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XTE J1701−462 is a neutron star low-mass X-ray binary (NS LMXB) discovered in 2006 as the first system to demonstrate unambiguously that the 'Atoll' and 'Z' classes of accreting neutron stars are separated by accretion rate. Radio observations during the 2006/7 outburst provided evidence for the formation of a relativistic jet, as now expected for all accreting neutron star and black hole X-ray binaries at high-accretion rates. The source entered a new outburst in 2022, and we report 29 observations made with the MeerKAT radio telescope. The first radio detection was on the 16th September 2022, we continued detecting the source until mid-December 2022. Thereafter, establishing radio upper limits till 25 March 2023. We present the radio analysis alongside analysis of contemporaneous X-ray observations from MAXI. The radio light curve shows evidence for at least three flare-like events over the first hundred days, the most luminous of which has an associated minimum energy of |$1\times 10^{38}$|  erg. We provide a detailed comparison with the 2006/7 outburst, and demonstrate that we detected radio emission from the source for considerably longer in the more recent outburst, although this is probably a function of sampling. We further constrain the radio emission from the source to have a polarization of less than 9 per cent at the time of 2022 IXPE detection of X-ray polarization. Finally, we place the source in the radio–X-ray plane, demonstrating that when detected in radio it sits in a comparable region of parameter space to the other Z-sources. [ABSTRACT FROM AUTHOR]

High-performance and low-cost metal halide perovskite scintillator materials have important applications in X-ray detection and imaging. However, large-sized perovskite scintillator panel with high transmittance, low toxicity and long-term stability is still lacking. Herein, Eu2+ doped cesium alkaline earth chloride nanocrystals (Eu2+: CsCaCl 3 , Eu2+: CsSrCl 3 , and Eu2+: Cs 2 BaCl 4 NCs) embedded in glasses through conventional melt-quenching and subsequent heat-treatment were developed. These NCs doped glasses exhibited highly efficient and narrowband photoluminescence in blue region. Benefitting from the large X-ray absorption coefficient and high photoluminescence quantum yied, these Eu2+: CsCaCl 3 NCs doped glass scintillator shows light yield of ∼9300 photons/MeV, detection limit of ∼213.8 μGy/s, and spatial resolution of 30.0 lp/mm. These results demonstrated that glasses containing Eu2+ doped cesium alkaline earth chloride nanocrystals are promising for X-ray detection and imaging. [ABSTRACT FROM AUTHOR]

We describe an X-ray source detection method entirely based on the maximum likelihood analysis, in application to observations with the ART-XC telescope onboard the Spectrum Roentgen Gamma observatory. The method optimally combines the data taken at different conditions, a situation commonly found in scanning surveys or mosaic observations with a telescope with a significant off-axis PSF distortion. The method can be naturally extended to include additional information from the X-ray photon energies, detector grades, etc. The likelihood-based source detection naturally results in an optimal use of available information for the sources detection and stable and uniform definition of detection thresholds under different observing conditions (PSF, background level). This greatly simplifies the statistical calibration of the survey needed to, e.g. obtain the |$\log N - \log S$| distribution of detected sources or their luminosity function. The method can be applied to the data from any imaging X-ray telescope. [ABSTRACT FROM AUTHOR]

In this study, the development of a deep learning approach for distinguishing cardiomegaly in chest X-ray images and its validation process are presented. Typically, radiologists diagnose cardiomegaly by examining X-ray images. However, their interpretations can vary owing to subjective judgments, and mild cardiomegaly can be missed. For this reason, there is ongoing research into the use of AI-based deep learning algorithms as an adjunct to X-ray interpretation. In this study, radiologists collected 10,000 public images, from which 718 useful images were selected to create a dataset. A DenseNet121 algorithm was then used to develop an AI model for cardiomegaly detection. The results demonstrate an accuracy of 0.95, a recall of 0.91, and an F1 score of 0.94. Additional validation was performed to ensure the accuracy of the cardiomegaly detection model. The validation methods included saliency maps and guided backpropagation, which indicated the significance of the model. In conclusion, this study demonstrates the potential to develop a high-quality AI model with fewer data than previous studies, suggesting its applicability in the medical field. [ABSTRACT FROM AUTHOR]

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model's detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model's mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection. [ABSTRACT FROM AUTHOR]

Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substantial classification problem that we were able to address with the assistance of Vision Transformer models. In order to provide a basis for comparison, we conducted a parallel analysis in which we sought to solve the same problem by employing traditional convolutional neural networks (CNNs), which are well-known and commonly used techniques for the solution of image categorization issues. The findings of our research led us to conclude that ViT is capable of achieving superior outcomes compared to CNN. Furthermore, provided that methods have access to a sufficient quantity of training data, the probability increases that both methods arrive at more appropriate solutions to critical issues. [ABSTRACT FROM AUTHOR]

The article focuses on the critical role of soldering in microelectronics assembly and the impact of voids within solder balls on device performance. Topics include the challenges posed by voids, which can affect electrical resistance and mechanical stability, the importance of accurate void detection, and the various methods, including traditional and machine learning-based approaches, used for void detection in 2D x-ray images of ball grid array packages.

This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020–2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace. [ABSTRACT FROM AUTHOR]

The emerging pseudohalide metal-free perovskite (pseudohalide–MFPs) X-ray detector caters to the demands of timely mobile diagnosis owing to its lightweight, flexibility, and cost-effectiveness. However, the performance of these devices is severely limited by poor X-ray absorption, ultra-wide band gap, relative instability, and their unknown biotoxicity. Herein, we construct heavy atom covalent bonds (C–Br/Cl) on the A-site organic cation to reinforce component coordination to modulate X-ray absorption and band gap in pseudohalide–MFPs and further enhance its stability. Molecular dynamics simulations demonstrate that the introduction of halogen atoms can strengthen hydrogen bonding interactions, thereby improving the coordination between different components. The resultant (MDABCOBr)–NH4(BF4)3 (MDABCO = N-methyl-N'-diazabicyclo[2.2.2]octonium) single crystal significantly increases X-ray absorption cross-section and crystalline density (from 1.728 to 1.950 g cm−3), and synergistically realizes the band nature modulation (from 7.4 to 5.5 eV) and enhanced ionic migration inhibition (628 meV) with optimized stability. As such, our X-ray detectors realized a sensitivity of 2377 μC Gyair−1 cm−2, an ultralow detection limit of 50.1 nGyair s−1, and impressive operation stability. Moreover, cytotoxicity assay confirmed the compatibility of pseudohalide metal-free perovskite. Finally, within this framework, we successfully fabricate the (MDABCOBr)–NH4(BF4)3-based flexible device to create an ideal in vitro wearable X-ray detection. [ABSTRACT FROM AUTHOR]

Quasi‐2D perovskite quantum wells are increasingly recognized as promising candidates for direct‐conversion X‐ray detection. However, the fabrication of oriented and uniformly thick quasi‐2D perovskite films, crucial for effective high‐energy X‐ray detection, is hindered by the inherent challenges of preferential crystallization at the gas‐liquid interface, resulting in poor film quality. In addressing this limitation, a carbonyl array‐synergized crystallization (CSC) strategy is employed for the fabrication of thick films of a quasi‐2D Ruddlesden‐Popper (RP) phase perovskite, specifically PEA2MA4Pb5I16. The CSC strategy involves incorporating two forms of carbonyls in the perovskite precursor, generating large and dense intermediates. This design reduces the nucleation rate at the gas‐liquid interface, enhances the binding energies of Pb2+ at (202) and (111) planes, and passivates ion vacancy defects. Consequently, the construction of high‐quality thick films of PEA2MA4Pb5I16 RP perovskite quantum wells is achieved and characterized by vertical orientation and a pure well‐width distribution. The corresponding PEA2MA4Pb5I16 RP perovskite X‐ray detectors exhibit multi‐dimensional advantages in performance compared to previous approaches and commercially available a‐Se detectors. This CSC strategy promotes 2D perovskites as a candidate for next‐generation large‐area flat‐panel X‐ray detection systems. [ABSTRACT FROM AUTHOR]

We report the photon (PL), electron (CL) and X-ray (XEL) induced luminescence characteristics of high aspect ratio ultra-long (~ 50 µm) ZnO nanorods (NRs) and discuss the potential for fast X-ray detection based on the consistent and efficient visible emission (~ 580 nm) from ZnO NRs. Nanostructured ZnO scintillators were rearranged to form a vertically well-aligned NR design in order to help light absorption and coupling resulting in luminescent and fast scintillation properties. The design of the nanorod array combines the key advantages of a low-cost growth technique together with environmentally friendly and widely available materials. A low temperature hydrothermal method was adopted to grow ZnO NRs in one cycle growth and their structural, optical and X-ray scintillation properties were investigated. The relatively short (~ 10 µm) ZnO NRs emitting in the near-band-edge region were found to be almost insensitive to X-rays. On the other hand, the higher XEL response of long ZnO NRs, which is a key parameter for evaluation of materials to be used as scintillators for high quality X-ray detection and imaging, along with a decay time response in the order of ns confirmed promising scintillation properties for fast and high-resolution X-ray detector applications. [ABSTRACT FROM AUTHOR]

Strategy to enhance radioluminescence of lanthanide metal–organic frameworks scintillators through isolation of conjugated structures is proposed. [Display omitted] • The spacing between conjugated groups is used to control triplet-based RL. • The distance between the conjugated groups of MA-Tb reaches 8.483 Å. • The light yield of MA-Tb reaches 28000 MeV−1. • MA-Tb is used for X-ray detection with a detection limit of 82 nGy/s. Triplet exciton is both a luminescence quenching factor and an important luminescence sensitization technology solution, which is widely concerned in the field of optoelectronic materials. Since X-ray excited triplet excitons are dissipated through various pathways, there are still huge difficulties in achieving efficient triplet sensitized emission. Here, the antenna ligand is regulated through the carboxyl group, increasing the steric hindrance between the conjugated groups and improving triplet-enhanced radioluminescence (RL) efficiencies of Tb3+. The lanthanide metal–organic frameworks (Ln-MOFs) formed by the coordination of Tb3+ with mellitic acid (MA), pyromellitic acid (PMA) and trimesic acid (TMA) under low temperature preparation conditions. Among them, MA-Tb has a longer spacing between conjugated groups than PMA-Tb and TMA-Tb, and its triplet RL is relatively strongest, with a light yield of 28,000 photons MeV−1. Mechanistic studies revealed that the RL efficiency of Ln-MOFs is related to the π-π stacking effect in the benzene ring. In addition, the application of MA-Tb in the field of X-ray detection was demonstrated. The RL intensity of MA-Tb has a good linear relationship with the X-ray dose rate, and the detection limit for X-ray reaches 82 nGy/s, which is 66 times lower than the typical medical imaging dose. These results will provide a universal strategy for the design of Ln-MOFs scintillator. [ABSTRACT FROM AUTHOR]

Human bone is an essential structure that allows the body to move. It is a common observation in contemporary society that bone fractures occur frequently. The doctors use X-rays, Computed Tomography scans, and Magnetic Resonance Imaging to determine the location of the broken bone. The previous method of evaluating broken bones in person was inefficient, often leading to errors. However, introducing new, advanced evaluation techniques has obliterated these issues. Consequently, it is essential to develop an automated system for identifying fractured bones. This research uses a new deep-learning model named "You Only Look Once (version 8)" to distinguish between healthy and broken bones from multi-modal images. We utilized a customized dataset named "Human Bone Fractures Multi-modal Image Dataset", which includes 641 images representing ten different classes of bone fractures. The small data set leads to an over-fitting of the model. To increase the amount of data, we utilized a data augmentation technique. Three experiments were conducted to assess the effectiveness of the model. The findings of the experiments show that the proposed study effectively identifies and classifies different types of fractures in this area. Our system attained 95% precision, 93% recall, and 92% of mean average precision. The outcomes demonstrated that the method achieves cutting-edge performance. [ABSTRACT FROM AUTHOR]

In this work, we use 8 years of deep near-infrared imaging to select and study a new set of 601 active galaxies identified through long-term near-infrared (NIR) variability in the UKIDSS Ultra Deep Survey (UDS). These objects are compared to 710 X-ray bright Active Galactic Nuclei (AGN) detected by the Chandra X-ray observatory. We show that infrared variability and X-ray emission select distinct sets of active galaxies, finding only a 37 per cent overlap of galaxies detected by both techniques and confirming NIR-variable AGN to be typically X-ray quiet. Examining the mass functions of the active galaxies shows that NIR variability detects AGN activity in galaxies over a significantly wider range of host stellar mass compared to X-ray detection. For example, at z ∼ 1, variable AGN are identified among approximately 1 per cent of galaxies in a roughly flat distribution above the stellar mass completeness limit (⁠|$\gt 10^{9}\rm \, {\rm M}_{\odot }$|⁠), while X-ray detection primarily identifies AGN in galaxies of higher mass (⁠|$\gt 10^{10}\rm \, {\rm M}_{\odot }$|⁠). We conclude that long-term near-infrared variability provides an important new tool for obtaining more complete samples of AGN in deep survey fields. [ABSTRACT FROM AUTHOR]

We report on the SRG /eROSITA detection of ultra-soft (⁠|$kT=47^{+5}_{-5}$|  eV) X-ray emission (L X = |$2.5^{+0.6}_{-0.5} \times 10^{43}$|  erg s−1) from the tidal disruption event (TDE) candidate AT 2022dsb ∼14 d before peak optical brightness. As the optical luminosity increases after the eROSITA detection, then the 0.2–2 keV observed flux decays, decreasing by a factor of ∼39 over the 19 d after the initial X-ray detection. Multi-epoch optical spectroscopic follow-up observations reveal transient broad Balmer emission lines and a broad He  ii 4686 Å emission complex with respect to the pre-outburst spectrum. Despite the early drop in the observed X-ray flux, the He  ii 4686  Å complex is still detected for ∼40 d after the optical peak, suggesting the persistence of an obscured hard ionizing source in the system. Three outflow signatures are also detected at early times: (i) blueshifted H α emission lines in a pre-peak optical spectrum, (ii) transient radio emission, and (iii) blueshifted Ly α absorption lines. The joint evolution of this early-time X-ray emission, the He  ii 4686 Å complex, and these outflow signatures suggests that the X-ray emitting disc (formed promptly in this TDE) is still present after optical peak, but may have been enshrouded by optically thick debris, leading to the X-ray faintness in the months after the disruption. If the observed early-time properties in this TDE are not unique to this system, then other TDEs may also be X-ray bright at early times and become X-ray faint upon being veiled by debris launched shortly after the onset of circularization. [ABSTRACT FROM AUTHOR]

We report the first detection of evolving low-frequency quasi-periodic oscillation (LFQPO) frequencies in hard X-rays upto 100 keV with AstroSat/ LAXPC during 'unusual' outburst phase of Swift J1727.8−1613 in hard intermediate state (HIMS). The observed LFQPO in 20–100 keV has a centroid |$\nu _{_{\rm QPO}}=1.43$| Hz, a coherence factor Q  = 7.14 and an amplitude |${\rm rms_{_{\rm QPO}}} = 10.95{{\ \rm per\ cent}}$| with significance σ = 5.46. Type-C QPOs (1.09–2.6 Hz) are found to evolve monotonically during HIMS of the outburst with clear detection in hard X-rays (80−100 keV), where |${\rm rms_{_{\rm QPO}}}$| decreases (⁠|$\sim 12\!-\!3{{\ \rm per\ cent}}$|⁠) with energy. Further, |$\nu _{_{\rm QPO}}$| is seen to correlate (anticorrelate) with low- (high-) energy flux in 2–20 keV (15–50 keV). Wide-band (0.7−40 keV) energy spectrum of NICER/ XTI and AstroSat/ LAXPC is satisfactorily described by the 'dominant' thermal Comptonization contribution (∼88 per cent) in presence of a 'weak' signature of disc emissions (kT in ∼ 0.36 keV) indicating the harder spectral distribution. Considering source mass |$M_{\rm BH}=10\, \mathrm{M}_\odot$| and distance 1.5 < d (kpc) < 5, the unabsorbed bolometric luminosity is estimated as |$\sim 0.03\!-\!0.92{{\ \rm per\ cent}}\, L_{\rm Edd}$|⁠. Finally, we discuss the implications of our findings in the context of accretion dynamics around black hole X-ray binaries. [ABSTRACT FROM AUTHOR]

Organic scintillators have recently gained considerable attentions in X‐ray detection for their potential applications in biomedical radiograph and security inspection. However, the weak X‐ray absorption and/or inefficient exciton utilization have limited the development and commercialization of organic scintillators. Currently, high‐performance X‐ray organic scintillators are scarce and organic scintillators with dual triplet‐harvesting channels have not been explored before. Here, we develop several proof‐of‐concept sulfone‐based organic molecules, C1–C7, using different alkoxy chains to manipulate molecular packing mode. These materials exhibit dual triplet‐harvesting channels of thermally activated delayed fluorescence (TADF) and room‐temperature phosphorescence (RTP) in aggregated state. Inspiringly, these molecules display distinct radioluminescence under the X‐ray stimulation. Among them, C6 behaves the highest light yield of 16,558 photons MeV−1. Moreover, clear X‐ray images are demonstrated in both aggregated state and single‐molecule level. High spatial resolutions of 15.0 and 10.6 line pairs per millimeter (lp mm−1) are achieved for rigid and flexible scintillator screens, exceeding most reported organic and conventional inorganic scintillators. These results highlight the great potential of organic molecules with TADF and RTP nature for efficient X‐ray scintillation and imaging. [ABSTRACT FROM AUTHOR]

Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively. [ABSTRACT FROM AUTHOR]

X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while addressing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot on a metal target held micrometers away from the sample of interest, while the TES spectrometer isolates target photons with a high signal-to-noise ratio. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enables nanoscale, element-specific X-ray imaging in a compact footprint. The proof of concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in six layers of a Cu-SiO2 integrated circuit, and a path toward finer resolution and enhanced imaging capabilities is discussed. [ABSTRACT FROM AUTHOR]

The Snake is a remarkable Galactic Centre radio filament with a morphology characterized by two kinks along its ∼20 arcmin extent. The major and minor kinks are located where the filament is most distorted from a linear magnetized structure running perpendicular to the Galactic plane. We present Chandra , VLA, and MeerKAT data and report the detection of an X-ray and radio source at the location of the major kink. High-resolution radio images of the major kink reveal a compact source with a steep spectrum with spectral index α ∼ −2.7 surrounded by extended emission. The radio luminosity and steep spectrum of the compact source are consistent with a pulsar. We also show flattening of the spectrum and enhanced synchrotron emissivity away from the position of the major kink along the Snake, which suggests injection of relativistic particles along the Snake. We argue that the major kink is created by a fast-moving (∼500–1000 km s−1) object punching into the Snake, distorting its magnetic structure, and producing X-ray emission. X-ray emission pinpoints an active acceleration site where the interaction is taking place. A secondary kink is argued to be induced by the impact of the high-velocity object producing the major kink. [ABSTRACT FROM AUTHOR]

The detection of inelastically scattered soft x-rays with high energy resolution usually requires large grating spectrometers. Recently, photoelectron spectrometry for analysis of x-rays (PAX) has been rediscovered for modern spectroscopy experiments at synchrotron light sources. By converting scattered photons to electrons and using an electron energy analyser, the energy resolution for resonant inelastic x-ray scattering (RIXS) becomes decoupled from the x-ray spot size and instrument length. In this work, we develop PAX towards high energy resolution using a modern photoemission spectroscopy setup studying Ba2Cu3O4Cl2 at the Cu L 3-edge. We measure a momentum transfer range of 24% of the first Brillouin zone simultaneously. Our results hint at the observation of a magnon excitation below 100 meV energy transfer and show intensity variations related to the dispersion of dd -excitations. With dedicated setups, PAX can complement the best and largest RIXS instruments, while at the same time opening new opportunities to acquire RIXS at a range of momentum transfers simultaneously and combine it with angle-resolved photoemission spectroscopy in a single instrument. [ABSTRACT FROM AUTHOR]

The accurate assessment of peanut seed plumpness is crucial for optimizing peanut production and quality. The current method is mainly manual and visual inspection, which is very time-consuming and causes seed deterioration. A novel imaging technique is used to enhance the detection of peanut seed fullness using a non-destructive soft X-ray, which is suitable for the analysis of the surface or a thin layer of a material. The overall grayscale of the peanut is similar to the background, and the edge of the peanut seed is blurred. The inaccuracy of peanut overall and peanut seed segmentation leads to low accuracy of seed plumpness detection. To improve accuracy in detecting the fullness of peanut seeds, a seed plumpness detection method based on level set and multi-threshold segmentation was proposed for peanut images. Firstly, the level set algorithm is used to extract the overall contour of peanuts. Secondly, the obtained binary image is processed by morphology to obtain the peanut pods (the peanut overall). Then, the multi-threshold OTSU algorithm is used for threshold segmentation. The threshold is selected to extract the peanut seed part. Finally, morphology is used to complete the cavity to achieve the segmentation of the peanut seed. Compared with optimization algorithms, in the segmentation of the peanut pods, average random index (RI), global consistency error (GCE) and variation of information (VI) were increased by 10.12% and decreased by 0.53% and 24.11%, respectively. Compared with existing algorithms, in the segmentation of the peanut seed, the average RI, VI and GCE were increased by 18.32% and decreased by 9.14% and 6.11%, respectively. The proposed method is stable, accurate and can meet the requirements of peanut image plumpness detection. It provides a feasible technical means and reference for scientific experimental breeding and testing grading service pricing. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the characteristics of radiation-induced current in nano-porous pellet and thin-film anodized tantalum exposed to kVp X-ray beams. We aim at developing a large area (≫cm2) thin-film radiation sensor for medical, national security and space applications. Methods: Large area (few cm2) micro-thin Ta foils were anodized and coated with a counter electrode made of conductive polymer. In addition, several types of commercial electrolytic porous tantalum capacitors were assembled and prepared for irradiation with kVp X-rays. We measured dark current (leakage) as well as transient radiation-induced currents as a function of external voltage bias. Results: Large transient currents (up to 50 nA) under X-ray irradiation (dose rate of about 3 cGy/s) were measured in Ta2O5 capacitors. Small nano-porous Ta and large-area flat Ta foil capacitors show similar current–voltage characteristic curve after accounting for different X-ray attenuation in capacitor geometry. The signal is larger for thicker capacitor oxide. A non-negligible signal for null external voltage bias is observed, which is explained by fast electron production in Ta foils. Conclusions: Anodized tantalum is a promising material for use in large-area, self-powered radiation sensors for X-ray detection and for energy harvesting. [ABSTRACT FROM AUTHOR]

In response to the problems of complex background, multi-scale dangerous goods and severe stacking in X-ray security images, this paper proposes a high-accuracy dangerous goods detection algorithm for X-ray security images based on the improvement of YOLOv7. Firstly, by combining the coordinate attention mechanism, the downsampling structure of the backbone network is improved to enhance the model's target feature localization ability. Secondly, a weighted bidirectional feature pyramid network is used as the feature fusion structure to achieve multi-scale feature weighted fusion and further simplify the network. Then, combined with dynamic snake convolution, a downsampling structure was designed to facilitate the extraction of features at different scales, providing richer feature representations. Finally, drawing inspiration from the idea of group convolution and combining it with Conv2Former, a feature extraction module called a multi-convolution transformer (MCT) was designed to enhance the network's feature extraction ability by combining multi-scale information. The improved YOLOv7 in this article was tested on the public datasets SIXRay, CLCXray, and PIDray. The average detection accuracy (mAP) of the improved model was 96.3%, 79.3%, and 84.7%, respectively, which was 4.7%, 2.7%, and 3.1% higher than YOLOv7. This proves the effectiveness and universality of the method proposed in this article. Compared to the current mainstream X-ray image dangerous goods detection models, this model effectively reduces the false detection rate of dangerous goods in X-ray security inspection images and has achieved significant improvement in the detection of small and multi-scale targets, achieving higher accuracy in dangerous goods detection. [ABSTRACT FROM AUTHOR]

This paper focuses on the intelligent detection of prohibited items in X‐ray images during the security checking process. An intelligent semantic segmentation model of prohibited items in X‐ray images is proposed based on the attention‐based object localization method. Based on the pre‐trained CNN classification framework, the attention mechanism can map the high‐layer semantic information of objects into the input space, while generating energy saliency maps to locate the prohibited items. In order to make the obtained attention maps discriminative, the lateral and contrastive inhibition strategies are introduced and combined together which can highlight the responses of activated neurons. Under the guidance of attention responses, two traditional image segmentation algorithms are employed to achieve the semantic segmentation results for the prohibited items detection in X‐ray images. The proposed semantic segmentation model relies on weakly supervised learning mechanism, and only depends on the category labels of prohibited items, which greatly avoids the work cost of data semantic annotation. The experimental results based on the public SIXray baseline and the self‐built X‐ray image database demonstrate the proposed method can achieve about 65% IoU localization precise averagely. In addition, comparison experiments were carried out with the state‐of‐the‐arts and ablation experiments to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Scintillators, which can convert high‐energy particles (X‐rays) into detectable low‐energy ultraviolet–visible–near‐infrared photons, are essential components of X‐ray detectors and show extensive practical applications in nondestructive detection and medical imaging. Traditionally, inorganic scintillators represented by CsI:Tl have achieved definite progress. However, the harsh preparation conditions, high production cost, and poor mechanical properties impede their potential development in the high‐end X‐ray imaging field. Organic–inorganic hybrid metal complexes could be excellent alternatives, by virtue of their structural and spectral tunability, good solution processability, and excellent photophysical properties. This review mainly focuses on eco‐friendly lead‐free metal (Mn2+, Cu+, Sb3+, Sn2+, Ge2+, Ln3+, etc.) complex scintillators. The luminescence mechanisms are introduced and the scintillation performance, such as light yield, limit of detection, imaging resolution, etc., is highlighted. Moreover, the current challenges and perspectives in this emerging field are described. It is hoped to provide some theoretical guidance for the continuous development of the new scintillator systems. [ABSTRACT FROM AUTHOR]

Challenges associated with stretchable optoelectronic devices, such as pixel size, power consumption and stability, severely brock their realization in high-resolution digital imaging. Herein, we develop a universal detachable interface technique that allows uniform, damage-free and reproducible integration of micropatterned stretchable electrodes for pixel-dense intrinsically stretchable organic transistor arrays. Benefiting from the ideal heterocontact and short channel length (2 μm) in our transistors, switching current ratio exceeding 106, device density of 41,000 transistors/cm2, operational voltage down to 5 V and excellent stability are simultaneously achieved. The resultant stretchable transistor-based image sensors exhibit ultrasensitive X-ray detection and high-resolution imaging capability. A megapixel image is demonstrated, which is unprecedented for stretchable direct-conversion X-ray detectors. These results forge a bright future for the stretchable photonic integration toward next-generation visualization equipment. Pixel size, power consumption, and stability of stretchable optoelectronic devices limit their application in digital imaging. Bian et al. developed a universal detachable interface technique for damage-free micropattern, reproducible transfer and ideal heterocontact of intrinsically stretchable electrodes. [ABSTRACT FROM AUTHOR]

Objectives: Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. Methods: Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. Results: Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). Conclusion: The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. Critical relevance statement: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. Key points: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow. [ABSTRACT FROM AUTHOR]

Given that defect detection in weld X-ray images is a critical aspect of pressure vessel manufacturing and inspection, accurate differentiation of the type, distribution, number, and area of defects in the images serves as the foundation for judging weld quality, and the segmentation method of defects in digital X-ray images is the core technology for differentiating defects. Based on the publicly available weld seam dataset GDX-ray, this paper proposes a complete technique for fault segmentation in X-ray pictures of pressure vessel welds. The key works are as follows: (1) To address the problem of a lack of defect samples and imbalanced distribution inside GDX-ray, a DA-DCGAN based on a two-channel attention mechanism is devised to increase sample data. (2) A convolutional block attention mechanism is incorporated into the coding layer to boost the accuracy of small-scale defect identification. The proposed MAU-Net defect semantic segmentation network uses multi-scale even convolution to enhance large-scale features. The proposed method can mask electrostatic interference and non-defect-class parts in the actual weld X-ray images, achieve an average segmentation accuracy of 84.75% for the GDX-ray dataset, segment and accurately rate the valid defects with a correct rating rate of 95%, and thus realize practical value in engineering. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents' performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents' sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI's potential in radiology, emphasizing its role in training and interpretation improvement. [ABSTRACT FROM AUTHOR]

Metal-halide perovskite thin monocrystals featuring efficient carrier collection and transport capabilities are well suited for radiation detectors, yet their growth in a generic, well-controlled manner remains challenging. Here, we reveal that mass transfer is one major limiting factor during solution growth of perovskite thin monocrystals. A general approach is developed to overcome synthetic limitation by using a high solute flux system, in which mass diffusion coefficient is improved from 1.7×10–10 to 5.4×10–10 m2 s–1 by suppressing monomer aggregation. The generality of this approach is validated by the synthesis of 29 types of perovskite thin monocrystals at 40–90 °C with the growth velocity up to 27.2 μm min–1. The as-grown perovskite monocrystals deliver a high X-ray sensitivity of 1.74×105 µC Gy−1 cm−2 without applied bias. The findings regarding limited mass transfer and high-flux crystallization are crucial towards advancing the preparation and application of perovskite thin monocrystals. Liu et al. report a universal solution growth method for perovskite thin monocrystals by improving the mass transfer in the high solute flux system. The approach is applied to 29 types of perovskites with growth velocity up to 27.2 µm min-1 and enables efficient self-driven X-ray detectors. [ABSTRACT FROM AUTHOR]

X-ray weld seam images carry vital information about welds. Leveraging graphic–text recognition technology enables intelligent data collection in complex industrial environments, promising significant improvements in work efficiency. This study focuses on using deep learning methods to enhance the accuracy and efficiency of detecting weld seam information. We began by actively gathering a dataset of X-ray weld seam images for model training and evaluation. The study comprises two main components: text detection and text recognition. For text detection, we employed a model based on the DBNet algorithm and tailored post-processing techniques to the unique features of weld seam images. Through model training, we achieved efficient detection of the text regions, with 91% precision, 92.4% recall, and a 91.7% F1 score on the test dataset. In the text recognition phase, we introduced modules like CA, CBAM, and HFA to capture the character position information and global text features effectively. This optimization led to a remarkable text line recognition accuracy of 93.4%. In conclusion, our study provides an efficient deep learning solution for text detection and recognition in X-ray weld seam images, offering robust support for weld seam information collection in industrial manufacturing. [ABSTRACT FROM AUTHOR]

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results. [ABSTRACT FROM AUTHOR]

Computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. [ABSTRACT FROM AUTHOR]

In this paper, an overview of wide-bandgap (WBG) semiconductors for radiation detection applications is given. The recent advancements in the fabrication of high-quality wafers have enabled remarkable WBG semiconductor device applications. The most common 4H-SiC, GaN, and β-Ga2O3 devices used for radiation detection are described. The 4H-SiC and GaN devices have already achieved exceptional results in the detection of alpha particles and neutrons, thermal neutrons in particular. While β-Ga2O3 devices have not yet reached the same level of technological maturity (compared to 4H-SiC and GaN), their current achievements for X-ray detection indicate great potential and promising prospects for future applications. [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 article proposes a progressive frequency domain-guided depth model with adaptive preprocessing to solve the problem of defect detection with weak features based on X-ray images. In distinct intuitive surface defect detection tasks, non-destructive testing of castings using X-rays presents more complex and weak defect features, leading to lower accuracy and insufficient robustness on the part of current casting defect detection methods. To address these challenges, the proposed method establishes four specialized mechanisms to improve model accuracy. First, an adaptive image contrast enhancement method is proposed to enhance the features of defects in casting images to promote subsequent feature extraction and prediction. Second, a subtle clue mining module based on frequency domain attention is proposed to fully extract the discriminative features of casting defects. Third, a feature refinement module based on progressive learning is proposed to achieve a balance between feature resolution and semantic information. Finally, a refined deep regression supervision mechanism is designed to improve defect detection accuracy under strict intersection-to-union ratio standards. We established extensive ablation studies using casting defect images in GDXray, conducted detailed comparative experiments with other methods, and performed experiments to analyze the robustness of the resulting models. Compared with other X-ray defect detection methods, our framework achieves an average +4.6 AP. Compared to the baseline, our proposed refined deep regression supervision mechanism results in an improvement of 5.3 AP. [ABSTRACT FROM AUTHOR]

Metal halide perovskites are becoming increasingly attractive for X-ray detection applications due to their distinctive optoelectronic properties. However, the practical use of lead-based organic–inorganic hybrid perovskites have been significantly impeded by their toxicity and instability. In this study, we have achieved successful growth lead-free bismuth-based all-inorganic perovskite Cs3Bi2Br9 films using a chemical vapor deposition (CVD) method for the first time. The CVD process has the advantage of circumventing the solubility limit of raw materials in conventional solution process, enabling thick film deposition for enhanced X-ray attenuation. Through meticulous adjustments to the molar ratio of the precursor materials, we have produced high-quality films characterized by a smooth surface and compact grains, ideal for minimizing leakage current. Consequently, the optimized devices exhibit an exceptionally low dark current density of 0.47 nA cm−2, meeting industrial standards for integration with thin-film transistor backplanes. Furthermore, these devices demonstrate an impressive sensitivity of 593 μC Gyair−1 cm−2, a low detection limit of 187.7 nGyair s−1, as well as excellent storage and operational stabilities, which hold great promise as an eco-friendly candidate for high-performance X-ray detection and imaging applications. [ABSTRACT FROM AUTHOR]

Due to the low effectiveness and poor quality of manual crimping of grounding wires, the author proposes the design of an automatic crimping device for connecting tube grounding wires based on intelligent fully automatic technology. The device consists of a microcontroller, an upper computer control interface, an electric push rod, an infrared sensor, a pressure transmitter, and other devices. The staff used the upper computer monitoring interface to set the relevant parameters for grounding wire crimping, and used X-ray digital imaging technology to measure the crimping size of the grounding wire. The size met the set parameter conditions. Through the PID control algorithm in the microcontroller, the stepper motor was controlled to push the clamp to move, completing the automatic crimping of the grounding wire. The X-ray detection method was introduced to detect the quality of the grounding wire after the crimping was completed. The experimental results show that the average deviation between the measured crimping size of the grounding wire and the actual measurement size by the automatic crimping device is only 0.06 mm, indicating that its measurement results are accurate; The success rate of crimping exceeds 95%. The above experimental results verify that the designed crimping device has high stability and reliability, and good quality detection effect. [ABSTRACT FROM AUTHOR]

X-ray detection is widely used in various applications. However, to meet the demand for high image quality and high accuracy diagnosis, the raw data increases and imposes challenges for conventional X-ray detection hardware regarding data transmission and power consumption. To tackle these issues, we present a scheme of in-X-ray-detector computing based on CsPbBr3 single-crystal detector with convenient polarity reconfigurability, good linear dynamic range, and robust stability. The detector features a stable trap-free device structure and achieves a high linear dynamic range of 106 dB. As a result, the detector could achieve edge extraction imaging with a data compression ratio of ~50%, and could also be programmed and trained to perform pattern recognition tasks with a high accuracy of 100%. Our research shows that in-X-ray-detector computing can be used in flexible and complex scenarios, making it a promising platform for intelligent X-ray imaging. In-sensor computing requires detectors with polarity reconfigurability and linear responsivity. Pang et al. report a CsPbBr3 perovskite single crystal X-ray detector for edge extraction imaging with a data compression ratio of 46.4% and classification task with an accuracy of 100%. [ABSTRACT FROM AUTHOR]

The latest synchrotron radiation sources have the capability to produce X-ray beams with a photon flux that can be up to three orders of magnitude higher than previous-generation facilities, and that are not manageable by the currently available 2D photon-counting pixel detectors. The construction of new detectors that exceed the limitations of existing devices is a critical strategic need. Developing such detectors is a challenge in terms of readout electronics as well as sensor material, particularly in the case of devices intended to operate at X-ray energies above 30 keV. The approach adopted at the ESRF to deal with this major difficulty is twofold: the use of a novel semiconductor material with improved electrical properties, high-flux CdZnTe, and the investigation of a specific readout scheme, incremental digital integration, via the XIDer project in collaboration with the University of Heidelberg. Incremental digital integration is a method intended to be less sensitive to variations of the dark current than the conventional charge integration readout. However, this readout scheme requires that the leakage current from the sensor material stays below a certain threshold to reduce the leakage contributions. This paper introduces the ESRF strategy and few examples of the methods employed to evaluate the performance and leakage current behavior of high-flux CdZnTe pixelated sensors. These examples illustrate the first results obtained with this material under moderate to very high X-ray irradiation fluxes of up to 1012 photons/mm²/s. [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]

When first introduced, single-photon counting detectors reshaped crystallography at synchrotrons. Their fast readout speed enabled, for example, shutter-less data collection and fine slicing of the rotation angle and boosted the development of new experimental techniques like ptychography. Under optimal conditions, single-photon counting detectors provide an unlimited dynamic range with image noise only limited by the Poisson statistics of the incoming photons. Counting the pulses from individual photons, essentially what made the detectors so successful, also causes the main drawback, which is the loss of efficiency at high photon fluxes due to pulse pileup in the analog front end. To fully take advantage of diffraction-limited light sources, the next-generation single-photon counters need to improve their count rate capabilities in the same order of magnitude as the increased flux. Moreover, fast frame rates (a few kHz) are required to cope with the shorter dwell time achievable, thanks to the higher flux. Detector architecture with multiple comparators and counters can open new possibilities for energy-resolved imaging, while interpixel communication can overcome the issues arising from charge sharing and reduce the loss of efficiency at the pixel corners. Coupling single-photon counting detectors to high-Z sensors for hard X-ray detection (> 20 keV) and to low-gain avalanche diodes (LGADs) for soft X-rays is also necessary to make use of the increased coherence of the new light sources over the full radiation spectrum. In this paper, we present possible strategies to improve the performance of single-photon counting detectors at the fourth-generation synchrotron sources and compare them to charge integrating detectors. [ABSTRACT FROM AUTHOR]

The current challenge of wearable/implantable personal dosimeters for medical diagnosis and radiotherapy applications is lack of suitable detector materials possessing both excellent detection performance and biocompatibility. Here, we report a solution-grown biocompatible organic single crystalline semiconductor (OSCS), 4-Hydroxyphenylacetic acid (4HPA), achieving real-time spectral detection of charged particles with single-particle sensitivity. Along in-plane direction, two-dimensional anisotropic 4HPA exhibits a large electron drift velocity of 5 × 105 cm s−1 at "radiation-mode" while maintaining a high resistivity of (1.28 ± 0.003) × 1012 Ω·cm at "dark-mode" due to influence of dense π-π overlaps and high-energy L1 level. Therefore, 4HPA detectors exhibit the record spectra detection of charged particles among their organic counterparts, with energy resolution of 36%, (μt)e of (4.91 ± 0.07) × 10−5 cm2 V−1, and detection time down to 3 ms. These detectors also show high X-ray detection sensitivity of 16,612 μC Gyabs−1 cm−3, detection of limit of 20 nGyair s−1, and long-term stability after 690 Gyair irradiation. New detector materials are crucial for radiation beam monitoring in dosimeters and X-ray imaging. The authors report a solution-grown biocompatible organic single crystalline semiconductor for real-time spectral detection of charged particles with single-particle sensitivity, X-ray detection and imaging. [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]

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]