Advances in Inverse Transport Methods and Applications to Neutron Tomography

The purpose of the inverse-transport problems that we address is to reconstruct the material distribution inside an unknown object undergoing a nondestructive evaluation. We assume that the object is subjected to incident beams of photons or particles and that the exiting radiation is measured with detectors around the periphery of the object. In the present work we focus on problems in which radiation can undergo significant scattering within the optically thick object. We develop a set of reconstruction strategies to infer the material distribution inside such objects. When we apply these strategies to a set of neutron-tomography test problems we find that the results are substantially superior to those obtained by previous methods. We first demonstrate that traditional analytic methods such as filtered back projection (FBP) methods do not work for very thick, highly scattering problems. Then we explore deterministic optimization processes, using the nonlinear conjugate gradient iterative updating scheme to minimize an objective functional that characterizes the misfits between forward predicted measurements and actual detector readings. We find that while these methods provide more information than the analytic methods such as FBP, they do not provide sufficiently accurate solutions of problems in which the radiation undergoes significant scattering. We proceed to present some advances in inverse transport methods. Our strategies offer several advantages over previous reconstruction methods. First, our optimization procedure involves the systematic use of both deterministic and stochastic methods, using the strengths of each to mitigate the weaknesses of the other. Another key feature is that we treat the material (a discrete quantity) as the unknown, as opposed to individual cross sections (continuous variables). This changes the mathematical nature of the problem and greatly reduces the dimension of the search space. In our hierarchical approach we begin by learning some characteristics of the object from relatively inexpensive calculations, and then use knowledge from such calculations to guide more sophisticated calculations. A key feature of our strategy is dimension-reduction schemes that we have designed to take advantage of known and postulated constraints. We illustrate our approach using some neutron-tomography model problems that are several mean-free paths thick and contain highly scattering materials. In these problems we impose reasonable constraints, similar to those that in practice would come from prior information or engineering judgment. Our results, which identify exactly the correct materials and provide very accurate estimates of their locations and masses, are substantially better than those of deterministic minimization methods and dramatically more efficient than those of typical stochastic methods

DiffAnt: Diffusion Models for Action Anticipation

Anticipating future actions is inherently uncertain. Given an observed video segment containing ongoing actions, multiple subsequent actions can plausibly follow. This uncertainty becomes even larger when predicting far into the future. However, the majority of existing action anticipation models adhere to a deterministic approach, neglecting to account for future uncertainties. In this work, we rethink action anticipation from a generative view, employing diffusion models to capture different possible future actions. In this framework, future actions are iteratively generated from standard Gaussian noise in the latent space, conditioned on the observed video, and subsequently transitioned into the action space. Extensive experiments on four benchmark datasets, i.e., Breakfast, 50Salads, EpicKitchens, and EGTEA Gaze+, are performed and the proposed method achieves superior or comparable results to state-of-the-art methods, showing the effectiveness of a generative approach for action anticipation. Our code and trained models will be published on GitHub

A mechanistic model of a PWR-based nuclear power plant in response to external hazard-induced station blackout accidents

Natural hazard-induced nuclear accidents, such as the Fukushima Daiichi Accident that occurred in Japan in 2011, have significantly increased reactor safety studies in understanding nuclear power plant (NPP) responses to external hazard events such as earthquakes and floods. Natural hazards could cause the loss of offsite power in nuclear power plants, potentially leading to a Station Blackout (SBO) accident that significantly contributes to the overall risk of nuclear power plant accidents. Despite the fact that extensive research has been conducted on the station blackout accident for nuclear power plant, further understanding of these events is needed, particularly in the context of the dynamic nature of external hazards such as external flooding. This paper estimates the progression of station blackout events for a generic pressurized water reactor (PWR) in response to external flooding events. The original RELAP5-3D model of the Westinghouse four-loop design pressurized water reactor was adopted and modified to simulate the external flood-induced station blackout accident, including the short-term and long-term station blackout scenarios. A sensitivity analysis of long-term station blackout, examining reactor operation times and analyzing key parameters over time, was also conducted in this work. The results of the analyses, especially the critical timing parameters of key event sequences, provide useful insights about the time during the external flooding event, which is important for plant operators to make timely decisions to prevent potential core damage. This paper represents significant progress toward developing an integrated risk assessment framework for further identifying and assessing the effects of the critical sources of uncertainties of nuclear power plant under external hazard-induced events

Multi-omics analysis reveals the prognostic and tumor micro-environmental value of lumican in multiple cancer types

Background: Lumican (LUM), a proteoglycan of the extracellular matrix, has been reported to be involved in the regulation of immune escape processes, but the data supporting this phenomenon are not sufficient. In this study, we aimed to explore the links among LUM expression, survival, tumor microenvironment (TME), and immunotherapy in 33 cancer types.Methods: Data from several databases, such as UCSC Xena, GTEx, UALCAN, HPA, GEPIA2, TISIDB, PrognoScan, TIMER2, and GEO, as well as published studies, were used to determine the relationship between LUM expression and clinical features, TME, heterogeneity, and tumor stemness.Results: The expression of LUM was statistically different in most tumors versus normal tissues, both at the RNA and protein expression levels. High expression of LUM was typically associated with a poor prognosis in tumors. Additionally, immune scores, six immune cells, four immunosuppressive cells, cancer-associated fibroblasts (CAFs)-associated and immunosuppressive factors, tumor mutation burden (TMB), microsatellite instability (MSI), DNAss, and RNAss were all significantly associated with LUM. Among them, LUM expression displayed a significant positive correlation with CAFs and their factors, and exhibited immunosuppressive effects in six independent immunotherapy cohorts.Conclusion: Multi-omics analysis suggests that LUM may have been a prognostic marker, contributed to immunosuppression in the TME, and decreased the effectiveness of immune checkpoint inhibitors

Neural Network Based Deep Learning Method for Multi-Dimensional Neutron Diffusion Problems with Novel Treatment to Boundary

In this paper, the artificial neural networks (ANN) based deep learning (DL) techniques were developed to solve the neutron diffusion problems for the continuous neutron flux distribution without domain discretization in advance. Due to its mesh-free property, the DL solution can easily be extended to complicated geometries. Two specific realizations of DL methods with different boundary treatments are developed and compared for accuracy and efficiency, including the boundary independent method (BIM) and boundary dependent method (BDM). The performance comparison on analytic benchmark indicates BDM being the preferred DL method. Novel constructions of trial function are proposed to generalize the application of BDM. For a more in-depth understanding of the BDM on diffusion problems, the influence of important hyper-parameters is further investigated. Numerical results indicate that the accuracy of BDM can reach hundreds of times higher than that of BIM on diffusion problems. This work can provide a new perspective for applying the DL method to nuclear reactor calculations

An Economic Cost Assessment on HALEU Fuels for Small Modular Reactors

Small modular reactors (SMRs) are currently being considered as future investments for commercial entities due to perceived advantages over traditional large-scale power reactors, particularly their considerably lower capital costs. One strategy for lowering the levelized cost of electricity (LCOE) of SMRs is to increase their burnup by utilizing high-assay low-enriched uranium (HALEU) fuels, which range from 5 to 20 weight percent (w/o) of U-235. By increasing fuel enrichment to HALEU levels, with higher specific fuel costs compared to standard enrichment, a plant may achieve an increased capacity factor by extending its fuel cycle and thereby reducing average yearly fuel supply costs. It is expected that the benefits of optimizing fuel enrichment to extend a reactor’s fuel cycle outweigh the added cost due to more expensive fuel. In this paper, the net benefit of extending an SMR’s fuel cycle by enriching uranium fuel to HALEU levels was estimated using 2017 nuclear fuel production market data with NuScale’s 160 MWt SMR design as a case study. It was found that, for NuScale’s design, plant LCOE decreased with increasing cycle length enabled by higher fuel enrichment. It was also observed that doubling cycle time from 24 months to 48 months netted each reactor a 1.23 $/MWh reduction in LCOE. The total savings for a 12-module SMR design were estimated to be around$5,840,000 per year. Therefore, utilizing HALEU fuel in SMRs can vastly improve their economic efficiency