Visualization of Big Spatial Data using Coresets for Kernel Density Estimates

The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatial data suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatial datasets

Sickness, Violence and Reconciliation: Congenital Heart Disease in Iraq

Congenital heart disease affects tens of thousands of children and families throughout Iraq, where complex surgical treatment remains largely unavailable. Through participant-observation and in-depth interviews, I investigated the understandings of this disorder among families in two areas: Kurdish northern Iraq and Arab southern Iraq. I pay particular attention to families’ perspectives on causes and treatment of the disorder in relation to historical and current macrosocial forces. Among the families I spoke with, there is a strong connection between the recent history of violence in Iraq and congenital heart disease. This thesis is largely an attempt to understand the uses and implications of this connection between sickness and violence for Iraqi families pursuing treatment through an international non-governmental organization

Multipartite Graph Algorithms for the Analysis of Heterogeneous Data

The explosive growth in the rate of data generation in recent years threatens to outpace the growth in computer power, motivating the need for new, scalable algorithms and big data analytic techniques. No field may be more emblematic of this data deluge than the life sciences, where technologies such as high-throughput mRNA arrays and next generation genome sequencing are routinely used to generate datasets of extreme scale. Data from experiments in genomics, transcriptomics, metabolomics and proteomics are continuously being added to existing repositories. A goal of exploratory analysis of such omics data is to illuminate the functions and relationships of biomolecules within an organism. This dissertation describes the design, implementation and application of graph algorithms, with the goal of seeking dense structure in data derived from omics experiments in order to detect latent associations between often heterogeneous entities, such as genes, diseases and phenotypes. Exact combinatorial solutions are developed and implemented, rather than relying on approximations or heuristics, even when problems are exceedingly large and/or difficult. Datasets on which the algorithms are applied include time series transcriptomic data from an experiment on the developing mouse cerebellum, gene expression data measuring acute ethanol response in the prefrontal cortex, and the analysis of a predicted protein-protein interaction network. A bipartite graph model is used to integrate heterogeneous data types, such as genes with phenotypes and microbes with mouse strains. The techniques are then extended to a multipartite algorithm to enumerate dense substructure in multipartite graphs, constructed using data from three or more heterogeneous sources, with applications to functional genomics. Several new theoretical results are given regarding multipartite graphs and the multipartite enumeration algorithm. In all cases, practical implementations are demonstrated to expand the frontier of computational feasibility

1,3-Propanediol from Crude Glycerol

As a result of the rapid increase in biodiesel production, there is excess supply of crude glycerol, a byproduct of the transesterification process. Prices are depressed and expected to remain low for the foreseeable future, making it a very good time to enter the glycerol market. The proposed design is for 1,3-propanediol from crude glycerol. The plant has a capacity of 100 MM lb/year and will be located in Southeast Asia. It has an estimated IRR of 16.76% and an NPV of $46,963,200. The process begins with an aerobic fermentation section, consisting of lab scale fermenters, seven seed fermenters, and 14 production fermenters. Klebsiella pneumoniae, a genetically engineered, PDO producing microbe is used. In order to produce polymer grade PDO, a separation section is needed following fermentation. Separation operations include filtration, ion exchange, evaporation, hydrogenation, and distillation. The final product is 99.98% pure, by mass

Shifting logics: Limitations on the journey from 'state' to 'market' logic in UK higher education

© Policy Press 2018. Our study of UK higher education institutions (HEIs) offers insights into the role of institutional logics in the adoption of organisational practices - specifically outsourcing. We identify two logics prevalent within HEIs: a public service 'state logic' and a 'market logic'. While adherence to the market logic supports commercial-based practices such as outsourcing, organisations enact competing logics in complex ways. Outsourcing is mainly limited to peripheral activities segmented from the core while a nascent cooperative solution is emerging as HEIs co-opt practices and discourse of outsourcing to justify hybrid relationships that marry competing logics in a process of selective coupling