Abstract. Classical theory of binary homogeneous nucleation is extended to the ternary system H2SO4-NH3-H20. For NH3 mixing ratios exceeding about 1 ppt, the presence of NH3 enhances the binary H2SO4-H20 nucleation rate by several orders of magnitude. The Gibbs free energies of formation of the critical H2SO4-NH3-H20 cluster, as calculated by two independent approaches, are in substantial agreement. The finding that the H2SO4-NH3-H20 ternary nucleation rate is independent of relative humidity over a large range of H2SO4 concentrations has wide atmospheric consequences. The limiting component for ternary H2SO4-NH3-H20 nucleation is, as in the binary H2SO4-H20 case, H2SO4; however, the H2SO4 concentration needed to achieve significant nucleation rates is several orders of magnitude below that required in the binary case.

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In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point consisting of desirable aspiration levels for objective functions. The information is used in an evolutionary algorithm to generate a new population by combining the fitness function and an achievement scalarizing function. In multi-objective optimization, achievement scalarizing functions are widely used to project a given reference point into the Pareto optimal set. In our approach, the next population is thus more concentrated in the area where more preferred alternatives are assumed to lie and the whole Pareto optimal set does not have to be generated with equal accuracy. The approach is demonstrated by numerical examples.

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The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. Recent research has developed sophisticated financial polarity lexicons for investigating how financial sentiments relate to future company performance. However, based on experience from fields that commonly analyze sentiment, it is well known that the overall semantic orientation of a sentence may differ from that of individual words. This article investigates how semantic orientations can be better detected in financial and economic news by accommodating the overall phrasestructure information and domain-specific use of language. Our three main contributions are the following: (a) a human-annotated finance phrase bank that can be used for training and evaluating alternative models; (b) a technique to enhance financial lexicons with attributes that help to identify expected direction of events that affect sentiment; and (c) a linearized phrase-structure model for detecting contextual semantic orientations in economic texts. The relevance of the newly added lexicon features and the benefit of using the proposed learning algorithm are demonstrated in a comparative study against general sentiment models as well as the popular word frequency models used in recent financial studies. The proposed framework is parsimonious and avoids the explosion in feature space caused by the use of conventional n-gram features.