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Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury for which early diagnosis and evidence-based treatment can improve patient outcomes. Chest X-rays (CXRs) play a crucial role in the identification of ARDS; however, their interpretation can be difficult due to non-specific radiological features, uncertainty in disease staging, and inter-rater variability among clinical experts, thus leading to prominent label noise issues. To address these challenges, this study proposes a novel approach that leverages label uncertainty from multiple annotators to enhance ARDS detection in CXR images. Label uncertainty information is encoded and supplied to the model as privileged information, a form of information exclusively available during the training stage and not during inference. By incorporating the Transfer and Marginalized (TRAM) network and effective knowledge transfer mechanisms, the detection model achieved a mean testing AUROC of 0.850, an AUPRC of 0.868, and an F1 score of 0.797. After removing equivocal testing cases, the model attained an AUROC of 0.973, an AUPRC of 0.971, and an F1 score of 0.921. As a new approach to addressing label noise in medical image analysis, the proposed model has shown superiority compared to the original TRAM, Confusion Estimation, and mean-aggregated label training. The overall findings highlight the effectiveness of the proposed methods in addressing label noise in CXRs for ARDS detection, with potential for use in other medical imaging domains that encounter similar challenges. [ABSTRACT FROM AUTHOR]

Precise control and characterization of nanomaterials at working conditions are essential for further rational applications in many areas important for modern society. Penetrating properties of X-ray radiation in combination with advanced spectroscopy schemes are an ideal tool to investigate modifications of nanomaterials with extraordinary precision. Here, we present preliminary results on the controlled oxidation of copper nanoparticles and exploration of X-ray absorption spectroscopy to follow electronic and structural changes. The described pilot experiment raises questions on the applicability of high energy resolution X-ray detection schemes in potential future investigations aimed at following reversible reduction/oxidation processes at nanoparticle surfaces. [ABSTRACT FROM AUTHOR]

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]

Mn-doped UO2 is under consideration for use as an accident tolerant nuclear fuel. We detail the synthesis of Mn-doped UO2 prepared via a wet co-precipitation method, which was refined to improve the yield of incorporated Mn. To verify the Mn-doped UO2 defect chemistry, X-ray absorption spectroscopy at the Mn K-edge was performed, in addition to X-ray diffraction, Raman spectroscopy and high-energy resolved fluorescence detection X-ray absorption near edge spectroscopy at the U M4-edge. It was established that Mn2+ directly substitutes for U4+ in the UO2 lattice, accompanied by oxygen vacancy (Ov) charge compensation. In contrast to other divalent-element doped UO2 materials, compelling evidence for U5+ in a charge compensating role was not found. This work furthers understanding of the structure and crystal chemistry of Mn-doped UO2, which could show potential advantages as a novel efficient advanced nuclear fuel. [ABSTRACT FROM AUTHOR]

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra -like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 per cent at an 89 per cent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 per cent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC 4765, NGC 533, NGC 2300, NGC 3091, NGC 4073, NGC 4125, and NGC 5129) and a number of potential cavity candidates. [ABSTRACT FROM AUTHOR]

The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date, however, do not fully exploit recent developments in the signal and imaging processing research domains to optimize searches for short X-ray flashes. This paper addresses this issue by presenting a new source detection pipeline, STATiX (Space and Time Algorithm for Transients in X-rays), which directly operates on three-dimensional X-ray data cubes consisting of two spatial and one temporal dimension. The algorithm leverages wavelet transforms and the principles of sparsity to denoise X-ray observations and then detect source candidates on the denoised data cubes. The light curves of the detected sources are then characterized using the Bayesian blocks algorithm to identify flaring periods. We describe the implementation of STATiX in the case of XMM–Newton data, present extensive validation and performance verification tests based on simulations and also apply the pipeline to a small subset of seven XMM–Newton observations, which are known to contain transients sources. In addition to known flares in the selected fields, we report a previously unknown short duration transient found by our algorithm that is likely associated with a flaring Galactic star. This discovery demonstrates the potential of applying STATiX to the full XMM–Newton archive. [ABSTRACT FROM AUTHOR]

Mn-doped UO2 is under consideration for use as an accident tolerant nuclear fuel. We detail the synthesis of Mn-doped UO2 prepared via a wet co-precipitation method, which was refined to improve the yield of incorporated Mn. To verify the Mn-doped UO2 defect chemistry, X-ray absorption spectroscopy at the Mn K-edge was performed, in addition to X-ray diffraction, Raman spectroscopy and high-energy resolved fluorescence detection X-ray absorption near edge spectroscopy at the U M4-edge. It was established that Mn2+ directly substitutes for U4+ in the UO2 lattice, accompanied by oxygen vacancy (Ov) charge compensation. In contrast to other divalent-element doped UO2 materials, compelling evidence for U5+ in a charge compensating role was not found. This work furthers understanding of the structure and crystal chemistry of Mn-doped UO2, which could show potential advantages as a novel efficient advanced nuclear fuel. [ABSTRACT FROM AUTHOR]

Crystalline organic–inorganic hybrids, which exhibit colorimetric responses to ionizing radiation, have recently been recognized as promising alternatives to conventional X‐ray dosimeters. However, X‐ray‐responsive organic–inorganic hybrids are scarce and the strategy to fine‐tune their detection sensitivity remains elusive. Herein, an unprecedented mixed‐ligand strategy is reported to modulate the X‐ray detection efficacy of organic–inorganic hybrids. Deliberately blending the stimuli‐responsive terpyridine carboxylate ligand (tpc−) and the auxiliary pba− group with different ratios gives rise to two OD thorium‐bearing clusters (Th‐102 and Th‐103) and a 1D coordination polymer (Th‐104). Notably, distinct X‐ray sensitivity is evident as a function of molar ratio of the tpc− ligand, following the trend of Th‐102 > Th‐103 > Th‐104. Moreover, Th‐102, which is exclusively built from the tpc− ligands with the highest degree of π–π interactions, exhibits the most sensitive radiochromic and fluorochromic responses toward X‐ray with the lowest detection limit of 1.5 mGy. The study anticipates that this mixed‐ligand strategy will be a versatile approach to tune the X‐ray sensing efficacy of organic–inorganic hybrids. [ABSTRACT FROM AUTHOR]

X-ray diffraction imaging (XDI) is utilized for visualizing the structures of noncrystalline particles in material sciences and biology. In the structural analysis, phase-retrieval (PR) algorithms are applied to the diffraction amplitude data alone to reconstruct the electron density map of a specimen particle projected along the direction of the incident X-rays. However, PR calculations may not lead to good convergence because of a lack of diffraction patterns in small-angle regions and Poisson noise in X-ray detection. Therefore, the PR calculation is still a bottleneck for the efficient application of XDI in the structural analyses of non-crystalline particles. For screening maps from hundreds of trial PR calculations, we have been using a score and measuring the similarity between a pair of retrieved maps. Empirically, probable maps approximating the particle structures gave a score smaller than a threshold value, but the reasons for the effectiveness of the score are still unclear. In this study, the score is characterized in terms of the phase differences between the structure factors of the retrieved maps, the usefulness of the score in screening the maps retrieved from experimental diffraction patterns is demonstrated, and the effective resolution of similarity-score-selected maps is discussed. [ABSTRACT FROM AUTHOR]

Precisely acquiring the timing information of individual X-ray photons is important in both fundamental research and practical applications. The timing precision of commonly used X-ray single-photon detectors remains in the range of one hundred picoseconds to microseconds. In this work, we report on high-timing-precision detection of single X-ray photons through the fast transition to the normal state from the superconductive state of superconducting nanowires. We successfully demonstrate a free-running X-ray single-photon detector with a timing resolution of 20.1 ps made of 100-nm-thick niobium nitride film with an active area of 50  μ m by 50  μ m. By using a repeated differential timing measurement on two adjacent X-ray single-photon detectors, we demonstrate a precision of 0.87 ps in the arrival-time difference of X-ray photon measurements. Therefore, our work significantly enhances the timing precision in X-ray photon counting, opening a new niche for ultrafast X-ray photonics and many associated applications. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of optically selected GAMA groups and clusters in the SRG/eROSITA X-ray map of eROSITA Final Equatorial Depth Survey, in the halo mass range 1013−5 × 1014 M⊙ and at z < 0.2. All X-ray detections have a clear GAMA counterpart, but most of the GAMA groups in the halo mass range 1013−1014 M⊙ remain undetected. We compare the X-ray surface brightness profiles of the eROSITA detected groups with the mean stacked profile of the undetected low-mass haloes at fixed halo mass. Overall, we find that the undetected groups exhibit less concentrated X-ray surface brightness, dark matter, and galaxy distributions with respect to the X-ray-detected haloes. The mean gas mass fraction profiles are consistent in the two samples within 1.5σ, indicating that the gas follows the dark matter profile. The low-mass concentration and the magnitude gap indicate that these systems are young. They reside with a higher probability in filaments while X-ray-detected groups favour the nodes of the Cosmic Web. Because of the lower central emission, the undetected systems tend to be X-ray underluminous at fixed halo mass and to lie below the LX − M halo relation. Interestingly, the X-ray-detected systems inhabiting the nodes scatter the less around the relation, while those in filaments tend to lie below it. We do not observe any strong relationship between the system X-ray appearance and the active galactic nucleus (AGN) activity. We cannot exclude the role of the past AGN feedback in affecting the gas distribution over the halo lifetime. However, the data suggests that the observed differences might be related to the halo assembly bias. [ABSTRACT FROM AUTHOR]

We report a comprehensive spectro-polarimetric study of the black hole binary LMC X-3 using simultaneous Imaging X-ray Polarimetry Explorer (IXPE), NICER , and NuSTAR observations in 0.5–20 keV energy band. The broad-band energy spectrum (0.5–20 keV) with NICER and NuSTAR is well described by the disc emission of temperature ∼1.1 keV and a weak Comptonizing tail beyond ∼10 keV. This evidently suggests a disc-dominated spectral state of the source with disc contribution of ∼96  per cent. The lack of variability (rms ∼ 0.5  per cent) in the power spectrum further corroborates the high/soft nature of the source. A significant polarization degree (PD) of 3.04 ± 0.40  per cent (>7σ) at a polarization angle of −44.24° ± 3.77° (>7σ) is found in 2–8 keV energy range of IXPE. In addition, PD is seen to increase with energy up to ∼4.35 ± 0.98  per cent (>3σ) in 4–8 keV band. Further, we attempt to constrain the source spin (a *) using broad-band spectral modelling that indicates a weakly rotating 'hole' in LMC X-3 with |$a_{*}=0.273_{-0.012}^{+0.011}\,\text{to}\,0.295_{-0.021}^{+0.008}$| (90  per cent confidence). Based on the spectro-polarimetric results, we infer that the polarization in LMC X-3 is resulted possibly due to the combined effects of the direct and/or reflected emissions from a partially ionized disc atmosphere. Finally, we discuss the relevance of our findings. [ABSTRACT FROM AUTHOR]

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles. Recently, two‐dimensional (2D) perovskites have been shown to be promising materials for medical X‐ray imaging, but they are mostly used in low‐energy (≤130 keV) regions. Direct detection of MeV X‐rays, which ensure thorough penetration of the thick shell walls of containers, trucks, and aircraft, is also highly desired in practical industrial applications. Unfortunately, scintillation semiconductors for high‐energy X‐ray detection are currently scarce. Here, This paper reports a 2D (C4H9NH3)2PbBr4 single crystal with outstanding sensitivity and stability toward X‐ray radiation that provides an ultra‐wide detectable X‐ray range of between 8.20 nGyair s−1 (50 keV) and 15.24 mGyair s−1 (9 MeV). The (C4H9NH3)2PbBr4 single‐crystal detector with a vertical structure is used for high‐performance X‐ray imaging, delivering a good spatial resolution of 4.3 lp mm−1 in a plane‐scan imaging system. Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X‐ray radiation at high bias voltages, leading to a sensitivity of 46.90 μC Gyair−1 cm−2 (−1.16 V μm−1) with 9 MeV X‐ray radiation, demonstrating that 2D perovskites have enormous potential for high‐energy industrial applications. [ABSTRACT FROM AUTHOR]

Signal amplification is vitally important for sensing low‐dose X‐rays in medical diagnosis by amplifying the generated electric read‐out signal. However, the complexity of external amplification circuits hampers device miniaturization and portability, while integrating amplification functionality directly into sensors or detectors remains a significant and formidable challenge. In this work, a direct high‐gain X‐ray detector with facile electron drift speed manipulation is reported in perovskite single‐crystal film (SCF). By employing laser‐assisted nucleation, high‐quality MAPbBr3 SCF is fabricated with precise control of thickness from ≈20 to ≈500 µm and the area up to 3 by 2 cm, while the architecture of ITO/MAPbBr3/Au is constructed to form the Schottky junction in opposite polarity. With the assistance of applied bias, the space electric field over MAPbBr3 SCF can be tunable, which ensures the manipulation of charge carrier drift speed to form recirculation for internal gain. The resultant photodetector exhibits an ultrahigh sensitivity of 1.44 × 105 µC Gy−1 cm−2 with a gain of 5.14 × 105, an ultralow detection limit of 39.8 nGy s−1, and the X‐ray array imaging is achieved at a low dose rate of 5 µGy s−1. These results confirm the advance of high‐gain detectors in constructing sensing arrays for practical safe medical diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model's performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model's spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning. [ABSTRACT FROM AUTHOR]

Abstract The introduction of YOLOv9, the latest version of the you only look once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer‐assisted diagnosis to help radiologists and surgeons to interpret X‐ray images. Specifically, this paper trained the model on the GRAZPEDWRI‐DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50–95 of the current state‐of‐the‐art model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9‐Fracture‐Detection.

The introduction of YOLOv9, the latest version of the you only look once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer‐assisted diagnosis to help radiologists and surgeons to interpret X‐ray images. Specifically, this paper trained the model on the GRAZPEDWRI‐DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50–95 of the current state‐of‐the‐art model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9‐Fracture‐Detection. [ABSTRACT FROM AUTHOR]

High‐resolution X‐ray imaging is increasingly required for medical diagnosis and large‐area detection. However, the issues of scattering and optical crosstalk are limiting the spatial resolution of the indirect X‐ray imaging. In this study, a feasible and efficient strategy is proposed to in situ synthesize flexible Cs3Cu2I5:2%In+@paper as a superior scintillator film, which can be scaled up to an ultra‐large area of 4800 cm2. The as‐obtained Cs3Cu2I5:2%In+@paper performs a fascinating photoluminescence quantum efficiency up to 88.14%, a steady state light yield of 70169 photons/MeV, and spatial resolution of 15 lp mm−1. Moreover, the suppressed physical scattering and optical crosstalk of the corresponding film are demonstrated. Accordingly, this work explores a feasible fabrication of customizable scintillation films with large area for high‐resolution X‐ray detection. [ABSTRACT FROM AUTHOR]

X-ray detection for personal dosimetry requires sensitive, stable and non-toxic materials. At the same time, scalability onto large-area and flexible substrates is emerging as a desirable property. To satisfy these requirements, novel materials to be employed as the active layer of direct X-ray detectors are needed. In this search for easy-processability, large area, efficient and non-toxic materials for direct X-ray detection, we assess the performance of a layered metal-organic chalcogenide [AgSePh]M, recently proposed as representative of a novel excitonic semiconductors platform. Here we demonstrate that [AgSePh]M can be successfully applied as direct ionizing radiation detecting layer, reaching sensitivities up to (180 ± 10) ^C Gy-1 cm-2 and competitive limit of detection down to (100 ± 30) nGy s-1. Moreover, it offers good stability and reproducibility of detection after 100 Gy of irradiation and upon bending to a curvature radius of 5 mm. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) of the Knee is a degenerative joint disease mainly caused due to loss of articular cartilages. The paper introduces an approach to quantify knee osteoarthritis (OA) severity using KL grades. This approach combines EDA (Exploratory Data Analysis), Pre-processing and Feature Engineering techniques. The amount of damage to the knee can be graded using KL scale (0-4). The automated detection of Knee Osteoarthritis (KOA) based on KL grades which corresponds to severity stages has been given in the paper. In the study public dataset from Osteoarthritis Initiative (OAI) has been used to evaluate the proposed approach with very promising results. Different accuracy metrices like F1 score, Receiver operating characteristic curve (ROC), Area Under Curve (AUC) and Precision were used to find the best algorithm amongst the classification models in Machine learning. Random forest and Decision trees algorithms were considered efficient giving an accuracy of 96.9% and 91.6% respectively. Our study is an economically better approach when compared to x-rays for OA detection. [ABSTRACT FROM AUTHOR]

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model's performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model's performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications. [ABSTRACT FROM AUTHOR]

This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, consultations with practicing radiologists indicate that clinical data is highly informative and essential for interpreting medical images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising different modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision compared to a standard Mask R-CNN using chest X-rays alone. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. In the interest of fostering scientific reproducibility, the architecture proposed within this investigation has been made publicly accessible(https://github.com/ChihchengHsieh/multimodal-abnormalities-detection). [ABSTRACT FROM AUTHOR]

X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second. [ABSTRACT FROM AUTHOR]

In this paper, we performed a spectro-polarimetric analysis of the transient NS-LMXB XTE J1701−462 using IXPE, NICER , and NuSTAR data during its 2022 outburst. We report significant detection of energy-dependent polarization in the X-ray signal from the source on 2022 September 29 in the 2–4, 4–8, and 2–8 keV energy bands with a polarization degree of 3.9 ± 0.3, 5.5 ± 0.6, and 4.5 ± 0.4 per cent, respectively. The polarization angle in the overall 2–8 keV band was ∼143° ± 2°. The spectra were modelled using a combination of thermal emission from an accretion disc, Comptonized emission from a hot electron plasma (or corona) and a Gaussian line. From spectro-polarimetric analysis, the polarization degree due to the disc emission had an upper limit of ∼11.5 per cent, and that of the Comptonized emission was constrained at 7.7 ± 2.5 per cent (at the 3σ level). The results suggest that the Comptonized component probably originates from a spreading-layer/boundary-layer above the neutron star surface. IXPE observation of the source on 2022 October 8 does not show significant polarization which can be attributed to a weakening of the coronal Comptonized emission. The implication of these results are discussed. [ABSTRACT FROM AUTHOR]

In this study, it is shown how the photoluminescence, scintillation, and optical thermometric properties are managed via the introduction of Gd3+ ions into Pr3+:Lu2Zr2O7. Raman spectra validate that partial replacement of Lu3+ with Gd3+ can promote the phase transition of Lu2Zr2O7 host from the defective fluorite structure to the ordered pyrochlore one. Upon 289 nm excitation, all the samples emit the 483 (3P0 → 3H4), 581 (1D2 → 3H4), 611 (3P0 → 3H6), 636 (3P0 → 3F2), and 714 nm (3P0 → 3F4) emissions from Pr3+ ions, which are enhanced with the addition of Gd3+ ions due to the modification of crystal structure. Dissimilarly, the X‐ray excited luminescence spectra consist of a strong emission located at 314 nm from Gd3+ ions (6P7/2 → 8S7/2), together with the typical emissions from Pr3+ ions, which exhibit different dependences on the Gd3+ concentration. When the luminescence intensity ratio between the 3P0 → 3H6 (611 nm) and 1D2 → 3H4 (581 nm) transitions is selected for temperature sensing, Pr3+:(Lu0.75Gd0.25)2Zr2O7 shows the optimal relative sensing sensitivity of 0.78% K−1 at 303 K, which is higher than that of the Gd3+‐free sample. Therefore, the developed Pr3+:(Lu, Gd)2Zr2O7 phosphors have the applicative potential for optical thermometry, X‐ray detection, and photodynamic therapy. [ABSTRACT FROM AUTHOR]

Our study focuses on analysing the coronal, transition and chromospheric activity of four rapidly rotating stars located within 50 pc in the solar neighborhood. We have used the multi-wavelength capabilities of AstroSat to investigate the outer atmospheres of AB Dor, BO Mic, DG CVn and GJ 3331. These stars, classified as M and K type active stars, are known for their short rotation periods, leading to increased surface magnetic activity. Our soft X-ray observations provide the coronal properties, such as emission measures, temperatures and elemental coronal abundances. We reported the detection of X-ray flares from AB Dor, BO Mic and DG CVn, while UV light curves reveal flares in both BO Mic and DG CVn. [ABSTRACT FROM AUTHOR]

Background: Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. Method: A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. Results: In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. Conclusions: Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels. [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]

In our previous studies, the silicon drift detector (SDD) structure with a constant spiral ring cathode gap (g) and a given surface electric field has been partially investigated based on the physical model that gives an analytical solution to the integrals in the calculations. Those results show that the detector has excellent electrical characteristics with a very homogeneous carrier drift electric field. In order to cope with the implementation of the theoretical approach with a complete set of technical parameters, this paper performs different theoretical algorithms for the technical implementation of the detector performance using the Taylor expansion method to construct a model for cases where the parameter "j" is a non-integer, approximating the solution with finite terms. To verify the accuracy of this situation, we performed a simulation of the relevant electrical properties using the Sentaurus TCAD tool 2018. The electrical properties of the single and double-sided detectors are first compared, and then the effects of different equal gaps g (g = 10 μm, 20 μm, and 25 μm, respectively) on the electrical properties of the double-sided detectors are analyzed and demonstrated. By analyzing and comparing the electrical characteristics data from the simulation results, we can show that the double-sided structure has a larger transverse drift electric field, which improves the spatial position resolution as well as the response speed. The effect of the gap size on the electrical characteristics of the detector is also analyzed by analyzing three different gap bifacial detectors, and the results show that a 10 μm equal gap is the optimal design. Such results can be used in applications requiring large-area SDD, such as the pulsar X-ray autonomous navigation. in the future to provide navigation and positioning space services for spacecraft deep-space exploration. [ABSTRACT FROM AUTHOR]

By expanding the applications of GEM detectors, a newer pattern of such detectors was introduced in 2004, named THGEM detectors. In this work, a sample of an X-ray detector was designed and constructed using 2 cm x 2 cm THGEMs domestically produced with a thickness of 250 µm, a hole diameter of 300 µm and a pitch of 500 µm, for the first time. The triple THGEM detector working in Ar/CO2 gas mixture was characterized. Influence of gas pressure and gas mixture on gain of the detector was investigated. Results show the detector operated in a stable mode with no discharges. The gain of the detector increased with high voltage across the THGEM electrodes exponentially. This verified the performance of a detector as a proportional counter. Also, the detector's gain is maximum at Ar/CO2 (80/20) gas mixture and voltage of 700 V applied to each multiplier. The detector is promising for localization applications such as particle physics experiments. [ABSTRACT FROM AUTHOR]

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The process parameters in the low-pressure casting of large-size aluminum alloy wheels are systematically optimized in this work using numerical casting simulation, response surface methodology (RSM), and genetic algorithm (NSGA-II). A nonlinear input–output relationship was established based on the Box–Behnken experimental design (BBD) for the crucial casting parameters (pouring temperature, mold temperature, holding pressure, holding time), and response indicators (defect volume fraction, spokes large plane mean secondary dendrite spacing (SDAS)), and a mathematical model was developed by regression analysis. The Isight 2017 Design Gateway and NSGA-II algorithm were used to increase the population and look for the best overall solution for the casting parameters. The significance and predictive power of the model were assessed using ANOVA. Casting numerical simulation was used to confirm the best option. To accomplish systematic optimization in its low-pressure casting process, the mold cooling process parameters were adjusted following the local solidification rate. The results showed that the mathematical model was reliable. The optimal solutions were a pouring temperature of 703 °C, mold temperature of 409 °C, holding pressure of 1086 mb, and holding time of 249 s. The mold cooling process was further optimized, and the sequence solidification of the optimal solution was realized under the optimized cooling process. Finally, the wheel hub was manufactured on a trial basis. The X-ray detection, mechanical property analysis, and metallographic observation showed that the wheel hub had no X-ray defects and its mechanical properties were well strengthened. The effectiveness of the system optimization process scheme was verified. [ABSTRACT FROM AUTHOR]

Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 and DenseNet169 models bolstered by the HyperColumn and the CBAM, distinct improvements in fracture site prediction emerge. Significantly, when HyperColumn and CBAM integration is applied, both DenseNet169 and EfficientNet-B0 showed noteworthy accuracy improvements, with increases of approximately 0.69% and 0.70%, respectively. The HyperColumn-CBAM-DenseNet169 model particularly stood out, registering an uplift in the AUC score from 0.8778 to 0.9145. The incorporation of Grad-CAM technology refined the heatmap's focus, achieving alignment with expert-recognized fracture sites and alleviating the deep-learning challenge of heavy reliance on bounding box annotations. This innovative approach signifies potential strides in streamlining training processes and augmenting diagnostic precision in fracture detection. [ABSTRACT FROM AUTHOR]

Prohibited item detection plays a significant role in ensuring public safety, as the timely and accurate identification of prohibited items ensures the safety of lives and property. X-ray transmission imaging technology is commonly employed for prohibited item detection in public spaces, producing X-ray images of luggage to visualize their internal contents. However, challenges such as multiple object overlapping, varying angles, loss of details, and small targets in X-ray transmission imaging pose significant obstacles to prohibited item detection. Therefore, a dual attention mechanism network (DAMN) for X-ray prohibited item detection is proposed. The DAMN consists of three modules, i.e., spatial attention, channel attention, and dependency relationship optimization. A long-range dependency model is achieved by employing a dual attention mechanism with spatial and channel attention, effectively extracting feature information. Meanwhile, the dependency relationship module is integrated to address the shortcomings of traditional convolutional networks in terms of short-range correlations. We conducted experiments comparing the DAMN with several existing algorithms on datasets containing 12 categories of prohibited items, including firearms and knives. The results show that the DAMN has a good performance, particularly in scenarios involving small object detection, detail loss, and target overlap under complex conditions. Specifically, the detection average precision of the DAMN reaches 63.8%, with a segmentation average precision of 54.7%. [ABSTRACT FROM AUTHOR]

Kaonic atom X-ray spectroscopy is a consolidated technique for investigations on the physics of strong kaon–nucleus/nucleon interaction. Several experiments have been conducted regarding the measurement of soft X-ray emission (<20 keV) from light kaonic atoms (hydrogen, deuterium, and helium). Currently, there have been new research activities within the framework of the SIDDHARTA-2 experiment and EXCALIBUR proposal focusing on performing precise and accurate measurements of hard X-rays (>20 keV) from intermediate kaonic atoms (carbon, aluminum, and sulfur). In this context, we investigated cadmium–zinc–telluride (CdZnTe or CZT) detectors, which have recently demonstrated high-resolution capabilities for hard X-ray and gamma-ray detection. A demonstrator prototype based on a new cadmium–zinc–telluride quasi-hemispherical detector and custom digital pulse processing electronics was developed. The detector covered a detection area of 1 cm2 with a single readout channel and interesting room-temperature performance with energy resolution of 4.4% (2.6 keV), 3% (3.7 keV), and 1.4% (9.3 keV) FWHM at 59.5, 122.1, and 662 keV, respectively. The results from X-ray measurements at the DAΦNE collider at the INFN National Laboratories of Frascati (Italy) are also presented with particular attention to the effects and rejection of electromagnetic and hadronic background. [ABSTRACT FROM AUTHOR]

Based on the expected population of core collapse supernova remnants and the huge number of detected pulsars in the Galaxy, still representing only a fraction of the real population, pulsar wind nebulae are likely to constitute one of the largest classes of extended Galactic sources in many energy bands. For simple evolutionary reasons, the majority of the population is made of evolved systems, whose detection and identification are complicated by their reduced luminosity, the possible lack of X-ray emission (that fades progressively away with the age of the pulsar), and by their modified morphology with respect to young systems. Nevertheless they have gained renewed attention in recent years, following the detection of misaligned X-ray tails protruding from an increasing number of nebulae created by fast moving pulsars, and of extended TeV halos surrounding aged systems. Both these features are clear signs of an efficient escape of particles, with energy close to the maximum acceleration limit of the pulsar. Here we discuss the properties of those evolved systems and what we have understood about the process of particle escape, and the formation of observed features. [ABSTRACT FROM AUTHOR]

Diodes are exposed to radiation in many operating environments, and it is important to investigate the radiation effect. In this study, the impact of X-ray radiation on the electrical properties of Mo/Cu(In0.7Ga0.3)Te2/p-Si/Al semiconductor diode was explored by I-V measurements performed before and after radiation doses between 200 and 1000 centiGray. The semiconductor diode was exposed to radiation by a linear accelerator having 6 MV X-ray. I-V measurements and the Cheung-Cheung method demonstrated the differences in series resistance, ideality factor, and barrier height. Moreover, the interface state density was obtained from I-V results. The radiation dose increased the ideality factor and decreased the barrier height. This result was thought to be due to the increase in the interface state density and the defects in the diode interface. The series resistance was increased by increasing the radiation dose due to a possible decrease in mobility and free carrier concentration. As a result of exposure to X-ray, defects occurred and due to these defects, the diode deviated from the ideal. It has been observed that the electrical properties of the diode were sensitive to X-ray radiation. The study demonstrated that the Mo/Cu(In0.7Ga0.3)Te2/p-Si/Al semiconductor diode can be implemented in X-ray radiation detection systems. [ABSTRACT FROM AUTHOR]

This article experimentally investigates the inception of an innovative hard X-ray photon energy attenuation layer (PAL) to advance high-energy X-ray detection (20–50 keV). A bi-layer design with a thin film high-Z PAL on the top and Si image sensor on the bottom has previously demon-strated quantum yield enhancement via computational methods by the principle of photon energy down conversion (PEDC), where high-energy X-ray photon energies are attenuated via inelastic scattering down to ≤10 keV, which is suitable for efficient photoelectric absorption by Si. Quantum yield enhancement has been experimentally confirmed via a preliminary demonstration using PAL-integrated Si-based CMOS image sensors (Si CIS). Furthermore, substituting the high-Z PAL with a lower-Z material—Sn—and alternatively coupling it with a conventional scintillator ma-terial—Lutetium-yttrium oxyorthosilicate (LYSO)—have been compared to demonstrate the most prominent efficacy of monolithic integration of high-Z PAL on Si CIS to detect hard X-rays, paving the way for next-generation high-energy X-ray detection methods. [ABSTRACT FROM AUTHOR]

The bronze Zun was one of the more prevalent high-class wine containers of the Han dynasty, representing the highest level of decoration in bronze at the time. However, little has been reported about its technical characteristics and scientific value. In this paper, the samples were selected for analysis based on scientific analysis, following the principle of "minimal intervention", and a bronze Zun from the Han dynasty in the Gansu Provincial Museum collection was studied using ultra-deep field microscopy, X-ray flaw detection, X-ray fluorescence spectrometry (XRF), scanning electron microscopy (SEM), and energy spectrometry (EDS). The results show that the gold and silver decoration on the bronze is inlaid rather than gilt. Secondly, the body and lid of the vessel are molded in one shot, with the bird-head-shaped and animal-foot-shaped components cast separately and then attached to the lid and body. Thirdly, the corrosion of the bronze Zun is characterized by the copper matrix being corroded first and most severely, followed by the silver and, finally, the gold. The high purity of the gold wire embedded in this bronze Zun, the fine width of gold wire (154–190 μm), and the magnificent decoration show the excellent processing technology level of the precious metal and the high aesthetic level of ancient man during the Han dynasty. The results of the analysis of this bronze Zun can provide an essential reference for research on bronze vessels of the same type, the techniques of gold and silver misalignment, and the development of the history of bronze manufacture and technology during the Han dynasty. [ABSTRACT FROM AUTHOR]

Currently, X-ray inspection systems may produce false detections due to factors such as the varying sizes of contraband images, complex backgrounds, and blurred edges. To address this issue, we propose the YOLO-CID method for contraband image detection. Firstly, we designed the MP-OD module in the backbone network to enhance the model's ability to extract key information from complex background images. Secondly, at the neck of the network, we designed a simplified version of BiFPN to add cross-scale connection lines in the feature fusion structure, to preserve deeper semantic information and enhance the network's ability to represent objects in low-contrast or occlusion situations. Finally, we added a new object detection layer to improve the model's accuracy in detecting small objects in dense environments. Experimental results on the PIDray public dataset show that the average accuracy rate of the YOLO-CID algorithm is 82.7% and the recall rate is 81.2%, which are 4.9% and 3.2% higher than the YOLOv7 algorithm, respectively. At the same time, the mAP on the CLCXray dataset reached 80.2%. Additionally, it can achieve a real-time detection speed of 40 frames per second and 43 frames per second in real scenes. These results demonstrate the effectiveness of the YOLO-CID algorithm in X-ray contraband detection. [ABSTRACT FROM AUTHOR]

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures—as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications. [ABSTRACT FROM AUTHOR]

Rapid sensory detection of X-ray stimulation has been documented across a wide variety of species, but few studies have explored the underlying molecular mechanisms. Here we report the discovery of an acute behavioral avoidance response in wild type Caenorhabditis elegans to X-ray stimulation. The endogenous C. elegans UV-photoreceptor protein LITE-1 was found to mediate the locomotory avoidance response. Transgenic expression of LITE-1 in C. elegans muscle cells resulted in paralysis and egg ejection responses to X-ray stimulation, demonstrating that ectopic expression of LITE-1 can confer X-ray sensitivity to otherwise X-ray insensitive cells. This work represents the first demonstration of rapid X-ray based genetically targeted (X-genetic) manipulation of cellular electrical activity in intact behaving animals. Our findings suggest that LITE-1 has strong potential for use in this minimally invasive form of neuromodulation to transduce transcranial X-ray signals for precise manipulation of neural activity in mammals, bypassing the need for invasive surgical implants to deliver stimulation. [ABSTRACT FROM AUTHOR]

Lead halide perovskites have made remarkable progress in the field of radiation detection owing to the excellent and unique optoelectronic properties. However, the instability and the toxicity of lead‐based perovskites have greatly hindered its practical applications. Alternatively, lead‐free perovskites with high stability and environmental friendliness thus have fascinated significant research attention for direct X‐ray detection. In this review, the current research progress of X‐ray detectors based on lead‐free halide perovskites is focused. First, the synthesis methods of lead‐free perovskites including single crystals and films are discussed. In addition, the properties of these materials and the detectors, which can provide a better understanding and designing satisfactory devices are also presented. Finally, the challenge and outlook for developing high‐performance lead‐free perovskite X‐ray detectors are also provided. [ABSTRACT FROM AUTHOR]

Perovskite single crystals are actively studied as X-ray detection materials with enhanced sensitivity. Moreover, the feasibility of using perovskites for self-powered devices such as photodetectors, UV detectors, and X-ray detectors can significantly expand their application range. In this work, the charge carrier transport and photocurrent properties of MAPbBr3 single crystals (MSCs) are improved by the mechanochemical surface treatment using glycerin combined with an additional electrode design that forms an ohmic contact. The sensitivity of MSC-based detectors and pulse shape generated by X-rays are enhanced at various bias voltages. The synthesized MSC detectors generate direction-dependent photocurrents, which indicate the presence of a polarization-induced internal electric field. In addition, photocurrent signals are produced by X-rays with energies greater than 1 MeV under a zero-bias voltage. This work demonstrates a high application potential of perovskites as self-powered detectors for X-rays with energies exceeding 1 MeV. [ABSTRACT FROM AUTHOR]

This article explores various theoretical models proposed to explain the soft x‐ray excess phenomenon, including warm Comptonization, ionized reflection models, and ionized outflowing disc winds. The soft x‐ray excess is better understood thanks to crucial observations made by ROSAT, XMM‐Newton, and eROSITA. The article briefly touches upon historical discoveries in this field, specifically the initial detection of the soft x‐ray excess in Narrow Line Seyfert 1 galaxies. While progress has been made in comprehending the soft x‐ray excess, the article also acknowledges the challenges and limitations of the different models. We emphasize the importance of further studies to refine theoretical models by, for example, building up a large database of reverberation time lags between the disc and coronal x‐ray emission to achieve a more comprehensive understanding of this intriguing phenomenon. [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]

Two-dimensional transition metal dichalcogenides (TMDCs) are demonstrated to be appealing semiconductors for optoelectronic applications, thanks to their remarkable properties in the ultraviolet-visible spectral range. Interestingly, TMDCs have not yet been characterized when exposed to x rays, although they would be ideal candidates for optoelectronic applications in this spectral range. They benefit from the high cross section of the constituent heavy atoms, while keeping the absorption very low, due to the ultrathin structure of the film. This encourages the development of photodetectors based on TMDCs for several applications dealing with x rays, such as radioprotection, medical treatments, and diagnosis. Given the atomic thickness of TMDCs, they can be expected to perform well at low dose measurements with minimal perturbation of the radiation beam, which is required for in vivo applications. In this paper, the use of TMDCs as active materials for direct x-ray detection is demonstrated, using a photodetector based on a MoS2 monolayer (1L-MoS2). The detector shows a response to x rays in the range of 101–102 keV, at dose rates as low as fractions of mGy/s. The sensitivity of 1L-MoS2 reaches values in the range of 108–109µC Gy−1 cm−3, overcoming the values reported for most of the organic and inorganic materials. To improve the x-ray photoresponse even further, the 1L-MoS2 was coupled with a polymeric film integrating a scintillator based on terbium-doped gadolinium oxysulfide (Gd2O2S:Tb). The resulting signal was three times larger, enabled by the indirect x ray to visible photoconversion mechanism. This paper might pave the way toward the production of ultrathin real-time dosimeters for in vivo applications. [ABSTRACT FROM AUTHOR]

Broadband photodetectors (PDs) with low detection limits hold significant importance to next‐generation optoelectronic devices. However, simultaneously detecting broadband (i.e., X‐ray to visible regimes) and weak lights in a single semiconducting material remains highly challenging. Here, by alloying iodine‐substituted short‐chain cations into the 3D FAPbI3 (FA = formamidine), a new 2D bilayered lead iodide hybrid perovskite, (2IPA)2FAPb2I7 (1, 2IPA = 2‐iodopropylammonium), that enables addressing this challenge is reported. Such a 2D multilayered structure and lead iodide composition jointly endow 1 with a minimized dark current (6.04 pA), excellent electrical property, and narrow bandgap (2.03 eV), which further gives it great potential for detecting broadband weak lights. Consequently, its high‐quality single crystal PDs exhibit remarkable photoresponses to weak ultraviolet–visible lights (377–637 nm) at several tens of nW cm−2 with high responsivities (>102 mA W−1) and significant detectivities (>1012 Jones). Moreover, 1 has an excellent X‐ray detection performance with a high sensitivity of 438 µC Gy−1 cm−2 and an ultralow detection limit of 20 nGy s−1. These exceptional attributes make 1 a promising material for broadband weak lights detection, which also sheds light on future explorations of high‐performance PDs based on 2D hybrid perovskites. [ABSTRACT FROM AUTHOR]