The effect of laryngeal mask cuff pressure on postoperative sore throat incidence

OBJECTIVE: To determine whether a significant association occurs between the presence of various periodontal diseases and recoverable infectious HIV-I in the saliva of injecting drug users. DESIGN: Five hundred and fifty-one injecting drug users were recruited from various programs associated with the Beth Israel Medical Center. Examiners were 'blinded' to the subject's HIV-I serostatus. A socio-economic and risk factors' survey was conducted and a complete oral examination, including periodontal disease indices was performed. Whole saliva and blood were collected for virus culture. MAIN OUTCOME MEASUREMENTS: Recovery of infectious HIV-I in saliva related to presence of periodontal diseases. RESULTS: Those HIV-I seropositive subjects with periodontal diseases did not differ from those HIV-I seropositive subjects without periodontal disease in mean age and immune status. Less than 1% of the HIV-I seropositive subjects had cultivable HIV-I in their saliva while it was present in 78% of PBMCs and 35% of the sera. There was no significant association between infectious HIV-I in saliva, serum, or PBMCs and any of the various periodontal diseases. CONCLUSIONS: The presence of periodontal disease in HIV-I seropositive injecting drug users does not appear to be a potential risk factor for infectious HIV-I in saliva, probably due to the various anti-viral components of saliva.     

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.     

Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization

In this paper, we present a vision-based approach to mobile robot localization that integrates an image-retrieval system with Monte Carlo localization. The image-retrieval process is based on features that are invariant with respect to image translations and limited scale. Since it furthermore uses local features, the system is robust against distortion and occlusions, which is especially important in populated environments. To integrate this approach with the sample-based Monte Carlo localization technique, we extract for each image in the database a set of possible viewpoints using a two-dimensional map of the environment. Our technique has been implemented and tested extensively. We present practical experiments illustrating that our approach is able to globally localize a mobile robot, to reliably keep track of the robot's position, and to recover from localization failures. We furthermore present experiments designed to analyze the reliability and robustness of our approach with respect to larger errors in the odometry.     

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.     

Remote sensing of ammonia and sulfur dioxide from on-road light duty vehicles

This study reports the largest data set of on-road, fuel-based mass emissions of ammonia and sulfur dioxide from vehicles of known make, model year, and fuel type. Ammonia is the first pollutant observed for which the emissions decrease with increasing fleet age from 10 to 20 years. The fixed nitrogen emission ratio is 15.0% by mass and 24.7% by mole, larger than current models predict. Diesel fueled vehicles emit more SO2 than gasoline, and unexpectedly, gasoline SO2 emissions decrease continuously with newer model year vehicles.     

Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RBPF for learning grid maps. We propose an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation. This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations, which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor, as well as outdoor, environments illustrate the advantages of our methods over previous approaches