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The composition and physiochemical properties of aquatic-phase natural organic matter (NOM) are most important problems for both environmental studies and water industry. Laser desorption/ionization (LDI) mass spectrometry facilitated successful examinations of NOM, as humic and fulvic acids in NOM are readily ionized by the nitrogen laser. In this study, hydrophobic NOMs (HPO NOMs) from river, reservoir and waste water were characterized by this technique. The effect of analytical variables like concentration, solvent composition and laser energy was investigated. The exact masses of small molecular NOM moieties in the range of 200–1200 m/z were determined in reflectron mode. In addition, spectra of post-source-decay experiments in this range showed that some compounds from different natural NOMs had the same fragmental ions. In the large mass range of 1200–15 000 Da, macromolecules and their aggregates were found in HPO NOMs from natural waters. Highly humic HPO exhibited mass peaks larger than 8000 Da. On the other hand, the waste water and reservoir water mainly had relatively smaller molecules of about 2000 Da. The LDI-MS measurements indicated that highly humic river waters were able to form large aggregates and membrane foulants, while the HPO NOMs from waste water and reservoir water were unlikely to form large aggregates. Copyright © 2014 John Wiley & Sons, Ltd.
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.