Abstract
The European Space Research and Technology Center ESTEC, ESA's premises in Noordwijk, The Netherlands, has a long lasting cooperation with the ZARM-FAB (Centre of Applied Space Technology and Microgravity-Drop Tower Operation and Service Company) in Bremen on the utilization of the Drop Tower for ground-based microgravity research and space hardware development studies. During the period January 2000 to December 2011 ESA will have procured in total some 840 drops addressing a variety of scientific and technological disciplines. The experiments are usually carried out in campaigns of 15 to 20 drops each, with an annual average of about 5 campaigns. The cooperation agreement between ESA and the ZARM-FAB includes experiment preparation advice by ZARM's experts, the integration of the hardware into the drop capsule, dedicated safety reviews, the execution of the drop or catapult experiments, the post-flight payload de-integration as well as the handover of acquired data to the experimenters. The experiment hardware itself is provided by the scientists or has to be procured from sources outside of ESA's drop tower utilization contract. ESA appreciates the cooperation of the ZARM-FAB in Bremen whose drop-and catapult facility provides excellent microgravity quality, is operated by a highly competent, flexible and extremely supportive expert team, allows campaign integration at relatively short notice throughout the entire year, offers real-time experiment operations and immediately after each drop delivers experiment results and provides on-site hardware modification possibilities.

Abstract

Abstract
In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation operators, showing experimentally that it is effective in solving a set of benchmark problems.

Abstract
This paper presents a multistage transmission expansion planning (TEP) which alleviates the level of transmission congestion during the planning horizon. Due to the combinatorial nature of the proposed TEP model, a Benders decomposition approach intertwined with a manipulated disjunctive model is applied to decompose the original TEP problem into a master problem and two sub-problems standing for system security and optimal operation. The security sub-problem applying the N-1 contingency criterion to assess the transmission system security. Besides, the optimal operation sub-problem makes it possible for the proposed TEP model to fulfill the optimal operation as well as to calculate the difference between system lacational marginal prices (LMPs) for all buses. Also, this model appends a specific term into the TEP objective function as the congestion level (CL) reflecting the degree of market competitiveness. Therefore, the proposed TEP model minimizes the total costs comprising the investment cost of candidate transmission lines as well as the level of transmission congestion. The proposed approach is applied to the northeastern area of the Iranian power grid. © 2011 IEEE.

Abstract

Abstract
Wildfires can importantly affect the ecology and economy of large regions of the world. Effective prevention techniques are fundamental to mitigate their consequences. The design of such preemptive methods requires a deep understanding of the factors that increase the risk of fire, particularly when we can intervene on these factors. This is the case for the maintenance of ecological balances in the landscape that minimize the occurrence of wildfires. We use an inductive logic programming approach over detailed spatial datasets: one describing the landscape mosaic and characterizing it in terms of its use; and another describing polygonal areas where wildfires took place over several years. Our inductive process operates over a logic term representation of vectorial geographic data and uses spatial predicates to explore the search space, leveraging the framework of Spatial-Yap, its multi-dimensional indexing and tabling extensions. We show that the coupling of a logic-based spatial database with an inductive logic programming engine provides an elegant and powerful approach to spatial data mining.