Anti-MET monoclonal antibody, clone TQ55

Many modern technologies require permanent magnets with combinations of properties that cannot be met by conventional metallic or ceramic magnets. Ferrite/polymer composite magnets are a type of rare-earth free magnet with a wide range of magnetic and material property combinations. The uncertainty surrounding the supply and pricing of rare-earth elements, along with environmental issues of using these elements, have motivated many researchers to develop high-performance ferrite-based magnets via an exchange spring method. The present study explores magnetite coated M-type ferrite nanocomposites synthesised via a hydrothermal and coprecipitation method, and investigates the mechanical and magnetic properties of warm compressed high-performance exchange-coupled nanocomposites in an epoxy matrix. We show how the powder-to-resin ratio and preparation conditions lead to optimised mechanical properties, and enhancement in the maximum energy product of the composite magnet by up to 120% compared to a commercial SrM bonded plasto-ferrite magnet. These high performance composite magnets can lower the final cost of ferrite based bonded magnets without reducing the permanent magnetic properties or can be used in applications that a ferrite magnet has inadequate performances.

Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its extensions have been widely developed for industrial applications. Nevertheless, an AE commonly uses the last hidden layer for regression modeling with the output, which seems to be a kind of information waste as the shallow layers are also abstractions of input data. Besides, when there are excessive unlabeled samples, AE-based models are less likely to make full use of them or even degrade the performance. To deal with these issues, a method called ensemble semi-supervised gated stacked AE (ES(2)GSAE) is proposed in this article. Gate units are used to develop connections between different layers and the output layer, which also help quantify the contribution of different hidden layers. Moreover, the idea of ensemble learning is combined with semi-supervised learning, in which different unlabeled datasets are used for training different submodels to ensure their diversities. In this way, unlabeled samples can be utilized more efficiently and help enhance the model performance. The effectiveness and superiority are verified in a real industrial process by comparing the proposed method with other typical AE-based models.