Non-isothermal kinetic analysis of the crystallization of metallic glasses using the master curve method

The non-isothermal transformation rate curves of metallic glasses are analyzed with the Master Curve method grounded in the Kolmogorov-Johnson-Mehl-Avrami theory. The method is applied to the study of two different metallic glasses determining the activation energy of the transformation and the experimental kinetic function that is analyzed using Avrami kinetics. The analysis of the crystallization of Cu47Ti33Zr11Ni8Si1 metallic glassy powders gives Ea = 3.8 eV, in good agreement with the calculation by other methods, and a transformation initiated by an accelerating nucleation and diffusion-controlled growth. The other studied alloy is a Nanoperm-type Fe77Nb7B15Cu1 metallic glass with a primary crystallization of bcc-Fe. An activation energy of Ea = 5.7 eV is obtained from the Master Curve analysis. It is shown that the use of Avrami kinetics is not able to explain the crystallization mechanisms in this alloy giving an Avrami exponent of n = 1

numpywren: serverless linear algebra

Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algebra at scales beyond a single machine. Traditional approaches either require access to supercomputing clusters, or impose configuration and cluster management challenges. In this paper we show how the disaggregation of storage and compute resources in so-called "serverless" environments, combined with compute-intensive workload characteristics, can be exploited to achieve elastic scalability and ease of management. We present numpywren, a system for linear algebra built on a serverless architecture. We also introduce LAmbdaPACK, a domain-specific language designed to implement highly parallel linear algebra algorithms in a serverless setting. We show that, for certain linear algebra algorithms such as matrix multiply, singular value decomposition, and Cholesky decomposition, numpywren's performance (completion time) is within 33% of ScaLAPACK, and its compute efficiency (total CPU-hours) is up to 240% better due to elasticity, while providing an easier to use interface and better fault tolerance. At the same time, we show that the inability of serverless runtimes to exploit locality across the cores in a machine fundamentally limits their network efficiency, which limits performance on other algorithms such as QR factorization. This highlights how cloud providers could better support these types of computations through small changes in their infrastructure

Preserving invariance properties of reaction–diffusion systems on stationary surfaces

We propose and analyse a lumped surface finite element method for the numerical approximation of reaction–diffusion systems on stationary compact surfaces in R3. The proposed method preserves the invariant regions of the continuous problem under discretization and, in the special case of scalar equations, it preserves the maximum principle. On the application of a fully discrete scheme using the implicit–explicit Euler method in time, we prove that invariant regions of the continuous problem are preserved (i) at the spatially discrete level with no restriction on the meshsize and (ii) at the fully discrete level under a timestep restriction. We further prove optimal error bounds for the semidiscrete and fully discrete methods, that is, the convergence rates are quadratic in the meshsize and linear in the timestep. Numerical experiments are provided to support the theoretical findings. We provide examples in which, in the absence of lumping, the numerical solution violates the invariant region leading to blow-up

Limited dependent variable and structural equations models: empirical applications to traffic operations and safety

Thesis (Ph. D.)--University of Washington, 1997This dissertation presents empirical applications of econometric and statistical methodologies to traffic operations and safety. The motivation of this research was to develop methodological frameworks to investigate factors affecting rural freeway safety and operations in an intelligent transportation system (ITS) setting. The research effort focuses on methodological frameworks for evaluating pre-ITS conditions but is applicable to post-ITS settings as well.Methodologies used in this dissertation include limited dependent variable models such as Poisson and negative binomial regressions as well as nested logit structures, and structural models involving simultaneous equations. The Poisson and negative binomial models investigate accident likelihoods on roadway sections, while the nested logit structure examines the conditional likelihood of accident severity. Simultaneous equations models examine factors affecting the cross-sectional endogeneity between lane mean speeds and lane-speed deviations. Accounting for cross-sectional endogeneity captures traffic flow dynamics which critically affect safety. These techniques are applied to an empirical setting where ITS infrastructure is being installed by the Washington State Department of Transportation (WSDOT) on a 61-kilometer section of rural Interstate 90 (I-90) located some 50 kilometers east of Seattle. Variable message signs at critical roadway locations coupled with in-vehicle signing in a selected number of vehicles will be provided to inform travelers of adverse driving conditions. The study area includes the Snoqualmie Pass summit, and experiences significant climatic interactions coupled with challenging roadway geometrics.Findings from this dissertation provide significant insights into the complex interactions and contemporaneous effects of spatial, temporal, environmental, geometric and traffic flow factors affecting accident causality and cross-sectional traffic flow dynamics. The approaches embodied in this dissertation, while being local in model specification, have broader implications beyond ITS, encompassing critical regional and national infrastructure design, programming and investment issues relating to traffic safety and operations. They afford greater flexibility in decision making through enhancement of the design strategy identification and definition process

The joint impact of commitment to disclosure and prior forecast accuracy on managers' forecasting credibility

textAlthough managers rate concerns about being seen as committed disclosers as an important consideration in their voluntary disclosure decisions, prior research has paid limited attention to how investors view commitment to disclosure. This study experimentally tests two competing perspectives relating to how managers' commitment to disclosure and prior forecast accuracy jointly influence managers' forecasting credibility. The first perspective (the normative perspective) draws on economic theory and the second perspective (the omission bias perspective) draws on theory from psychology. The normative perspective suggests that commitment to disclosure and prior forecast accuracy will independently influence managers' forecasting credibility. In contrast, the omission bias literature suggests that the influence of commitment to disclosure on managers' forecasting credibility depends on managers' prior forecast accuracy. In other words, the normative perspective suggests two main effects, whereas the omission bias perspective suggests a commitment to disclosure x accuracy interaction. To test the competing predictions relating to the joint impact of commitment to disclosure and prior forecast accuracy on managers' forecasting credibility, I conduct an experiment. Results of this experiment support the omission bias perspective. Participants in the role of investors rate more (less) committed managers as more (less) credible, but only when they are also accurate. When managers are inaccurate, however, this relationship reverses. That is, more committed managers are viewed as less credible relative to their less committed peers. These results suggest that managers' concerns about commitment to disclosure are indeed valid, but only when they are accurate. When managers are less accurate, commitment to disclosure hurts, rather than helps, managers' credibility. Participants' valuation judgments as well as their judgments relating to a current disclosure are positively associated with their judgments of managers' forecasting credibility, suggesting that their assessment of managers' credibility may have significant valuation consequences. This study contributes to the voluntary disclosure literature and has implications for managers who provide earnings forecasts and for investors who use these forecasts in their investment decisions.Accountin

SOME INSIGHTS ON ROADWAY INFRASTRUCTURE DESIGN FOR SAFE ELDERLY PEDESTRIAN TRAVEL

This paper presents insights into the relationship between road infrastructure and elderly pedestrian involvement in traffic accidents. We combine insights from empirical studies involving the probability of a pedestrian accident with insights from studies involving the probability of injuries to elderly pedestrians who are involved in vehicle-pedestrian accidents. The combined insights provide some direction to the methodology for identifying non-motorized improvements for supporting safe elderly travel. The results of the study indicate that after controlling for vehicle volumes, road infrastructure variables posing the greatest risk of pedestrian accidents in urban corridors include the presence of center turning lanes, traffic signal spacing exceeding 0.5 miles and roadway illumination. Center turning lanes indicate the presence of long corridors which may induce elderly pedestrians especially to attempt to cross roadways mid-block using center turning lane sections as refuges. Presence of traffic signals provides reduced pedestrian accident risk if the spacing is less than 0.5 miles. Especially for elderly pedestrians, the availability of protected crossings at signalized intersections is important considering the fact they cannot travel long block lengths in order to use signalized crossings. Presence of continuous roadway lighting decreases elderly pedestrian accident risk. The results also show the greatest impacts on injury severity probabilities are from the occurrence of elderly pedestrian accidents in non-intersection locations. Specifically, if elderly pedestrians are involved in marked crosswalk accidents, the probability of lower severity injury is higher; in contrast, if they are involved in unmarked, non-intersection locations such as mid-blocks, the probability of high severity injury is higher. We obtained these results through the use of Bayesian analysis. Bayesian analysis allows us to use subjective prior information on the distribution of parameters in combination with information from the observed data. The advantage of Bayesian analysis in the assessment of key road variables on safe elderly travel is that we can examine the robustness of results

Perspectives from Ab-initio and Tight-binding: Applications to Transition Metal Compounds and Superlattices

The experimental and theoretical study of transition metal compounds have occupied condensed matter physicists for the best part of the last century. The rich variety of physical behaviour exhibited by these compounds owes its origin to the subtle balance of the energy scales at play for the $d$ orbitals. In this thesis, we study three different systems comprised of transition metal atoms from the third, the fourth, and the fifth group of the periodic table using a combination of \emph{ab-initio} density functional theory (DFT) computations and effective tight-binding models for the electronic properties. We first consider the electronic properties of artificially fabricated perovskite superlattices of the form [(\ce{SrIrO3})$_m$ / \ce{SrTiO3}] with integer $m$ denoting the number of layers of \ce{SrIrO3}. After discussing the results of experiments undertaken by our collaborators, we present the results of our DFT calculations and build tight-binding models for the $m=1$ and $m=2$ superlattices. The active ingredient is found to be the \ce{Ir} 5$d$ orbitals with significant spin-orbit coupling. We then study the energies of magnetic ground states within DFT and compare and contrast our results with those obtained for the bulk Ruddlesden-Popper iridates. Together with experimental measurements, our results suggest that these superlattices are an exciting venue to probe the magnetism and metal-insulator transitions that occur from the intricate balance of the spin-orbit coupling and electron interactions, as has been reported for their bulk counterparts. Next, we consider $\alpha$-\ce{RuCl3}, a honeycomb lattice compound. We first show using DFT calculations in conjunction with experiments performed by our collaborators, how spin-orbit coupling in the 4$d$ orbitals of \ce{Ru} is essential to understand the insulating state realized in this compound. Then, in the latter half of the chapter, we study the magnetic ground states of a two-dimensional analogue of $\alpha$-\ce{RuCl3} in weak and strong-coupling regimes obtained from a tight-binding model for the 4$d$ orbitals. We further compare these results with energies obtained from DFT calculations. We obtain a zig-zag magnetic ground state for this compound, in all the three approaches. Within DFT, we find that correlations enhance the spin-orbit coupling in this compound and that the anisotropic Kitaev interactions between the spins are dominant in a strong-coupling model. Then, we move on to study the electronic band structures of the higher manganese silicides, which are good thermoelectric materials. Using results from DFT calculations on \ce{Mn4Si7} and structural arguments, we construct an effective tight-binding model for the first three members of this series - \ce{Mn4Si7}, \ce{Mn11Si19}, and \ce{Mn15Si26}.Ph.D

Modeling the endogeneity of lane-mean speeds and lane-speed deviations: a structural equations approach

This paper attempts to macroscopically address endogeneity issues related to lane-mean traffic speeds and lane-speed deviations. Methodologically, we seek to provide a better understanding of mean speeds and speed deviations across the lanes of a multilane highway. In so doing, the work may eventually be applied to better understand highway safety and the effects that lane-mean and lane speed deviations have on highway safety. We propose a structural model that relates mean speed and speed deviations by lane and is contemporaneously influenced by environmental, temporal, and traffic flow factors. Spot speed and vehicle classification data measured by lane in both the eastbound and westbound directions of Interstate 90 (I-90) in Washington State are used to develop the empirical relationships. The findings show that lane-mean speeds are endogenously related with adjacent lane speeds and exogenously related with associated environmental, traffic flow and temporal factors, while lane-speed deviations are endogenously related not only with adjacent lane speed deviations but also, through forward causality, lane-mean speeds and exogenously related with environmental, traffic flow and temporal factors as well. The approach shows significant promise in unraveling cause-effect relationships affecting macroscopic traffic flow continuums.