The Effect of Framing on Layoff Decisions: Are More-Experienced Managers More Rational? Does Computerized Data Make Any Difference?

[Excerpt] The effects of framing on decisions has been widely studied, producing research that suggests individuals respond to framing in predictable and fairly consistent ways (Bazerman, 1984, 1990; Tversky & Kahneman, 1986; Thaler, 1980). The essential finding from this body of research is that "individuals treat risks concerning perceived gains (for example, saving jobs and plants) differently from risks concerning perceived losses (losing jobs and plants)" (Bazerman, 1990, pp. 49-50). Specifically, individuals tend to avoid risks concerning gains, and seek risks concerning losses.

Application of phytotoxicity data to a new Australian soil quality guideline framework for biosolids

To protect terrestrial ecosystems and humans from contaminants many countries and jurisdictions have developed soil quality guidelines (SQGs). This study proposes a new framework to derive SQGs and guidelines for amended soils and uses a case study based on phytotoxicity data of copper (Cu) and zinc (Zn) from field studies to illustrate how the framework could be applied. The proposed framework uses normalisation relationships to account for the effects of soil properties on toxicity data followed by a species sensitivity distribution (SSD) method to calculate a soil added contaminant limit (soil ACL) for a standard soil. The normalisation equations are then used to calculate soil ACLs for other soils. A soil amendment availability factor (SAAF) is then calculated as the toxicity and bioavailability of pure contaminants and contaminants in amendments can be different. The SAAF is used to modify soil ACLs to ACLs for amended soils. The framework was then used to calculate soil ACLs for copper (Cu) and zinc (Zn). For soils with pH of 4-8 and OC content of 1-6%, the ACLs range from 8 mg/kg to 970 mg/kg added Cu. The SAAF for Cu was pH dependant and varied from 1.44 at pH 4 to 2.15 at pH 8. For soils with pH of 4-8 and OC content of 1-6%, the ACLs for amended soils range from 11 mg/kg to 2080 mg/kg added Cu. For soils with pH of 4-8 and a CEC from 5-60, the ACLs for Zn ranged from 21 to 1470 mg/kg added Zn. A SAAF of one was used for Zn as it concentrations in plant tissue and soil to water partitioning showed no difference between biosolids and soluble Zn salt treatments, indicating that Zn from biosolids and Zn salts are equally bioavailable to plants.

Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model

Background: Standard methods for quantifying IncuCyte ZOOM™ assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM™ images to the solution of the Fisher-Kolmogorov model. Results: The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM™ assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM™ assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF. Conclusions: Our approach for estimating D, λ and K from an IncuCyte ZOOM™ assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Comprehensive Path-sensitive Data-flow Analysis

Data-flow analysis is an integral part of any aggressive optimizing compiler. We propose a framework for improving the precision of data-flow analysis in the presence of complex control-flow. W initially perform data-flow analysis to determine those control-flow merges which cause the loss in data-flow analysis precision. The control-flow graph of the program is then restructured such that performing data-flow analysis on the resulting restructured graph gives more precise results. The proposed framework is both simple, involving the familiar notion of product automata, and also general, since it is applicable to any forward data-flow analysis. Apart from proving that our restructuring process is correct, we also show that restructuring is effective in that it necessarily leads to more optimization opportunities. Furthermore, the framework handles the trade-off between the increase in data-flow precision and the code size increase inherent in the restructuring. We show that determining an optimal restructuring is NP-hard, and propose and evaluate a greedy strategy. The framework has been implemented in the Scale research compiler, and instantiated for the specific problem of Constant Propagation. On the SPECINT 2000 benchmark suite we observe an average speedup of 4% in the running times over Wegman-Zadeck conditional constant propagation algorithm and 2% over a purely path profile guided approach.

A computationally efficient technique for data-clustering

A computationally efficient agglomerative clustering algorithm based on multilevel theory is presented. Here, the data set is divided randomly into a number of partitions. The samples of each such partition are clustered separately using hierarchical agglomerative clustering algorithm to form sub-clusters. These are merged at higher levels to get the final classification. This algorithm leads to the same classification as that of hierarchical agglomerative clustering algorithm when the clusters are well separated. The advantages of this algorithm are short run time and small storage requirement. It is observed that the savings, in storage space and computation time, increase nonlinearly with the sample size.

The effect of data sources and quality on the predictive capacity of CLIMEX models: an assessment of Teleonemia scrupulosa and Octotoma scabripennis for the biocontrol of Lantana camara in Australia

Understanding the effects of different types and quality of data on bioclimatic modeling predictions is vital to ascertaining the value of existing models, and to improving future models. Bioclimatic models were constructed using the CLIMEX program, using different data types – seasonal dynamics, geographic (overseas) distribution, and a combination of the two – for two biological control agents for the major weed Lantana camara L. in Australia. The models for one agent, Teleonemia scrupulosa Stål (Hemiptera:Tingidae) were based on a higher quality and quantity of data than the models for the other agent, Octotoma scabripennis Guérin-Méneville (Coleoptera: Chrysomelidae). Predictions of the geographic distribution for Australia showed that T. scrupulosa models exhibited greater accuracy with a progressive improvement from seasonal dynamics data, to the model based on overseas distribution, and finally the model combining the two data types. In contrast, O. scabripennis models were of low accuracy, and showed no clear trends across the various model types. These case studies demonstrate the importance of high quality data for developing models, and of supplementing distributional data with species seasonal dynamics data wherever possible. Seasonal dynamics data allows the modeller to focus on the species response to climatic trends, while distributional data enables easier fitting of stress parameters by restricting the species envelope to the described distribution. It is apparent that CLIMEX models based on low quality seasonal dynamics data, together with a small quantity of distributional data, are of minimal value in predicting the spatial extent of species distribution.

Modelling the distribution and relative abundance of feral camels in the Northern Territory using count data

The objectives of this study were to predict the potential distribution, relative abundance and probability of habitat use by feral camels in southern Northern Territory. Aerial survey data were used to model habitat association. The characteristics of ‘used’ (where camels were observed) v. ‘unused’ (pseudo-absence) sites were compared. Habitat association and abundance were modelled using generalised additive model (GAM) methods. The models predicted habitat suitability and the relative abundance of camels in southern Northern Territory. The habitat suitability maps derived in the present study indicate that camels have suitable habitat in most areas of southern Northern Territory. The index of abundance model identified areas of relatively high camel abundance. Identifying preferred habitats and areas of high abundance can help focus control efforts.

Seabed discrimination data from SeaScan™

Raw data from SeaScan™ transects off Wide Bay (south Queensland) taken in August 2007 as part of a study of ecological factors influencing the distribution of spanner crabs (Ranina ranina). The dataset (comma-delimited ascii file) comprises the following fields: 1. record number 2. date-time (GMT) 3. date-time (AEST) 4. latitude (signed decimal degrees) 5. longitude (decimal degrees) 6. speed over ground (knots) 7. depth (m) 8. seabed roughness (v) 9. hardness (v) Indices of roughness and hardness (from the first and second echoes respectively) were obtained using a SeaScan™ 100 system (un-referenced) on board the Research Vessel Tom Marshall, with the ship’s Furuno FCV 1100 echo sounder and 1 kW, 50 kHz transducer. Generally vessel speed was kept below about 14 kt (typically ~12 kt), and the echo-sounder range set to 80 m. The data were filtered to remove errors due to data drop-out, straying beyond system depth limits (min. 10 m), or transducer interference.

Queensland Coastal Wetlands Resources Mapping Data

1:100,000 coastal wetland vegetation mapping for Queensland including mangrove communities, saltpans and saline grasslands. Mapping taken from Landsat TM images with ground truthing. Additional metadata is available for details of techniques and accuracy for each section of coastline. Data Currency for each section of coast: NT border to Flinders River - 1995 SE Gulf of Carpentaria - 1987, 1988, 1991, 1992 Cape York Peninsula - 1986-88, 1991 Cape Trib to Bowling Green Bay - 1997-99 The Burdekin Region - 1991 The Bowen Region - 1994-95 The Whitsunday Region - 1997 Repulse Bay - 1989 Central Qld - 1995, 1997 The Curtis Coast Region - 1997 Round Hill Head to Tin Can Inlet - 1997 Moreton Region - 1995. Article Links: 1/ #1662. Queensland Coastal Wetland Resources: the Northern Territory Border to Flinders River. Project Report. Information Series QI00099. 2/ #1663. Queensland Coastal Wetland Resources: Sand Bay to Keppel Bay. Project Report. Information Series QI00100. 3/ #1664. Queensland Coastal Wetland Resources: Cape Tribulation to Bowling Green Bay. Project Report. Information Series QI01064. 4/ #1666. Coastal Wetlands Resources Investigation of the Burdekin Delta for declaration as fisheries reserves. Report to Ocean Rescue 2000. Project Report. 5/ #1667. Queensland Coastal Wetland Resource Investigation of the Bowen Region: Cape Upstart to Gloucester Island. Project Report. 6/ #1784. Resource Assessment of the Tidal Wetland Vegetation of Western Cape York Peninsula, North Queensland, Report to Ocean Rescue 2000. Project Report. 7/ #1785. Marine Vegetation of Cape York Peninsula. Cape York Peninsula Land Use Strategy. Project Report. 8/ #3544. Queensland Coastal Wetland Resources: The Whitsunday Region. Project Report.Information Series QI01065. 9/ #3545. Queensland Coastal Wetland Resources: Round Hill Head to Tin Can Inlet. Project Report. Information Series QI99081.

Great Barrier Reef Fish Biological Data

Biological measurements on fish sampled during the course of FRDC funded project Growth, Reproduction and Recruitment of Great Barrier Reef Food Fish Stocks (FRDC 90/18). The comma-delimited ascii file comprises the following fields: 1. Cruise number 2. Date (d-m-y) 3, Region (descriptor of part of Queensland coast or Great Barrier Reef system) 4. Reef (name or number) 5. Data source (Res=research, Rec=recreational fisher, Com=commercial fisher) 6. Capture method 7. Trap number (where appropriate) 8. Species name 9. LthStd (standard length, cm) 10. LthFrk (fork length, cm) 11. LthTot (total length, cm) 12. WtTot (approx total weight, g; weighed at sea) 13. FrameWt (weight of frame [after filleting, with viscera], g; weighed in lab) 14. Sex (macroscopic examination only) 15. GonadWt (g) Data obtained by the Department Employment, Economic Development and Innovation (formerly Primary Industries and Fisheries) between 1988 and 1993, primarily in the southern Great Barrier Reef (Capricorn-Bunker and Swain Groups), with fish traps and handlining.

The Victorian 260 cc Legislation. Evaluation Report Using Accident Data to December, 1982.

Learner and first year probationary motorcyclists are over-represented in traffic accidents, being involved about four times as often as full motorcycle licence holders in relation to their numbers. In an attempt to reduce this over-involvement, the Victorian Government amended the law in 1979 to restrict learner and first year probationary motorcyclists to motorcycles with engine capacities of less than 260 cc. This paper reports an evaluation which showed that casualty rates for learner and first year probationers began to decrease from mid 1979 and continued to do so until the end of 1980. A further analysis indicated that compared to full licence holder casualties, learner permit casualties were about 40% less than expected while first year probationary casualties were about 39% lower.

Assessment of the underreporting rate of death cause data in Shandong province using capture-mark-recapture method [应用捕获再捕获方法评估山东省死因登记漏报率水平]

To evaluate the underreporting rate of death -cause data in Shandong province during 2012 to 2013 by capture -mark -recapture method and to provide the base for health strategy. Methods All counties were divided into 5 stratifications according the death rates of 2012, and 14 counties were selected, then 3 towns or streets were selected in each country, 10 villages or neighborhood committees were selected in each town （street）. The death data collected from security bureau and civil affairs bureau were compared with the reporting death data from the National Cause of Death Surveillance, and the underreporting rate was calculated. Results In present study, 6 929 death cases were collected, it was found that 1 556 cases were underreported. The death cases estimated by CMR method were 6 227 cases （95%CI: 7 593-7 651）, and the average underreporting rate was 23.15%. There were significantly differences between different stratifications （P<0.01）. The underreporting rate in 0-4 years old group was 56.93%, the male underreporting rate was 22.31% and the female underreporting rate was 24.09%. There was no significant difference between male and female groups （P>0.05）. Conclusion There is an obvious underreport in the cause of death surveillance of Shandong province, and the underreporting rates are different among the 5 stratifications. The underreporting rate is higher in 0-4 years old group, and the investigation of the death cause surveillance for young residents is not perfect in some countries. The investigation quality of the death cause surveillance should be improved, increasing the integrity of the report data and adjusting the mortalities in different stratifications for obtaining a accurate mortality in Shandong province.

A new formalism for representation of binary thermodynamic data

The applicability of a formalism involving an exponential function of composition x1 in interpreting the thermodynamic properties of alloys has been studied. The excess integral and partial molar free energies of mixing are expressed as: $$\begin{gathered} \Delta F^{xs} = a_o x_1 (1 - x_1 )e^{bx_1 } \hfill \\ RTln\gamma _1 = a_o (1 - x_1 )^2 (1 + bx_1 )e^{bx_1 } \hfill \\ RTln\gamma _2 = a_o x_1^2 (1 - b + bx_1 )e^{bx_1 } \hfill \\ \end{gathered} $$ The equations are used in interpreting experimental data for several relatively weakly interacting binary systems. For the purpose of comparison, activity coefficients obtained by the subregular model and Krupkowski’s formalism have also been computed. The present equations may be considered to be convenient in describing the thermodynamic behavior of metallic solutions.