Engineering economic analysis of a rail extension from Dunbar siding to Livengood, Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2011The Dunbar Siding to Livengood rail extension study is an economic prefeasibility investigation, and is conducted from two perspectives as a cost benefit analysis. The first perspective is, that of the Alaska Railroad Corporation (ARRC) in which the capital and operating costs of the proposed extension are recovered through the revenue stream resulting from the out-bound mineral freight loads, the in-bound re-supply freight loads, and the potential commuter passenger service to mining projects and communities in the Livengood area. The second perspective is that of the private sector in which a shipping sensitivity and employee transport analysis with respect to mining project developments. The large mineral resource base within the Dunbar-Livengood Corridor indicates an excellent freight potential with generous benefits for Alaska's economy of greater than $2 billion annually in gross revenues; whereas, resource and rail development are synergistic.Alaska Department of Transportation/Public Facilities1.0. Introduction -- 1.1. Opening -- 1.2. Foreground -- 2.0. Location and geologic hazards -- 2.1. General route setting -- 2.2. Bedrock geology -- 2.3. Surficial geology -- 2.4. Seismicity -- 2.5. Aufies/Icings -- 2.6. Frozen ground -- 3.0 History -- 3.1. Brief history of the Alaska Railroad -- 3.2. History of mining in Livengood -- 4.0. Methods and models -- 4.1. Freight modeling summary -- 4.2. ARRC model -- 4.3. Initial rail operation cost estimates -- 4.4. Final rail operation cost estimates -- 4.5. Freight sources -- 4.5.1. ITH, money knob project freight model -- 4.5.2. Shorty Creek project -- 4.5.3. Globe Creek limestone project -- 4.5.4. Probable prospects -- 4.5.5. Ore prospect tonnage model -- 4.5.6. Timber resources -- 4.5.7. Tourism -- 4.5.8. Truck freight -- 4.6. Rail freight model results -- 5.0. Livengood money knob project mine model -- 5.1. Introduction to Money Knob project model -- 5.2. Pit costs -- 5.3. Heap leach costs -- 5.4. Mill (floatation) costs -- 5.5. Gravity recovery circuit cost -- 5.6. Cyanide agitated leach costs -- 5.7. Carbon in pulp costs -- 5.8. Electric power -- 5.9. Trolley assisted haul summary -- 5.10. Mine model cash flow analysis -- 5.11. Mine modeling results, at specific tonnages -- 6.0. Economic benefits -- 7.0. Cost benefit analysis -- 7.1. ARRC perspective -- 7.2. Public perspective 7.3. Cost benefit results -- 8.0. Discussion -- 9.0. Conclusions and recommendations 9.1. Conclusions -- 9.2. Recommendations -- 10.0. References -- Appendix

Arduous implementation: Does the Normalisation Process Model explain why it's so difficult to embed decision support technologies for patients in routine clinical practice

Background: decision support technologies (DSTs, also known as decision aids) help patients and professionals take part in collaborative decision-making processes. Trials have shown favorable impacts on patient knowledge, satisfaction, decisional conflict and confidence. However, they have not become routinely embedded in health care settings. Few studies have approached this issue using a theoretical framework. We explained problems of implementing DSTs using the Normalization Process Model, a conceptual model that focuses attention on how complex interventions become routinely embedded in practice.Methods: the Normalization Process Model was used as the basis of conceptual analysis of the outcomes of previous primary research and reviews. Using a virtual working environment we applied the model and its main concepts to examine: the 'workability' of DSTs in professional-patient interactions; how DSTs affect knowledge relations between their users; how DSTs impact on users' skills and performance; and the impact of DSTs on the allocation of organizational resources.Results: conceptual analysis using the Normalization Process Model provided insight on implementation problems for DSTs in routine settings. Current research focuses mainly on the interactional workability of these technologies, but factors related to divisions of labor and health care, and the organizational contexts in which DSTs are used, are poorly described and understood.Conclusion: the model successfully provided a framework for helping to identify factors that promote and inhibit the implementation of DSTs in healthcare and gave us insights into factors influencing the introduction of new technologies into contexts where negotiations are characterized by asymmetries of power and knowledge. Future research and development on the deployment of DSTs needs to take a more holistic approach and give emphasis to the structural conditions and social norms in which these technologies are enacte