Search Results (2,719)

Hydrogen embrittlement is known to adversely affect the mechanical properties of low-carbon steels used for pipelines and pressure vessels, leading to accelerated crack growth and lowered fracture toughness. To overcome the limitations of surface-based analysis, this study employs X-ray micro-computed tomography (µ-CT) to provide a comprehensive 3D evaluation of the crack evolution. This approach is used to assess hydrogen-assisted crack growth in P355NH compact tension samples from previous fracture mechanical tests and enables a precise quantification of the internal crack path and the crack tip opening angle (CTOA) across the entire specimen thickness as well as the local damage morphology. By integrating these spatial parameters, a deeper understanding of the impact of hydrogen on local fracture mechanisms is achieved, revealing insights that have remained hidden in previous two-dimensional microscopy observations. For instance, µ-CT results clearly demonstrate that the hydrogen-assisted crack propagation is associated with increased void formation and secondary cracking in vicinity of the crack tip. However, it is proposed that the results are superimposed with continuous hydrogen desorption, which implies a need for in situ charging during mechanical loading and an analysis of the hydrogen concentration profile. Both will be the scope of further studies. Full article

Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant attention in medical image analysis. This comprehensive review examines the current state of the art in diffusion models for medical image segmentation, covering theoretical foundations, methodological innovations, computational efficiency strategies, and clinical applications. We analyze recent advances in latent diffusion frameworks, transformer-based architectures, and ambiguous segmentation modeling while addressing the practical challenges of implementing these models in clinical environments. The review encompasses applications across multiple medical imaging modalities including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and X-ray imaging, providing insights into performance achievements and identifying future research directions. Through systematic analysis of publications mostly from 2019 to 2025, we demonstrate that diffusion models have achieved remarkable progress in addressing fundamental challenges including data scarcity, inter-observer variability, and uncertainty quantification. Notable achievements include inference time being reduced from 91.23 s to 0.34 s for echocardiogram segmentation (LDSeg, Echo dataset), DSC scores up to 0.96 for knee cartilage MRI segmentation, and a +13.87% DSC improvement over baseline methods for breast ultrasound segmentation. This review serves as a comprehensive resource for researchers and clinicians interested in leveraging diffusion models for medical image segmentation, providing a roadmap for future research and clinical translation. Full article

This study proposes a four-dimensional Historical Building Defect Information Modeling (HBDIM) framework designed to support the documentation, diagnosis, and conservation of deteriorated historic stucco elements. The framework integrates multi-source digital documentation techniques, including terrestrial laser scanning (TLS), high-resolution photogrammetry, and automated total station measurements with laboratory-based material diagnostics to create a unified digital environment for defect detection and conservation assessment. The approach was applied to the Baron Empain Palace in Egypt as a representative case study of complex architectural heritage affected by material deterioration. Within the HBDIM workflow, point cloud processing and defect-oriented information modeling were used to identify and spatially localize deterioration features such as cracking, erosion, and material loss. Laboratory investigations—including computed tomography (CT), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray fluorescence (XRF)—were conducted to evaluate the effectiveness of calcium hydroxide nanoparticle consolidation treatments and to relate microstructural material behavior to spatially mapped defects within the digital model. Mechanical testing demonstrated a significant improvement in material performance, with treated stucco samples exhibiting an average compressive strength increase of approximately 69.06% compared to untreated specimens. The results demonstrate that integrating digital documentation, defect-oriented modeling, and material diagnostics within a four-dimensional framework provides a robust platform for linking geometric deterioration patterns with material-level conservation performance. By embedding diagnostic data and treatment outcomes within a temporally structured digital model, the HBDIM approach supports preventive conservation strategies, long-term monitoring, and data-driven decision-making in sustainable heritage management. Full article

This work provides an integrated experimental and computational evaluation of the cationic surfactant Quaternium-22 (Q-22) as a potentially eco-compatible corrosion inhibitor for carbon steel (CS) in 1 M hydrochloric acid. Gravimetric analysis and electrochemical techniques, including electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP), were employed over a temperature range of 20–50 °C. Q-22 exhibited mixed-type inhibition behavior, with efficiency rising to 97% at an optimal concentration of 277 μmol L⁻¹. Performance was concentration-dependent but diminished with increasing temperature, indicating partial inhibitor desorption at elevated temperatures. Thermodynamic evaluation confirmed a spontaneous adsorption process consistent with the Langmuir isotherm, involving a combined physisorption and chemisorption mechanism. Surface characterization via scanning electron microscopy (SEM), atomic force microscopy (AFM), contact angle (CA) measurement, and X-ray photoelectron spectroscopy (XPS) confirmed the formation of a coherent, hydrophobic inhibitor layer that substantially reduced surface roughness and corrosion damage. Theoretical investigations using density functional theory (DFT), natural bond orbital (NBO) analysis, and molecular dynamics (MD) simulations revealed strong adsorption energies and favorable electronic properties consistent with the inhibitor’s high experimental efficacy. Overall, the results demonstrate that Q-22 is a highly effective, eco-compatible corrosion inhibitor for CS in acidic environments, operating through a stable adsorptive film-forming mechanism. Full article

Background/Objectives: AI-based tools for chest radiograph interpretation are increasingly used as decision-support systems, yet their performance must be validated in local clinical environments before deployment. This study evaluated the diagnostic performance of qXR (Qure.ai, v3.2) for detecting pulmonary nodules, cardiomegaly, and pleural effusion in adult patients at Hospital Clínica Bíblica, San José, Costa Rica. Methods: Three radiologists independently interpreted 225 chest radiographs, providing the reference standard. qXR outputs were compared against radiologist assessments for each finding. The sensitivity, specificity, Cohen’s kappa, and area under the ROC curve (AUC) were calculated. Due to the convenience-stratified sampling design, predictive values were not used for clinical interpretation. Results: For pulmonary nodules, qXR achieved a sensitivity of 0.71, specificity of 0.90, Cohen’s kappa of 0.51, and AUC of 0.80. For pleural effusion, sensitivity and specificity were both 0.86, with a kappa of 0.63 and AUC of 0.86. Cardiomegaly showed the lowest agreement, with a sensitivity of 0.64, specificity of 0.91, kappa of 0.57, and AUC of 0.77. Conclusions: qXR demonstrated moderate diagnostic agreement with radiologist assessments for pulmonary nodules and pleural effusion, and lower agreement for cardiomegaly under local imaging conditions. These results reflect technical concordance between the AI system and individual radiologists and do not constitute evidence of clinical utility or real-world impact. Context-specific validation is essential prior to integrating AI tools into routine radiological workflows. Full article

Silica-based bioceramics are promising bone substitutes with tunable degradation and mechanical properties. We aimed to assess bone response in critical-size calvarial defects in rats, empty or filled with 3D-printed sphene ceramic (CaTiSiO₅) scaffolds produced using direct ink writing from preceramic polymers and reactive fillers. Scaffold characterization was performed using scanning electron microscopy, X-ray diffraction, porosity analysis, and compressive strength testing. Bilateral cylindrical 5 mm calvarial defects were created in 20 rats: one was randomly filled with sphene scaffold, while the contralateral remained empty. Ten animals were killed at 4 weeks, the rest at 8 weeks. Specimens were collected for micro-X-ray computed tomography (micro-CT) analysis, followed by undecalcified histology. The scaffolds exhibited porous structure with complete sphene phase purity and compressive strength of 17.91 MPa (SD 4.6). In vivo, no adverse event was noted during healing. Overall bone regeneration—as measured by BV/TV—was comparable between groups: Bone volume/total volume (BV/TV) increased over time in the empty and sphene groups, reaching ~40%, with no significant differences between groups or time points. BV/TV was significantly higher in the external regions of the defects compared to the internal areas in both groups at the two time points. The sphene group showed a significantly greater volume of new bone extending beyond the original cortical boundary at both 4 and 8 weeks (p = 0.013). In the sphene group histology revealed partial bone ingrowth within the scaffold, while bone in the control group was limited to defect edges. After 8 weeks, new bone adjacent to the cortical surface was thicker in the sphene group (p < 0.05). These initial findings are consistent with prior preclinical studies, supporting the biocompatibility and osteoconductive nature of sphene ceramic scaffolds. Full article

Automated identification of lumbar vertebrae plays a key role in modern spine analysis, offering valuable assistance for diagnostic assessment and preoperative decision-making. Despite recent progress in deep learning-based detection methods, accurately localizing vertebral structures remains challenging due to anatomical variability and heterogeneous image quality. To address the difficulty of capturing subtle vertebral structures, we introduce a Multi-Scale Channel Attention Block (MSCABlock) integrated into the YOLO11 backbone. Unlike conventional attention-based or multi-scale convolutional designs, MSCABlock jointly exploits channel-wise feature interaction and multi-scale receptive fields to enhance both local detail sensitivity and contextual representation, while preserving computational efficiency. The proposed approach is designed to improve detection performance without significantly increasing model complexity. Our model is trained and validated using only the AP-view images from the Burapha University Lumbar-Spine Dataset (BUU-LSPINE), which provides well-annotated lumbar spine X-ray images from 400 unique patients. The proposed approach operates in a fully end-to-end manner, allowing vertebrae to be identified directly from input images without relying on handcrafted feature engineering or complex preprocessing pipelines. Experimental evaluations show that the proposed model achieves strong detection performance, with mAP@0.5 and mAP@0.5–0.95 reaching 0.982 and 0.79, respectively, alongside a precision of 0.93 and a recall of 0.975. Compared with the YOLO11 baseline, ablation and efficiency analyses demonstrate that MSCABlock consistently improves detection performance. It introduces only marginal increases in model parameters and computational cost, thereby preserving a lightweight architecture and maintaining efficient inference. These results show that the optimized YOLO11-based system generalizes well across lumbar levels. It maintains reliable detection under challenging conditions, providing robust automated localization to support large-scale clinical spine analysis. Full article

Background: Point mutations to the androgen receptor (AR) ligand-binding domain (LBD) are becoming increasingly recognized as a mechanism of therapeutic resistance in castration resistant prostate cancer (CRPC). The present review explores how point mutations induce molecular changes that contribute to the eventual treatment failure of androgen receptor pathway inhibitors (ARPIs) in CRPC. Methods: The PubMed database was searched for structural studies on the AR LBD. Eligible articles included molecular docking analysis and emphasized changes in ligand–receptor interactions after point mutation. Structural data were obtained from the Protein Data Bank (PDB) using the search parameters “Androgen receptor ligand binding domain”, “Homo sapiens”, and “X-ray diffraction”. PDB files of wild-type and point mutant AR LBDs were accumulated for analysis. Results: A functional shift from inhibiting to activating AR has been documented for multiple ARPIs. Crystallography data and in silico evaluation have deciphered how changes in steric hindrance of the AF-2 domain contribute to ARPI loss of function. To combat therapeutic resistance, discovery efforts have begun to consider combination approaches of orthosteric and allosteric inhibitors, as well as compounds that target other AR domains. Although lead compounds have been identified, none have progressed into the clinic. Conclusions: Questions remain regarding the best approach for rationally designing new AR targeting therapeutics. Understanding how structural changes to the AR LBD lead to the failure of clinical therapeutics is a necessary step that should precede drug discovery campaigns. Moreover, computational modeling is a powerful tool that should be leveraged to streamline therapeutic development. Full article

This study develops a multi-scale fiber-reinforced cementitious composite (MFRC) by hybridizing calcium carbonate whisker (CW), polyvinyl alcohol (PVA) fiber, and steel fiber. The interfacial micromechanical properties between steel fiber/matrix and PVA fiber/matrix under the influence of CW were systematically examined through single-fiber pull-out tests. The two-dimensional and three-dimensional distribution characteristics of fibers in the MFRC were analyzed using backscattered electron imaging (BSE) and X-ray computed tomography (X-CT), respectively. Based on the fiber distribution characteristics, flexural strength prediction models were developed with R² values of 0.79 (2D) and 0.82 (3D). Experimental validation via splitting tensile tests and three-point bending tests confirmed the model’s effectiveness in simultaneously predicting splitting tensile strength (R² = 0.89) and flexural strength (R² = 0.93). These findings demonstrate the reliability and universality of the proposed model for predicting flexural–tensile strength in an MFRC. Full article

To address the challenge of coarse aggregates hindering steel fiber dispersion and reducing toughening efficiency in ultra-high-performance concrete containing coarse aggregate (UHPC-CA), this study proposes a hybrid fiber design method based on reverse adaptation to the aggregate structure: a paradigm where fiber proportions are inversely designed to match the quantified void size distribution within the coarse aggregate skeleton. Industrial X-ray computed tomography (X-CT) was employed to capture the internal structure of UHPC-CA. Digital image processing techniques were used to quantitatively characterize the size distribution within the coarse aggregate skeleton gap. Based on this distribution, the blending proportions of multi-scale (3–16 mm) copper-plated steel fibers were systematically determined. Three fiber configurations were compared: mono-sized 13 mm fibers (Type A), an empirical model based on aggregate size (Type B), and a quantitatively designed blend based on skeleton gap distribution (Type C). At the same fiber volume fraction, the mechanical property test results show that the C type achieves approximately 18.6% higher flexural strength and 29.1% higher splitting tensile strength compared to the A type, while showing 5.3% and 6.7% improvements over the B type, and the compressive strength also increased slightly (about 3.0%). The microanalysis further confirms that the fiber distribution in the C-type design was more uniform, and the bridging effect and crack resistance were more sufficient. The proposed gap-adaptive fiber design paradigm offers an effective approach for optimizing reinforcement distribution in composites, providing theoretical and practical value for high-performance UHPC-CA applications. Full article

Background/Objectives: A ratio of subcutaneous adipose tissue to muscle mass may be more informative than defining low subcutaneous adipose tissue and muscle mass separately. The objective of this study was to determine which ultrasound measurement points in the upper and lower extremities predict the subcutaneous adipose tissue (SAT)-to-muscle ratio as measured by gold-standard computed tomography (CT) at the third lumbar vertebra (L3) level. Methods: Two hundred hospitalised patients (41% female; median (Q1–Q3) age: 61.3 (51.0–70.1) years) who underwent an abdominal CT scan for any clinical reason within 48 h prior to extremity ultrasound were included in this prospective observational study conducted from 2017 to 2019. Ultrasound measurements of subcutaneous adipose tissue and muscle thickness were obtained at three measuring points on the thigh and two on the upper arm. On the CT scan at the L3 level, subcutaneous (SAT) and visceral adipose tissue and skeletal muscle area were measured. A linear LASSO (Least Absolute Shrinkage and Selection Operator) model was used to identify which ultrasound sites best predicted the CT L3 SAT-to-muscle ratio. Results: Height, weight, sex, SAT-to-muscle ratio at four ultrasound measuring points and abdominal circumference predicted the CT SAT-to-muscle ratio in the LASSO model (R² = 0.70; cross-validated R² = 0.63; p values are not reported in LASSO regression and R² is used instead). The upper-arm anterolateral ultrasound site most strongly influenced the CT SAT-to-muscle ratio (estimate × standard deviation of predictor: 0.24). Conclusions: The CT SAT-to-muscle ratio at the L3 level can be predicted non-invasively using bedside ultrasound, particularly at the anterolateral measuring point of the upper arm. Bedside ultrasound assessment of the ratio of subcutaneous adipose tissue to muscle on the anterolateral upper arm provides a within-patient comparison of body compartments. Full article

Computed Tomography (CT) is a widely used imaging modality that employs X-rays and computational reconstruction to visualize internal anatomy. Although higher radiation doses produce higher-quality images, they also increase long-term cancer risk, motivating the use of low-dose protocols. However, low-dose CT data inherently suffer from elevated Poisson–Gaussian noise, necessitating effective denoising strategies. In myocardial CT perfusion (CTP) imaging, this challenge is compounded by residual cardiac motion, which misaligns consecutive time points and impairs accurate estimation of perfusion maps for diagnosing coronary artery disease. Traditional approaches typically treat these two problems, noise and motion, separately, denoising the reconstructed images first or applying the registration first. Such serial pipelines often degrade clinically significant features; e.g., denoising may destroy structural details essential for registration, while motion correction can distort subtle intensity cues needed for noise modelling. To overcome these limitations, we propose a unified deep learning framework that performs noise suppression and motion correction jointly for low-dose myocardial CTP. The method integrates two complementary components through a parallel ensemble strategy: (i) a modified Fast and Flexible Denoising Network (FFDNet) that incorporates noise-level maps to mitigate blended noise effectively, and (ii) a CNN-based registration model, extended with Time Enhancement Curve (TEC) correction and 4D physiological consistency constraints to estimate temporally coherent and anatomically plausible motion fields. By combining their outputs without iterative dependencies, the proposed framework produces motion-corrected and denoised CTP sequences in a single unified processing step, thereby better preserving myocardial structure and perfusion dynamics than conventional serial pipelines. The model has been evaluated using both reference-based (MSE, PSNR, SSIM, PCC, Noise Variance, TRE) and no-reference (NIQE, FID, KID, AUC) image quality metrics, supplemented by expert human assessment. Results demonstrate that jointly learning noise characteristics and motion patterns enables restoration of low-dose CTP images while minimizing feature corruption, thereby advancing the clinical utility of low-dose myocardial CTP imaging. Full article

Purpose: To investigate whether an AI-based approach combining deep learning myocardial segmentation with attenuation-normalized myocardial mapping (colormaps) improves detection of myocardial ischemia and infarction on emergency ECG-gated CT angiography. Materials and Methods: In this retrospective study, 119 patients with acute chest pain who underwent ECG-gated CT angiography to exclude pulmonary embolism or acute aortic syndrome and invasive coronary angiography within 48 h were included. A deep learning model (nnU-Net) was used for automatic left-ventricular myocardial segmentation, serving as the basis for voxel-wise attenuation normalization to generate AI-based myocardial attenuation maps. Six readers with varying experience levels evaluated all cases for myocardial hypoattenuation in a multi-reader, multi-case design, with and without AI-generated attenuation maps. Results: AI-based myocardial attenuation mapping increased mean sensitivity for detection of myocardial ischemia or infarction by 12% [IQR 2–20%] compared with standard CT interpretation alone. Sensitivity improved by 15% [IQR 10–22%] in STEMI (ST-Elevation Myocardial Infarction) and 11% [IQR −1–18%] in NSTEMI (Non-STEMI) cases. The AI-assisted approach resulted in the correct reclassification of 11% of patients and improved inter-reader agreement, particularly among less experienced readers, demonstrating reduced reader dependency. Conclusions: AI-based myocardial segmentation and attenuation mapping enhance the detection of myocardial ischemia and infarction on emergency CT angiography and improve inter-reader agreement. This AI-assisted image processing approach provides clinically meaningful decision support in acute chest pain imaging workflows. Full article

Permeability is affected by nanopores and pore structure, and anisotropic permeability is the result of shale lamination, orientation, and stratification of minerals. To understand the reasons for permeability anisotropy, the pore networks of over-mature shale has been studied. The mineral compositions, petrophysical properties, and pore structures of the Lower Cambrian Niutitang Formation shales were analyzed using subcritical gas adsorption, field-emission scanning electron microscopic, and X-ray micro-computed tomographic methods. Quartz, clay minerals, and carbonate are the dominant minerals in the shales. The bedding-parallel and bedding-perpendicular permeabilities are 1.25–46.21 × 10⁻² and 1.38–6.62 × 10⁻² mD, respectively. The anisotropy of permeability, which is the ratio between the bedding-parallel and bedding-perpendicular permeability, is 0.21–26.87. The micropore and Barrett–Joyner–Halenda pore volumes are 0.54–3.62 and 0.05–0.69 mL/100 g, respectively. The bedding-parallel permeability is correlated positively with the micropore and Barrett–Joyner–Halenda pore volumes. Thin-section observations indicate the shales exhibit a bedding-parallel alignment of phyllosilicate minerals and planar deformation bands. The scanning electron microscopy shows deformation of the lamination and parallel alignment of the clay minerals due to compaction or differential compaction over coarser-grained quartz grains. The scanning electron microscopy images and subcritical gas adsorption data indicate that the pore fracture system is parallel to bedding and formed after diagenesis. Furthermore, X-ray micro-computed tomographic analysis shows that the micro-fractures are also preferentially oriented, parallel to bedding. Full article

This study examines the carbonation behavior and CO₂ storage potential of a Ca-rich alkali-activated binder produced entirely from industrial residues-ladle furnace slag (LFS), coal ash (CA), and cement kiln dust (CKD). The system was designed as a one-part alkali-activated material (AAM), with CKD acting as an internal activator, and subjected to ambient curing, water curing, and accelerated CO₂ curing at ambient pressure. Phase evolution, microstructural development, and pore-structure characteristics were investigated using X-ray diffraction, FTIR spectroscopy, DSC–TG analysis, scanning electron microscopy, and X-ray micro-computed tomography, together with measurements of density, water absorption, and compressive strength. Loss-on-ignition measurements combined with chemical analysis were further used to quantify CO₂ uptake and evaluate the degree of carbonation of the binder system. CO₂ curing fundamentally altered the reaction pathway of the binder, shifting it from hydration-dominated to carbonation-controlled phase evolution, leading to the decomposition of calcium-bearing hydrates and complete carbonation of non-hydraulic γ-belite with the formation of vaterite, aragonite, and calcite. These transformations induced pronounced microstructural densification, reflected in a near-doubling of compressive strength (>48 MPa), increased apparent density, reduced water absorption, and simplified pore-network topology. A preliminary carbon footprint assessment indicates that the production of 1 m³ of the developed LFS–CA–CKD concrete generates about 14.36 kg CO₂-eq, while the carbonation process enables significant CO₂ sequestration, resulting in a net negative carbon balance. The results demonstrate that controlled carbonation is an effective post-treatment strategy for waste-derived alkali-activated binders, enabling simultaneous performance enhancement and permanent CO₂ sequestration. Full article