Thesis advisor: Michael Kelly / This thesis is an assessment of the notion of justice through the eyes of various philosophers including Aristotle, Montesquieu, the Federalists/Anti-Federalists, Rawls, and Sandel. These philosophies of justice are then applied to the Republican and Democratic platforms to assess which platform is the most just. / Thesis (BA) — Boston College, 2013. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Carroll School of Management Honors Program. / Discipline: Philosophy Honors Program. / Discipline: Philosophy.

Thesis (Ph. D.)--Marquette University, 2009. / Access available to Marquette University only. Available for download on June 17, 2010. Robert J. Stango, Nicholas J. Nigro, Stephen Heinrich, Vikram Cariapa, Shuguang Huang, Advisors.

Individuals rely on digital messaging to form and maintain intimate relationships, trusting that the mediums through which they communicate are protective of their privacy. The screenshot feature revokes attempts to establish such trust. This tool allows individuals to capture and store pieces of a private conversation as a separate file on their device, rendering them usable and shareable with third parties. While the screenshot feature serves utilitarian purposes, this dissertation focuses on its ability to breach privacy expectations, termed within communication privacy management theory (CPM) as privacy turbulence. This dissertation extends the scope of CPM beyond its interpersonal bounds, recognizing the power of platforms to create rules that users follow when making decisions about others' information. Experimental results suggest that blurring received messages upon use of the screenshot feature (i.e., obscurity) and creating an explicit confidentiality expectation (i.e., explicit privacy rule) significantly reduce screenshot collection and sharing, respectively. Additionally, reflections from participants reveal that many individuals are willing to compromise others’ privacy on digital messaging platforms while simultaneously expecting protection of their own. Qualitative analysis of relevant case law and complaints/opinions from the Federal Trade Commission (FTC) reveal inconsistencies both within law and policy and as compared to empirical evidence. Judges have provided overly broad definitions of “authorization” and lofty thresholds to sustain individual harm, making statutory regulation of screenshot collection and sharing unlikely. However, guidance from the FTC demonstrates a more nuanced regulatory approach to privacy that recognizes the influence of platform design. Results from this study suggest that design-based strategies—both ex-ante and ex-post—would be a promising first step toward adjusting the norms around screenshot collection and sharing of digital messages. Together, the results of this dissertation will inform policymakers and platform designers of the privacy harm enabled by the screenshot feature, providing tangible recommendations to create messaging platforms that are truly private. / 2026-03-08T00:00:00Z

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Context. The present trend in a large variety of applications are ranging from the web and social networking to telecommunications, is to gather and process very large and fast growing amounts of information leading to a common set of problems known collectively as “Big Data”. The ability to process large scale data analytics over large number of data sets in the last decade proved to be a competitive advantage in a wide range of industries like retail, telecom and defense etc. In response to this trend, the research community and the IT industry have proposed a number of platforms to facilitate large scale data analytics. Such platforms include a new class of databases, often refer to as NoSQL data stores. Apache Cassandra is a type of NoSQL data store. This research is focused on analyzing the performance of different compaction strategies in different use cases for default Cassandra stress model. Objectives. The performance of compaction strategies are observed in various scenarios on the basis of three use cases, Write heavy- 90/10, Read heavy- 10/90 and Balanced- 50/50. For a default Cassandra stress model, so as to finally provide the necessary events and specifications that suggest when to switch from one compaction strategy to another. Methods. Cassandra single node network is deployed on a web server and its behavior of read and write performance with different compaction strategies is studied with read heavy, write heavy and balanced workloads. Its performance metrics are collected and analyzed. Results. Performance metrics of different compaction strategies are evaluated and analyzed. Conclusions. With a detailed analysis and logical comparison, we finally conclude that Level Tiered Compaction Strategy performs better for a read heavy (10/90) workload while using default Cassandra stress model , as compared to size tiered compaction and date tiered compaction strategies. And for Balanced Date tiered compaction strategy performs better than size tiered compaction strategy and date tiered compaction strategy.

The reliability of ocean systems is dependent on organizational factors. It has been shown that low probability / high consequence system failures are overwhelmingly induced by organizational factors. However, no methodology is yet widely accepted for the evaluation of this phenomenon or its accurate quantification. A qualitative complementary approach is proposed based on the CANL (Complex Adaptive Non-Linear) model. In the first part, the understanding of organizational processes that affect reliability is sought. The approach is applied to several case studies based on published information: the "Story of a Platform Audit" (where no failure occurred) and some offshore accidents. A methodology is proposed to complement regular safety audit procedures. The approach is shown useful also to improve post-mortem investigations. In the second part, quantitative probabilistic formulations are revised, based on the understanding obtained through the previous approach. Some of the limitations of these quantitative methods are pointed out. The Reliability State of an Organization is defined and a ranking for its evaluation is proposed. Preliminary guidelines are presented for the use of this approach as a framework to identify suitable quantitative methods for a given case. The use of a qualitative approach is demonstrated. A different insight into organizational factors is achieved based on a disciplined approach that relies on experience. Significant conclusions regarding quantitative methods, their limitations and appropriate use, are obtained. / Graduation date: 1999

No description available.

Interest in evaluating the performance and safety of offshore oil and gas platforms has been expanding due to the growing world energy supply and recent offshore catastrophes. In order to accurately assess the reliability of an offshore platform, all relevant uncertainties must be properly accounted for. This necessitates the development of a probabilistic demand model that accounts for the relevant uncertainties and model errors. In this study, a probabilistic demand model is developed to assess the deformation demand on asymmetric offshore jacket platforms subject to wave and current loadings. The probabilistic model is constructed by adding correction terms and a model error to an existing deterministic deformation demand model. The correction terms are developed to capture the bias inherent in the deterministic model. The model error is developed to capture the accuracy of the model. The correction terms and model errors are estimated through a Bayesian approach using simulation data obtained from detailed dynamic analyses of a set of representative asymmetric offshore platform configurations. The proposed demand model provides accurate and unbiased estimates of the deformation demand on offshore jacket platforms. The developed probabilistic demand model is then used to assess the reliability of a typical offshore platform considering serviceability and ultimate performance levels. In addition, a sensitivity analysis is conducted to assess the effect of key parameters on the results of the analyses. The proposed demand model can be used to assess the reliability of different design options and for the reliability-based optimal design of offshore jacket platforms.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Hiroshi Iwasaki. / M.S.

Our related research review on propagation models reveals six factors that are significant in last mile connectivity via LAP: path loss, elevation angle, LAP altitude, coverage area, power consumption, operation frequency, interference, and antenna type. These factors can help with monitoring system performance, network planning, coverage footprint, receivers' line-of-sight, quality of service requirements, and data rates which may all vary in response to geomorphology characteristics. Several competing propagation models have been proposed over the years but whilst they collectively raise many shortcomings such as limited altitude up to few tens of meters, lack of cover across different environments, low perdition accuracy they also exhibit several advantages. Four propagation models, which are representatives of their types, have been selected since they exhibit advantages in relation to high altitude, wide coverage range, adaption across different terrains. In addition, all four have been extensively deployed in the past and as a result their correction factors have evolved over the years to yield extremely accurate results which makes the development and evaluation aspects of this research very precise. The four models are: ITU-R P.529-3, Okumura, Hata-Davidson, and ATG. The aim of this doctoral research is to design a new propagation model for last-mile connectivity using LAPs technology as an alternative to aerial base station that includes all six factors but does not exhibit any of the shortcomings of existing models. The new propagation model evolves from existing models using machine learning. The four models are first adapted to include the elevation angle alongside the multiple-input multiple-output diversity gain, our first novelty in propagation modelling. The four adapted models are then used as input in a Neural Network framework and their parameters are clustered in a Self-Organizing-Map using a minimax technique. The framework evolves an optimal propagation model that represents the main research contribution of this research. The optimal propagation model is deployed in two proof-of-concept applications, a wireless sensor network, and a cellular structure. The performance of the optimal model is evaluated and then validated against that of the four adapted models first in relation to predictions reported in the literature and then in the context of the two proof-of-concept applications. The predictions of the optimised model are significantly improved in comparison to those of the four adapted propagation models. Each of the two proof-of-concept applications also represent a research novelty.