Confirming Fundamental Parameters of the Exoplanet Host Star epsilon Eridani Using the Navy Optical Interferometer

We measured the angular diameter of the exoplanet host star epsilon Eridani using the Navy Optical Interferometer. We determined its physical radius, effective temperature, and mass by combining our measurement with the star's parallax, photometry from the literature, and the Yonsei-Yale isochrones (Yi et al. 2001), respectively. We used the resulting stellar mass of 0.82 +/- 0.05 M_Sun plus the mass function from Benedict et al. (2006) to calculate the planet's mass, which is 1.53 +/- 0.22 M_Jupiter. Using our new effective temperature, we also estimated the extent of the habitable zone for the system.Comment: Accepted for publication in The Astrophysical Journa

Effectiveness of Monetary Incentives: Mail Surveys to Members of Multinational Professional Groups

Members of professional groups were much more likely to respond to a mail survey than nonmembers who were also experts in the area (43.7% versus 13.7%). A one-dollar (U.S.) prepaid monetary incentive increased the response rates, and it was as effective for members as for nonmembers (gains of 18.6% and 15.3%, respectively). Surprisingly, the U.S. dollar monetary incentive had a greater effect on foreign than U.S. response rates (gains of 32.6% and 12.9%, respectively).monetary incentives, marketing

Potential Diffusion of Expert Systems in Forecasting

We drew upon findings from the diffusion literature to assess the prospects for the diffusion of expert systems in forecasting. Forecasters judged potential adoption of expert systems in relation to two techniques that had been widely adopted in the past, Box-Jenkins and scenarios. They also rated each technique on seven innovation characteristics: relative advantage, compatibility, divisibility, communicability, complexity, product risks, and psychological risks. The respondents were classified by four forecaster roles: researcher, educator, practitioner, and decision maker. In general, the expected probabilities of adoption for expert systems were slightly higher than for the two other techniques. Additionally, the respondents rated expert systems nearly equivalent to Box-Jenkins and scenarios on relative advantage and communicability. In relating the probabilities of adoption to the characteristic ratings, the groups perceived significant negative psychological and product risks with expert systems. However the experts, especially practitioners and decision makers, rated expert systems positive on compatibility, divisibility, and communicability, so it may be desirable to ensure that these positive traits are stressed with potential adopters, especially researchers and educators. © 2001 Elsevier Science In

Effectiveness of Monetary Incentives: Mail Surveys to Members of Multinational Professional Groups

Members of professional groups were much more likely to respond to a mail survey than nonmembers who were also experts in the area (43.7% versus 13.7%). A one-dollar (U.S.)prepaid monetary incentive increased the response rates, and it was as effective for members as for nonmembers (gains of 18.6% and 15.3%, respectively). Surprisingly, the U.S. dollar monetary incentive had a greater effect on foreign than U.S. response rates (gains of 32.6% and 12.9%, respectively)

Beyond Accuracy: Comparison of Criteria Used to Select Forecasting Methods

Two studies were conducted to examine expert opinions of criteria used to select forecasting techniques. In Study One, while accuracy was a dominant criterion, the ratings of five of thirteen criteria varied by the role of the forecaster. Researchers rated accuracy relatively higher than did practitioners, educators or decision-makers. Decision makers rated implementation-related criteria, such as ease criteria, relatively higher than the other groups. In Study Two, forecasting experts significantly varied their ratings on six of seven criteria according to situations. Other criteria were often as important or more important than accuracy, especially when the situation involved making many forecasts. In general, there was much agreement across roles and across situations that accuracy was the most important criterion, but other criteria were rated as being almost as important. In particular, factors related to implementation, such as ease of interpretation and ease of use, were highly rated

Decomposition of Time-Series by Level and Change

This article examines whether decomposing time series data into two parts – level and change – produces forecasts that are more accurate than those from forecasting the aggregate directly. Prior research found that, in general, decomposition reduced forecasting errors by 35%. An earlier study on decomposition into level and change found a forecast error reduction of 23%. The current study found that nowcasts consisting of a simple average of estimates from preliminary surveys and econometric models of the U.S. lodging market, improved the accuracy of final estimates of levels. Forecasts of change from an econometric model and the improved nowcasts reduced forecast errors by 29% when compared to direct forecasts of the aggregate. Forecasts of change from an extrapolation model and the improved nowcasts reduced forecast errors by 45%. On average then, the error reduction for this study was 37%

Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series

Causal forces are a way of summarizing forecasters\u27 expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting causal forces; we refer to these as complex times series. It would seem that forecasting these times series would be easier if one could decompose the series to eliminate the effects of the conflicts. Given forecasts subject to high uncertainty, we hypothesized that a time series could be effectively decomposed under two conditions: 1) if domain knowledge can be used to structure the problem so that causal forces are consistent for two or more component series, and 2) when it is possible to obtain relatively accurate forecasts for each component. Forecast accuracy for the components can be assessed by testing how well they can be forecast on early hold-out data. When such data are not available, historical variability may be an adequate substitute. We tested decomposition by causal forces on 12 complex annual time series for automobile accidents, airline accidents, personal computer sales, airline revenues, and cigarette production. The length of these series ranged from 16 years for airline revenues to 56 years for highway safety data. We made forecasts for one to ten horizons, obtaining 800 forecasts through successive updating. For nine series in which the conditions were completely or partially met, the forecast error (MdAPE) was reduced by more than half. For three series in which the conditions were not met, decomposition by causal forces had little effect on accuracy

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Lean job design and musculoskeletal disorder risk: A two plant comparison

This study examined the relationship between lean job design and work-related musculoskeletal disorder (WMSD) risk factors. Repetition, force, and posture were assessed for a sample of 56 production jobs across departments at a lean automobile-manufacturing plant and compared to 56 similar jobs at a traditional automobile-manufacturing plant. The results showed greater productivity in the lean plant: less waiting ( p = .006) and walking ( p < .001); and greater repetition exposure ( p = .001). The mean rating for repetition was 5.5 in the lean plant, compared to 5.0 in the traditional plant based on the Latko (1997) hand activity level scale. However, the lean plant had significantly lower peak hand force ratings ( p = .01). When examining force and repetition combined, the lean plant had a lower percentage of jobs above the American Conference for Governmental Industrial Hygienists (ACGIH)-recommended Threshold Limit Value (TLV ® ). The findings suggest that lean manufacturing does not necessarily increase workers' risk for WMSD injuries. © 2009 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63064/1/20159_ftp.pd