Moxifloxacin sensitivity of respiratory pathogens in the United Kingdom

The in vitro activity of moxifloxacin and comparator agents against respiratory isolates from a range of geographically distinct centres around the United Kingdom was investigated in the following study. Clinical isolates of Streptococcus pneumoniae (n = 257), Haemophilus influenzae (n = 399) and Moraxella catarrhalis (n = 253) were obtained between March 1998 and April 1999 from nine centres in the United Kingdom. Sensitivity was determined by testing each isolate for its minimum inhibitory concentration (MIC) by agar dilution. Against Streptococcus pneumoniae moxifloxacin and grepafloxacin were the most active (MIC90 = 0.25 mg/l). Trovafloxacin and sparfloxacin were the next most active (MIC90 = 0.5 mg/l) followed by levofloxacin and ciprofloxacin. MIC90 values of the six fluoroquinolones versus H. influenzae ranged from ciprofloxacin > levofloxacin. Against M. catarrhalis the lowest MIC90 was that of grepafloxacin at 0.0625 mg/l followed by moxifloxacin, sparfloxacin, levofloxacin and ciprofloxacin. Trovafloxacin demonstrated the highest MIC90 at 0.5 mg/l. These results demonstrate that moxifloxacin has superior in vitro activity against respiratory tract pathogens than any other comparator quinolones available for clinical use.

Massive MIMO Systems with Hardware-Constrained Base Stations

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas. Such large antenna arrays offer huge spatial degrees-of-freedom for transmission optimization; in particular, great signal gains, resilience to imperfect channel knowledge, and small inter-user interference are all achievable without extensive inter-cell coordination. The key to cost-efficient deployment of large arrays is the use of hardware-constrained base stations with low-cost antenna elements, as compared to today's expensive and power-hungry BSs. Low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the excessive degrees-of-freedom of massive MIMO would bring robustness to such imperfections. We herein prove this claim for an uplink channel with multiplicative phase-drift, additive distortion noise, and noise amplification. Specifically, we derive a closed-form scaling law that shows how fast the imperfections increase with the number of antennas.

Tangible applications for regular objects:An end-user model for pervasive computing at home

This paper describes an end-user model for a domestic pervasive computing platform formed by regular home objects. The platform does not rely on pre-planned infrastructure; instead, it exploits objects that are already available in the home and exposes their joint sensing, actuating and computing capabilities to home automation applications. We advocate an incremental process of the platform formation and introduce tangible, object-like artifacts for representing important platform functions. One of those artifacts, the application pill, is a tiny object with a minimal user interface, used to carry the application, as well as to start and stop its execution and provide hints about its operational status. We also emphasize streamlining the user's interaction with the platform. The user engages any UI-capable object of his choice to configure applications, while applications issue notifications and alerts exploiting whichever available objects can be used for that purpose. Finally, the paper briefly describes an actual implementation of the presented end-user model. © (2010) by International Academy, Research, and Industry Association (IARIA).

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.