The magnesium sulfate-water system at pressures to 4 kilobars

Hydrated magnesium sulfate constitutes up to 1/6 of the mass of carbonaceous chondrites, and probably is important in many icy asteroids and satellites. It occurs naturally in meteorites mostly as epsomite. MgSO4, considered anhydrously, comprises nearly 3/4 of the highly soluble fraction of C1 chondrites. Thus, MgSO4 is probably an important solute in cryovolcanic brines erupted on certain icy objects in the outer solar system. While the physiochemical properties of the water-magnesium sulfate system are well known at low pressures, planetological applications of these data are hindered by a dearth of useful published data at elevated pressures. Accordingly, solid-liquid phase equilibria was recently explored in this chemical system at pressures extending to about 4 kilobars. The water magnesium sulfate system in the region of the eutectic exhibits qualitatively constant behavior between pressures of 1 atm and 2 kbar. The eutectic melting curve closely follows that for water ice, with a freezing point depression of about 4 K at 1 atm decreasing to around 3.3 K at 2 kbars. The eutectic shifts from 17 pct. MgSO4 at 1 atm to about 15.3 pct at 2 kbars. Above 2 kbars, the eutectic melting curve again tends to follow ice

Semantics-based information extraction for detecting economic events

As today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process

Ontology population from web product information

With the vast amount of information available on the Web, there is an increasing need to structure Web data in order to make it accessible to both users and machines. E-commerce is one of the areas in which growing data congestion on the Web has serious consequences. This paper proposes a frame- work that is capable of populating a product ontology us- ing tabular product information from Web shops. By for- malizing product information in this way, better product comparison or recommendation applications could be built. Our approach employs both lexical and syntactic matching for mapping properties and instantiating values. The per- formed evaluation shows that instantiating consumer elec- Tronics from Best Buy and Newegg.com results in an F1 score of approximately 77%

Sentiment Analysis of Text Guided by Semantics and Structure

As moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many stakeholders, like consumers. One challenge for such automated sentiment analysis systems is to identify whether pieces of natural language text are positive or negative. Typical methods of identifying this polarity involve low-level linguistic analysis. Existing systems predominantly use morphological, lexical, and syntactic cues for polarity, like a text's words, their parts-of-speech, and negation or amplification of the conveyed sentiment. This dissertation argues that the polarity of text can be analysed more accurately when additionally accounting for semantics and structure. Polarity classification performance can benefit from exploiting the interactions that emoticons have on a semantic level with words – emoticons can express, stress, or disambiguate sentiment. Furthermore, semantic relations between and within languages can help identify meaningful cues for sentiment in multi-lingual polarity classification. An even better understanding of a text's conveyed sentiment can be obtained by guiding automated sentiment analysis by the rhetorical structure of the text, or at least of its most sentiment-carrying segments. Thus, the sentiment in, e.g., conclusions can be treated differently from the sentiment in background information. The findings of this dissertation suggest that the polarity of natural language text should not be determined solely based on what is said. Instead, one should account for how this message is conveyed as well

Sentiment Analysis of Text Guided by Semantics and Structure

As moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many stakeholders, like consumers. One challenge for such automated sentiment analysis systems is to identify whether pieces of natural language text are positive or negative. Typical methods of identifying this polarity involve low-level linguistic analysis. Existing systems predominantly use morphological, lexical, and syntactic cues for polarity, like a text's words, their parts-of-speech, and negation or amplification of the conveyed sentiment. This dissertation argues that the polarity of text can be analysed more accurately when additionally accounting for semantics and structure. Polarity classification performance can benefit from exploiting the interactions that emoticons have on a semantic level with words – emoticons can express, stress, or disambiguate sentiment. Furthermore, semantic relations between and within languages can help identify meaningful cues for sentiment in multi-lingual polarity classification. An even better understanding of a text's conveyed sentiment can be obtained by guiding automated sentiment analysis by the rhetorical structure of the text, or at least of its most sentiment-carrying segments. Thus, the sentiment in, e.g., conclusions can be treated differently from the sentiment in background information. The findings of this dissertation suggest that the polarity of natural language text should not be determined solely based on what is said. Instead, one should account for how this message is conveyed as well

Safety in Design: Human factors influencing safe design of a new technology

The aim of this study was to explore the role of safety in the design of new technologies in a high-risk industry. For this study 7 interviews were conducted with designers and developers who worked together as a team to develop a new technology for the oil and gas industry. The data analysis was done using grounded theory based on the ideas of Strauss and Corbin (1990). The analysis showed that the interaction between humans and the technology is critical to the safety of the design. Therefore it is not possible to base safe design solely on technical features; latent failures are not just technical. A more holistic approach to safety is necessary when judging the safety of design, and the designer’s understanding of the competencies of the end-users is central to this