Development of an exposure database and surveillance system for use by practicing OSH professionals

This report summarizes the development of an occupational exposure database and surveillance system for use by health and safety professionals at Rocky Flats Environmental Technology Site (RFETS), a former nuclear weapons production facility. The site itself is currently in the cleanup stage with work expected to continue into 2006. The system was developed with the intent of helping health and safety personnel not only to manage and analyze exposure monitoring data, but also to identify exposure determinants during the highly variable cleanup work. Utilizing a series of focused meetings with health and safety personnel from two of the major contractors at RFETS, core data elements were established. These data elements were selected based on their utility for analysis and identification of exposure determinants. A task-based coding scheme was employed to better define the highly variable work. The coding scheme consisted of a two-tiered hierarchical list with a total of 34 possible combinations of work type and task. The data elements were incorporated into a Microsoft Access database with built-in data entry features to both promote consistency and limit entry choices to enable stratified analyses. In designing the system, emphasis was placed on the ability of end users to perform complex analyses and multiparameter queries to identify trends in their exposure data. A very flexible and user-friendly report generator was built into the system. This report generator allowed users to perform multiparameter queries using an intuitive system with very little training. In addition, a number of automated graphical analyses were built into the system, including ex posure levels by any combination of building, date, employee, job classification, type of contaminant, work type or task, exposure levels over time, exposure levels relative to the permissible exposure limit (PELS), and distributions of exposure levels. Both of these interfaces, allow the user to ''drill down'' or gradually narrow query criteria to identify specific exposure determinants. A number of other industrial hygiene processes were automated by the use of this database. Exposure calculations were coded into the system to allow automatic calculation of time-weighted averages and sample volumes. In addition, a table containing all the PELs and other relevant occupational exposure limits was built into the system to allow automatic comparisons with the current standards. Finally, the process of generating reports for employee notification was automated. The implementation of this system demonstrates that an integrated database system can save time for a practicing hygienist as well as provide useful and more importantly, timely information to guide primary prevention efforts.

Weakly monotone averaging functions

Averaging behaviour of aggregation functions depends on the fundamental property of monotonicity with respect to all arguments. Unfortunately this is a limiting property that ensures that many important averaging functions are excluded from the theoretical framework. We propose a definition for weakly monotone averaging functions to encompass the averaging aggregation functions in a framework with many commonly used non-monotonic means. Weakly monotonic averages are robust to outliers and noise, making them extremely important in practical applications. We show that several robust estimators of location are actually weakly monotone and we provide sufficient conditions for weak monotonicity of the Lehmer and Gini means and some mixture functions. In particular we show that mixture functions with Gaussian kernels, which arise frequently in image and signal processing applications, are actually weakly monotonic averages. Our concept of weak monotonicity provides a sound theoretical and practical basis for understanding both monotone and non-monotone averaging functions within the same framework. This allows us to effectively relate these previously disparate areas of research and gain a deeper understanding of averaging aggregation methods. © Springer International Publishing Switzerland 2014.

Three types of monotonicity of averaging functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions, which are one of the basic tools in knowledge-based systems. The functions known as means (or averages) are idempotent and typically are monotone, however there are many important classes of means that are non-monotone. Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging functions. In this paper we discuss the concepts of directional and cone monotonicity, and monotonicity with respect to majority of inputs and coalitions of inputs. We establish the relations between various kinds of monotonicity, and illustrate it on various examples. We also provide a construction method for cone monotone functions.

Fuzzy measures of pixel cluster compactness

Pixel-scale fine details are often lost during image processing tasks such as image reduction and filtering. Block or region based algorithms typically rely on averaging functions to implement the required operation and traditional function choices struggle to preserve small, spatially cohesive clusters of pixels which may be corrupted by noise. This article proposes the construction of fuzzy measures of cluster compactness to account for the spatial organisation of pixels. We present two construction methods (minimum spannning trees and fuzzy measure decomposition) to generate measures with specific properties: monotonicity with respect to cluster size; invariance with respect to translation, reflection and rotation; and, discrimination between pixel sets of fixed cardinality with different spatial arrangements. We apply these measures within a non-monotonic mode-like averaging function used for image reduction and we show that this new function preserves pixel-scale structures better than existing monotonie averages.

On the weak monotonicity of Gini means and other mixture functions

Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging aggregation, and weakly monotone functions were shown to have desirable properties when averaging data corrupted with outliers or noise. We extended the study of weakly monotone averages by analyzing their ϕ-transforms, and we established weak monotonicity of several classes of averaging functions, in particular Gini means and mixture operators. Mixture operators with Gaussian weighting functions were shown to be weakly monotone for a broad range of their parameters. This study assists in identifying averaging functions suitable for data analysis and image processing tasks in the presence of outliers.

The local power of the CADF and CIPS panel unit root tests

Very little is known about the local power of second generation panel unit root tests that are robust to cross-section dependence. This article derives the local asymptotic power functions of the cross-section argumented Dickey–Fuller Cross-section Augmented Dickey-Fuller (CADF) and CIPS tests of Pesaran (2007), which are among the most popular tests around.