Influence of substrate aggregation state on the enzymatic-induced crosslinking of soy protein isolate.

Transglutaminase (TGase) induced-crosslinking of soy protein isolate (SPI) was markedly influenced by the substrate aggregation state. Results showed that appropriate heating significantly accelerated the TGase crosslinking, and the 7S and 11S acidic subunits were more susceptible to the enzyme than the 11S basic proteins. The content of ε-(γ-glutamyl)-lysine isopeptide bonds increased from 4.74 to 8.61 µmol/g protein when the heating intensity was increased from 75 °C for 15 min to 95 °C for 30 min, due to sufficient unfolding of the protein structure. Rheological data indicated that the gel formed from the SPI heated at 95 °C for 30 min exhibited the best properties, with a 60 % increase in the storage modulus compared with the unheated sample. However, excessive heating (95 °C, 60-120 min) caused severe aggregation of SPI and formation of insoluble aggregates, resulting in poor crosslinking efficiency and weaker gel properties.

An Overview of Laser Metal Deposition for Cladding: Defect Formation Mechanisms, Defect Suppression Methods and Performance Improvements of Laser-Cladded Layers.

With the development of society and the economy, there is an increasing demand for surface treatment techniques that can efficiently utilize metal materials to obtain good performances in the fields of mechanical engineering and the aerospace industry. The laser metal deposition (LMD) technique for cladding has become a research focus in recent years because of its lower dilution rate, small heat-effect zone and good metallurgical bonding between the coating and substrate. This paper reviews the simulation technology for the melt pool's grain growth mechanism, temperature and stress distribution that are directly related to defect formation in LMD technology. At the same time, the defect suppression method and the performance improvement method of the cladded layer in LMD technology are introduced. Finally, it is pointed out that the active selection of materials according to the required performance, combined with the controllable processing technology, to form the corresponding microstructure, and finally, to actively realize the expected function, is the future development direction of LMD technology.

V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data.

We present V2V, a novel deep learning framework, as a general-purpose solution to the variable-to-variable (V2V) selection and translation problem for multivariate time-varying data (MTVD) analysis and visualization. V2V leverages a representation learning algorithm to identify transferable variables and utilizes Kullback-Leibler divergence to determine the source and target variables. It then uses a generative adversarial network (GAN) to learn the mapping from the source variable to the target variable via the adversarial, volumetric, and feature losses. V2V takes the pairs of time steps of the source and target variable as input for training, Once trained, it can infer unseen time steps of the target variable given the corresponding time steps of the source variable. Several multivariate time-varying data sets of different characteristics are used to demonstrate the effectiveness of V2V, both quantitatively and qualitatively. We compare V2V against histogram matching and two other deep learning solutions (Pix2Pix and CycleGAN).