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The healthcare system in India faces an acute shortage of medical staff, significantly impacting the nation's ability to provide sufficient healthcare services. X-ray reports are paramount to orthopaedic surgeons for making critical patient care decisions. Implementing automated systems for detecting bone fractures offers a potentially efficient solution to supplement the limited workforce. However, these systems encounter substantial hurdles, such as inconsistent image quality and the risk of incorrect diagnosis due to the limitations of current technology. The research provides a specialised tool for identifying bone fractures with high precision (97% accuracy) and an excellent overall rating (F1 score of 0.98), showing it can reliably detect fractures. The assessment of this model is augmented by visual tools like heatmaps and performance graphs, providing an in-depth analysis of its operational effectiveness. Furthermore, creating a user-friendly interface is intended to streamline the adoption of this tool in clinical settings. Notwithstanding its demonstrated precision, the model must navigate data-related challenges, precisely data complexity and the representation of different categories, to prevent skewed outcomes. Clarifying the model's decision-making process and adaptability to various clinical situations are also critical. The study emphasises the need for thorough testing to ensure the model's dependability in diagnosing fractures before being deployed effectively in the medical field. [ABSTRACT FROM AUTHOR]

[Display omitted] Amyloid- β (A β) plaques from Alzheimer's Disease (AD) can be visualized ex vivo in label-free brain samples using synchrotron X-ray phase-contrast tomography (XPCT). However, for XPCT to be useful as a screening method for amyloid pathology, it is essential to understand which factors drive the detection of A β plaques. The current study was designed to test the hypothesis that A β -related contrast in XPCT could be caused by A β fibrils and/or by metals trapped in the plaques. Fibrillar and elemental compositions of A β plaques were probed in brain samples from different types of AD patients and AD models to establish a relationship between XPCT contrast and A β plaque characteristics. XPCT, micro-Fourier-Transform Infrared spectroscopy and micro-X-Ray Fluorescence spectroscopy were conducted on human samples (one genetic and one sporadic case) and on four transgenic rodent strains (mouse: APPPS1, ArcA β , J20; rat: TgF344). A β plaques from the genetic AD patient were visible using XPCT, and had higher β -sheet content and higher metal levels than those from the sporadic AD patient, which remained undetected by XPCT. A β plaques in J20 mice and TgF344 rats appeared hyperdense on XPCT images, while they were hypodense with a hyperdense core in the case of APPPS1 and ArcA β mice. In all four transgenic strains, β -sheet content was similar, while metal levels were highly variable: J20 (zinc and iron) and TgF344 (copper) strains showed greater metal accumulation than APPPS1 and ArcA β mice. Hence, a hyperdense contrast formation of A β plaques in XPCT images was associated with biometal entrapment within plaques. The role of metals in Alzheimer's disease (AD) has been a subject of continuous interest. It was already known that amyloid- β plaques (A β), the earliest hallmark of AD, tend to trap endogenous biometals like zinc, iron and copper. Here we show that this metal accumulation is the main reason why A β plaques are detected with a new technique called X-ray phase contrast tomography (XPCT). XPCT enables to map the distribution of A β plaques in the whole excised brain without labeling. In this work we describe a unique collection of four transgenic models of AD, together with a human sporadic and a rare genetic case of AD, thus exploring the full spectrum of amyloid contrast in XPCT. [ABSTRACT FROM AUTHOR]

The growth of the CsI(Tl) scintillators deposited on glass substrates with proper wavelength matching the mainstream semiconductor circuits is crucial for X-ray imaging industry. But the coupling loss between the scintillators and silicon based photo sensors has limited the performance. So fabricating CsI(Tl) films on monocrystalline silicon is greatly important for next-generation high performance X-ray image devices. However, the lattice and thermal mismatch between CsI(Tl) and Si always deteriorates the morphology and structure, and thus degrades the property of the CsI(Tl) scintillation converters. Here, by utilizing crystalline graphene(Gr) as a buffer layer, the van der Waals epitaxial growth of CsI(Tl) film on Si(111) substrate is reported and its advanced applications are demonstrated. The introduced graphene buffer layer extremely frees the stress between CsI(Tl) and Si substrate. Therefore, the CsI(Tl) films show perfect micro-columnar and crystalline structures, and then, exhibit much higher light output and signal to noise ratio, and better modulation transfer functions and a bar pattern X-ray image compared to that on bare Si(111) substrate. This facile method not only lays the foundation for the epitaxy of the alkali halide crystals, including CsI(Tl), onto Si-based detectors and its applications in radiation detection, but also may broaden a new perspective of deposition of CsI(Tl) scintillators into flexible substrates. [ABSTRACT FROM AUTHOR]

This article explore lung cancer using a hybrid deep learning (DL) model. Thus, this article offers a hybrid CNN along with the SVM classification method with tuned hyperparameters, which is the orthogonal Convolution neural network-support vector machine OCNN-SVM model. Each lung image may be automatically categorized and analyzed by this system to determine whether cancer cells are present. CNN has fewer parameters and is easier to train than a fully linked network with an exact count of hidden units. SVM has also been used to remove irrelevant data that has an adverse effect on accuracy. The effectiveness of this method is assessed in this research, and the findings show that the proposed orthogonal Convolution neural network-support vector machine (OCNN-SVM) model has been successful in categorizing lung images. [ABSTRACT FROM AUTHOR]

[Display omitted] Tissue-equivalent pure organic semiconducting crystals (OSCs) have unique advantages in direct X-ray detectors (DXDs), especially for biological tissue fluoroscopy, but their low carrier mobility and inherent weak absorption restrict the limit-of-detection (LoD) and sensitivity of DXDs. High-mobility OSCs theoretically facilitate charge collection and performance leaps, however, they are rare and suffer from poor solubility due to high conjugation, leading to nearly impossible crystal growth from solution and subsequent device applications. Here we report an ingenious solution-processed crystal-growth and high-performance DXD using 2,6-diphenylanthracene (2,6-DPA), a high-mobility OSC we developed recently. In contrast to previous OSCs, high-mobility 2,6-DPA exhibits low dark current density and large photoconductive gain, resulting in record-breaking LoD of <85 nGy air s−1 and sensitivity of >103 μC Gy air −1 cm−2. Compared with semiconductors containing high- Z atoms (Z is atomic number), the accuracy of 2,6-DPA based DXDs is not affected by near-edge absorption effects. Moreover, the non-linear relationship between irradiation current and dose rate is confirmed as a high injection effect. High mobility and ingenious crystal growth strategy make 2,6-DPA an ideal active material for DXDs and also provide the possibility for more optoelectronic applications. [ABSTRACT FROM AUTHOR]

Pore detection by x-ray computed tomography (CT) is widely used for additively manufactured (AM) parts. The ability of a CT device to reliably detect pores of a certain shape and size is still being investigated, though. Typical methods to evaluate the error of CT-based pore detection utilize reference objects or microscope images of cross-sections. This work improves the cross-section method by directly matching the two-dimensional (2D) plane depicted in the microscope image to the corresponding plane within the three-dimensional (3D) scan. For this purpose, a specially designed test part was scanned for a first time before preparing a cross-section and a second time afterward. The test part's design allowed to identify the cross-section's plane parameters relative to the part's local coordinate system in the second scan. The plane parameters were then transferred to the first CT scan in which pores had been detected to identify voxels that lay within the plane that was to become the cross-section. An image was extracted from this plane that directly matched a microscope image of the cross-section. Comparing CT and microscope images highlighted that CT's relative lack of resolution may lead to relevant errors when analyzing pore area and pore form. • Used additively manufactured test parts to assess CT's ability to detect pores. • Deployed micrographs of cross sections as reference. • Matched actual micrographs and virtual micrographs from CT. • Investigated CT settings' influence on ability to detect pores. • Found that CT reliably detects pores with an area of about 4 × 4 voxels. [ABSTRACT FROM AUTHOR]

Micron/sub-micron hard X-ray imaging is an important development direction of X-ray detection technology. Scintillation screens with excellent X-ray stopping power and high light output are desired to obtain good imaging quality. Europium-doped lutetium oxide (Lu 2 O 3 :Eu) film with micro-columnar structure is an ideal candidate for this application. In this study, micro-columnar structured Lu 2 O 3 :Eu films were prepared on quartz glass substrates by laser chemical vapor deposition (LCVD) method. The influences of preparation conditions on structure and morphology properties of films were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Photoluminescence and scintillation properties of the films, including photoluminescence excitation (PLE), photoluminescence (PL), PL decay, X-ray excitation luminescence (XEL), and afterglow curves, were recorded. The relationships between structure, morphology, luminescence properties of films, and preparation conditions were investigated and discussed as well. The preparation conditions include laser power, oxygen flow rate, and precursor evaporation temperature. In addition, dual-layer Lu 2 O 3 :Eu film was prepared for the first time by just turning off the laser. [ABSTRACT FROM AUTHOR]

Compared with single-crystal scintillator, the glass scintillators have the benefits of low cost, big size, straightforward manufacturing method, and adjustable component. In this work, a succession of diaphanous gallogermanate glass scintillators doped with Tb3+ or Eu3+ were manufactured using melt-quenching technique. The thermal, structural, photoluminescent, and scintillating properties were studied. For photoluminescent properties, 12 mol% Tb3+-doped glass sample has high internal quantum efficiency (87.8%) and no obvious concentration quenching phenomenon can be observed. For scintillating properties, the optimal doping concentration of Tb3+ and Eu3+ are 4 mol% and 8 mol%, respectively. The integral intensity of X-ray excited luminescence of Tb3+-doped sample is 73.1% of that of Bi 4 Ge 3 O 12 scintillator, while the ratio of Eu3+-doped sample is 26.5%. The radiation damage of 4 mol% Tb3+-doped sample resulted by high power X-ray could be repaired through heat-treatment, while 8 mol% Eu3+-doped sample exhibits fine stability that the transmittance keeps constant after exposed to high power X-ray. Our research indicates Tb3+ or Eu3+ doped gallogermanate glass might be candidate for X-ray detection of slow events. [ABSTRACT FROM AUTHOR]

The CsPbX 3 perovskites have demonstrated promising applications in the field of X-ray detection. Appropriate element doping to substitute Pb in CsPbX 3 not only improves the photoresponse performance of the resulting material but also reduces the Pb content for achieving environmental protection. Herein, X-ray detectors based on Sr-doped CsPb 1-x Sr x Br 3 thick films were developed. The effects of Sr doping concentration were investigated. The incorporation of Sr increased the density of the CsPbBr 3 thick film and modulated the bandgap of the materials. CsPb 1-x Sr x Br 3 exhibited good optical responses at different bias voltages and X-ray doses. The X-ray response improved as the Sr-doping concentration increased. Therefore, the partial substitution of Sr into CsPbBr 3 is suitable for reducing the Pb content in X-ray detectors. Furthermore, the development of CsPb 1-x Sr x Br 3 thick films provides a new strategy for the detection of X-rays. [ABSTRACT FROM AUTHOR]

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan. [ABSTRACT FROM AUTHOR]

• Global Context Feature Extraction (GCFE) module is proposed, which can extract multi-scale global contextual information. • A Dual Scale Feature Aggregation (DSFA) module is proposed to fuse the features from different scales. • A learnable Gabor convolution layer is designed to enhance its ability to capture the edge and contour information of object. • A Spatial Attention (SA) mechanism is designed to further enhance the spatial structure information of object. Prohibited Object Detection (POD) in X-ray images plays an important role in protecting public safety. Automatic and accurate POD is required to relieve the working pressure of security inspectors. However, the existing methods cannot obtain a satisfactory detection accuracy, and especially, the problem of object occlusion also has not been solved well. Therefore, in this paper, according to the specific characteristics of X-ray images as well as low-level and high-level features of Convolutional Neural Network (CNN), different feature enhancement strategies have been elaborately designed for occluded POD. First, a learnable Gabor convolutional layer is designed and embedded into the low layer of the network to enhance the network's capability to capture the edge and contour information of object. A Spatial Attention (SA) mechanism is then designed to weight the output features of the Gabor convolutional layer to enhance the spatial structure information of object and suppress the background noises simultaneously. For the high-level features, Global Context Feature Extraction (GCFE) module is proposed to extract multi-scale global contextual information of object. And, a Dual Scale Feature Aggregation (DSFA) module is proposed to fuse these global features with those of another layer. To verify the effectiveness of the proposed modules, they are embedded into typical one-stage and two-stage object detection frameworks, i.e., Faster R-CNN and YOLO v5L, obtaining POD-F and POD-Y methods, respectively. The proposed methods have been extensively evaluated on three publicly available benchmark datasets, namely SIXray, OPIXray and WIXray. The experimental results show that, compared with existing methods, the proposed POD-Y method can achieve a state-of-the-art detection accuracy. And POD-F can also achieve a competitive detection performance among the two-stage detection methods. 1 1 https://github.com/Open-my-paper-code/POD. [ABSTRACT FROM AUTHOR]

Pr3+ doped high density germanate glasses were prepared by the melt-quenching method. The physical and luminescent properties were investigated. The glass containing BaF 2 represented maximum phonon energy of 810 cm−1, and the glass density was greater than 6 g/cm3. The glass exhibited good radiation resistance under 2 h of X-ray irradiation. Intense blue, green emissions and weak red emissions were observed when the glass was excited by X-ray, and the blue fluorescence lifetimes were in the range of 9.15–12.59 μs The results show that Pr3+ doped germanate glass could be a potential scintillating material in the field of X-ray detection for fast events. [ABSTRACT FROM AUTHOR]

COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification. [ABSTRACT FROM AUTHOR]

[Display omitted] Ultralong phosphorescent materials have numerous applications across biological imaging, light-emitting devices, X-ray detection and anti-counterfeiting. Triplet-state molecular phosphorescence typically accompanies the singlet-state fluorescence during photoluminescence, and it is still difficult to achieve direct triplet photoemission as ultralong room temperature phosphorescence (RTP). Here, we have designed Zn-IMDC (IMDC, 4,5-imidazoledicarboxylic acid) and Cd-IMDC, two-dimensional (2D) hydrogen-bond organized metal–organic crystalline microsheets that exhibit rarely direct ultralong RTP upon UV excitation, benefiting from the appropriate heavy-atom effect and multiple triplet energy levels. The excitation-dependent and thermally stimulated ultralong phosphorescence endow the metal–organic systems great opportunities for information safety application and temperature-gated afterglow emission. The well-defined 2D microsheets present color-tunable and anisotropic optical waveguides under different excitation and temperature conditions, providing an effective way to obtain intelligent RTP-based photonic systems at the micro- and nano-scales. [ABSTRACT FROM AUTHOR]

In recent years, copper-based halide scintillators have attracted tremendous attention due to their excellent luminescent properties and low manufacturing costs. Herein, a high-performance and low-cost Cs 5 Cu 3 Cl 6 I 2 scintillator is prepared by simple mechanical grinding. The mixed-halide Cs 5 Cu 3 Cl 6 I 2 phosphor exhibits a blue emission centered at 475 nm with a large stokes shift, originating from the self-trapped exciton emission of Cu sites. The photoluminescence quantum yield (PLQY) of Cs 5 Cu 3 Cl 6 I 2 can be increased to 88.4% from 20.4% by the efficient suppression of the anion vacancy defects. In addition, the Cs 5 Cu 3 Cl 6 I 2 scintillator shows a high steady-state X-ray to light conversion efficiency of about 57000 photons/MeV, good X-ray linear response, and low detection limit of 71.9 nGy air /s. The X-ray imaging results based on the Cs 5 Cu 3 Cl 6 I 2 scintillator screen display a high spatial resolution of 9.0 line-pairs per millimeter (lp/mm). Furthermore, Cs 5 Cu 3 Cl 6 I 2 exhibits good radiation stability under a total dose of 113.58 Gy air. Overall, the Cs 5 Cu 3 Cl 6 I 2 scintillator prepared by mechanical grinding method has the advantages of low cost, high efficiency, and good radiation resistance, suggesting its huge potential in X-ray detection and imaging. [ABSTRACT FROM AUTHOR]

Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%. [ABSTRACT FROM AUTHOR]

CsI(Tl) scintillator has been widely used in X-ray flat-panel detectors due to excellent performance characteristics including a suitable Si-photodiode emission wavelength, excellent optical output, and fast decay time. Here, a solution-based space-confined growth method was presented to achieve CsI(Tl) scintillator single-crystal flakes with controllable thickness. The grown CsI(Tl) single-crystal flake exhibits a comparable light yield (∼72,000 photons/MeV) and a lower afterglow level (0.1% @ 21 ms, 0.01% @ 118 ms) in comparison to that by the traditional melting method. This space-confined method allows for the achievement of a single crystal thickness of only 0.27 mm, making it suitable for direct application in X-ray detection and other related fields. It may open up new possibilities for the development of halide scintillator single crystals. A solution-based space-confined growth method enables the growth of high-quality CsI(Tl) single crystal flakes with controlled thickness for X-ray flat-panel detectors. [Display omitted] • A space-confined solution method is used to grown CsI(Tl) single-crystal flakes. • The thickness of the CsI(Tl) flake grown using this method can be controlled to be as thin as approximately 0.27 mm. • The CsI(Tl) flake exhibits a lower afterglow and a comparable light yield in comparison to that by the melting method. [ABSTRACT FROM AUTHOR]

• Novel method enabling tracking of obscured lung tumor in X-ray. • Validation on real-world patient data (CyberKnife 1 View patients). • Zero-shot deep learning framework leveraging deep learning infrastructure yet independent of specific data or patient cohort. Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available – fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm – Deep Match. Deep Match consists of four self-definable stages – training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10–15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy. [ABSTRACT FROM AUTHOR]

In this paper, we synthesized silver nanoclusters using bovine serum albumin (BSA) as a template (BSA@AgNCs). Then, we anchored hydroxyphenyl fluorescein (HPF) to yield HPF-BSA@AgNCs. When exposed to X-rays, hydroxyl (∙ OH) radicals generated by radiolysis of water react with HPF to produce fluorescein, which emits enhanced fluorescence at 515 nm (λ ex = 480 nm). The fluorescence intensity of BSA@AgNCs at 685 nm (λ ex = 480 nm) remains stable when exposed to X-rays. This HPF-BSA@AgNCs ratiometric fluorescence sensor can rapidly detect 0.1–20 Gy (the energy deposited per unit mass, J/kg) of X-rays. In addition, HPF-BSA@AgNCs exhibit good durability and temperature stability. Finally, HPF-BSA@AgNCs were used to measure the absorbed doses of A549 cells and evaluate the cell irradiation damage. [Display omitted] • We synthesize silver nanoclusters using bovine serum albumin (BSA) as a template, then anchored hydroxyphenyl fluorescein (HPF) to yield HPF-BSA@AgNCs. • HPF-BSA@AgNCs ratiometric fluorescence sensor can rapidly detect 0.1–20 Gy of X-rays. • HPF-BSA@AgNCs exhibit good durability and temperature stability. • HPF-BSA@AgNCs can rapidly measure the absorbed doses of A549 cells and evaluate the cell irradiation damage. [ABSTRACT FROM AUTHOR]

Perovskite X-ray detectors are actively investigated because of their exceptional properties, including their significant attenuation coefficient, low defect density, excellent transport characteristics, easy growth, and high sensitivity to X-ray photons. However, perovskite-based high-energy X-ray detectors on the MeV scale are rarely explored. In this study, we investigate the optoelectronic characteristics and radiation detection capabilities of triple-cation Cs 0.05 FA 0.9 MA 0.05 PbI 3 single crystals (CSC) grown using the inverse temperature crystallization method. The CSCs with a reduced Goldschmidt tolerance factor owing to the presence of Cs and MA ions exhibit exceptional properties, including a bandgap of 1.447 eV, low dark current, and self-powered response to X-ray irradiation. The X-ray absorption rate of 36 keV exceeded 99% with a CSC thickness of 2 mm, demonstrating its strong stopping power. Moreover, the CSC detectors exhibit precise linear responses to varying X-ray doses and dose rates with R2 values close to 1 and relative standard deviation (RSD) values below 3% under all the tested conditions. In addition, the detector effectively detected high-energy X-rays of up to 15 MV with minimal variations in the RSD values. These findings highlight the potential of CSC as promising materials for high-performance X-ray detectors. • Triple-cation lead–halide perovskites for high-energy X-ray detection were studied. • CSC detector has uniform linear response to varying X-ray doses and rates. • CSC detector can detect high-energy X-rays of up to 15 MeV. • CSC demonstrated remarkable promise as excellent self-powered X-ray detectors. [ABSTRACT FROM AUTHOR]

• Liquid epitaxy boosts δ-CsPbI 3 's one-dimensional growth, enhancing carrier transition. • Sensitivity reaches 1768.46 μC Gy air -1 cm−2 at 432 V mm−1, outperforming α-Se and single crystalline δ-CsPbI 3 detectors. • Retains 91.34 % sensitivity after 7 days in air, showcasing impressive long-term stability. • Successful integration with a thin-film transistor array for 8-bit imaging in a 64 × 64 matrix. • Opens the door for commercial high-performance direct X-ray detectors, solving the "absorption vs. transportation" issue. An ideal direct X-ray detector should convert a low dose of X-ray photons into large quantity of electrical signals, coupled with sustained long-term stability. Nevertheless, a notable conflict arises between X-ray absorption efficiency and carrier transport in thick polycrystalline perovskite films. Moreover, conventional perovskite materials exhibit inherent challenges regarding the stability of their crystal phase and the consistency of the photocurrent. To circumvent these limitations, a polycrystalline perovskite thick film is proposed using orthorhombic CsPbI 3 (δ-CsPbI 3) as a highly stable active material. Combined with the liquid epitaxy process, δ-CsPbI 3 prefers one-dimensional growth along the carrier transportation direction, which suppresses the formation of grain boundaries, enabling high carrier mobility while maintaining X-ray absorption for a thick polycrystalline film. Consequently, the polycrystalline δ-CsPbI 3 based detector exhibits a highest sensitivity of 1768.46 μC Gy air -1 cm−2 at an electric field of 432 V mm−1 which is 88.42-fold higher than α-Se based detectors and 190-fold higher than the single crystalline δ-CsPbI 3 based detector, respectively. The sensitivity maintains 91.34 % of its initial state after 7 days exposure in the air. Combing with a thin-film transistor array, the detector achieves 8-bit imaging within a 64 × 64 matrix. This work provides a feasible method for commercialized production of high-performance direct X-ray detectors. [ABSTRACT FROM AUTHOR]

X-ray-prohibited item inspection is an important measure to maintain public safety and has been widely applied in industries such as transportation and logistics. Without bells and whistles, it is a classic few-shot object detection task due to the long-tailed distribution of data in the real world, i.e., rare items occur at low frequencies. Taking into account that most previous few-shot methods are designed to detect known categories and are unable to perceive unknown objects, this is not conducive to the screening of novel types of prohibited items because the predefined categories in specific datasets cannot cover all possible classes in the natural world. In this work, we propose a Few-shot Open-set X-ray Object Detection method (FOXOD) composed of four novel components, namely the Overlapping Object Separator (OOS), Unknown Interest Advisor (UIA), Knowledge Augmentation (KA), and Discriminant Classifier (DC). Our method aims to achieve perceptual alertness for all known prohibited items and potential unknown objects based on a few data. To alleviate the problem of high overlap between objects, OOS utilizes a hierarchical attention mechanism to sample local knowledge and decouple the features of overlapping individual targets. In addition, UIA is carefully designed to assist RPN in perceiving potential unknown proposals without supervision, while KA mitigates catastrophic forgetting of known classes by the model. Moreover, DC prevents overfitting and further identifies unknown positive objects from a large number of background proposals. Extensive experiments on SIXray and VOC datasets demonstrate that FOXOD outperforms other baselines in both X-ray and common scenarios. Code is available at: https://github.com/liumaozhen-lmz/-FOXOD. [ABSTRACT FROM AUTHOR]

The Aluminium Conductor Composite Core (ACCC) is a critical component of power transmission that needs automated nondestructive defect detection to prevent wire breaking accidents and ensure regular city operations. Unfortunately, the current supervised target detection scheme is limited in its generalization ability due to the diversity of defect morphology and the high cost of acquiring samples. To overcome these challenges, we propose an automatic defect detection method based on a modified Skip-GANomaly model using X-ray images of ACCC wires as input. We experiment with semi-supervised deep learning reconstruction methods to mitigate the lack of abnormal samples and improve the generalization ability of unknown defects. We also explore hierarchical reconstruction and Skip-Connection adjustment to enhance the reconstruction effect and defect identification based on Resnet50. The results show that our method can achieve high accuracy, recall, and precision levels of over 99 %, significantly highlighting the difference in defect morphology. Compared to existing supervised schemes, our Defect Detection Scheme with Hierarchical Reconstruction and Anomaly-Subtracted Image Discrimination (DHRAD) eliminates dependence on the number of abnormal samples and the diversity of defect types in the training set, and has the ability to detect defects with unknown morphology. Therefore, DHRAD provides an effective detection scheme for automatic nondestructive defect detection of ACCC in operation, based on computer vision and semi-supervised learning. [ABSTRACT FROM AUTHOR]

It is of great significance to detect solder defects of ceramic packaging chips by X-ray detection to improve the quality of electronic products. However, the scale of the solder defects varies violently and there are a lot of tiny, dense, and long narrow defects, making it difficult to identify timely and accurately. To solve the issues mentioned above, an improved object detection model based on the YOLOv5 network, namely YOLO-STPN, is proposed in this paper. We add another prediction head to the original model to detect tiny solder defects effectively. In addition, the swin transformer block and convolutional block attention module are integrated into the path aggregation network, which improve the networks' ability to identify dense and long narrow defects. The positive sample matching mechanism and the complete intersection over union (CIoU) loss are modified to make the model focus on long narrow defects. Experimental results of 10 types of ceramic packaging chips show that the mean average precision at 50 % IoU (mAP 50) of YOLO-STPN is 10.05 % higher than the original YOLOv5 on average, while the decrease of frames per second (FPS) is acceptable. The proposed method has high accuracy and speed. Therefore, it is applicable to the real-time detection of chip packaging. [ABSTRACT FROM AUTHOR]

A new optical beam splitting method is proposed, based on which the optical frame camera capable of capturing multiple frames in a single exposure is designed and experimentally verified. The operation of the frame camera is based on an ultra-fast response semiconductor detector. It is equipped with an optical beam splitter and an optical imaging module. The ultrafast semiconductor detector receives an optical pulse that produces a transient refractive index change, and ultrafast physical processes are recorded by diffracting the probe laser through the transient phase grating. The interaction of an X-ray pulse with a semiconductor detector to produce a phase grating is simulated, based on the Monte Carlo method. The optical beam splitting mode separates a laser into two optical pulses with a certain time difference in the direction of polarization perpendicular to each other. The imaging module filters the diffracted probe laser in the spectral plane and then images multiple frames. The frame camera was used to record the temporal and spatial distribution characteristics of femtosecond laser pulses with a temporal resolution of 4.1 ps. This frame camera has great potential and value for applying to experimental studies of inertial confinement fusion. [ABSTRACT FROM AUTHOR]

We present an analysis of publicly available X-ray and optical observations of TYC 7275-1968-1, a contact binary, red nova progenitor candidate. The long optical time series of ASAS-3 , SuperWASP , CRTS , Gaia , ASAS-SN , and TESS enabled us to improve its orbital period to 0.3828071 ± 0.0000026 d. We show the presence of an X-ray and UV source associated with TYC 7275-1968-1 from Neil Gehrels Swift Observatory, that was previously assumed to be the counterpart of CD -36 8436 (V1044 Cen), a symbiotic star located 22 arcsec from the red nova candidate. The X-ray data indicate the presence of a region with a temperature of k T = 0.8 − 0. 1 + 0. 9 keV and a luminosity of 1.4 − 0. 2 + 0. 1 × 1031 erg s −1 in the range 0.3 – 10 keV. The detection of X-rays and modulated UV emission suggests that both components of the binary are chromospherically active. • TYC 7275-1968-1 is a binary eclipsing system suggested as red novae progenitor. • Swift and TESS data are related to red nova progenitor and not for CD-36 8436. • We improved the orbital period of TYC 7275-1968-1 using the TESS data. • The period found is considered as limits to orbital instability. • The X-ray emission originates in a corona around the contact binary. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) hybrid perovskite single crystals are promising photovoltaic materials. However, the single crystals grown from solution usually respond poorly to light and other charged particles. The 2D (BDA)CsPb 2 Br 7 single crystals (BDA = 1,4-butanediamine) are grown from a supersaturated solution. The macroscopical growth steps are present on the surface, leading to a large leakage current during device operation. The surface defects are removed to expose a clean and flat surface. This well-defined crystallographic surface enables an Ohmic contact with the electrode, which minimized the dark current and baseline drift. Consequently, the assembled devices exhibit an enhanced response to LED light and X-rays, revealing the surface defect to be one of the culprits underneath the poor performance of solution-grown perovskite single crystals. • The (BDA)CsPb 2 Br 7 perovskite single crystal was grown by solution cooling method. • Surface defects worsen the detection performance of crystals for photons and X-rays. • A surface treatment method was used to remove defects and improve device performance. [ABSTRACT FROM AUTHOR]

Thanks to the low melting point of hybrid glass, (ETP) 2 MnBr 4 namely, scintillators of arbitrary shapes were synthesized in a flexible silicon mold at low temperature (ca. 180 ℃), representing a new route to a transparent bulk of highly luminescence. [Display omitted] • The (ETP) 2 MnBr 4 glass was synthesized at a low temperature down to 180 ℃. • The luminescent glass exhibited a high transparency over 85% and a nearly-unity PL QY. • The internal crystallization of the hybrid glass was effectively driven by a mild annealing process at 60 ℃. • The low melting point of glass enabled the use of silicone molds for varied shapes, such as slide, monolith, and rod. • The ensuing scintillating screen for X-ray imaging achieved a high spatial resolution (26.8 lp/mm). In contrast to all-inorganic scintillators, organic–inorganic hybrid glass of low melting-point has many advantages in fabricating bulky and transparent scintillator at low temperature. Here, the (ETP) 2 MnBr 4 (ETPBr = ethyltriphenylphosphonium bromide) glass was successfully synthesized at 180 ℃, which exhibited a high transparency over 85 % (ranging from 500 to 800 nm) and a nearly-unity photoluminescence quantum yield. A mild annealing process at 60 ℃ was found effective to drive the interior crystallization of hybrid glass, which was only achievable at several hundred degrees for inorganic glass. The interior crystallization significantly improved the luminescence intensity at the expense of optical transparency. Importantly, based on the low melting point of (ETP) 2 MnBr 4 glass, flexible silicone molds were used as container to obtain a variety of scintillators of arbitrary sizes and shapes, such as alphabet, slide, monolith, and rod. The (ETP) 2 MnBr 4 glass was used as scintillating screen in X-ray detection and imaging, demonstrating a high light yield (66000 photons/MeV of annealed glass) and a high spatial resolution (26.8 lp/mm of transparent glass), respectively. This work not only refined the synthesis process of bulk scintillator, but also opened many avenues in luminescent volumetric display and three-dimensional imaging. [ABSTRACT FROM AUTHOR]

As one of the commonly used scintillating materials, single crystals inhibit their further practical applications considering the high cost, limited volume, and time-wasting preparation technique. By the compelling requirement for radiative detection in a variety of fields, numerous novel scintillators have been developed. Here, a novel efficient and transparent Eu3+-doped boroaluminate glass was successfully fabricated by melt-quenching technique. The optical responses to ultraviolet–visible (UV) and X-ray are both significantly enhanced by heavily doping Eu3+ into the glass host. The optimal doping concentration of Eu3+ is 8 mol%. Moreover, the internal quantum yield (IQY) was evaluated to 53.2% with high transparency (83% @ 600 nm). More notably, the best specimen has 18.4% X-ray excited luminescence (XEL) integrated intensity of that of the commercial Bi 4 Ge 3 O 12 single crystal scintillator. According to our findings, heavily doped boroaluminate glass could be one of the alternative scintillating materials for slow process detection. [ABSTRACT FROM AUTHOR]

Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent need in the current pandemic situation of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN's sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images. [ABSTRACT FROM AUTHOR]

• We analyze testing protocols used for Covid-19 detection used in the literature. • We successfully classify COVID images whose lungs areas are turned to black. • We try using different datasets for training and testing. • We show that many highly cited paper on this topic use biased testing protocols. In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose. [ABSTRACT FROM AUTHOR]

The development of Eu2+-activated oxide materials with high efficiency, fast decay and narrow bandwidth emission in the visible region is still challenging for the field of luminescence and scintillation applications. Generally, Eu2+ in oxide hosts can emit intense and broad 5d-4f emission in the visible region, but efficient emitting with stable bivalence of Eu is rare in inorganic oxide system. In this work, a series of ASL:Eu2+ (Sr 1-x La x Mg x Al 12-x O 19) powder samples with various La, Mg doping ratios were designed and synthesized to modulate the UV or X-ray excited luminescence properties, including redshift, quantum yield, and decaying lifetime. Moreover, an ASL:Eu2+ crystal with the optimal ratio x = 1/3 was grown, its scintillation performance was compared with standard Bi 4 Ge 3 O 12 crystal sample and evaluated in detail. These results indicate that the ASL:Eu2+ crystal is potential scintillator as a kind of X-ray detection and provide a reference for the further development of high-performance Eu-activated oxide scintillation materials. • The La, Mg dopants in SrAl 12 O 19 host promotes the red shift of Eu2+ emission under UV excitation. • The ASL:Eu2+ shows the self-trapped excitons radiation in XEL spectra. • The light yield of the as-grown ASL:Eu2+ crystal was determine to be 111.6 p.e./MeV. [ABSTRACT FROM AUTHOR]

The perovskite scintillators have been extensively studied recently for their merits of tunable emission spectra and simple preparation processes. However, the practical applications of perovskite scintillator-based X-ray image sensor are still impeded by inadequate radioluminescence, poor environmental stability, and low imaging resolution. Herein, we demonstrate a scalable co-firing method to fabricate high-quality lead-free Cs 3 Cu 2 I 5 perovskite scintillator and an Indium (In)-doping strategy is introduced to enhance its radioluminescence performance at the same time. The In-doped Cs 3 Cu 2 I 5 obtains a high PLQY of 77.9% and a relative light output of 53372 ph/MeV, which are 0.34 and 1.08 times higher than those of the undoped counterpart, respectively. The X-ray detection limit of the In:Cs 3 Cu 2 I 5 can reach 150.55 nGy air /s, 36.53 times lower than the requirement for X-ray medical diagnosis. The synthesized scintillator also shows superior stability under continuous high dose X-ray irradiation of 6800 μGy air /s for 120 minutes, maintaining 95% of its initial radioluminescence intensity. Furthermore, a large-area (300 cm2) flexible perovskite scintillator film is prepared, which owns a much competitive resolution of 10 lp/mm and less distortion in X-ray imaging. This work provides a practical path for the wide application of perovskite scintillator in the field of X-ray detection and imaging in near future. [Display omitted] • A co-firing method for lead-free indium doped Cs 3 Cu 2 I 5 perovskite scintillators has been proposed. • The indium doped Cs 3 Cu 2 I 5 shows superior environmental and radiation stability. • A large-area flexible scintillator film is prepared for high-resolution imaging. [ABSTRACT FROM AUTHOR]

• Presents a novel framework combining residual network (ResNet) with bidirectional long short-term memory network (BiLSTM) for enhanced terahertz signal analysis. • Introduces a sophisticated method for automatic defect detection and interface localization in non-metallic materials, surpassing traditional manual analysis in efficiency and accuracy. • Achieves interface positioning accuracy surpassing 80 µm in coating sample thickness, demonstrating superior performance to conventional approaches. • Provides robust validation against X-ray CT results, revealing an error margin of only 1.07% to 5.39% in detecting adhesive layer defects. This manuscript presents a framework that integrates a residual network (ResNet) with a bidirectional long short-term memory network (BiLSTM) for effective feature extraction and interface positioning in terahertz signal analysis. This innovative approach employs spatial and temporal feature extraction modules to process terahertz signals, enabling the automatic detection of interfaces and defects in both coating and bonded structure samples. The experimental results indicate that the interface positioning accuracy of the method aligns with the manual analysis outcomes for coating samples exceeding 80 µm in thickness. Furthermore, it exhibits a high degree of consistency with X-ray CT detection results when detecting defects in the adhesive layer, with an error margin ranging from 1.07 % to 5.39 %. Significantly, we have also conducted preliminary analyses on actual samples, further underscoring the robustness and generalizability of our model. This method offers an efficient automated tool for terahertz signal analysis and holds promise for industrial detection applications. Future research will aim to broaden the range of samples and application scenarios, thereby increasing the technology's practicality in quality control and production line detection. [ABSTRACT FROM AUTHOR]

Low-dose X-rays in the order of milligrays (mGy) are employed in diagnostic radiology. This poses a challenge, considering commercially used dosimeters have limitations in detecting low-dose X-rays. Accordingly, the goal of this research was to fabricate a nitro blue tetrazolium-doped polyvinyl alcohol (PVA/NBT) film dosimeter that could respond to low-dose X-rays while also investigating the effects of introducing copper(II) and bismuth(III) ions on the film's detecting ability. The films were synthesized by the solvent casting method and then exposed to X-ray radiation at doses in the range of 0–2.626 mGy. Optical density and UV–Vis transmittance measurements were carried out to identify changes in the film's optical characteristics brought on by X-ray radiation. In addition, changes in the film's surface morphology and chemical structure were examined by scanning electron microscopy (SEM) and FTIR spectroscopy. According to the results of the optical density measurement, introducing copper(II) ions to the film increased its sensitivity and response linearity. In contrast, the opposite was shown in the case of bismuth(III)-doped films. According to FTIR analysis, film X-ray irradiation caused the PVA to degrade. The development of grain structures on the film surface might be related to the reduction of the copper(II) and bismuth(III) ions. This study discovered that copper(II) ion-doped PVA/NBT film has promising low-dose X-ray detection properties. [Display omitted] • Cu 2 + and Bi 3 + were doped to PVA/NBT film to increase its sensitivity to X-rays. • PVA/NBT and metal ion-doped PVA/NBT films were prepared via solvent casting method. • The UV–Vis spectrum of the film shifted in response to X-ray exposure. • Copper(II)-doped PVA/NBT film has promising low-dose X-ray detection properties. [ABSTRACT FROM AUTHOR]

The backscatter X-ray system makes an image by detecting scattered particles from an object in the opposite direction by Compton scattering. This system has the advantage of detecting drugs or explosives composed of organic substances. In this study, we developed a backscatter X-ray detection system. The 6-ch DAQ system is able to store acquired data for 50 μs as 8 bytes and the 6-ch high voltage power supply board provides adjustable power from 0 to 1600 V. Using this detection system, we varied the tube voltage from 80 to 160 kV and observed backscatter X-ray images of two plastic phantoms at 160 kV. The results indicate a need to optimize the speed of the collimator and data acquisition time of the DAQ system to improve image quality. The backscatter X-ray imaging technology exhibits high scalability, making it applicable not only to security screening but also for medical and industrial purposes. [ABSTRACT FROM AUTHOR]

Metal halide perovskite single crystal (SC) has attracted wide attentions in the field of photoelectric detection. All-inorganic perovskite SCs are much more stable than the hybrid counterparts, but its rapid fabrications usually end up low quality. We proposed a modified solvent-evaporation method that can rapidly obtain smooth and transparent high-quality CsPbBr 3 single crystal with a surface roughness of only 5 nm in a few hours. In this method, the lattice phase of the SC and the growth rate is precisely controlled by the solution temperature, which is raised to the narrow range above the cubic-to-tetragonal phase transition temperature. This precisely controlled solvent temperature allows the CsPbBr 3 SC growing immediately into cubic phase, and limits the volatilization rate of solvent, thus inhibiting the defect density. The SCs grown this way are of high crystallinity, as the half-height width of the (100) and (200) peaks of XRD rocking curve is 0.0154° and 0.0176°, respectively. Characterization of photoelectric properties shows great potential for applications in X-ray detection and photodetection. • CsPbBr 3 single crystals were grown by a modified evaporative solvent method. • The crystal quality is very high crystallinity as the half-height width of the rocking curve as low as 0.0176°. • The effect of growth temperature on crystal quality is investigated. • The growth time of large size (3 mm) CsPbBr 3 single crystals is reduced to 10 h. [ABSTRACT FROM AUTHOR]

Ce3+ activated high-density lanthanum lutetium germanate glass was fabricated by melt quenching method. Their densities, fluorescence decay curves, transmittance spectra, and luminescence excited by UV light and X-ray were measured and investigated. The densities of the glasses are between 5.90 g/cm3 and 5.97 g/cm3. The glasses present blue emission upon 379 nm light and X-ray. The blue emission lifetimes of the glasses are between 11.26 ns and 18.83 ns. The results shows that Ce3+ activated high-density lanthanum lutetium germanate glass would be a potential fast-event X-ray detection scintillating material. • Ce3+ doped germanate glasses with density around 6 g/cm3 were prepared. • The glass showed blue emissions upon UV light and X-ray. • The blue emission lifetimes were in the range of 11.26–18.83 ns. [ABSTRACT FROM AUTHOR]

In the field of X-ray space detection, the optical blocking filter which can transmit X-ray and block UV, visible and infrared light is required. The combination of polyimide film and Al coating is the most commonly used design of OBF. The Al island structure created during magnetron sputtering deposition affects the optical properties of OBF. In this work, the potential of Mg as a seed layer to improve the optical performance of OBF was investigated. Polyimide films coated with Mg–Al bilayer with different thickness parameters (the total thickness was the same, equaled to 80 nm) were prepared, and the effect of Mg on the optical properties of optical blocking filters was analyzed. The results show that the island structure of the Al coating is weakened by the addition of Mg seed layer. For optical blocking filter with 10 nm and 20 nm Mg seed layer, the UV, visible and infrared transmittance decreased significantly, and the X-ray transmittance increased slightly. The blocking performance of UV, visible and infrared light has no change with the variation of storage time. • The Mg seed layer was used between the Al coating and Polyimide film. • The island structure of the Al coating is weakened by Mg seed layer. • The optical performance of optical blocking filter is improved by Mg seed layer. [ABSTRACT FROM AUTHOR]

Scintillators have been commercialized as radiation detectors in several practical applications. However, fabrication of commercial inorganic crystal scintillators is time-consuming and expensive. Herein, we report inorganic glass scintillators for gamma and X-ray detection based on phosphate glasses, with alkali and alkali earth metals in the host and Ce3+ ions as emission centers. Unlike inorganic crystals, inorganic glasses have industrial advantages owing to their low cost and ease of fabrication, which facilitate the large-scale production of scintillators. Previously studied inorganic glass scintillators for gamma and X-ray detection exhibited low light yields compared to inorganic crystal scintillators. In contrast, our glass exhibits a high light yield of 3200 photons/MeV. This light yield is higher than that of previously reported glasses, whose light yields are less than several hundred photons/MeV in most cases and approximately 2000 photons/MeV as the highest value. In addition, this glass has a relatively high density of 3.33 g/cm3 and a fast scintillation decay of approximately 30 ns, which is attributed to the 5d–4f transition of Ce3+ ions acting as emission centers. These properties are suitable for gamma and X-ray detection and this glass has the potential to expand the practical applications of inorganic glass scintillators. • We fabricated a phosphate glass scintillator for gamma- and X-ray detection. • This glass exhibited a high light yield of 3200 photons/MeV. • This glass has the potential to expand applications of glass scintillators. [ABSTRACT FROM AUTHOR]

Redox-active heteropolyanions are widely applied to redox catalysts and electron acceptors and the understanding of their reduced states in solution is essential. In this study, the structural changes of Keggin-type tungstophosphate: [PW 12 O 40 ]3− induced by electrochemical reduction were investigated using W L 3 -edge X-ray absorption near-edge structure (XANES) spectra in high-energy-resolution fluorescence detection (HERFD) mode. W L 3 -edge HERFD-XANES spectra revealed the changes in the electronic state of W species and local structure of {WO 6 } units, which were negligible in conventional transmittance mode; the peak intensities of the split two white line peaks were switched by the electrochemical reduction accompanied by enlargement of the splitting width. Our findings suggest that the injected electron was delocalized on whole {WO 6 } units in [PW 12 O 40 ]3− and that the improved symmetry of {WO 6 } units compensated for the change in valence due to the injected electron. [ABSTRACT FROM AUTHOR]

[Display omitted] • BN nanosheets act as a barrier, inhibiting ion migration within perovskite layers. • BN also improved charge transport and carrier mobility. • Perovskite photodiodes incorporated with BN exhibit exceptional X-ray stability. Metal halide perovskites have been widely used in optoelectronic applications due to their excellent optoelectronic properties. However, ensuring their stability during operation remains a significant challenge. In this study, we investigated the charge carrier dynamics of perovskite films and devices, comparing those with and without the insertion of boron nitride on the grain boundaries. It was revealed that the charge transport and stability could be significantly enhanced with the present of boron nitride nanosheets on the grain boundaries. The incorporation of boron nitride led to a substantial improvement in charge carrier mobility and lifetime, without any noticeable changes in phase segregation or photostability. Moreover, the perovskite detectors incorporating boron nitride exhibited suppressed ion migration and dark current, while demonstrating an enhanced photoresponse and radiation exposure capability. These findings indicate that the addition of insulating boron nitride nanosheets acts as a stabilizer, enhancing the stability of both the perovskite thin films and the devices. Additionally, the incorporation of boron nitride significantly improved the device performance benefiting from the enhanced charge transport, indicating that the boron nitride nanosheets enhanced perovskite photodiodes possess great potential for X-ray detection and imaging. [ABSTRACT FROM AUTHOR]

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency • The IoT is now playing a vital role in the medical field's approach to providing people. • This paper presents DKCNN-AK architecture for disease prediction. • The DKCNN-AK architecture is used for both disease prediction and severity estimation. • The GSO optimized Fuzzy TOPSIS algorithm is used. [ABSTRACT FROM AUTHOR]

Waste management plays an important role in environmental protection and resource recycling. In fact, traditional waste recycling requires a large number of workers to sort manually, which is not only labor-intensive and time-consuming, but also increases the health risk for workers. To further improve the efficiency of waste detection and classification, related scholars have used computer vision technology to achieve automatic waste management. However, existing methods tend to use RGB images as input, requiring workers to open waste bags and disperse waste items in advance. To avoid comprehensive preparation procedures and identify occluded waste items, we use deep learning models to detect and recognize waste items in X-ray images. Compared to RGB images, X-ray images can effectively preserve boundary information and reflect material types. To take advantage of the inherent characteristics of X-ray images, we propose a novel X-ray Characteristic Enhancement Module (XCEM) to improve the performance of X-ray waste detection. XCEM consists of three sub-modules, namely Hybrid Interaction Module (HIM), Multi-scale Material Perception Module (MMPM), and Boundary Guidance Module (BGM). Specifically, HIM not only generates soft and hard boundary maps, but also achieves information interaction and feature enhancement of different boundary maps. Meanwhile, MMPM can effectively capture different multi-scale material features. In addition, BGM uses auxiliary boundary information and attention mechanisms to effectively enhance critical material features. As a plug-and-play module, XCEM can be readily inserted into the most popular detection pipelines. We select several representative one-stage and two-stage methods and perform related experiments on the WIXRay dataset. Experimental results show that our method can achieve consistent performance gains on diverse detection paradigms. This study promotes the development of intelligent waste inspection and recycling. • An efficient solution based on X-ray images for waste detection and classification. • The proposed XCEM can make use of the inherent characteristics of X-ray images. • The XCEM can achieve consistent performance gains on diverse detection models. • Several representative methods are explored for waste detection in X-ray images. • Using computer vision and deep learning can achieve automatic waste management. [ABSTRACT FROM AUTHOR]

Deep learning-based X-ray detection of hazardous materials is crucial for public safety, as it can automatically detect them in baggage. However, most existing algorithms for X-ray detection rely on supervised learning, necessitating large amounts of labeled data for training. Obtaining such data is challenging due to the unique nature of X-ray safety images, requiring significant time and money investment from trained staff for accurate labeling. In this paper, we propose a new X-ray dangerous goods detection framework called EslaXDET, whose backbone is trained by a hybrid Self-Supervised Learning (SSL) strategy and whose detection head is designed for a non-multistage backbone. First, the hybrid strategy named ESLA is an abbreviation for E nhanced S elf-supervised L earning with masked A utoencoders, which does not require the involvement of labels as it is derived from a hybrid architecture of Contrastive Learning (CL) and Masked Image Model (MIM). Then, the detection head, called Head-Tail Feature Pyramid (HTFP), creates multi-level feature maps comparable to that produced by a multi-level backbone structure by downsampling the output feature of the last stage of the plain Vision Transformers (ViT) multiple times. Finally, the experimental results show that with the backbone of ViT-B, the top-1 accuracy of ESLA on the ImageNet-1K dataset is 77.2%, which is 3% higher than BYOL. Moreover, the average precision (AP, the same evaluation metrics as COCO) of EslaXDET on the PIDray dataset is 69.2%, nearly 8% higher than the original method SDANet, providing a new idea for the subsequent SSL-based X-ray baggage security detection method. [Display omitted] • A new hybrid SSL framework named ESLA. • A new SSL detection framework for X-ray baggage screening named EslaXDET. • A new detector head called HTFP is adapted to a plain ViT. [ABSTRACT FROM AUTHOR]

[Display omitted] CsPbBr 3 -CsPb 2 Br 5 wafer is obtained by water-assisted coprecipitation method, and a spray-coating method is further utilized to convert it into CsPbBr 3 -CsPb 2 Br 5 -CsPbI x Br 3-x composite wafer, which exhibits superior performance and reliability of X-ray detection. • Water-assisted coprecipitation is proposed to prepare high-quality CsPbBr 3 -CsPb 2 Br 5 powder. • CsPbBr 3 -CsPb 2 Br 5 wafer is obtained by pressure-induced aggregation of the powder. • Spray-coating of CsI/H 2 O onto CsPbBr 3 -CsPb 2 Br 5 wafer can convert it into CsPbBr 3 -CsPb 2 Br 5 -CsPbI x Br 3-x composite wafer. • X-ray detector with the composite wafer exhibits much improved performance and reliability. Lead halide perovskite wafers with large lateral size, controllable thickness, and desirable optoelectronic characteristics are promising for X-ray detectors. Herein, CsPbBr 3 -CsPb 2 Br 5 -CsPbI x Br 3-x composite wafers are prepared via water-assisted coprecipitation and spray coating. The preparation involved pressure-induced aggregation of the CsPbBr 3 -CsPb 2 Br 5 powder produced through coprecipitation using of H 2 O/MeOH mixed solvent into CsPbBr 3 -CsPb 2 Br 5 wafers, followed by spraying a CsI/H 2 O solution onto the wafer surface to transform it into a CsPbBr 3 -CsPb 2 Br 5 -CsPbI x Br 3-x composite wafer. Multiple favorable properties, including compact surface, high crystallinity, excellent carrier transportation, large/adjustable size, low cost, and mass producibility, can be identified for the CsPbBr 3 -CsPb 2 Br 5 -CsPbI x Br 3-x composite wafer. An X-ray detector with high performance and excellent stability is realized with this wafer. It yields high resistivity (ρ; 1.3 × 109 Ω cm−2, exceptional carrier mobility (μ)/lifetime (τ) (μτ) product (1.01 × 10−3 cm2 V−1), high sensitivity (20555.1 μC Gy air −1 cm−2), and low detection limit (127.7 nGy s−1). Hence, our research provides a reliable strategy for optimizing the structure and performance of perovskite wafer-based X-ray detectors and enabling mass preparation. [ABSTRACT FROM AUTHOR]

Gotthard-II (G-II) is a 1-D silicon microstrip hybrid detector developed by the Paul Scherrer Institut within the framework of a collaboration agreement with the European XFEL. The ASIC features a dynamic gain switching architecture, in order to cope with the luminosity of European XFEL, and a 12-bit analog-to-digital converter with a sampling/conversion rate of more than 18 MS/s as well as a Static random-access memory, making it capable of matching the European XFEL pulse train structure, thus acquiring up to 2700 images at 4.5 MHz per burst. The sensor's strip pitch can be either of 50 μ m or 25 μ m , for a total of 1280 or 2560 output channels per detector, respectively, with a spectral sensitivity that allows either X-ray detection (optimized in the 5–20 keV range) or visible light detection. Its exceptionally good compliance with the European XFEL beam conditions will make G-II the most widely employed detector across the facility, with a total of 29 modules of different flavors that will be installed in scientific instruments and beam diagnostic setups. Although the detector will be predominantly used for spectroscopic measurements it will have a variety of other applications, including X-ray diffraction/emission/absorption experiments, relative pulse arrival time monitoring (of fundamental importance for pump–probe experiments), spectral diagnostics, and beam quality monitoring, with the possibility for the detector itself to generate a veto patterns for the large area MHz framerate pixel detectors employed at the scientific instruments, such as AGIPD, LPD and DSSC. In this paper, an overview of G-II detector technology and its usage at the European XFEL will be presented; the focus will then move on towards the process of detector integration in the European XFEL control system, data acquisition, and data correction infrastructure, highlighting its challenges. Finally, an overview of the first results obtained at scientific instruments will be given. [ABSTRACT FROM AUTHOR]

This study examines a perovskite-based graphene field effect transistor (P-GFET) device for X-ray detection. The device architecture consisted of a commercially available GFET-S20 chip, produced by Graphenea, with a layer of methylammonium lead iodide (MAPbI 3) perovskite spin coated onto the top of it. This device was exposed to the field of a molybdenum target X-ray tube with beam settings between 20 and 60 kVp (X-ray tube voltage) and 30–300 μA (X-ray tube current). Dose measurements were taken with an ion-chamber and thermo-luminescent dosimeters and used to determine the sensitivity of the device as a function of the X-ray tube voltage and current, as well as source-drain voltage. The X-ray tube was also simulated in this work with GEANT4 and MCNP to determine the dose rate and power incident on the device during irradiation. These simulations were then used to determine the responsivity as a function of the X-ray tube voltage and current, as well as the source-drain voltage. Overall, a strong positive correlation between sensitivity and source-drain voltage was found. Conversely, the sensitivity was found to decrease – roughly exponentially – as a function of both the X-ray tube current and energy. Similar trends were seen with responsivity. We report the models used for the study as well as address the feasibility of the device as a low-energy (<70 keV) X-ray photon detector. [Display omitted] • A perovskite-based graphene field effect transistor was examined under x-rays. • Charges in the perovskite and recirculating holes in the graphene provide gain. • Found decreasing responsivity with increasing x-ray intensity. • Buildup of charges at the junction limits charge transfer. [ABSTRACT FROM AUTHOR]

As for the characteristics of the objects in the airtight package in the X-ray image, some prohibited items of the airtight package are difficult to be detected from the X-ray images with complex and overlapped backgrounds. In this article, the cooperative knowledge distillation method is used to enhance the prohibited items detection model in the X-ray image. To efficiently implement hard example mining, this article designs an Multi-task Classification Head (MCH) for teachers to provide prior knowledge of image-level and instance-level predictions. Different from the distillation method in which the students imitate the teacher, the algorithm in this article is implemented by the cooperation between teacher and student. In order to verify the effectiveness of this algorithm, a series of related experiments are carried out on PIDray and SIXray respectively. Experiments show that the algorithm improves the AP of State-of-the-Art dense object detectors (e.g., RetinaNet, ATSS, GFL, and TOOD) in SIXray by 1% ∼ 2%. Especially for prohibited items that are difficult to be found in X-ray images, the algorithm is more effective, and the dense object detectors can achieve a performance improvement of about AP of 2% on the Hidden subset of PIDray. The experiments demonstrate that the cooperative knowledge distillation algorithm proposed in this article can effectively enhance the performance of prohibited items detection, particularly showing more pronounced improvements in detecting hard examples. [ABSTRACT FROM AUTHOR]