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.

Effect of Tool Edge Geometry on Energy Dissipation in Ultraprecision Machining

Abstract: Summary An experimental study of the effect of single crystal diamond tool edge geometry on the resulting cutting and thrust forces and specific energy in the ultraprecision orthogonal flycutting of Te-Cu was made. The effects of both the nominal rake angle and tool edge profile were investigated over uncut chip thicknesses from 20μm down to 10 nm. Characterization of the tool edge was performed with the use of atomic force microscopy. Both the nominal rake angle and tool edge profile were found to have significant effects on he resulting forces and energies.

Reconceptualizing multicompetence as a theory of language knowledge

Abstract: Over the last decade or so, the concept of multicompetence has attracted significant research attention in the field of applied linguistics and in particular in the study of multiple language use and learning. We argue that while research efforts concerned with multicompetence have been useful in advancing a more positive view of second language learners, they have been less successful in transforming understandings of language knowledge. One reason for their lack of success is the fact that these efforts have been mired in a state of theoretical confusion arising from a continued reliance on three assumptions. These assumptions include (1) a view of L1 and L2 language knowledge as distinct systems; (2) the presumption of a qualitative distinction between multicompetence and monocompetence; and (3) the assumption of homogeneity of language knowledge across speakers and contexts. Our intent here is to redress these theoretical inadequacies by making a case for a usage-based view of multicompetence. We do so by drawing on empirical evidence and theoretical insights from other areas concerned with language and language development that expose the theoretical flaws in current research efforts on multicompetence. We then use these new understandings of language to reconsider findings on the language knowledge of multiple language users and to offer new directions for research on multicompetence.

Data-Driven Stochastic Unit Commitment for Integrating Wind Generation

Abstract: Considering recent development of deregulated energy markets and the intermittent nature of renewable energy generation, it is important for power system operators to ensure cost effectiveness while maintaining the system reliability To achieve this goal, significant research progress has recently been made to develop stochastic optimization models and solution methods to improve reliability unit commitment run practice, which is used in the day-ahead market for ISOs/RTOs to ensure sufficient generation capacity available in real time to accommodate uncertainties Most stochastic optimization approaches assume the renewable energy generation amounts follow certain distributions However, in practice, the distributions are unknown and instead, a certain amount of historical data are available In this research, we propose a data-driven risk-averse stochastic unit commitment model, where risk aversion stems from the worst-case probability distribution of the renewable energy generation amount, and develop the corresponding solution methods to solve the problem Given a set of historical data, our proposed approach first constructs a confidence set for the distributions of the uncertain parameters using statistical inference and solves the corresponding risk-averse stochastic unit commitment problem Then, we show that the conservativeness of the proposed stochastic program vanishes as the number of historical data increases to infinity Finally, the computational results numerically show how the risk-averse stochastic unit commitment problem converges to the risk-neutral one, which indicates the value of data

The CMC Interactivity Model: How Interactivity Enhances Communication Quality and Process Satisfaction in Lean-Media Groups

Abstract: Process satisfaction is one important determinant of work group collaborative system adoption, continuance, and performance. We explicate the computermediated communication (CMC) interactivity model (CMCIM) to explain and predict how interactivity enhances communication quality that results in increased process satisfaction in CMC-supported work groups. We operationalize this model in the challenging context of very large groups using extremely lean CMC. We tested it with a rigorous field experiment and analyzed the results with the latest structural equation modeling techniques. Interactivity and communication quality dramatically improved for very large groups using highly lean CMC (audience response systems) over face-to-face groups. Moreover, CMC groups had fewer negative status effects and higher process satisfaction than face-to-face groups. The practical applications of lean CMC rival theoretical applications in importance because lean CMC is relatively inexpensive and requires minimal training and support compared to other media. The results may aid large global work group continuance, satisfaction, and performance in systems, product and strategy development, and other processes in which status effects and communication issues regularly have negative influences on outcomes.

Search for the Standard Model Higgs boson produced in association with top quarks and decaying into bb¯ in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: A search for the Standard Model Higgs boson produced in association with a top-quark pair, , is presented. The analysis uses 20.3 fb(-1) of pp collision data at , collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the decay mode and uses events containing one or two electrons or muons. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities. A neural network is used to discriminate between signal and background events, the latter being dominated by +jets production. In the single-lepton channel, variables calculated using a matrix element method are included as inputs to the neural network to improve discrimination of the irreducible background. No significant excess of events above the background expectation is found and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95 % confidence level. The ratio of the measured signal cross section to the Standard Model expectation is found to be assuming a Higgs boson mass of 125 Gev.