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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.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

The purpose of this study is to simulate the temperature distribution during an electron beam freeform fabrication (EBF³) process based on a fully threaded tree (FTT) technique in various scales and to analyze the temperature variation with time in different regions of the part.

This study presented a revised model for the temperature simulation in the EBF³ process. The FTT technique was then adopted as an adaptive grid strategy in the simulation. Based on the simulation results, an analysis regarding the temperature distribution of a circular deposit and substrate was performed.

The FTT technique was successfully adopted in the simulation of the temperature field during the EBF³ process. The temperature bands and oscillating temperature curves appeared in the deposit and substrate.

The FTT technique was introduced into the numerical simulation of an additive manufacturing process. The efficiency of the process was improved, and the FTT technique was convenient for the 3D simulations and multi-pass deposits.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.