Abstract
This paper presents a framework for a robotic production line simulation learning environment using Autonomous Ground Vehicles (AGV). An eLearning platform is used as interface with the simulator. The objective is to introduce students to the production robotics area using a familiar tool, an eLearning platform, and a framework that simulates a production line using AGVs. This framework allows students to learn about robotics but also about several areas of industrial management engineering without requiring an extensive prior knowledge on the robotics area. The robotic production line simulation learning environment simulates a production environment using AGVs to transport materials to and from the production line. The simulator allows students to validate the AGV dynamics and provides information about the whole materials supplying system which includes: supply times, route optimization and inventory management. The students are required to address several topics such as: sensors, actuators, controllers and an high level management and optimization software. This simulator was developed with a known open source tool from robotics community: Player/Stage. This tool was extended with several add-ons so that students can be able to interact with a complex simulation environment. These add-ons include an abstraction communication layer that performs events provided by the database server which is programmed by the students. An eLearning platform is used as interface between the students and the simulator. The students can visualize the effects of their instructions/programming in the simulator that they can access via the eLearning platform. The proposed framework aims to allow students from different backgrounds to fully experience robotics in practice by suppressing the huge gap between theory and practice that exists in robotics. Using an eLearning platform eliminates installation problems that can occur from different computers software distribution and makes the simulator accessible by all students at school and at home.

Abstract
In this paper, long (>40 years) hourly tide gauge records from the North Atlantic are analyzed. A new time series clustering approach which combines Bayesian methodology, extreme value theory, and classification techniques is adopted for the analysis of the regional variability of sea-level extremes. The tide gauge records are clustered on the basis of their corresponding predictive distributions for 25-, 50-, and 100-year return values. The results of the cluster analysis show a clear distinction between the higher latitude stations for which the return values are largest and the remaining locations. This distinction reflects in the U.S. east coast the transition between the Scottian shelf and Gulf of Maine area and the mid-Atlantic Bight area. For the stations at lower latitudes the results show a grouping based on return levels that is not a function of geographical proximity but reflects local effects in extreme sea levels associated with the specific location of each tide gauge.

Abstract
Radon is a naturally occurring radioactive noble gas generated within mineral grains of uranium bearing rocks by alpha decay from radium. The Amram tunnel (A. Bloch Geophysical Observatory) is a particularly suitable location for the investigation of radon variability. Located in the arid environment of the Arava desert, near Elat, the 170 m tunnel that constitutes the observatory enables radon monitoring in a desert environment and under fairly stable environmental conditions. The analysis of the temporal variability of continuous measurements of radon and environmental parameters at the Amram tunnel over a period of several years shows a complex temporal pattern characterized by non-stationary and multiscale features. Radon concentrations exhibit multiyear variability in the form of a increasing trend of similar to 1000 Bq m(-3) yr(-1) in the mean and much larger trends up to similar to 2500 Bq m(-3) yr(-1) in the maximum radon levels. Radon concentrations also display strong seasonal patterns, with maxima in summer and minima in winter, ranging from 2.5 kBq m(-3) in winter to 35 kBq m(-3) in summer. Intraseasonal variability is characterized by very large radon anomalies, with sharp increases of more than 20 kBq m(-3) relative to the base level, that occur in spring and summer and last for several days. Daily periodic variability with maxima around midnight appears also in spring and summer, being absent in the cold months. Radon variability at seasonal, intraseasonal and daily timescales is associated with the air temperature outside the tunnel, specifically the temperature gradient between the external environment and the more stable environment inside the tunnel where the measurements are performed.

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Abstract

Abstract
We propose and theoretically study a novel surface-plasmon-resonance sensor based on an H-shaped, elliptical-core optical fibre. The two grooves of the H-fibre are coated with a thin, uniform metal layer that in turn is covered with a high-index dielectric layer to allow broad spectral tunability. The sensor maintains linear polarization and facilitates effortless splicing. Electromagnetic mode analysis indicates a sensitivity of 1800 nm/RIU (refractive-index unit) for aqueous analytes.