31 research outputs found
Boron containing vinyl aromatic polymers: synthesis, characterization and applications
No full textNovel luminescent polystyrene-based organoborane polymers were synthesized via facile silicon-boron exchange reactions with boron tribromide. The Lewis acidity as well as the photophysical properties of the polymers were then fine tuned by variation of the organic pi-system. The key step is a selective boron-tin exchange that allows for controlled and selective replacement of one of the bromine substituents by the chromophoric system, followed by substitution of the second bromine by a sterically hindered aryl group. The polymers and model compounds were fully characterized by multinuclear NMR spectroscopy. Molecular weights were determined by GPC. DSC and TGA were used to determine their thermal properties. Both polymers and model systems are highly emissive and UV-visible and fluorescence spectroscopy were used to ascertain their photophysical characteristics. To probe the use of the polymers as potential anion sensors, they were subjected to complexation with fluoride ([Bu4N]F in THF) and their complexation was studied by 11B NMR as well as UV-vis and fluorescence spectroscopy.
To counter the oxidative degradation of the systems a new strategy was adapted in which we prepared stannylcarbazole precursors which would act as chromophores. To further enhance the stability of the boron center in these systems the mesityl group was replaced by the bulkier 1,3,5-triisopropylphenyl group. The models and polymers synthesized show superior stability as compared to the compounds synthesized earlier. The compounds are also highly emissive in the blue region.
Systems that contain electroactive ferrocenyl groups in the side-chain of polystyrene were also synthesized. Cyclic voltammetry studies confirm the electroactive nature of these polymers. A quasi-reversible boron redox couple and a ferrocene-centered redox process were observed.
Finally, a trimethylsilyl-functionalized vinyl bithiophene monomer was prepared. This monomer was polymerized by a variety of methods including standard free radical polymerization (BPO, AIBN), via nitroxide-mediated polymerization (NMP) and also by anionic polymerization. Moderate molecular weights in the range of 4000 – 9000 were obtained.Ph.D.Includes bibliographical references (p. 146-161)by Kshitij K. Para
Predicting bitcoin price fluctuations by analyzing global currency patterns & sentiments
No full textFrom the past two years with increasing geopolitical and economic issues, global currency values have been falling and stock markets have been having a poor run & investors losing wealth. This has led to a renewal of interest in digital currencies. Bitcoin, one of the most prominent digital currency has found itself in spotlight with investors wanting a piece of it and business establishments accepting it as a source of payment due to its stable performance in the last few years. A lot of research has been done on predicting Bitcoin prices using Machine Learning and Twitter Sentiment Analysis. On the same lines, we are analyzing bitcoin prices using Machine Learning and Sentiment Analysis. We also study stock market trends in order to better predict bitcoin prices quantitively. In this work we analyze the impact of global currencies like US Dollar, foreign exchanges on Bitcoin prices and whether Bitcoin has the stability to dethrone global currencies and become the single medium of transaction.M.S.Includes bibliographical referencesby Kshitij Chhatwan
Multimodal Learning Experience for Deliberate Practice
No full textWhile digital education technologies have improved to make educational resources more available, the modes of interaction they implement remain largely unnatural for the learner. Modern sensor-enabled computer systems allow extending human-computer interfaces for multimodal communication. Advances in Artificial Intelligence allow interpreting the data collected from multimodal and multi-sensor devices. These insights can be used to support deliberate practice with personalised feedback and adaptation through Multimodal Learning Experiences (MLX). This chapter elaborates on the approaches, architectures, and methodologies in five different use cases that use multimodal learning analytics applications for deliberate practice.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System
Survey on Noise Estimation and Removal Methods through SVM
No full textThe Support vector machine is statistical learning method but it is also recognized as another approach to solve and simplify data classification. SVM have been discovered as one of the successful classification techniques for many areas and application and it works on different datasets and gives appropriate result. There is a noise or irrelevant data present in datasets which leads to poor result so to remove those meaningless data some approaches are introduced for better result. In this paper an introduction of SVM (Support Vector Machine) and various noise estimation and noise removal methods based on support vector machine is presented
Effect of Substrate Concentration & Elevated CO2 Partial Pressure on the Odd & Even Carboxylate Formation
No full textOf all the greenhouse gases (GHGs), carbon dioxide (CO2) has been the target of most climate recovery efforts as it is the most abundantly emitted GHG by mass. In fact, in 2015 a legally binding international treaty was adopted by 196 parties in Paris, France to constrain the anthropogenic warming to 1.5-2.0˚C above the pre-industrial level. In order to meet this goal, a carbon budget was formulated as an estimate of the amount of carbon that can be emitted while limiting the anthropogenic warming to prescribed levels. However, the global CO2 emissions from industries are rapidly depleting this budget. Therefore, to mitigate the effects of climate change, CO2 emissions must be reduced by employing alternative commodities that can replace petrochemical resources. In this context, mixed culture fermentation presents an opportunity for redefining CO2 and waste streams as raw material for production of commodities traditionally derived from petrochemical resources. Previous studies by on this topic have indicated a potential association between elevated CO2 levels (pCO2) and butyrate formation from mixed culture fermentation. However, the cellular mechanism underlying this association are still poorly understood. Therefore, the principal objective of this research was to investigate the effects of initial substrate concentrations (g/L) and elevated pCO2 (bar) conditions on selectivity (moli/moltotal) of biomolecules produced from anaerobic conversion of glucose. For this purpose, a between-subject mixed factorial experimental design was developed to gauge the main and interaction effects of initial substrate concentrations (g/L) and elevated pCO2 (bar) conditions on selectivity of biomolecules. The principal findings of this research indicate that a strong positive relationship exists between the pCO2 and butyrate formation as the application of CO2 in reactor (EPBs) headspace resulted in higher butyrate selectivity compared to the control reactors (APBs). However, contrary to the conclusions reached by previous studies it was found that increasing the initial substrate concentration steered the product formation towards lactate and not butyrate. Whereas the highest recorded butyrate selectivity for EPBs was 30.41% for experimental condition with 5 g/L substrate concentration and 4 bar pCO2, the highest recorded butyrate selectivity for APBs was only 11.72% for 10 g/L substrate concentration and atmospheric pressure conditions. Conversely, the highest recorded lactate selectivity for EPBs was 15.13% for 20 g/L substrate and 3 bar pCO2 while the highest recorded lactate selectivity for APBs was 47.95% for 25 g/L substrate concentration and atmospheric pressure conditions. As a result of these investigations, theories concerning formation of butyrate and lactate were proffered in context of the role of CO2 in mixed culture fermentation. By confronting the existing understanding regarding product formation with new evidence this investigation seeks to advance theories concerning mixed culture fermentation.Civil Engineering | Environmental Engineerin
Novel Approach to Hide Sensitive Association Rules by Introducing Transaction Affinity
No full textIn this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide association rules. The first two algorithms, MaxaffinityDSR and MinaffinityDSR, hide the sensitive information by selecting the victim item as the right-hand side of the sensitive association rule. The next two algorithms, MaxaffinityDSL and MinaffinityDSL, select the victim item from the left-hand side of the rule whereas the Hybrid approach picks the victim item from either the left-hand side or right-hand side. The performance of proposed algorithms has been evaluated by comparison with state-of-art methods (Algo 1.a and Algo 1.b), MinFIA, MaxFIA and Naive algorithms. The experiments were performed using the dataset generated from IBM synthetic data generator, and implementation has been performed in R language
Investigations in Privacy Preserving Data Mining
No full textData Mining, Data Sharing and Privacy-Preserving are fast emerging as a field of the high level of the research study. A close review of the research based on Privacy Preserving Data Mining revealed the twin fold problems, first is the protection of private data (Data Hiding in Database) and second is the protection of sensitive rules (Knowledge) ingrained in data (Knowledge Hiding in the database). The first problem has its impetus on how to obtain accurate results even when private data is concealed. The second issue focuses on how to protect sensitive association rule contained in the database from being discovered, while non-sensitive association rules can still be mined with traditional data mining projects. Undoubtedly, performance is a major concern with knowledge hiding techniques. This paper focuses on the description of approaches for Knowledge Hiding in the database as well as discuss issues and challenges about the development of an integrated solution for Data Hiding in Database and Knowledge Hiding in Database. This study also highlights directions for the future studies so that suggestive pragmatic measures can be incorporated in ongoing research process on hiding sensitive association rules
