Investigation into Constitutive Equation and Hot Compression Deformation Behavior of 6061 Al Alloy

Hot compression tests of 6061 Al alloy were applied under the Gleeble-3500 system at temperature range of 300 – 450 °C and strain rate range of 0.01-10 s−1. The true stress-strain curves of 6061 Al alloy were acquired and the flow stress was recorded and corrected. The associated microstructure of 6061 Al alloy after hot deformation process was observed. The results suggest that the stress level of 6061 Al alloy during hot compression process decreases with increasing compression temperature and decreasing strain rate. Arrhenius equation and the Zener-Hollomon parameter in the hyperbolic sine-type equation were utilized in present research to formulate the constitutive equation of 6061 Al alloy. The microstructure after hot deformation consists of elongated grains and the dynamic recovery of 6061 Al alloy occurs during hot compression. However, for the alloy deformed at low Z value, the existence of newly refined grains around the serrated grain boundaries indicates the occurrence of partial dynamic recrystallization

Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov random field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation--Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Magnetic Skyrmion Transport in a Nanotrack With Spatially Varying Damping and Non-adiabatic Torque

Reliable transport of magnetic skyrmions is required for any future skyrmion-based information processing devices. Here we present a micromagnetic study of the in-plane current-driven motion of a skyrmion in a ferromagnetic nanotrack with spatially sinusoidally varying Gilbert damping and/or non-adiabatic spin-transfer torque coefficients. It is found that the skyrmion moves in a sinusoidal pattern as a result of the spatially varying Gilbert damping and/or non-adiabatic spin-transfer torque in the nanotrack, which could prevent the destruction of the skyrmion caused by the skyrmion Hall effect. The results provide a guide for designing and developing the skyrmion transport channel in skyrmion-based spintronic applications.Comment: 5 pages, 6 figure

Mutual effects between Pinus armandii and broadleaf litter during mixed decomposition

Mixed-decomposition effects are commonly observed in natural and planted forests and affect nutrient cycling in a forest ecosystem. However, how one litter type affects the decomposition of another is still poorly understood. In this study, Pinus armandii litter was mixed with Betula albosinensis, Catalpa fargesii, Populus purdomii, Eucommia ulmoides, and Acer tsinglingense litter. The mixtures were placed in litterbags and buried in soil with consistent moisture for a 180-day indoor simulated decomposition experiment. The litterbags were periodically harvested during decomposition; the litter residues of different species were separated, and the biomass dynamics of each litter type were simulated. In addition, the soil sucrase, cellulase and polyphenol oxidase activities were also detected three times. The mutual effects of needle and broadleaf litter during mixed decomposition and the possible underlying mechanisms were investigated. The results indicated that (i) during the decomposition experiment, P. armandii needles significantly inhibited the decomposition of broadleaf litter in the first 3 months, while the broadleaf litter accelerated the decomposition of P. armandii needles in only approximately 40% of the cases. However, the inhibitory effects of needles on broadleaf litter decomposition subsequently exhibited significant weakening, while the accelerating effects of broadleaf litter were significantly enhanced. The effects of mixed decomposition on the activities of three enzymes can only partially explain the interactions between different litter types; (ii) the prediction by the decomposition model showed that most of the broadleaf litter types could continuously accelerate the decomposition of P. armandii needles throughout the mixed decomposition process, while the decomposition of broadleaf litter would be significantly inhibited at least in the short term. In general, four of the five broadleaf litter types (excluding E. ulmoides) could accelerate the early decomposition of P. armandii needles and consequently accelerate nutrient cycling in P. armandii pure forests. These species could be used for the transformation of pure P. armandii pure forests to mixed forests

Morphological Investigation of Calcium Carbonate during Ammonification-Carbonization Process of Low Concentration Calcium Solution

Ultrafine calcium carbonate is a widely used cheap additive. The research is conducted in low degree supersaturation solution in order to study the polymorphic phases’ change and its factors of the calcium carbonate precipitate in the ammonification-carbonization process of the solution with calcium. Fine particles of calcium carbonate are made in the solution containing 0.015 mol/L of Ca2+. Over 98% of the calcium carbonate precipitate without ammonification resembles the morphology of calcite, while the introduction of ammonia can benefit the formation of vaterite. It was inferred that the main cause should be serious partial oversaturation or steric effects. Ammonia also helps to form the twin spherical calcium carbonate. However, particles formed in the process of ammonification-carbonization in solution with low concentration degree of calcium are not even with a scale of the particle diameter from 5 to 12 μm. Inorganic salts, alcohol, or organic acid salts have significant controlling effect on the particle diameter of calcium carbonate and can help to decrease the particle diameter to about 3 μm. Anionic surfactants can prevent the conglobation of calcium carbonate particles and shrink its diameter to 500 nm–1 μm

Power Control of Diesel Engine-Generator Set Subject to Emission Constraints

Series Hybrid Electric Vehicle (SHEV) is a promising solution to reducing fuel consumption and emissions. It equipped with large battery packs that allow the SHEV first operates in full electrical mode, once the on-board batteries are depleted, the engine generator set turns on to sustain the power demand. Therefore, the efficiency and emissions of a SHEV depend heavily on the operation of the engine generator set. For the simultaneous power and emission control, the model based engine generator set control was developed. Then, emission estimation and Exhaust Gas Recirculation (EGR) model were implemented. The amount of EGR was determined based on the trade-off between NOx and soot emissions. Finally, Model Predictive Control (MPC) was designed and applied to control the power of the engine generator set at its operation point to achieve the best fuel economy as well as to satisfy the power demand

INFORMATIONAL DISCONTINUITY IN SOYBEAN FUTURES PRICE FROM THE 2018-2019 SINO-AMERICA TRADE WAR

There are plenty of studies testing and verifying the price signaling effects between China and US futures markets. This paper is built up based on this consensus but further elaborate on method application. Results show that previous patterns of price signaling effect on soybean futures prices between China and US no longer exist after the US-China trade war in 2018, during which the trade of soybeans was adversely affected and almost stopped. Different from previous studies, this paper differentiates price signaling effects between opening prices and closing prices of soybean futures, building up a circulating price signaling structure. We also consider signaling effect between US soybean futures price and Chinses spot price. Besides, existing studies typically predict future price shocks by simulations. This paper applies an existing shock, the 2018 US-China trade war, to examine whether the price signaling effects still exist. Key words: US-China trade war, price signaling, soybean future

A Bayesian Image Analysis of the Change in Tumor/Brain Contrast Uptake Induced by Radiation via Reversible Jump Markov Chain Monte Carlo

This work is motivated by a pilot study on the change in tumor/brain contrast uptake induced by radiation via quantitative Magnetic Resonance Imaging. The results inform the optimal timing of administering chemotherapy in the context of radiotherapy. A noticeable feature of the data is spatial heterogeneity. The tumor is physiologically and pathologically distinct from surrounding healthy tissue. Also, the tumor itself is usually highly heterogeneous. We employ a Gaussian Hidden Markov Random Field model that respects the above features. The model introduces a latent layer of discrete labels from an Markov Random Field (MRF) governed by a spatial regularization parameter. We further assume that conditional on the hidden labels, the observed data are independent and normally distributed, We treat the regularization parameter of the MRF, as well as the number of states of the MRF as parameters, and estimate them via the Reversible Jump Markov chain Monte Carlo algorithm. We propose a novel and nontrivial implementation of the Reversible Jump moves. The performance of the method is examined in both simulation studies and real data analysis. We report the pixel-wise posterior mean and standard deviation of the change in contrast uptake marginalized over the number of states and hidden labels. We also compare the performance with a Markov chain with fixed number of states and a parallel Expectation-Maximization approach from a frequentist perspective

How to Learn from An Inconclusive Translation of An African Woman’s Writing?

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149535/1/paf2000078.pd