U-Pu-Zr nuclear fuels experience a redistribution of constituents and a number of phase transformations when subjected to the thermal gradient present in nuclear reactors. This redistribution and phase separation leads to several undesirable fuel performance issues. In an effort to better understand how different alloys compositions are affected by this thermal gradient, we utilize the recently introduced Hybrid Potts-phase Field Method to study the U-Pu-Zr system. The recently introduced Hybrid method couples microstructural and compositional evolutions of a system so that the two phenomena can be studied together rather than separately, as is frequently done. However, simulation of the U-Pu-Zr system required several adaptations to the modeling framework. First the model was adapted to incorporate a thermodynamic database for free energy calculations, as well as thermal diffusion (the Soret effect). These abilities were tested in the Al-Si system. Second, the modeling framework was expanded to simulate three component systems such that ternary U-Pu-Zr alloys could be studied.Simulations capture constituent redistribution and the appropriate phase transformations as compared to experimentally irradiated a U-16Pu-23Zr (at%) nuclear fuel. Additional simulations analyze constituent redistribution over the entire spectrum of U-Pu-Zr compositions. Analysis of these simulation results indicate alloys that are likely to experience minimal constituent redistribution and fewer phase boundaries, such that their fuel performance should be improved. The outcomes of the work include a coupled microstructural-compositional modeling framework for ternary alloys and suggestions of U-Pu-Zr alloys that could lead to improved fuel performance.

As society becomes more data-driven, new alternative technologies to the more traditional relational database model arise. One of these technologies is the graph database model, which stores the data as nodes and edges, where the edges define the relationships between the nodes representing entities. Graph databases are a great fit for data with dense and dynamic relationships, such as social networks, fraud detection and recommendation engines. At the Swedish Pensions Agency, the unit working with fraud detection has rapidly increased personnel due to increased demand for fraud prevention. New technologies are being investigated to improve the efficiency in both detection of current fraudulent activities and also to detect suspicious activities and persons to prevent fraud from even happening. The scope of this thesis is to develop a tool as a proof of concept to import data through the web browser into a graph database, namely Neo4j. The evaluations show that importing nodes and relationships with the tool is slower than importing with Neo4j LOAD CSV. However, the performance is still reasonable for data sets of a few hundred thousand nodes or relationships. The evaluations with the Swedish Pensions Agency show that the tool could bring value to them by removing the complexity of writing code.

Henry Stewart Talks: The Biomedical & Life Sciences Collection is a subscription database containing a variety of lectures on basic science and medical topics. Lectures in this database may be used as a supplement to existing college courses. Additional features of Henry Stewart Talks include several full courses available to faculty upon request and syllabus assistance to link course goals to lectures in the database. Other aspects of Henry Stewart Talks include evidence and expert transparency and ADA compliance of content.

With the proliferation of inexpensive cameras and the availability of high-speed wired and wireless networks, networks of distributed cameras are becoming an enabling technology for a broad range of interdisciplinary applications in domains such as public safety and security, manufacturing, transportation, and healthcare. Today’s live video processing systems on networks of distributed cameras, however, are designed for specific classes of applications. To provide a generic query processing platform for applications of distributed camera networks, we designed and implemented a new class of general purpose database management systems, the live video database management system (LVDBMS). We view networked video cameras as a special class of interconnected storage devices, and allow the user to formulate ad hoc queries over real-time live video feeds. In the first part of this dissertation, an Internet scale framework for sharing and dissemination of general sensor data is presented. This framework provides a platform for general sensor data to be published, searched, shared, and delivered across the Internet. The second part is the design and development of a Live Video Database Management System. LVDBMS allows users to easily focus on events of interest from a multitude of distributed video cameras by posing continuous queries on the live video streams. In the third part, a distributed in-memory database approach is proposed to enhance the LVDBMS with an important capability of tracking objects across cameras

The most common mode of levee failure, breach due to overtopping, is generally considered as a function of a complex set of contributing factors. The goal of this research is to enhance the state of the art and practice for performing levee overtopping risk assessment. For this purpose, a dataset of levee overtopping event records within the portfolio of levee systems maintained by the U.S. Army Corps of Engineers (USACE) is presented. The dataset is utilized with logistic regression analysis to develop a probabilistic model to calculate system response probabilities and assess risk related to levee overtopping. The presented dataset can be used for identifying key factors controlling overtopping behavior, validation of model results, and providing new insight into the phenomenon of levee overtopping. The proposed model offers a practical yet robust tool for levee risk analysis and can be readily employed by engineers and other stakeholders.

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