Attachment, Self-Esteem, Worldviews, and Terror Management: Evidence for a Tripartite Security System.

On the basis of prior work integrating attachment theory and terror management theory, the authors propose a model of a tripartite security system consisting of dynamically interrelated attachment, self-esteem, and worldview processes. Four studies are presented that, combined with existing evidence, support the prediction derived from the model that threats to one component of the security system result in compensatory defensive activation of other components. Further, the authors predicted and found that individual differences in attachment style moderate the defenses. In Studies 1 and 2, attachment threats motivated worldview defense among anxiously attached participants and motivated self-enhancement (especially among avoidant participants), effects similar to those caused by mortality salience. In Studies 3 and 4, a worldview threat and a self-esteem threat caused attachment-related proximity seeking among fearful participants and avoidance of proximity among dismissing participants. The authors' model provides an overarching framework within which to study attachment, self-esteem, and worldviews. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Validity of the Five-Factor Model Prototypes for Personality Disorders in Two Clinical Samples.

The authors examined the validity of D. R. Lynam and T. A. Widiger's (2001) prototypes for personality disorders (PDs) derived from the facets of the 5-factor model (FFM) of personality in 2 clinical samples. In the 1st sample (N = 94), there was good agreement between the prototypes generated by experts and the profiles reported by patients. These FFM PD similarity scores also demonstrated good convergent and discriminant validity with results from a semistructured interview and a self-report measure of Axis II pathology. In the 2nd sample (N = 132), the FFM PD similarity scores demonstrated excellent longitudinal stability and good predictive validity with regard to consensus ratings of PD features. The implications of these findings are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Overconfidence in Interval Estimates.

Judges were asked to make numerical estimates (e.g., "In what year was the first flight of a hot air balloon?"). Judges provided high and low estimates such that they were X% sure that the correct answer lay between them. They exhibited substantial overconfidence: The correct answer fell inside their intervals much less than X% of the time. This contrasts with choices between 2 possible answers to a question, which showed much less overconfidence. The authors show that overconfidence in interval estimates can result from variability in setting interval widths. However, the main cause is that subjective intervals are systematically too narrow given the accuracy of one's information-sometimes only 40% as large as necessary to be well calibrated. The degree of overconfidence varies greatly depending on how intervals are elicited. There are also substantial differences among domains and between male and female judges. The authors discuss the possible psychological mechanisms underlying this pattern of findings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Simple Penning ion source for laboratory research and development applications

A simple Penning ion generator (PIG) that can be easily fabricated with simple machining skills and standard laboratory accessories is described. The PIG source uses an iron cathode body, samarium cobalt permanent magnet, stainless steel anode, and iron cathode faceplate to generate a plasma discharge that yields a continuous 1 mA beam of positively charged hydrogen ions at 1 mTorr of pressure. This operating condition requires 5.4 kV and 32.4 W of power. Operation with helium is similar to hydrogen. The ion source is being designed and investigated for use in a sealed-tube neutron generator; however, this ion source is thoroughly described so that it can be easily implemented by other researchers for other laboratory research and development applications.     

Human, rhesus macaque, and feline sequences highly similar to mouse mammary tumor virus sequences

Sequences highly similar (>95%) to the mouse mammary tumor virus (MMTV) env gene have been amplified from human DNA samples, including DNA samples from patients with breast cancer (BC) and persons who did not have BC. The sequences from human DNA were distinct from the MMTV sequences used as controls in these PCR reactions, indicating that these results are not simply due to contamination. In addition to both, mouse and human-related sequences were also amplified from some monkey and cat genomic DNA samples. These products were shown to be distinct from, but highly related to, the MMTV env gene, whereas, testing of other sources (lambda phage, snake, cockroach, sea urchin, chicken, or dog) demonstrated no specific amplification. A sequence 90% similar to the MMTV group antigen gene (gag) was amplified from cat DNA. These results indicate that DNA from vertebrate species other than rodents, including some but not all humans, monkeys, and cats, can contain sequences closely related to MMTV.     

A semantic framework for automatic generation of computational workflows using distributed data and component catalogues

Computational workflows are a powerful paradigm to represent and manage complex applications, particularly in large-scale distributed scientific data analysis. Workflows represent application components that result in individual computations as well as their interdependences in terms of dataflow. Workflow systems use these representations to manage various aspects of workflow creation and execution for users, such as the automatic assignment of execution resources. This article describes an approach to automating a new aspect of the process: the selection of application components and data sources. We present a novel approach that enables users to specify varying degrees of detail and amount of constraints in a workflow request, including the specification of constraints on input, intermediate or output data in the workflow, abstract workflow component classes rather than specific component implementations, and generic reusable workflow templates that express a pre-defined combination of components. The algorithm elaborates the user request into a set of fully ground workflows with specific choices of data sources and codes to be used so that they can be submitted for mapping and execution. The algorithm searches through the space of possible candidate workflows by creating increasingly more specialized versions of the original template and eliminating candidates that violate constraints cumulated in the candidate workflow as components and data sources are selected. A novel feature of our approach is that it assumes a distributed architecture where data and component catalogues are separate from the workflow system. The algorithm explicitly poses queries to external catalogues, and therefore any reasoning regarding data or component properties is not assumed to occur within the workflow system. We describe our implementation of this approach in the Wings workflow system. This implementation uses the W3C Web Ontology Language and associated reasoners to implement the workflow system as well as the data and component catalogues. This research demonstrates the use of artificial intelligence techniques to support the kinds of automation envisioned by the scientific community for large-scale distributed scientific data analysis.     

Propagation of Data Error and Parametric Sensitivity in Computable General Equilibrium Models

While computable general equilibrium (CGE) models are a well-established tool in economic analyses, it is often difficult to disentangle the effects of policies of interest from that of the assumptions made regarding the underlying calibration data and model parameters. To characterize the behavior of a CGE model of carbon output with respect to two of these assumptions, we perform a large-scale Monte Carlo experiment to examine its sensitivity to base year calibration data and elasticity of substitution parameters in the absence of a policy change. By examining a variety of output variables at different levels of economic and geographic aggregation, we assess how these forms of uncertainty impact the conclusions that can be drawn from the model simulations. We find greater sensitivity to uncertainty in the elasticity of substitution parameters than to uncertainty in the base-year data as the projection period increases. While many model simulations were conducted to generate large output samples, we find that few are required to capture the mean model response of the variables tested. However, characterizing standard errors and empirical probability distribution functions is not possible without a large number of simulations.