Monitoring Aerosols With Time Resolution With a Rotating Drum Sampler Using LA-ICPMS for Elemental Analysis

This thesis presents a comprehensive study on the characterization and application of a rotating drum sampler for air quality monitoring, with a focus on aerosol composition analysis using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS). The study aimed to determine (1) the cut-off points of the drum sampler to better understand its performance characteristics and (2) to investigate indoor air quality, specifically within makerspaces on Bucknell University’s campus. The research highlighted the novelty of applying LA-ICPMS for time-resolved aerosol composition analysis, demonstrating the potential for low-cost pollution concentration studies. The characterization experiments did not yield expected results, but the results provided valuable insights into the sampler\u27s operation and suggested improvements for future research. The indoor air quality monitoring did not reveal significant diurnal variations in metal aerosol concentrations, suggesting effective ventilation in the studied locations. This work lays the groundwork for future studies on aerosol sampling and analysis, emphasizing the need for further methodological development in LA-ICPMS standards and data processing techniques

A Text-based Model for Identifying Online Trust Relationships

Trust has been a long-standing issue in online communities and is gaining importance with the popularity of online social networks. Traditional trust models theorize and explain trust, but they do not directly provide operationalization of trust relationships. In view that the text is the dominant medium of online communication, this paper develops a text-based model for identifying online trust relationships. Building on organizational trust models, social exchange theory, and speech-act theory, the proposed model conceptualizes trust relationship as a sequence of speech acts. The model is validated with the data collected from a real-world online community. This research not only creates a text-based method for identifying online trust but also lays the groundwork for automated analysis of online trust

withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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Trust Discovery in Online Communities

This research aims to discover interpersonal trust in online communities. Two novel trust models are built to explain interpersonal trust in online communities drawing theories and models from multiple relevant areas, including organizational trust models, trust in virtual settings, speech act theory, identity theory, and common bond theory. In addition, the detection of trust in online communities is automated by leveraging natural language processing techniques. Online communities continue to grow on the internet and vary from grass roots organizations to communities facilitated by large corporations. Examples of increased use of social networks include seeking healthcare, financial, and technical advice. Topics such as these stress the importance of trust between individuals in online communities. Although trust has been widely studied in the literature, the question of how trust evolves in online communities remains as a research gap. This research seeks to model the evolution of trust in online communities to address this gap. Establishing practical trust models provides opportunities for new algorithms for discovering trust relationships in online communities. Today trust is typically measured through the use of psychometric surveys that do not scale with the growth of online communities. Alternatively, the creation of automated trust discovery tools would provide benefit to online community managers in moderating communities. First the research extends organizational trust theories to online communities. Specifically, the Calculus-Based Trust (CBT) and Knowledge-Based Trust (KBT) theories showed high correlation to trust relationships in various online communities. Moreover, in view of the evolvement of trust relationships, CBT was found to precede KBT. The extension of CBT and KBT was validated through empirical survey using active participants in online communities such as financial investing, healthcare, shopping, and technology communities. To help operationalize the theory, a formal trust model was proposed using speech act theory. The model was tested in a financial investing community, and discussion threads were discovered that matched this model. The formal trust model sets a foundation for applying natural language processing techniques to text in discussion threads, allowing the development of new tools for online community managers. Next, an identity-based trust model was developed using the artifacts of virtual co-presence, deep profiling, and self-presentation to predict CBT and KBT. This finding resulted from an empirical study using the same online community participants that validated CBT and KBT in online communities. Algorithms for discovering likely trustees in online communities can be facilitated by knowing that artifacts provide potential indicators of individuals serving as trustees. Lastly, a two-part trust discovery algorithm is proposed to automatically find trust relationships in online communities. The first part of the algorithm consists of a speech act classifier to categorize each sentence in a discussion thread as one of four speech acts that are relevant to the trust model in this dissertation. The second part of the algorithm involves applying similarity measures to rank speech act pairs and then using the ranking score with additional features to find trustors in a discussion thread. &#8195

Trust Discovery in Online Communities

This research aims to discover interpersonal trust in online communities. Two novel trust models are built to explain interpersonal trust in online communities drawing theories and models from multiple relevant areas, including organizational trust models, trust in virtual settings, speech act theory, identity theory, and common bond theory. In addition, the detection of trust in online communities is automated by leveraging natural language processing techniques. Online communities continue to grow on the internet and vary from grass roots organizations to communities facilitated by large corporations. Examples of increased use of social networks include seeking healthcare, financial, and technical advice. Topics such as these stress the importance of trust between individuals in online communities. Although trust has been widely studied in the literature, the question of how trust evolves in online communities remains as a research gap. This research seeks to model the evolution of trust in online communities to address this gap. Establishing practical trust models provides opportunities for new algorithms for discovering trust relationships in online communities. Today trust is typically measured through the use of psychometric surveys that do not scale with the growth of online communities. Alternatively, the creation of automated trust discovery tools would provide benefit to online community managers in moderating communities. First the research extends organizational trust theories to online communities. Specifically, the Calculus-Based Trust (CBT) and Knowledge-Based Trust (KBT) theories showed high correlation to trust relationships in various online communities. Moreover, in view of the evolvement of trust relationships, CBT was found to precede KBT. The extension of CBT and KBT was validated through empirical survey using active participants in online communities such as financial investing, healthcare, shopping, and technology communities. To help operationalize the theory, a formal trust model was proposed using speech act theory. The model was tested in a financial investing community, and discussion threads were discovered that matched this model. The formal trust model sets a foundation for applying natural language processing techniques to text in discussion threads, allowing the development of new tools for online community managers. Next, an identity-based trust model was developed using the artifacts of virtual co-presence, deep profiling, and self-presentation to predict CBT and KBT. This finding resulted from an empirical study using the same online community participants that validated CBT and KBT in online communities. Algorithms for discovering likely trustees in online communities can be facilitated by knowing that artifacts provide potential indicators of individuals serving as trustees. Lastly, a two-part trust discovery algorithm is proposed to automatically find trust relationships in online communities. The first part of the algorithm consists of a speech act classifier to categorize each sentence in a discussion thread as one of four speech acts that are relevant to the trust model in this dissertation. The second part of the algorithm involves applying similarity measures to rank speech act pairs and then using the ranking score with additional features to find trustors in a discussion thread. &#8195

Modeling programmer navigation: A head-to-head empirical evaluation of predictive models

Software developers frequently need to perform code maintenance tasks, but doing so requires time-consuming navigation through code. A variety of tools are aimed at easing this navigation by using models to identify places in the code that a developer might want to visit, and then providing shortcuts so that the developer can quickly navigate to those locations. To date, however, only a few of these models have been compared head-to-head to assess their predictive accuracy. In particular, we do not know which models are most accurate overall, which are accurate only in certain circumstances, and whether combining models could enhance accuracy. Therefore, we have conducted an empirical study to evaluate the accuracy of a broad range of models for predicting many different kinds of code navigations in sample maintenance tasks. Overall, we found that models tended to perform best if they took into account how recently a developer has viewed pieces of the code, and if models took into account the spatial proximity of methods within the code. We also found that the accuracy of single-factor models can be improved by combining factors, using a spreading-activation based approach, to produce multi-factor models. Based on these results, we offer concrete guidance about how these models could be used to provide enhanced software development tools that ease the difficulty of navigating through code. © 2011 IEEE