Highly Efficient Microwave-assisted Aminolysis of Epoxides in Water

Zuo, Hua,Li, Zhu-Bo,Zhao, Bao-Xiang,Miao, Jun-Ying,Meng, Li-Juan,Jang, Ki-Wan,Ahn, Chul-Jin,Lee, Dong-Ha,Shin, Dong-Soo Korean Chemical Society 2011 Bulletin of the Korean Chemical Society Vol.32 No.suppl8

Highly efficient and rapid aminolysis of epoxides with various amines in water under microwave irradiation in the absence of catalyst was developed. Chiral ${\beta}$-amino alcohols were formed in a short time with excellent yields.

Modeling of ion diffusion coefficient in saturated concrete

Xiao-Bao Zuo,Wei Sun,Cheng Yu,Xu-Rong Wan 사단법인 한국계산역학회 2010 Computers and Concrete, An International Journal Vol.7 No.5

This paper utilizes the modified Davis model and the mode coupling theory, as parts of the electrolyte solution theory, to investigate the diffusivity of the ion in concrete. Firstly, a computational model of the ion diffusion coefficient, which is associated with ion species, pore solution concentration, concrete mix parameters including water-cement ratio and cement volume fraction, and microstructure parameters such as the porosity and tortuosity, is proposed in the saturated concrete. Secondly, the experiments, on which the chloride diffusion coefficient is measured by the rapid chloride penetration test,have been carried out to investigate the validity of the proposed model. The results indicate that the chloride diffusion coefficient obtained by the proposed model is in agreement with the experimental result. Finally, numerical simulation has been completed to investigate the effects of the porosity, tortuosity, water-cement ratio, cement volume fraction and ion concentration in the pore solution on the ion diffusion coefficients. The results show that the ion diffusion coefficient in concrete increases with the porosity, water-cement ratio and cement volume fraction, while we see a decrease with the increasing of tortuosity. Meanwhile, the ion concentration produces more obvious effects on the diffusivity itself, but has almost no effects on the other ions.

Towards Improving Causality Mining using BERT with Multi-level Feature Networks

Wajid Ali,WanLi Zuo,Rahman Ali,Gohar Rahman,Xianglin Zuo,Inam Ullah 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.10

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

Novel organic-inorganic hybrid polyvinylidene fluoride ultrafiltration membranes with antifouling and antibacterial properties by embedding N-halamine functionalized silica nanospheres

Han Wang,Zuo-Ming Wang,Xi Yan,Jun Chen,Wan-Zhong Lang,Ya-Jun Guo 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.52 No.-

Novel polyvinylidenefluoride (PVDF) hybrid ultrafiltration membranes with antifouling and antibacterialproperties are prepared by embedding N-halamine functionalized silica nanospheres (HFSNs). With theaddition of HFSNs, the antifouling properties of PVDF membranes are significantly improved. The resultsreveal that the highest pure water permeationflux of 559.8 L m 2 bar 1 h 1 is attained when the 0.6 wt%HFSNs is added in the casting solution. The membrane of M-3 with 0.9 wt% HFSNs addition shows thehighest sterilization ratios of 97.1% and 97.0% against (escherichia coli) E. coli and (staphylococcus aureus)S. aureus respectively. After 6 times of inhibition-activation cycles, the membrane still remains 72.3%against E.coli.

Time Label Topic Model

YongHeng Chen,Wanli Zuo,kerui Chen,Yaojin lin 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.1

Most of the models not aware of these dependencies on document time stamps. Not modeling time can confound co-occurrence patters and results in exchangeability of topic problem, which is important factor to deal with when finding dynamic topic discovery. This limitation has thus motivated work on developing a generalized framework for incorporating time information into topic models. Consequently, a topic model named Topics over Time (TOT) is proposed, which introduces a time node in topic model to handle the exchangeability of topics problem. However it lacks the capability to accommodate data type of side information. In this paper, we present a generative time LDA-style topic model with a variety of side information named Time Label Topic(TLT), which can find not only how the latent low-dimensional structure of document-response pairs changes over time, but also overcome the exchangeability of topics problem. Empirical results demonstrate significant improvements accuracy of time stamp and response variable prediction, and lower perplexity of our proposed model and dominance over other models.

Sentiment-Aspect Analysis through Semi-Supervised Topic Modeling

Yong Heng Chen,Wanli Zuo,Hao Yue,Yaojin Lin 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6

Sentiment analysis based on the aspects of products or services is designed to explore subjective information such as attitudes and opinions in user-generated reviews. Although a great many of approaches have been proposed in detecting aspects and the relevant aspect-specific sentiments, most of them detect the latent aspects with no proper classifying them or classify them employing unsupervised topic modeling without predicting the sentiment towards these aspects. This paper proposes a novel sentiment-aspect analysis probabilistic modeling framework consisting of Seeding words extraction and semi-supervised topic (SST) model based on Sentence-LDA. More specifically, the proposed methodology starts by capturing seeding words from the websites inherent semi-structured information about products or services description. Then, it employs the captured seeding words to instruct the discovery of aspects and relevant sentiment of products or services simultaneously. Experimental results show that significant improvements have been achieved by the proposed method with respect to other state-of-the-art methods.

Phrase-based Topic and Sentiment Detection and Tracking Model using Incremental HDP

( YongHeng Chen ),( YaoJin Lin ),( WanLi Zuo ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.12

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.

Self-assembled magnetic lamellar hydroxyapatite as an efficient nanovector for gene delivery

Guangyao Xiong,Yizao Wan,Guifu Zuo,Kaijing Ren,Honglin Luo 한국물리학회 2015 Current Applied Physics Vol.15 No.7

Magnetic lamellar hydroxyapatite (ML-HA) nanoparticles were synthesized by a template-assisted selfassembly process. The as-prepared ML-HA nanoparticles self-assembled under different conditions were characterized by XRD, TEM, cytotoxicity assessment, and DNA-loading and transfection efficiency measurements. We found that the structure and morphology of ML-HA were controlled by self-assembly conditions. The ML-HA synthesized in this work exhibited good biocompatibility. The DNA-loading capacity and z-potential of ML-HA were much lower in comparison to bare lamellar HA (L-HA) without magnetic nanoparticles. Despite that, the ML-HA with good lamellar structure showed 47% higher transfection efficiency than L-HA. Results suggested that the ordered lamellar structure is a key factor in controlling transfection efficiency and magnetization is an effective way of improving the transfection efficiency of lamellar HA. Mechanisms were proposed to interpret these experimental results. It is demonstrated that the ML-HA may be a promising gene vector to deliver DNA into the cells effectively and safely.

Deep Image Annotation and Classification by Fusing Multi-Modal Semantic Topics

( YongHeng Chen ),( Fuquan Zhang ),( WanLi Zuo ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.1

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

A Novel Dummy-Based KNN Query Anonymization Method in Mobile Services

Huan Zhao,Jiaolong Wan,Zuo Chen 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.6

Due to the advances of mobile devices with GPS (Global Positioning System), a user's privacy threat is increased in location based services (LBSs).So, various Location Privacy-Preserving Mechanisms (LPPMs) have been proposed in the literature to address the privacy risks derived from the exposure of user locations through the use of LBSs. However, these methods obfuscate the locations disclosed to the LBS provider using a variety of strategies, most of which come at a cost of resource consumption. Therefore, we propose a privacy-protected KNN query anonymization method based on Bayesian estimation for Location-based services. Unlike previous dummy-based approaches, in our method, the request to the LBS server doesn't contain the genuine user location, so we can't calculate whether meet the threshold condition of two location directly, but must to decision making by transition probability. In addition, our method just requires the server returns the results the client needs. Further, we propose an effective search algorithm to improve the server processing. So it can reduce bandwidth usages and efficiently support K-nearest neighbor queries without revealing the private information of the query issuer. An empirical study shows that our proposal is effective in terms of offering location privacy, and efficient in terms of computation and communication costs.