Global strong solutions to the planar compressible magnetohydrodynamic equations with large initial data and vaccum

This paper considers the initial boundary problem to the planar compressible magnetohydrodynamic equations with large initial data and vacuum. The global existence and uniqueness of large strong solutions are established when the heat conductivity coefficient $\kappa(\theta)$ satisfies \begin{equation*} C_{1}(1+\theta^q)\leq \kappa(\theta)\leq C_2(1+\theta^q) \end{equation*} for some constants $q>0$, and $C_1,C_2>0$.Comment: 19pages,some typos are correcte

Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning

The coupling of computational thermodynamics and kinetics has been the central research theme in Integrated Computational Material Engineering (ICME). Two major bottlenecks in implementing this coupling and performing efficient ICME-guided high-throughput multi-component industrial alloys discovery or process parameters optimization, are slow responses in kinetic calculations to a given set of compositions and processing conditions and the quality of a large amount of calculated thermodynamic data. Here, we employ machine learning techniques to eliminate them, including (1) intelligent corrupt data detection and re-interpolation (i.e. data purge/cleaning) to a big tabulated thermodynamic dataset based on an unsupervised learning algorithm and (2) parameterization via artificial neural networks of the purged big thermodynamic dataset into a non-linear equation consisting of base functions and parameterization coefficients. The two techniques enable the efficient linkage of high-quality data with a previously developed microstructure model. This proposed approach not only improves the model performance by eliminating the interference of the corrupt data and stability due to the boundedness and continuity of the obtained non-linear equation but also dramatically reduces the running time and demand for computer physical memory simultaneously. The high computational robustness, efficiency, and accuracy, which are prerequisites for high-throughput computing, are verified by a series of case studies on multi-component aluminum, steel, and high-entropy alloys. The proposed data purge and parameterization methods are expected to apply to various microstructure simulation approaches or to bridging the multi-scale simulation where handling a large amount of input data is required. It is concluded that machine learning is a valuable tool in fueling the development of ICME and high throughput materials simulations.publishedVersio

A colorimetric method for point mutation detection using high-fidelity DNA ligase

The present study reported proof-of-principle for a genotyping assay approach that can detect single nucleotide polymorphisms (SNPs) through the gold nanoparticle assembly and the ligase reaction. By incorporating the high-fidelity DNA ligase (Tth DNA ligase) into the allele-specific ligation-based gold nanoparticle assembly, this assay provided a convenient yet powerful colorimetric detection that enabled a straightforward single-base discrimination without the need of precise temperature control. Additionally, the ligase reaction can be performed at a relatively high temperature, which offers the benefit for mitigating the non-specific assembly of gold nanoparticles induced by interfering DNA strands. The assay could be implemented via three steps: a hybridization reaction that allowed two gold nanoparticle-tagged probes to hybrid with the target DNA strand, a ligase reaction that generates the ligation between perfectly matched probes while no ligation occurred between mismatched ones and a thermal treatment at a relatively high temperature that discriminate the ligation of probes. When the reaction mixture was heated to denature the formed duplex, the purple color of the perfect-match solution would not revert to red, while the mismatch gave a red color as the assembled gold nanoparticles disparted. The present approach has been demonstrated with the identification of a single-base mutation in codon 12 of a K-ras oncogene that is of significant value for colorectal cancers diagnosis, and the wild-type and mutant type were successfully scored. To our knowledge, this was the first report concerning SNP detection based on the ligase reaction and the gold nanoparticle assembly. Owing to its ease of operation and high specificity, it was expected that the proposed procedure might hold great promise in practical clinical diagnosis of gene-mutant diseases

Characterization of coal fines generation: a micro-scale investigation

Coal fines are commonly generated as by-product during coalbed methane production mainly due to the interaction of coal with inseam water flow. A portion of the created coal fines may settle and plug the coal cleats and hydraulic fractures due to the gravity and coal pore size constraint. This could result in the reduction of coal permeability and blockage of coalbed methane wells or gas drainage boreholes. Despite the increasing awareness of the importance of understanding coal fines, limited research has been carried out on the characterization of coal fines creation. This study aimed to numerically characterize the generation process of coal fines in micro-scale coal cleats. The Scanning Electron Microscopy (SEM) images for a coal sample from Bulli Seam of the Sydney Basin in Australia were obtained and analysed to determine the actual cleat geometries and the characteristics of coal fines distribution. Then a fully coupled fluid-structure numerical model was developed to identify the creation process of coal fines at micro-scale. The impact of pertinent production conditions on coal fines generation was studied, including production pressure drawdown, temperature, coal fines Young's modulus and strength. The SEM images revealed that the particle size distributions of the coal fines in the examined cleats were in the order of hundreds of nanometres to several microns. The results of the numerical studies showed the coal fines production increased with pressure build-up, and decreased with increasing coal fines strength with more sensitivity compared with pressure. Critical values for production pressure drawdown were obtained, above which failure area began to expand; threshold values were also determined, below which remarkable reduction of coal fines production was achieved. Coal cleat geometry plays an important role in determining coal fines production. It was noted that exposed microstructures, cleat elbow regions and micro-fracture tips are more likely to generate coal fines. Based on these findings, guidance can be provided on the control of production conditions to mitigate coal fines issue, and new insight into where and how coal fines are created by inseam water flow can be achieved. (C) 2015 Elsevier B.V. All rights reserved

A Semi-Empirical Inversion Model for Assessing Surface Soil Moisture using AMSR-E Brightness Temperatures

In 2004-2005, 2007 and 2009, three major drought disasters occurred in Guangdong Province of southern China, which caused serious economic losses. Hence, it has recently become an important research subject in China to monitor surface soil moisture (SSM) and the drought disaster quickly and accurately. SSM is an effective indicator for characterizing the degree of drought. First, using the brightness temperatures (T[subscript b]) of the Advanced Microwave Scanning Radiometer on the EOS Aqua Satellite (AMSR-E), a modified surface roughness index was developed to map the land surface roughness. Then by combining microwave polarization difference indices (MPDI)-based vegetation cover classification and the modified surface roughness index, a simple semi-empirical model of SSM was derived from the passive microwave radiative transfer equation using AMSR-E C-band T[subscript b] and observed surface soil temperature (T[subscript s]). The model was inverted to calculate SSM. The results show the ability to discriminate over a broad range of SSM (7%~73%) with an accuracy of 2.11% in bare ground and flat areas (R² =0.87), 2.89% in sparse vegetation and flat surface areas (R²=0.85), about 6%~9% in dense vegetation areas and rough surface areas (0.80≤R²≤0.83). The simulation results were also validated using in-situ SSM data (R²=0.87, RMSE=6.36%). Time series mapping of SSM from AMSR-E imageries further demonstrated that the presented method was effective to detect the initiation, duration and recovery of the drought events.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/journal-of-hydrology/.Keywords: Drought disaster, Brightness temperature (T[subscript b]), South China, Semi-Empirical model, Surface soil moisture (SSM), AMSR-E, Passive microwave remote sensin