Search

The Netherlands' Cadastre, Land Registry and Mapping Agency – in short Kadaster – collects and registers administrative and spatial data on property and the rights involved. Currently, the Kadaster is publishing its geo-spatial data assets as Linked Open Data. The Kadaster manages hundreds of datasets that describe hundreds of millions of geospatial objects, including all Dutch buildings, roads, and forests. The Kadaster exposes this large collection of data to thousands of daily users that operate from within different contexts and that need to be supported in different use cases. Therefore, Kadaster must offer diverse, yet complementary, approaches for browsing and exploring the data it publishes. Specifically, it supports the following paradigms for browsing and exploring its data assets: hierarchical browsing, graph navigation, faceted browsing, and tabular browsing. These paradigms are useful for different tasks, cover different use cases, and are implemented by reusing and/or developing Open Source libraries and applications.

Dietary exposures to the seven food, drug, and cosmetic (FD&C) colour additives that are approved for general use in food in the United States were estimated for the US population (aged 2 years and older), children (aged 2–5 years) and teenage boys (aged 13–18 years) based on analytical levels of the FD&C colour additives in foods. Approximately 600 foods were chosen for analysis, based on a survey of product labels, for the levels of FD&C colour additives. Dietary exposure was estimated using both 2-day food consumption data from the combined 2007–10 National Health and Nutrition Examination Survey (NHANES) and 10–14-day food consumption data from the 2007–10 NPD Group, Inc. National Eating Trends – Nutrient Intake Database (NPD NET-NID). Dietary exposure was estimated at the mean and 90th percentile using three different exposure scenarios: low exposure, average exposure and high exposure, to account for the range in the amount of each FD&C colour additive for a given food. For all populations and all exposure scenarios, the highest cumulative eaters-only exposures in food were determined for FD&C Red No. 40, FD&C Yellow No. 5 and FD&C Yellow No. 6. In addition, the eaters-only exposure was estimated for individual food categories in order to determine which food categories contributed the most to the exposure for each FD&C colour additive. Breakfast Cereal, Juice Drinks, Soft Drinks, and Frozen Dairy Desserts/Sherbet (also referred to as Ice Cream, Frozen Yogurt, Sherbet (including Bars, Sticks, Sandwiches)) were the major contributing food categories to exposure for multiple FD&C colour additives for all three populations.

The dietary intake of industrially-produced trans fatty acids (IP-TFA) was estimated for the US population (aged 2 years or more), children (aged 2-5 years) and teenage boys (aged 13-18 years) using the 2003-2006 National Health and Nutrition Examination Survey (NHANES) food consumption database, market share information and trans fat levels based on label survey data and analytical data for packaged and in-store purchased foods. For fast foods, a Monte Carlo model was used to estimate IP-TFA intake. Further, the intake of trans fat was also estimated using trans fat levels reported in the US Department of Agriculture (USDA) National Nutrient Database for Standard Reference, Release 22 (SR 22, 2009) and the 2003-2006 NHANES food consumption database. The cumulative intake of IP-TFA was estimated to be 1.3 g per person per day (g/p/d) at the mean for the US population. Based on this estimate, the mean dietary intake of IP-TFA has decreased significantly from that cited in the 2003 US Food and Drug Administration (FDA) final rule that established labelling requirements for trans fat (4.6 g/p/d for adults). Although the overall intake of IP-TFA has decreased as a result of the implementation of labelling requirements, individuals with certain dietary habits may still consume high levels of IP-TFA if certain brands or types of food products are frequently chosen.

Usability is considered an important quality attribute for software systems. To ensure a particular level of usability, a certain amount of time and money have to be invested; however this has proven to be expensive. Most of the costs spent on usability are spent after an initial development e.g. during maintenance. These high costs often prevent developers from meeting all the usability requirements. The challenge is therefore to cost effectively develop usable software e.g. minimize the costs & time spent on usability. We believe architecture analysis of usability is an important tool to achieve this. Our experiences with software architecture analysis of usability allowed us to identify a series of problems that explain why usability is not achieved cost effectively in current software development practice.