Assessment of Airborne Exposure to Trihalomethanes from Tap Water in Residential Showers and Baths

This study evaluates airborne concentrations of common trihalomethane (THM) compounds in bathrooms during showering and bathing in homes supplied with chlorinated tap water. Three homes in an urban area were selected, each having three bedrooms, a full bath, and approximately 1,000 square feet of living area. THMs were concurrently measured in tap water and air in the shower/bath enclosure and the bathroom vanity area using Summa canisters. Chloroform (TCM), bromodichloromethane (BDCM), and chlorodibromomethane (CDBM) were quantified using U.S. Environmental Protection Agency (EPA) Method TO-14. Air samples were collected prior to, during, and after the water-use event for 16 shower and 7 bath events. Flow rate and temperature were measured, but not controlled. The increase in average airborne concentration (+/- standard error) during showers (expressed as microg/m3 in shower enclosure or bathroom air per microg/L in water) was 3.3+/-0.4 for TCM, 1.8+/-0.3 for BDCM, and 0.5+/-0.1 for CDBM (n = 12), and during baths was 1.2+/-0.4 for TCM, 0.59+/-0.21 for BDCM, and 0.15+/-0.05 for CDBM (n = 4). The relative contribution of each chemical to the airborne concentrations was consistent for all shower and bath events, with apparent release of TCM > BDCM > CDBM. The results are therefore consistent with their relative concentration in tap water and their vapor pressures. When the shower findings for TCM are normalized for water concentration, flow rate, shower volume, and duration, the average exposure concentrations in these urban residences are about 30% lower than those reported by other investigators using EPA analytical methods. This difference is likely attributable primarily to greater air exchange rates in residential shower/bath stalls compared to more "airtight" laboratory shower chambers. This appears to be the first field study to thoroughly evaluate THM exposures from residential showers and baths, and can be used to validate previously published models of tap water volatile chemical transfer to indoor air.     

Adoption in Clinical Psychology: A Review of the Absence, Ramifications, and Recommendations for Change

Practicing clinical psychologists reported that 5 to 10% of their patients are adoption triad members (Sass & Henderson, 1999) and yet recent studies concluded that the majority of psychologists are not being taught about adoption-related issues in their preservice raining (Post, 1999; Sass & Henderson, 1999). This article discusses the need for training on adoption in the context of the APA Guidelines and Principles for Accreditation of Programs in Professional Psychology; Domain D, which states that programs must provide students with relative knowledge and experiences about the role of cultural and individual diversity in psychological phenomenon as they relate to the science and practice of professional psychology (1996, p. 15), the omission of adoption from curricula, and potential ramifications of this absence.     

Who Holds the Stakes? A Case Study of Stakeholder Identification at Two Nuclear Weapons Production Sites

Traditional risk assessments, including those involving the United States Department of Energy (USDOE), are often criticized for producing useless or noncredible management responses because they did not meaningfully involve the public. The first step to involve the public is to identify appropriate active participants (stakeholders). This study was done to understand the processes used to identify stakeholders to serve on advisory boards established at the two largest remediation sites in the United States: the Hanford Nuclear Reservation in Washington state and the Savannah River Site in South Carolina. The Hanford stakeholder identification process produced an interest-based board whereas the Savannah River Site strategy produced population-based representation. The basic goals of the stakeholder advisory groups were similar. However, different processes were used to identify the participants for the groups in part because of distinctly different social and cultural conditions in the areas affected by the operations of the two facilities, and in part because of the different level of trust of the USDOE and their contractors at Hanford compared with Savannah River. The discussion analyzes their different needs and potential for successful citizen participation.     

Mlonte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Most public health risk assessments assume and combine a series of average, conservative, and worst-case values to derive a conservative point estimate of risk. This procedure has major limitations. This paper demonstrates a new methodology for extended uncertainty analyses in public health risk assessments using Monte Carlo techniques. The extended method begins as do some conventional methods--with the preparation of a spreadsheet to estimate exposure and risk. This method, however, continues by modeling key inputs as random variables described by probability density functions (PDFs). Overall, the technique provides a quantitative way to estimate the probability distributions for exposure and health risks within the validity of the model used. As an example, this paper presents a simplified case study for children playing in soils contaminated with benzene and benzo(a)pyrene (BaP).     

French and British Top Managers' Understanding of the Structure and the Dynamics of Their Industries: a Cognitive Analysis and Comparison

This paper presents the findings of an exploratory study of the content of and differences between managerial frames of references with regard to the structure and dynamics of competitive environments. These frames of references are examined and compared at both the industry level and the country level; and the paper shows the influences of both industry and institutional structures and national culture on managerial thinking. It therefore contributes to the field of comparative management, as well as the growing interest in cognitive analysis in the field of management. The study is particularly significant since (a) its context is the rapidly changing industry environment of Europe, and (b) the managers are all involved in the development of strategies for their firms. Thirty three top managers (16 English and 17 French) were interviewed in four industries (brewing, car manufacturing, retail banking and book publishing) on their understanding of their industry and the strategy of their company. Content analysis of the transcribed interviews surfaces cognitive constructs which facilitate the identification of similarities and differences between managers, (a) within industries, (b) across industries, (c) within countries, (d) between countries and (e) between countries within the different industries. The analysis also provides means whereby the constructs can be represented in the form of cognitive maps.     

Towards an Objective Classification Scheme for Organizational Task Environments

This study replicates, refines, cross-validates and simplifies a scheme of operationalization and measurement of environmental dimensions proposed by Dess and Beard (1984). Employing a sample of 60 industries and data over a 16-year period, this study found considerable support for the viability of the three dimensions of munificence, dynamism and complexity. The data intensive and computationally complex operationalization scheme was simplified using a discriminant analysis approach and the discriminant functions were cross-validated using an alternate data set.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Control of interrelated research and development projects

The purpose of this paper is to examine a type of resource allocation problem arising in the context of research and development activities. The particular problem analyzed involves scheduling a group of projects in such a way that total cost is minimized while several precedence relations are satisfied and specific completion times are maintained. The primary difficulty results from the existence of commonalities that allow the successful completion of a particular project to be applied to several different purposes. A solution approach is developed which combines a one-pass network algorithm to deal with the precedence and time restrictions and a dynamic programming procedure to allocate the resources to each project.