Oklahoma State University–Stillwater

About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.

Risk Perceptions, Risk Preference, and Acceptance of Risky Food

Abstract: Consumers' risk preferences are often overlooked in studies of consumer demand for risky food. We find that risk preferences elicited through context-less lottery choices are significantly related to consumers' stated preferences for genetically modified (GM) food. These results suggest risk preferences elicited in the laboratory are not artificial in the sense that they appear to be related to the same risk preferences that govern other individual decisions such as food choice. Consistent with theoretical expectations, risk perceptions and risk preferences were found to be significant determinants of acceptance of GM food, which has important implications for explaining consumer behavior.

An introduction to the use of neural networks in control systems

Abstract: SUMMARY The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The paper also presents three control architectures: model reference adaptive control, model predictive control, and feedback linearization control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks. We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system. Copyright # 2002 John Wiley & Sons, Ltd.

Predicting Accuracy of Early Cost Estimates using Factor Analysis and Multivariate Regression

Abstract: The importance of accurate estimates during the early stages of capital projects has been widely recognized for many years. Early project estimates represent a key ingredient in business unit decisions and often become the basis for a project's ultimate funding. However, a stark contrast arises when comparing the importance of early estimates with the amount of information typically available during the preparation of an early estimate. Such limited scope definition often leads to questionable estimate accuracy. Even so, very few quantitative methods are available that enable estimators and business managers to objectively evaluate the accuracy of early estimates. The primary objective of this study was to establish such a model. To accomplish this objective, quantitative data were collected from completed construction projects in the process industry. Each of the respondents was asked to assign a one-to-five rating for each of 45 potential drivers of estimate accuracy for a given estimate. The data were analyzed using factor analysis and multivariate regression analysis. The factor analysis was used to group the 45 elements into 11 orthogonal factors. Multivariate regression analysis was performed on the 11 factors to determine a suitable model for predicting estimate accuracy. The resulting model, known as the estimate score procedure, allows the project team to score an estimate and then predict its accuracy based on the estimate score. In addition, a computer software tool, the Estimate Score Program, was developed to automate the estimate score procedure. The multivariate regression analysis identified 5 of the 11 factors that were significant at the α = 10% level. The five factors, in order of significance, were basic process design, team experience and cost information, time allowed to prepare the estimate, site requirements, and bidding and labor climate.

Research frontiers for improving our understanding of drought-induced tree and forest mortality.

Abstract: Accumulating evidence highlights increased mortality risks for trees during severe drought, particularly under warmer temperatures and increasing vapour pressure deficit (VPD). Resulting forest die-off events have severe consequences for ecosystem services, biophysical and biogeochemical land–atmosphere processes. Despite advances in monitoring, modelling and experimental studies of the causes and consequences of tree death from individual tree to ecosystem and global scale, a general mechanistic understanding and realistic predictions of drought mortality under future climate conditions are still lacking. We update a global tree mortality map and present a roadmap to a more holistic understanding of forest mortality across scales. We highlight priority research frontiers that promote: (1) new avenues for research on key tree ecophysiological responses to drought; (2) scaling from the tree/plot level to the ecosystem and region; (3) improvements of mortality risk predictions based on both empirical and mechanistic insights; and (4) a global monitoring network of forest mortality. In light of recent and anticipated large forest die-off events such a research agenda is timely and needed to achieve scientific understanding for realistic predictions of drought-induced tree mortality. The implementation of a sustainable network will require support by stakeholders and political authorities at the international level.