Detailed Analysis of the Solution Heat Treatment of a Third-Generation Single-Crystal Nickel-Based Superalloy CMSX-10K^®

Abstract A detailed analysis of the response of as-cast third-generation single-crystal nickel-based superalloy CMSX-10K® to solution heat treatment (SHT) has been carried out, alongside an SHT optimization exercise. The analysis was conducted through microstructural characterization, differential scanning calorimetry, and compositional homogeneity measurements, quantifying (i) the dissolution and microstructural evolution of the inter-dendritic constituents, (ii) the shift in thermo-physical characteristics of the material, and (iii) the change in compositional homogeneity across the microstructure, in order to gain further understanding of these phenomena during the progression of the SHT. During the early stages of SHT, the coarse cellular γ′/narrow γ channel inter-dendritic constituents which were the last areas to solidify during casting, progressively dissolve; homogenization between these inter-dendritic areas and adjacent dendritic areas leads to a rapid increase in the incipient melting temperature T IM. The fine γ/γ′ morphology which were the first inter-dendritic constituents to solidify after primary γ dendrite solidification were found to progressively coarsen; however, subsequent dissolution of these coarsened γ/γ′ inter-dendritic areas did not result in significant increases in the T IM until the near-complete dissolution of these inter-dendritic areas. After the final SHT step, residual compositional micro-segregation could still be detected across the microstructure despite the near-complete dissolution of these remnant inter-dendritic areas; even so the T IM of the material approached the solidus temperature of the alloy.The authors would like to acknowledge funding through the EPSRC/Rolls-Royce Strategic Partnership (EP/H500375/1 and EP/M005607/1). The authors also wish to express appreciation to Dr. Chris Hayward at the School of Geosciences, University of Edinburgh for carrying out the EPMA composition measurements and to Mr. Kevin Roberts of Dept. of Materials Science and Metallurgy for assistance in carrying out the solution heat treatment runs. Requests for access to the underlying research data should be directed to the corresponding author and will be considered against commercial interests and data protection.This is the final version of the article. It was first available from Springer via http://dx.doi.org/10.1007/s11661-015-3252-

Pengaruh Aplikasi Gofood Terhadap Minat Konsumen Untuk Produk Makanan Dan Minuman Secara Online Menurut Model Utaut2

Online transportation services are one of the applications that are widely used by the people of Indonesia, giving rise to the idea of conducting this study to analyze the effect of performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, and price value as factors that influence customer’s intention to use GoFood application to purchase food and beverage products online. This research uses a quantitative method using 100 samples data. The results of multiple linear regression analysis show that among the six variables studied, performance expectancy, social influence, hedonic motivation, and price value found to have a significant effect on customer’s intention to use GoFood application to purchase food and beverage products online, while effort expectancy and facilitating condition are found not affect customer’s intention to use GoFood application

The Directed Dominating Set Problem: Generalized Leaf Removal and Belief Propagation

A minimum dominating set for a digraph (directed graph) is a smallest set of vertices such that each vertex either belongs to this set or has at least one parent vertex in this set. We solve this hard combinatorial optimization problem approximately by a local algorithm of generalized leaf removal and by a message-passing algorithm of belief propagation. These algorithms can construct near-optimal dominating sets or even exact minimum dominating sets for random digraphs and also for real-world digraph instances. We further develop a core percolation theory and a replica-symmetric spin glass theory for this problem. Our algorithmic and theoretical results may facilitate applications of dominating sets to various network problems involving directed interactions.Comment: 11 pages, 3 figures in EPS forma

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices In A Coarse-To-Fine Framework

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approache

Higgsing M2 to D2 with gravity: N=6 chiral supergravity from topologically gauged ABJM theory

We present the higgsing of three-dimensional N=6 superconformal ABJM type theories coupled to conformal supergravity, so called topologically gauged ABJM theory, thus providing a gravitational extension of previous work on the relation between N M2 and N D2-branes. The resulting N=6 supergravity theory appears at a chiral point similar to that of three-dimensional chiral gravity introduced recently by Li, Song and Strominger, but with the opposite sign for the Ricci scalar term in the lagrangian. We identify the supersymmetry in the broken phase as a particular linear combination of the supersymmetry and special conformal supersymmetry in the original topologically gauged ABJM theory. We also discuss the higgsing procedure in detail paying special attention to the role played by the U(1) factors in the original ABJM model and the U(1) introduced in the topological gauging.Comment: 53 pages, Late

Zero Sound in Effective Holographic Theories

We investigate zero sound in $D$-dimensional effective holographic theories, whose action is given by Einstein-Maxwell-Dilaton terms. The bulk spacetimes include both zero temperature backgrounds with anisotropic scaling symmetry and their near-extremal counterparts obtained in 1006.2124 [hep-th], while the massless charge carriers are described by probe D-branes. We discuss thermodynamics of the probe D-branes analytically. In particular, we clarify the conditions under which the specific heat is linear in the temperature, which is a characteristic feature of Fermi liquids. We also compute the retarded Green's functions in the limit of low frequency and low momentum and find quasi-particle excitations in certain regime of the parameters. The retarded Green's functions are plotted at specific values of parameters in $D=4$, where the specific heat is linear in the temperature and the quasi-particle excitation exists. We also calculate the AC conductivity in $D$-dimensions as a by-product.Comment: 29 pages, 1 figur

Demonstrating approaches to chemically modify the surface of Ag nanoparticles in order to influence their cytotoxicity and biodistribution after single dose acute intravenous administration

With the advance in material science and the need to diversify market applications, silver nanoparticles (AgNPs) are modified by different surface coatings. However, how these surface modifications influence the effects of AgNPs on human health is still largely unknown. We have evaluated the uptake, toxicity and pharmacokinetics of AgNPs coated with citrate, polyethylene glycol, polyvinyl pyrolidone and branched polyethyleneimine (Citrate AgNPs, PEG AgNPs, PVP AgNPs and BPEI AgNPs, respectively). Our results demonstrated that the toxicity of AgNPs depends on the intracellular localization that was highly dependent on the surface charge. BPEI AgNPs ( potential=+46.5mV) induced the highest cytotoxicity and DNA fragmentation in Hepa1c1c7. In addition, it showed the highest damage to the nucleus of liver cells in the exposed mice, which is associated with a high accumulation in liver tissues. The PEG AgNPs ( potential=-16.2mV) showed the cytotoxicity, a long blood circulation, as well as bioaccumulation in spleen (34.33 mu g/g), which suggest better biocompatibility compared to the other chemically modified AgNPs. Moreover, the adsorption ability with bovine serum albumin revealed that the PEG surface of AgNPs has an optimal biological inertia and can effectively resist opsonization or non-specific binding to protein in mice. The overall results indicated that the biodistribution of AgNPs was significantly dependent on surface chemistry: BPEI AgNPs>Citrate AgNPs=PVP AgNPs>PEG AgNPs. This toxicological data could be useful in supporting the development of safe AgNPs for consumer products and drug delivery applications

Building pathway clusters from Random Forests classification using class votes

<p>Abstract</p> <p>Background</p> <p>Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.</p> <p>Results</p> <p>We propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster.</p> <p>Conclusion</p> <p>Our proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data.</p