CONUP: A code that calculates tag gas concentrations for reactor components

The CONUP code calculates the current tag isotopic concentrations for the Tag Gas Analysis Code, SMTAG. The combined codes, CONUP and SMTAG, represent the Tag Gas System. CONUP produces tag concentrations that are decayed and transmuted over specific reactor core cycles. The calculated concentrations are used, together with measured concentrations, as input for the SMTAG code, which identifies the failed reactor components that have released tag gas. The CONUP code has two modes for calculating isotopic concentrations: absolute and incremental. In the absolute mode, the CONUP code calculates concentrations from the beginning of the reactor startup through the current cycle. In the incremental mode, the CONUP code processes concentrations from the last reactor component cycle for each component. The incremental mode saves significant processing time because the concentrations are updated only for the current cycle. A description of the underlying physical model and method of solution are presented. A description of the code and a user`s guide are also given, along with example input and corresponding concentration output

Life cycle assessment in road infrastructure planning using spatial geological data

Purpose: The purpose of the study was to outline and demonstrate a new geographic information system (GIS)-based approach for utilising spatial geological data in three dimensions (i.e. length, width and depth) to improve estimates on earthworks during early stages of road infrastructure planning. Methods: This was undertaken by using three main methodological steps: mass balance calculation, life cycle inventory analysis and spatial mapping of greenhouse gas (GHG) emissions and energy use. The mass balance calculation was undertaken in a GIS environment using two assumptions of geological stratigraphy for two proposed alternative road corridors in Sweden. The estimated volumes of excavated soil, blasted rock and filling material were later multiplied with the GHG emission and energy use factors for these processes, to create spatial data and maps in order to show potential impacts of the studied road corridors. The proposed GIS-based approach was evaluated by comparing with actual values received after one alternative was constructed. Results and discussion: The results showed that the estimate of filling material was the most accurate (about 9 % deviation from actual values), while the estimate for excavated soil and blasted rock resulted in about 38 and 80 % deviation, respectively, from the actual values. It was also found that the total volume of excavated and ripped soils did not change when accounting for stratigraphy. Conclusions: The conclusion of this study was that more information regarding embankment height and actual soil thickness would further improve the model, but the proposed GIS-based approach shows promising results for usage in LCA at an early stage of road infrastructure planning. Thus, by providing better data quality, GIS in combination with LCA can enable planning for a more sustainable transport infrastructure.</p

The Role of Patriotism in Explaining the TV Audience of National Team Games—Evidence From Four International Tournaments

In the literature addressing the determinants of TV audiences in sports, both the absolute and relative playing strength of the opponents play a prominent role. Regarding national team competitions, however, this study conjectures that patriotism matters as well. Analyzing the Swiss TV audience at 2 World Cups and 2 European Football Championships, this study finds strong evidence that TV ratings are highly affected by the sizes of the groups of foreign residents affiliated with the teams playing on the field.

Testing structural equation models: the impact of error variances in the data generating process

This is the authors’ final, accepted and refereed manuscript to the article. The final publication is available at www.springerlink.comYet another paper on fit measures? To our knowledge, very few papers discuss how fit measures are affected by error variance in the Data Generating Process (DGP). The present paper deals with this. Based upon an extensive simulation study, this paper shows that the effects of increased error variance differ significantly for various fit measures. In addition to error variance the effects depend on sample size and severity of misspecification. The findings confirm the general notion that good fit as measured by the chi-square, RMSEA and GFI etc. does not necessarily mean that the model is correctly specified and reliable. One finding is that the chi square test may give support to misspecified models in situations with a high level of error variance in the DGP, for small sample sizes. Another finding is that the chi-square test looses power also for large sample sizes when the model is negligible misspecified. Other results include incremental fit indices as NFI and RFI which prove to be more informative indicators under these circumstances. At the end of the paper we formulate some guidelines for use of different fit measures