1,720,994 research outputs found
Dataset for Surface Modification and Porosimetry of Vertically Aligned Hexagonal Mesoporous Silica Films
No full textDataset supporting:
Robertson, Calum et al (2016) Surface Modification and Porosimetry of Vertically Aligned Hexagonal Mesoporous Silica Films. RSC Advances. DOI:10.1039/C6RA23059H</span
The fabrication of nanoporous template thin films for supercritical fluid electrodeposition
No full textThis work investigates the fabrication of nanoporous templates as a part of the supercritical fluid electrodeposition (SCFED) project. The goals set for this investigation was to produce films with pore channels (diameter < 5 nm) orientated perpendicularly on top of an electrode. During the course of this work, two techniques were investigated, Stöber-derived method and electrochemical-assisted self-assembly (EASA).The Stöber films produced perpendicularly orientated pore structure through a hydrothermal process. EASA film were generated through electrodeposition, resulting in highly ordered vertically aligned pore channels. Both these techniques were transferred from indium-tin oxide (ITO) onto titanium nitride (TiN), which increased the potential window of the substrate. The pore diameters of the Stöber and EASA films were determined as 2.6 and 1.6 nm respectively. This could be increased with the addition of a swelling agent or decreased by using a surfactant with a shorter tail length. Helium-ion microscopy was shown to provide high-resolution images of silica films. It provided detailed images of the topography and pores structure at the surface of the films.Tin was deposited into the pores of an EASA film using SCFED method. The EASA films were also subjected to post-synthesis chemical modification based on grafting functionalised silane molecules. As a result, the pore size and chemical properties were altered using a range of functionalised silane based grafting agents. Trimethylchlorosilane was found to be most successful at coating the pore walls. Other larger grafting agents were shown only to partially coat the surface of the films. This was only possible due to the films being placed on the reflective surface of the TiN substrates, which allowed for changes in porosity to be analysed using ellipsometric porosimetry.<br/
Real time financial information analysis
No full textThe efficient market hypothesis states that an efficient market incorporates all available information to provide an accurate valuation of an asset. Presently investors and researchers attempt to forecast future returns (profit/loss if the asset is held for a certain period) and volatility (variance of the returns) of the asset based on past trading behaviour, and commonly ignore non-numerical information. It is almost impossible to forecast future returns for frequently traded assets such as stocks, bonds, and currencies, so many institutional investors prefer to forecast future volatility. Volatility is frequently used by traders and fund managers to measure the risk of continuing to own the asset. Most volatility forecasting models completely disregard the arrival of news and therefore theoretically violate the efficient market hypothesis. The aim of this research is to investigate how the inclusion of details of the arrival of asset specific news (news which is relevant to the asset) can improve the volatility forecasts of a model. The problem is that the efficient market hypothesis indicates that only new information will cause the market to react, and therefore it is necessary to determine whether the news contains any new information. Most news does not include any new information and therefore assuming all news will trigger abnormal market behaviour is unlikely to improve the performance of a model. Furthermore news which causes a shock, i.e., news which contains highly unexpected new information, will cause a greater change in volatility than news which contains expected information. Therefore to produce a model that factors in the arrival of news into volatility forecasts, it is beneficial to examine the content to predict the reaction to the news. This research combines the field of econometrics with machine learning and intelligent data analysis. All hypotheses tested within this thesis are tested on a large collection of stocks traded in the US, UK and Australia. To my knowledge, this is the largest dataset used for the types of experiments conducted in this thesis. In this thesis evidence is provided to suggest that asset specific news is correlated with abnormal returns, volatility, and volatility forecast errors. There is also evidence to suggest that abnormal volumes and trading activity correlate to asset specific news. This confirms the findings of previous studies though in most cases only a small dataset was used and often only one or two time series (i.e., return, volatility, volume etc.) were used. Furthermore many studies did not investigate the intraday effect of news (i.e., the reaction on the day the news was released). The studies which investigated the intraday effect tended to focus on macroeconomic news, which is scheduled and eagerly anticipated by investors. Therefore the behaviour is easier to detect that for asset specific news. It is demonstrated that the content of news can be used to forecast abnormal returns and forecast periods when the given volatility forecasting model exhibits abnormally large errors (the difference between the realised volatility and the volatility which the given model forecast) with a high degree of accuracy. This was achieved by analysing the content of past news which correlated with abnormal market behaviour. For this research a new method for ranking terms is introduced and demonstrated to be very effective. Previous studies have revealed that the content of news can be used to forecast abnormal returns but, to my knowledge, no study has investigated the volatility forecast error. Furthermore, most previous studies have used a small dataset, and to forecast at relatively low frequencies (most are daily, though one is hourly). To the best of my knowledge no previous study has use such a large dataset to predict the high frequency (as little as 5 minutes) market reaction to news. Nor has any previous study achieved classification accuracies as high as those achieved in this thesis. Finally, a news aware volatility forecasting model is produced and the evidence demonstrates that the performance is better than an alternative model which does not account for news under certain circumstances. Furthermore it is demonstrated that using the content of news to choose documents which are more likely to cause the market to react yields better forecasts. Very few researchers have included the arrival of news in a volatility forecasting model, and all of these have used small datasets. Furthermore, to my knowledge, none of these researchers have used the content of the news to choose news which is more likely to cause the market to react
Parallel data mining on cycle stealing networks
No full textIn a world where electronic databases are used to store ever-increasing quantities of data it is becoming harder to mine useful information from them. Therefore there is a need for a highly scalable parallel architecture capable of handling the ever-increasing complexity of data mining problems.\ud
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A cycle stealing network is one possible scalable solution to this problem. A cycle stealing network allows users to donate their idle cycles to form a virtual supercomputer by connecting multiple machines via a network. \ud
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This research aims to establish whether cycle stealing networks, specifically the G2 system developed at the Queensland University of Technology, are viable for large scale data mining problems. The computationally intensive sequence mining, feature selection and functional dependency mining problems are deliberately chosen to test the usefulness and scalability of G2.\ud
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Tests have shown that G2 is highly scalable where the ratio of computation to communication is approximately known. However for combinatorial problems where computation times are difficult or impossible to predict, and communication costs can be unpredictable, G2 often provides little or no speedup. \ud
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This research demonstrates that existing sequence mining and functional dependency mining techniques are not suited to a client-server style cycle stealing network like G2. However the feature selection is well suited to G2, and a new sequence mining algorithm offers comparable performance to other existing, non-cycle stealing, parallel sequence mining algorithms. Furthermore new functional dependency mining algorithms offer substantial benefit over existing serial algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
What types of events provide the strongest evidence that the stock market is affected by company specific news?
Full text linkThe efficient market hypothesis states that an efficient market immediately incorporates all available information into the price of the traded entity. It is well established that the stock market is not an efficient market as it consists of numerous traders with differing strategies and interpretations of information. However there is substantial evidence to suggest that the stock market does incorporate new information into prices. Unfortunately little research has focussed on the high frequency effect of real time news, across a broad base of assets. This paper investigates how the US, UK, and Australian markets incorporate all real time news, not just Press Announcements, Annual Reports, etc. We find that there is strong evidence to suggest that the markets do incorporate news quickly
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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