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Lost X‐ray films waste time, delay treatment, and may necessitate a patient being exposed to further radiation. Audit of a 132‐bed orthopaedic and trauma department over a 2‐month period showed that 16 patients' X‐ray films were lost. Fifteen (93%) had been stored in anonymous polythene packets. Only 5 (31%) were found within half an hour, and a mean of 67 minutes' working time was occupied locating each one. Recognition of why and where they were lost reduced the number of losses.

Computer tomography (CT) for 3D reconstruction entails a huge number of coplanar fan‐beam projections for each of a large number of 2D slice images, and excessive radiation intensities and dosages. For some applications its rate of throughput is also inadequate. A technique for overcoming these limitations is outlined.Design methodology/approach – A novel method to reconstruct 3D surface models of objects is presented, using, typically, ten, 2D projective images. These images are generated by relative motion between this set of objects and a set of ten fanbeam X‐ray sources and sensors, with their viewing axes suitably distributed in 2D angular space.Findings – The method entails a radiation dosage several orders of magnitude lower than CT, and requires far less computational power. Experimental results are given to illustrate the capability of the techniquePractical implications – The substantially lower cost of the method and, more particularly, its dramatically lower irradiation make it relevant to many applications precluded by current techniquesOriginality/value – The method can be used in many applications such as aircraft hold‐luggage screening, 3D industrial modelling and measurement, and it should also have important applications to medical diagnosis and surgery.

Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays.

The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection.

The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods.

The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Orange peel formation remains to be understood clearly because it is difficult to directly observe a laser-sintered process in a partcake. Therefore, this study aims to provide insight into the orange peel formation mechanism through the nondestructive observation of laser-sintered specimens and their surrounding powders.

This study observed polyamide 12 powder in the vicinity of a laser-sintered specimen via X-ray computed tomography (CT) scanning. The specimen for nondestructive observation was 3D modeled in a hollow box using 3D CAD software. The boxes built using a laser-sintering system contained unsintered surrounding powder and sintered specimens. The box contents were preserved even after the boxes were removed from the partcake. After X-ray CT scanning, the authors broke the boxes and evaluated the unevenness formed on the specimen surface (i.e. the orange peel evaluation).

Voids (not those in sintered parts) generated in the powder in the vicinity of the specimen triggered the orange peel formation. Voids were less likely to form in the build with a 178.5° powder bed than in the build with a 173.5° powder bed. Similarly, the increment in laser energy density effectively suppressed void formation, although there was a tradeoff with overmelting. Thin-walled parts avoided void growth and made the orange peel less noticeable.

To the best of the authors’ knowledge, this study is the first to observe and understand the relationship between voids generated in the powder in the vicinity of sintered parts and orange peel formation.

The X‐ray diffraction patterns of epoxy resins: four samples with different epoxide equivalents and coal‐tar blended epoxy resins: three samples with different epoxide equivalents were recorded using CuKa X‐ray radiation. These X‐ray diffraction patterns were indicating the amorphous nature of the resins. Their intensity curves were subjected to Fourier Analysis for the first time in order to get more information about the difference between epoxy and coal‐tar blended epoxy resins in terms of their internal structure such as particle size, percentage crystallanity and electron density fluctuations. Also, the effect of different epoxide equivalent on these physical parameters was interpreted successfully in epoxy as well as coal‐tar blended epoxy resins.

Micro-focus X-ray computed tomography (CT) can be used to quantitatively evaluate the packing density, pore connectivity and provide the basis for specimen derived simulations of gas permeability of sand mould. This non-destructive experiment or following simulations can be done on any section of any size sand mould just before casting to validate the required properties. This paper aims to describe the challenges of this method and use it to simulate the gas permeability of 3D printed sand moulds for a range of controlling parameters. The permeability simulations are compared against experimental results using traditional measurement techniques. It suggests that a minimum volume of only 700 × 700 × 700 µm³ is required to obtain, a reliable and most representative than the value obtained by the traditional measurement technique, the simulated permeability of a specimen.

X-ray tomography images were used to reconstruct 3D models to simulate them for gas permeability of the 3D printed sand mould specimens, and the results were compared with the experimental result of the same.

The influence of printing parameters, especially the re-coater speed, on the pore connectivity of the 3D printed sand mould and related permeability has been identified. Characterisation of these sand moulds using X-ray CT and its suitability, compared to the traditional means, are also studied. While density and 3PB strength are a measure of the quality of the moulds, the pore connectivity from the tomographic images precisely relates to the permeability. The main conclusions of the present study are provided below. A minimum required sample size of 700 × 700 × 700 µm³ is required to provide representative permeability results. This was obtained from sand specimens with an average sand grain size of 140 µm, using the tomographic volume images to define a 3D mesh to run permeability calculations. Z-direction permeability is always lower than that in the X-/Y-directions due to the lower values of X-(120/140 µm) and Y-(101.6 µm) resolutions of the furan droplets. The anisotropic permeability of the 3D printed sand mould is mainly due to, the only adjustable, X-directional resolution of the furan droplets; the Y-directional resolution is a fixed distance, 102.6 µm, between the printhead nozzles and the Z-directional one is usually, 280 µm, twice the size of an average sand grain.A non-destructive and most representative permeability value can be obtained, using the computer simulation, on the reconstructed 3D X-ray tomography images obtained on a specific location of a 3D printed sand mould. This saves time and effort on printing a separate specimen for the traditional test which may not be the most representative to the printed mould.

The experimental result is compared with the computer simulated results.

One of the most frustrating aspects of gas turbine engineering since the work of Whittle has been the inability of the designer and development engineer to see or visualise the behaviour of components within a jet engine on test.

Radiography has been used in industry for many decades and it has been of major significance in the aerospace industry. When an inspection method such as Radiography has been successfully used for such a long time, it becomes difficult to realize that its full potential has not yet been reached.