Changing Governance, Business Elites, and Local Regulation in Nashville

In this dissertation, I explore how power and governance in Nashville is related to economic and institutional change on a national and global scale—the transition from Fordism to post-Fordism. By investigating the political and social institutions of the city, it is my objective to understand how the elites are operating to control and direct development. The study is an in-depth look at the activities of local business and how they decide to respond in a situation of change and uncertainty. Here I stress the importance of collective action among the business elite, which is equally meaningful to explore as the notion of capitalism driven by competition. The period of analysis stretches from the 1980s to the late 1990s; but the early 1990s was a particularly crucial time period in Nashville when a new local regime emerged as a catalyst for the local post-Fordist mode of regulation. New forms of governance, which reached maturation stage during the 1990s, have evolved in Nashville affecting both political and business institutions. Governance is increasingly based on power diffusion, transparency and inclusiveness in decision-making, yet dominated by business-friendly policies, which should be viewed in the context of urban competition and the need to position the city to take advantage of economic restructuring. To comprehend the interaction between local development and the national regime, I have studied three relevant policy areas that can exemplify the nature of competitive governance: image and city promotion; airport development; and emerging engagement of business interests in social policy as exemplified by education. The first two areas conform to the idea of the city driven by entrepreneurial considerations, while the latter concerns social reproduction. All cases, however, depend on the capacity of business to pursue its goals through collective action

Towards a model for managing uncertainty in logistics operations – A simulation modeling perspective

Uncertainty rules supply chains. Unexpected changes constantly occur on all levels; strategically through globalization, introduction of novel technology, mergers and acquisitions, volatile markets, and on an operational level through demand fluctuations, and events such as late arrival of in-bound material, machine equipment breakdown, and quality problems. The problem with uncertainty is increasing as the focus on cost reductions and efficiency in the industry tends to stretch supply chains to become longer and leaner, thus making them more vulnerable to disturbances. The aim of this thesis is to explore strategies for evaluating and managing uncertainties in a logistics context with the objectives; “to propose a method for modeling and analyzing the dynamics of logistics systems with an emphasize on risk management aspects”, and “to explore the impact of dynamic planning and execution in a logistics system”. Three main strategies for handling uncertainties are being discussed; robustness, reliability, and resilience. All three strategies carry an additional cost that must be weighed against the cost and risk of logistical disruptions. As an aid in making this trade-off, a hybrid simulation approach, based on discrete-event simulation and Monte Carlo simulation, is proposed. A combined analytical, and simulation approach is further used to explore the impact of dynamic planning and execution in a solid waste management case. Finally, a draft framework for how uncertainty can be managed in a logistics context is presented along with the key reasons why the proposed simulation approach has proven itself useful in the context of logistics systems

Productivity of a prototype truck-mounted logging residue bundler and a road-side bundling system

When recovering logging residues (LR) for bioenergy its density should be increased before road transport, otherwise a low proportion of the trucks’ load capacity will be used. One way this can be currently done is to compress LR into bundles that are forwarded to roadside landing. A less well-developed alternative is to forward loose LR and bundle it at landing. In the presented study, a prototype specifically developed for road-side bundling was found to produce larger, heavier bundles than bundling machinery intended for in-field use (mean length, diameter and raw bulk density 4.7 m, 0.8 m and 285 kg m–3, respectively, with 299–445 kg oven dry matter per bundle). The machine was a so at least 30% more productive than previously described in-field bundling systems, producing 14–19 bundles per productive work hour (PWh), equivalent to 5.2–7.8 oven-dry tonnes PWh–1. Bundles were estimated to use 67–86% of an LR truck’s 30 tonnes load capacity, similar to proportions used when transporting loose LR. However, a continuous feeding and compressing process would probably almost double productivity, while longer bundles would enable full use of truck load capacity. With such improvements bundling at road-side could provide a viable alternative to current LR-recovering systems

Complete genome sequences of three novel human papillomavirus types, 175, 178, and 180.

We report the characterization of three novel human papillomavirus (HPV) types of the genus Gammapapillomavirus. HPV175 and HPV180 were isolated from a condyloma. HPV178 was isolated from healthy skin adjacent to an actinic keratosis

A comparison of the ForestMan AI software with the Heureka system regarding forest growth simulations and carbon calculations

The purpose of this study is to evaluate the reliability of the forest growth simulations and carbon calculations of the ForestMan AI forest planning system. The ForestMan AI software is developed by and the property of Skogr Kaupa Group AB. ForestMan AI is based on the ProdMod model from SLU and carbon calculations according to methodologies recommended by the IPCC in Guidelines for National Greenhouse Gas Inventories reporting.The Heureka system calculations are used as benchmark for the evaluation. Heureka is a planning system that has been developed over more than 20 years and has found extensive use in both research and among forest companies. The Heureka system calculations is used as the basis for evaluation since it can be considered state-of-the-art with respect to forest research and because it is also used for assessments for the Swedish reporting of greenhouse gas emissions.The evaluation is based on the data from a forest estate in the Halland county. The estate encompasses 2.037 ha and has spruce as dominating species. It is, by and large, representative of forest in the region.The evaluation shows ForestMan AI to perform growth projections and the associated carbon stock assessment to agree to a great extent. No major deviations between the outputs of the systems are identified.The result of the study can be used as a basis for further development of standards for evaluating forest planning systems. Carbon as a commodity represents an emerging branch of forestry. Furthermore, it is the quantity to report by financial institutions and forest owners in Climate Benefit Analyses (CBA) mandated by the EU Taxonomy. Thus, it is crucial that different actors, SMEs (Small, Medium Enterprises) and others, are given access to research in various forms to meet the needs of this emerging branch of forestry