Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Artificial neural network & Control theory. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Selective binding of O2 over N2 in a redox-active metal-organic framework with open iron(II) coordination sites.

Abstract: The air-free reaction between FeCl2 and H4dobdc (dobdc4– = 2,5-dioxido-1,4-benzenedicarboxylate) in a mixture of N,N-dimethylformamide (DMF) and methanol affords Fe2(dobdc)·4DMF, a metal–organic framework adopting the MOF-74 (or CPO-27) structure type. The desolvated form of this material displays a Brunauer–Emmett–Teller (BET) surface area of 1360 m2/g and features a hexagonal array of one-dimensional channels lined with coordinatively unsaturated FeII centers. Gas adsorption isotherms at 298 K indicate that Fe2(dobdc) binds O2 preferentially over N2, with an irreversible capacity of 9.3 wt %, corresponding to the adsorption of one O2 molecule per two iron centers. Remarkably, at 211 K, O2 uptake is fully reversible and the capacity increases to 18.2 wt %, corresponding to the adsorption of one O2 molecule per iron center. Mossbauer and infrared spectra are consistent with partial charge transfer from iron(II) to O2 at low temperature and complete charge transfer to form iron(III) and O22– at room temper...

Slow Magnetic Relaxation in a High-Spin Iron(II) Complex

Abstract: Slow magnetic relaxation is observed for [(tpaMes)Fe]−, a trigonal pyramidal complex of high-spin iron(II), providing the first example of a mononuclear transition metal complex that behaves as a single-molecule magnet. Dc magnetic susceptibility and magnetization measurements reveal a strong uniaxial magnetic anisotropy (D = −39.6 cm−1) acting on the S = 2 ground state of the molecule. Ac magnetic susceptibility measurements indicate the absence of slow relaxation under zero applied dc field as a result of quantum tunneling of the magnetization. Application of a 1500 Oe dc field initiates slow magnetic relaxation, which follows a thermally activated tunneling mechanism at high temperature to give an effective spin-reversal barrier of Ueff = 42 cm−1 and follows a temperature-independent tunneling mechanism at low temperature. In addition, the magnetic relaxation time shows a pronounced dc-field dependence, with a maximum occurring at ∼1500 Oe.

Knowledge Maps as Scaffolds for Cognitive Processing

Abstract: Knowledge maps are node-link representations in which ideas are located in nodes and connected to other related ideas through a series of labeled links. The research on knowledge mapping in the last 12 years has produced a number of consistent findings. Students recall more central ideas when they learn from a knowledge map than when they learn from text and those with low verbal ability or low prior knowledge often benefit the most. The use of knowledge maps also appears to amplify the benefits associated with scripted cooperation. Learning from maps is enhanced by active processing strategies such as summarization or annotation and by designing maps according to gestalt principles of organization. Fruitful areas for future research on knowledge mapping include examining whether knowledge maps reduce cognitive load, how map learning is influenced by the structure of the information to be learned, and the possibilities for transfer. Implications for practice are briefly delineated.

The use of data mining and neural networks for forecasting stock market returns

Abstract: It has been widely accepted by many studies that non-linearity exists in the financial markets and that neural networks can be effectively used to uncover this relationship. Unfortunately, many of these studies fail to consider alternative forecasting techniques, the relevance of input variables, or the performance of the models when using different trading strategies. This paper introduces an information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of several models. The results show that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy, as well as those guided by the level-estimation based forecasts of the neural network and linear regression models.