Fire as a fundamental ecological process: Research advances and frontiers

Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire‐dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above‐ground ecology, (d) fire effects on below‐ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. Synthesis : As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives

Learning Semantic Query Suggestions

Abstract. An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download.

Preconcentration of some trace elements via using multiwalled carbon nanotubes as solid phase extraction adsorbent

in present study preconcentration followed by solid phase extraction of heavy metal ions, Cu(II), Co(B), Ni(II) and Pb(II) using a multiwalled carbon nanotubes (MWNTs) and complexing reagent o-cresolphthalein complexone were investigated. The effects of parameters, including pH of the solutions, amounts of complexing reagent, eluent type, sample volume, flow rates of solution, and matrix ions, were examined for the optimum recoveries of the analyte ions. The preconcentration factor was 40. Detection limit (3s) obtained for the investigated metals in the optimal conditions were observed in the range of 1.64-5.68 mu g l(-1). The validation of the presented method was obtained by the analysis of certified reference material HR 1 (Humber river sediment), the obtained results were agreed with certified values. The optimum experimental conditions that ensure the efficiency of the procedure have been investigated and have been successfully applied to the determination of trace elements in environmental samples with satisfactory results. (C) 2009 Elsevier B.V. All rights reserved