Harbin Engineering University

About: Harbin Engineering University is a education organization based out in Harbin, Heilongjiang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Chiral polymers for resolution of enantiomers

Abstract: In 1979, the formation of one-handed helical poly(triphenylmethyl methacrylate) (PTrMA) was found through the helix-sense-selective polymerization of methacrylate using chiral anionic initiators, and the existence of a stable helical polymer without chiral side chains was proved. The chiral polymer exhibited unexpected high chiral recognition of various racemic compounds when used as the chiral packing material (CPM) for HPLC, which was commercialized in 1982 as the first chiral column based on an optically active polymer. This success encouraged us to develop further useful commercial chiral packing materials (CPMs) based on polysaccharides, cellulose, and amylose. By using these polysaccharide-based CPMs, particularly phenylcarbamate derivatives, nearly 90% of chiral compounds can be resolved not only analytically but also preparatively, and several chiral drugs have been produced using the CPMs.

2D Piezoelectric Bi 2 MoO 6 Nanoribbons for GSH‐Enhanced Sonodynamic Therapy

Abstract: Reducing the scavenging capacity of reactive oxygen species (ROS) and elevating ROS production are two primary goals of developing novel sonosensitizers for sonodynamic therapy (SDT). Hence, ultrathin 2D Bi2 MoO6 -poly(ethylene glycol) nanoribbons (BMO NRs) are designed as piezoelectric sonosensitizers for glutathione (GSH)-enhanced SDT. In cancer cells, BMO NRs can consume endogenous GSH to disrupt redox homeostasis, and the GSH-activated BMO NRs (GBMO) exhibit an oxygen-deficient structure, which can promote the separation of electron-hole pairs, thereby enhancing the efficiency of ROS production in SDT. The ultrathin GBMO NRs are piezoelectric, in which ultrasonic waves introduce mechanical strain to the nanoribbons, resulting in piezoelectric polarization and band tilting, thus accelerating toxic ROS production. The as-synthesized BMO NRs enable excellent computed tomography imaging of tumors and significant tumor suppression in vitro and in vivo. A piezoelectric Bi2 MoO6 sonosensitizer-mediated two-step enhancement SDT process, which is activated by endogenous GSH and amplified by exogenous ultrasound, is proposed. This process not only provides new options for improving SDT but also broadens the application of 2D piezoelectric materials as sonosensitizers in SDT.

Software defect prediction based on kernel PCA and weighted extreme learning machine

Abstract: Context Software defect prediction strives to detect defect-prone software modules by mining the historical data. Effective prediction enables reasonable testing resource allocation, which eventually leads to a more reliable software. Objective The complex structures and the imbalanced class distribution in software defect data make it challenging to obtain suitable data features and learn an effective defect prediction model. In this paper, we propose a method to address these two challenges. Method We propose a defect prediction framework called KPWE that combines two techniques, i.e., Kernel Principal Component Analysis (KPCA) and Weighted Extreme Learning Machine (WELM). Our framework consists of two major stages. In the first stage, KPWE aims to extract representative data features. It leverages the KPCA technique to project the original data into a latent feature space by nonlinear mapping. In the second stage, KPWE aims to alleviate the class imbalance. It exploits the WELM technique to learn an effective defect prediction model with a weighting-based scheme. Results We have conducted extensive experiments on 34 projects from the PROMISE dataset and 10 projects from the NASA dataset. The experimental results show that KPWE achieves promising performance compared with 41 baseline methods, including seven basic classifiers with KPCA, five variants of KPWE, eight representative feature selection methods with WELM, 21 imbalanced learning methods. Conclusion In this paper, we propose KPWE, a new software defect prediction framework that considers the feature extraction and class imbalance issues. The empirical study on 44 software projects indicate that KPWE is superior to the baseline methods in most cases.