Alonso's General Theory of Movement: Advances in Spatial Interaction Modeling

The Spatial Interaction Model proposed by Alonso as Theory ofMovements offers a new specification of spatial origin-destination flow models. Equations for flows between regions, totaloutflow from and total inflow to a region are linked bybalancing factors. The paper presents a consistent formulation ofSpatial Interaction Models in the Wilson tradition andAlonso's General Theory of Movement. It will be demonstrated thatAlonso's model contains Wilson's Family of SpatialInteraction Models as special cases, but that the reverse is nottrue. The paper discusses interpretations of the model andanalyzes its statistical properties. Several approaches will bediscussed which aim to make the meaning of the balancingfactors more explicit. The paper shows that simultaneous equationtechniques are required to estimate the variousrelevant parameters

What can Recycling in Thermal Reactors Accomplish?

Thermal recycle provides several potential benefits when used as stop-gap, mixed, or backup recycling to recycling in fast reactors. These three roles involve a mixture of thermal and fast recycling; fast reactors are required to some degree at some time. Stop-gap uses thermal reactors only until fast reactors are adequately deployed and until any thermal-recycle-only facilities have met their economic lifetime. Mixed uses thermal and fast reactors symbiotically for an extended period of time. Backup uses thermal reactors only if problems later develop in the fast reactor portion of a recycling system. Thermal recycle can also provide benefits when used as pure thermal recycling, with no intention to use fast reactors. However, long term, the pure thermal recycling approach is inadequate to meet several objectives

Exponential or power distance-decay for commuting? An alternative specification

In this paper we investigate the form of the distance-decay function for commuting, on the basis of an analysis of home-to-work relationships between municipalities in Denmark. The equation for the number of commuters is taken from Alonso’s Theory of Movements, in which the Spatial Interaction Models of Wilson’s Family are nested. Our estimation method separates the decay function F from the balancing factors, and includes a weighting procedure that takes specification error and heteroscedasticity into account. It appears that neither an exponential nor a power distance-decay function fits the data well. The specification of log F as a (downwards) logistic function of log cost results in a better fit. We find that the cost elasticity reaches a value of –4 for distances around 24 km, while it is close to for both very short and very long distances. Finally, we demonstrate that the choice of functional form for distance-decay can make an important difference for predictions concerning the effect of infrastructure improvements on commuting flows.

Modeling the Nuclear Fuel Cycle

The Advanced Fuel Cycle Initiative is developing a system dynamics model as part of their broad systems analysis of future nuclear energy in the United States. The model will be used to analyze and compare various proposed technology deployment scenarios. The model will also give a better understanding of the linkages between the various components of the nuclear fuel cycle that includes uranium resources, reactor number and mix, nuclear fuel type and waste management. Each of these components is tightly connected to the nuclear fuel cycle but usually analyzed in isolation of the other parts. This model will attempt to bridge these components into a single model for analysis. This work is part of a multi-national laboratory effort between Argonne National Laboratory, Idaho National Laboratory and United States Department of Energy. This paper summarizes the basics of the system dynamics model and looks at some results from the model

Lessons Learned From Dynamic Simulations of Advanced Fuel Cycles

Years of performing dynamic simulations of advanced nuclear fuel cycle options provide insights into how they could work and how one might transition from the current once-through fuel cycle. This paper summarizes those insights from the context of the 2005 objectives and goals of the Advanced Fuel Cycle Initiative (AFCI). Our intent is not to compare options, assess options versus those objectives and goals, nor recommend changes to those objectives and goals. Rather, we organize what we have learned from dynamic simulations in the context of the AFCI objectives for waste management, proliferation resistance, uranium utilization, and economics. Thus, we do not merely describe “lessons learned” from dynamic simulations but attempt to answer the “so what” question by using this context. The analyses have been performed using the Verifiable Fuel Cycle Simulation of Nuclear Fuel Cycle Dynamics (VISION). We observe that the 2005 objectives and goals do not address many of the inherently dynamic discriminators among advanced fuel cycle options and transitions thereof

User Guide for VISION 3.4.7 (Verifiable Fuel Cycle Simulation) Model

The purpose of this document is to provide a guide for using the current version of the Verifiable Fuel Cycle Simulation (VISION) model. This is a complex model with many parameters and options; the user is strongly encouraged to read this user guide before attempting to run the model. This model is an R&D work in progress and may contain errors and omissions. It is based upon numerous assumptions. This model is intended to assist in evaluating 'what if' scenarios and in comparing fuel, reactor, and fuel processing alternatives at a systems level. The model is not intended as a tool for process flow and design modeling of specific facilities nor for tracking individual units of fuel or other material through the system. The model is intended to examine the interactions among the components of a fuel system as a function of time varying system parameters; this model represents a dynamic rather than steady-state approximation of the nuclear fuel system. VISION models the nuclear cycle at the system level, not individual facilities, e.g., 'reactor types' not individual reactors and 'separation types' not individual separation plants. Natural uranium can be enriched, which produces enriched uranium, which goes into fuel fabrication, and depleted uranium (DU), which goes into storage. Fuel is transformed (transmuted) in reactors and then goes into a storage buffer. Used fuel can be pulled from storage into either separation or disposal. If sent to separations, fuel is transformed (partitioned) into fuel products, recovered uranium, and various categories of waste. Recycled material is stored until used by its assigned reactor type. VISION is comprised of several Microsoft Excel input files, a Powersim Studio core, and several Microsoft Excel output files. All must be co-located in the same folder on a PC to function. You must use Powersim Studio 8 or better. We have tested VISION with the Studio 8 Expert, Executive, and Education versions. The Expert and Education versions work with the number of reactor types of 3 or less. For more reactor types, the Executive version is currently required. The input files are Excel2003 format (xls). The output files are macro-enabled Excel2007 format (xlsm). VISION 3.4 was designed with more flexibility than previous versions, which were structured for only three reactor types - LWRs that can use only uranium oxide (UOX) fuel, LWRs that can use multiple fuel types (LWR MF), and fast reactors. One could not have, for example, two types of fast reactors concurrently. The new version allows 10 reactor types and any user-defined uranium-plutonium fuel is allowed. (Thorium-based fuels can be input but several features of the model would not work.) The user identifies (by year) the primary fuel to be used for each reactor type. The user can identify for each primary fuel a contingent fuel to use if the primary fuel is not available, e.g., a reactor designated as using mixed oxide fuel (MOX) would have UOX as the contingent fuel. Another example is that a fast reactor using recycled transuranic (TRU) material can be designated as either having or not having appropriately enriched uranium oxide as a contingent fuel. Because of the need to study evolution in recycling and separation strategies, the user can now select the recycling strategy and separation technology, by year

VISION User Guide - VISION (Verifiable Fuel Cycle Simulation) Model

The purpose of this document is to provide a guide for using the current version of the Verifiable Fuel Cycle Simulation (VISION) model. This is a complex model with many parameters; the user is strongly encouraged to read this user guide before attempting to run the model. This model is an R&D work in progress and may contain errors and omissions. It is based upon numerous assumptions. This model is intended to assist in evaluating “what if” scenarios and in comparing fuel, reactor, and fuel processing alternatives at a systems level for U.S. nuclear power. The model is not intended as a tool for process flow and design modeling of specific facilities nor for tracking individual units of fuel or other material through the system. The model is intended to examine the interactions among the components of a fuel system as a function of time varying system parameters; this model represents a dynamic rather than steady-state approximation of the nuclear fuel system. VISION models the nuclear cycle at the system level, not individual facilities, e.g., “reactor types” not individual reactors and “separation types” not individual separation plants. Natural uranium can be enriched, which produces enriched uranium, which goes into fuel fabrication, and depleted uranium (DU), which goes into storage. Fuel is transformed (transmuted) in reactors and then goes into a storage buffer. Used fuel can be pulled from storage into either separation of disposal. If sent to separations, fuel is transformed (partitioned) into fuel products, recovered uranium, and various categories of waste. Recycled material is stored until used by its assigned reactor type. Note that recovered uranium is itself often partitioned: some RU flows with recycled transuranic elements, some flows with wastes, and the rest is designated RU. RU comes out of storage if needed to correct the U/TRU ratio in new recycled fuel. Neither RU nor DU are designated as wastes. VISION is comprised of several Microsoft Excel input files, a Powersim Studio core, and several Microsoft Excel output files. All must be co-located in the same folder on a PC to function. We use Microsoft Excel 2003 and have not tested VISION with Microsoft Excel 2007. The VISION team uses both Powersim Studio 2005 and 2009 and it should work with either

A multi-centre, phase IV study to evaluate the steady-state plasma concentration and serum bactericidal activity of a generic teicoplanin preparation

INTRODUCTION : Teicoplanin is an effective treatment option against methicillin-resistant, Gram-positive bacteria, like Staphylococcus aureus. It is a glycopeptide antibiotic, produced through microbial fermentation, a process resulting in variations in the N-acyl side chain. Concerns that these variations may affect the pharmacokinetic profile and the clinical efficacy of generic teicoplanin preparations have been raised. METHOD : To address this issue, a multi-centre observational study was conducted to evaluate steady-state peak and trough serum concentrations, and the serum bactericidal activity (SBA) and safety of a generic teicoplanin preparation in critically ill patients. Additionally, the composition of the generic teicoplanin was compared to that of the innovator drug to assess differences in the composition. RESULTS: Following pre-determined loading and maintenance dose schedules, the mean peak and trough teicoplanin serum concentrations were 20.98 mg/l and 10.38 mg/l, respectively. A statistically significant association was observed between teicoplanin pharmacotherapy and increased ex vivo SBA. It was found using independent analysis that the composition of the generic teicoplanin preparation was similar to that of the innovator drug, and that both formulations met the European Pharmacopoeia specifications. CONCLUSION : The loading and maintenance schedules employed in this study were effective in establishing therapeutic serum teicoplanin concentrations in critically ill patients. Evidence of bactericidal activity measured in patients’ ex vivo serum samples, following treatment with the generic preparation, supports this finding.http://creativecommons.org/licenses/by-nc-nd/4.0am201

Adaptation to Climate Change in the Transport Sector

In this study, we review the literature on climate change adaptation measures in the transport sector. Many of the measures proposed are rather conceptual and far from concrete, probably due to the fact that climate change effects on transport are either unknown or highly uncertain. Given the limited information on the potential magnitude of climate damages and the various uncertainties involved, postponement of adaptation investments may well be the most sensible strategy at the moment, especially when investments are substantial and irreversible. Furthermore, monitoring of relevant climatic changes and ongoing research into climate change effects are important elements of a pro-active adaptation strategy. Irreversible decisions, such as the ones on spatial organization, likely require a more active strategy, e.g. in the form of making spatial reservations. We further discuss the interdependency between optimal mitigation and adaptation, an issue that is often overlooked in the literature. Finally, most operators and governmental bodies are not used to dealing with risk and uncertainty, and generally base their decisions on single risk values only, likely leading to under- or overinvestment. We discuss several relevant topics in this area and highlight methods that can be used to better deal with these issues. © 2012 Copyright Taylor and Francis Group, LLC