‘DEVOTED TO THE INTERESTS OF HIS RACE’: BLACK OFFICEHOLDERS AND THE POLITICAL CULTURE OF FREEDOM IN WILMINGTON, NORTH CAROLINA, 1865-1877

This dissertation examines black officeholding in Wilmington, North Carolina, from emancipation in 1865 through 1876, when Democrats gained control of the state government and brought Reconstruction to an end. It considers the struggle for black office holding in the city, the black men who held office, the dynamic political culture of which they were a part, and their significance in the day-to-day lives of their constituents. Once they were enfranchised, black Wilmingtonians, who constituted a majority of the city’s population, used their voting leverage to negotiate the election of black men to public office. They did so by using Republican factionalism or what the dissertation argues was an alternative partisanship. Ultimately, it was not factional divisions, but voter suppression, gerrymandering, and constitutional revisions that made local government appointive rather than elective, Democrats at the state level chipped away at the political gains black Wilmingtonians had made.

Europeanization beyond the EU: The case of Georgia.

320 p.

Adding Value to Manufacturing: Thirty Years of European Framework Program Activity

Part 1: Introduction

Capital structure and dividends : evidence from Portugal (2003-2014)

Mestrado em Finanças

Determinantes financeiros da estrutura de capital das PME industriais portuguesas ao longo dos ciclos económicos

Dissertação de Mestrado em Gestão Empresarial. Faculdade de Economia, Univ. do Algarve, 2004

SVR-GARCH com misturas de kernels gaussianos

Dissertação (mestrado)—Universidade de Brasília, Departamento de Administração, Programa de Pós-graduação em Administração, 2016.

Maximizing real-time streaming services based on a multi-servers networking framework

In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client-server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a (1-?) approximation algorithm to solve the NP-complete "maximum services"(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.

Informed options trading prior to takeovers - Does the regulatory environment matter?

We investigate the prevalence of informed options trading prior to takeover announcements, when the legal prohibition against insider trading is strictest. Although insider trading laws apply equally to the options and stock markets, the options market is considerably more transparent than the equity market, which makes insider trading in options more easily detectable. We find that privately informed investors trade in the options market prior to takeover announcements; however, their transactions are limited to liquid call options and options with high inherent leverage. Furthermore, we find that prior to takeover announcements, informed investors trade on their private information in the options market only when a SEC investigation of insider trading is unlikely to occur. Our results suggest that even prior to takeover announcements informed investors are attracted to the options market, which increases profit making potential due the greater leverage it affords, although they trade in a way which minimizes the likelihood of detection.

Capital structure of Chinese listed SMEs: an agency theory perspective

Prior work examining the antecedents of capital structure for small and medium-sized enterprises in emerging markets is limited. This paper sheds light on how the corporate governance mechanisms adopted by firms on the newly established Growth Enterprise Market (GEM) in China influence their use of debt. We find that the financial leverage of GEM firms is positively influenced by executives’ shareholding and their excess cash compensation. Ownership concentration appears to reduce leverage, whereas the percentage of tradable shares increases leverage. In contrast, institutional investors’ shareholding does not influence the level of debt. Traditional factors such as tax and operating cash flow are insignificant in explaining the debt levels among GEM firms.

Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.

Innovation practice using pervasive mobile technology solutions to improve population health management: a pilot study of gestational diabetes patient care in Australia

Healthcare service delivery is moving forward from individual care to population health management, because of the fast growth of health records. However, to improve population health performance, it is necessary to leverage relevant data and information using new technology solutions, such as pervasive diabetes mobile technology solution of Inet International Inc., which offers the potential to facilitate patient empowerment with gestational diabetic care. Hence, this article examines the pilot study outcomes of a small clinical trial focusing on pregnant patients affected by gestational diabetes mellitus, in an Australian not for profit healthcare context. The aims include establishing proof of concept and also assessing the usability, acceptability, and functionality of this mobile solution and thereby generate hypotheses to be tested in a large-scale confirmatory clinical trial.

Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering

Developing complex computational-intensiveand data-intensive scientific applications requires effectiveutilization of the computational power of the availablecomputing platforms including grids, clouds, clusters, multicoreand many-core processors, and graphical processingunits (GPUs). However, scientists who need to leverage suchplatforms are usually not parallel or distributed programmingexperts. Thus, they face numerous challenges whenimplementing and porting their software-based experimentaltools to such platforms. In this paper, we introduce asequential-to-parallel engineering approach to help scientistsin engineering their scientific applications. Our approach isbased on capturing sequential program details, plannedparallelization aspects, and program deployment details usinga set of domain-specific visual languages (DSVLs). Then, usingcode generation, we generate the corresponding parallelprogram using necessary parallel and distributedprogramming models (MPI, OpenCL, or OpenMP). Wesummarize three case studies (matrix multiplication, N-Bodysimulation, and signal processing) to evaluate our approach.

R1SVM: a randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and support vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep autoen-coder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Auditee religiosity, external monitoring, and the pricing of audit services

Based on prior studies which show that firms headquartered in high religiosity counties exhibit high level of business ethics, this study examines whether these firms are associated with low audit risk, and therefore low audit fees. In investigating this relationship, we draw a distinction between intrinsic and extrinsic religiosity of auditees. Using a sample of 25,872 U.S. observations from 2003 to 2012, we find that intrinsic religiosity of the auditees is associated with low audit fees after controlling for auditee extrinsic religiosity, social capital, firm-specific characteristics, and county-specific characteristics. Furthermore, we find that external monitoring (institutional ownership and leverage) weakens the negative relationship between auditee intrinsic religiosity and audit fees. Finally, we conclude that the effect of auditor religiosity on audit fees is a regional effect that may affect the relationship between audit fees and auditee intrinsic religiosity.

SYSTEM DYNAMICS MODELING AS A QUANTITATIVE-QUALITATIVE FRAMEWORK FOR SUSTAINABLE WATER RESOURCES MANAGEMENT: INSIGHTS FOR WATER QUALITY POLICY IN THE GREAT LAKES REGION

Early water resources modeling efforts were aimed mostly at representing hydrologic processes, but the need for interdisciplinary studies has led to increasing complexity and integration of environmental, social, and economic functions. The gradual shift from merely employing engineering-based simulation models to applying more holistic frameworks is an indicator of promising changes in the traditional paradigm for the application of water resources models, supporting more sustainable management decisions. This dissertation contributes to application of a quantitative-qualitative framework for sustainable water resources management using system dynamics simulation, as well as environmental systems analysis techniques to provide insights for water quality management in the Great Lakes basin. The traditional linear thinking paradigm lacks the mental and organizational framework for sustainable development trajectories, and may lead to quick-fix solutions that fail to address key drivers of water resources problems. To facilitate holistic analysis of water resources systems, systems thinking seeks to understand interactions among the subsystems. System dynamics provides a suitable framework for operationalizing systems thinking and its application to water resources problems by offering useful qualitative tools such as causal loop diagrams (CLD), stock-and-flow diagrams (SFD), and system archetypes. The approach provides a high-level quantitative-qualitative modeling framework for "big-picture" understanding of water resources systems, stakeholder participation, policy analysis, and strategic decision making. While quantitative modeling using extensive computer simulations and optimization is still very important and needed for policy screening, qualitative system dynamics models can improve understanding of general trends and the root causes of problems, and thus promote sustainable water resources decision making. Within the system dynamics framework, a growth and underinvestment (G&U) system archetype governing Lake Allegan's eutrophication problem was hypothesized to explain the system's problematic behavior and identify policy leverage points for mitigation. A system dynamics simulation model was developed to characterize the lake's recovery from its hypereutrophic state and assess a number of proposed total maximum daily load (TMDL) reduction policies, including phosphorus load reductions from point sources (PS) and non-point sources (NPS). It was shown that, for a TMDL plan to be effective, it should be considered a component of a continuous sustainability process, which considers the functionality of dynamic feedback relationships between socio-economic growth, land use change, and environmental conditions. Furthermore, a high-level simulation-optimization framework was developed to guide watershed scale BMP implementation in the Kalamazoo watershed. Agricultural BMPs should be given priority in the watershed in order to facilitate cost-efficient attainment of the Lake Allegan's TP concentration target. However, without adequate support policies, agricultural BMP implementation may adversely affect the agricultural producers. Results from a case study of the Maumee River basin show that coordinated BMP implementation across upstream and downstream watersheds can significantly improve cost efficiency of TP load abatement.