Coercivity Control of Variable-Length Iron Chains in Phthalocyanine Thin Films

The magnetic characteristics of iron phthalocyanine thin films are studied with a vibrating sample magnetometer, identifying a ferromagnetic transition temperature at 4.5?K. The metal ions at the center of the molecule are self-assembled along chains producing quasi one-dimensional magnetic chains of variable length in the thin films. The average chain length is varied from 20 to 300?nm via substrate temperature during deposition. Below the critical transition temperature, the magnetization curves have the shape of wasp-waisted or constricted loops. The in-plane chain length modulates the coercivity and saturation field and larger grains increase the coercivity significantly. First-order reversal curves of the wasp-waisted hysteresis loops reveal a long narrow strip that suggests a broad distribution of coercive fields and weak intergrain magnetic interactions. These findings are also supported through simulations based on the Preisach model.     

Large Eddy Simulations of Double-Ruler Electromagnetic Field Effect on Transient Flow During Continuous Casting

Transient flow during nominally steady conditions is responsible for many intermittent defects during the continuous casting of steel. The double-ruler electromagnetic field configuration, or “FC-Mold EMBr,” is popular in commercial slab casting as it provides independent control of the applied static field near the jet and free surface regions of the mold. In the current study, transient flow in a typical commercial caster is simulated in the absence and in the presence of a double-ruler magnetic field, with rulers of equal strengths. Large eddy simulations with the in-house code CU-FLOW resolve the important transient behavior, using grids of over five million cells with a fast parallel solver. In the absence of a magnetic field, a double-roll pattern is observed, with transient unbalanced behavior, high surface velocities (~0.5 m/s), surface vortex formation, and very large surface-level fluctuations (~±12 mm). Applying the magnetic field suppresses the unbalanced behavior, producing a more complex mold flow pattern, but with much lower surface velocities (~0.1 m/s), and a flat surface level with small level fluctuations (<±1 mm). Nail board measurements taken at this commercial caster, in the absence of the field, matched reasonably well with the calculated results, both quantitatively and qualitatively.     

Local,global, and elementary stoichiometry

Stoichiometry refers to conservation of atomic species. In this article, local refers to a point at the continuum level, global refers to the macroscopic balance level, and elementary refers to conservation of atomic species associated with distinct kinetic steps. The role of stoichiometry in the determination of the pivot matrix and the mechanistic matrix is presented. The elements of both these matrices are referred to as stoichiometric coefficients; however, both sets of coefficients are different and both play different roles in the analysis of chemical reactors. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Correcting evaluation bias of relational classifiers with network cross validation

Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess the models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). We propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1−Type II error).     

Prediction of rock brittleness using nondestructive methods for hard rock tunneling

The material and elastic properties of rocks are utilized for predicting and evaluating hard rock brittleness using artificial neural networks(ANN). Herein hard rock brittleness is defined using Yagiz’method. A predictive model is developed using a comprehensive database compiled from 30 years’ worth of rock tests at the Earth Mechanics Institute(EMI), Colorado School of Mines. The model is sensitive to density, elastic properties, and P- and S-wave velocities. The results show that the model is a better predictor of rock brittleness than conventional destructive strength-test based models and multiple regression techniques. While the findings have direct implications on intact rock, the methodology can be extrapolated to rock mass problems in both tunneling and underground mining where rock brittleness is an important control.