Extended Hyperbolic Model for Sand-to-Concrete Interfaces

A relatively simple, four-parameter extended hyperbolic model for interfaces was developed for use in soil-structure interaction analyses. The model accommodates arbitrary stress path directions and includes three important elements: (1) development of a yield surface during interface shear; (2) a formulation for yield-inducing shear stiffness that is applicable to any stress path orientation; and (3) a formulation for unloading-reloading shear stiffness. The model was evaluated against the results of shear tests performed at the interface between three different types of sand and a concrete surface under a variety of stress paths. Comparisons between measured and calculated interface response indicate that the model provides accurate estimates of the response of sand-to-concrete interfaces.     

Integrated model-driven dashboard development

Business performance modeling and model-driven business transformation are two research directions that are attracting much attention lately. In this study, we propose an approach for dashboard development that is model-driven and can be integrated with the business performance models. We adopt the business performance modeling framework, and we extend it in order to capture the reporting aspect of the business operation. We describe models that can effectively represent all the elements necessary for the business performance reporting process, and the interactions among them. We demonstrate how all these models can be combined and automatically generate the final solution. We further extend the proposed framework with mechanisms that can detect changes in the models and incrementally update the deployed solutions. Finally, we discuss our experience from the application of our technique in a real-world scenario. This case study shows that our technique can be efficiently applied to and handle changes in the underlying business models, delivering significant benefits in terms of both development time and flexibility.     

Quality of manual data collection in Java software: an empirical investigation

Data collection, both automatic and manual, lies at the heart of all empirical studies. The quality of data collected from software informs decisions on maintenance, testing and wider issues such as the need for system re-engineering. While of the two types stated, automatic data collection is preferable, there are numerous occasions when manual data collection is unavoidable. Yet, very little evidence exists to assess the error-proneness of the latter. Herein, we investigate the extent to which manual data collection for Java software compared with its automatic counterpart for the same data. We investigate three hypotheses relating to the difference between automated and manual data collection. Five Java systems were used to support our investigation. Results showed that, as expected, manual data collection was error-prone, but nowhere near the extent we had initially envisaged. Key indicators of mistakes in manual data collection were found to be poor developer coding style, poor adherence to sound OO coding principles, and the existence of relatively large classes in some systems. Some interesting results were found relating to the collection of public class features and the types of error made during manual data collection. The study thus offers an insight into some of the typical problems associated with collecting data manually; more significantly, it highlights the problems that poorly written systems have on the quality of visually extracted data.     

A complete VARMA modelling methodology based on scalar components

Abstract. This article proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.     

How the Emotional Climate (Field) Impacts Performance

Independently of the creativity movement, others have demonstrated the benefits of creating positive fields. I have reviewed a number of cases involving field below and added some from my own creativity experiences. I then explore the psychological explanation of why such fields are effective and go on to the findings of recent brain research, which not only reinforce the benefits of positive fields but also throw light on the difficulty of creating them. Finally, I offer some suggestions from my own experience of what can be done to create positive fields. I believe that this should now be the focus of our efforts: to the extent that we are successful, we should get the creativity and innovation we need as a by‐product.     

A connectionist implementation of the theory of planned behavior: Association of beliefs with exercise intention.

The theory of planned behavior suggests attitudes are a product of salient beliefs. This study examined whether aggregating salient beliefs was plausible within a more biologically centered information-processing environment. A neural network was used to examine associations among beliefs relating to exercise intention. Data on intentions and behavioral, normative, and control beliefs from 114 respondents were used to train (by error backpropagation) a neural network to associate beliefs with intention. The R2 between the network's estimated and self-reported intention was .66. The network's representation comprised 6 belief profiles associated with high, moderate, or low behavioral intention. The neural network accommodated complex relationships among beliefs and belief-intention associations and indicated how high-level constructs such as attitudes may be viewed as the best fit (compromise state) between aroused beliefs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)