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Neutron imaging can nondestructively detect the composition and structure of a sample, and has been used in many fields for quantitative analysis. Neutron imaging can be used to detect elements that are difficult to detect with X-rays, such as hydrogen. When neutron imaging is used for quantitative analysis of hydrogen-containing samples, the neutron beam decays because of scattering with hydrogen rather absorption. However, neutrons scattered in the direction of the neutron beam may still reach the detector, and these scattered neutrons can bring additional intensity to the sample and its edges, bring artifacts to the image, and cause serious interference to quantitative analysis. The elimination of scattering effect is one of the most important problems in the quantitative analysis of neutron imaging. Because samples with hydrogen have a larger total scattering than absorption cross section, the influence of the scattered neutrons on the transmitted image is not negligible. The results of neutron imaging quantitative analysis are also affected by the energy spectrum effect. Since the incident neutron beam is not unienergetic, the probability of interaction between the lower energy neutrons and the sample is high, resulting in the macroscopic cross section of the neutron varying with the thickness of the sample. Several existing scattering correction methods, such as increasing the distance between the sample and the detector, the point scattered functions method, using microchannel plates, and using the blackbody grids, are not applicable to all samples, or not applicable to dynamic neutron imaging, or will increase exposure time or complicate data processing. A new scattering correction method, the double-detector method was presented. The double-detector method used two detectors at two locations to image. The close detector can obtain a high-resolution image, but the image is affected by scattered neutrons. The far detector obtain images that are unaffected by scattered neutrons, but with poor resolution. The scattering neutron distribution can be obtained by processing the two images. By subtracting the scattering neutron distribution from the close image, an image with high resolution and free from scattering neutrons can be obtained to complete scattering correction. The method was used to correct the water steps sample images. And the macroscopic cross section was corrected by the energy spectrum correction factor. The results show that the thickness of the water steps in the close-position image differs greatly from the actual thickness due to the influence of scattered neutrons. And the thickness of the water steps corrected by the double-detector method is in good agreement with the actual thickness. The double-detector method is a feasible scattering correction method.

Neutron imaging is a transmission imaging method, which has a wide range of applications in the fields of non-destructive testing of internal defects in materials, the distribution of light elements in samples, and the detection of radioactive samples due to the advantages of neutrons with deep penetration, ability to distinguish isotopes, and sensitivity to light elements. With the continuous development of various application fields, the requirements for the spatial resolution of neutron imaging are getting higher and higher, and improving the spatial resolution of neutron imaging and continuously approaching the micron level has become a key research direction in the field of neutron imaging. At present, the international advanced neutron imaging device has been able to complete the high-resolution neutron imaging test with spatial resolution at the micron level, and the domestic research in this field has also made some progress with the completion of the three major neutron sources, and is gradually approaching the international advanced level. The thermal neutron imaging device and the cold neutron imaging device of the China Advanced Research Reactor have been completed and put into use in recent years, and the maximum neutron flux density at the sample of the device can reach 4×109 cm−2·s−1, the optical path system has been greatly improved compared with the original neutron imaging device, and the spatial resolution of the detector has reached the sub-millimeter level, which has been widely used in aerospace, cultural relics and archaeology, hydrogen content characterization and other fields. The main factors affecting the spatial resolution of neutron imaging in neutron imaging devices are neutron sources, neutron optical path systems and neutron imaging detectors. In this study, based on the relevant test requirements in the fields of zirconium alloy hydrogen content and cultural relics production technology, the influencing factors of neutron imaging spatial resolution were studied, and a set of high-spatially resolved neutron imaging detectors based on the thermal neutron imaging device of China Advanced Research Reactor was built. At the same time, the spatial resolution of the detector was calculated theoretically, and the detector was debugged as a whole. Finally, the relevant band bundle commissioning of the detector system was completed on the thermal neutron imaging facility of China Advanced Research Reactor, and the syringe needle samples were tested. The test results show that the spatial resolution of the detector reaches 11.3 μm, and the actual available field of view size reaches 23.6 mm×28 mm, achieving a spatial resolution of 10 μm.

Link prediction, the process of uncovering potential relationships between nodes in a network through the use of deep learning techniques, is commonly applied in fields such as network security and information mining. It has been utilized to identify social engineering attacks, fraudulent activities, and privacy breach risks by predicting links between nodes within a network. However, the topology of mobile social networks is subject to change over time, and the sparsity of links affects the accuracy of predictions. To address the issue of strong sparsity in link prediction for mobile social networks, a deep learning-based prediction method named DLMSSLP (deep learning-based method for mobile social networks with strong sparsity for link prediction) was developed. This method was designed to employ a combination of a Graph Auto-Encoder (GAE), feature matrix aggregation, and multi-layer long short-term memory networks (LSTM). It aimed to reduce the learning cost of the model, process high-dimensional and nonlinear network structures more effectively, and capture the temporal dynamics within mobile social networks, thereby enhancing the model’s predictive capability for the generation of existing links. When compared to other methods, DLMSSLP demonstrated significant improvements in AUC and ER metrics, showcasing the model’s high accuracy and robustness in predicting uncertain links.

Tin-based perovskite solar cells are expected to replace lead-based perovskite solar cells to achieve environmentally friendly devices. Currently, a significant challenge lies in low filling factor and short-circuit current density, leading to an overall lower efficiency of these cells. In this context, we conducted a comprehensive comparative study on the deposition of these three inorganic small-molecule materials (CsBr, CsCl, CsI) on tin-based perovskite layers. The results showed that depositing these three inorganic small-molecule materials (CsBr, CsCl, CsI) on tin-based perovskite layers can improve the topography of the thin film and display an increased grain size. Simultaneously, the presence of the passivation layer facilitates preferred crystal orientation and enhanced charge carrier transport capabilities. Furthermore, devices with passivation layers exhibit reduced series resistance and increased shunt resistance, leading to a higher filling factor, a higher short-circuit current density, and a reduced leakage current in the passivated devices. This results in an elevated overall conversion efficiency of the devices. Notably, among the three halide materials employed for passivation, CsI demonstrates the most effective passivation, with the champion device achieving an efficiency of 6.0%. This study contributes valuable insights into the passivation strategies for tin-based perovskite solar cells. [ABSTRACT FROM AUTHOR]

BackgroundWith the development of nuclear medicine, the amount of medical radioactive waste increase rapidly. The radioactivity of nuclides in medical radioactive liquid waste must be monitored to meet the relevant standards before discharge of the radioactive liquid waste. The volume of the waste sample and its distribution around the detector have a direct impact on the detection efficiency.PurposeThis study aims to explore the variation law of the size parameters of the optimal Marlin cup sample box and provide a basis for subsequent monitoring methods.MethodsThe LaBr3(Ce) crystal was applied to the detection of nuclide activity in medical radioactive waste liquid. Geant4 tool was employed to establish a LaBr3(Ce) crystal detection model. The changing rules of the optimal Marin cup sample box size parameters were explored using a Ø25.4 mm×25.4 mm LaBr3(Ce) detector, and 3D printed photosensitive resin samples were used in the laboratory box for verification experiments.ResultsExperimental results show that simply increasing the sample volume cannot improve the detection efficiency, and the change trend of the detection efficiency in the depth direction of the annular part of the sample container tends to be flat with increase of the sample volume. The optimal size ratio of the Marinelli beaker is that the depth of the annular portion (h2) and the radius (r) are approximately two times the length of the detector crystal and the diameter of the hollow cavity, respectively, and the ratio of the radius (r) to the height of the sample container (H) is approximately 0.5. The experimental results of the full energy peak detection efficiency with optimized sample container size are consistent with the simulation results, and the relative deviation is better than 2.5%.ConclusionsThe results of the study provide an important technical reference for detector selection, sampling container design, and processing and traceability methods of medical radioactive liquid waste monitoring devices.

Hydrogen ingress into zirconium cladding can cause embrittlement and affects the service safety of nuclear fuel cladding. Traditional hydrogen analysis methods like hydrogen determination instrument method and metallographic method, can only give the hydrogen content or density in a small area of the sample, however, the hydrogen distribution within the zirconium alloy cannot fully understand owing to limited scope of methods. In addition, those methods require the zirconium alloy to be cut, which prevents any opportunity for other performance analysis. A nondestructive analysis of hydrogen content and distribution is helpful to predict and evaluate zirconium alloy cladding service performance. Neutron has a higher sensitivity to hydrogen than the other elements (Zr, Sn, Fe, Nb et al.) in commercial zirconium cladding. Therefore, the hydrogen in zirconium alloys can easily be seen by neutron imaging method. Herein, the neutron tomography for threedimensional (3D) hydrogen distribution analysis and quantitative determination in hydrided zirconium alloys were introduced. A series of ZrSnNb alloy calibration samples with designed hydrogen concentrations up to 1 150 ppm were prepared, and studied using neutron tomography. In this way, the 3D hydrogen distribution of the calibration samples was obtained and the quantitative relationship between the hydrogen contents and image gray values was determined. It is observed that the image gray value increases linearly with the hydrogen content. Accordingly, the hydrogen content and distribution in hydrided samples are determined accurately. In addition, the metallographic method and inert gas pulse infrared absorption method were used to verify the feasibility of quantitative method for hydrogen distribution in zirconium alloys with neutron tomography. The hydrogen content and distribution determined using neutron tomography are consistent with the results of contrast methods. Consequently, neutron tomography can be efficient for 3D hydrogen distribution analysis and quantitative determination in hydrided zirconium alloys. This work provides a threedimensional, nondestructive testing method for the study of hydrogen distribution and content in zirconium alloy cladding, which can provide technical support for its optimization of manufacturing process and improvement of service performance.

With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-time monitoring and rapid response. This study aims to address the issue of rice pest detection through deep learning techniques to enhance agricultural productivity and sustainability. The research utilizes the IP102 large-scale rice pest benchmark dataset, publicly released by CVPR in 2019, which includes 9,663 images of eight types of pests, with a training-to-testing ratio of 8:2. By optimizing the YOLOv8 model, incorporating the CBAM (Convolutional Block Attention Module) attention mechanism, and the BiFPN (Bidirectional Feature Pyramid Network) for feature fusion, the detection accuracy in complex agricultural environments was significantly improved. Experimental results show that the improved YOLOv8 model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 78.6%, respectively, representing increases of 2.8% and 2.35% over the original model. This study confirms the potential of deep learning technology in the field of pest detection, providing a new technological approach for future agricultural pest management. [ABSTRACT FROM AUTHOR]

Tin-based perovskite has been considered as one of the most potential candidates for lead-based perovskite. The solution proceed method was widely utilized in fabricating tin perovskite solar cells. So far, all fabrication processes for tin perovskite solar cells involved toxic organic solvents, which is contrary to the development of environmentally friendly perovskite solar cells. In this study, we report for the first time, by using a mixed green solvent N-diethyl formamide and green 1,3-dimethyl-3,4,5,6-Tetrahydro-2 (1H)-pyrimidinone as precursor solvent, and a green solvent dibutyl ether as antisolvent, a high-quality FA0.75MA0.25SnI3 film was achieved. The optical band gap of the prepared perovskite layer was 1.36 eV, which was close to the ideal band gap. The green-solution-proceed perovskite films showed reduced defect density. As a consequence, the champion green-solution-proceed photovoltaic device achieved a power conversion efficiency of 4.4%. Moreover, it still maintains 80% of the initial efficiency after 600 h of storage in a nitrogen atmosphere. This work would promote the perovskite solar cells from a 'new' technique to a 'new and green' technique. [ABSTRACT FROM AUTHOR]

To overcome the time-resolution limitation inherent in the airflow-through scintillation cell method for radon measurement, this study introduces a compartmental model elucidating the behaviors of radon and its progeny within such cells. The computed results of the normalized equilibrium functions derived from the compartmental model, provide quantitative insights into the equilibrium progression of 222Rn and its progeny over time, substantiating the identified time-resolution limitation of 2–3 hours. Laboratory experiments confirm the efficacy of the proposed correction algorithm, showcasing its ability to surmount the time-resolution limitation and achieve a rapid response in radon measurement using airflow-through scintillation cells. [ABSTRACT FROM AUTHOR]