Analysis of Evidence from Core Sample Fractures in Stewardson Lake Uranium Prospect

This study was performed to characterize evidence of potential unconformity-type U mineralizing fluids in drill core fractures from the Stewardson Lake prospect, in the Athabasca Basin, located in Northern Saskatchewan and Alberta, Canada. Fractures were visually classified into eight varieties. This classification scheme was improved with the use of mineralogical characterization through SEM (Scanning Electron Microscope) and XRD analyses of the fracture fills and resulted in the identification of various oxides, hydroxides, sulfides, and clays or clay-sized minerals. Fractures were tallied to a total of ten categories with some commonalities in color. The oxidative, reductive or mixed nature of the fluids interacting with each fracture was determined based on its fill mineralogy. The measured Pb isotopic signature of samples was used to distinguish fractures affected solely by fluids emanating from a U mineralization source, from those affected by mixed fluids. Anomalies in U and U-pathfinder elements detected in fractures assisted with attributing them to the secondary dispersion halo of potential mineralization. Three types of fracture functions (chimney, composite and drain) were defined based on their interpreted flow vector and history. A secondary dispersion halo boundary with a zone of dominance of infiltrating fluids was suggested for two boreholes. The control of fill mineralogy on fracture color was investigated and the indicative and non-indicative colors and minerals, with respect to a secondary dispersion halo, were formally described. The fracture colors and fills indicative of proximity to the basement host of the potential mineralization were also identified. In addition, three zones of interest were delineated in the boreholes with respect to their geochemical dynamics and their relationship to the potential mineralization: a shallow barren overburden zone, a dispersion and alteration zone at intermediate depth, and a second deeper zone of dispersion and alteration.

The Most Complete Photometric Analysis of 548 CALIFA Galaxies

We present an extensive photometric catalog for 548 CALIFA galaxies observed as of the summer of 2015. CALIFA is currently lacking photometry matching the scale and diversity of its spectroscopy; this work is intended to meet all photometric needs for CALIFA galaxies while also identifying best photometric practices for upcoming integral field spectroscopy surveys such as SAMI and MaNGA. This catalog comprises gri surface brightness profiles derived from Sloan Digital Sky Survey (SDSS) imaging, a variety of non-parametric quantities extracted from these pro files, and parametric models fitted to the i-band pro files (1D) and original galaxy images (2D). To compliment our photometric analysis, we contrast the relative performance of our 1D and 2D modelling approaches. The ability of each measurement to characterize the global properties of galaxies is quantitatively assessed, in the context of constructing the tightest scaling relations. Where possible, we compare our photometry with existing photometrically or spectroscopically obtained measurements from the literature. Close agreement is found with Walcher et al. (2014), the current source of basic photometry and classifications of CALIFA galaxies, while comparisons with spectroscopically derived quantities reveals the effect of CALIFA's limited field of view compared to broadband imaging surveys such as the SDSS. The colour-magnitude diagram, star formation main sequence, and Tully-Fisher relation of CALIFA galaxies are studied, to give a small example of the investigations possible with this rich catalog. We conclude with a discussion of points of concern for ongoing integral field spectroscopy surveys and directions for future expansion and exploitation of this work.

IMPROVING THE PERFORMANCE OF DATABASE-CENTRIC APPLICATIONS THROUGH PROGRAM ANALYSIS

Modern software applications are becoming more dependent on database management systems (DBMSs). DBMSs are usually used as black boxes by software developers. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use nowadays. Using ORM, objects in Object-Oriented languages are mapped to records in the database, and object manipulations are automatically translated to SQL queries. As a result of such conceptual abstraction, developers do not need deep knowledge of databases; however, all too often this abstraction leads to inefficient and incorrect database access code. Thus, this thesis proposes a series of approaches to improve the performance of database-centric software applications that are implemented using ORM. Our approaches focus on troubleshooting and detecting inefficient (i.e., performance problems) database accesses in the source code, and we rank the detected problems based on their severity. We first conduct an empirical study on the maintenance of ORM code in both open source and industrial applications. We find that ORM performance-related configurations are rarely tuned in practice, and there is a need for tools that can help improve/tune the performance of ORM-based applications. Thus, we propose approaches along two dimensions to help developers improve the performance of ORM-based applications: 1) helping developers write more performant ORM code; and 2) helping developers configure ORM configurations. To provide tooling support to developers, we first propose static analysis approaches to detect performance anti-patterns in the source code. We automatically rank the detected anti-pattern instances according to their performance impacts. Our study finds that by resolving the detected anti-patterns, the application performance can be improved by 34% on average. We then discuss our experience and lessons learned when integrating our anti-pattern detection tool into industrial practice. We hope our experience can help improve the industrial adoption of future research tools. However, as static analysis approaches are prone to false positives and lack runtime information, we also propose dynamic analysis approaches to further help developers improve the performance of their database access code. We propose automated approaches to detect redundant data access anti-patterns in the database access code, and our study finds that resolving such redundant data access anti-patterns can improve application performance by an average of 17%. Finally, we propose an automated approach to tune performance-related ORM configurations using both static and dynamic analysis. Our study shows that our approach can help improve application throughput by 27--138%. Through our case studies on real-world applications, we show that all of our proposed approaches can provide valuable support to developers and help improve application performance significantly.