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Each metal has different properties with another and mechanical properties were important in the construction. Dissimilar welding still has problem because of thermal properties from both base metal. This study discussed performance from a Stainless Steel 304 and ST 37 plate joined by the MIG welding and Inconel as a filler wire with thin of both plate less than 1 mm. Performance of a dissimilar weld join was analyzed with tensile performance, macrostructure, and weld joint condition scanned by X-ray. The lowest tensile performance was around 1.5 kN which could be represented by specimen 2a, and the second tensile performance was not more than 2.5 kN, it could be shown specimen 7a. Specimen 1 of macrostructure appeared a crack at base metal with filler, Specimen 2 of macrostructure shown that a weld join change, a weld join of specimen 7 was not complete. Specimen 3 and 4 of X-ray analysis was complete weld join although specimen 4 was better than specimen 3. [ABSTRACT FROM AUTHOR]

The current (mA) control is necessary to controlling the number of electrons and x-ray intensity at x-ray machine. Radiographers are likewise impacted by the presence of high radiation levels. This study aimed to improve performance the x-ray machine with wireless mA control system prototype. One of the important benefits of the proposed study is the simplicity to controlling intensity of x-ray based on wireless mA control system. This research method uses rheostat as current selection (mA). The current settings that can be selected are 20 mA, 30 mA, 40 mA, 50 mA and 60 mA, which can be done manually and via the mobile phone (cellphone) connected to this device via wireless using Wemos D1Mini. Tool testing is carried out by comparing the results of measuring the output voltage of the filament transformer using AVOmeter with the results of voltage calculation through formula analysis. In addition, tests are carried out for the distance of the tool control connection in the presence of obstacles and no obstacles. Based on the test results obtained, it is known that the smallest correction value is 0,01 VAC for the mA option of 30 mA. The x-ray device can be controlled wirelessly using Android up to a distance of 20 meters with a 14 cm thick wall obstructed condition. [ABSTRACT FROM AUTHOR]

In medicine, X-ray images are important, on the basis of which medical professionals can obtain necessary information about the internal structures of patients. The diagnosis is determined using the information that has been received. In some cases, it will not be possible to obtain information sufficient for diagnosis from X-ray image. For example, if the radiation is not delivered to the patient in sufficient quantity, the contrast of the medical X-ray image will not meet the requirement, which means that the analysis of the image will be complicated. This does not allow for making an accurate diagnosis. So, when fine-tuning the contrast of the X-ray image, it's important to guide the patient through the initial processing of the X-ray image, sparing them from undergoing a re-examination. The automation of this process requires the use of objective evaluation indicators. The primary objective of this research is to enhance the contrast of X-ray images through the utilization of an algorithm. Additionally, it aims to evaluate the resulting images using the widely recognized RMS (Root Mean Square) evaluation indicator. Furthermore, the research seeks to identify the most effective algorithm or sequence of algorithms based on this evaluation indicator. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common causes of death according to WHO in 2020. One of the ways to detect lung cancer is through X-rays. Because the examination is generally carried out manually through the naked eye of experts, it is advisable to have an alternative to detect the disease as early as possible so that the patient can get the right treatment. This study aims to detect the presence of lung cancer from chest x-ray images with the application of deep learning, namely Convolutional Neural Network (CNN). In this study, the authors used 90 cancer images and 90 normal images. The research design stage consisted of pre-processing on x-ray images and identification using CNN. The research obtained an accuracy rate of 87%. [ABSTRACT FROM AUTHOR]

Novel Corona-virus (COVID-19) must be recognized immediately and precisely to avoid or contain a possible pandemic by immediate quarantine and appropriate medical treatment. Detecting a disease will be challenging due to the increased number of COVID-19 patients and the virus' mutation. However, computer-assisted medical diagnosis has attained cutting-edge outcomes using artificial intelligence and deep learning methodologies. This study uses current resources and powerful deep learning methods to present an alternative diagnostic tool for COVID-19 instances. This work aims to investigate the feasibility of combining state-of-the-art classifiers for the most accurate detection of COVID-19 from chest X-ray images. The model utilizes the IEEE covid chest X-ray dataset (https://github.com/ieee8023/covid-chestxray-dataset) and the EfficientNet architecture to predict an accuracy of 99 percent. As a result, a model such as this one can assist medical practitioners in diagnosing COVID-19 cases more quickly than a radiologist reading through each scan one by one, mainly when many patients must be evaluated in short period. [ABSTRACT FROM AUTHOR]

Serious disease known as pneumonia, which can occur in one or both lungs, is frequently brought on by bacteria, fungi, or viruses. Based on the x-rays we have, we will be able to identify this lung illness. In this study we conduct investigation and comparison of the identification of lung illness with the use of several deep learning method. This study uses four adaptable and effective deep learning methodologies, and a chest X-beam image to predict and identify a patient with and without the condition. VGG-16, Res-Net, Inception-Net, and Dense-Net. In this study models CNN(5 layes), Dense-Net, VGG-16, Res-Net, Inception-Net achieve the validation accuracy of 92.5, 90, 80, 85 and 83.3. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

An assessment of the Convolutional Neural Network (CNN) is presented in this study, which makes the test faster and more reliable in recognizing COVID-19 from chest X-Ray images. In light of the large number of studies already conducted, the proposed model strives to improve accuracy and metrics by incorporating new methodologies. CNN models such as VGG16 have been used to achieve better outcomes. Order metrics were used to estimate the exhibition's size in this assessment. There is a strong correlation between this research and the ability to detect SARS-CoV-2 from CXR images of the lungs. In terms of accuracy, a model is the best option. VGG-16 may be used to train a CNN network to determine if a person has COVID-19 just by looking at a chest X-ray images, improving the radiography dataset's success rate. [ABSTRACT FROM AUTHOR]

X-ray machines have been used for a long time in industries for non-destructive testing. X-ray imaging can detect surface and subsurface defects in the materials. An X-ray machine consists of some components that work together to produce adequate amounts of the x-ray beam to produce a clear image of the materials. If one or more components are damaged, it will harm the worker. The X-ray failure will cause unrestraint x-ray production which means we cannot control the radiation exposure. The failure and damage of the X-ray machine are also will cost a lot of money for a replacement. Due to prevent this risk from happening, the X-Ray Laboratory team National Research and Innovation Agency (BRIN) has identified and analyzed the risk using a systematic risk assessment framework. The first step was to identify some critical components in the X-ray machine, then analyzed all the hazards associated with Failure Mode Effect Analysis (FMEA). The FMEA method is combining technology and experience to identify and eliminate potential failure modes. The result of the FMEA analysis will be guidance for the maintenance and safety procedures for an X-Ray machine personnel. Following these procedures, it is expected can prolong the life of the X-ray machine. [ABSTRACT FROM AUTHOR]

Anthropogenic activities such as industry are primary sources of heavy metals and other elements, which can contaminate the surrounding environment. One of the most significant problems is soil and air pollution, which is harmful to humans and animals because it accumulates hazardous metals in plant tissue when growing in contaminated environments. This study identified and evaluated the magnitude of heavy metal contamination in leaves of plant samples at 22 locations, which consist of radii of 0.5-1; 3; and 5 KM from the industrial site in Gresik, East Java. The level of elements in the plant has been determined using Total X-Ray Fluorescence. An estimation of human health risks from consuming the plants was also conducted. The analysis result of the element content in plant samples was in the following order: Pb < Cu < Zn < Mn < Fe. The range of concentrations for each element in the samples was obtained and compared with the permissible levels set by the Food and Agricultural Organization (FAO) and World Health Organization (WHO) standards. The results obtained from this analysis revealed that Mn, Fe, Cu, Zn, and Pb are in general accordance with the recommended standard, and the hazard quotient (HQ) was lower than 1 for a crop, indicating no appreciable health risk due to the consumption of these samples in the study area. [ABSTRACT FROM AUTHOR]

Pneumonia is one amongst the most happening diseases all around and requires proper diagnosis at an early stage. A huge number of children die each year because of pneumonia all over the world. According to the latest estimation Pneumonia kills many children than any other infectious disease, claiming the lives of over 8,00,000 children under the age of five. The image processing would help better for prior diagnosis of diseases such as cancer, pneumonia etc. Convolutional neural networks (CNN) are widely used in image processing. The diagnosis of pneumonia can be achieved by using the X-ray image of the lungs. This diagnosis was done by considering the factor of having cloud kind of formation on X-ray images. Hence, artificial intelligences-based frameworks are required. [ABSTRACT FROM AUTHOR]

COVID-19 is an extremely serious disease caused by COVID-2 (SARS-CoV-2), an extremely severe respiratory condition. COVID-19 was discovered in Wuhan City, China, in December of 2019 before spreading around the world and becoming a pandemic. It has had a significant impact on daily life, general well-being, and the global economy. Distinguishing the effective positive cases at the right time is critical in the early stages of treatment. Detecting this virus necessitates a large number of tests, which takes time, and no other automated tool kits are currently available. X-ray images of the chest obtained through radiology imaging methods reveal important information about the COVID-19 infection. Deep learning methods on radiological images using advanced technologies such as artificial intelligence provide precise analysis of the infection and can be useful in treating patients in remote locations where medical facilities are not immediately available. Convolution neural networks, one of the deep learning methods used in the proposed model, are used to classify the person as Covid positive or negative by analysing images of chest X-rays. [ABSTRACT FROM AUTHOR]

The objective of the study is to detect the covid-19 positive patient with the help of Chest X-ray image dataset by using machine learning based on Multi-dimensional Convolutional Neural Network Algorithm. To achieve accuracy a novel augmented dataset classification is used. Accuracy and Loss of Covid-19 detection are Performed from kaggle library. Dataset of 2400 chest image dataset with total sample size 30. The two groups Multi-dimensional Convolutional Neural Network(N=15) and Convolutional Neural Network(N=15). The study proved that Multi-dimensional Convolutional Neural Network achieved better accuracy as 99.66% which is higher, compared to Convolutional Neural Network accuracy as 99.13%. Finally, Multi-dimensional CNN appears significantly better than CNN. The two algorithm Multi-dimensional CNN and CNN are statistically satisfied with the independent sample T-test value(p<0.001) with confidence level of 95%. Study shows Multidimensional CNN seems better (Std. Error Mean= 1.96525) than CNN(Std. Error Mean= 6.13974). [ABSTRACT FROM AUTHOR]

A globally affected pandemic SARS-CoV-2 created health emergencies the world all over. After originating in China in 2019, COVID-19 viruses obtained various mutations in the past two years and created severe impacts on the quality of human life with a high mortality rate. Early diagnosis and rapid isolation is the best solution to prevent the spread of the viruses. Among various diagnosis tests, a chest X-ray scan shows better results. Since manual analysis of X-ray images of the chest is burdensome, many deep learning-based methods have evolved in the past two years. However, the methods developed so far still need improvements for successful implementation in clinical settings. This piece of writing proposes an avant-garde deep feature fusion model for the effective detection of COVID-19. The experiments in this work are arranged in two stages. In the first phase, the images of chest X-ray are tried to classify using three robust deep learning networks such as ResNet-50,Inception-v3, and DenseNet. We have combined the features from the highly performed deep learning networks and designed a deep feature fusion model in the second stage. The promising results in a combined dataset (combination of two publically available data sets) of X-ray images of the chest show the competence of the schemed model. We have also used four various data augmentation methods to generate more COVID-19-infected samples in this work. [ABSTRACT FROM AUTHOR]

A high voltage (kV) on the x-ray device is necessary to create x-ray radiation. High voltage (kV) is created by the High Tension Transformator (HTT), which increases the Alternating Current (AC) voltage from the Volt (V) scale to Kilo Volt (kV). However, many regional public hospitals in Indonesia continue to employ X-ray equipment with analog kV selection systems. As a result, radiographers will be continuously exposed to radiation since they must directly operate the instrument. This project aims to develop a wireless kV controller prototype for x-ray equipment to decrease radiographers' radiation exposure. This study methodology comprises four major phases: hardware design, software design, voltage suitability testing on x-ray instruments, and wireless remote-control testing. This study uses Android smartphones to set the kV parameter. The WEMOS D1 MINI and the Arduino MEGA 2560 microcontroller are used for serial connection. The selectable kV options are 40kV, 50kV, 60kV, and 70kV, whose outputs are emulated with 300V, 350V, 400V, and 450V. The largest adjustment is +9.263V at the 60kV level, while the smallest is +2.316V at the 40kV setting. The adjustment value is still within the 5 percent tolerance value limit. With a barrier wall thickness of 14 centimetres, the farthest distance measured was 20 meters. [ABSTRACT FROM AUTHOR]

Any urgent diagnosis of pneumonia can be affected by a chest x-ray (CXR henceforth), which is one of the most common types of radiography. Artificial intelligence (AI) has been employed by most researchers for the automatic detection of the complicated patterns in imaging data and to provide quantitative analysis of radiographic properties as well. In recent years, many advances have taken place, namely in the computer vision domain. As a consequence, these advances enable the researchers to provide in-depth analysis and investigation, especially with the existence of thousands of samples of CXR images. CXR samples and techniques provide a huge support for the radiologists in making crucial decisions while examining hundreds of images from different perspectives. Moreover, time consumption will be reduced because the most urgent cases will be correctly identified in a short period of time. The current study will analyze ten of the well-known deep learning (DL)techniques that tackled identifying CXR images in relation to their actual diagnosis. Each technique will be briefly addressed regarding data preparation, methodology, and model design. Furthermore, the accuracy measure generated by each model will be compared to the dataset used. It is hoped that the current study will be a reliable resource for DL to strategies in depicting CXR images in the medical domain. [ABSTRACT FROM AUTHOR]

X-ray quasi-periodic oscillations (QPOs) were reported for 19 different observations of the atoll source 4U 1728-34. Three of them reveal triplets of QPOs: a lower and a upper kHz QPOs and a low-frequency QPO. Even though they are discernible only after averaging we assume them to be simultaneous. We apply a separate model (formula) for each of the three frequencies of the observed QPOs and compose a system which contains three algebraic equations for the three unknown parameters - the mass and the specific angular momentum of the central neutron star, and the radius of the orbit of orgin of the given triplet of QPOs, which allows us to find the numerical values of the later. Error propagation formulas are applied for the obtaining of interval estimates of the parameters of the star. This is done through implicit function differentiation. The calculations are repeated with several different models for the kHz QPOs. [ABSTRACT FROM AUTHOR]

Since early months of 2020s, when Corona virus disease-2019 (COVID-19) first appeared, the major challenge for the healthcare sector was to identify, monitor the disease and take timely decision for the treatment and management of the patients. However, the extreme spread of the disease in 2020 brought scarcity of laboratory diagnostic kits. This is how radiology became a forefront method during the outbreak of COVID-19 for diagnosis and treatment of the disease. Several studies have recently been reported using AI-based methods to diagnose COVID-19 from non-COVID pictures after training them on X-ray and CT scan images. However, to overcome the limitations of the CT, the purpose of the present paper is to develop a deep learning model to distinguish and diagnose COVID-19 and non-COVID pneumonia in chest radiographs using ResNet50 model. Only three steps: Resizing, Training and Heatmap generation were conducted on dataset. An overall accuracy of predicting COVID-19 pneumonia on ResNet50 model was found to be 85% with a specificity of 88% and sensitivity of 81% using Confusion matrix. The precision value for COVID and non-COVID pneumonia cases was 90% and 80% respectively. Thus, the current model of ResNet50 would aid radiologists in the deployment of AI-based approaches for the speedy and accurate classificationof COVID-19 cases and further control future behavior of the pandemic. [ABSTRACT FROM AUTHOR]

In global population, the well-being and health of individuals are heavily affected by the Corona virus Disease 2019 (COVID19). Efficient screening regarding the infected patients is considered as one of the essential steps for fighting against the pandemic, with radiology examination with the use of chest radiography being one of the key screening method. Early researches discovered that COVID19 patients have abnormalities in the chest radiography images. The current study assists readers in identifying study starting points in active contour models for COVID19 research that has been specified as a topic of high priority for practitioners and scholars to follow. Additionally, in the case when there were insufficient images, machine learning (ML) methods, like deep learning (DL) approaches can be utilized. For a purpose of training a convolution neural network (CNN), one can use pseudo-coloring approaches as well as a platform to annotate X-ray and computed tomography (CT) images. Strong correlations between the clinical indicators and lesion areas in the images can be found using neural network based regression. Sensitivity of 93.84%, accuracy of 98.50%, specificity of 99.18%, and average receiver operating properties–area under the curve score of 96.51% have been reached as a result of previous work. [ABSTRACT FROM AUTHOR]

In global population, the wellbeing and health of individuals are heavily affected by the Corona virus Disease 2019 (COVID19). Efficient screening regarding the infected patients is considered as one of the essential steps for fighting against the pandemic, with radiology examination with the use of chest radiography being one of the key screening method. Early researches discovered that COVID19 patients have abnormalities in the chest radiography images. The modus operandi of the proposed system's components will be clarified in this article. There are two sections to the system. Detecting and recognizing COVID-19. The recognition part of the proposed work used histogram orientation gradient (HOG) algorithm to specify lungs, and convolution neural networks (CNNs) used to classify lung have COVID-19 or not. Convolutional neural networks (CNNs) have been widely used in the applications of deep learning that have rapidly evolved over the last decade, most notably as a method for analyzing medical pictures. Detection part consist gray level co-occurrence matrix (GLCM) to detect the feature of COVID-19 region. The results of proposed method get 100% in training, 99.86 in validation, and 98.5 in testing with dataset contains two classes (each class contains 200 image) [ABSTRACT FROM AUTHOR]

The aim of the study is to identify the fractured bone area from X Ray images using Laplacian method of edge detection and compared with CT scan images. The input images of X-Rays are collected from Kaggle.com. The sample size of 2 groups was 20. The data set contains bone fractures which are considered as an input image. The analysis of the Laplancianmethod efficiency and an outperforming rate compared with CT scan images in Fracture detection are done using MATLAB (2013) tool. Version 21 of the Statistical Package for Social Sciences (SPSS) was used for the statistical analysis. A total of 20 samples were processed for the 2 groups for the better accurate detection of bone fracture using G power of 80%. The comparison of Laplacian method over CT scan images was done using an independent sample t-test using SPSS software. The efficiency appeared for Laplacian was 89.64% and when compared with CT scan was 83.75%. Significance was observed for the comparison of parameter efficiency through SPSS Version 2.1 and the efficiency of Laplancian was minimum (74.82 ± 5.29) and compared with CT scan images (61.52 ± 11.68). Independent sample test reveals statistical significance P value obtained as 0.004. The Laplacian algorithm has better efficiency when compared with CT scan images for fracture detection. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019-Covid-19 is one of the most severe and acute disease that affected the economy and wellbeing of the globe. The Covid-19 diagnostic method was carried out in two broad categories: RT-PCR in the laboratory and chest radiography (X-ray and CT scan). Recently, the rapid researches have pioneered to collaborate the healthcare with Artificial Intelligence (AI). With focus of AI, the huge studies on going in the COVID-19 disease for obtaining the ultimate accuracy results and diagnosis the disease at an early time. This survey provides systematic review and meta-analysis with highlighting novelty for the future research directions and mostly concentrated on analysis work of previous models on classification of dataset which is containing Covid-19, Pneumonia, SARS-COV2, and Normal lungs of CT (Computerized Tomography) scan images using the techniques of Deep Learning. [ABSTRACT FROM AUTHOR]

The purpose of this research is to use a deep learning algorithm and machine learning on a dataset of chest X-ray images to find and explore novel covid-19 prediction. This precision is accomplished with the use of an innovative categorization technique that augments existing datasets. However, there has been little research on how well current CAD can detect pneumonia on CXRs when using the Innovative Covid-19 Prediction. Using the kaggel library, we can assess the sample loss and accuracy for detecting covid-19. There are a total of 30 samples from the collection of 2000 chest X-ray pictures. Residual neural network and Vgg-16 are the two subsets. Compared to VGG-16 (93%), the accuracy attained by the Residual neural network methods was confirmed in this investigation to be 94%. It ends up seeming like the Residual Neural Network is a lot more superior than the VGG-16. Independent sample t-test with 95% confidence shows that both of these methods are statistically significant (p=0.001; P0.05). Research demonstrates that the effort is meant to be put into action in order to invent new techniques and enhance the Innovative Covid-19 prediction algorithms. The Residual neural network outperformed VGG-16, as seen by the obtained result. [ABSTRACT FROM AUTHOR]

This article describes the results of physical modeling of deformation and filtration processes in the rocks of the Astrakhanskoye gas-condensate field when implementing the method of increasing the permeability of low-permeability rocks - the directional unloading method. The experiments were carried out on the unique Triaxial Independent Load Test System (TILTS) of the Institute for Problems in Mechanics of the Russian Academy of Sciences (IPMech RAS). A digital model of the internal structure of the rock by X-ray computed tomography methods was obtained. A digital analysis of changes in the filtration properties of rocks based on the experimental data of the micro CT study is carried out. It is shown that the directional unloading method can be successfully applied to low-permeability rocks of the Astrakhanskoye field. [ABSTRACT FROM AUTHOR]

Breast density from breast fibroglandular tissue reduces mammography sensitivity. To enhance mammography sensitivity without using x ray, morphology and texture patterns analysis have been proposed resulting with a precision from 60 to 93%. To support mammography, artificial neural network (ANN) or deep learning (DL) have been employed. These techniques using thousands of mammograms have achieved 99.7% precision for benign/malign mammograms classification. The propose of this work is to analyze a minor database of specific mammograms from Oaxaca population. We propose their analysis using mathematical morphology by means of circularity (κ) and texture using mean height-width ratio of extrema (ρ). Database consists of mammograms from 2011 to 2013 of Hospital Regional de Alta Especialidad de Oaxaca classified as BI-RADS 0, 4 and 5. At this moment, database have 73 abnormal mammograms. All patients had a biopsy procedure after mammography resulting on 35 benign and 38 malign conditions. For ρ we analyze profiles with pike height of 10 (ρ10) and 100 (ρ100) gray levels. Main benign pathology was phyllodes tumor (17%) and main malignant pathology was invasive ductal carcinoma (36%). Results of k, ρ10 and ρ100 have shown focalization zones of benign and malign mammograms. These zones are not clear separated between all of them. This suggests use of a nonlinear criterion to get their correct classification. In conclusion, circularity and mean height-width ratio of extrema are descriptors that could be used to analyze digital mammograms. Further work will consist of enlarge database and design an ANN as a nonlinear criterion to classify benign and malignant mammograms. [ABSTRACT FROM AUTHOR]

The SSDL-ININ has developed the infrastructure to set up the Calibration and Measurement Capabilities (CMC) by determining the Practical Peak Voltage (PPV) for the RQR, RQA RQT and RQR-M reference field qualities for diagnostic X-ray dosimetry. In this present work, emphasis is given to the PPV quantity, whose purpose is to normalize the contrast resolution in images obtained with X-ray generators operated at different kVp. The PPV concept is defined in the IEC 61267:2005 and the IEC 61676:2002 standards and in the IAEA code of practice TRS 457:2007. The PPV is, essentially, a weighted average of the X-ray tube voltages given any arbitrary shape of the voltage ripple in the X-ray high voltage HV generator. The characterization of quality reference fields: RQR, RQT, RQA and RQR-M for diagnostic X-ray dosimetry in terms of PPV is a pending task for the work of the dosimetrist in Mexico. The PPV should be used in the calibration of equipment for patient dosimetry and image quality control, with the purpose to attain the uncertainty goal of U(k=2)=7% in the air kerma in diagnostic X-Rays, according the IAEA TRS 457:2007. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze in a wavelength range from 400 to 500 nm, the additional absorption bands-like in the characteristic net absorption spectra of the EBT3 radiochromic films irradiated with 6 MV X-ray in a dose range from 0 to 10 Gy through a theoretical model describing the interference effects of a Fabry-Perot cavity containing poly-DCHD. [ABSTRACT FROM AUTHOR]

OSL dosimeter as a passive radiation measuring dosimeter can be an option to replace radiation measurement instrument containing electronic circuit. In this study, dose response on environmental OSL (optically stimulated luminescence) dosimeters to industrial and medical at tube potential 80 kV X ray exposure and their linearity were tested. The effect of fading on reading repeatability after the OSL dosimeters stored were examined. Forty-eight OSL dosimeters were annealed and read to ensure that the dose information was reset to zero. Then they were packed using Al foil to prevent light exposure. The dosimeters are divided into 2 groups, namely 24 dosimeters for irradiation of industrial X ray and the rest for medical X ray. Each of them is divided into 6 groups consisting of 4 dosimeters each. The five groups were irradiated at 0.5 mSv, 1 mSv, 2.5 mSv, 5 mSv and 10 mSv at a distance of 200 cm with an additional filter of 0.59 mm Cu for industrial X ray and 0.3 mm Cu for medical X ray. While the rest consisting of 8 dosimeters were as control dosimeters. All dosimeters were read using the OSL microStar Reader. The result shows that the ratio of the measured dose to the exposure dose on industrial X ray is ≥0.89 for≥2.5 mSv exposure. It was different when it compared to the response with 1 mSv and 0.5 mSv exposure that the ratio is ≤0.61. Moreover, the smaller the dose of exposure, the deviation of the measured dose to the exposure dose is greater. Meanwhile, ratio measured dose to exposure dose in the medical X ray did not show a tendency as in the industrial X-ray exposure. Deviation of the measured dose to exposure dose in medical X ray is ≤22%. The fading effect of OSL dosimeter on the 12th day reading dose on industrial X ray was ≤6% while for medical X-ray showed a fading ≤7% on 14th day. It can be seen that the fading effect on the OSL dosimeter to X ray is relatively small. Linearity curve shows that the correlation between the measured dose to the exposure dose at industrial X-ray is linear with a correlation factor (R2)≥0.999. Meanwhile, linearity on medical X ray indicates R2 value≥0.997. The experiment showed that the relationship between measured dose to exposure dose on industrial and medical at tube potential 80 kV X ray was linier. After 2 weeks stored the fading effect of OSLD was relatively small. [ABSTRACT FROM AUTHOR]

Recent advances in numerical methods combined with the use of 3D X-ray micro computed tomography acquisition systems improved the characterization of reservoir rocks at pore scale. This approach, known as digital rock physics (DRP), consists of simulating rock properties using 3D X-ray micro computed tomography images at pore scale. DRP has been extensively used to estimate numerically rock properties like porosity and permeability. This methodology was successful in sandstone reservoir rocks due to their relative homogeneity. Nevertheless, this approach failed in many cases when applied for carbonate reservoirs due to their heterogeneity at several length scales. In order to overcome this limitation, we propose to use the texture information in the images to identify and segment the most representative textural regions. Indeed, several studies showed that texture information is correlated to variations of physical rock properties. In recent years, the advancements made in deep learning algorithms improved largely the performance of segmentation methods. In particular, we focus on a machine learning method based on convolutional neural network called the U-NET architecture to segment 3D X-Ray micro computed tomography images in terms of textures. The challenge is to identify precisely representative textures in highly heterogeneous rocks such as carbonate rocks. We investigate the performance of the proposed segmentation method on both synthetic and real data. [ABSTRACT FROM AUTHOR]

An experimental set up to characterize the microspall phenomenon is proposed. It involves velocity measurements and a multi-time transversal radiographic observation. Five experiments show that a quantitative characterization is achieved, leading to a better understanding of the phenomenon. [ABSTRACT FROM AUTHOR]

The most common method of analysing armour performance is the Depth of Penetration (DoP). However, this one-dimensional measurement does not provide insight into the method of penetration or energy absorbed by the target; the crater could be particularly narrow or very wide and yield the same DoP. Analysis of the crater through Crater Volume (CV) provides a more detailed metric to be used alongside DoP to visualise the crater, indicating whether energy was dispersed over a large area. CV provides a wider insight into how a material resisted penetration events, giving evidence of potential defeat mechanisms. Digital reconstruction of the craters using X-ray radiographs or Computed Tomography (CT) scanning can also provide a useful tool for computational models to be compared against. The simple calculation of CV through X-ray radiography and image processing has been demonstrated to be accurate to within ±6% of the CT scanned CV. Success in utilising this analytical tool was demonstrated through comparison of three armour configurations. A consistent difference in the ratio of DoP:CV was seen between steel targets, ceramic-steel targets and ceramic-air-steel targets, indicating variation in the defeat mechanism between the three target configurations. [ABSTRACT FROM AUTHOR]

Focused X-ray beam induced deposition is a recently developed approach for fabrication of metallic nanostructures by selective activation of metal-organic precursors with the focused beam of a scanning transmission soft X-ray microscope. While a proof-of-concept has been successfully achieved, the current setup requires fundamental improvements to be competitive with other nanolithography tools. Tools from theory of inventive problem solving have been applied for an in-depth analysis of potential improvements and to generate ideas for a next-level setup. [ABSTRACT FROM AUTHOR]

X-ray phase-contrast imaging is a powerful imaging modality for non-destructive visualisation of weakly-absorbing materials inside an object. We present here a two-directional beam-tracking approach for phase-contrast imaging in a laboratory setup. A single absorption mask is used to modulate the polychromatic x-ray beam, and the divergence of the beam is exploited to analyse the beamlets with detector pixels comparable in size to the mask pitch. The method gives access to two orthogonal phase gradients, which allows for robust phase integration. Two implementations are demonstrated: one with a low-noise photon-counting device for radiography, and the other with an indirect conversion flat-panel detector for phase-contrast tomography. Finally, images of biological specimens are presented to show the potential of the approach for biomedical applications. [ABSTRACT FROM AUTHOR]

X-ray micro/nano-tomography (multiscale tomography) has been developed at SPring-8 BL47XU. The X-ray optics consists of a simple projection type imaging system and a full-field x-ray microscope using a Fresnel zone plate (FZP) as an objective. There are two objectives with different parameter FZPs, therefore three modes are available in this system. 1. Micro-tomography mode, 2. Large field nano-tomography mode 3. High resolution nano-tomography mode. Furthermore, two types of detectors with different effective pixel sizes can be used in the two nano-tomography modes. All components are mounted on high precision stages driven by stepper motor. It takes about three minutes to change the mode from "micro" to "nano" or "nano" to "nano". This system enables 3D measurements at several levels of spatial resolution without dismount the sample in the energy range between 7 keV and 15keV. [ABSTRACT FROM AUTHOR]

High-energy x-ray nanotomography (nano-CT) based on a full-field x-ray microscope with Fresnel zone plate objective has been developed at BL20XU of SPring-8. An extremely long sample-to-detector distance of 165 m allows for a magnification of much more than x100 in high-energy x-ray region. Achieved spatial resolution is 150 - 200 nm at the x-ray energy region from 15 keV to 37.7 keV. A large working distance (WD) of more than 100 mm around the sample makes it easy to insert various devices and equipment. This can be used for multi-scale and multi-dimensional measurements combined with a projection type x-ray microtomography (µ-CT) and a microbeam x-ray diffraction (XRD), as well as in-situ observation by introducing various types of testing equipment. Since samples that are often entirely larger than the FOV of the nano-CT are measured in the multi-scale measurement, reduction of cupping effect and ring artifacts in the interior CT condition is also discussed. [ABSTRACT FROM AUTHOR]

Phase-contrast X-ray imaging (PCXI) techniques employ both X-ray refraction and attenuation to generate image contrast, and are therefore capable of forming high-quality images of weakly attenuating samples, for example, soft-tissues in mammography. Propagation-based phase-contrast X-ray imaging (PB-PCXI) is an especially simple PCXI technique as it requires no additional precision optics in the experimental set-up, and hence can be easily implemented in numerous disciplines. We recently published a PB-PCXI algorithm capable of extracting the real, δ, and imaginary, β, refractive index components of composite materials within an unknown sample [1]. This was previously applied to PB-PCXI data collected using a synchrotron facility where the X-ray wavefield was monochromatic, highly coherent, and paraxial. Within this work, we have validated, and further extended, this approach by applying it to PB-PCXI data collected using a conventional laboratory X-ray source that produces a polychromatic, relatively temporally incoherent and divergent wavefield. We demonstrate that our previously published phase and attenuation extraction algorithm can be applied to biological tissue sample data collected using such a laboratory X-ray source, thereby broadening the applicability of this algorithm. A link to a repository is provided, where a Python3 script implementing this approach can be downloaded. [ABSTRACT FROM AUTHOR]

A transmission full-field microscope of cryo-soft X-ray tomography (SXT) was developed as a platform to image three-dimensional frozen hydrated cells in beamline TPS 24A at Taiwan Photon Source (TPS). This microscope consists of two main compositions in a vacuum chamber: The first composition is the optical system of the microscope, and the second composition is the cryogenic cooling system. The optical system includes a high demagnification capillary condenser and an objective of Fresnel zone plate. The cryogenic cooling system consists of sample transferring, robot and rotational sample systems. In our preliminary results, a spatial resolution of around 25∼30 nm half-pitch was obtained from a 2D image of standard calibration Nested L sample, and a clear crista of inner membrane in mitochondria from a frozen lung carcinoma cell was imaged at water window energy of 520 eV. [ABSTRACT FROM AUTHOR]

We are developing SAKAS (SAga light source data KArte System), which is an image data carte system for synchrotron radiation imaging, such as micro- and phase-contrast X-ray imaging and computed tomography (CT). It handles not only the name and condition of a sample but also all measurement conditions, such as the X-ray imager and beamline used, and image processing, such as the reconstruction and noise reduction filter with a single record file. We have completed the specifications, such as metadata, of the first version, produced the corresponding control and processing software, and installed this version in the micro CT system at the beamline 07 (BL07) at SAGA Light Source and the X-ray crystal-interferometer-based phase-contrast imaging system at the beamline 14C (BL-14C) of the Photon Factory. [ABSTRACT FROM AUTHOR]

A nanopipe in a GaN crystal was nondestructively observed by X-ray ptychographic coherent diffraction imaging. A sharp-tipped nanopipe with a length of ∼3 µm and a width of ∼200 nm was identified at a depth of∼15 µm from the surface in a hexagonal pit with ∼6 µm per side. The position, length, thickness, and depth of the nanopipe can be used to efficiently perform detailed structural analysis by transmission electron microscopy and atom probe tomography. [ABSTRACT FROM AUTHOR]

X-ray micro/nano-tomography (multiscale tomography) has been developed at SPring-8 BL47XU. The X-ray optics consists of a simple projection type imaging system and a full-field x-ray microscope using a Fresnel zone plate (FZP) as an objective. There are two objectives with different parameter FZPs, therefore three modes are available in this system. 1. Micro-tomography mode, 2. Large field nano-tomography mode 3. High resolution nano-tomography mode. Furthermore, two types of detectors with different effective pixel sizes can be used in the two nano-tomography modes. All components are mounted on high precision stages driven by stepper motor. It takes about three minutes to change the mode from "micro" to "nano" or "nano" to "nano". This system enables 3D measurements at several levels of spatial resolution without dismount the sample in the energy range between 7 keV and 15keV. [ABSTRACT FROM AUTHOR]

X-ray phase-contrast imaging is a powerful imaging modality for non-destructive visualisation of weakly-absorbing materials inside an object. We present here a two-directional beam-tracking approach for phase-contrast imaging in a laboratory setup. A single absorption mask is used to modulate the polychromatic x-ray beam, and the divergence of the beam is exploited to analyse the beamlets with detector pixels comparable in size to the mask pitch. The method gives access to two orthogonal phase gradients, which allows for robust phase integration. Two implementations are demonstrated: one with a low-noise photon-counting device for radiography, and the other with an indirect conversion flat-panel detector for phase-contrast tomography. Finally, images of biological specimens are presented to show the potential of the approach for biomedical applications. [ABSTRACT FROM AUTHOR]

Focused X-ray beam induced deposition is a recently developed approach for fabrication of metallic nanostructures by selective activation of metal-organic precursors with the focused beam of a scanning transmission soft X-ray microscope. While a proof-of-concept has been successfully achieved, the current setup requires fundamental improvements to be competitive with other nanolithography tools. Tools from theory of inventive problem solving have been applied for an in-depth analysis of potential improvements and to generate ideas for a next-level setup. [ABSTRACT FROM AUTHOR]

High-energy x-ray nanotomography (nano-CT) based on a full-field x-ray microscope with Fresnel zone plate objective has been developed at BL20XU of SPring-8. An extremely long sample-to-detector distance of 165 m allows for a magnification of much more than x100 in high-energy x-ray region. Achieved spatial resolution is 150 - 200 nm at the x-ray energy region from 15 keV to 37.7 keV. A large working distance (WD) of more than 100 mm around the sample makes it easy to insert various devices and equipment. This can be used for multi-scale and multi-dimensional measurements combined with a projection type x-ray microtomography (µ-CT) and a microbeam x-ray diffraction (XRD), as well as in-situ observation by introducing various types of testing equipment. Since samples that are often entirely larger than the FOV of the nano-CT are measured in the multi-scale measurement, reduction of cupping effect and ring artifacts in the interior CT condition is also discussed. [ABSTRACT FROM AUTHOR]

We are developing SAKAS (SAga light source data KArte System), which is an image data carte system for synchrotron radiation imaging, such as micro- and phase-contrast X-ray imaging and computed tomography (CT). It handles not only the name and condition of a sample but also all measurement conditions, such as the X-ray imager and beamline used, and image processing, such as the reconstruction and noise reduction filter with a single record file. We have completed the specifications, such as metadata, of the first version, produced the corresponding control and processing software, and installed this version in the micro CT system at the beamline 07 (BL07) at SAGA Light Source and the X-ray crystal-interferometer-based phase-contrast imaging system at the beamline 14C (BL-14C) of the Photon Factory. [ABSTRACT FROM AUTHOR]

A transmission full-field microscope of cryo-soft X-ray tomography (SXT) was developed as a platform to image three-dimensional frozen hydrated cells in beamline TPS 24A at Taiwan Photon Source (TPS). This microscope consists of two main compositions in a vacuum chamber: The first composition is the optical system of the microscope, and the second composition is the cryogenic cooling system. The optical system includes a high demagnification capillary condenser and an objective of Fresnel zone plate. The cryogenic cooling system consists of sample transferring, robot and rotational sample systems. In our preliminary results, a spatial resolution of around 25∼30 nm half-pitch was obtained from a 2D image of standard calibration Nested L sample, and a clear crista of inner membrane in mitochondria from a frozen lung carcinoma cell was imaged at water window energy of 520 eV. [ABSTRACT FROM AUTHOR]

Phase-contrast X-ray imaging (PCXI) techniques employ both X-ray refraction and attenuation to generate image contrast, and are therefore capable of forming high-quality images of weakly attenuating samples, for example, soft-tissues in mammography. Propagation-based phase-contrast X-ray imaging (PB-PCXI) is an especially simple PCXI technique as it requires no additional precision optics in the experimental set-up, and hence can be easily implemented in numerous disciplines. We recently published a PB-PCXI algorithm capable of extracting the real, δ, and imaginary, β, refractive index components of composite materials within an unknown sample [1]. This was previously applied to PB-PCXI data collected using a synchrotron facility where the X-ray wavefield was monochromatic, highly coherent, and paraxial. Within this work, we have validated, and further extended, this approach by applying it to PB-PCXI data collected using a conventional laboratory X-ray source that produces a polychromatic, relatively temporally incoherent and divergent wavefield. We demonstrate that our previously published phase and attenuation extraction algorithm can be applied to biological tissue sample data collected using such a laboratory X-ray source, thereby broadening the applicability of this algorithm. A link to a repository is provided, where a Python3 script implementing this approach can be downloaded. [ABSTRACT FROM AUTHOR]

A nanopipe in a GaN crystal was nondestructively observed by X-ray ptychographic coherent diffraction imaging. A sharp-tipped nanopipe with a length of ∼3 µm and a width of ∼200 nm was identified at a depth of∼15 µm from the surface in a hexagonal pit with ∼6 µm per side. The position, length, thickness, and depth of the nanopipe can be used to efficiently perform detailed structural analysis by transmission electron microscopy and atom probe tomography. [ABSTRACT FROM AUTHOR]

The aim of the work is to analyze X-ray synchrotron microtomography data obtained for polymers reinforced with carbon fibers and to reveal the multiscale regularities of damage development caused by both the technological manufacturing process and the deformation contribution to the loading process of a sample of layered polymer composite material. It is known that the orientation distribution determined by the fiber stacking technology causes the mechanical properties of composites, and the production quality primarily determines the performance characteristics of material. Generally accepted methods for characterizing composites involve identifying fibers as individual objects, which requires high-resolution X-rays and large computational resources. This paper investigates damage analysis based on the field determination of the structure tensor of a representative volume of the composite, which makes it possible to determine both technological defects and accumulated damage during the deformation process. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is harming many people's health and lives in 150 nations. The primary line of defence against the Corona virus is early detection and isolation of infected patients. COVID-19 may be detected in radiography and radiology images, making infection diagnosis simple. Early research showed particular irregularities in COVID-19-inflamed individual's chest radiographs. From prior attempts, we saw using different methods of deep learning to locate infections from X-ray. It has beeen started by collecting 6000 chest X-rays from publicly accessible sources. Radiologist will identify COVID-19 illness in these images. Pneumonia kills 15% of children globally. Identified and categorised Normal, Covid-19 and pneumonia in Chest X-rays are derived by VGG16 deep learning model. To identify pixels, the identification model uses CNN, a deep neural network that integrates exterior and inner features. Based on the results, the medical sector should investigate the use of X-ray pictures in the treatment of illness, and more research into assessing the X-ray technique should be conducted. To compare pneumonia-related and non-pneumonia chest X-rays, the proposed detection method performed better. [ABSTRACT FROM AUTHOR]

This paper presents an approach to study the structure of modified multicomponent fine-grained concretes based on the results of computer tomography. The proposed technique for processing layer-by-layer halftone images makes it possible to evaluate the course of hydration and structure formation of the composite at all stages of hardening. The development of non-destructive testing methods capable of correctly identifying the structural units of the system at the early stages of hardening in order to analyze the overall picture of hydration contributes to the operational control of cement concretes characteristics at a qualitatively new level. [ABSTRACT FROM AUTHOR]