Analysis of the Spatial Separation Effects of Thorium/Uranium Fuels in Block‐Type HTRs

With the rapid development of nuclear energy, thorium has been gaining attention because of its abundant reserves and excellent physical properties. Compared with light-water reactors, block-type high temperature gas cooled reactors (HTRs) are a better choice for thorium-based fuel for higher burnup and harder neutron spectrum. When using thorium in block-type HTRs, the composition and spatial distribution of thorium/uranium fuels are two determined factors of nuclear performance. Four spatial separation levels of thorium/uranium fuels, no separation level, TRISO level, channel level, and block level, are defined for the block-type thorium-fueled HTRs. A two-step calculation scheme was used to obtain the neutronic performance, including the initial inventory of U-235, effective multiplication factor, and average conversion ratio. Based on these data, the fuel cycle cost of different spatial separation levels can be calculated by the levelized lifetime cost method as a function of thorium content. The fuel cycle cost changes with the same trend as the initial inventory of U-235 in the reactor cores because the latter determines 70% of the total cost. When the thorium content is constant, the initial inventory of U-235 decreases with the increase of the spatial separation level because spatial self-shielding effect is strengthened by the latter

Molecular docking via quantum approximate optimization algorithm

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.Comment: 10 pages, 5 figures, All comments are welcom

Effect of state-dependent time delay on dynamics of trimming of thin walled structures

Acknowledgments This work was supported by the National Key R&D Program of China (2020YFA0714900), National Natural Science Foundation of China (52075205, 92160207, 52090054, 52188102).Peer reviewedPostprin

A snoRNA modulates mRNA 3' end processing and regulates the expression of a subset of mRNAs.

mRNA 3' end processing is an essential step in gene expression. It is well established that canonical eukaryotic pre-mRNA 3' processing is carried out within a macromolecular machinery consisting of dozens of trans-acting proteins. However, it is unknown whether RNAs play any role in this process. Unexpectedly, we found that a subset of small nucleolar RNAs (snoRNAs) are associated with the mammalian mRNA 3' processing complex. These snoRNAs primarily interact with Fip1, a component of cleavage and polyadenylation specificity factor (CPSF). We have functionally characterized one of these snoRNAs and our results demonstrated that the U/A-rich SNORD50A inhibits mRNA 3' processing by blocking the Fip1-poly(A) site (PAS) interaction. Consistently, SNORD50A depletion altered the Fip1-RNA interaction landscape and changed the alternative polyadenylation (APA) profiles and/or transcript levels of a subset of genes. Taken together, our data revealed a novel function for snoRNAs and provided the first evidence that non-coding RNAs may play an important role in regulating mRNA 3' processing

Continual Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing a fixed document-token graph and cannot make inferences on new documents. It is a challenge to deploy them in online systems to infer steaming text data. In this work, we present a continual GCN model (ContGCN) to generalize inferences from observed documents to unobserved documents. Concretely, we propose a new all-token-any-document paradigm to dynamically update the document-token graph in every batch during both the training and testing phases of an online system. Moreover, we design an occurrence memory module and a self-supervised contrastive learning objective to update ContGCN in a label-free manner. A 3-month A/B test on Huawei public opinion analysis system shows ContGCN achieves 8.86% performance gain compared with state-of-the-art methods. Offline experiments on five public datasets also show ContGCN can improve inference quality. The source code will be released at https://github.com/Jyonn/ContGCN.Comment: 9 pages, 4 figures, AAAI 2023 accepted pape