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Buckwheat is a typical medicinal and edible crop with high nutritional value, and the application of appropriate fertilizer can improve the quality of buckwheat starch. The changes of internal structure and physicochemical characteristics of common buckwheat grain starch under low, medium, and high fertilization levels are investigated. With increasing fertilizer amount, the volume proportion is increased, while the granule size distribution is reduced significantly. All of the common buckwheat starch samples show a typical A‐type pattern in X‐ray diffraction detection. Furthermore, with the increase of fertilization level, the amylose content, retrogradation, and the ratio of 1045/1022 cm−1 reduce and then raise, but the relative crystallinity, the ratio of 1022/995 cm−1, light transmittance, solubility, and swelling power increase and then decrease. In comparison to other fertilization levels, common buckwheat starch at the medium level has greater pasting temperatures and gelatinization enthalpy but lower pasting viscosities. These results indicate the medium fertilization level of 180 (N), 115.2 (P), and 84.6 (K) kg h−1 m−2 is more suitable for buckwheat starch quality, which can give information for buckwheat cultivation. [ABSTRACT FROM AUTHOR]

Organic materials show good mechanical flexibility, easy processing, and large‐area manufacturing, while weak X‐ray absorption and low sensitivity for X‐ray scintillator media. As seen originally, this is a synergistic result of the lack of heavy elements in the molecule that can effectively truncate X‐rays and the massive dissipation of triplet excitons produced by molecular ionization. Herein, cocrystal with phosphorescence enhancement is found to be brilliant in addressing the shortcomings of organic scintillators. The results show that heavy atoms can be easily introduce into the donor unit of organic cocrystal to satisfy the X‐ray absorption and effectively activate the triplet excitons via promoting intersystem crossing (ISC). Moreover, the rational structural design of the acceptor molecule can achieve coupling with the donor for inhibiting the non‐radiative loss of the triplet exciton. Finally, the preferred PNPA (4,4′‐Dibromobiphenyl and N‐phenylnaphthalen‐2‐amine) cocrystals achieve record X‐ray‐induce exciton utilization and emit bright phosphorescent light. Meanwhile, the related device shows durable radiation stability, spatial resolution (>11 lp mm−1), low detection limit (<0.6 µGyair s−1), and effective dynamic imaging application. Hence, it is believed the organic cocrystal‐designed principle can provide guidance to develop advanced sensitive X‐ray scintillators in the future. [ABSTRACT FROM AUTHOR]

The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogues of astrophysical sources. Classification is useful for the study of individual objects, statistics for population studies, as well as for anomaly detection, that is, the identification of new unexplored phenomena, including transients and spectrally extreme sources. Despite the importance of this task, classification remains challenging in X-ray astronomy due to the lack of optical counterparts and representative training sets. We develop an alternative methodology that employs an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources with a limited number of labelled sources, and without ancillary information from optical and infrared catalogues. We provide a catalogue of probabilistic classes for 8756 sources, comprising a total of 14 507 detections, and demonstrate the success of the method at identifying emission from young stellar objects, as well as distinguishing between small- and large-scale compact accretors with a significant level of confidence. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses such as the unified active galactic nucleus model. This provides interpretability to the probabilistic classifier. Code and tables are available publicly through GitHub. We provide a web playground for readers to explore our final classification at https://umlcaxs-playground.streamlit.app. [ABSTRACT FROM AUTHOR]

This study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice. [ABSTRACT FROM AUTHOR]

2D Dion–Jacobson (DJ) perovskites recently have attracted extensive attention for X‐ray detection owing to their structural rigidity and environmental stability. The chemical incorporation of π‐conjugation aromatic spacing cations into DJ phase perovskites will increase the rigidity of cation and electron cloud density between adjacent inorganic slabs, thus facilitating structural rigidity and charge transport. However, it is still a challenge to synthesize high‐quality single crystals of DJ phase perovskite with aromatic spacers for detecting X‐ray applications. Here, a new DJ phase perovskite by introducing π‐conjugated aromatic cation spacer, (4ABA)PbI4 (1, 4ABA = 4‐aminobenzylamine), eliminating the van der Waals gap is designed. High‐quality single crystals of 1 are grown via the facile solution method, exhibiting high resistivity (1.97 × 1011 Ω cm), low trap density (3.36 × 109 cm−3), and large carrier mobility‐lifetime product (1.03 × 10−3 cm2 V−1) under X‐ray illumination. Such remarkable physical characteristics endow single crystal 1 detector with high‐performance X‐ray detection including a high sensitivity of 572 µC Gyair−1cm−2, an ultralow detection limit of 7.50 nGyair s−1 and exceptional radiation stability. The X‐ray detection limit of 1 is over 700 times lower than that of routine medical diagnosis (5500 nGyair s−1), demonstrating potential applications in medical examination. [ABSTRACT FROM AUTHOR]

Recent developments in x-ray image based pulmonary nodule detection have achieved remarkable results. However, existing methods are focused on transferring off-the-shelf coarse-grained classification models and fine-grained detection models rather than developing a dedicated framework optimized for nodule detection. In this paper, we propose PN-DetX, which as we know is the first dedicated pulmonary nodule detection framework. PN-DetX incorporates feature fusion and self-attention into x-ray based pulmonary nodule detection tasks, achieving improved detection performance. Specifically, PN-DetX adopts CSPDarknet backbone to extract features, and utilizes feature augmentation module to fuse features from different levels followed by context aggregation module to aggregate semantic information. To evaluate the efficacy of our method, we collect a LA rge-scale P ulmonary NO dule D etection dataset, LAPNOD, comprising 2954 x-ray images along with expert-annotated ground truths. As we know, this is the first large-scale chest x-ray pulmonary nodule detection dataset. Experiments demonstrates that our method outperforms baseline by 3.8% mAP and 5.1% AP 0.5. The generality of our approach is also evaluated on the publicly available dataset NODE21. We aspire for our method to serve as an inspiration for future research in the field of pulmonary nodule detection. The dataset and codes will be made in public. [ABSTRACT FROM AUTHOR]

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time. [ABSTRACT FROM AUTHOR]

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model's integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model's high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment. [ABSTRACT FROM AUTHOR]

The recently reported two-dimensional lead halide perovskites have attracted much attention in high-energy radiation detector applications due to its high structural stability and low ion migration. However, the increased exciton binding energy and the charge transport barrier resulting from the A-site cation can easily limit the charge extraction and device performance. As an emerging material in high-energy detection field, guanidinium {GA+ = [C(NH2)3]+} is a relatively large organic cation composed of three amino groups, which can enhance the hydrogen bond formation and the van der Waals interactions with the metal halide framework. Meanwhile, the polymer symmetry of GA+ cation leads to almost zero dipole moments, excellent electronic properties, and high thermodynamic stability. However, there is a lack of report on the preparation, structure, and properties of GA-based 2D perovskite single crystals as well as their applications in x-ray detection. Herein, we have synthesized large-size two-dimensional GA2PbI4 single crystals with a bandgap of 2.28 eV by cooling crystallization and applied them to x-ray detectors. GA2PbI4 single crystals exhibit a high μτ product of 7.5 × 10−4 cm2 V−1 and a low trap density of 6.44 × 108 cm−3. An effective x-ray photoresponse was realized with a sensitivity as high as 392.4 μC Gyair−1 cm−2. The unique structure and excellent charge transport properties of GA+ organic cations exhibit promising prospects in high-performance x-ray detection. [ABSTRACT FROM AUTHOR]

Wavelength dispersive X-ray fluorescence (WD-XRF) spectroscopy is a widely used instrumental spectroscopy method for a variety of samples and it requires no or a minimal of sample preparation. Compared to using the more conventional energy dispersive XRF spectrometers, WD-XRF spectroscopy has the advantage of a highly improved energy resolution, making this technique suitable for the detection of characteristic X-rays separated by only a few eV. WD-XRF is established both in industry for routine analyses and in the academic world for high-end research and development of the method. WD-XRF spectroscopy can be probed with a laboratory X-ray source and if higher X-ray-beam intensities, monochromatic and polarized beams are required a synchrotron X-ray radiation source can be used. In (synchrotron) radiation facilities all over the world interesting new setups are characterized and used. This review paper will give an overview of the important characteristic properties, such as energy resolution, sensitivity, and the reachable energy range of the different types of WD-XRF setups available and reported in the last 20 years, based on laboratory X-ray sources, radiation facility sources or availability as a commercial instrument. The commercially available instruments are listed by the manufacturer and type of instrument, and for each instrument at least one example of an application is shown. [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]

Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million. [ABSTRACT FROM AUTHOR]

Hybrid pixel detectors have become indispensable at synchrotron and X-ray free-electron laser facilities thanks to their large dynamic range, high frame rate, low noise, and large area. However, at energies below 3 keV, the detector performance is often limited because of the poor quantum efficiency of the sensor and the difficulty in achieving single-photon resolution due to the low signal-to-noise ratio. In this paper, we address the quantum efficiency of silicon sensors by refining the design of the entrance window, mainly by passivating the silicon surface and optimizing the dopant profile of the n+ region. We present the measurement of the quantum efficiency in the soft X-ray energy range for silicon sensors with several process variations in the fabrication of planar sensors with thin entrance windows. The quantum efficiency for 250 eV photons is increased from almost 0.5% for a standard sensor to up to 62% as a consequence of these developments, comparable to the quantum efficiency of backside-illuminated scientific CMOS sensors. Finally, we discuss the influence of the various process parameters on quantum efficiency and present a strategy for further improvement. [ABSTRACT FROM AUTHOR]

This literature review charts the early evolution of silver halide photographic technology in photo-mechanical printing for artwork reproduction. It concentrates on a key point in time around the 1890s and most specifically around 1895 as these dates are significant in this evolution. By this time dry gelatin Process and Half-tone plates were commercially available as an alternative to the wet collodion process and a comparison is made between these. The work contains references to the contemporary literature to aid further research and uses this to show how studies published on the spectral sensitivities and reciprocity behaviour of commercial products can provide an insight into the formulation of these materials. It places these products in context of the development of the photographic detection of X-rays and radioactivity and later products such as line film. [ABSTRACT FROM AUTHOR]

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]

Hyperluminous infrared galaxies (HyLIRGs) are the most extreme star-forming systems observed in the early Universe, and their properties still elude comprehensive understanding. We have undertaken a large XMM – Newton observing programme to probe the total accreting black hole population in three HyLIRGs at z = 2.12, 3.25, and 3.55, gravitationally lensed by foreground galaxies. Selected from the Planck All-Sky Survey to Analyse Gravitationally lensed Extreme Starbursts (PASSAGES), these HyLIRGs have apparent infrared luminosities >1014 L⊙. Our observations revealed X-ray emission in each of them. PJ1336+49 appears to be dominated by high-mass X-ray binaries (HMXBs). Remarkably, the luminosity of this non-AGN X-ray emission exceeds by a factor of about 3 the value obtained by calibration with local galaxies with much lower star formation rates. This enhanced X-ray emission most likely highlights the efficacy of dynamical HMXB production within compact clusters, which is an important mode of star formation in HyLIRGs. The remaining two (PJ0116−24 and PJ1053+60) morphologically and spectrally exhibit a compact X-ray component in addition to the extended non-AGN X-ray emission, indicating the presence of Active Galactic Nuclei (AGNs). The AGN appears to be centrally located in the reconstructed source plane images of PJ0116−24, which manifests its star-forming activity predominantly within an extended galactic disc. In contrast, the AGN in the field of PJ1053+60 is projected 60 kpc away from the extreme star-forming galaxy and could be ejected from it. These results underline the synergistic potential of deep X-ray observations with strong lensing for the study of high-energy astrophysical phenomena in HyLIRGs. [ABSTRACT FROM AUTHOR]

We report on IXPE, NICER, and XMM–Newton observations of the magnetar 1E 2259+586. We find that the source is significantly polarized at about or above 20  per cent for all phases except for the secondary peak where it is more weakly polarized. The polarization degree is strongest during the primary minimum which is also the phase where an absorption feature has been identified previously. The polarization angle of the photons are consistent with a rotating vector model with a mode switch between the primary minimum and the rest of the rotation of the neutron star. We propose a scenario in which the emission at the source is weakly polarized (as in a condensed surface) and, as the radiation passes through a plasma arch, resonant cyclotron scattering off of protons produces the observed polarized radiation. This confirms the magnetar nature of the source with a surface field greater than about 1015 G. [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]

With the increasing demands of X‐ray detection and medical diagnosis, organic scintillators with intense and tunable X‐ray excited emission have been becoming important. To guarantee the X‐ray absorption, heavy atoms were widely added in reported organic scintillators, which led to emission quenching. In this work, we propose a new strategy to realize organic scintillators through the host‐guest doping strategy. Then the X‐ray absorption centers (host) and emission centers (guest) are separated. Under X‐ray excitation, these materials displayed intense and readily tunable emissions ranging from green (520 nm) to near infrared (NIR) regions (682 nm). Besides, the relationship between the X‐ray absorption and spatial arrangement of the heavy atoms in the host matrix was also revealed. The potential application of these wide‐range color tunable organic host‐guest scintillators in X‐ray imaging were demonstrated. This work provides a new feasible strategy for constructing high‐performance organic scintillators with tunable luminescence properties. [ABSTRACT FROM AUTHOR]

With the increasing demands of X‐ray detection and medical diagnosis, organic scintillators with intense and tunable X‐ray excited emission have been becoming important. To guarantee the X‐ray absorption, heavy atoms were widely added in reported organic scintillators, which led to emission quenching. In this work, we propose a new strategy to realize organic scintillators through the host‐guest doping strategy. Then the X‐ray absorption centers (host) and emission centers (guest) are separated. Under X‐ray excitation, these materials displayed intense and readily tunable emissions ranging from green (520 nm) to near infrared (NIR) regions (682 nm). Besides, the relationship between the X‐ray absorption and spatial arrangement of the heavy atoms in the host matrix was also revealed. The potential application of these wide‐range color tunable organic host‐guest scintillators in X‐ray imaging were demonstrated. This work provides a new feasible strategy for constructing high‐performance organic scintillators with tunable luminescence properties. [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]

In recent years, the research and development of perovskite long afterglow materials has received extensive attention. In this paper, we review the research progress of ABO3-type inorganic halide perovskite (including semiconductor and metal halides) long afterglow materials. Initially, we have introduced in detail the afterglow properties of perovskite long afterglow materials (including the afterglow wavelength, afterglow intensity, and afterglow time), their afterglow mechanism (based on the three most widely accepted basic theoretical models, namely the conduction band–valence band model, quantum tunneling model, oxygen vacancy model, and the complex experimental models of specific materials) and afterglow applications (including optical information storage, information encryption, anti-counterfeiting, X-ray detection and imaging, and biological imaging and therapy). Finally, the customization and regulation of the afterglow properties of perovskite long afterglow materials, the expansion of their applications, and the development of perovskite matrix materials are prospected, hoping to provide a reference for the reasonable design and application of long afterglow materials in the future. [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]

2D perovskites have attracted wide attention for optoelectronic applications because of their unique layer structure and tunable outstanding optical/electrical properties. In addition, 2D Cs3Bi2Br9 nanoflakes possess large effective atomic number, high resistivity, high density as well as excellent stability, rendering it a promising material for X‐ray detection. Nevertheless, it is full of challenges to synthesize 2D Cs3Bi2Br9 nanoflakes by conventional inversion temperature crystallization (ITC) strategy due to the existence of Br‐ vacancies in the Cs3Bi2Br9 crystal nucleus. Herein, an Ag+ assisted ITC (SAITC) strategy to grow 2D Cs3Bi2Br9 nanoflakes is proposed. The synthesis mechanism revealed by both experiments and theoretical calculations can be mainly ascribed to the passivated Br− vacancies and enhanced structure stability by adding Ag+ which can effectively prevent the oxidation of 2D Cs3Bi2Br9 nanoflakes from growth of hybrid crystals. The synthesized high‐crystallinity 2D Cs3Bi2Br9 nanoflakes possess direct bandgap characteristic, and the mobility lifetime can reach 9.8 × 10−4 cm2 V−1. Excitingly, the fabricated device based on 2D Cs3Bi2Br9 nanoflakes demonstrates ultrahigh sensitivity of detecting X‐ray (1.9 CGyair−1cm−2) at very low driven voltage (0.5 V) due to the photoconductive gain mechanism. The 2D Cs3Bi2Br9 nanoflakes synthesized by SAITC method have great potential for developing highly sensitive optoelectronic devices. [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]

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]

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]

More than 200 million X-ray images are taken annually in the United States of America using several medical imaging procedures like conventional X-ray radiography, computed tomography (CT), fluoroscopy, and mammography. About 12% of mammograms result in false positives due to poor image resolution between dense breast tissues and cancerous tissues. False positive results lead to additional follow-up screening that cost billion s of dollars each year. This work aims to improve image resolution utilizing a novel direct conversion perovskite–graphene imaging detector that will enhance the X-ray detection sensitivity, which will improve the overall image quality compared to existing a-Se sensors installed in tomosynthesis and mammography units. A graphene field effect transistor perovskite sensor prototype made with metal halide perovskites was built and tested at Oregon State University. The sensor that was exposed to a micro-CT X-ray beam with 35 kVp, registered a sensitivity of 870 uC/cm2/R. The Planar design of the perovskite–graphene sensor was exposed to a 35 kVp and 50 kVp energy beams and registered a 500 uC/cm2/R and 350 uC/cm2/R sensitivity, respectively. The sensor sensitivity was found to be 3–4 orders of magnitude greater than current a-Se sensors used in mammography systems. Improvements in sensitivity should lead to better image quality, and thus reduce the percentage of false positive results, which will decrease the need for secondary screenings. [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]

Conventional scintillator‐based X‐ray imaging typically captures the full spectral of X‐ray photons without distinguishing their energy. However, the absence of X‐ray spectral information often results in insufficient image contrast, particularly for substances possessing similar atomic numbers and densities. In this study, an innovative multi‐energy X‐ray linear‐array detector is presented that leverages side‐illuminated X‐ray scintillation using the emerging metal halide Cs3Cu2I5. The negligible self‐absorption characteristic not only improves the scintillation output but is also beneficial for improving the energy resolution for the side‐illuminated scintillation scenarios. By exploiting Beer's law, which governs the absorption of X‐ray photons with different energies, the incident X‐ray spectral can be reconstructed by analyzing the distribution of scintillation intensity when the scintillator is illuminated from the side. The relative error between the reconstructed and measured X‐ray spectral is < 5.63%. This method offers an additional energy‐resolving capability for X‐ray linear‐array detectors commonly used in computed tomography (CT) imaging setups, surpassing the capabilities of conventional energy‐integration approaches, all without requiring extra hardware components. A proof‐of‐concept multi‐energy CT imaging system featuring eight energy channels is successfully implemented. This study presents a simple and efficient strategy for achieving multi‐energy X‐ray detection and CT imaging based on emerging metal halides. [ABSTRACT FROM AUTHOR]

Cs3Bi2I9 (CBI) is a promising material for direct X‐ray detectors. However, for the solution method, due to the difference in the chemical potential of Cs+, Bi3+, I− ions in the solvent, the composition of CBI single crystals (SCs) prepared by the solution method often deviates from the stoichiometric ratio, resulting in the formation of many defects in the material, which degrades the quality of the SC. Herein, Br− and Cl− are used as dopants to produce CBI SCs by the top seed solution method. It can be speculated that the dopant ions can reduce the VI defects in the CBI by means of density functional theory calculations. The carrier lifetimes of CBI SCs doped with Br− and Cl− have been increased to 41.7 and 13.0 ns, respectively. Meanwhile, the defect densities of the SCs are reduced to 1.02 × 109 and 1.85 × 109 cm−3, respectively. X‐ray detectors based on Br‐doped CBI and Cl‐doped CBI SCs exhibit high X‐ray sensitivity of 23071.3 and 18525.3 μC Gyair−1 cm−2 at an electric field of 40 V mm−1, respectively. In addition, the X‐ray detection limit reaches 1.07 and 1.35 nGyairs−1 at an electric field of 2.5 V mm−1, respectively. [ABSTRACT FROM AUTHOR]

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]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [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]

Inorganic Bi‐based perovskites have shown great potential in X‐ray detection for their large absorption to X‐rays, diverse low‐dimensional structures, and eco‐friendliness without toxic metals. However, they suffer from poor carrier transport properties compared to Pb‐based perovskites. Here, we propose a mixed‐halogen strategy to tune the structural dimensions and optoelectronic properties of Cs3Bi2I9−nBrn (0≤n≤9). Ten centimeter‐sized single crystals are successfully grown by the Bridgman technique. Upon doping bromine to zero‐dimensional Cs3Bi2I9, the crystal transforms into a two‐dimensional structure as the bromine content reaches Cs3Bi2I8Br. Correspondingly, the optoelectronic properties are adjusted. Among these crystals, Cs3Bi2I8Br exhibits negligible ion migration, moderate resistivity, and the best carrier transport capability. The sensitivities in 100 keV hard X‐ray detection are 1.33×104 and 1.74×104 μC Gyair−1 cm−2 at room temperature and 75 °C, respectively, which are the highest among all reported bismuth perovskites. Moreover, the lowest detection limit of 28.6 nGyair s−1 and ultralow dark current drift of 9.12×10−9 nA cm−1 s−1 V−1 are obtained owing to the high ionic activation energy. Our work demonstrates that Br incorporation is an effective strategy to enhance the X‐ray detection performance by tuning the dimensional and optoelectronic properties. [ABSTRACT FROM AUTHOR]

Inorganic Bi‐based perovskites have shown great potential in X‐ray detection for their large absorption to X‐rays, diverse low‐dimensional structures, and eco‐friendliness without toxic metals. However, they suffer from poor carrier transport properties compared to Pb‐based perovskites. Here, we propose a mixed‐halogen strategy to tune the structural dimensions and optoelectronic properties of Cs3Bi2I9−nBrn (0≤n≤9). Ten centimeter‐sized single crystals are successfully grown by the Bridgman technique. Upon doping bromine to zero‐dimensional Cs3Bi2I9, the crystal transforms into a two‐dimensional structure as the bromine content reaches Cs3Bi2I8Br. Correspondingly, the optoelectronic properties are adjusted. Among these crystals, Cs3Bi2I8Br exhibits negligible ion migration, moderate resistivity, and the best carrier transport capability. The sensitivities in 100 keV hard X‐ray detection are 1.33×104 and 1.74×104 μC Gyair−1 cm−2 at room temperature and 75 °C, respectively, which are the highest among all reported bismuth perovskites. Moreover, the lowest detection limit of 28.6 nGyair s−1 and ultralow dark current drift of 9.12×10−9 nA cm−1 s−1 V−1 are obtained owing to the high ionic activation energy. Our work demonstrates that Br incorporation is an effective strategy to enhance the X‐ray detection performance by tuning the dimensional and optoelectronic properties. [ABSTRACT FROM AUTHOR]

The quality of welding is directly related to the performance and life of the welding structure. The nondestructive testing technology is mainly used for welding defect defection, while the X-ray testing technology can directly and reliably reflect the shape, location and size of defects. For X-ray images, manual inspection is used at present, and it is easily caused by subjective factors, such as professional level, which may lead to low accuracy. This paper focuses on establishing an end-to-end automatic detection model of X-ray welding defects to improve the accuracy and efficiency of detection based on a deep learning algorithm. Considering the feature information of welding defects, this paper improves on the basis of Faster R-CNN and uses the deep residual network Res2Net to enhance the original backbone network to improve the feature extraction ability. And the weighted feature fusion module is studied, which combines the high-level semantic information and the low-level high-resolution edge detail information to predict the feature map of each layer respectively, to improve the detection performance, especially for small targets. The experimental data show that this method can effectively improve the accuracy and efficiency of welding defect detection. [ABSTRACT FROM AUTHOR]

Objectives: Artificial intelligence (AI)-based applications for augmenting radiological education are underexplored. Prior studies have demonstrated the effectiveness of simulation in radiological perception training. This study aimed to develop and make available a pure web-based application called Perception Trainer for perception training in lung nodule detection in chest X-rays. Methods: Based on open-access data, we trained a deep-learning model for lung segmentation in chest X-rays. Subsequently, an algorithm for artificial lung nodule generation was implemented and combined with the segmentation model to allow on-the-fly procedural insertion of lung nodules in chest X-rays. This functionality was integrated into an existing zero-footprint webbased DICOM viewer, and a dynamic HTML page was created to specify case generation parameters. Results: The result is an easily accessible platformagnostic web application available at: https://castlemountain.dk/mulrecon/perceptionTrainer.html. The application allows the user to specify the characteristics of lung nodules to be inserted into chest X-rays, and it produces automated feedback regarding nodule detection performance. Generated cases can be shared through a uniform resource locator. Conclusion: We anticipate that the description and availability of our developed solution with open-sourced codes may help facilitate radiological education and stimulate the development of similar AI-augmented educational tools. Advances in knowledge: A web-based application applying AI-based techniques for radiological perception training was developed. The application demonstrates a novel approach for on-the-fly generation of cases in chest X-ray lung nodule detection employing deep-learning-based segmentation and lung nodule simulation. [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]

The amount of labeled samples always plays a significant role in deep learning. When there are insufficient training samples, deep learning usually struggles to carry out its task successfully. The problem becomes more prominent in that labeled data are expensive to obtain. Therefore, we want to address the problem by introducing a method that utilizes unlabeled samples to improve object detection performance. We propose a framework that utilizes a pseudo‐labeling approach in semi‐supervised learning and applies it to disease diagnosis of chest X‐ray images. Our objective is to train a model that works as a labeling expert and perform the labeling task for improving the object (disease area) detection model by these newly labeled samples. In addition, we also apply an ensemble technique to our framework to reduce the model bias and improve the generalization ability for pseudo‐labeling, which results in improvements in object detection performance. Our proposed method improves the mean average precision up to 5.32, compared with the method without the pseudo‐labeling. Additionally, the mean average precision further increases by up to 19.11 when applying ensemble learning. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The problem of detecting dangerous or prohibited objects in luggage is a very important step during the implementation of Security setup at Airports, Banks, Government buildings, etc. At present, the most common techniques for detecting such dangerous objects are by using intelligent data analysis algorithms such as deep learning techniques on X-ray imaging or employing a human workforce for inferring the presence of these threat objects in the obtained X-ray images. One of the major challenges while using deep-learning methods to detect such objects is the lack of high-quality threat image data containing the "dangerous" objects (objects of interest) versus the non-threat image data in practical scenarios. So, to tackle this data scarcity problem, anomaly detection techniques using normal data samples have shown great promise. Also, among the available Deep Learning Strategies for anomaly detection for computer vision applications, generative adversarial networks have achieved state-of-the-art results. Considering these insights, we adopted a newly proposed architecture known as Skip-GANomaly and devised a modified version of it by using a UNet++ style generator which performed better than Skip-GANomaly, getting an AUC of 94.94% on Compass-XP, a public X-ray dataset. Finally, for targeting better latent space exploration, we combine these two architectures into an Ensemble, which gives another boost to the performance, getting an AUC of 96.8% on the same Compass-XP, a public X-ray dataset. To further validate the effectiveness of ensemble-based architecture, its performance was tested on patch-based training data on a subset of randomly chosen images of another huge public X-ray dataset named as SIXray, and obtained an AUC of 75.3% on this reduced dataset. To demonstrate the prowess of the discriminator and to bring some explainability to the working of our ensemble, we have used Uniform Manifold Approximation and Projection to plot the latent-space vectors for the dangerous and non-dangerous objects of the test-set; this analysis indicates that the Ensemble learns better features for separating the anomalous class from non-anomalous with respect to the individual architectures. Thus, our proposed architecture provides state-of-the-art results for threat object detection. Most importantly, our models are able to detect threat objects without ever being trained on images containing threat objects. [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 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]

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]

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]

A novel radio-photoluminescence material featuring fluorochromic responses toward UV or X-ray irradiation has been obtained. Such a unique monomer- to excimer-based luminescence transition allows for dosimetry of ionizing radiation in a ratiometric manner. Rather than quenching the luminescence, the radiation-induced radical species of Th-105 boost the excimer emission, rendering it as a rare material possessing radical-excimers. [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]

Metal‐free perovskites are a promising class of materials for X‐ray detection due to their unique structural, optical, and electrical properties. Here, we first delve into the stoichiometry and geometric argument of metal‐free perovskites. Followed, the alternative A/B/X ions and hydrogen‐bonding are clearly introduced to further optimize the materials' stability and properties. Finally, we provide a comprehensive overview of their potential applications for flexible X‐ray images and prospects for metal‐free perovskite development. In conclusion, metal‐free perovskite is a promising material for X‐ray detection. Its stoichiometric and geometric parameters, ion, and hydrogen bond selection, and application prospects are worthy of further study. [ABSTRACT FROM AUTHOR]