagnosis, clinical treatment decisions, and advancing research. CNNs and Transformers have dominated this area, but CNNs struggle with long-range modeling, whereas Transformers are limited by the high computational costs of self-attention. Recently, Mamba has garnered significant attention due to its remarkable performance in long sequence modeling. However, the original Mamba architecture, designed primarily for 1D sequence modeling, fails to effectively capture the spatial and structural relationships essential for brain tumor segmentation. In this paper, we propose FocusMamba, a Mamba-based model inspired by human visual observation patterns, which jointly enhances local detail modeling and global contextual understanding. FocusMamba consists of three components: (i) a novel hierarchical and tri-directional Mamba unit that elevates attention from the global to the window level, reinforcing local semantic feature extraction, while simultaneously achieving window-level interactions to maintain broader global awareness, (ii) a large kernel convolution unit that captures long-range dependencies within whole-volume features, overcoming the limitations of Mamba’s single-scale context modeling, and (iii) a fusion unit that enhances the overall feature representation by fusing information from different levels. Extensive experiments on the BraTS 2023 and BraTS 2020 datasets demonstrate that FocusMamba achieves superior segmentation performance compared with several advanced methods. Full article

Aileron fault diagnosis in fixed-wing unmanned aerial vehicles (UAVs) faces significant challenges due to strong noise, multi-modal coupling, and limited fault samples. This paper presents a hybrid fault diagnosis framework that integrates variational mode decomposition (VMD) with a cross-attention-based feature fusion mechanism. First, residual signals are generated from UAV kinematic models and decomposed into multi-scale intrinsic mode functions (IMFs) using VMD to extract multiscale frequency-localized features. An integrated framework is then constructed, where Transformer encoders capture the global features and bidirectional long short-term memory (BiLSTM) networks extract local temporal dynamics. To effectively combine these complementary features, a cross-attention fusion module is designed to focus on the discriminative time-frequency features. Furthermore, a hybrid pooling strategy integrating max pooling and attention pooling is introduced to enhance classification robustness. Experiments on the AirLab failure and anomaly (ALFA) dataset demonstrate that the proposed method achieves 95.12% accuracy with improved fault separability, outperforming VMD + BiLSTM (87.66%), VMD + Transformer (86.89%), Transformer + BiLSTM (84.83%), Transformer (72.24%), CNN + LSTM (94.05%), and HDMTL (94.86%). Full article

Distinguishing surfaces from underwater targets in complex marine environments is challenging when relying solely on physical sonar features. To address the high uncertainty inherent in single-modal features and the conflicts arising from heterogeneous data, we propose a Dual-View Entropy-Driven Negation Dempster–Shafer (DVE-NDS) fusion method that integrates AIS kinematic priors with passive sonar signals. First, a heterogeneous recognition framework is constructed. LOFAR and DEMON features are extracted via convolutional neural networks (CNNs), while a Negation Basic Probability Assignment (Negation BPA) strategy is introduced to transform AIS spatiotemporal mismatches into effective "negation support" for non-cooperative underwater targets. Instead of relying on a single conflict coefficient, the proposed method jointly considers evidence self-information and inter-source consistency. Evidence quality is quantified using improved Deng entropy and negation belief entropy, while mutual trust is evaluated via the Jousselme distance. Heterogeneous evidence is weighted and corrected by generated coupling weights, effectively suppressing low-quality evidence and sharpening decision boundaries. Simulation results confirm that DVE-NDS improves macro-F1 over classical fusion, indicating the framework’s potential for handling conflicting evidence, though the current validation remains simulation-based and should be regarded as a methodological proof-of-concept. Full article