Search Results (5,926)

Radar echo extrapolation is a fundamental task in precipitation nowcasting. However, existing models based on RNNs, CNNs, and Transformers are often constrained by error accumulation, the loss of temporal information, and quadratic computational complexity. Consequently, these limitations can lead to prediction blurring and the distortion of fine-grained details in extrapolated radar images. To address these challenges, we propose RadarEchoMamba, a novel extrapolation framework built on the efficient Mamba model. RadarEchoMamba uses a pyramidal architecture to capture multi-scale features, and its core is a Bidirectional Spatiotemporal Mamba backbone that models dependencies within the observed historical input window. Furthermore, we introduce a spatiotemporal prior module driven by the input sequence to guide the reconstruction of high-resolution features during the decoding phase. This design enables the model to generate detail-rich predictions, improving upon the blurring issues prevalent in traditional extrapolation models. Experimental results on the South China and Shanghai-2020 datasets show the effectiveness of our method. Compared with strong Transformer-based baselines, the lightweight variant uses fewer parameters and achieves lower measured inference latency, while maintaining competitive prediction quality. Its main advantages are observed in visual-fidelity metrics, with improved SSIM and reduced LPIPS on the South China dataset. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality worldwide, and accurate histopathological tissue segmentation is critical for timely and reliable diagnosis. Healthcare systems represent complex adaptive environments where diagnostic tools must function reliably across heterogeneous clinical settings, varying staining protocols, and resource-constrained infrastructures. However, existing deep learning segmentation models often require substantial computational resources, limiting their deployment in such settings. This study proposes a novel, resource-efficient colorectal histopathology segmentation network (RCHS-Net) designed for robust clinical deployment across diverse and resource-constrained healthcare environments. Methods: RCHS-Net employs a compact multi-scale encoder with channel recalibration blocks, a gland context module (GCM) with three parallel atrous convolutions and lightweight self-attention for multi-scale contextual feature extraction, and a feature pyramid decoder (FPD) for fine-grained spatial reconstruction. To address the demands of real-world healthcare systems, feature-wise linear modulation (FiLM) conditioning enables class-aware segmentation across multiple tissue categories, while MixStyle augmentation improves stain domain generalization across heterogeneous laboratory and scanner conditions. Results: The model was evaluated on two publicly available benchmark datasets: the EBHI-Seg dataset and the GlaS dataset. On EBHI-Seg, RCHS-Net achieved a mean Dice coefficient of 95.20% and a mean IoU of 91.10% across six colorectal tissue classes, with only 243,226 trainable parameters. On the GlaS benchmark, RCHS-Net attained a Dice score of 93.39% and an IoU of 88.32%, outperforming state-of-the-art methods. Conclusions: RCHS-Net demonstrates that high-accuracy histopathology segmentation can be achieved with a compact architecture, offering a scalable and practical solution for AI-assisted cancer diagnosis across the complex, heterogeneous conditions of real-world healthcare systems, supporting scalable and equitable cancer diagnostics globally. Full article

Walking is essential for daily physical activity, yet most existing studies focus on pedestrian counts while neglecting varied on-street activities, largely owing to data shortages. Targeting this gap, the research defines walking activity quality as the occurrence likelihood of walking-related activities and explores built environment influences. It adopts deep-learning video recognition to capture fine-grained pedestrian behaviours and quantifies walkability via activity quality. Structural equation modelling (SEM) is applied to decode causal links between urban design quality, pedestrian volume and activity quality. According to the results, urban design qualities exhibit a more pronounced influence on activity quality compared to pedestrian volume. Among a broad array of 20 physical features examined, interface density, the quantity of street furniture, walkway width, and shading rate all demonstrated significant positive effects on stationary activities. Interestingly, higher interface density and shorter crossing distances facilitated the occurrence of social activities, whereas the proportion of ground-floor windows had a notable negative impact on social activities. The findings of this study can directly inform the development of sustainable and liveable streets. Full article

Lightweight, privacy-aware and adversarial robust intrusion detection is required for the proliferation of Internet of Things (IoT) devices. In the Industrial Internet of Things (IIoT), centralized detectors can be compromised by adversarial perturbations via gradient-based attacks, making them susceptible to raw traffic. We suggest Federated Learning-Adaptive Gated Recurrent Unit (FL-AdGRU), a Federated approach that combines a lightweight Gated Recurrent Unit (GRU) classifier with alternating adversarial fine-tuning on each client using FGSM and PGD, without any communication overhead. A two-stage resampling scheme (UCAS-SMOTE) reduces the class-imbalance ratio from 4081:1 to ≈4:1, followed by 61 features being reduced to 40 by a mutual-information selector (MI-SelectK). Under this scenario, FL-AdGRU achieves 99.9% accuracy and 0.999 weighted F1 (+6.5 pp over the federated DNN baseline), with no loss of accuracy when facing clean attacks, and boosts Fast Gradient Sign Method FGSM/Projected Gradient Descent (PGD) robustness by +19.3/+19.0 p.p at the same level of $ϵ$ = 0.1, thus effectively balancing the accuracy–robustness trade-off. It is robust (97.8%/84.2% on UNSW-NB15) and generalizes well to UNSW-NB15, while decaying slowly in skeptical scenarios (≈99.9% weighted F1 for moderate skew, 93.9%/86.7% for severe). Assuring data-locality privacy through exchange of only model weights; defenses against inference attack are left for future work. FL-AdGRU, with a total communication of 43.8 MB (≈50× less than centralized training), is deployable on bandwidth-constrained IIoT networks. Full article

As autonomous-driving scenarios become increasingly complex, object detection in road environments remains challenging, especially for small-scale, visually ambiguous, and partially occluded targets. These difficulties are closely related to the loss of fine-grained spatial information caused by repeated downsampling and the limited consistency of multi-scale feature fusion. To address these issues, this paper proposes MFP-YOLOv11 (Multi-dimensional Focused P2 YOLOv11), a YOLOv11-based detector with enhanced multi-scale feature fusion for complex road-scene object detection. The proposed method improves the YOLOv11 architecture from the perspectives of high-resolution feature preservation, deep contextual representation, and multi-scale feature fusion consistency. Specifically, a Multi-Scale Dynamic Alignment Feature Fusion module (MDAF) is designed as the main fusion component to enhance multi-scale feature representation by modelling channel-, spatial-, and scale-level relationships among features at different resolutions. In addition, C3Ghost is selectively employed in shallow high-resolution stages to partially offset the additional computational cost introduced by the enhanced architecture, AIFI-RepBN is introduced to strengthen deep contextual representation, and Detect-P2 is added to provide high-resolution prediction compensation for small-scale object detection. Experimental results on the SODA10M dataset show that MFP-YOLOv11 achieves an mAP@0.5 of 0.697 and an mAP@0.5:0.95 of 0.483, corresponding to absolute gains of 7.0 and 5.7 percentage points over the YOLOv11 baseline, respectively. Comparative experiments, ablation studies, component-wise analysis, and qualitative visualizations show the contribution of the proposed modifications to detection performance in representative complex road scenes. Cross-dataset testing on the KITTI dataset further evaluates the performance of the proposed method under heterogeneous road-scene distributions. Overall, MFP-YOLOv11 improves Recall and mAP in complex road-scene object detection, while introducing higher computational complexity than the original baseline model. Full article

The increasing frequency of water anomalies poses severe threats to aquatic ecosystem stability, water resource security, and socioeconomic sustainability. Rapid and accurate identification of these events is critical for early warning and informed environmental decision-making. However, existing type identification methods often fail to balance high precision with rapid inference since many depend on hand-crafted feature engineering or computationally intensive post-classification procedures that slow processing and increase confusion among anomalies with similar spectral characteristics. Therefore, in this study, a deep multisphere decision boundaries (DMSDB) method for unified, fine-grained identification of diverse water anomalies is developed. The method first involves constructing a compact 5-dimensional feature space by deriving physically interpretable indices from Sentinel-2 imagery. Then, a modified VM-UNet-v2 network is employed to extract water anomaly response features (WARFs) that capture the spatial–spectral characteristics of each anomaly. Finally, a multi-sphere-contrast loss function optimizes class-specific decision boundaries, compacting intraclass features while increasing the separation between spectrally similar anomaly categories. The validation results demonstrate that DMSDB achieves an average F1 score of 0.8789 and an mIoU of 0.7932, enabling efficient large-scale inference, and completing classification over a 10,000 km² scene within 33 s. These results highlight the method’s potential for effective or ground-based rapid environmental monitoring, supporting timely and category-specific responses to aquatic disturbances. Full article

State-of-the-art generative models exhibit powerful image-generation capabilities, raising ethical and legal challenges for service providers. Consequently, Content Removal Techniques (CRTs) have emerged to control outputs without requiring full retraining. However, the problem of unlearning in Generative Adversarial Networks (GANs) remains largely unexplored. We propose Text-to-Unlearn, a novel framework that selectively unlearns concepts from pre-trained GANs using only text prompts, enabling feature and identity unlearning, as well as fine-grained tasks such as expression and multi-attribute removal in models trained on human faces. Our approach leverages natural language descriptions to guide unlearning without additional datasets or supervised finetuning, offering a scalable solution. To evaluate the effectiveness of our method, we introduce an automated unlearning assessment method using state-of-the-art image–text alignment metrics and propose a new metric: degree of unlearning. Additionally, we assess robustness by introducing adversarial attacks to subvert unlearning. Our results demonstrate that Text-to-Unlearn achieves robust unlearning, resisting adversarial attempts to recover erased concepts while preserving model utility. To our knowledge, this is the first cross-modal unlearning framework for GANs, advancing the management of generative model behavior. Full article

The quantitative characterization of microscopic pore structure has long been a challenge in reservoir evaluation for igneous reservoirs, owing to their pronounced heterogeneity and complex pore geometry. In this study, thin-section casting, X-ray diffraction, high-pressure mercury intrusion, nuclear magnetic resonance, and fractal theory were employed to investigate the reservoir-space types, pore-structure characteristics, and fractal features of the igneous rocks both quantitatively and qualitatively. The relationships among reservoir petrophysical properties, pore structure, movable-fluid saturation, and fractal dimension were examined. The results indicate that the reservoirs in the study area are characterized by medium-to-low porosity and medium-to-low permeability, with mean values of 6.57% and 2.06 mD, respectively; the storage performance of andesite was found to exceed that of tuff. Based on the morphology of the mercury intrusion curves and the petrophysical parameters, the reservoirs were classified into three categories. From Class I to Class III, the displacement pressure increased progressively, the movable-fluid saturation declined from 9.65% to 8.54%, and the heterogeneity was markedly enhanced. The fractal analysis revealed that the reservoirs exhibit distinct piecewise fractal behavior with a well-defined inflection point, allowing two fractal intervals to be distinguished: large pore-throats (D₁) and small pore-throats (D₂). The mean total fractal dimension was 2.8996, and the large pore-throat fractal dimension (mean = 2.9607) exceeded that of the small pore-throats (mean = 2.3863), indicating that large pore-throats serve not only as the principal contributor to reservoir space but also as the dominant control on heterogeneity. Correlation analysis demonstrated that D₁ is significantly negatively correlated with both porosity and permeability, making it a key indicator for evaluating reservoir flow capacity, whereas D₂ is positively correlated with petrophysical properties, reflecting the role of fine throats in improving the connectivity of isolated pores. Notably, the large-pore-throat fractal dimension (D₁) of these igneous reservoirs generally exceeds that of tight sandstone, whereas the small-pore-throat fractal dimension (D₂) is positively correlated with petrophysical properties rather than negatively, in contrast to sandstone reservoirs; this indicates that the pore-structure behavior of igneous reservoirs is distinct from that of conventional clastic reservoirs. This study offers a new perspective on the quantitative characterization of pore structure in igneous reservoirs and provides a scientific basis for reservoir evaluation and exploration-and-development efforts in the study area. Full article

Remote sensing imagery provides a primary data source for large-scale surface water body monitoring, which is crucial for quantifying climate-related hydrological impacts, supporting flood control, and sustaining integrated water resource management. However, remote sensing images generally face the trade-off between spatial resolution and temporal coverage. To address this issue, the Real-ESRGAN super-resolution algorithm is employed to reconstruct temporally continuous, wide-coverage medium-resolution imagery to a 2.5 m resolution, effectively improving its capability to identify sub-pixel river boundaries. Water body segmentation (WBS) is an effective method for fine-detail surface water extraction. Nonetheless, when applied in complex hydrological environments, it still faces several limitations, such as ambiguous delineation of land–water boundaries and the difficulty in capturing multiscale water body characteristics. To address these issues, a Dynamic Weight Fusion SegFormer (DWFSeg) network is constructed, integrating a MixVision Transformer (MVT) encoder with a multiscale decoding architecture. Specifically, a Dynamic Multiscale Feature Fusion (DMFF) mechanism is proposed, which adaptively assigns semantic-guided fusion weights to multiscale feature water bodies. Furthermore, the Dual Attention-Enhanced (DAE) module strengthens discriminative essential features and suppresses background noise in both channel and spatial dimensions. Evaluated on a self-constructed super-resolution imagery dataset (SID) and the public GID, DWFSeg achieves overall accuracies of 98.08% and 96.14%, respectively. It outperforms representative benchmark models across multiple quantitative metrics, while maintaining competitive inference efficiency and favorable segmentation stability. Ablation studies verify the effectiveness and necessity of each proposed component. The presented network provides a reliable technical solution and supports refined water resource evaluation and sustainable watershed management. Full article

Clay mineral diagenesis occurs in all buried siliciclastic sediments, albeit fine-grained and low-permeable or coarse-grained and highly permeable. Because of the limited permeability after compaction, this sheds doubt on the presumption that fluid flow is a causal factor in burial diagenesis besides temperature and effective pressure conditions. To assess the influence of fluid flow on their diagenesis, a number of sandstones have been studied focusing on clay minerals. In the studied sandstones, the authigenic clay minerals show a considerable variation in their chemical composition and mineralogy. In particular, authigenic illite and chlorite are common as authigenic cements in sandstones, often forming grain-rimming cements. Each of these two authigenic clay minerals varies distinctly in chemical composition, mineralogy and crystal habit at a small scale, compared to individual pores and laminae/beds. This indicates that local conditions determined not only which authigenic clay minerals formed, but also their chemical composition. Local conditions include the detrital mineral assemblage, specific textural features including clay grain coatings, and the ratio of the volume of susceptible components to pores. During burial diagenesis, a number of elements involved in clay mineral authigenesis, including Al, K, Mg and Fe, are locally fixed by clay mineral precipitation. In addition, diagenesis is driven by the amount and distribution of susceptible detrital components and the changes in physical conditions including temperature and effective pressure. The physical conditions allow chemical processes to commence and continue. The evidence presented is inconsistent with external fluid flow as a causal agent of burial diagenesis, supporting a largely closed, diagenetic system. Full article

High-accuracy parcel-level agricultural mapping is fundamental to precision agriculture. However, in fragmented agricultural regions of the Yangtze River Delta, identifying cropping patterns at the parcel level faces two compounding challenges: asynchronous multi-source observations and mixed-pixel effects in small parcels. When historical archive records are used as training labels, inter-annual cropping pattern changes further introduce label noise that undermines model reliability. To address these challenges and the label noise issue, we propose PAST (Parcel-level Asynchronous SpatioTemporal), a parcel-level cropping pattern classification framework comprising three stages: K-Shape-based label quality control, parallel dual-branch classification, and decision-level fusion. PAST employs a dual-branch architecture: the temporal branch achieves interpolation-free cross-modal phenological fusion of Sentinel-1 and Sentinel-2 data, while the image branch extracts canopy texture features from 0.8 m high-resolution imagery to partially address mixed-pixel interference. Experiments in a typical fragmented agricultural region of the Yangtze River Delta demonstrate that PAST achieves an overall F1 score of 0.926 and a small-parcel F1 score of 0.906, outperforming mainstream time-series baselines. These results confirm that combining K-Shape label quality control at the data level with a dual-branch interference-robust architecture at the model level provides a complete integrated three-stage pipeline for fine-grained crop mapping under weakly supervised historical archive label conditions. Full article

Infrared small target detection (ISTD) remains challenging in complex cluttered environments because targets usually occupy only a few pixels and exhibit weak thermal radiation with limited texture information. The problem becomes more severe in high-resolution infrared imaging systems, where sliding-window inference is commonly adopted under memory and computational constraints. However, the truncated field of view may lead to contextual information loss and increased false alarms in cluttered regions. To address these issues, we propose the Selective Contextual Uplift and Attention Network (SCUA-Net). The proposed network adopts a U-Net++-style densely nested encoder–decoder architecture to enhance multi-scale feature interaction and preserve fine-grained weak-target features. In addition, a Global-Context Calibration Coordinate Attention (GCC-CA) module is introduced to inject window-level contextual statistics into coordinate attention, thereby improving clutter suppression and localization robustness under sliding-window inference. During training, a joint optimization strategy combining Online Hard Example Mining (OHEM) and Dice Loss is employed to alleviate severe foreground–background imbalance. During inference, Gaussian-weighted fusion is adopted to reduce stitching artifacts between adjacent windows. Experimental results on NUDT-SIRST and IRSTD-1k validate the effectiveness of the proposed method. SCUA-Net achieves 99.15% Pd, 0.558 × 10⁻⁶ Fa, and 0.9570 IoU on NUDT-SIRST, while maintaining competitive performance on IRSTD-1k at 161.6 FPS on an NVIDIA RTX 4090 platform, demonstrating favorable accuracy, robustness, and real-time performance in complex infrared scenarios. Full article

Background/Objectives: Skin cancer is one of the most common diseases worldwide, with a high mortality rate. Due to its ability to metastasize, the disease can progress to more serious stages over time. This article proposes a hybrid model based on feature engineering that will play a critical role in the early diagnosis of the disease. Methods: The developed model in this paper utilizes the well-known Vision Transformer (ViT) and Convolutional Neural Network (CNN) models for feature extraction from images in the dataset, while the FT-Transformer, Excel Former, SAINT, GRANDE, PTaRL, and TabTransformer architectures are used for feature extraction from clinical data. Furthermore, this study was developed using a very large pool of classifiers, including 13 classifiers. Fine-tuning was applied to improve the performance of the developed model. Channel attention mechanisms were incorporated into the study to ensure that the proposed model focuses on the diseased area. The PAD-UFES-20 dataset was used during the experiments. Class weighting was applied to the proposed model to prevent class-based imbalance in the PAD-UFES-20 dataset. Results: Six distinct CNN and four distinct ViT models were compared to the developed model. The developed model achieved a highly competitive Area Under the Curve (AUC) rate of 96.41%. The study was conducted using a dataset containing both clinical and imaging data. Conclusions: The proposed model is thought to help dermatologists diagnose skin cancer. Full article

With the acceleration of global population aging, the incidence of knee osteoarthritis (KOA) has risen significantly, placing unprecedented pressure on healthcare resources and creating an urgent need for automated segmentation technologies to enhance clinical diagnostic efficiency. Therefore, this paper proposes a novel multi-target tissue segmentation network for knee joints, SwMrNet, which integrates improved Swin Transformer units and a proposed multi-scale residual module within the decoder to enhance both segmentation accuracy and robustness. Firstly, a sliding-window mechanism is used to iteratively exchange feature information, allowing for the extraction of global tissue features. Then, features are extracted at multiple scales, with residual connections preserving the fine details of each tissue type. Through the repeated fusion of global and local features, the SwMrNet segmentation performance and robustness are significantly enhanced. Finally, the proposed model was evaluated on a public knee MRI dataset and a local clinical knee MRI dataset. On the public dataset, the model achieved a Dice score of 98.2%, with Dice scores for all segmented tissues exceeding 94%. On the local clinical dataset, the model showed visually consistent segmentation results, suggesting its potential as an efficient multi-tissue segmentation tool for automated knee joint analysis and auxiliary clinical assessment. Full article

With the increasing diversity of remote sensing image acquisition conditions and imaging scenarios, building extraction models often experience significant performance degradation in cross-dataset applications due to variations in sensors and scene characteristics. Improving their cross-domain generalization ability has therefore become a critical research problem. To address the challenges of appearance style discrepancy and feature distribution shift in cross-domain building extraction, this paper proposes WCMNet, a wavelet-guided and CNN–Mamba hybrid network for unsupervised domain adaptation in building extraction. Specifically, a Mamba Wavelet Alignment (MWA) module is designed to align low-frequency style information in the wavelet domain while preserving directional high-frequency edge structures, thereby mitigating cross-domain appearance discrepancies and reducing structural degradation during domain translation. In addition, a Global–Local Mamba Block (GLMB) is developed to jointly model local textures and global semantic dependencies. In GLMB, the CNN branch captures fine-grained local details and boundary cues, while the Mamba branch models long-range contextual information; an adaptive gated fusion mechanism further integrates the two types of features. Experimental results on six cross-domain transfer tasks across the WHU, Massachusetts, and Potsdam datasets demonstrate that WCMNet consistently outperforms existing state-of-the-art domain adaptation methods. In particular, WCMNet achieves an average IoU of 65.13% and an average BIoU of 74.80% across all transfer settings, with improvements of up to 27.35 percentage points in IoU and 38.32 percentage points in BIoU compared with the strongest competing methods. These results demonstrate that the proposed MWA and GLMB effectively improve building completeness, boundary delineation accuracy, and cross-domain robustness. Full article