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Measuring coalignment of retroreflectors with large lateral incoming-outgoing beam offset

A method based on phase-shifting Fizeau interferometry is presented with which retroreflectors with large incoming-outgoing beam separations can be tested. The method relies on a flat Reference Bar that is used to align two auxiliary mirrors parallel to each other to extend the aperture of the interferometer. The method is applied to measure the beam coalignment of a prototype Triple Mirror Assembly of the GRACE Follow-On Laser Ranging Interferometer, a future satellite-to-satellite tracking device for Earth gravimetry. The Triple Mirror Assembly features a lateral beam offset of incoming and outgoing beam of 600 mm, whereas the acceptance angle for the incoming beam is only about ±2 mrad. With the developed method, the beam coalignment of the prototype Triple Mirror Assembly was measured to be 9 μrad with a repeatability of below 1 μrad

The Gradient Free Directed Search Method as Local Search within Multi-objective Evolutionary Algorithms

Recently, the Directed Search Method has been proposed as a point-wise iterative search procedure that allows to steer the search, in any direction given in objective space, of a multi-objective optimization problem. While the original version requires the objectives’ gradients, we consider here a possible modification that allows to realize the method without gradient information. This makes the novel algorithm in particular interesting for hybridization with set oriented search procedures, such as multi-objective evolutionary algorithms. In this paper, we propose the DDS, a gradient free Directed Search method, and make a first attempt to demonstrate its benefit, as a local search procedure within a memetic strategy, by integrating the DDS into the well-known algorithmMOEA/D. Numerical results on some benchmark models indicate the advantage of the resulting hybrid

Discovering web services to specify more complete system requirements

Service-centric systems pose new challenges and opportunities for requirements processes and techniques. This paper reports new techniques developed by the EU-funded SeCSE Integrated Project that enable service discovery during early requirements processes and exploit discovered services to enhance requirements specifications. The paper describes the algorithm for discovering services from requirements expressed using structured natural language, and demonstrates it using an automotive example. The paper also reports a first evaluation of the utility of the environment that implements this algorithm when improving the specification of requirements with retrieved services

A study on using genetic niching for query optimisation in document retrieval

International audienceThis paper presents a new genetic approach for query optimisation in document retrieval. The main contribution of the paper is to show the effectiveness of the genetic niching technique to reach multiple relevant regions of the document space. Moreover, suitable merging procedures have been proposed in order to improve the retrieval evaluation. Experimental results obtained using a TREC sub-collection indicate that the proposed approach is promising for applications

Kinetics of the urea–urease clock reaction with urease immobilized in hydrogel beads

Feedback driven by enzyme catalyzed reactions occurs widely in biology and has been well characterized in single celled organisms such as yeast. There are still few examples of robust enzyme oscillators in vitro that might be used to study nonlinear dynamical behavior. One of the simplest is the urea–urease reaction that displays autocatalysis driven by the increase in pH accompanying the production of ammonia. A clock reaction was obtained from low to high pH in batch reactor and bistability and oscillations were reported in a continuous flow rector. However, the oscillations were found to be irreproducible and one contributing factor may be the lack of stability of the enzyme in solution at room temperature. Here, we investigated the effect of immobilizing urease in thiol-poly(ethylene glycol) acrylate (PEGDA) hydrogel beads, prepared using emulsion polymerization, on the urea–urease reaction. The resultant mm-sized beads were found to reproduce the pH clock and, under the conditions employed here, the stability of the enzyme was increased from hours to days

Impact of soil properties on critical concentrations of cadmium, lead, copper, zinc and mercury in soil and soil solution in view of ecotoxicological effects

Concern about the input of metals to terrestrial ecosystems is related to (i) the ecotoxicological impact on soil organisms and plants (Bringmark et al. 1998; Palmborg et al. 1998) and also on aquatic organisms resulting from runoff to surface water and (ii) the uptake via food chains into animal tissues and products, which may result in health effects on animals and humans (Clark 1989). Effects on soil organisms, including microorganisms/macrofungi and soil fauna, such as nematodes and earthworms, are reduced species diversity, abundance, and biomass and changes in microbe-mediated processes (Bengtsson and Tranvik 1989; Giller et al. 1998; Vig et al. 2003). Effects on vascular plants include reduced development and growth of roots and shoots, elevated concentrations of starch and total sugar, decreased nutrient contents in foliar tissues, and decreased enzymatic activity (Prasad 1995; Das et al. 1997). A review of these phytotoxic effects is given by Balsberg-Påhlsson (1989). Effects on aquatic organisms, including algae, Crustacea, and fish, include effects on gill function (Sola et al. 1995), nervous systems (Baatrup 1991), and growth and reproduction rates (Mance 1987). Environmental quality standards or critical limits, often also denoted as Predicted No Effect Concentrations, or PNECs, for metals in soils and surface waters related to those effects serve as a guide in the environmental risk assessment process for those substances

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey