The effects of surface modifications of multiwalled carbon nanotubes on their dispersibility in different solvents and poly(ether ether ketone)

The effects of surface modifications of multi-walled carbon nanotubes (MWCNTs) on their dispersibility in different solvents and poly (ether ether ketone) (PEEK) have been studied. MWCNTs were treated by mixed acids to obtain acid-functionalized MWCNTs. The acid-functionalized MWCNTs were modified with different chemical agents separately, such as 1,6-diaminohexane, hexadecyl trimethyl ammonium bromide, silane coupling agent 3-aminopropyltriethoxysilane, anhydrous sulfanilic acid and ethanolamine. MWCNT/PEEK composite films were fabricated in order to explore systematically the dispersibility of differently modified MWCNTs PEEK as well as in different solvents. The morphology and structures of MWCNTs and the compatibility between MWCNTs and PEEK have been investigated. It was observed that the MWCNTs modified with anhydrous sulfanilic acid have an excellent dispersion in the PEEK grafted by sulfonic acid groups and that the MWCNTs modified with ethanolamine are also dispersed well in pure PEEK. The results herein provide useful insights into the development of MWCNT/PEEK composites for a wide variety of applications.Peer reviewe

Effects of different immunosuppressive drugs on the periodontal status and changes in periodontal pathogenic bacterial flora in rheumatoid arthritis patients

Purpose: To investigate the prevalence of periodontal disease(s) and the associated bacteria among rheumatoid arthritis (RA) patients treated with different immunosuppressive drugs.Methods: Patients aged 18 – 65 years who had a 6-month history of RA, and were diagnosed as per the American College of Rheumatology and European League against Rheumatism, were included in the study. Supragingival plaque was removed by dentists. Using sterile paper strips, sub-gingival biofilm samples were collected from 5 of the deepest periodontal pockets. The samples were sent to pathologists for assessment. Polymerase chain reaction was carried out on them. Detection thresholds were >102 for Aggregatibacter actinomycetemcomitans, while the detection threshold for Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Prevotella intermedia,  Fusobacterium nucleatum, Campylobacter rectus, Eubacterium nodatum, Eikenellacorrodens, and Capnocytophaga species was 103.Results: There was a higher number of patients with bleeding-on-probing amongst cohorts who received a combination of methotrexate and tumor necrosis factor-α antagonist than in those given leflunomide only (52 vs. 29, p = 0.041, q = 3.064), or methotrexate + rituximab (52 vs. 30, p = 0.041, q = 3.131, Fig. 1). Papilla bleeding index was lowest in patients who were treated with leflunomide. Almost all patients had dental infection with Fusobacterium nucleatum.Conclusion: These results indicate that treatment of RA with methotrexate results in periodontal inflammation

Modelling financial volatility using Bayesian and conventional methods

This thesis investigates different volatility measures and models, including parametric and non-parametric volatility measurement. Both conventional and Bayesian methods are used to estimate volatility models. Chapter 1: We model and forecast intraday return volatility based on an extended stochastic volatility (SV) specification. Compared with the standard SV, we incorporate the trading duration information which includes both actual and expected durations. We use the Autoregressive Conditional Duration (ACD) model to calculate the expected duration that can be used to measure the surprise in durations. We find that the effect of surprise in durations on intraday volatility is highly significant. If there is an unexpected increase for the lag actual duration, the current volatility tends to decrease, and vice versa. We also take into account the duration and volatility intraday patterns. Our empirical results is based on the SPDR S&P 500 (SPY) and Microsoft Corporation (MSFT) data. According to the in-sample and out-of-sample empirical results, the extend SV model outperforms the GARCH and GARCH augmented with duration information. Chapter 2: We examine contagion effects resulting from the US subprime crisis on a sample of EU countries (UK, Switzerland, Netherlands, Germany and France) using a Multivariate Stochastic Volatility (MSV) framework augmented with implied volatilities. The MSV framework is estimated using Bayesian techniques. We compare the the MSV framework with the Multivariate GARCH (M-GARCH) framework and find the contagion effect is more significant under MSV framework. Moreover, augmenting the MSV framework with implied volatilities further increases model fit. Compared with the original MSV framework, we find that the contagion effect becomes more significant when we incorporate implied volatilities. Therefore, implied volatility information is useful for detecting financial contagion, or double checking some cases of market interdependence (strong linkages but insignificant increase in correlations). Chapter 3: We extend the Heterogeneous AR (HAR) model to allow the autoregressive parameter of daily realized volatility (RV) to be time varying (TV-HAR). The daily lag weights are adjusted according to the fluctuations of RV around its longer time average level (monthly RV). We compare the TV-HAR model with the HAR model and the recently introduced HARQ model. We observe a regular pattern of RV which the HAR and HARQ models do not fully capture: if there is an increase in the lag daily RV compared with its longer-term average level (monthly RV), the current RV tends to decrease rapidly to its long term level; conversely, if there is a decrease in the lag daily RV compared with its longer-term average level (monthly RV), that reversion takes longer. The TV-HAR model can capture this RV pattern. We find that the TVHAR model performs better than the benchmark HAR model and the HARQ model for both simulated and empirical data. Our empirical analysis is based on the S&P 500 equity index, SPY index and ten series of stocks data from 2000 to 2010

A Study of Knowledge Construction in Virtual Product User Communities

In this research the virtual product user community is defined as a producer sponsored customer aggregation existing on the Internet to share usage experience and to collaboratively find technical solutions to problems with specific brand products. Such groups have a variety of benefits to members and organisations, one being that they are a knowledge resource for product users to look for solutions to specific problems with products and identify how to use them effectively. They are also a platform for the producer to communicate with its customers, to collect market intelligence, and to incorporate users’ innovative insights and problem solving skills. However, how knowledge is constructed and shared in such groups has been rarely studied. Previous literature that focuses on cognitive development and critical thinking stages in a formal online learning context may offer some relevant insights and methodologies but requires translation to the new context, and is not likely to provide a comprehensive understanding of this area. Accordingly, this thesis aims to explore knowledge construction in virtual product user communities. The philosophical basis of the research design was constructivism and interpretivism. A qualitative research methodology was adopted. Dozens of discussion threads of theoretical interest were chosen from a typical virtual product user community on the Dell User Support Forum (and from the Dell Idea Storm Community) and were analyzed through a qualitative content analysis method. In addition, semi-structured interviews with 20 community members of the Dell User Support Forum were conducted via e-mail. A deductive thematic analysis method was used for analysing the interview transcripts. More threads were chosen from a range of other virtual product user communities for content analysis in order to explore the influences of attributes such as language, national culture and technology platform on knowledge construction. A new content analysis tool, which is based on a combination of prior codes and new categories identified from the data, was created, in order to analyze the knowledge construction embedded in the discussion of technical problems. The research identified five types of key knowledge construction episodes that make up the knowledge building process and which are characterised by low-level cognitive engagement. A knowledge construction model which represents knowledge building in reality was developed. Furthermore, problem description episodes, non-constructive episodes, and moderation episodes were identified and their relations clarified. The problem description episodes were found to facilitate knowledge construction by providing knowledge about the problem and knowledge about its context. Following from this the peer advisor could diagnose the cause of technical problems and propose tailored solutions ideas based on the users’ experiential knowledge. The moderation episode can offset the negative influence of non-constructive episodes, maintaining social order and promoting knowledge contribution. The findings illustrate that knowledge construction needs collective contribution through various types of participation by community members at different knowledge levels. The influences of contextual attributes of a virtual product user community, including communication technology, sponsorship, national language and culture, moderation, and discussion topics, on knowledge construction, were all explored in this research. Models of different types of knowledge transfer across the boundaries between the virtual product user community and the organization, highlighting the role of moderators, were constructed. Besides the above findings, this research identified and defined this specific type of online community on the Internet, i.e. the virtual product user community. In addition, it provided a content analysis tool which is tailored to the purpose of examining low-level critical knowledge construction, which complements existing analytical frameworks, derived from formal learning contexts. The study mainly contributes to the general area of information and knowledge management, specifically knowledge construction in the virtual product user community and other low-level cognitive engagement contexts. It provides a theoretical basis for practices in managing online communities, and offers useful suggestions for educators in designing and managing formal online learning communities

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods