The changing dynamic of crisis management suggests that we should be leveraging social media and accessible geotagged text data to assist with making emergency evacuations more eective and increasing the eciency of emergency rst responders. This paper presents a preliminary visualization tool for automatically clustering geotagged text data, and visualizing such data contextually, graphically, and geographically. Such a tool could be used to allow emergency management personnel to quickly assess the scope and location of a current crisis, and to quickly summarize the state of aairs. Discussion herein includes details about the clustering algorithm, design and implementation of the visualization, and ideas for improving the utility for use in a variety of circumstances. 1

This article addresses the problem of detecting crisisrelated messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine-learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis-á-vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real-world data set to assess its practical value.

Military command and control (C2) organizations are complex socio-technical systems which must constantly adapt to meet changing operational requirements. We describe our experiences in developing a work-centred support system (WCSS) to aid weather forecasting and monitoring in a military airlift C2 organization as an illustrative case. As part of the development process we conducted field observations both before and after introduction of the WCSS in their operations centre. A striking finding was the constant changes that operations personnel faced (changes in goals and priorities, changes in scale of operations, changes in team roles and structure, and changes in information sources and systems). We describe the changes in workplace demands that we observed and the modifications we needed to make to the WCSS in response. For today's fielded systems, it is seldom possible to make changes that are responsive to users' changing requirements in a timely manner. We argue for the need to incorporate facilities that enable users to adapt their systems to the changing requirements of work and point to some promising directions towards evolvable work-centred support systems.

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In this contribution two planar micros trip band-pass filters based on closed loop resonators with reconfigurable center frequency are proposed at K-band. Two geometries, a square ring and a meander loop, are closely investigated. The frequency reconfigurability is achieved by means of PIN diodes. The filters are designed for and fabricated on a standard printed circuit board technology. Dual-mode resonators are used to obtain a compact design suitable for integration into an active antenna frontend. The paper presents the filter design, the tuning range, an analysis of the occurring losses, and the verification through simulation and measurement.