Algorithms and Data Structures for Multi-Adaptive Time-Stepping

Multi-adaptive Galerkin methods are extensions of the standard continuous and discontinuous Galerkin methods for the numerical solution of initial value problems for ordinary or partial differential equations. In particular, the multi-adaptive methods allow individual and adaptive time steps to be used for different components or in different regions of space. We present algorithms for efficient multi-adaptive time-stepping, including the recursive construction of time slabs and adaptive time step selection. We also present data structures for efficient storage and interpolation of the multi-adaptive solution. The efficiency of the proposed algorithms and data structures is demonstrated for a series of benchmark problems.Comment: ACM Transactions on Mathematical Software 35(3), 24 pages (2008

Comparison of Density and Water Content Determinations Using Soil Cores and a Dual Probe Density Gauge

Wet bulk density and water content were determined with the standard soil-core method and by using a density and moisture gauge (gamma radiation and fast neutrons). Four soils collected at different forest sites were tested in the laboratory under different degrees of compression and at various water contents. Using the count ratios of the gauge for density and water as independent variables and wet bulk density and water content determined by soil cores as dependent variables, calibration equations were developed. For the soils used, the gauge values concerning wet bulk density were in close agreement with values determined with soil cores. However, the water content readings of the gauge had to be recalculated using the equations developed. The equations were tested on soil cores collected in the field after measurements with the gauge. The dry bulk density calculated as the wet bulk density given by the gauge minus the water content recalculated using the presented equations differed by an average of -1.6 percent from the soil-core values

HAnS: IDE-based editing support for embedded feature annotations

One of the most common and widespread activities among software developers is locating features (Krüger, Berger, & Leich, 2019). However, feature location is a costly activity that is challenging and takes considerable effort to perform, as records of a features’ explicit location are rarely kept. In this thesis, HAnS is presented, a plugin for IntelliJ with editing support for embedding feature annotations in source code during development, as embedding feature annotations is shown to be a cheap and reliable way to keep track of feature locations. To implement the plugin, a design science methodology was adopted where we performed user studies to find what features would make HAnS effective. The results of the user studies show that the most important functionality of HAnS is its support for writing feature annotations through convenient code completion, refactoring and navigation of features. The user studies also found that HAnS was effective at reducing the errors created during development that require correction later on. The major drawback of HAnS is its refactoring performance which is slow in larger projects. From these results, we draw the conclusion that the plugin is effective for its purpose, but that further research is needed in the form of a longitudinal study where long term use data is gathered. We also suggest further development to extend the plugin’s functionality

Determinants of customer satisfaction with socially responsible investments: Do ethical and environmental factors impact customer satisfaction with SRI profiled mutual funds?

Although much research has been published on green/ethical consumer behaviour, the question of how consumers evaluate pro-socially positioned products in the post-purchase stage is still virtually unexplored. This is troubling given the significance of post-purchase evaluations within general marketing theory. To address this gap in the literature, this study examines how a set of technical and functional quality attributes contribute to customer satisfaction in a socially responsible investment (SRI) setting. The results of the study show that perceived financial quality of the SRI mutual fund is the most important predictor of customer satisfaction. However, perceived social, ethical, and environmental (SEE) quality is also positively related to satisfaction for the SRI mutual fund. Based on these results, it is argued that although SEE quality is important to customers, marketers of pro-socially profiled products should primarily focus on conventional quality attributes, as a good SEE record unlikely to generate customer satisfaction alone.Customer satisfaction; ethics; perceived quality; socially responsible investment; mutual funds

Performance Indicators in Agricultural Financial Markets. Factor Markets Working Document No. 43, May 2013

This study attempts to develop performance indicators for the financial markets based on the findings in an earlier Factor Markets Working Paper (No. 33, “Agricultural credit market institutions: A comparison of selected European countries”) and on FADN (Farm Accountancy Data Network) data. Two indicators were developed. One measured the long-term economic sustainability of agricultural firms since the financial characteristics of the firms were perceived as important factors when rejecting a loan applicant. If the indicator works, it should show that a low value in this indicator is related to the performance in the financial markets. The second indicator was the loan-to-value (LTV), or debt-to-asset ratio, the reasoning behind this indicator is that low values can point to credit constraints, and in WP 33 we saw that the interviewed experts expected LTVs to be much higher than what is actually the case. We find that the first indicator can’t be used to measure the performance of the financial institutions, since we can’t show any relationship between the indicator and activities in the financial markets. However, the indicator is valuable for its measurement of the long-term financial sustainability of the agricultural sector, or of the firms. The loan-to-value indicator does imply that most countries would have room to increase the credit