Subjects: Physics >> Nuclear Physics submitted time 2025-06-25
Xiang, Dr. Jiabing
Wang, Mr. Yanxin
Yang, Mr. Yuhang
Zhao, Prof. Wei
Li, Dr. Baolei
Sun, Dr. Bao-Hua
Abstract： In this paper, we propose a novel design for a stationary CT system, termed the Alternating Source-Detector Array stationary CT (ASDA-sCT). The ASDA-sCT system comprises an array of miniature carbon nanotube X-ray sources and a detector array strategically positioned in the gaps between sources. To minimize projection loss caused by ray path obstruction, the X-ray sources are distributed within a short-scan trajectory that takes advantage of the fan-beam symmetry. After interpolation-based restoration of the discontinuities, CT images can be directly reconstructed using the filtered backprojection (FBP) algorithm with Parker’s weighting function. We further investigate the influence of the number of X-ray sources on the reconstruction quality of the ASDA-sCT system and determine the optimal source number for different X-ray exit window sizes. However, the limited number of sources and the interpolation errors introduced during sinogram restoration remain critical barriers to achieving high-quality image reconstruction. To tackle these issues, we propose a tailored triple-stage dual-domain cascade neural network (TSDDC-Net), which incorporates prior knowledge to correct interpolation errors in the sinogram and compensate for the missing projection views. In the projection domain, we introduce a novel multi-scale deformable convolution module (DFInception) that enhances feature extraction and improves the accuracy of sinogram refinement. In the image domain, a dual-encoder architecture is employed to independently extract features from the initial CT image reconstructed from raw interpolated projections and from the refined CT image reconstructed using the corrected sinogram. Ultimately, the well-designed deep learning model significantly enhances the quality of the reconstructed images. Experiments conducted on the Shepp-Logan phantom and abdominal CT datasets demonstrate the promising potential of the ASDA-sCT system for practical applications.
Peer Review Status:
Awaiting Review
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2025-06-25
Abstract： [Background]: In the field of nuclear thermal propulsion reactor engineering, real-time prediction of complex multi-physics temperature fields remains a critical challenge. [Purpose]: To address this issue, this study proposes a latent space neural operator (L-DeepONet)-based approach for real-time prediction of temperature fields in nuclear thermal propulsion reactors. [Methods]: A lightweight "encoder-operator learning-decoder" framework is constructed by coupling an autoencoder (AE) with a deep operator network (DeepONet). First, high-dimensional temperature fields are compressed into a 100-dimensional latent space using AE. Subsequently, DeepONet is trained in the low-dimensional space to learn coolant dynamic evolution patterns. Finally, high-fidelity reconstruction of predictions is achieved through the decoder. [Results]: Validation using OpenFOAM-generated coolant temperature field datasets demonstrates that the method achieves average relative errors below 1% for fuel temperature fields in both 40-second iterative predictions and 100-second long-term predictions, with errors for coolant and cladding below 0.5%. The training time of 79.23-192.83 seconds represents a two-order-of-magnitude acceleration compared to traditional CFD simulations, enabling real-time single-step long-term prediction with error distributions concentrated in gradient-sensitive regions. [Conclusions]: This work innovatively introduces latent space operator learning into multi-physics modeling of nuclear thermal propulsion reactors and achieves real-time prediction. The framework provides insights for real-time simulation and decision-making under extreme operating conditions, and can be extended to neutronics-thermomechanical coupling scenarios, offering new pathways for digital twin applications in advanced nuclear systems.
Subjects: Physics >> Nuclear Physics submitted time 2025-06-25
Abstract： Automatic identification of radioactive isotopes through energy spectrum analysis is vital for remote, unmanned monitoring of radioactive contamination and rapid early warning. In recent years, deep learning methods have advanced significantly, outperforming traditional approaches in recognition accuracy. However, their purely data-driven nature and the "black-box" characteristics of neural networks result in poor interpretability, a high risk of overfitting, and uncontrollable errors, limiting their use in high-reliability fields like the nuclear industry. We present FhyMetric-Net, a novel multi-label classification model that integrates physical constraints with data-driven techniques. This model automatically infers the probability of mixed nuclides and provides weight interpretations consistent with expert knowledge. Our approach is groundbreaking in embedding prior characteristic peak physical information into neural networks, effectively constraining the feature weight optimization space for improved reliability and interpretability. We also introduce a novel metric constraint method in the feature space, tailored for mixed nuclide samples, which enhances the model’s ability to extract discriminative features. By establishing a clear causal link between predicted probabilities and channel addresses, FhyMetric-Net overcomes the interpretability challenges of traditional dense fully connected layers. We conducted more challenging quantitative tests than previous studies. When faced with challenges such as an increased number of mixed radionuclides, variations in Gaussian broadening coefficients, and differences in detector types, the proposed model consistently maintained an F1 score above 95%, achieving state-of-the-art (SOTA) performance, while the model's parameter count was only 1.58% of the ResNet-18 model. In scenarios with low gross count and low signal-to-noise ratio (SNR), its overall performance also demonstrated significant advantages. Qualitative analysis further confirmed the model's strong physical interpretability. This achievement will advance the application of automated mixed radionuclide identification technology in high-reliability fields of the nuclear industry.
Peer Review Status:Awaiting Review
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2025-06-25
Abstract： [Background]: Graphite is a critical structural material in high-temperature gas-cooled reactors. Ensuring its integrity under seismic loading is essential for core safety. [Purpose]: This study aims to evaluate the seismic response of a gas-cooled microreactor core and verify its compliance with the ASME design code. [Methods]: Collision tests and simulations of small-size graphite blocks were conducted to identify stiffness and damping parameters. A simplified core model with equivalent spring-damper elements was established and analyzed through time-history simulations. [Results]: The Kelvin collision model effectively captured graphite block interactions, with stiffness ranging from 1.70×10⁹ to 2.20×10⁹ N/m for collision angles of 0.02°–0.05°. The maximum collision force between graphite components reached 1.21×10⁵ N. Stress evaluation based on the Weibull distribution indicated a failure probability below 0.5% under design-level seismic loads. [Conclusions]: The proposed stiffness-equivalent method provides an efficient approach for simulating nonlinear core behavior, confirming the structural safety of the graphite core under seismic conditions.
Subjects: Physics >> Nuclear Physics submitted time 2025-06-24
Wang, Mr. Haochen
Zhang, Mr. Zhao
Yu, Dr. Pei
Bao, Miss Yuxin
Zhai, Prof. Jiajia
Xu, Dr. Yu
Deng, Dr. Li
Xiao, Dr. Sa
Zhang, Dr. Xueheng
Yu, Dr. Yuhong
He, Dr. Weibo
Chen, Dr. Liangwen
Zhang, Prof. Yu
Yang, Prof. Lei 杨磊
Sun, Prof. Zhiyu 孙志宇
Abstract： Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive high-Z nuclear elements. Most of the traditional identification methods are based on complex muon event reconstruction and trajectory fitting processes. Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of target materials, significantly limiting their practical applicability in detecting concealed materials. For the first time, transfer learning is introduced into the field of muon tomography in this work. We propose two lightweight neural network models for fine-tuning and adversarial transfer learning, utilizing muon tomography data of bare materials to predict the Z-class of coated materials. By employing the inverse cumulative distribution function method, more accurate scattering angle distributions could be obtained from limited data, leading to an improvement by nearly 4% in prediction accuracy compared with the traditional random sampling based training. When applied to coated materials with limited labeled or even unlabeled muon tomography data, the proposed method achieves an overall prediction accuracy exceeding 96%, with high-Z materials reaching nearly 99%. Simulation results indicate that transfer learning improves prediction accuracy by approximately 10% compared to direct prediction without transfer. This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data, highlights the promising potential of transfer learning in the field of muon tomography.
Peer Review Status:Awaiting Review
Subjects: Physics >> Nuclear Physics submitted time 2025-06-24
Abstract： Cancer is the second leading cause of mortality globally. As a critical technological approach in oncology treatment, radiation therapy includes conventional dose rate radiation therapy, high dose rate radiotherapy and ultra-high dose rate radiation therapy (FLASH-RT). With the significant escalation in radiotherapy dose rates, real-time dosimetry monitoring faces the dual challenges of enhancing both response time and measurement precision. This work successfully developed a real-time dosimetry monitoring system for radiotherapy, designed to accommodate a broad range of dose rates. The system consists of a dual-gated integrator architecture front-end circuit and a high-speed data acquisition circuit, providing accurate detection of bipolar current pulse signals spanning from -190 µA to +200 µA, the minimum current measurement range is from -1 pA to 1 pA. Two significant technological advancements were accomplished: (1) The elimination of signal processing dead time resulted in a reduction of the single-event readout time to 5 µs; (2)The nonlinear error from -190 µA up to the maximum current is within 0.67%, with a linear correlation coefficient (R2)of 0.99992. The experiments were conducted using an ionization chamber detector at the Heavy Ion Research Facility in Lanzhou (HIRFL-TR4), this system, combined with a dose detector, achieves real-time dose measurement within the dose rate range of 65 Gy/min to 120 Gy/min. It demonstrates excellent real-time monitoring performance in the high-dose rate range of radiation therapy and shows potential for further application in dose monitoring for electron and proton beam radiotherapy.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Cognitive Psychology submitted time 2025-06-24
Abstract： A Brain-Computer Interface (BCI) establishes a direct communication channel between the brain and external devices by acquiring, decoding, and translating neural signals, opening novel pathways to understand and enhance cognitive potential. This study explores the theoretical foundations and clinical applications of BCI in cognitive enhancement and psychotherapy. This paper introduces an ethical framework for analyzing psychological adaptability to long-term BCI use and proposes the ’Technological Dependence Risk Index’ (TDRI) to quantify the impact of such reliance on individual autonomy. Furthermore, the integration of BCI with cutting-edge technologies like artificial intelligence (AI) and virtual reality (VR) offers innovative pathways for therapeutic interventions targeting complex psychological processes. Future research should prioritize enhancing the user-centered experience of BCI applications and further investigate the long-term impacts of technological dependence on psychological autonomy and emotional regulation. Moreover, applying principles from cognitive psychology (e.g., attention, memory) and neuroplasticity is crucial for optimizing BCI decoding features and neurofeedback design, ultimately creating more adaptable and personalized psychological intervention paradigms.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2025-06-24
Abstract： We studied the energy partition between two well-separated fission fragments associated with the partition of nucleons owing to quantum entanglement. This is different from most fission models that invoke an explicit statistical partition of excitation energies. The dynamical fission evolution is described within the timedependent Hartree-Fock+BCS framework. Excitation energies of isotopic fission fragments were obtained using the particle-number projection method after the dynamical splitting of 238U. The resulting excitation energies of the light and heavy fragments are consistent with the appearance of sawtooth structures. We found that the pairing correlation strengths have a significant influence on the partition of the excitation energies. Furthermore, the excitation energies of isotopic fragments increase with increasing neutron number, implying the suppression of the production of neutron-rich beams in rare-isotope beam facilities.
Peer Review Status:Awaiting Review
Subjects: Physics >> Nuclear Physics submitted time 2025-06-24
Abstract： The presence of hydrogen will affect the formation of irradiation defects in reactor structural materials, which in turn will influence the degradation of their mechanical properties. A deeper understanding of the mechanisms behind irradiation effects in structural materials in the presence of hydrogen poses an important scientific challenge in the field of fusion and fission energy. Reduced-activation ferritic/martensitic (RAFM) steel, a potential structural material for fusion and fission reactors, was selected as the research material. Using positron annihilation Doppler broadening spectroscopy, it was characterized that a significant amount of hydrogen atoms in the H + irradiated RAFM steel are captured in vacancies, resulting in the formation of relatively stable vacancy-H complexes. Furthermore, the first-principles calculations revealed that this behavior of hydrogen atoms being captured at vacancies inhibits the recombination of vacancies and SIAs generated by cascade collisions, especially when the concentration of hydrogen is high, thereby promoting the formation of irradiation defects.
Peer Review Status:Awaiting Review
Subjects: Medicine, Pharmacy >> Clinical Medicine submitted time 2025-06-24 Cooperative journals: 《中国全科医学》
Abstract： Background Acute ST-segment elevation myocardial infarction（STEMI）has a high mortality and disability rate. Percutaneous coronary intervention（PCI）is an important revascularization method that can improve prognosis. However，some patients experience recurrent angina after PCI，which affects their quality of life and long-term prognosis. Drug-metabolizing enzyme gene polymorphisms influence drug efficacy and adverse reactions. Cytochrome P450 2C19（CYP2C19）is involved in the metabolism of multiple drugs，and its gene polymorphisms can alter enzyme activity and affect drug metabolism. The correlation between different CYP2C19 metabolic levels and recurrent angina after PCI in STEMI patients is worth exploring. Objective To investigate the correlation between different CYP2C19 metabolic levels and recurrent angina after PCI in STEMI patients. Methods A total of 128 patients who underwent emergency PCI for acute coronary occlusion at the Chest Pain Center of the First Affiliated Hospital of Inner Mongolia Medical University in 2022 were selected as the study subjects. The patients' medical records and CYP2C19 gene test results were collected. Follow-up was conducted via telephone or outpatient visits at 1，3，6，and 12 months after PCI，with the follow-up ending on December 31，2023. The endpoint event was angina attack. Lasso regression analysis was used to screen variables related to angina attacks，followed by the construction of a predictive model using multivariate logistic regression analysis and the development of a nomogram. Bootstrap resampling was used for internal model validation. The training and validation sets were evaluated using receiver operating characteristic（ROC）curves，goodness-of-fit tests，calibration curves，and decision curve analysis（DCA）to construct a risk prediction model for recurrent angina after PCI in elderly STEMI patients. Results A total of 128 patients were included，with 92 males（71.9%）and 36 females（28.1%），and a median age of 63.5（61.0，66.0）years. During follow-up，45 patients（35.2%）experienced recurrent angina，while 83 patients（64.8%）did not. There were statistically significant differences in gender，low-density lipoprotein cholesterol（LDL-C），high-density lipoprotein cholesterol（HDL-C），and CYP2C19 genotype between patients with and without recurrent angina （P<0.05）. Lasso regression analysis identified 7 independent predictive variables，including gender，LDL-C，HDL-C，homocysteine（Hcy），apolipoprotein B（ApoB），D-dimer，and CYP2C19 genotype. Multivariate logistic regression analysis showed that female gender（OR=3.492 9，95%CI=1.288 8-15.066 2），elevated LDL-C（OR=3.123 7，95%CI=1.685 9-6.348 4），and elevated Hcy（OR=1.061 4，95%CI=1.028 8-1.103 6）were risk factors for recurrent angina after STEMI intervention，while elevated HDL-C（OR=0.016 7，95%CI=0.000 9-0.209 1），intermediate CYP2C19 metabolism（OR=0.273 4，95%CI=0.0747-0.923 7），and normal CYP2C19 metabolism（OR=0.086 7，95%CI=0.025 5-0.256 1）were protective factors against recurrent angina after STEMI intervention. The model was internally validated using Bootstrap resampling with 1 000 replications，and the Hosmer-Lemeshow calibration curve showed good model fit. ROC curves were plotted for the training and validation sets，with areas under the ROC curve（AUC）of 0.869（95%CI=0.796-0.943）and 0.789（95%CI=0.701-0.877），respectively，indicating good discrimination in both the modeling and validation populations. Further DCA showed that the model had good clinical utility. Conclusion Intermediate and normal CYP2C19 metabolic types are protective factors against recurrent angina after STEMI intervention. This study established a risk prediction model for recurrent angina that includes five clinical indicators: female gender，LDL-C，Hcy，HDL-C，and CYP2C19. The model can be used to predict the risk of recurrent angina in patients for early screening and has good fit，discrimination，and clinical application value.
Subjects: Nuclear Science and Technology >> Radiation Physics and Technology submitted time 2025-06-24
Abstract： The deep inelastic collision (DIC) process of the ^{18}\text{O}+^{238}\text{U} system at an incident energy of \SI{8.5}{MeV/u} was explored by coupling the improved quantum molecular dynamics (ImQMD) model with the GEMINI++ statistical decay model.Through a comprehensive analysis of the total kinetic energy - mass distribution of the reaction products, it was confirmed that the projectile-like fragments mainly stem from DIC mechanisms. Detailed calculations reveal three critical phenomena for isotopes of carbon (C), oxygen (O), and fluorine (F) in the projectile-like region：(1) The differential cross sections of neutron-rich fragments peak at near-zero emission angles, with ^{21-23}\text{F} isotopes exhibiting particularly enhanced; (2) Strong correlations emerge between fragment neutron-proton ratios (N/Z) and collision dynamics，higher N/Z values correlate with longer projectile-target contact times and smaller emission angles; (3) The collision dynamics feature a rotating dinuclear system with neck, persisting for \SI{>200}{fm/c} with \SI{\sim90}{\degree} rotation, during which substantial nucleon transfer occurs. The positive Q-values for ^{238}\text{U} \to ^{18}\text{O} transfer channels (\SI{4.212}{MeV} for 1p+2n, \SI{3.492}{MeV} for 1p+3n, and \SI{5.805}{MeV} for 1p+4n) thermodynamically favor ^{21-23}\text{F} production. The model demonstrates excellent agreement with experimental data, successfully reproducing the differential cross sections of neutron-rich fragments. These results provide critical insights for optimizing exotic beam production in next-generation zero-degree spectrometer facilities.
Subjects: Psychology >> Applied Psychology submitted time 2025-06-23
Abstract： To clarify the intricate pathways and internal mechanisms of exiting intimate partner violence (IPV), this study systematically examines the multi-level factors influencing IPV disengagement. By integrating the phase divisions of the transtheoretical model of change with the dynamic continuum framework of the psychosocial readiness model, we propose the Staged-Continuum Dynamic Interaction Model (SCDIM). The SCDIM synthesizes triggering mechanisms for phase transitions and the dynamic interplay between risk and protective factors during phase shifts, offering a comprehensive theoretical framework for understanding the complexity of IPV disengagement. This model addresses limitations of traditional models in explaining multi-factor interactions and nonlinear relationships between phases. Future research should validate the model’s efficacy and applicability, while expanding its applications and empirical directions through interdisciplinary perspectives.
Subjects: Other Disciplines submitted time 2025-06-23
Abstract：为满足反应堆压力容器（RPV）在役检查发现裂纹（役致裂纹）的快速评估需求，需进行役致裂纹验收标准制定方法研究并制定RPV役致裂纹的验收标准。本文分析了美国电力研究协会（EPRI）提出的役致裂纹验收标准制定原则和制定方法，以RPV设计阶段防断裂分析的假想裂纹为基准裂纹，结合役致裂纹验收标准制定的安全准则，提出了一种基于设计假想裂纹和设计安全系数的役致裂纹验收标准制定力学分析方法；应用本文提出的方法对RPV筒体制定的役致裂纹验收标准与ASME给出的验收标准基本接近。本文提出的役致裂纹验收标准制定力学分析方法也适用于其它核电承压设备。
Subjects: Nuclear Science and Technology >> Other Disciplines of Nuclear Science submitted time 2025-06-22
Lanlu Lu
Wenming Qin
Luozhen Jiang
Yang Liu
Kangwen Bao
Chunyu Li
Zhongjie Zhu
Yijun Gu
Jianchao Tang
Qingjie Xiao
Tingting Wu
Yupu Zhang
Weizhe Zhang
Shuyu Zhou
Yayun Yang
Zheng Jiang
Abstract：
The synchrotron radiation beamline BL17B of National Facility for Protein Science in Shanghai (NFPS), situated at the Shanghai Synchrotron Radiation Facility (SSRF), was originally designed for diffraction experiments, accommodating techniques such as single crystal diffraction, powder diffraction, and grazing-incidence wide-angle X-ray scattering (GIWAXS), enabling the characterization of long-range ordered atomic structure. Its academic community engages in research domains encompassing biology, environment, energy and materials, where there also exists a pronounced demand for characterizing short-range ordered structures. To cater to these requirements, BL17B has established an advanced X-ray absorption fine structure (XAFS) experiment platform, enabling it to address a wide range of systems, from crystalline to amorphous, and from long-range order to short-range order. The XAFS platform allows simultaneous XAFS data acquisition for both transmission and fluorescence modes within an energy range of 5-23 keV, encompassing the K-edges of titanium to ruthenium and the L3-edges of cesium to bismuth. The platform exemplifies high levels of automation, which is achieved through automated sample assessment and data collection based on large-capacity sample wheels that facilitate remote sample loading. Integrated with highly-integrated control system that simplifies experimental preparation and data collection, it significantly not only bolsters experimental efficiency but also enhance user experience. Notably, the platform boasts an impressively low extended X-ray absorption fine structure (EXAFS) detection limit of 0.04 wt% for dilute Copper Phthalocyanine (CuPc) samples and an even more remarkable X-ray absorption near edge structure (XANES) detection threshold of 0.01 wt%. These figures stand as a hallmark of the platform’s unwavering commitment to high-fidelity XAFS data acquisition, setting a new benchmark for structural characterization.
Subjects: Psychology >> Cognitive Psychology submitted time 2025-06-22
Abstract： Presenting multiple face images of the same person has been shown to enhance a person’s ability to recognize faces, which is known as multiple image advantage (MIA). However, MIA appears selective as evidenced by it manifesting in delayed face matching tasks but being absent in non-delayed face matching tasks. Two hypotheses are proposed to account for this selectivity. In one hypothesis, the delay after multiple face images are presented facilitates face representation formation, which strengthens participants’ face recognition. In the other hypothesis, it is proposed that the quantity of image information and task difficulty are key variables. That is, the more face images presented, the more information is processed, which improves recognition performance. Additionally, delayed face matching tasks are more difficult than non−delayed ones. Together, these yield MIA in delayed face matching tasks but the ceiling effect in non−delayed tasks.
In the current study, we conducted two experiments to investigate the cognitive mechanisms underlying MIA in face identity recognition. Experiment 1A adopted a face-matching paradigm where university students (N = 81) judged whether a target face matched a set of one, two, or three study faces presented simultaneously (non-delayed condition) or sequentially (delayed condition). In the simultaneous condition, target and study faces were displayed concurrently, allowing direct perceptual comparison; in the sequential condition, study faces were presented for 5,000 ms followed by a 500 ms blank screen and the target face, requiring memory-based matching. In Experiment 1B, the simultaneous matching task was modified to enforce a mandatory 5,000 ms viewing period for studying faces before allowing responses, which ensured identical exposure duration across both simultaneous and sequential conditions. This eliminated potential confounds arising from differences in processing time. Both experiments utilized unfamiliar face images, counterbalanced across tasks and participants, with familiarity screening applied post-test to exclude recognizable faces. In Experiment 2, we repeated Experiment 1 but inverted all faces to disrupt the integration processes of facial representation in the delay time.
The results of Experiment 1 show that: (1) MIA appears only when the images were presented sequentially as participants’ discriminability improved when the number of images increased and (2) When presenting three face images, participants’ discriminability under the sequential condition was higher than under the simultaneous condition. After controlling for duration of faces presented in Experiment 1B, results remain unchanged. For Experiment 2, (3) regardless of whether faces were single or multiple, participants’ discriminability under the sequential condition was lower than under the simultaneous condition, and (4) no MIA appeared in either task. Altogether, these findings suggest that MIA in facial identity recognition in delayed face matching tasks is derived from the formation of facial representation that happens in memory.
Subjects: Psychology >> Personality Psychology submitted time 2025-06-22
Abstract： Being moved is an involuntary and immediate emotional state triggered by external stimuli. Upon its occurrence, it exerts a pronounced social connection effect, strengthening not only individuals’ bonds with the direct elicitors but also extending their inclination toward broader social affiliation beyond the immediate context. Communal Sharing Strengthening Theory and Meaning Salience Theory offer two complementary perspectives on the psychological mechanisms underlying this effect: the former emphasizes an evolutionarily adaptive process that promotes social bonds through activation of communal sharing, while the latter highlights cognitive processes whereby the recognition of core values motivates voluntary social connection. This paper reviews the manifestations of both direct and extended connection effects induced by being moved, analyzes the similarities and differences between the two explanatory frameworks, and suggests that future research should further explore the boundary conditions of connection expansion, the distinction and integration of theoretical mechanisms, and the moderating role of individual differences, in order to deepen the understanding and application of the social functions of being moved.
Peer Review Status:Awaiting Review
Subjects: Chemistry >> Nuclear Chemistry submitted time 2025-06-22
Abstract： In order to more accurately assist physicians in diagnosing liver diseases and making surgical plans, accurate and stable automatic liver region segmentation of CT images is an urgent issue to be addressed. However, medical images inevitably generate noise and artifacts, and segmentation algorithms are susceptible to such information. To address this problem, this paper proposes the MCAF-Net, which embeds the MCCA into the bottleneck layer, generates abundant and complete feature representations through multi-feature complementation to reduce information loss and mitigates the effects of noise and artifacts by feature-interaction. Further, in order to achieve more accurate recognition of liver edges, the encoder and decoder side is connected by the AMFM, which enhances the perception of contextual and multi-scale information and thus realizes the fine segmentation of liver edges. The experimental results on the LiTS2017 and the noise-containing LDCT dataset show that the MCAF-Net outperforms other mainstream algorithms in mitigating the effects of noise and artifacts and the recognition of liver edges, in which the DSC and Jarracd on the LiTS2017 dataset reach 96.24% and 92.83%, respectively, and the results on the LDCT dataset show that the MCAF-Net has certain robustness and anti-noise performance.
Subjects: Physics >> Nuclear Physics submitted time 2025-06-22
CHEN, Dr. Zhen Hua
Zou, Prof. Ying
Li, Dr. Junqin
Zhu, Dr. Fangyuan
Cheng, Dr. Yiwei
Huang, Prof. Yaobo
Wang, Prof. Yong
Tai, Prof. Renzhong
Abstract： The ultrahigh-energy-resolution soft X-ray beamline, "Dreamline," at the Shanghai Synchrotron Radiation Facility (SSRF), has been successfully constructed and is now fully operational for conducting angle-resolved photoemission spectroscopy (ARPES) and photoelectron emission microscopy (PEEM) experiments. Both branches of the beamline utilize a sophisticated plane-grating monochromator equipped with four variable- line-spacing gratings, enabling it to span an energy range of 20–2000 eV. The beam spot size at the ARPES endstation is H:60μm×V:30μm, while the vertical size is a crucial factor in determining the energy resolution of the beam, which is influenced by the exit slit. Notably, the energy resolution at the ARPES sample positions has been measured to be an 17.2 meV at 867.1 eV, setting a new benchmark for the highest resolution capability within this energy range among similar international facilities. Furthermore, under the 4σ opening of the white light slit, the full energy range flux of double undulator exceeds 1012 photons per second per 0.01% bandwidth (phs/s/0.01%BW) below 800 eV when selecting the appropriate grating, while the flux across the full energy range remains above 1011 phs/s/0.01%BW.
Peer Review Status:Awaiting Review
Subjects: Other Disciplines submitted time 2025-06-21
Abstract： To assess the impact of space radiation on astronauts, this study estimates the average organ absorbed dose of astronauts by calculating the product of the fluence-to-dose conversion coefficient and the cosmic ray fluence inside the spacecraft. The dose equivalent is then derived by applying the radiation quality factor Q. The dose conversion coefficients used in this study are sourced from ICRP publications 118 and 123. The cosmic ray fluence inside the spacecraft is obtained through simulations of the transport of space radiation particles penetrating the spacecraft walls using Geant4. The results indicate that, with the increase in aluminum shielding thickness, the dose from particles originating from the Earth's radiation belt decreases, while the dose from Galactic Cosmic Rays increase. During Extravehicular Activity, 87% of the radiation dose for low Earth orbit astronauts comes from particles of the Earth's radiation belt, and 13% comes from Galactic Cosmic Rays. The calculated effective dose is approximately 70% higher than the effective dose equivalent, and the effective dose equivalent used by NASA (HNASA) is about 18% higher than the value recommended in ICRP Publication 60 (HICRP60). These findings comprehensively validate the applicability of cosmic ray fluence-to-dose conversion coefficients in radiological dosimetry and provide a scientific basis for optimizing astronaut radiation protection strategies.
Subjects: Physics >> Nuclear Physics submitted time 2025-06-21
Yuting Wei
Changlin Lan
Bo Gao
Xianlin Yang
Gong Jiang
Jiahao Wang
Bo Xie
Yangbo Nie
Yanliang Chang
Ge Zhang
Fan Wu
Kuozhi Xu
Shilong Liu
Xichao Ruan
Abstract： Benchmark experiments are indispensable for the development of neutron nuclear data evaluation libraries. Given the lack of domestic benchmarking of nuclear data in the fission energy region, this study developed a neutron leakage spectrum measurement system using a spherical sample based on the 252Cf spontaneous fission source. The EJ309 detector (for high-energy measurements) and CLYC detector (for low-energy measurements) were combined to measure the time-of-flight spectrum using the γ tagging method. To assess the performance of the system, the time-of-flight spectrum without a sample was measured first. The experimental spectra were consistent with those simulated using the Monte Carlo method and the standard 252Cf spectrum from ISO:8529-1. This demonstrates that the system can effectively measure the neutron events in the 0.15--8.0 MeV range. Then, a spherical polyethylene sample was used as the standard to verify the accuracy of the system for the benchmark experiment. The simulation results were obtained using the Monte Carlo method with evaluated data from the ENDF/B-VIII.0, CENDL-3.2, JEFF-3.3, and JENDL-5 libraries. The measured neutron leakage spectra were compared with the corresponding simulated results for the neutron spectrum shape and calculated C/E values. The results showed that the simulated spectra with different data libraries reproduced the experimental results well in the 0.15--8.0 MeV range. This study confirms that the leakage neutron spectrum measurement system based on the 252Cf source can perform benchmarking and provides a foundation for evaluating neutron nuclear data through benchmark experiments.
Peer Review Status:Awaiting Review
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