Search Results (1,131)

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

Background/Objectives: Accurate bone fracture detection is essential for orthopedic diagnosis and trauma management. Manual interpretation of X-ray or CT images can be time-consuming and may lead to inter-observer variability, particularly in subtle or multi-site fracture cases. This study proposes an interpretable Hybrid Selective Feature Network (Hybrid SFNet) to improve multi-site bone fracture detection performance and boundary localization. Methods: The proposed Hybrid SFNet extends the original SFNet architecture by incorporating multi-scale convolutional feature extraction and a semantic flow mechanism to enhance structural representation and fracture boundary delineation. Preprocessing techniques, including Canny edge detection, normalization, and data augmentation, were applied to improve feature quality. Model interpretability was addressed using Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize regions contributing to predictions. The model was evaluated on publicly available multi-site fracture datasets using both standard and class-weighted loss configurations. Results: For binary fracture classification, the proposed model achieved 90 accuracy, 94% precision, 77% recall, and an F1-score of 85% for fractured cases. When class-weighted loss was applied, recall improved to 85%, reducing false negatives from 145 to 94 cases (approximately 35%). Under the weighted configuration, Cohen’s Kappa reached 0.79 and the Matthews Correlation Coefficient (MCC) reached 0.76. Conclusions: The proposed Hybrid SFNet provides an interpretable and effective framework for multi-site bone fracture detection. The integration of multi-scale feature extraction and semantic flow mechanisms enhances detection performance and boundary localization, while Grad-CAM supports clinical interpretability. These results indicate the model’s potential for supporting clinical decision-making in orthopedic imaging. 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

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

This study evaluates the effectiveness of laser cleaning techniques for the non-contact removal of unwanted deposits from the surface of contemporary urban mural paintings. Two sets of mock-up samples, painted with popular graffiti spray paints on lime-based plaster, and artificially contaminated, were subjected to various cleaning procedures using Nd:YAG lasers operated in Q-switched (QS), long Q-switched (LQS) or short free-running mode (SFR). A multi-analytical approach—including X-ray fluorescence spectroscopy (XRF), Fourier-transform infrared spectroscopy (FTIR), colorimetry, and hyperspectral imaging (HSI)—was used to identify pigments and binders, and to evaluate cleaning efficiency and pigment preservation. XRF and FTIR were useful in understanding the composition of the sprays, while colorimetric ΔE values quantified cleaning efficiency and potential damage, and hyperspectral reflectance and LSU (linear spectral unmixing) abundance maps provided spatial distribution insights into contaminant removal and pigment preservation. The results demonstrate that laser cleaning effectiveness and selectivity are strongly dependent on the operational regime and fluence. In particular, long Q-switched laser irradiation at moderate fluence levels achieved effective contaminant removal with minimal chromatic and chemical alteration of the original paint layers. These findings support the development of tailored, sustainable, and non-contact laser cleaning protocols for the conservation of contemporary urban murals and contribute to the establishment of objective, multi-parameter criteria for evaluating cleaning outcomes in street art conservation. Full article

Background and Objectives: Bile duct injury is a relatively rare, but critical complication of laparoscopic cholecystectomy and is most commonly attributed to misinterpretation of biliary anatomy. Intraoperative biliary imaging may enhance anatomical recognition and reduce operative uncertainty, yet the optimal imaging modality remains debated. This study aimed to compare conventional intraoperative X-ray cholangiography with two fluorescence-based techniques—intravenous and intracholecystic indocyanine green fluorescence cholangiography—with respect to biliary visualization, perioperative outcomes, and surgeon satisfaction during elective laparoscopic cholecystectomy. Materials and Methods: This prospective, single-center, single-blind randomized controlled trial included 240 adult patients scheduled for elective laparoscopic cholecystectomy between June 2021 and December 2022. Participants were randomized equally to standard intraoperative cholangiography, intravenous indocyanine green fluorescence cholangiography, or intracholecystic indocyanine green fluorescence cholangiography. The primary outcome was successful visualization of predefined extrahepatic biliary landmarks, including the critical junction. Secondary outcomes included cholangiography duration, perioperative complications, postoperative inflammatory markers, and surgeon satisfaction assessed using a five-point Likert scale. This study was registered at ClinicalTrials.gov (NCT04908826). Results: Visualization rates of the critical junction and major extrahepatic bile ducts were comparable among three groups, with no statistically significant differences observed. Both fluorescence-based techniques achieved a 100% technical success rate, whereas standard cholangiography failed in a small proportion of cases. Cholangiography duration was significantly shorter in the fluorescence groups compared with standard cholangiography (p < 0.001). Surgeon satisfaction scores were significantly higher for both fluorescence approaches, with a slight preference for intravenous administration. Perioperative complication rates and postoperative inflammatory markers were com-parable among groups. Conclusions: Intravenous and intracholecystic indocyanine green fluorescence cholangiography are non-inferior to conventional intraoperative cholangiography for biliary anatomy visualization and offer advantages in procedural efficiency and surgeon satisfaction. Fluorescence-based imaging represents a safe and effective alternative for intraoperative biliary mapping during elective laparoscopic cholecystectomy. Full article

CoNi layered double hydroxides (CoNiLDHs) were successfully synthesized on nickel foam (NF) using a hydrothermal method. X-ray diffraction (XRD) analysis confirmed the formation of a well-defined hydrotalcite-like phase, including a strong (003) peak, indicating layered stacking. Scanning electron microscopy (SEM) revealed a 3D hierarchical nanosheet structure resembling flower-like arrays, which was further supported by EDS mapping showing a uniform distribution of Co, Ni, and O. Electrochemical studies demonstrated excellent OER activity, with a low overpotential of 188 mV at 10 mA/cm² and a Tafel slope of 97.48 mV/dec, inferring rapid reaction kinetics. Furthermore, the material exhibited a significant electrochemical surface area (ECSA) compared to bare NF. Chronoamperometry over 24 h confirmed the operational durability catalyst, stabilizing around 7–8 mA/cm², validating its potential as a cost-effective and efficient OER electrocatalyst in alkaline media. Full article

In X-ray security inspection imagery, hazardous object detection is challenged by severe object overlap/occlusion, ambiguous boundaries of small objects, and complex texture representations caused by material diversity. Although YOLOv8-seg provides real-time instance segmentation capability, it still has clear limitations in this application scenario. Specifically, the original SPPF module has limited ability to model long-range spatial dependencies, making it difficult to accurately separate boundaries of densely overlapped objects, while the C2f module is insufficient for multi-scale feature parsing of hazardous items with diverse sizes and materials and introduces feature redundancy, which degrades segmentation accuracy in occluded scenes. To address these issues, this paper proposes an improved YOLOv8-seg framework for X-ray hazardous object detection, termed LM-YOLOv8. For feature enhancement, an SPPF-LSKA module is constructed by integrating large-kernel separable attention with dynamic receptive-field adjustment, thereby improving global contextual modeling and alleviating boundary ambiguity. For multi-scale feature fusion, a C2f-MSC module is designed by combining multi-branch dilated convolutions with the C2f structure to enhance complex contour parsing and cross-scale feature interaction. Experiments on the PIDray dataset show that the proposed method achieves 84.8% mAP50 in instance segmentation, representing an improvement of approximately 4.0 percentage points over the baseline YOLOv8-seg. In addition, the method demonstrates stronger robustness on challenging hard/hidden subsets, validating its effectiveness for X-ray security inspection hazardous object detection. Full article

PrFeTiO₃ perovskite composite was synthesized, and its structural, morphological, chemical, and optical properties were comprehensively characterized. X-ray diffraction (XRD) and a selected area electron diffraction (SAED) confirm the formation of an orthorhombic distorted perovskite phase with no secondary impurities. Transmission electron microscope (TEM) observations show aggregated nanocrystalline domains, while EDS mapping reveals homogeneous cation distribution (Pr, Fe, Ti, O), confirming successful incorporation of Fe and Ti into the perovskite lattice. X-ray photoelectron spectroscopy (XPS) analysis identifies Pr³⁺, Fe³⁺, and Ti⁴⁺ as the dominant oxidation states, supporting charge-compensated B-site substitution. Optical analysis reveals a bandgap of ~2.0 eV, significantly narrower than pristine titanates, indicating enhanced visible-light absorption. This multi-modal characterization verifies the successful formation of PrFeTiO₃ and highlights its potential as a visible-light-active photocatalyst. Although PrTiO₃ showed little reactivity to visible light, PrFeTiO₃ showed excellent efficiency in visible light photocatalytic reactions. PrFeTiO₃ showed more than 20 times better performance than PrTiO₃ in the photodegradation of methylene blue in the liquid phase and formaldehyde in the gas phase. Furthermore, PrFeTiO₃ showed more than 95% superior bactericidal activity against the pathogenic bacterium Staphylococcus aureus than PrTiO₃. Its high photocatalytic efficiency can be attributed to its strong photosensitivity to visible light and small band gap energy. Full article

Accurate interpretation of chest radiographs is central to the early diagnosis and management of pulmonary disorders. This study introduces an explainable deep learning framework that integrates biomedical signal fidelity analysis with transfer learning to enhance diagnostic reliability and transparency. Using the publicly available COVID-19 Radiography Dataset (21,165 chest X-ray images across four classes: COVID-19, Viral Pneumonia, Lung Opacity, and Normal), three architectures, namely baseline Convolutional Neural Network (CNN), ResNet-50, and EfficientNetB3, were trained and evaluated under varied class-balancing and hyperparameter configurations. Signal preservation was quantitatively verified using the Structural Similarity Index Measure (SSIM = 0.93 ± 0.02), ensuring that preprocessing retained key diagnostic features. Among all models, ResNet-50 achieved the highest classification accuracy (93.7%) and macro-AUC = 0.97 (class-balanced), whereas EfficientNetB3 demonstrated superior generalization with reduced parameter overhead. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed anatomically coherent activations aligned with pathological lung regions, substantiating clinical interpretability. The integration of signal fidelity metrics with explainable deep learning presents a reproducible and computationally efficient framework for medical image analysis. These findings highlight the potential of signal-aware transfer learning to support reliable, transparent, and resource-efficient diagnostic decision-making in radiology and other imaging-based medical domains. Full article

This study investigates the protective performance of a triazole-based Mannich base corrosion inhibitor, 4-((1,2,4-triazolyl)methyl) dibutylamine (TZMBA), on P110 carbon steel in dissolved oxygen environments. TZMBA was synthesized via a Mannich reaction, and its molecular structure was confirmed by Fourier transform infrared spectroscopy (FT-IR). The corrosion inhibition behavior and underlying mechanisms were systematically explored through weight loss measurements, surface characterization, and multiscale molecular simulations. Weight loss results indicated that TZMBA significantly mitigates the corrosion of P110 steel, with inhibition efficiency reaching 81.5% at 1.67 mmol/L and 82.0% at 2.14 mmol/L. Adsorption thermodynamic analysis revealed that the process follows the Langmuir isotherm model. The calculated standard Gibbs free energy ${∆G}_{ads}^0$ of −38.69 kJ/mol suggests a spontaneous, mixed-type adsorption mechanism involving both physisorption and chemisorption. Scanning electron microscopy (SEM) observations confirmed a marked reduction in surface degradation, characterized by suppressed corrosion products and minimized localized attack. X-ray photoelectron spectroscopy (XPS) further verified that TZMBA anchors to the metal surface through chemical coordination, forming a robust organic-inorganic composite film. From a theoretical perspective, frontier molecular orbital (FMO) analysis showed that TZMBA’s high E_HOMO and narrow energy gap facilitate efficient electron transfer. Combined Fukui function and molecular electrostatic potential (MEP) maps identified the nitrogen atoms in the triazole ring and amine group as the primary active sites. Furthermore, molecular dynamics (MD) simulations demonstrated that TZMBA molecules adopt a nearly parallel configuration on the Fe surface. The high negative interaction energy obtained from MD simulations confirms a strong binding affinity and a potent inherent driving force for the formation of a stable protective layer. Overall, the integration of experimental data and theoretical calculations establishes TZMBA as an effective inhibitor that provides superior protection by forming a stable, compact adsorption film on P110 carbon steel. Full article

Background/Objectives: Follitropin delta is an approved recombinant follicle-stimulating hormone (rFSH) expressed in a human cell line. Correct disulfide connectivity is a critical quality attribute for rFSH, a heterodimeric glycoprotein composed of noncovalently associated α and β subunits and stabilized by an extensive network of intramolecular disulfide bonds. Disulfide characterization is typically performed by mass spectrometry (MS). However, the closely spaced disulfide bonds within the FSH α-subunit are particularly resistant to proteolytic cleavage, complicating conventional MS-based disulfide mapping. Methods: To overcome limitations of MS-based methods, an X-ray crystallography strategy was employed using a ternary complex of the recombinant FSH heterodimer with an anti-FSHα Fab and a stabilizing anti-kappa VHH. Crystals of the desialylated rFSH/Fab/VHH complex were obtained and diffraction data were collected. Results: The structure of recombinant FSH was determined at 2.29 Å resolution. Electron density surrounding cysteine residues in both the α and β subunits was well defined, allowing unambiguous assignment of all intramolecular disulfide bonds in the crystallized protein. The observed cysteine connectivity is fully consistent with the disulfide architecture of FSH from other sources and supports correct folding of the recombinant Follitropin delta. Full article

This study investigates the gel characteristics of alkali-activated materials (AAMs) synthesized using wood ash (WA), and metakaolin (MK) as solid precursors. The research explores the influence of precursor type and sodium hydroxide (NaOH) concentrations in the alkali activator solution on the resulting physicochemical, microstructural, mechanical, and radiological properties of gels. The alkaline activators were prepared by mixing sodium hydroxide solutions (6 M and 12 M) with a sodium silicate (water glass) solution at a volume ratio of 1.5. The physicochemical characteristics of raw materials and AAMs were thoroughly analyzed using X-ray fluorescence (XRF), Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM) with EDS elemental mapping. FTIR analysis confirmed the formation of an amorphous gels geopolymer network. XRD revealed the presence of characteristic crystalline phases (quartz, calcite) within an amorphous matrix. Mechanical properties, such as compressive strength, depended on precursor type and alkali molarity: metakaolin (12 M) reached ~14 MPa, while wood ash showed ~4 MPa (6 M) and ~0.5 MPa (12 M) due to high CaO, low Si and Al, and unfavorable SiO₂/Al₂O₃ (5.71) and Na₂O/Al₂O₃ (3.19) ratios. Furthermore, this research estimates radiological doses by quantifying radionuclide content via gamma-spectrometry. Alkali activation significantly reduced radiological hazard parameters, with radium equivalent activity (Ra_eq) decreasing to 238.0 Bq/kg and the external hazard index (H_ex) to 0.643 for A₁₂MK, while the annual effective dose rate for A₁₂WA was only 0.265 nSv/y-all values remaining well below the recommended safety limit of 370 Bq/kg (≤1 mSv/y). The decrease in activity concentration index (I_γ), Ra_eq, and H_ex with increasing NaOH concentration indicates effective radionuclide immobilization within the geopolymer matrix, confirming the suitability of these alkali-activated materials for safe use in construction from a radiation protection perspective. Full article

Eight H-bonded salts of arsenic acid and nitrogen bases (2,4,6-trimethylpyridine, pyridine-2,6-diamine, pyridin-4-ol, 4-methoxypyridine, 4-methoxyaniline, 1,3,5-triazine-2,4,6-triamine, diethylamine and N¹,N¹,N²,N²-tetraethylethane-1,2-diamine) were studied in the solid state by single crystal X-ray diffraction technique and DFT calculations. In all cases quite short (≤2.65 Å) OHO bonds were found in the self-assembled supramolecular ribbons or 2D networks of dihydrogen arsenates, constituting a repertoire of five different H-bonding patterns (motifs). The electron localization function maps revealed the spots of the nucleophilic sites on oxygen atoms that determine the preferable directions for H-bonding of H₂AsO₄⁻ anions observed in the crystal packing. Analysis of the electrostatic potential maps for isolated species has demonstrated that upon H-bonding between H₂AsO₄⁻ anions and protonated nitrogen bases, NH⁺⋯⁻OAsO(OH)₂, the redistribution of electron density within the anion provides otherwise virtually non-existent electrophilic sites on hydrogen atoms, which balances the Coulomb repulsion and allows for the anion⋯anion pairing within the crystal. The topological analysis of the calculated crystalline electron density after relaxation of the hydrogen atoms’ positions was used to classify the OHO bonds as moderately strong ones (with an interaction energy up to 65 kJ/mol) and revealed a high degree of ionicity of molecular moieties within ion pairs (with an absolute charge up to 0.87 e). For the strongest OHO and NHO bonds, the noticeable covalent character was shown by using the crystal orbital Hamiltonian population analysis. Full article

The relative position of the oxime group within pharmaceutically relevant pyridinium oximes is a pivotal factor that governs their intrinsic physicochemical properties and their biological reactivity. However, studies providing in-depth, molecular-level insight into these structure–reactivity relationships are still limited. In this work, we present an integrated experimental and computational study of N-methylpyridinium-2-aldoxime chloride (PAM2-Cl), N-methylpyridinium-3-aldoxime iodide (PAM3-I), and N-methylpyridinium-4-aldoxime iodide (PAM4-I), aimed at elucidating discrete differences in their ionization behavior, electronic structure, σ-donor properties, and nucleophilicity. The crystal structure of PAM3-I was determined by X-ray diffraction. Comparative structural and spectroscopic (UV–Vis, NMR, IR) analyses elucidated the structural and electronic effects arising from the position of the oxime group. Kinetic studies of substitution reactions with aquapentacyanoferrate(II) in aqueous solution enabled the determination of pentacyano(PAM)ferrate(II) formation and dissociation rate constants, coordination modes, pK_a values of the coordinated ligands, complex stability constants, and σ-donating capabilities. The DFT-based analysis of atomic charge distribution transcended experimental limitations, offering a new perspective on electronic structure-related properties. This study presents the first side-by-side, internally consistent structure–reactivity map across PAM2, PAM3, and PAM4 isomers that triangulates crystallography, UV–Vis-derived pK_a values, substitution kinetics, and DFT descriptors in a single framework. Full article