Economic sustainability of the Flambeau Mine Ladysmith, Wisconsin

Today sustainable development is a very pertinent issue. Communities do not want companies, specifically mining companies, to deplete a natural resource and leave. The goal is to minimize the negative impacts of mining and the boom/bust cycles of natural resource extraction. In this study a three part framework was developed to analyze the sustainability of the Flambeau Mine in Ladysmith, Wisconsin. The first and second part dealt with an in-depth local and regional analysis and whether the community was developing within its own vision. The third part used nine sustainability measures including: 1. Need Present Generation 2. Future Need 3. Acceptable Legacy 4. Full-Cost 5. Contribution to Economic Development 6. Equity 7. Consent 8. Respect for Ecological Limits, Maintenance of Ecological Integrity and Landscape Requirements 9. Offsetting Restoration This study concluded that the Flambeau Mine was sustainable relative to the first two criteria and that it can be considered mostly sustainable relative to the nine criteria. Overall it can be stated that the Flambeau Mine was a beneficial project to the Ladysmith Wisconsin area. Additionally it appeared to decrease the public’s negative perception of mining. Recommendations for future analytical work are made. Suggestions are made as to how mining companies could increase the potential for the attainment of sustainability in projects. It is recommended that this framework be used by other industries.

Finite element analysis on Titan STL3 OTR tires

Finite element tire modeling can be a challenging process, due to the overall complexities within the tire and the many variables that are required to produce capable predictive simulations. Utilizing tools from Abaqus finite element software, adequate predictive simulations that represent actual operational conditions can be made possible. Many variables that result from complex geometries and materials, multiple loading conditions, and surface contact can be incorporated into modeling simulations. This thesis outlines modeling practices used to conduct analysis on specific tire variants of the STL3 series OTR tire line, produced by Titan Tire. Finite element models were created to represent an inflated tire and rim assembly, supporting a 30,000 lb load while resting on a flat surface. Simulations were conducted with reinforcement belt cords at variable angles in order to understand how belt cord arrangement affects tire components and stiffness response.

A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method.