Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant

Reliable power and temperature control in pressurized water reactor (PWR) nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC), by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC) scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation

Specify Robust Causal Representation from Mixed Observations

Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models. In this paper, we develop a learning method to learn such representation from observational data by regularizing the learning procedure with mutual information measures, according to the hypothetical factored causal graph. We theoretically and empirically show that the models trained with the learned causal representations are more robust under adversarial attacks and distribution shifts compared with baselines. The supplementary materials are available at https://github.com/ymy $4323460 / \mathrm{CaRI} /$.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0838

Neutrophil: A New Player in Metastatic Cancers

The interaction between cancer cells and immune cells is important for the cancer development. However, much attention has been given to T cells and macrophages. Being the most abundant leukocytes in the blood, the functions of neutrophils in cancer have been underdetermined. They have long been considered an “audience” in the development of cancer. However, emerging evidence indicate that neutrophils are a heterogeneous population with plasticity, and subpopulation of neutrophils (such as low density neutrophils, polymorphonuclear-myeloid-derived suppressor cells) are actively involved in cancer growth and metastasis. Here, we review the current understanding of the role of neutrophils in cancer development, with a specific focus on their pro-metastatic functions. We also discuss the potential and challenges of neutrophils as therapeutic targets. A better understanding the role of neutrophils in cancer will discover new mechanisms of metastasis and develop new immunotherapies by targeting neutrophils

CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through "do-operation" to the causal factors

Gas flow-directed growth of aligned carbon nanotubes from nonmetallic seeds

Kite growth is a process that utilizes laminar gas flow in chemical vapor deposition to grow long, well-aligned carbon nanotubes (CNTs) for electronic application. This process uses metal nanoparticles (NPs) as catalytic seeds for CNT growth. However, these NPs remain as impurities in the grown CNT. In this study, nanodiamonds (NDs) with negligible catalytic activity were utilized as nonmetallic seeds instead of metal catalysts because they are stable at high temperatures and facilitate the growth of low-defect CNTs without residual metal impurities. Results demonstrate the successful growth of over 100-$\mu$m-long CNTs by carefully controlling the growth conditions. Importantly, we developed an analysis method that utilizes secondary electron (SE) yield to distinguish whether or not CNTs grown from metal impurities. The absence of metallic NPs at the CNT tips was revealed by the SE yield mapping, whereas the presence of some kind of NPs at the same locations was confirmed by atomic force microscopy (AFM). These results suggest that most of the aligned CNTs were grown from nonmetallic seeds, most likely ND-derived NPs, via the tip-growth mode. Structural characterizations revealed the high crystallinity of CNTs, with relatively small diameters. This study presents the first successful use of nonmetallic seeds for kite growth and provides a convincing alternative for starting materials to prepare long, aligned CNTs without metal impurities. The findings of this study pave the way for more convenient fabrication of aligned CNT-based devices, potentially simplifying the production process by avoiding the need for the removal of metal impurities.Comment: Accepted version. Main manuscript: 26 pages, 6 figures. Supporting information: 8 pages, 9 figure