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The accurate calculation of pressurized water reactor burned fuel neutron source intensity plays a significant role in the subcritical reactivity measurement. The calculation methods of both spontaneous fission source and (α, n) neutron source were researched in this paper. There were multiple sources of alpha particle in reactor core, including 238Pu, 239Pu, 240Pu, 241Am, 242Cm, 244Cm, etc. Among these sources, the dominant source was coming from 242Cm decay. 242Cm approximation method as well as ratio fitting method were proposed for obtaining (α, n) neutron source intensity. In 242Cm approximation method, all alpha particle sources were neglected with the exception of 242Cm. The ORIGEN code was used to analyze the ratio of (α, n) to the spontaneous fission neutron of 242Cm for different enrichment assemblies at different shutdown time. It is found that the ratio of (α, n) to the spontaneous fission neutron of 242Cm is fixed around 0.191. Therefore, the neutron number density of spontaneous fission neutron is multiplied by the factor of 0-191 to obtain (α, n) neutron source intensity due to 242Cm decay. On account of neglecting all (α, n) neutron source intensity with the exception of 242Cm, it is foreseeable that 242Cm approximation method would underestimate (α, n) neutron source intensity. In ratio fitting method, the ratio of (α, n) to the total neutron source intensity was analyzed. It is found that the ratio of (α, n) to the total neutron source intensity is linearly related to the burunp when the burnup is greater than 20 000 MW·d/tU. Therefore, two fitting methods were proposed to obtain the ratio of (α, n) to the total neutron source intensity. The polynomial fitting method was used with burnup less than 20 000 MW·d/tU, on the other hand, the liner fitting method was adopted with burnup greater than 20 000 MW·d/tU. Based on the in-house core design code package, the burned fuel neutron source calculation module was developed. In order to account for reactor core space effect and burnup history effect, the micro-depletion correction method in core design code package was used to obtain accurate number densities of actinide nuclides. The developed module was verified by calculating fuel assembly examples. Comparing with the reference code, the maximum errors of total source intensity is about 5% provided by both 242Cm approximation method and ratio fitting method. With the developed burned fuel neutron source intensity calculation module, both radial and axial distributions of burned fuel neutron source intensity in the reactor core are obtained. These distributions are of significant importance to ex-core detector signal measurements. The work in this paper is an important technology support for subcritical rod worth measurement development.

Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. Methods: In this retrospective study, CT images of 1070 T3–4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. Results: The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model's capability to predict adverse clinical outcomes was superior to those of traditional assessments. Conclusion: AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques. [ABSTRACT FROM AUTHOR]