Structure-property characterization of rheocast and VADER processed IN-100 superalloy

Two recent solidification processes have been applied in the production of IN-100 nickel-base superalloy: rheocasting and vacuum arc double electrode remelting (VADER). A detailed microstructural examination has been made of the products of these two processes; associated tensile strength and fatigue crack propagation (FCP) rate at an elevated temperature were evaluated. In rheocasting, processing variables that have been evaluated include stirring speed, isothermal stirring time and volume fraction solid during isothermal stirring. VADER processed IN-100 was purchased from Special Metals Corp., New Hartford, NY. As-cast ingots were subjected to hot isostatic pressing (HIP) and heat treatment. Both rheocasting and VADER processed materials yield fine and equiaxed spherical structures, with reduced macrosegregation in comparison to ingot materials. The rheocast structures are discussed on the basis of the Vogel-Doherty-Cantor model of dendrite arm fragmentation. The rheocast ingots evaluated were superior in yield strength to both VADER and commercially cast IN-100 alloy. Rheocast and VADER ingots may have higher crack propagation resistance than P/M processed material

VADER - A Satellite Mission Concept For High Precision Dark Energy Studies

We present a satellite mission concept to measure the dark energy equation of state parameter w with percent-level precision. The Very Ambitious Dark Energy Research satellite (VADER) is a multi-wavelength survey mission joining X-ray, optical, and IR instruments for a simultaneous spectral coverage from 4microns (0.3eV) to 10keV over a field of view (FoV) of 1 square degree. VADER combines several clean methods for dark energy studies, the baryonic acoustic oscillations in the galaxy and galaxy cluster power spectrum and weak lensing, for a joint analysis over an unrivalled survey volume. The payload consists of two XMM-like X-ray telescopes with an effective area of 2,800cm^2 at 1.5keV and state-of-the-art wide field DEPFET pixel detectors (0.1-10keV) in a curved focal plane configuration to extend the FoV. The X-ray telescopes are complemented by a 1.5m optical/IR telescope with 8 instruments for simultaneous coverage of the same FoV from 0.3 to 4 microns. The 8 dichroic-separated bands (u,g,r,z,J,H,K,L) provide accurate photometric galaxy redshifts, whereas the diffraction-limited resolution of the central z-band allows precise shape measurements for cosmic shear analysis. The 5 year VADER survey will cover a contiguous sky area of 3,500 square degrees to a depth of z~2 and will yield accurate photometric redshifts and multi-wavelength object parameters for about 175,000 galaxy clusters, one billion galaxies, and 5 million AGN. VADER will not only provide unprecedented constraints on the nature of dark energy, but will additionally extend and trigger a multitude of cosmic evolution studies to very large (>10 Gyrs) look-back times.Comment: 14 pages, 7 figures, accepted for publication in the SPIE conference proceeding

Higher yields of cyclodepsipetides from Scopulariopsis brevicaulis by random mutagenesis

The ascomycete Scopulariopsis brevicaulis, which was isolated from the marine sponge Tethya aurantium, produces two cyclodepsipeptides, scopularides A and B [1]. Both peptides exhibit activity against several tumor cell lines. Within the EU-project MARINE FUNGI (EU FP7, 265926) one of our aims is to enhance the production of these secondary metabolites. We are in the process to establish two ways of random mutagenesis by both UV radiation and transposon-mediated. To this end we created UV-mutants and a miniaturised screening method was developed. UV-radiation was performed at 312 nm and the survival rate was set to 1 %. With this method a mutant library was established. To screen these mutants for higher secondary metabolites production, we developed a miniaturised screening method which includes decreased cultivation volume, fast extraction and an optimised LC-MS analysis format. Using the UV mutagenesis, we were able to identify several mutants with a higher scopularide production in comparison to the wild type. One of these mutants, which produces three times more biomass and more than double the amount of scopularide A, has been used for another round of mutation. Next generation sequencing is being employed to identify the molecular genetic basis of the observed mutations. In parallel we employ transposable elements to introduce mutants [2]. The impact of transposons on gene expression as well as their ability to cause major mutations within the genome or single genes makes them an interesting tool for random mutagenesis [3, 4, 5]. We employ the Vader transposon in its homologous host and found that Vader mostly integrates within or very close to genes. Thus it appears to be a useful tool for transposon-mediated mutagenesis in A. niger (6). At current we try to enhance its usability by modifying the Vader element

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

Serial fiction, the End?

Andrew McGonigal presents some interesting data concerning truth in serial fictions.1 Such data has been taken by McGonigal, Cameron and Caplan to motivate some form of contextualism or relativism. I argue, however, that many of these approaches are problematic, and that all are under-motivated as the data can be explained in a standard invariantist semantic framework given some independently plausible principles

Optical and IR luminosity functions of IRAS galaxies

The optical and infrared luminosity functions are determined for a 60 micron flux-limited sample of 68 IRAS galaxies covering a total area of 150 deg sq. The IR function is in good agreement with that obtained by other authors. The shape of the optical luminosity function is similar to that of optically selected galaxy samples. The integrated light of most objects in the sample have (NII) to H alpha line flux ratios characteristic of HII-region galaxies. In the absolute magnitude range M sub J = -18, -22 about 14% of late-type galaxies are IRAS galaxies. The apparent companionship frequency is about twice as large as that for a comparable sample of non-IRAS late-type galaxies

Predicting Cyber Events by Leveraging Hacker Sentiment

Recent high-profile cyber attacks exemplify why organizations need better cyber defenses. Cyber threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into groups' collective malicious activity. We propose a novel approach to predict cyber events using sentiment analysis. We test our approach using cyber attack data from 2 major business organizations. We consider 3 types of events: malicious software installation, malicious destination visits, and malicious emails that surpassed the target organizations' defenses. We construct predictive signals by applying sentiment analysis on hacker forum posts to better understand hacker behavior. We analyze over 400K posts generated between January 2016 and January 2018 on over 100 hacking forums both on surface and Dark Web. We find that some forums have significantly more predictive power than others. Sentiment-based models that leverage specific forums can outperform state-of-the-art deep learning and time-series models on forecasting cyber attacks weeks ahead of the events