Search

Due to the remarkable ability of Deep learning (DL) to abstract hidden information, it has been proven to be a powerful tool in many tasks related to the design of metamaterials. DL-aided design techniques can be generally categorized into two types, including forward designs, which are training surrogate models to accelerate the simulation process, and inverse design, which uses inverse modeling techniques to generate the design that satisfies the corresponding requirement. Although generative models have a unique capability to generate multiple designs instantly with random information, they often underperform in accuracy compared to designs based on optimization techniques. In this paper, a hybrid design framework combining the advantages of both DL forward design and the inverse design based on the mixture density network (MDN) is proposed. Then the proposed framework is implemented for the inverse design of S-shaped perforated auxetic metamaterial. The hybrid design framework inherited the one-to-many mapping capability of MDN and has great capability of generating designs with designated mechanical properties at less than 10% relative errors, in most design scenarios (over 95% in the test set), at one to two orders of magnitude less computational cost compared to optimization-based forward design. • A hybrid method with MDN and DNN generates designs at low cost but good accuracy. • A case study of auxetic metamaterial shows the effectiveness of the hybrid method. • The comparison with current DL design models shows the advantage of our model. [ABSTRACT FROM AUTHOR]

The progress of machine learning (ML) in the past years has opened up new opportunities to the field of gas turbine (GT) modelling. However, successful implementation of ML algorithms remains challenging, particularly for complex problems such as multi-mode faults. An important tool for enabling applications are the feature selection and feature learning (FSFL) techniques. In particular, FL techniques have recently facilitated and improved the applicability of ML to GT modelling. This review paper conducts a review on 46 studies that utilised FSFL for GT modelling with ML. The purpose of this review is to investigate how FSFL techniques can help address GT modelling challenges and when researchers should deploy them. Therefore, the theories behind the techniques are illustrated in depth along with practical application examples from the analysed literature. The advantages and limitations of FSFL are discussed, the computational costs of different techniques are compared, and trends in the field are highlighted. Consequently, a novel categorisation framework for FSFL techniques and recommendations regarding when and how to implement them are provided. A new knowledge accumulation, extraction, and transfer concept is proposed to address GT modelling challenges. [ABSTRACT FROM AUTHOR]

• BTV utilizes NS3 and NS4 to antagonize IFN-Ⅰ signaling in the JAK-STAT pathway. • NS3 and NS4 inhibit phosphorylation of STAT1 in a dose-dependent manner. • NS3 and NS4 target the SH2 domain of STAT1. Previous studies have pointed out that bluetongue virus (BTV) down-regulates the expression levels of type Ⅰ interferon (IFN-Ⅰ) and inhibits IFN-Ⅰ signaling by targeting on the Janus tyrosine kinase (JAK)-signal transducer and activator of transcription protein (STAT) pathway. However, individual viral protein could not effectively block IFN-Ⅰ signaling. There is a need to explore the underlying mechanisms by which viral proteins of BTV coordinate to antagonize the IFN-Ⅰ signaling. We investigated the coordinative role of BTV-1 nonstructural protein 3 (NS3) and NS4 in counteracting IFN-Ⅰ signaling in the JAK-STAT pathway by directly interacting with STAT1. The NS3 and NS4 targeted the SH2 domain of STAT1 to inhibit its phosphorylation, heterodimerization, nuclear translocation, as well as activation of downstream genes of the JAK-STAT pathway. NS3 and NS4 impaired STAT1 phosphorylation induced by IFN-Ⅰ in a dose dependent manner. Overall, this study confirmed that NS3 and NS4 of BTV participate in interfering with IFN-Ⅰ signaling process. Also, a new mechanism employed by BTV to evade host innate immune responses was revealed. [ABSTRACT FROM AUTHOR]