Abstract
Electricity markets based on simple bids provide a very high degree of transparency and simplicity. However, simple bids fail to capture many well-known characteristics of generating units and, therefore, the responsibility for obtaining feasible schedules is transferred to market participants. The purpose of this paper is to help the generating utility to automatically analyze the last energy program cleared in the market and, in case this program is technically unfeasible, to provide an alternative schedule by redispatching the generating units. This is achieved by formulating an optimization problem where the objective is to find the cheapest and feasible instantaneous power trajectory of each generator, trying to minimize the differences between its hourly average values and the last energy program. As the objectives of the utility can vary during the day, three different models are presented. Two of them are formulated as a joint energy and reserve dispatch in order to take into account possible commitments acquired in the ancillary services market of AGC regulation. In this sense, a novel approach for considering discontinuous ancillary regulation curves is proposed. Some numerical examples are included to illustrate the essential features of the models.

Abstract
High time resolution monitoring of radon (= (222) Rn) in three boreholes, 4, 10 and 53 m deep, along a 0.6 km transect is carried out in massive granite in southern Israel. Three components of variation occur in the measured signal (MS) -seasonal radon (SR -periodic), multiday (MD), and daily radon (DR -periodic). Temporal variation of the components suggests an association between the overall level of the long-term variation and the amplitude of the daily variation. The daily mean level of radon and the daily standard deviation vary periodically throughout the year. Time offsets occur among time series of the MS and were investigated also for the MD and DR components, using consecutive 20-day intervals spanning + 900 days. The resulting time series show that systematic time offsets occur, whereby the radon signal always occurs first at the easternmost site. The MD shows a gradually varying lag of 0 -12 h, and the DR a stable 1 -3 h lag. Spectral analysis shows that diurnal (24-h) and semidiurnal (12-h) periodic components characterize the DR. The amplitudes of these components exhibit regular temporal variation having a seasonal pattern. The ratios of co-occurring amplitudes of these components define a linear pattern indicating a fundamental statistical property in the frequency domain of the radon time series. The results indicate that unrecognized dynamic processes are driving the radon signal in the subsurface regime of the pluton, suggesting new prospects for radon behavior in the frame of interacting geodynamic (tectonic?) and Earth-Sun system related processes.

Abstract
The design and testing of a "dry" active electrode for electroencephalographic recording is described. A comparative study between the EEG signals recorded in human volunteers simultaneously with the classical Ag-AgCl and "dry" active electrodes was carried out and the reported preliminary results are consistent with a better performance of these devices over the conventional Ag-AgCl electrodes.

Abstract
The integration of different robot automation technologies, with the aim for reusing available production solutions, is a major obstacle for deployment of low-cost components into productive (high-performance) systems. Technologies demanding high processing power, like machine vision or voice recognition systems, are normally easy to program but require proprietary languages and platforms, which constitutes an important problem during communications and setup. Instead of the current need for trained specialist, in particular flexible manufacturing in SMEs call for solutions that are easy easy to use and (re)configure. One attempt in that direction is the service-oriented architecture (SOA) approach, which here is accomplished by the use of Universal Plug-and-Play (UPnP) technologies and confronted with real robot application demand represented by an experimental manufacturing cell. Contributions include the way of building software applications to program manufacturing cells whose building blocks are represented by UPnP devices. Such devices encapsulate both manufacturing equipment and interaction methods. The latter is exemplified by a speech recognition system, for which a tool for automatic generation of UPnP devices based on the information contained in speech recognition XML grammars is presented. Experiences form experiments confirms the desired efficiency and simplicity when setting up advanced manufacturing equipment. © 2007 IFAC.

Abstract
A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts. In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic. To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task. As a proof-of-concept, the extended system was used to analyse an artificial dataset of a credit scoring problem with eighty million records.

Abstract