Search Results (1,449)

Multiscale surface characterization is essential for understanding the surface condition of laser powder bed fusion (LPBF)-fabricated metallic structures and regions subjected to different post-processing conditions. This study examines LPBF-fabricated 17-4 PH stainless steel (SS) triply periodic minimal surface (TPMS) prototypes to characterize geometry-dependent manufacturing features and representative flat plate regions associated with aging, shot blasting, hydrophobic coating, and combined blasting-coating conditions. Hirox digital microscopy was used to examine macro- to microscale surface features on the TPMS prototypes and flat plates, while Hirox 3D profiling was used to evaluate the roughness response of the flat plate treatment regions. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) provided higher-resolution surface morphology and elemental composition from selected flat plate regions. Hirox imaging revealed anisotropic surface textures aligned with LPBF scan paths, pore presence, and staircase-like features on curved TPMS surfaces. Three-dimensional profiling identified different geometric mean Rz values among the examined flat plate regions; the blasted-plus-coated regions on the untreated baseline plate, U-BC, and the aged plate, A-BC, had the smallest reported values within their respective plate sets. SEM observations identified LPBF-related features, including balling, pores, particle adhesion, localized surface irregularities, and coating cracks. Localized EDS analysis showed Fe-, Cr-, Ni-, Cu-, and Si-containing elemental profiles in the examined U-0 and A-0 regions, whereas the analyzed U-C region exhibited strong Si and O signals, coating-associated elemental features, and reduced substrate contribution. These results demonstrate the importance of combining surface morphology, profile roughness, coating condition, and localized elemental composition to document geometry-dependent TPMS features and regional surface condition differences across the examined flat plates. Full article

Confirming seed variety identity is important in certification, breeding-material management and grain trade, but visually similar cultivars remain difficult to distinguish consistently. We present GAFE-Stack, a morphology-guided classifier that expands seven kernel measurements into twenty-one interpretable descriptors and combines five complementary learners. The method is evaluated on the small, balanced public UCI Seeds benchmark ($blackN=210$; $black70$ kernels per variety), whose measurements were extracted from soft X-ray images. Across ten repeats of stratified five-fold cross-validation, GAFE-Stack achieved $96.33black\pm 2.29\%$ accuracy and $96.67\%$ under leave-one-out validation. Its observed mean differences from seven re-implemented references ranged from $black0.48$ to $black4.29$ percentage points; after accounting for dependence among repeated folds and applying Holm adjustment, none of the comparisons was significant at $black\alpha =0.05$. The strongest individual member, LightGBM, achieved a slightly higher mean accuracy ($black96.76\%$), whereas GAFE-Stack had lower fold-level dispersion ($black2.29\%$ versus $black2.60\%$) and fewer pooled Kama–Canadian confusions than the RBF-SVM baseline. The complete pipeline achieved $black93.33\%$ and $black94.52\%$ accuracy with $black30$ and $black60$ labelled training kernels, respectively, compared with $black96.33\%$ using the full training folds. From stored morphometric inputs, single-thread CPU training required $black0.85$ s and batch prediction processed approximately $black52,600$ kernels/s without a GPU. Applying the same dimension-typed construction rules to Raisin, Rice and Dry Bean datasets produced small positive mean changes of $black0.09$–$black0.37$ points, with corrected intervals including zero. Lot-purity results are reported only as an exploratory resampling analysis of UCI observations. The present evidence therefore supports GAFE-Stack as an interpretable proof-of-concept approach to seed screening under benchmark conditions; validation on independently acquired kernels, measurement systems and physical seed lots remains future work. Full article

Postharvest fruit quality differs among various fruits and alters during sorting, packaging, transportation and storage. Therefore, it is important to have an efficient and objective grading method that causes little mechanical damage to ensure the uniformity of products and decrease market price and losses in the supply chain. This paper describes the grading norms, detection techniques, processing algorithms, structural designs and practical uses in different types of fruits. It examines the external features, internal quality and hidden faults by using machine vision, visible-near-infrared spectroscopy, hyperspectral and X-ray imaging, acoustic and mechanical detection, electronic noses and the integration of multiple sensors. In addition, it also investigates conventional machine learning, deep learning, transfer learning and lightweight implementations. The research has developed from grading according to size, weight and color to a complete evaluation of ripeness, juice volume, hardness, internal defects and shelf life. Moreover, single detection devices are joined together to construct integrated systems including feeding, separation, inspection, classification, redirection, packaging and data management. However, the application is restricted by the discrepancy between the grading criteria and measurable results, the lack of cross-species and batch generalization ability, and the difficulty in coordinating multiple sensors in real time. The systems should maintain a balance between production speed, mechanical damage and costs. Some matters needing attention are to standardize the quality description, choose multi-source fusion, develop adaptive lightweight models, design modular structures and guarantee end-to-end traceability. Solving these problems will facilitate the transition from accurate laboratory identification to reliable, economic and extensive commercial grading. Full article

A seven-layer broadband antireflection coating consisting of Al₂O₃, TiO₂, SiO₂, and MgF₂ was designed and deposited on both sides of K9 glass for the 400–1100 nm range. The deposition parameters for TiO₂ and SiO₂ single layers were optimized, and the measured optical constants were used in TFCalc to refine the multilayer design. Layer-specific thickness-tolerance analysis was performed to identify the layers requiring strict process control. The initially characterized double-sided coating showed an average transmittance of 98.72% over 400–1100 nm. Four samples prepared in one additional deposition batch exhibited closely matching transmittance spectra, indicating low sample-to-sample variation within that batch. Five-position profilometry indicated consistent total-thickness control. Cross-sectional scanning electron microscopy (SEM) revealed well-defined multilayer contrast without obvious cracking, and regional energy-dispersive X-ray spectroscopy (EDS) analysis detected the constituent elements of the coating. These results demonstrate a practical design-to-fabrication approach that combines process-specific optical constants with tolerance analysis, with potential applications in imaging systems, optical windows, and lidar. Full article

The eddy covariance (EC) technique is a key tool in environmental monitoring, enabling continuous and non-invasive measurement of carbon dioxide (CO₂) fluxes at the ecosystem–atmosphere interface. In environments characterized by high levels of airborne particulates, such as volcanic regions, the reliability of enclosed-path EC measurements can be compromised by frequent filter clogging, potentially affecting data continuity, and increasing maintenance requirements. This study investigates whether the chemical and mineralogical signatures of particulate matter accumulated on clogged Swagelok pre-Licor filters can be used to identify dominant particle sources and provide insights into filter clogging processes. A multi-analytical workflow combining scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS), portable Raman spectroscopy, and hyperspectral imaging (HSI) was applied to recovered filter residues. The combined approach provided complementary chemical, mineralogical, and morphological information, allowing discrimination among volcanogenic material (e.g., glass shards, crystals, and lithic fragments), aeolian lithogenic dust, including Saharan inputs, and biogenic particles such as plant fibers. The results revealed two dominant particulate groups, volcanogenic mineral phases and biogenic material, with a minor contribution from wind-transported lithogenic dust. Volcanogenic phases, enriched in Si, Al, and Fe, dominated the inorganic fraction, whereas O-, C-, and N-rich particles were mainly associated with local biogenic sources. No clear evidence of significant anthropogenic contributions was identified. These findings demonstrate that multi-analytical characterization of particles accumulated on EC pre-filters can provide qualitative source attribution and valuable information on the processes responsible for filter loading and clogging. By linking particle characteristics with meteorological and environmental conditions, this approach has the potential to support site-specific, predictive, and event-driven maintenance strategies, contributing to improved EC data quality and more efficient long-term monitoring in high-aerosol environments. Full article

Wrist fractures present significant challenges in clinical diagnosis, often leading to treatment delays and compromised recoveries. Manual diagnosis is resource-intensive and error-prone. To address these challenges, we develop DeepWristFNet, a compact convolutional architecture designed for end-to-end wrist fracture classification using a small dataset of 193 wrist X-ray images. The DeepWristFNet architecture integrates multi-scale convolutional operations with Fire and Shuffle modules within a compact network design, followed by fully connected layers for binary classification. We applied data pre-processing techniques such as data augmentation, image enhancement, and image resizing to increase the number of images, improve image quality, and resize images to match the DeepWristFNet input size. The proposed method comprised three phases. In the first phase, we trained, validated, and tested end-to-end and achieved validation and testing accuracies of 99.04% and 87.93%, respectively. Testing was performed on a hold-out subset of image instances that was kept separate from model development. The evaluated hold-out images originated from the same dataset distribution and included the corresponding augmented variants. In the second phase, we further evaluated the learned representation by extracting deep features from the first fully connected layer of DeepWristFNet. ReliefF was then used to select informative features, which were subsequently evaluated using 10 conventional machine learning classifiers. Out of 10 classifiers, 5 classifiers, i.e., Efficient linear SVM, quadratic SVM, Narrow NN, wide NN, and medium NN, achieved 100% testing accuracy on unseen samples. In the third phase, an auxiliary Fuzzy Inference System provides an intensity-based foreground-background representation of the X-ray images. This representation provides complementary visual information for interpretation but is not intended to directly classify or localize fractures. Grad-CAM is additionally used to visualize image regions contributing to the DeepWristFNet predictions, providing a model-specific explanation of the classification decision. Additionally, we evaluated how well the proposed DeepWristFNet approach performed against cutting-edge deep transfer learning models. In the evaluated experiments, DeepWristFNet outperformed the compared pre-trained deep learning architectures on the unseen hold-out subset from the same dataset distribution (test set). This study demonstrates the potential of DeepWristFNet for wrist fracture classification under a small-data setting. However, further evaluation on larger, independently collected clinical datasets is required to establish its robustness, generalizability, and suitability for clinical decision support. Full article

Hand X-ray image segmentation is an important step in automated radiographic image analysis. However, conventional U-shaped segmentation networks often have relatively high model complexity, while variations in grayscale distributions across hand X-ray images may affect segmentation performance. To address these issues, this study proposes a lightweight hand X-ray image segmentation network, CSP-UNet (Cross-Stage Partial U-Net). The network integrates cross-stage partial feature processing into the U-Net encoder–decoder framework to reduce redundant feature computation and the number of model parameters while preserving effective feature representation. In addition, an adaptive Gaussian histogram-matching strategy is employed to reduce variations in grayscale distributions across X-ray images. CSP-UNet was evaluated on a dataset comprising 2000 hand X-ray images and compared with Classic U-Net, Res-UNet, Attention U-Net, and Swin U-Net. Experimental results show that CSP-UNet maintained comparable segmentation performance, achieving a Dice coefficient of 0.9927, PA of 0.9839, MPA of 0.9810, and mIoU of 0.9542, while requiring only 20.01 M parameters. Compared with Classic U-Net, CSP-UNet maintained a comparable Dice coefficient (0.9927 vs. 0.9910) while reducing the parameter count from 69.1 M to 20.01 M, corresponding to a reduction of approximately 71.04%. These results indicate that CSP-UNet maintains comparable segmentation performance while substantially reducing model complexity, offering a favorable trade-off between segmentation performance and model size for hand X-ray image segmentation. Full article

Metallic and organometallic nanoparticles exhibit intriguing size- and morphology-dependent magnetic properties that differ markedly from their bulk counterparts. Here, we report a detailed synthesis and characterization of iron (Fe), copper (Cu), nickel (Ni), and cobalt (Co) nanostructures dispersed in carbon matrices derived from phthalocyanine (Pc), tetrakis(4-carboxyphenyl)porphyrin (TCPP), and tetraphenylporphyrin (TPP), with the detailed quantitative analysis focused primarily on iron phthalocyanine (FePc), iron tetrakis(4-carboxyphenyl)porphyrin (FeTCPP), and iron tetraphenylporphyrin (FeTPP). Using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), energy-dispersive X-ray spectroscopy (EDS), and magnetometry, we systematically investigate how precursor composition, annealing conditions, and nanostructure formation impact the resulting magnetic behaviors. We introduce a validation-aware image-analysis workflow for morphological, local periodic-contrast, and two-dimensional connectivity descriptors while distinguishing these image-derived quantities from direct measurements of bulk crystallinity, porosity, and three-dimensional connectivity. Hysteresis measurements at low temperatures reveal that iron-based compounds, particularly iron phthalocyanine (FePc) and iron tetrakis(4-carboxyphenyl)porphyrin (FeTCPP), exhibit notable saturation-like magnetization and stronger coercivity, respectively, whereas other precursors (e.g., Cu tetraphenylporphyrin, CuTPP) show weaker magnetic responses. These contrasting behaviors underscore the importance of understanding how candidate phase composition (e.g., metallic Fe, graphite-like carbon, or iron-carbide-related contributions), local morphology, and carbon structure correlate with magnetic characteristics. Microscopy and EDS support Fe-rich regions dispersed within carbonaceous matrices, and a representative Fe/O/C STEM–EDS field provides local evidence for a core–shell-like morphology without establishing a uniform shell thickness, composition, or local core phase across the full population. Our findings highlight the feasibility of tuning carbon–metal nanocomposites through controlled synthesis and post-annealing, thereby motivating future application-specific evaluations in areas such as magnetic hyperthermia, drug delivery, sensing, and electromagnetic materials. The image-analysis workflow also offers a reproducible framework for future studies seeking to relate nanoparticle morphology and magnetic properties to controlled processing. Full article

Contemporary mural paintings contribute a significant part of urban cultural heritage, yet their conservation remains challenging due to the complex materials used and the aggressive conditions of the urban environment. Among the main deterioration factors, vandalic graffiti is particularly problematic, as its removal must be carried out without damaging the original paint layer, which often has a similar chemical composition. In this context, laser cleaning is a promising alternative to conventional mechanical and chemical methods. This study evaluates the effectiveness and selectivity of a nanosecond Nd:YAG laser (1064 nm) for the removal of a blue alkyd graffiti spray paint applied over mock-ups prepared with alkyd and polyvinyl acetate (PVA) paints on concrete substrates. The cleaning results were evaluated by stereomicroscopy, colour spectrophotometry, measurement of static contact angle, profilometry, near-infrared (NIR) hyperspectral imaging, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) to assess physical and chemical changes after laser treatment. The results show that the effectiveness and selectivity of the process depend strongly on the chemical composition of both the vandalism layer and the original paint system, highlighting the importance of preliminary material characterisation prior to laser cleaning interventions. Although laser cleaning enabled the partial or substantial removal of the blue alkyd graffiti in all cases, alkyd-based paints exhibited greater resistance to laser irradiation and allowed more effective graffiti removal with fewer surface alterations than PVA-based paints. Among them, the green alkyd paint achieved the highest cleaning efficiency. These results indicate that the interaction between laser radiation and the materials was governed not only by the binder type, but also by the pigment composition and the optical properties of the paint layers. Full article

The three-dimensional (3D) documentation of the Similaun mummy (Ötzi the Iceman) and the associated Copper Age equipment and clothing presents unique challenges due to diverse material properties and strict conservation constraints. This study presents a comprehensive digital preservation workflow, primarily utilizing Structure from Motion (SfM) close-range photogrammetry (a method that reconstructs precise 3D geometry from overlapping 2D digital photographs), integrated with Image-Based Modeling (IBM) and Neural Radiance Field (NeRF) algorithms (a machine learning approach that models a complex scene as a continuous volumetric function method). To overcome the non-Lambertian properties of the mummy’s protective ice layer and wet skin (surfaces that reflect light specularly rather than diffusely, creating glares that can disorient standard reconstruction algorithms), a specialized Polarized Light Photography (PLP) strategy was implemented using custom-built hardware. This integration required advanced anatomical segmentation to resolve postural discrepancies caused by taphonomic processes. The resulting web-based application provides the scientific community with a metrically accurate digital twin, featuring interactive tools for cross-sectioning and X-ray visualization. By adopting a Free/Libre and Open-Source Software (FLOSS) ecosystem, this project establishes a sustainable, modular framework for future forensic investigations and diachronic monitoring, ensuring the long-term digital life of one of the world’s most significant archaeological finds. Full article

Preconcentration before grinding is important for reducing unnecessary downstream processing and improving ore utilization. Dual-energy X-ray transmission imaging provides paired responses of ore particles under different energy levels, which can be used for particle-level classification. However, adjacent categories, such as waste rock and low-grade copper ore, may exhibit similar transmission appearances, and discriminative cues may be distributed across both local attenuation details and global transmission patterns. In this study, we propose a dual-energy X-ray image classification method, named the Difference-Guided Cross-Level Feature Fusion Network (DGCF-Net), for three-class copper ore classification. Waste rock and copper ore samples from the Dexing Copper Mine were used to construct a three-class dual-energy X-ray image dataset. DGCF-Net incorporates response-difference cues from paired low- and high-energy images and combines local and global feature representations for ore-particle classification. Experimental results on the constructed dataset show that the proposed method achieved an Overall Accuracy of 0.9570, a Macro-F1 of 0.9664, and an AUC of 0.9953, with 3.6424 M parameters. These results indicate that the proposed method provides effective classification performance on the current dataset, particularly for categories with relatively similar image responses, while its broader practical applicability requires further validation under more realistic operating conditions. Full article

Three-dimensional images of multiphase natural and engineered materials obtained by X-ray micro-computed tomography require quantitative processing methods that can describe not only phase volume but also connectivity, spatial heterogeneity, and anisotropy. In this article, X-ray micro-computed tomography is abbreviated as X-$\mu$CT. Scalar descriptors such as fractal dimension, Betti numbers, Euler characteristic, and Minkowski functionals provide compact phase-level summaries of segmented X-$\mu$CT data, but they do not encode where structural heterogeneity occurs, whether connectivity is directionally spanning, how finite sample boundaries affect topological measurements, or how surface-normal orientation is distributed. We propose a unified methodological framework that extends scalar topological and Minkowski-functional analysis of segmented multiphase 3D images by adding cut-response analysis, including its boundary-sensitivity interpretation, directional connectivity and orientation descriptors, and the rank-two surface Minkowski tensor $W_1^{0,2}$. The framework is demonstrated on a previously published segmented geological X-$\mu$CT volume used as a benchmark multiphase geometry with four X-ray-density phases and on synthetic validation geometries with analytically known topology. The results show that the proposed extensions reveal spatial sensitivity, boundary-to-boundary connectivity, and surface fabric that are not captured by scalar phase-level invariants alone. The proposed framework can be used to analyze segmented 3D images of multiphase geological, porous, composite, and engineered samples, thereby expanding quantitative knowledge about their internal structure beyond scalar phase-level descriptors. Full article

Ce³⁺-activated Gd₃(Al,Ga)₅O₁₂:Ce scintillation ceramics have been widely studied due to their excellent scintillation properties. However, the relatively long radiative lifetime and slow decay components limit their applications in X-ray imaging. To address these issues, Lu³⁺ ions were introduced to partially substitute Gd³⁺ ions, thereby weakening the role of self-trapped states in the excitation process of Ce³⁺ and reducing the negative effects caused by shallow electron traps. As a result, the scintillation decay time was, overall, shortened, and an average decay time of 63 ns was obtained when x = 0.997. Meanwhile, the afterglow behavior induced by shallow electron traps was significantly suppressed (for the sample with x = 0.5, the afterglow intensity was measured to be approximately 0.48% of the initial intensity at 100 ms after the X-ray excitation was turned off). Meanwhile, an X-ray imaging spatial resolution comparable to that of commercial CsI:Tl (10 lp mm⁻¹) was achieved for the (Gd,Lu)₃Ga₂Al₃O₁₂:Ce³⁺ scintillation ceramics. Full article

The effect of internal pressure on the layered microstructural evolution of N36 zirconium alloy cladding tubes was systematically studied under simulated loss-of-coolant accident (LOCA) biaxial creep conditions at 900 °C. The tested specimens were characterized by electron channeling contrast imaging, energy-dispersive X-ray spectroscopy, electron backscatter diffraction, and transmission electron microscopy. The results show that all specimens formed a typical layered cross-sectional structure consisting of an oxide film, an oxygen-rich α-Zr (α(O)) layer, and a prior-β transformed layer. The thickness of the α(O) layer and the oxygen diffusion depth changed markedly with internal pressure. The thickness of the α(O) layer was approximately 21 μm for the 0.8 MPa specimen and 11 μm for the 1.9 MPa specimen, respectively. The lower-pressure specimen exhibited a wider oxygen-affected region, whereas the higher-pressure specimen showed a steeper oxygen gradient. In the prior-β transformed layer, lath-like α structures formed under both conditions, but their spatial arrangement and orientation distribution were different. Under lower internal pressure, the laths were more regularly arranged and showed a more complete colony structure. Under higher internal pressure, the laths were more interwoven, and the orientation distribution became more scattered. Meanwhile, the high-pressure specimen retained a higher local orientation gradient and a higher degree of lattice distortion. These results indicate that the above microstructural differences mainly arise from the effect of internal pressure on the high-temperature exposure history. A higher internal pressure causes earlier instability of the specimen, thereby shortening the effective time for oxygen diffusion and microstructural evolution, rather than directly changing the oxidation or phase transformation process. Full article

Background/Objectives: Radiation protection in dental medicine is based on the principles of justification and optimization, yet available evidence indicates inconsistent application of these principles in everyday practice. The aim of this systematic review was to assess dentists’ understanding of radiation protection and their adherence to guidelines for justification and optimization of dental X-ray procedures. Methods: A systematic search of the PubMed and Scopus databases was conducted from January 2010 to April 2026, following PRISMA guidelines. The systematic review included surveys and cross-sectional studies that examined dentists’ knowledge of radiation protection, as well as justification and optimization practices. The quality of the included studies was assessed using the Newcastle–Ottawa scale adapted for cross-sectional studies. Results: The systematic review included 22 original scientific articles that assessed knowledge of radiation protection, justification, and optimization practices among 5277 dentists from 12 countries. The findings indicate a high theoretical understanding of the basic rules of radiation protection, but a lower level of understanding of the technical and optimization factors of dental X-ray devices. A relevant inconsistency was observed in the justification process, with frequent referral for X-ray imaging without clinical indication and inconsistent adherence to guidelines. The optimization of X-ray imaging varied, especially in failing to adjust exposure parameters and in the restricted use of rectangular collimators. Inadequate quality control of X-ray devices and insufficient continuous professional training in radiation protection and imaging optimization are examples of organizational and infrastructural factors that lead to higher radiation exposure. Conclusions: Although many guidelines exist, there remains a noteworthy disparity between theoretical understanding and clinical practice in dental radiation protection. To reduce patients’ and dentists’ exposure to ionizing radiation, it is important to include radiation protection education in the basic dental curriculum, establish and improve continuous training, standardize clinical procedures, and enable broader application of systems for optimization and quality control. Full article