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To save the economic cost and improve the performance of enterprises, this study aims to synthesize high performance immobilized penicillin G acylase (PGA) carriers with fast reaction speed, high recovery rate of enzyme activity and good reusability through corresponding theoretical guidance and experimental exploration.

A diblock resin was synthesized by reversible addition-fragmentation chain transfer polymerization method using N, N-diethylacrylamide (DEA) and β-hydroxyethyl methacrylate (HEMA) as functional monomers poly(N, N-diethylacrylamide)-b-poly(β-hydroxyethyl methacrylate) (PDEA-b-PHEMA) was obtained, and the effect of the ratio of DEA and HEMA on the activity of PGA was investigated, and the appropriate block ratio of DEA and HEMA was obtained. After that, the competitive rate of HEMA and glycidyl methacrylate (GMA) under the carrier preparation conditions was investigated. Based on the above work, a thermosensitive resin carrier PDEA-b-PHEMA-b-P(HEMA-co-GMA) with different target distances was synthesized, and the chemical structures and molecular weight of copolymers were investigated by hydrogen NMR (¹H NMR).

The lower critical solution temperature of the resin support decreases with the increase of the monomer HEMA in the random copolymerization; the catalytic performance study indicated that the response rate of the immobilized PGA is fast, and the recovery rate of the enzyme activity of the immobilized PGA varies with the distance between the targets. When the molar ratio of HEMA to GMA in the resin block is 8.15:1 [i.e. resin PDEA100-b-PHEMA10-b-P(HEMA65-co-GMA8)], the activity recovery rate of immobilized PGA can reach 50.51%, which was 15.49% higher than that of pure GMA immobilized PGA.

This contribution provides a novel carrier for immobilizing PGA. Under the optimal molar ratio, the enzyme activity recovery could be up to 50.51%, which was 15.49% higher than that of PGA immobilized on the carrier with nonregulated distance between two immobilization sites.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.