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Abstract: The properties of a novel X-ray diffraction (XRD) technique having very low sensitivity to the sample morphology were previously elucidated through theoretical considerations and model simulations (Hansford, 2011 [1]). This technique opens up the possibility of mineralogical analysis by XRD without sample preparation. Here, the results of proof-of-principle experimental tests are presented. Two sets of experiments were performed using a vacuum chamber equipped with an X-ray tube source, sample holder and charge-coupled detector. Firstly, a pressed-powder pellet of α-quartz was placed in three different positions relative to the X-ray source and detector. The changes in position represent gross sample movements which would be inconceivable in conventional XRD analysis. The resulting back-reflection energy-dispersive spectra show a very high degree of correspondence other than an overall intensity factor dependent on the distance between the sample and detector. Secondly, the back-reflection spectrum of an unprepared limestone hand specimen, having mm-scale surface morphology, was compared to the spectrum of a calcite pressed-powder pellet. The correspondence of the diffraction peaks in the spectra demonstrate that the limestone is comprised dominantly of calcite. In both cases, the claims of the earlier paper are fully supported by the results of these experimental tests. [Copyright &y& Elsevier]

Abstract: Conventional semiconductor X-ray detectors for medical imaging have either a planar or a pixelated structure. The options available for detection materials are limited by the natural trade-off between the absorption of incident photons and the collection of free charge carriers with these two structures. This trade-off can be avoided by using az 3D structure, in which electrodes are drilled into the detector's volume. This article describes a prototype 3D semiconductor detector, using semi-insulating GaAs. A laser drilling technique was used to create electrodes in the volume of the material. The holes created were characterized by scanning electron microscopy. Electrode contacts were created using electroless Au deposition. The manufacturing process and the first gamma counting results obtained with 241Am and 57Co sources are presented. The system is capable of individual photon-counting without energy discrimination but requires further development to improve efficiency. [Copyright &y& Elsevier]

• An improved deep transfer learning model is proposed for spacecraft composite structures. • Our method can detect inclusion and void defects. It still applies if there are enough samples of other types of defects. • Small-size defects can be detected using the proposed method. • It is suitable for the small-scale sample dataset. This is important in the application. Usually, defect detection in X-ray images of composite materials is mainly performed by humans, which is time consuming and inefficient. Some deep learning methods have been proposed, but they are only used to identify a certain type of defect or need a large number of training samples. Besides, the size of void and inclusion defects in X-ray images of spacecraft composite structures (SCS) is very small, which is difficult for common object detection methods, especially when the number of training samples is small. Thus, we propose a transfer learning object detection model for detection of both of these defects. Based on domain adaptive Faster R-CNN (DA Faster), the feature pyramid network (FPN) is added into the feature extraction sub-module for multi-scale features adaptation, and small anchor strategies and ROI Align are utilized, which are helpful for localization of small-size defects. Besides, big NMS threshold and conditional domain adaptation (CDAN) is adopted in the box classification branch for more accurate features alignment. The experiment results demonstrate that strong ability of the proposed model in detecting small-size void and inclusion defects of SCS. [ABSTRACT FROM AUTHOR]

Despite advances in Deep Learning, the Convolutional Neural Networks methods still manifest limitations in medical applications because datasets are usually restricted in the number of samples or include poorly contrasted images. Such a case is found in stenosis detection using X-rays coronary angiography. In this study, the emerging field of quantum computing is applied in the context of hybrid neural networks. So, a hybrid transfer-learning paradigm is used for stenosis detection, where a quantum network drives and improves the performance of a pre-trained classical network. An intermediate layer between the classical and quantum network post-processes the classical features by mapping them into a hypersphere of fixed radius through a hyperbolic tangent function. Next, these normalized features are processed in the quantum network, and through a SoftMax function, the class probabilities are obtained: stenosis and non-stenosis. Furthermore, a distributed variational quantum circuit is implemented to split the data into multiple quantum circuits within the quantum network, improving the training time without compromising the stenosis detection performance. The proposed method is evaluated on a small X-ray coronary angiography dataset containing 250 image patches (50%–50% of positive and negative stenosis cases). The hybrid classical–quantum network significantly outperformed the classical network. Evaluation results showed a boost concerning the classical transfer learning paradigm in the accuracy of 9%, recall of 20%, and F 1 -score of 11%, reaching 91.8033%, 94.9153%, and 91.8033%, respectively. • A quantum network boosts the performance of classical neural architecture. • An L 2 hyperbolic tangent layer bounds the features between classical and quantum stages. • The X-ray angiography images are analyzed to develop a robust stenosis detector system. • Transfer learning and quantum network substantially improved stenosis detection. • An efficient hybrid classical–quantum architecture is focused on stenosis detection. [ABSTRACT FROM AUTHOR]

Flexible Pb-MOF film serves as a scintillator screen for sensitive X-ray detection and high-resolution X-ray imaging. [Display omitted] • The Pb-MOF composite film is firstly used as the scintillating screen for X-ray imaging. • Lead-based metal–organic framework scintillator is prepared via the mild method. • Synergistic effect of inorganic and organic motifs in Pb-MOF is responsible for sensitive X-ray response. Scintillators that exhibit X-ray stimulated light response have attracted increasing attention for their wide application in medical diagnosis and treatment, security inspection, industrial flaw detection and other aspects. However, the broader application of traditional inorganic and organic scintillators has been limited by their high preparation cost or poor adaptability to changeable environments. Here, a scintillating 1D metal–organic framework (MOF) [Pb(adda)(DMF)] n 1 (H 2 adda = (2 E ,2′ E)-3,3′-(anthracene-9,10-diyl) diacrylic acid, DMF = N , N -dimethylformamide) was prepared by a low-cost and short-cycle solvothermal process. Benefiting from the intrinsic synergistic effect of functional motifs and robust architecture, sensitive and steady X-ray response signals of 1 are demonstrated with an excellent linearity to the X-ray dose rates and stable light output under 9 h per day continuous irradiation during five days. In addition, the flexible MOF-based scintillator film is firstly applied in X-ray imaging application, which achieves a relatively high spatial resolution of 5.5 lp mm−1. This work provides an effective visualization tool for X-ray radiography and verifies the potential application of flexible MOF-based scintillator film for X-ray imaging. [ABSTRACT FROM AUTHOR]

• Rb 2 CuBr 3 has low cost, high mass yield, high PLQY and suitable emission wavelength. • The materials have self-trapped excitons (STEs) for large stokes shift (90 nm). • Rb 2 CuBr 3 -PS composite screens films shown a sensitive response to X-ray signals. Environmentally stable, low cost, large stokes shift, adjustable area and flexibility are basic factors to prepare scintillator films for their commercial application. Here, a low cost (∼3 $/g) room-temperature ligand-assisted reprecipitation method is used to synthesize one-dimensional crystal structure based on Rb 2 CuX 3 (X = Br, Cl). Rb 2 CuBr 3 exhibits quite high mass yield of 90% with high Photoluminescence quantum yield (PLQY) of 59.06%. Importantly, its bright blue emission (90 nm stokes shift) is consistent with the highest optical detection efficiency wavelength range of commercial SiPM. Further characterizations point out that such effective blue emission comes from the recombination of excitons rather than the defects or combination of electron-hole pairs. And the existing strong exciton-photon coupling is conducive to the formation of self-trapped excitons (STEs). Finally, uniform and adjustable area Rb 2 CuBr 3 -polystyrene composite films have been successfully obtained. It's fascinating that the films exhibit ultra-flexibility (unchanged performance after multiple bending of 400 times) and excellent stability (subtle attenuation after 30 days of storage in a harsh environment). As an X-ray imaging screen, it presents a sensitive scintillation response to X-ray signals with superior linearity with high-quality images. Therefore, our prepared Rb 2 CuBr 3 scintillator shows great commercial prospects in the field of flexible X-ray detection. [ABSTRACT FROM AUTHOR]

Scintillator screens have been used on multiple devices for X-ray tomography, being responsible for conversion of the X-ray beam into visible light that is then focused to form an image. In this context, CdWO 4 -polystyrene composite films were tested as detector screens at the Imaging Beamline of the Brazilian Synchrotron Light Laboratory. Samples with varied thickness (150 μ m , 300 μ m and 600 μ m) and CdWO 4 load (10% and 40%) were characterized as a microtomography image screen. The performance of the composites as radiation sensors demonstrates the feasibility of using CdWO 4 -PS scintillator for digital imaging applications. Spatial inhomogeneities of the composites were corrected by a digital flat image, thus allowing successful acquisition of absorption coefficients of the projected reference. Photoluminescence emission spectra provided understanding of the role of CdWO 4 particles dispersion, which was optimized in order to reduce artifacts related to light scattering. In addition, spatial resolution of images acquired with CdWO 4 -PS was demonstrated to be improved with the higher scintillator concentrations and the smaller screen thickness. [ABSTRACT FROM AUTHOR]

A novel Eu3+ ions-based metal–organic coordination polymer with a peculiar "snake-shaped" chain structure has been synthesized, which exhibits efficient light emission, excellent line response to X-rays and high structure and luminescence stability against continuous irradiation. [Display omitted] • A new type of luminescent Eu-MOCP was synthesized by simple solvothermal method. • Synthesized Eu-based MOCP shows an efficient and stable light emission under X-ray excitation, and delivers an excellent linear response to X-ray dose. • Compound 1 demonstrates high structure and luminescence stability against continuous X-ray irradiation. In this paper, a novel Eu ions-based metal–organic coordination polymer (MOCP) named Eu(1,3,6,8-PTS)(H 2 O) 7 ·4H 2 OHbipy (1) (PTS = pyrenetetrasulfonic acid) has been prepared, and its structure, stability, photoluminescence and scintillation properties were systematically investigated. Compound 1 gives a peculiar "snake-shaped" chain structure with the aid of π-π stacking effect of 4,4′-bipyridine auxiliary template. An efficient and stable light emission was detected under X-ray irradiation, which presents an excellent linear response to X-ray dose. Moreover, this compound demonstrates high radiation stability, illustrating its potential as a scintillator for X-ray detection. This work not only provides guidance for the design and fabrication of sulfonic ligand derived MOCPs with Eu (III) ions coordiantion, but also demonstrates the potential of MOCPs scintillators for X-ray detection technology. [ABSTRACT FROM AUTHOR]

• The 25 µm-thick ZnO film using sputtering method was characterized for X-ray detection applications. • p+-Si/ZnO X-ray detector based on the 25 µm-thick ZnO film shows a high sensitivity of 128 μC∙Gy−1∙cm−2 at − 20 V. • The on-off response is related to the V O defect, which was verified by the transmittance spectrum and the TSC spectrum. [Display omitted] In the field of direct-conversion flat panel X-ray detector, semiconductor with thickness of tens to hundreds microns is needed. In this paper, we demonstrate that thick ZnO semiconductor deposited by RF magnetron sputtering can be candidate for the X-ray detector. The 25 µm ZnO film was prepared by sputtering method under 200 °C and a p+-Si/ZnO X-ray detector based on the thick ZnO film was investigated to explore its X-ray detection properties. The detector shows nonzero short circuit current and open circuit voltage indicating the heterojunction nature of the ZnO film in contact with the p+-Si substrate. The detector shows linear response to the X-ray dose rate and has a high sensitivity of 128 μC∙Gy−1∙cm−2 at −20 V under 30 kV p X-rays indicating its great potential in the flat panel X-ray detector applications. The on-off response of the detector is related to the V O defect in the ZnO film, which can be verified from the transmission spectrum, the thermal stimulated current spectrum, and the photoluminescence spectrum. [ABSTRACT FROM AUTHOR]

• A foreground and background separation framework is proposed for prohibited item detection in heavily overlapping X-ray image. • Method can separate the pure prohibited items from the X-ray images and eliminate the interference of irrelevant information to the detection. • Its performance is demonstrated on a synthetic dataset and two public datasets. Experiment results verify the effectiveness of our method. X-ray imagery security screening is an essential component of transportation and logistics. In recent years, some researchers have used computer vision algorithms to replace inefficient and tedious manual baggage inspection. However, X-ray images are complicated, and objects overlap with one another in a semi-transparent state, which underperforms the existing object detection frameworks. To solve the severe overlapping problem of X-ray images, we propose a foreground and background separation (FBS) X-ray prohibited item detection framework, which separates prohibited items from other items to exclude irrelevant information. First, we design a target foreground and use recursive training to adaptively approximate the real foreground. Thereafter, with the constraints of X-ray imaging characteristics, a decoder is employed to separate the prohibited items from other irrelevant items to obtain the foreground and background (FB). Finally, we use the attention module to make the detection framework focus more on the foreground. Our method is evaluated on a synthetic dataset with FB ground truth and two public datasets with only bounding box annotations. Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art solutions. Furthermore, experiments are performed in the case where only a small number of images contain the FB ground truth. The results indicate that our method requires only a small number of FB ground truths to obtain a performance equivalent to that of all FB ground truths. [ABSTRACT FROM AUTHOR]

• An n -ZnO/p-Si/ n -Si heterojunction bipolar phototransistor was proposed to detect the visible light and the X-ray. • The detector fabricated using the sputtering method shows linear and repeatable response to the visible light, and has a fast response with a decay time of about 7.75 μs. • The high sensitivity and well repeatable response of the n -ZnO/p-Si/ n -Si detector to the X-ray indicates its potential in the nuclear-radiation detection applications. We fabricated an n -ZnO/p-Si/ n -Si heterojunction bipolar phototransistor by the low cost RF magnetron sputtering method and characterized its photoresponse to both the 780 nm visible light and the X-ray in this paper. The device structure and the film transmittance were characterized by the SEM, XRD and transmittance spectra. The phototransistor shows high sensitivity, linearity and repeatable response to both the 780 nm visible light and the X-ray illumination indicating the promising potential of the n -ZnO/p-Si/ n -Si detector. The sensitivity of the phototransistor to the X-ray is about 280 μC·Gy−1·cm−2 under the bias voltage of 10 V with the X-ray tube voltage of 30 kV. The phototransistor also shows a fast response with a decay time of about 7.75 μs, which corresponds to the lifetime of the sputtered p-Si film. The temperature dependent dark current of the device indicated the activation energy of 0.32 eV, which is probably caused by the deep-level defect in the high resistivity silicon substrate. The photo current of the phototransistor to the 780 nm visible light linearly increases with increasing the bias voltage in the saturation region indicating an Early voltage of about 5.6 V, from which the effective impurity density of the p-Si film was deduced to be ∼1 × 1016 cm−3 revealing the low activation rate of the sputtering method, and the unactivated dopant could act as recombination centers and responsible for the short carrier lifetime of the p-Si film and the fast response of the phototransistor. This paper demonstrated that the n -ZnO/p-Si/ n -Si heterojunction bipolar transistor has potential for the development of an X-ray detector. [ABSTRACT FROM AUTHOR]

• We are achieving High Performance COVID-19 Detection compared to existing methods. • This method is less time consuming that RTPCR. The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score. [ABSTRACT FROM AUTHOR]

The physical, structural, optical, shielding, thermal and luminescence properties of WO 3 -Gd 2 O 3 –B 2 O 3 glasses were studied in this work. The effect of WO 3 replacement by Gd 2 O 3 on radiation shielding and radiation detection potential was evaluated. The glass density increases with increasing Gd 2 O 3 concentration and the maximum value is 5.97 g/cm3. The FTIR and XRD result confirm that the trigonal BO 3 , tetrahedral BO 4 and tungsten complex are the main chemical groups and coorperate into the amorphous structure of glass. The optical absorption spectra exhibit the absorbance of glasses in ultraviolet and visible light region. The shielding parameters of glasses, such as mass attenuation coefficient, effective atomic number, electron density and half value layer, were investigated theoretically and experimentally. They perform a small degradation of shielding property by increasing Gd 2 O 3 content. There is no obvious dependence of Gd 2 O 3 concentration on glass thermal conductivity and expansion. The strong photoluminescence of [WO 6 ]6- group with 524 nm occurred under its charge transfer excitation and under the energy transfer from excited Gd3+ ion. In the radioluminescence, WGB glasses emitted the visible light with 562 nm based on the mixed emission from [WO 6 ]6- group and oxygen vacancy. The glass possesses the X-ray detection efficiency around 18.65% of BGO crystal. The WO 3 –Gd 2 O 3 –B 2 O 3 glass with 17.5 and 27.5 mol% of Gd 2 O 3 is a promising γ-ray shielding material at room temperature and high temperature circumstance, respectively and one with 25 mol% of Gd 2 O 3 owns the attractive potential for the X-ray radiation detection applications. • The self-luminescence of WO 3 –Gd 2 O 3 –B 2 O 3 glass. • X-ray detection ability. • High radiation shielding quality compared to shielding concrete and Schott's glass. • High-density glass. [ABSTRACT FROM AUTHOR]

• Combined feature set for Diaphyseal Tibial fracture detection using X-Ray images is defined. • Knowledge gained by Orthopaedic doctors are deployed on image processing algorithms. • The work is useful to give second opinion to practicing Orthopaedic doctors. • The proposed method is compared with state-of-the-art methods in the related study. In Orthopaedics, fracture detection is considered one of the challenging tasks with x-ray images. The proposed methodology uses a novel feature-set to classify diaphyseal Tibial fractures using an Artificial Neural Network. The task of classification is carried out in two levels. The first level involves the classification of images into normal and fractured. The second level comprises of classification into three types of fractures, namely, simple, wedge and complex type. Around 12,000 X-Ray images are used as a dataset, collected from local hospitals and publicly available musculoskeletal radiographs. The local features such as Hough lines, texture values, number of intersection points, number of fragments and local binary patterns are deployed in the work. Performance-based feature reduction is carried out. The experimentation performed with individuals, a combination of two, three, four and five features, has revealed an average classification accuracy of 98.59%. Along with BPNN, other classifiers, namely, k-NN and DT are used. Results show that the method outperforms the state-of-the-art works and are found encouraging. The work is useful for Orthopaedic practitioners and extendable to other types of bones. [ABSTRACT FROM AUTHOR]

Abstract: Post-growth annealing is a potentially promising method of improving the properties of CZT for fabricating room-temperature X-ray and gamma-ray detectors. In this paper, we summarize some of our recent research on annealing detector-grade CZT crystals. Our results show that annealing in a Cd vapor effectively removes Te inclusions from CZT. The migration of Te inclusions was also observed for annealing in a temperature-gradient field. We recorded a loss of resistivity of the detector-grade CZT after annealing in a Cd vapor. The underlying mechanism of this loss was discussed, and solutions including two-step annealing (Cd annealing followed by Te annealing) and one-step annealing with Cd and Zn pressure control were proposed to maintain high resistivity. [Copyright &y& Elsevier]

Abstract: Interventional Radiology (IR) is a subspecialty of radiology comprehensive of all minimally invasive diagnostic and therapeutic procedures performed using radiological devices to obtain image guidance. The interventional procedures are potentially harmful for interventional radiologists and medical staff due to the X-ray diffusion by the patient's body. The characteristic energy range of the diffused photons spans few tens of keV. In this work we will present a proposal for a new X-ray sensing element in the energy range of interest for IR procedures. The sensing element will then be assembled in a dosimeter prototype, capable of real-time measurement, packaged in a small form-factor, with wireless communication and no external power supply to be used for individual operators dosimetry for IR procedures. For the sensor, which is the heart of the system, we considered three different Active Pixel Sensors (APS). They have shown a good capability as single X-ray photon detectors, up to several tens keV photon energy. Two dosimetric quantities have been considered, the number of detected photons and the measured energy deposition. Both observables have a linear dependence with the dose, as measured by commercial dosimeters. The uncertainties in the measurement are dominated by statistic and can be pushed at for all the sensors under test. [Copyright &y& Elsevier]

The capability of polycarbonate cladded ZnO nanowires as scintillating optical fibers, in the presence of the 10 keV X-ray is simulated using the Geant4 Monte Carlo simulation code. The simulated results show that using millions of scintillating fibers in a 1 mm 2 area, which are packed together in a polycarbonate membrane, have additional light guiding role that reduces the scattering of the X-ray generated optical photons inside the detector. This new low-cost technique can provide a higher spatial resolution X-ray imager with sharper edges at the object boundaries in comparison to a single crystal scintillating macro detectors. [ABSTRACT FROM AUTHOR]

Abstract: We demonstrate the stereo-PIXE method by measurement of surface topography of the relief details on 1€ coin. Two X-ray elemental maps were simultaneously recorded by two X-ray detectors positioned at the left and the right side of the proton microbeam. The asymmetry of the yields in the pixels of the two X-ray maps occurs due to different photon attenuation on the exit travel path of the characteristic X-rays from the point of emission through the sample into the X-ray detectors. In order to calibrate the inclination angle with respect to the X-ray asymmetry, a flat inclined surface model was at first applied for the sample in which the matrix composition and the depth elemental concentration profile is known. After that, the yield asymmetry in each image pixel was transferred into corresponding local inclination angle using calculated dependence of the asymmetry on the surface inclination. Finally, the quantitative topography profile was revealed by integrating the local inclination angle over the lateral displacement of the probing beam. [Copyright &y& Elsevier]

Abstract: The left and right-turning scheme of weldments was adopted to detect small porosities in laser weldments with complex structures. Detection images were obtained in this study. An algorithm that determines the optimal structure elements for morphology simulation was provided to achieve fidelity segmentation of small porosities. Mathematical models for the defect depth and deviation of bulk porosities were established based on the geometric features of laser weldments. Thus, the features of spatial distribution of small porosities were determined. To improve detection efficiency, the automatic extraction of spatial position data for bulk porosities was divided into two parts: automatic extraction of projection distance, which is important for the determination of weld porosity depth and deviation, and automatic extraction of porosity size. The porosity distribution in the longitudinal direction was obtained by automatic searching, and the features of the spatial distribution of weld porosities were procured. A 3D model of laser weldments was established to visualize the spatial distribution of porosities. Extracted data were inputted to the model, and the spatial positions of bulk porosities were visualized. [Copyright &y& Elsevier]

Abstract: This paper presents the results of the characterisation of a back-illuminated pixel sensor manufactured in Silicon-on-Insulator technology on a high-resistivity substrate with soft X-rays. The sensor is thinned and a low energy phosphorus implantation is performed on the back-plane. The response to X-rays from 2.12 to 8.6keV is evaluated with fluorescence radiation at the LBNL Advanced Light Source. [Copyright &y& Elsevier]

Abstract: Kaonic hydrogen atoms provide a unique laboratory to probe the kaon–nucleon strong interaction at the energy threshold, allowing an investigation of the interplay between spontaneous and explicit chiral symmetry breaking in low-energy QCD. The SIDDHARTA Collaboration has measured the K-series X rays of kaonic hydrogen atoms at the DAΦNE electron–positron collider of Laboratori Nazionali di Frascati, and has determined the most precise values of the strong-interaction induced shift and width of the 1s atomic energy level. This result provides vital constraints on the theoretical description of the low-energy interaction. [Copyright &y& Elsevier]

Abstract: We investigated the X-ray detection capability of a fabricated Pt/ZnO diode using a high-resistivity ZnO single crystal grown by the hydrothermal method. The X-ray sensor consists of a Pt electrode on the Zn-face, an Au/Ti electrode on the O-face and a (0001) ZnO substrate with high resistivity. The fabricated X-ray sensor showed ohmic-like characteristics in the measurement of current-applied voltage characteristics. We attributed these ohmic characteristics to degradation of the surface quality of the ZnO substrate caused by handling damage that occurred when carrying out a series of electrical and optical ZnO characterizations. The fabricated X-ray sensor at a bias of 20V responded to X-rays in current-mode measurement. The sensor current increased linearly with X-ray tube current at 60keV, and the results showed that sensitivity was approximately 1.5μC/Gy. We demonstrated that a ZnO single crystal has potential for the development of an X-ray detector. [Copyright &y& Elsevier]

Abstract: A new class of X-ray sensor – in which there is a blend of poly(triarylamine) (PTAA) and 6,13-bis(triisopropylsilylethynyl) (TIPS)-pentacene in the active layer of a diode structure – has been developed. The crystalline pentacene provides a fast route for charge carriers and leads to enhanced performance of the sensor. The first time-of-flight charge-carrier mobility measurement of this blend is reported. The mobility of PTAA and TIPS-pentacene in a 1:25molar ratio was found to be 2.2×10−5 cm2 V−1 s−1 (averaged for field strengths between 3×104 and 4×105 Vcm−1), which is about 17 times higher than that obtained in PTAA over the same range of field strengths. This higher mobility is correlated with a fourfold increase in the X-ray detection sensitivity in the PTAA:TIPS-pentacene devices. [Copyright &y& Elsevier]

Abstract: The system at threshold is a sensitive testing ground for low energy QCD, especially for the explicit chiral symmetry breaking. Therefore, we have measured the K-series X-rays of kaonic hydrogen atoms at the electron–positron collider of Laboratori Nazionali di Frascati, and have determined the most precise values of the strong-interaction energy-level shift and width of the 1s atomic state. As X-ray detectors, we used large-area silicon drift detectors having excellent energy and timing resolution, which were developed especially for the SIDDHARTA experiment. The shift and width were determined to be and , respectively. The new values will provide vital constraints on the theoretical description of the low-energy interaction. [Copyright &y& Elsevier]

Abstract: The Pauli exclusion principle (PEP), as a consequence or the spin-statistics connection, is one of the basic principles of the modern physics. Being at the very basis of our understanding of matter, it spurs a lively debate on its possible limits, deeply rooted as it is in the very foundations of Quantum Field Theory. The VIP (VIolation of the Pauli exclusion principle) experiment established the world''s best limit on the probability that PEP is violated by electrons, using the method of searching for PEP forbidden atomic transitions in copper. We describe the experimental method and the obtained results; we briefly present future plans to go beyond the actual limit by upgrading the experiment using vetoed new spectroscopic fast Silicon Drift Detectors. We also shortly mention the possibility of using a similar experimental technique to search for possible X-rays generated in the spontaneous collapse models of quantum mechanics. [Copyright &y& Elsevier]

Abstract: This paper presents a new power efficient asynchronous multiplexer (MUX) for application in analog front-end electronics (AFE) used in X-ray medical imaging systems. Contrary to typical synchronous MUXes that have to be controlled by a clock, this circuit features a simple structure, as the clock is not required. The circuit dissipates power only while detecting the active signals and then automatically turns back to the power down mode. Medical imaging systems usually consist of several dozen to even several hundreds of channels that operate asynchronously. The proposed MUX enables an unambiguous choice of the active channel. In case of two or more channels that become active at the same time the MUX serializes the reading out data from particular channels. This characteristic leads to 100% effectiveness in data processing and no impulses’ loss. The proposed MUX together with an experimental readout ASIC has been implemented in the CMOS 0.18μm process and occupies 1100μm2/channel area. It works properly in a wide range of the voltage supply in between 0.8 and 1.8V. Energy consumed during the detection of one active channel is below 1pJ, while the detection time is about 1ns. [ABSTRACT FROM AUTHOR]

Abstract: Requirements for X- and gamma ray detectors for future High Energy Astrophysics missions include high detection efficiency and good energy resolution as well as fine position sensitivity even in three dimensions. We report on experimental investigations on the CZT drift detector developed DTU Space. It is operated in the planar transverse field (PTF) mode, with the purpose of demonstrating that the good energy resolution of the CZT drift detector can be combined with the high efficiency of the PTF configuration. Furthermore, we demonstrated and characterized the 3D sensing capabilities of this detector configuration. The CZT drift strip detector (10mm×10mm×2.5mm) was characterized in both standard illumination geometry, Photon Parallel Field (PPF) configuration and in PTF configuration. The detection efficiency and energy resolution are compared for both configurations . The PTF configuration provided a higher efficiency in agreement with calculations. The detector energy resolution was found to be the same (3keV FWHM at 122keV) in both in PPF and PTF . The depth sensing capabilities offered by drift strip detectors was investigated by illuminating the detector using a collimated photon beam of 57Co radiation in PTF configuration. The width ( FWHM at 122keV) of the measured depth distributions was almost equal to the finite beam size. However, the data indicate that the best achievable depth resolution for the CZT drift detector is FWHM at 122keV and that it is determined by the electronic noise from the setup. [ABSTRACT FROM AUTHOR]

Abstract: The system at rest makes a sensitive testing ground for chiral SU(3) symmetry in QCD, especially for the explicit symmetry breaking induced by the relatively large mass of the strange quark. At the DAΦNE electron-positron collider of Laboratori Nazionali di Frascati we study kaonic atoms, taking advantage of the low-energy kaons from Φ-mesons decaying nearly at rest. The low-energy antikaon-nucleon interaction in kaonic hydrogen and kaonic deuterium can be investigated under favorable conditions. The DEAR (DAΦNE Exotic Atom Research) experiment at LNF delivered the most precise data on kaonic hydrogen up to now. DEAR and its follow-up experiment SIDDHARTA (Silicon Drift Detector for Hadronic Atom Research by Timing Application) are using X-ray spectroscopy of kaonic atoms to measure the strong interaction induced shift and width of the ground state. SIDDHARTA is the first experiment on kaonic deuterium ever, and kaonic hydrogen is remeasured with improved precision. From the shift and width in K-p and K-d the isospin-dependent antikaon-nucleon scattering lengths can be determined, quantities essential as constraints for low-energy QCD. [Copyright &y& Elsevier]

Abstract: A Monte Carlo model has been developed to predict the X-ray spectroscopic performance of silicon carbide detectors over the energy range 1–60keV. Consideration of the charge creation and diffusion in the different active layers along with provision for fluorescence and Compton scattering produce spectra that are in good agreement with measurements. Direct comparison of modelled spectra with X-ray data from a novel detector, the Semi-Transparent SiC Schottky Diode (STSSD) and comparison of SiC data from the literature also shows good agreement between the reported spectra and the model. Predicted quantum efficiencies are also presented. [Copyright &y& Elsevier]

[Display omitted] • Superior nanoadsorbent for highly effective adsorption of Pb(II) ions. • Impressive adsorption capacity and selectivity of the adsorbent toward Pb(II). • Extremely small adsorbent dosage and very short adsorption time. • Very low detection limit of 2.3 pg mL−1 using a benchtop TXRF instrument. • Simple and accurate analysis of complex matrix samples. In this paper, the graphene oxide (GO) decorated with fullerenol nanoparticles C 60 (OH) 22 has been designed for the highly selective separation and ultrasensitive determination of lead ions. The grafting fullerenol nanoparticles to the surface of GO solves the problem of their high solubility in aqueous solutions and simultaneously uses their high hydrophilicity and deprotonation ability. The research has revealed unique adsorption properties of GO-C 60 (OH) 22 toward Pb(II) ions at pH 5.5, i.e., minimal adsorbent dose (5 mg L−1), impressive resistance to ionic strength (up to 1 mol L−1), and enormous adsorption capacity (1307 mg g−1), much higher than those of any of the currently reported sorbents. The adsorption isotherms, kinetics, and effect of ionic strength indicate that an inner-sphere model based on surface complexation is the main mechanism of Pb(II) adsorption on GO-C 60 (OH) 22. The high-resolution O1s and Pb4f X-ray photoelectron spectra confirm the strong chelation of Pb(II) ions and suggest the various coordination of Pb(II) ions to the oxygen functional groups. The exceptional properties of GO-C 60 (OH) 22 , including the possibility of application in micro-quantities, were the basis for the development of the method for ultra-sensitive detection of Pb(II) ions using such micro-analytical technique as total-reflection X-ray fluorescence spectrometry (TXRF). The method allows obtaining an extremely low detection limit of 2.3 pg mL−1 using a low-power TXRF instrument. Due to the impressive selectivity of the method, the ultra-trace Pb(II) ions can be highly accurately determined in complex matrix samples, including high salinity waters challenging to analyze using other analytical techniques. [ABSTRACT FROM AUTHOR]

Using a powder scintillator for a detection of gamma and X-rays allows to create detectors of various shapes, moreover the handling and fabrication of such scintillators is much easier than in the case of large or thin monocrystals. We report on the application of YAlO 3 :Ce powder to Mössbauer spectroscopy. The detection efficiency and the energy resolution of the scintillators made of powders of two different granularities (grain size ≤ 30 μ m and 50– 70 μ m) were investigated. The powder scintillators exhibited a comparable energy resolution and detection efficiency as YAlO 3 :Ce monocrystal with thickness of 400 μ m. Burying the YAlO 3 :Ce grains into the epoxy resin increased the energy resolution of powder scintillators. The studied scintillators were found to be suitable for experiments in Mössbauer spectroscopy. The performance of YAP is also compared to the NaI:Tl scintillator. [Display omitted] • Yap:Ce powder exhibits comparable detection efficiency as YAP:Ce monocrystal. • Covering YAP:Ce powder with epoxy resin increases its energy resolution and mechanical hardness. • YAP:Ce powder is efficient for a photon detection in Mössbauer spectroscopy. • Scintillation crystals of various shapes could be made from YAP:Ce powder. [ABSTRACT FROM AUTHOR]

A promising composite scintillator for used in ultrafast X-ray detection was successfully developed by compounding Cl-doped CuI phosphor with polystyrene (PS) (CuI:Cl-PS), and its crystal structure and luminescence characteristics were investigated in detail. The CuI:Cl-PS composite scintillator only exhibited an intense near-band-edge (NBE) luminescence with a decay time of sub-nanosecond, and the slow luminescence of CuI was entirely suppressed under the excitation of ultraviolet light or X-ray. Furthermore, the intensity of the ultrafast NEB emission for the composite scintillator was not basically deteriorated after storage in air at room temperature for six months. It is expected that the CuI:Cl-PS composite scintillator demonstrates a great potential application in ultrafast hard X-ray detection or imaging. • The CuI:Cl-polystyrene (CuI:Cl-PS) composite scintillator was successfully synthesized, and firstly proposed to use in ultrafast X-ray detection. • The CuI:Cl-PS scintillator has only an intense near-band-edge (NBE) emission under X-ray excitation. • The decay time of NBE emission for CuI:Cl-PS scintillator is up to sub-nanosecond, which is greatly significant for ultrafast X-ray detection. • The intensity of NBE for CuI:Cl-PS scintillator is not basically deteriorated after storage for 6 months in air. [ABSTRACT FROM AUTHOR]

The internal defects of the Aluminum Conductor Composite Core (ACCC) can be effectively visualized by X-ray imaging. However, the online detection of internal defects in the carbon core is challenging due to the huge data quantity and inconspicuous defects. In this paper, we propose an automatic defect detection framework for ACCC X-ray scans based on deep convolution neutral networks. The proposed method is based on an image classification network, with Inception-Resnet as backbone. Image normalization is conducted as the pre-processing step to reduce the training cost. We also propose a new data augmentation method according to the morphological character of the defect samples, to improve the extensiveness of the method. Experiment results have proven that the proposed method can effectively recognize the small and inconspicuous defects, with 3.5% improvement on mean Average Precision, compared to the object detection network (RetinaNet). Experiment also verifies the positive effect for data normalization and augmentation. [ABSTRACT FROM AUTHOR]

• A novel multi-kernel-size spatial-channel attention model for COVID-19 detection • The proposed method enables early diagnosis from chest X-ray images. • Evaluation with real-world data demonstrates improved accuracy at 98.2 Novel coronavirus 2019 (COVID-19) has spread rapidly around the world and is threatening the health and lives of people worldwide. Early detection of COVID-19 positive patients and timely isolation of the patients are essential to prevent its spread. Chest X-ray images of COVID-19 patients often show the characteristics of multifocality, bilateral hairy glass turbidity, patchy network turbidity, etc. It is crucial to design a method to automatically identify COVID-19 from chest X-ray images to help diagnosis and prognosis. Existing studies for the classification of COVID-19 rarely consider the role of attention mechanisms on the classification of chest X-ray images and fail to capture the cross-channel and cross-spatial interrelationships in multiple scopes. This paper proposes a multi-kernel-size spatial-channel attention method to detect COVID-19 from chest X-ray images. Our proposed method consists of three stages. The first stage is feature extraction. The second stage contains two parallel multi-kernel-size attention modules: multi-kernel-size spatial attention and multi-kernel-size channel attention. The two modules capture the cross-channel and cross-spatial interrelationships in multiple scopes using multiple 1D and 2D convolutional kernels of different sizes to obtain channel and spatial attention feature maps. The third stage is the classification module. We integrate the chest X-ray images from three public datasets: COVID-19 Chest X-ray Dataset Initiative, ActualMed COVID-19 Chest X-ray Dataset Initiative, and COVID-19 radiography database for evaluation. Experimental results demonstrate that the proposed method improves the performance of COVID-19 detection and achieves an accuracy of 98.2%. [ABSTRACT FROM AUTHOR]

Background: The accuracy of the chest x-ray (CXR) in the identification of lung cancer amongst symptomatic individuals is uncertain.Purpose: To determine the diagnostic accuracy of the CXR for the detection of non-small cell carcinomas (NSCLC) and all primary intrathoracic malignancies.Methods: A prospective cohort study of consecutive CXR reports obtained within a primary care open access initiative. Eligibility criteria were symptoms specified by National Institute for Clinical Excellence as indicative of possible lung cancer and age over 50-yrs. A positive test was a CXR which led directly or indirectly to investigation with CT. The reference standards were malignancies observed within a one- or two-year post-test period.Results: 8,948 CXR outcomes were evaluated. 496 positive studies led to a diagnosis of 101 patients with primary intrathoracic malignancy including 80 with NSCLC. Within two-years, a cumulative total of 168 patients with primary intrathoracic malignancies including 133 NSCLC were observed. The sensitivity and specificity for NSCLC were 76% (95 %CI 68-84) and 95% (95 %CI 95-96) within 1-year and 60% (95 %CI 52-69) and 95% (95 %CI 95-96) within 2-years. The 2-yr positive and negative likelihood ratios were 12.8 and 0.4. The results did not differ for NSCLC compared to all primary malignancies. Within this symptomatic population a negative test reduced the 2-year risk of lung cancer to 0.8%.Conclusions: A positive test strongly increases the probability of malignancy whereas a negative test does not conclusively exclude the disease. The findings allow the risk of malignancy following a negative test to be estimated. [ABSTRACT FROM AUTHOR]

• A deep learning method is proposed using different optimizers and transfer learning to classify Pneumonia patients. • Preparing a dataset of around 5300 X-ray images for pneumonia detection. • The proposed deep transfer learning method is trained on a benchmark open dataset of chest x-ray images. • Presenting the optimization results, precision, recall, accuracy, and F1-score for proposed method. • In proposed method achieve better accuracy in the detection rate than other techniques. Pneumonia is a disease that leads to the death of individuals within a short period since the flow of fluid in the lungs. Hence, initial diagnosis and drugs are very important to avoid the progress of the disease. This paper proposes a novel deep learning approach for automatic detection of pneumonia using deep transfer learning to simplify the detection process with improved accuracy. This work was aimed to preprocess the input chest X-ray images to identify the presence of pneumonia using U-Net architecture based segmentation and classifies the pneumonia as normal and abnormal (Bacteria, viral) using pre-trained on ImageNet dataset models such as ResNet50, InceptionV3, InceptionResNetV2. Besides, to extract the efficient features and improve accuracy of pre-trained models two optimizers, namely, Adam and Stochastic Gradient Descent (SGD) used and its performances are analyzed with batch sizes of 16 and 32. Based on the values obtained, the performances of undertaken pre-trained models are analyzed and compared with other Convolutional Neural Network (CNN) models such as DenseNet-169+SVM, VGG16, RetinaNet + Mask RCNN, VGG16 and Xception, Fully connected RCNN, etc using various measures. From the results observed that the proposed ResNet50 model work achieved 93.06% accuracy, 88.97 % precision rate, 96.78% Recall rate and 92.71% F1-score rate, which than is higher than the other models aforementioned. [ABSTRACT FROM AUTHOR]

X-ray detection is an important technology for medical diagnosis as well as industrial and security inspections. While today's commercial X-ray detectors are bulky, photodetectors based on organic semiconductors have attracted increasing attention owing to their low temperature processing capabilities, flexibility and low cost. Nonetheless, the low X-ray attenuation coefficient of organic semiconductors still hinders their practical application. Herein, a new organic-inorganic hybrid strategy is proposed to improve the X-ray sensitivity of organic photodetectors (OPDs). A solution-processed X-ray sensitive hybrid OPD is fabricated by embedding CsPbBr 3 quantum dots (QDs) into a P3HT:PC 61 BM bulk heterojunction photodiode. The QDs, acting as embedded scintillators in the organic active layer, maintain a high radioluminescence. The proposed hybrid structure enables indirect X-ray detection in a comprehensive manner. These hybrid photodetectors exhibit suppressed dark current densities in the range of tens of picoamperes per square centimeters for different weight ratios of blended QDs. The best OPD achieves a sensitivity of 229.6 e nGy−1 mm−2 (3.67 μC Gy−1 cm−2) and a dark current of 23.3 pA cm−2 at a low operating voltage (−3 V) for 20–80 kV "soft" X-rays, thus representing great potential for the development of next generation low cost, portable, and highly sensitive X-ray detectors. [Display omitted] • An all-in-one X-ray photodetector is realized by embedding CsPbBr 3 QDs into the P3HT:PC 61 BM photodiode. • The hybrid photodetectors exhibit suppressed dark current densities and high photosensitivity. • Great enhancement in X-ray sensitivity is achieved with CsPbBr 3 QDs. [ABSTRACT FROM AUTHOR]

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients. • COVID-19 detection deep learning architectures typically need many labels. • Also the datasets at the beginning of a virus outbreak are highly imbalanced. • Semi supervised data can be used to increase model's accuracy with few labels. • The effect of data imbalance on semi-supervised learning is under-explored. • A method to correct data imbalance for semi supervised learning is proposed. [ABSTRACT FROM AUTHOR]

The new type of coronavirus, COVID 19, appeared in China at the end of 2019. It has become a pandemic that is spreading all over the world in a very short time. The detection of this disease, which has serious health and socio-economic damages, is of vital importance. COVID-19 detection is performed by applying PCR and serological tests. Additionally, COVID detection is possible using X-ray and computed tomography images. Disease detection has an important position in scientific researches that includes artificial intelligence methods. The combined models, which consist of different phases, are frequently used for classification problems. In this paper, a new combined approach is proposed to detect COVID-19 cases using deep features obtained from X-ray images. Two main variances of the approach can be presented as single layer-based (SLB) and feature fusion-based (FFB). SLB model consists of pre-processing, deep feature extraction, post-processing, and classification phases. On the other side, the FFB model consists of pre-processing, deep feature extraction, feature fusion, post-processing, and classification phases. Four different SLB and six different FFB models were developed according to the number and binary combination of layers used in the feature extraction phase. Each model is employed for binary and multi-class classification experiments. According to experimental results, the accuracy performance for COVID-19 and no-findings classification of the proposed FFB3 model is 99.52%, which is better than the best performance accuracy (of 98.08%) in the literature. Concurrently, for multi-class classification, the proposed FFB3 model has an accuracy performance of 87.64% outperforming the best existing work (which reported an 87.02% classification performance). Various metrics, including sensitivity, specificity, precision, and F1-score metrics are used for performance analysis. For all performance metrics, the FFB3 model recorded a higher success rate than existing work in the literature. To the best of our knowledge, these accuracy rates are the best in the literature for the dataset and data split type (five-fold cross-validation). Composite models (SLBs and FFBs), which are generated in this paper, are successful ways to detect COVID-19. Experimental results show that feature extraction, pre-processing, post-processing, and hyperparameter tuning are the steps are necessary to obtain a higher success. For prospective works, different types of pre-trained models and other hyperparameter tuning methods can be implemented. • Using pre-trained CNN models is an effective way to extract deep features • Feature fusion-based model is more successful to detect COVID-19 in X-Ray images • Data pre-processing and post-processing techniques increase the performance of the models • Hyperparameter tuned SVM is more successful than without one • To the best of our knowledge, obtained accuracy rates are the best in the literature [ABSTRACT FROM AUTHOR]

The development of nanoscale X-ray sensors is of crucial importance to achieve higher spatial resolution in many X-ray-based techniques playing a key role in materials science, healthcare, and security. Here, we demonstrate X-ray detection using individual CdS, SnO 2 , and ZnO nanowires (NWs). The NWs were produced via vapor–liquid–solid technique and characterized using X-ray diffraction, scanning, and transmission electron microscopy. Electrical measurements were performed under ambient conditions while exposing two-terminal NW-based devices to X-rays generated by a conventional tungsten anode X-ray tube. Fast and stable nanoampere-range X-ray beam induced current (XBIC) in response to X-ray illumination was observed. The high XBIC measured in the NW devices could be attributed to the efficient transport and collection of generated charge carriers due to the single-crystalline nature of NWs and the short NW length. Such fast-response and high-sensitivity nanoscale X-ray detectors can find applications in sub-micron resolution imaging and nanofocused beam shape measurements. [Display omitted] • Vapor–liquid–solid synthesis of single-crystalline CdS, SnO 2 and ZnO nanowires. • Individual nanowire two-terminal devices were exposed to X-rays. • Fast and stable nanoampere-range X-ray beam induced current was observed. [ABSTRACT FROM AUTHOR]

In view of the demand for high-energy and high-bandwidth signal detection in X-ray communication (XCOM) during spacecraft reentry blackout, a detection module using fast-decay LYSO scintillator coupled with Silicon photomultiplier (SiPM) was proposed in this study. The theoretical model based on the Monte Carlo simulation and Personick' theory was established and the detector prototype was built. Moreover, the signal to noise ratio (SNR) and bit error rate (BER) performance were investigated. Results present that the SNR of 19.8 dB, 29.9 dB and 35.5 dB could be reached for X-ray with the energy of 17 keV, 59.5 keV and 122 keV respectively. This energy response characteristic is much suitable for the X-ray signal detection during spacecraft reentry blackout. Additionally, the deviation of SNR corresponding to the BER between the theoretical and experimental results does not exceed 0.3 dB when the data rate is 100 kbps, which proves the validity of the theoretical model. Furthermore, the numerical analysis shows that the scheme demonstrates high SNR and low BER performance in high bandwidth and low signal receiving power, which explains the superiorities in X-ray signal detection of the design. This work provides the theoretical basis and design reference for the LYSO–SiPM X-ray signal detection module. • A detection module LYSO–SiPM was proposed for blackout X-ray communication. • The theoretical model was established and the detector prototype was built. • The superiorities of high SNR and low BER was demonstrated in high bandwidth case. [ABSTRACT FROM AUTHOR]

The Schottky barrier diodes were fabricated from a bulk Sn-doped (001) n-type Ga 2 O 3 substrate with a Si-doped epitaxial layer grown by hydride vapor phase epitaxy (HVPE), which demonstrate a good response to X-rays. Circular metal contacts with diameters ranging from 50 to 1500 μ m and square metal contacts ranging from 100 × 100 μ m 2 to 1600 × 1600 μ m 2 were deposited on the wafer. The devices were characterized for their electrical performance including forward current–voltage (FIV), reverse current–voltage (RIV), and capacitance–voltage (CV) measurements. The best device showed a breakdown voltage of −804 V and the devices tested had an average ideality of 1.12. The devices exhibited a clear response to X-rays even at zero bias with an experimentally observed response time ∼ 1.03 s and a linear response of detector signal to the X-ray dose rate. The experimentally observed device response time improved to ∼ 0.25 s when bias voltage is applied. The device also survived a long-term stability test of over 2 h under a constant X-ray irradiation. The sensitivity and the lower limit of detection for X-ray by Ga 2 O 3 epitaxial Schottky detectors were discussed and determined as 43.5 μ C/mGy cm−2 at −200 V and 8.31 nGy Air /s, respectively. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4). • COVID-19 prediction using chest X-ray images. • The manual method i.e. PCR is time-consuming and high costs. • A novel automated image-based technique DenseNet and BiTModels like R101x1, which segment and identify Covid-19 patient x-ray. • Covid-19 normal and affected patient data is used. • Comparing with other models, training and testing accuracy, the R101x1 has an average accuracy, i.e., 91% in the Covid-19 class Prediction. [ABSTRACT FROM AUTHOR]

Abstract: Al0.8Ga0.2As p+–p−–n+ diodes with low reverse leakage currents (<10nA) were evaluated as room temperature (23°C), soft X-ray photon-counting detectors. X-ray spectra from a 55Fe radioactive source show these diodes can be used for spectroscopy with promising energy resolution of 1.47keV FWHM at 5.9keV. At elevated reverse bias levels (>20V), we observed avalanche multiplication which, while improving the signal-to-noise ratio, degraded the spectroscopic performance. [Copyright &y& Elsevier]

Abstract: The paper describes a comprehensive readout electronics system for X-ray imaging using gas-filled detectors to be used at the European Synchrotron Radiation Facility (ESRF). The front-end electronics (named BLADE3) relies on a multi-strips binary parallel readout architecture. BLADE3 features 256 channels in a small space (120×300mm2 printed circuit board) at low power (12mW per channel for the analog processing). It is based on a 16-channel preamplifier-discriminator ASIC (named ASG217) of the trans-impedance type. The ASG217 measured gain (220kΩ), peaking time (15ns), and RMS noise (1.5mV+0.1mV/pF) well match the simulated results. Front-end boards communicate with the back-end electronics using 2Gb/s optical links. The back-end electronics has been implemented in the CompactPCI format and can be operated through a PCI bridge by any host computer. The experience gained with a prototype detector indicates that the front-end electronics meets our requirements. The back-end electronics, already operational for one-dimensional readout, has to be significantly improved for supporting two-dimensional imaging at high photon flux. [Copyright &y& Elsevier]

Abstract: We describe a novel SiC Schottky diode architecture. The semi-transparent SiC Schottky diode has an “ultra-thin” (18nm Ni/Ti) Schottky contact, a gold annular overlayer and a gold corner-contact pad. We show that the new architecture exhibits the same essential characteristics as a more conventional “thick-contact” Schottky diode (⩾100nm). Such diodes will have a higher efficiency for low-energy (<5keV) X-rays than that of conventional structures combined with minimal self-fluorescence from the electrode materials. We present X-ray spectra from 55Fe, 109Cd and 241Am radioactive sources that show these diodes can be used for spectroscopy with promising energy resolution (1.47keV FWHM at 22keV) at room temperature (23°C). The reduction in contact thickness, however, does reduce the barrier height of the new diodes in comparison to those fabricated using the conventional process, and requires a trade-off between the low-energy detection threshold and the noise in the detector. [Copyright &y& Elsevier]

Abstract: In order to study the segmentation and quantitative problem of small defects in precision weldments with complex structure, an automated image processing system was setup based on the Visual Basic 6.0 development environment. The weld zones were extracted by adopting the method of twice automatic threshold, and the small defects were segmented successfully by using sharpen, smoothing processing and background subtraction in the extracted weld zones. To determine the spatial distribution features of small defects, calculation formula of defects depth and deviation were deduced individually, and the projection distance of small defects can be extracted automatically also. The image processing system can achieve the goal of small defects segmentation and automatic extraction of projection distance. The depth and deviation of small defects can be obtained through the above deduced formula, and the longitudinal distribution of small defects can be obtained from the detection image, then the spatial distribution features of small defects can be determined. [Copyright &y& Elsevier]

Abstract: This work is devoted to a more detail testing of overall imaging performance of our developed digital quantum scanner. We enhanced spatial resolution in direction of detection unit movement from 254 down to 85μm. In particular its spatial resolution, resolution in contrast and imaging ability through evaluation images, revealed with various testing objects are performed. For irradiation an X-ray tube was used. The obtained results are discussed in the scope of published results. [Copyright &y& Elsevier]

Abstract: The performance of a xenon high-pressure gas proportional scintillation counter (GPSC) instrumented with a large area avalanche photodiode (LAAPD) as the VUV-photosensor has been investigated for filling pressures from 1 up to 10bar, for 22- and 60-keV photons. The LAAPD photosensor is placed directly within the xenon envelope, as a substitute for the photomultiplier tube, avoiding the constraints of the use of a quartz scintillation window for GPSC-photosensor coupling, which absorbs a significant amount of scintillation and is a drawback for applications where large detection areas and high filling pressures are needed. The lowest energy resolutions are achieved for pressures around 5bar (4.5% and 3.0% full width at half-maximum (FWHM), for 22- and 60-keV photons, respectively). Increasing the pressure to the 8bar range, competitive energy resolutions of 5.0% and 3.6% are still achieved for 22- and 60-keV photons, respectively. This detector could be a compelling alternative in applications where compactness, large detection area, insensitivity to strong magnetic fields, room temperature operation, large signal-to-noise ratio and good energy resolution are important requirements. [Copyright &y& Elsevier]

Abstract: It is demonstrated that a careful X-ray diffraction analysis represents an effective way to determine imperfections and their density in complex oxide epitaxial thin films. A method for simulating X-ray intensities of the (00ℓ) reflexes is developed and demonstrated for the model system YBa2Cu3O7−δ . In a first step, the δ dependence of the intensities of the different (00ℓ) reflexes is simulated and compared to literature data of perfect YBa2Cu3O7−δ thin film and bulk samples with different oxygen content. In a second step, it is demonstrated that the δ dependence of the intensities of the different (00ℓ) reflexes depends strongly on the type of defects in case of imperfect YBa2Cu3O7−δ . Different types of defects (e.g., cation disorder, cation substitution, Cu deficiency) are discussed. Finally, the method is applied to low-pressure sputtered YBa2Cu3O7−δ thin film. It is shown that according to this analysis and in contrast to previous assumptions, Cu(1) deficiency seems to be responsible for the elongation of the c-axis and the reduction of the superconducting transition temperature. [Copyright &y& Elsevier]