Search Results (541)

Background/Objective: The COVID-19 pandemic profoundly transformed social life worldwide, indiscriminately affecting individuals across all age groups. Children have not been exempted from the risk of severe illness and death caused by COVID-19. Objective: This paper sought to describe the clinical findings, laboratory and imaging results, and hospital care provided for severe and critical cases of COVID-19 in unvaccinated children, with or without severe asthma, hospitalized in a public referral service for COVID-19 treatment in the Brazilian state of Pernambuco. Methods: This was a case series study of severe and critical COVID-19 in hospitalized, unvaccinated children, with or without severe asthma, conducted in a public referral hospital between March 2020 and June 2021. Results: The case series included 80 children, aged from 1 month to 11 years, with the highest frequency among those under 2 years old (58.8%) and a predominance of males (65%). Respiratory diseases, including severe asthma, were present in 73.8% of the cases. Pediatric multisystem inflammatory syndrome occurred in 15% of the children, some of whom presented with cardiac involvement. Oxygen therapy was required in 65% of the cases, mechanical ventilation in 15%, and 33.7% of the children required intensive care in a pediatric intensive care unit. Pulmonary infiltrates and ground-glass opacities were common findings on chest X-rays and CT scans; inflammatory markers were elevated, and the most commonly used medications were antibiotics, bronchodilators, and corticosteroids. Conclusions: This case series has identified key characteristics of children with severe and critical COVID-19 during a period when vaccines were not yet available in Brazil for the study age group. However, the persistence of low vaccination coverage, largely due to parental vaccine hesitancy, continues to leave children vulnerable to potentially severe illness from COVID-19. These findings may inform the development of public health emergency contingency plans, as well as clinical protocols and care pathways, which can guide decision-making in pediatric care and ensure appropriate clinical management, ultimately improving the quality of care provided. Full article

Accurate weld defect detection is critical for ensuring structural safety and evaluating welding quality in industrial applications. Manual inspection methods have inherent limitations, including inefficiency and inadequate sensitivity to subtle defects. Existing detection models, primarily designed for natural images, struggle to adapt to the characteristic challenges of weld X-ray images, such as high noise, low contrast, and inter-defect similarity, particularly leading to missed detections and false positives for small defects. To address these challenges, a multi-dimensional feature fusion model (MADet), which is a multi-branch deep fusion network for weld defect detection, was proposed. The framework incorporates two key innovations: (1) A multi-scale feature fusion network integrated with lightweight attention residual modules to enhance the perception of fine-grained defect features by leveraging low-level texture information. (2) An anchor-based feature-selective detection head was used to improve the discrimination and localization accuracy for five typical defect categories. Extensive experiments on both public and proprietary weld defect datasets demonstrated that MADet achieved significant improvements over the state-of-the-art YOLO variants. Specifically, it surpassed the suboptimal model by 7.41% in mAP@0.5, indicating strong industrial applicability. Full article

Large soil pores critically influence water and solute transport in soils. The presence of preferential flow paths created by soil macropores can profoundly impact water quality, underscoring the necessity of accurately assessing the characteristics of these macropores. However, it remains unclear whether variations in macropore structure exist between different altitudes and positions of terraced paddy fields. The primary objective of this research was to utilize X-ray computed tomography (CT) and image analysis techniques to characterize the soil pore structure at both the inner field and ridge positions across different altitude levels (high, medium, and low altitude) within terraced paddy fields. The results indicate that there are significant differences in the distribution of large soil pores at different altitudes, with large pores concentrated in the surface layer (0–10 cm) in low-altitude areas, while in high-altitude areas, the distribution of large pores is more uniform. Additionally, as altitude increases, the porosity of large pores shows an increasing trend. The three-dimensional equivalent diameter and large pore volume are primarily characterized by large pores ranging from 1 to 2 mm and 0 to 5 mm³, respectively, with their morphology predominantly appearing spherical or ellipsoidal. The connectivity of large pores in the surface layer of paddy soil is stronger than that in the bunds. However, this connectivity gradually weakens with increasing soil depth. The findings from this study provide valuable quantitative insights into the unique characteristics of soil macropores that vary according to the altitude and position in terraced paddy fields. Moreover, this study emphasizes the necessity for future research that encompasses a broader range of soil types, altitudes, and terraced paddy locations to validate and further explore the identified relationships between altitude and macropore characteristics. Full article

Hazelnut (Corylus avellana L.) is a significant global agricultural product due to its high economic and nutritional worth. The traditional methods used to measure the hazelnut kernel percentage for quality assessment are often time-consuming, expensive, and prone to human errors. Inaccurate measurements can adversely impact the market value, shelf life, and industrial applications of hazelnuts. This research introduces a novel system for calculating hazelnut kernel percentage utilizing a non-destructive X-ray imaging technique along with deep learning methods to assess hazelnut quality more efficiently and reliably. An image dataset of hazelnut kernels has been developed using X-ray technology, and defective areas are identified employing YOLOv7 architecture. Additionally, a novel bounding box regression technique called DCIoU and an algorithm for Neighborhood Relationship have been introduced to enhance object detection capabilities and to improve the selection of the target box with greater precision, respectively. The performance of these proposed methods has been evaluated using both the created hazelnut dataset and the COCO-128 dataset. The results indicate that the system can serve as a valuable tool for measuring hazelnut kernel percentages by accurately identifying defects in hazelnuts. Full article

A scalable synthesis of two-dimensional titanium nitride chloride (TiNCl) via flash Joule heating (FJH) using titanium tetrachloride (TiCl₄) precursor has been developed. This single-step method overcomes traditional synthesis challenges, including high energy consumption, multi-step procedures, and hazardous reagent requirements. The structural and chemical properties of the synthesized TiNCl were characterized through multiple analytical techniques. X-ray diffraction (XRD) patterns confirmed the presence of TiNCl phase, while Raman spectroscopy data showed no detectable oxide impurities. Fourier transform infrared spectroscopy (FTIR) analysis revealed characteristic Ti–N stretching vibrations, further confirming successful titanium nitride synthesis. Transmission electron microscopy (TEM) imaging revealed thin, plate-like nanostructures with high electron transparency. These analyses confirmed the formation of highly crystalline TiNCl flakes with nanoscale dimensions and minimal structural defects. The material exhibits excellent structural integrity and phase purity, demonstrating potential for applications in photocatalysis, electronics, and energy storage. This work establishes FJH as a sustainable and scalable approach for producing MXenes with controlled properties, facilitating their integration into emerging technologies. Unlike conventional methods, FJH enables rapid, energy-efficient synthesis while maintaining material quality, providing a viable route for industrial-scale production of two-dimensional materials. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

This paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which can be automatically implemented and made available for rapid differentiation between normal pneumonia and COVID-19 starting from X-ray images. The model developed in this work is capable of performing three-class classification, achieving 97.48% accuracy in distinguishing chest X-rays affected by COVID-19 from other pneumonias (bacterial or viral) and from cases defined as normal, i.e., without any obvious pathology. The novelty of our study is represented not only by the quality of the results obtained in terms of accuracy but, above all, by the reduced complexity of the model in terms of parameters and a shorter inference time compared to other models currently found in the literature. The excellent trade-off between the accuracy and computational complexity of our model allows for easy implementation on numerous embedded hardware platforms, such as FPGAs, for the creation of new diagnostic tools to support medical practice. Full article

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm⁻³). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article

Classification of thyroid images based on the Bethesda category using Diff-Quick stained images can assist in diagnosing thyroid cancer. This paper proposes a cross-domain approach that modifies the original deep learning network designed to classify X-ray images to classify stained thyroid images. Since the Diff-Quick stained images have large and high-quality sizes with tiny cells with essential characteristics that can help a doctor diagnose, resizing images is required to maintain this characteristic, which is significant. Thus, in this paper, we also research and evaluate the performance of different interpolation methods, including linear and cubic interpolation. The experiment results evaluated on a private dataset present promising results in the thyroid image classification of the proposed approach. Full article

Coumarin and cytisine and their derivatives have significant biological activity. In addition, the electronic properties of coumarin derivatives are very sensitive to the molecular environment, which allows for their use as sensors for bioluminescent imaging. Due to the fact that cytisine exhibits high activity in binding to nicotinic acetylcholine receptors, a compound combining parts of cytisine and coumarin may have a broader spectrum of biological activity and also act as a photoactive element for promising use in optoelectronic devices. This article reports the synthesis of a crystalline cytisine–coumarin complex (IUPAC: N-(2-oxo-2H-chromene-3-carbonyl)cytisine), along with the results of both theoretical and experimental investigations of its structural and electronic properties. The structure of this new compound was established on the basis of X-ray diffraction and Fourier transform infrared spectroscopy data and was confirmed through density functional theory calculations using periodic crystal and single-molecule approaches. Interpretations of the IR absorption peaks and the atomic patterns of the vibrational modes are given. The electronic band structure and the contributions of individual atoms to the electronic density of states are analyzed. The structural and optical properties considered may be useful for quality control of the compound and for studying similar matrices. Full article

The internal quality detection is extremely important. To solve the challenges of walnut quality detection, we presented the first comprehensive investigation of walnut quality detection method using X-ray imaging and deep learning model. An X-ray machine vision system was designed, and a walnut kernel detection (called WKD) dataset was constructed. Then, an effective walnut kernel detection network (called WKNet) was developed by employing Transformer, GhostNet, and criss-cross attention (called CCA) module to the YOLO v5s model, aiming to solve the time consuming and parameter redundancy issues. The WKNet achieved an mAP_0.5 of 0.9869, precision of 0.9779, and recall of 0.9875 for walnut kernel detection. The inference time per image is only 11.9 ms. Extensive comparison experiments with the state-of-the-art (SOTA) deep learning models demonstrated the advanced nature of WKNet. The online test of walnut internal quality detection also shows satisfactory performance. The innovative combination of X-ray imaging and WKNet provide significant implications for walnut quality control. Full article

Scattered light makes up a significant amount of recorded intensities during tomographic imaging, thereby leading to severe misinterpretation and artifacts in the reconstructed volume images. Correcting artificial intensities that stem from scattered light, therefore, is of primary interest and demands quantitative measurements. While numerous methods have been developed to reduce X-ray scattering artifacts, fewer methods deal with optical scattering. In this study, a measurement method for determining optical scattering in scintillators is presented with the aim of further developing correction algorithms. A theoretical model based on internal multiple reflections was developed for this purpose. This model assumes an additive exponential kernel with a certain scattering length to the system’s point spread function. This assumption was confirmed, and the scatter length was estimated from three new different kinds of experiments (hgap, rect, and LSF) on the BM18 beamline of the European synchrotron. The experiments further revealed significant differences in scattering proportion and length when different coatings are applied to the front and back faces of crystalline LuAG scintillators. Anti-reflective coatings on the backside show an effect of reducing the scattering magnitude while reflective coatings on the front side increase the proportion of the unscattered signal and, thus, show proportionally less scattering than black coating or no front coating. In particular, roughened black coating is found to worsen optical scattering. In summary, our results indicate that a combination of reflective (front) and anti-reflective (back) coatings yields the least optical scattering and, hence, the best image quality. Full article

Pneumonia remains a critical health concern, necessitating accurate and automated diagnostic tools. This study proposes a novel approach for the binary classification of pneumonia in chest X-ray images using an adaptive contrast enhancement model and a convolutional neural network (CNN). The enhancement model, an improvement over standard contrast-limited techniques, employs adaptive tile sizing, variance-guided clipping and entropy-weighted redistribution to optimize image quality for pneumonia detection. Applied to the Chest X-Ray Images (Pneumonia) dataset (5856 images), the enhanced images enable the CNN to achieve an accuracy of 98.7%, precision of 99.3%, recall of 98.6% and F1-score of 97.9%, outperforming baseline methods. The model’s robustness is validated through five-fold cross-validation, and its feature extraction is visualized to ensure clinical relevance. Limitations, such as reliance on a single dataset, are discussed, with future evaluations planned for larger datasets like CheXpert and NIH Chest X-ray to enhance generalizability. This approach demonstrates the potential of tailored preprocessing and efficient CNNs for reliable pneumonia classification, contributing to improved diagnostic support in medical imaging. Full article

Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and mechanical properties of poultry eggshells. It systematically reviews current advances in eggshell quality inspection technologies and compares the suitability and performance of techniques for key indicators, including shell strength, thickness, spots, color, and cracks. Furthermore, the paper discusses challenges in non-destructive testing, including individual egg variations, species differences, hardware precision limitations, and inherent methodological constraints. It summarizes commercially available portable and online non-destructive testing equipment, analyzing core challenges: the cost–accessibility paradox, speed–accuracy trade-off, algorithm interference impacts, and the technology–practice gap. Additionally, the paper explores the potential application of several emerging technologies—such as tactile sensing, X-ray imaging, laser-induced breakdown spectroscopy, and fluorescence spectroscopy—in eggshell quality inspection. Finally, it provides a comprehensive outlook on future research directions, offering constructive guidance for subsequent studies and practical applications in production. Full article

X-ray ptychography is an ultrahigh resolution imaging technique widely used in synchrotron radiation facilities. Its imaging performance relies on the quality of the acquired signals. However, the X-ray detectors used often suffer from signal loss due to sensor gaps, beamstops, defective pixels, overexposure, or other factors, resulting in degraded image quality. To suppress and compensate for the effects of signal loss, we proposed the known probe approach to partially recover the lost signals and introduced the high probe divergence strategy by investigating the effects of probe divergence on reconstruction quality under signal loss conditions. Both simulation and experiment results show that high probe divergence can effectively suppress the impact of signal loss on reconstruction quality while using a known probe as the initial probe for reconstruction can largely recover missing signals in Fourier space, resulting in a much better image than using a guessed initial probe. These strategies allow for high-quality imaging in the presence of signal loss without secondary data acquisition, significantly improving experimental efficiency and reducing radiation damage compared to previous strategies. Full article