Search Results (1,892)

This paper presents the design, optimization, and performance evaluation of a Satellite-Based Augmentation System (SBAS) ground segment tailored to Colombia’s air navigation infrastructure, with emphasis on ionospheric anomalies in equatorial latitudes. The configuration comprises six Reference Stations (RIMS), strategically sited via geometric dilution of precision (GDOP) minimization and airspace demand models from ADS-B data. A simulation suite—integrating STK^®, Radio Mobile™, and Stanford-ESA certified monitors—quantifies service volume, link margins, and protection level compliance. Ionospheric threat characterization uses regional scintillation datasets (σln ≈ 0.36, ROTI₉₅ ≈ 85 mm/km), informing GIVE inflation and dual-frequency pseudorange integrity validation. Simulations confirm the system sustains ≥ 99.8% APV-I availability over the CAR/SAM FIR, with Horizontal and Vertical Protection Levels (HPL/VPL) bounded below 28 m and 46 m. Uplink integrity and GEO broadcast continuity are modelled under worst-case masking and multipath, confirming ICAO Annex 10 SARPs compliance. The architecture achieves a high performance-to-cost ratio, enabling nationwide SBAS coverage with a 65% cost reduction versus legacy navaids. The system is forward-compatible with dual-frequency multi-constellation SBAS (DFMC), supporting future APV-II scalability. These results position Colombia as a regional node for GNSS augmentation, fostering safety, efficiency, and procedural harmonization. Full article

S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are essential intermediates in one-carbon metabolism and key regulators of cellular methylation capacity. Their concentrations and the SAM/SAH ratio are increasingly studied as biomarkers across metabolic, cardiovascular, neurological, and cancer-related diseases. This review outlines validated analytical methods for quantifying SAM and SAH, focusing primarily on liquid chromatography–tandem mass spectrometry (LC–MS/MS), which is considered the gold standard in both clinical and research settings. A comprehensive literature search identified studies on method development, validation, and clinical use of SAM and SAH measurements. Special attention is given to analytical challenges arising from their high polarity, structural similarity, endogenous presence, and limited stability. The review also discusses preanalytical variables, including biological matrix selection, sample handling, and storage conditions. LC–MS/MS methods are compared with alternative techniques, such as immunoassays, with respect to sensitivity, specificity, matrix effects, and clinical relevance. Additionally, the review summarizes the concentration ranges of SAM and SAH, and their ratio, in healthy and patient populations, noting current standardization limitations. Overall, the review highlights the importance of harmonized analytical protocols and matrix-specific validation to enable reliable clinical interpretation of SAM and SAH as methylation biomarkers. Full article

The paper presents a method for generating a pseudo-panchromatic (PPAN) orthophotomosaic that is spectrally compatible with the multispectral (MS) orthophotomosaic, and it targets the fusion of unmanned aircraft system (UAS) RGB–MS orthophotomosaics when no true panchromatic band is available. In typical UAS imaging systems, RGB and multispectral sensors operate independently and exhibit different spectral responses and spatial resolutions, making the construction of a spectrally compatible substitution intensity a critical challenge for component substitution fusion. The conventional RGB-derived PPAN preserves spatial detail but is constrained by RGB–MS spectral incompatibility, expressed as reduced corresponding-band similarity. The proposed hybrid intensity (PPAN_E) increases the mean corresponding-band correlation from 0.842 (PPAN_A) to 0.928 (PPAN_E) and reduces the across-site mean SAM from 5.782° to 4.264°, while maintaining spatial sharpness comparable to the RGB-derived intensity. It is proposed that the PPAN_E orthophotomosaic be produced as a hybrid intensity (single band) image. Specifically, a multispectral-visible-derived intensity is resampled onto the RGB grid and statistically integrated with RGB spatial detail, followed by mild high-frequency enhancement to produce the final PPAN_E orthophotomosaic. Principal Component Analysis (PCA) fusion is applied to seven archaeological sites in Northern Greece. Spectral quality is evaluated on the MS grid using band-wise (corresponding-band) correlation and the Spectral Angle Mapper (SAM), while the spatial sharpness of the fused NIR orthophotomosaic is assessed using Tenengrad and Laplacian variance. The PPAN_E orthophotomosaic consistently increases correlations relative to PPAN_A (especially in Red Edge/NIR) and reduces the mean site-mean SAM. PPAN_C yields the lowest SAM but also the lowest spatial sharpness/clarity, whereas PPAN_E maintains spatial sharpness/clarity comparable to PPAN_A, supporting a balance between spectral consistency and spatial detail, as also confirmed through comparative evaluation against established component substitution fusion methods. The approach is reproducible and avoids full histogram matching; instead, it relies on explicitly defined linear standardization steps (mean–std normalization) and controlled spatial sharpening, and performs consistently across different scenes. Full article

Liver disease encompasses a wide range of conditions, each requiring tailored therapeutic approaches. This review describes and critically discusses treatments with robust evidence for improving liver health. Ursodeoxycholic acid (UDCA) is a drug approved by the Food and Drug Administration of the USA to treat primary biliary cholangitis (PBC). In addition, UDCA has been demonstrated to protect against metabolic dysfunction-associated steatohepatitis, fibrosis, and drug-induced liver injury (DILI). The mechanism of action of UDCA has been attributed not only to decreasing the effects of toxic bile acids but also to protecting mitochondrial integrity and function, as well as to antioxidant, anti-inflammatory, and anti-apoptotic activities. UDCA can scavenge reactive oxygen species (ROS) and activate the nuclear factor-E2-related factor-2 (Nrf2) pathway, thereby exerting antioxidant activity. The anti-inflammatory activity of UDCA is associated with its ability to inhibit the nuclear factor-κB pathway. Pirfenidone is a well-recognized antifibrotic drug for the treatment of idiopathic pulmonary fibrosis; its effects on liver fibrosis have also been demonstrated. Pirfenidone exerts anti-inflammatory effects by attenuating the nucleotide-binding oligomerization domain-like receptor 3 inflammasome signaling pathway. The antioxidant actions of pirfenidone are associated with its ability to upregulate the Nrf2 pathway. Both the anti-inflammatory and antioxidant properties of pirfenidone act together to attenuate lung and liver fibrosis, decreasing transforming growth factor-β levels, inhibiting profibrogenic hepatic stellate cell activation, and increasing extracellular matrix degradation. Methyltransferases utilize S-adenosyl-L-methionine (SAM) as a methyl donor for most transmethylation reactions in the body. SAM increases reduced glutathione (GSH) levels, exerting important antioxidant effects. Evidence indicates that SAM prevents fibrosis and attenuates hepatocellular carcinoma development, improving patient survival. N-acetylcysteine (NAC) is a precursor to L-cysteine and GSH and is used in clinical settings to treat cancer, nephropathy, heart disease, pulmonary fibrosis, polycystic ovary syndrome, and influenza. Regarding the liver, NAC is the most accepted treatment for DILI, especially after paracetamol overdose. Owing to its antioxidant and anti-inflammatory actions, NAC has been successfully used to treat chronic liver injuries, including hepatosteatosis and fibrosis. Therefore, ursodeoxycholic acid, pirfenidone, S-adenosyl-L-methionine, and N-acetylcysteine could represent therapeutic strategies for the treatment of liver pathologies. Full article

Indoor scene reconstruction remains challenging due to the prevalence of low-texture regions such as walls, floors, and ceilings, where weak photometric signals hinder accurate geometric recovery. While 3D Gaussian Splatting (3DGS) achieves impressive novel view synthesis, existing methods struggle with geometric accuracy in textureless areas due to uniform treatment of scene regions. We propose a texture-complexity-based 3D Gaussian Splatting strategy that leverages geometric priors for high-fidelity indoor reconstruction. Our method extracts planar priors through Manhattan frame alignment and refines them with Segment Anything Model (SAM) masks, enabling texture-aware initialization: planar priors guide Gaussian placement in low-texture regions, while dense feature matching ensures accurate initialization in high-detail areas. During optimization, geometric regularization through depth-plane loss, normal-surface loss, and normal-consistency loss maintains structural integrity. Evaluations on ScanNet++, MuSHRoom, and Replica datasets demonstrate state-of-the-art performance, with training completed in under 1 h. Our approach balances geometric accuracy with photometric fidelity, providing a practical solution for high-fidelity indoor mesh extraction from Gaussian representations. Full article

This paper proposes a unified vision–language framework that translates user instructions into navigation for the mobile base and actions for the manipulator in indoor environments. In general, occupancy grid maps constructed via SLAM capture solely the geometric layout of the environment. This renders the robot incapable of leveraging the semantic information required for object distinction. The proposed method encodes semantic information from vision–language models and the robot’s pose in a textual format, referred to as a semantic topological graph. Specifically, the models including GroundingDINO, LG EXAONE, and SAM2 extract object-level semantic information, which is subsequently used to identify room characteristics. A large language model then interprets user instructions to identify the final destination for navigation within the semantic topological graph, followed by reasoning to determine the suitable action network. Notably, the proposed text-based representation facilitates a substantial reduction in inference time, and its effectiveness is validated through real-world experiments. Full article

Background: Accurate vertebral instance segmentation on full-spine radiographs is essential for spinal parameter assessment, but supervised methods require costly instance-level annotations and may be sensitive to domain shift. Methods: We investigated whether promptable segmentation foundation models can generalize zero-shot to raw DICOM spine radiographs without task-specific training. We evaluated SAM-ViT-Huge, SAM2-Hiera-Large, and MedSAM-ViT-Base on 144 full-spine radiographs with 1309 annotated vertebral masks using a standardized pipeline for DICOM decoding, intensity normalization, automatic prompt generation, and instance-level evaluation. For each prompt, models produced three candidate masks. Performance was reported under an oracle protocol selecting the candidate with the highest IoU against ground truth and a model-score protocol selecting the candidate with the highest predicted IoU. Metrics included IoU, Dice, precision, recall, ASSD, and HD95. Results: The best configuration was SAM-ViT-Huge with rectangle prompting, reaching a mean IoU/Dice of 0.782/0.870 under oracle selection and 0.737/0.837 under model-score selection. SAM2-Hiera-Large with rectangle prompting followed (0.744/0.848 oracle; 0.699/0.815 model-score), ahead of MedSAM-ViT-Base (0.599/0.737 oracle; 0.387/0.499 model-score). Point prompting yielded consistently low overlap (IoU 0.224–0.319; Dice 0.276–0.414) despite high recall, indicating systematic over-segmentation and large boundary errors. Conclusions: Zero-shot vertebral instance segmentation on raw DICOM spine radiographs is feasible with promptable foundation models when prompts sufficiently constrain target extent. Rectangle prompting is clearly more effective than point prompting in this setting. Full article

Accurate detection of individual trees is essential for urban forest management and ecological assessment, yet remains challenging due to the heterogeneous backgrounds, variable sizes of tree crowns, and significant variations across urban scenarios. To address these issues, we propose Tree-SAM, a city-scale individual tree detection architecture built upon the visual foundation model Segment Anything Model (SAM) and equipped with three task-specific modules, i.e., Cross-Correlation Feature Backbone (CCFB), Hierarchical Instance Aggregation Neck (HIAN), and Context-Aware Adaptation Head (CAAH). These modules synergistically fuse general semantics with fine-grained structural cues, enable multi-scale feature aggregation, and adaptively refine predictions based on specific scene contexts. On the GZ-Tree Crown dataset, Tree-SAM achieves F1-scores of 0.762, 0.732, and 0.830, with corresponding AP@50 values of 0.478, 0.454, and 0.526 in the forest, mixed, and urban scenarios, respectively, consistently ranking first across all scenes and demonstrating strong adaptability to diverse intra-city landscapes. Additional evaluations on the BAMFORESTS dataset and the SZ-Dataset further confirm its robustness across varied geographic contexts. Tree-SAM provides a reliable, automated framework for large-scale urban tree mapping, providing reliable data support for urban forest management, carbon stock estimation, and ecological assessment. Full article

Person Re-identification (Re-ID) plays an important role in dynamic evacuation path planning and safety monitoring. However, rapid appearance changes and limited long-term surveillance data significantly degrade model robustness in emergency scenarios. To address this issue, a virtual try-on-based data augmentation framework is proposed for person Re-ID. A prompt-based automatic clothing mask generation (PACMG) module integrating Grounding DINO and the Segment Anything Model (SAM) is developed to improve clothing mask accuracy under low-resolution, occlusion, and complex background conditions. A tiered augmentation strategy is further designed to alleviate identity-level imbalance. Experimental results demonstrate that the proposed method increases the clothing replacement validity rate from $52\%$ to $73.61\%$ while preserving identity consistency and distribution stability, as verified through multi-level analyses. When the augmented data are incorporated into the training set, consistent improvements in Rank-1 accuracy and mAP are observed on a ResNet-50-based person Re-ID benchmark. These results indicate that the augmented data enhance robustness to appearance variation, providing practical support for robust person tracking in evacuation scenarios. Full article

Positioning, Navigation and Timing (PNT) methods in GNSS-challenged environments require multi-sensor and cooperative approaches to mitigate the low or complete unavailability of GNSS measurements. Many methods also rely on map databases and the availability of sensors throughout the environment. Data like Signal of Opportunity (SoO) ranges, Inertial Measurement Units, and camera data are often used to ensure measurement redundancy. Given the recent advancements in Artificial Intelligence (AI) image segmentation, especially the Segment Anything Model (SAM) and Depth Anything (DA) model, there is an opportunity to treat AI as a modern SoO. SAM can quickly and efficiently recognise distinct objects in any image, while DA can create a pixel-based depth map from any image. A novel architecture for combining multi-sensor cooperative positioning and a position integrity method with SAM and DA is proposed. In this paper, the initial feasibility study of using SAM and DA to determine the ranges from images is carried out. SAM and DA are tested on photographs taken in Vienna, Austria. The feasibility of establishing a functional relation between determined depth and ground truth distances is studied and demonstrated. Full article

High-quality RGB–thermal infrared (RGB-T) semantic segmentation datasets are crucial for search-and-rescue (SAR) applications, yet their development is hindered by the scarcity of annotated ground-truth and by the challenges of thermal-camera calibration, which typically depends on heated targets with limited geometric definition. Recent approaches focus on using semantic segmentation annotation tools and transferring RGB masks to multi-spectral data, but they do not fully address the need for robust cross-modal geometric validation, quality control, or human-in-the-loop reliability assessment in RGB-T segmentation. To fill this gap, we propose a validated cross-modal annotation pipeline that combines deep correspondence matching, geometric transformation (affine or homography) of RGB-T pairs, and quantitative alignment validation. Our RGB-T pipeline integrates a semi-automatic annotation pipeline based on the Segment Anything Model 2 (SAM2) in Label Studio, with guided human refinement, and incorporates quantitative cost and quality control via inter-annotator agreement before being used in downstream model training. Results across three annotators show that the proposed approach reduces annotation time by 36% while achieving high annotation quality (mean IoU = 74.9%) and strong inter-annotator agreement (mean pixel accuracy = 74.3%, Cohen’s $\kappa$ = 65%). The proposed RGB-T pipeline was annotated on a SAR-oriented RGB-T dataset comprising 306 image pairs and trained on two SOTA RGB-T. These findings demonstrate the practical value of the proposed methodology and establish a reproducible framework for generating reliable RGB-T semantic segmentation datasets, complementing and extending recent multispectral auto-labeling approaches. Full article

Soil heavy metal contamination in mining areas poses a serious environmental challenge, requiring monitoring approaches with both wide coverage and high accuracy. Hyperspectral remote sensing provides an effective solution, yet its performance in complex mining environments is often limited by mixed-pixel effects and nonlinear spectral responses. To address these issues, this study proposes a Physically-Constrained Collaborative Endmember Extraction (PCCEE) framework that integrates spectral unmixing with machine learning for multi-element inversion. Using Gaofen-5 hyperspectral imagery, a collaborative workflow combining Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and prior-spectral-constrained Spectral Angle Mapper (SAM) was developed to improve endmember purity and physical interpretability. Among three unmixing models (LMM, NMF, and SVR), the Linear Mixing Model achieved the best balance between accuracy and efficiency. Random Forest regression using retrieved abundances enabled high-accuracy inversion of eight heavy metals (mean R² = 0.85). Spatial analysis revealed significant co-enrichment of Pb, Cd, and Zn related to sulfide weathering, while PCA distinguished compound and independent pollution sources. The proposed PCCEE framework effectively mitigates mixed-pixel interference and provides a transferable approach for heavy metal monitoring and risk assessment in complex mining environments. Full article

Background/Objectives: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Although chest computed tomography (CT) enables detailed assessment of lung abnormalities, manual interpretation is time-consuming, requires expert expertise, and is prone to diagnostic variability. To address these challenges, this study proposes DE-SAMNet, a hybrid deep learning framework for automated multi-class LC classification from CT scans. Methods: The model integrates two pre-trained convolutional neural networks—DenseNet121 and EfficientNetB0—operating in parallel to extract complementary multi-scale features. A Spatial Attention Module (SAM) is applied to each feature stream to emphasize clinically important regions. Final classification is performed through a compact fusion mechanism involving global average pooling, batch normalization, and a fully connected layer. DE-SAMNet was evaluated on two datasets: a public dataset (IQ-OTH/NCCD) with benign, malignant, and normal cases, and a private clinical dataset including benign, malignant, cystic, and healthy cases. Results: On the public dataset, the model achieved a 99.00% F1-score, 98.41% recall, 99.64% precision, and 99.54% accuracy. On the private dataset, it obtained 95.96% accuracy, 95.99% precision, 96.04% F1-score, and 96.21% recall, outperforming existing approaches. To enhance reliability, explainable AI (XAI) techniques such as Grad-CAM were used to visualize the model’s decision rationale. The resulting heatmaps effectively highlight lesion-specific regions, offering transparency and supporting clinical interpretability. Conclusions: This explainability strengthens trust in automated predictions and demonstrates the clinical potential of the proposed system. Overall, DE-SAMNet delivers a highly accurate and interpretable solution for early LC detection. Full article

Infrared thermography is a significant and extensively utilized method for assessing the operational condition of power equipment. Nonetheless, the constrained spatial resolution of infrared imaging systems, imaging noise, and the inadequate representational capacity of single-modality data render the precise segmentation of power equipment targets difficult, particularly in intricate backdrops and settings with weak structures. Simultaneously, obtaining high-quality pixel-level annotations for power equipment is expensive and laborious, leading to a scarcity of training samples and thus diminishing the efficacy of conventional supervised segmentation techniques. This research offers a super-resolution guided cross-modal segmentation strategy to tackle these issues in data-scarce circumstances and examines the applicability of the general-purpose segmentation model Segment Anything Model 3 (SAM3) for infrared image segmentation of power equipment. A super-resolution reconstruction framework based on a high-order degradation model is built to enhance low-resolution infrared images collected in real-world contexts. An Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) -based network incorporating residual-in-residual dense blocks (RRDB) is utilized to reconstruct infrared thermograms, hence improving structural features and boundary representations. Secondly, the concurrently obtained visible-light images are improved by low-light enhancement methods, and an anchor-free object detection framework is employed to ensure accurate localization of power equipment targets. The identified areas in visible images are aligned with the coordinate system of infrared super-resolution images via cross-modal geometric transformation, establishing a cross-modal spatial prior that efficiently limits the search space for infrared segmentation and mitigates background interference. The general-purpose segmentation model SAM3 is introduced, utilizing cross-modal detection boxes as prompts to facilitate precise segmentation of power equipment targets in infrared super-resolution images, achieving high-accuracy segmentation without the necessity for extensive task-specific annotated data. The experimental results demonstrate that our proposed approach significantly improves both the accuracy and robustness of infrared image segmentation for power equipment under complex conditions, attaining a Jaccard index of 89.86% and a Dice coefficient of 91.12%, thereby validating its efficacy and practical applicability in data-scarce environments. Full article

In metal additive manufacturing, the molten pool directly influences the performance of the fabricated components. Therefore, a comprehensive understanding of the molten pool behavior is essential for improving the quality of the parts and mitigating the formation of defects. Selective arc melting (SAM) is a promising additive manufacturing method for fabricating metal matrix composites. However, the melting and solidification process of the powder layer under the arc heat source remains unrevealed. This study aims to elucidate the formation mechanisms of surface morphology during SAM processing and the influence of carbide addition on the melting and solidification behavior of Inconel 718 powder. In this study, thin-walled parts of Inconel 718 and TiC/Inconel 718 composite were fabricated and their microstructures were studied. The melting and solidification behavior of Inconel 718 and TiC/Inconel 718 composite during single-track single-layer deposition was investigated using high-speed photography. Focusing on the differences in the sidewall surface morphology of the Inconel 718 and TiC/Inconel 718 composite parts, the edge feature formation of the deposition track of both materials was studied. Furthermore, the formation mechanism of the differences in forming height at different positions of the deposition track was explored. The results indicate that the melted material in the molten pool of Inconel 718 mainly comes from the mass transport of the beads generated around the molten pool, while the liquid material in the molten pool of TiC/Inconel 718 composite mainly comes from the in situ powder melted under the arc center. During the melting process of Inconel 718 powder, beads at the edge of the heating area come into contact with the boundary of the molten pool and solidify in situ, forming protrusion features. The randomness in the bead size leads to different volumes of molten material at different positions within the same time, thereby causing variations in building height. Full article