Abstract

Abstract
In this article we present a system capable of graphically representing the interactions between students and teachers in hierarchical online forums. By defining the 'reply-to' relation between the users the system builds a graph. During forum posts mining, the system computes metrics taken from social network analysis which are then applied to the graph drawing process. This system brings up new possibilities to e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation of students; to identify key students in information passing, and to find the implicit relations between forums participants. Preliminary tests lead to the conclusions that the system is able to rapidly help in identifying situations like outliers, sources and sinks of information. It also depicts rapidly sub communities formed from forum participants. © 2012 IEEE.

Abstract
In recent years, the FlexRay communication protocol been promoted as a de facto standard for in-vehicular communication. In the FlexRay protocol, the communication timeline is organized as a sequence of four segments, whereas the static segment defines a set of slots specifically designed for the transmission of synchronous messages. In this paper, we investigate the following problem: "how to allocate a minimum number of static slots to each FlexRay node, while guaranteeing that all synchronous messages will be transmitted before their deadlines." Unlike previous studies that use linear programming based techniques, we evaluate the number of allocated slots using traditional response time analysis (RTA) techniques. The use of RTA techniques allow us to consider the timing requirements imposed by the set of synchronous message streams. Unlike other approaches, the RTA-based technique proposed in this paper is able to deal with a) message stream sets where periods are not multiple of the FlexRay cycle duration, b) generation of messages at the application layer that are not synchronized with the FlexRay cycle, and c) on-line scheduling of the traffic transferred during the static segment, according to its deadline requirements. The proposed technique is integrated as a RT-Middleware in each node, to take full advantage of the proposed allocation scheme. © 2012 IEEE.

Abstract
The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (spline-based) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles.

Abstract

Abstract