Microbial Air Contamination in an Intensive Care Unit

Unit layout affects every aspect of intensive care services, including patient safety. A previous study has shown that patients admitted to beds adjacent to the sink and to the door of a large bayroom had the highest number of positive blood cultures and the highest blood culture incidence density, respectively. The present study measures microbial air contamination in a medical intensive care unit of a medical center in central Taiwan. Of the 17 rooms, 8 rooms with distinct physical environmental characteristics were selected. Sampling tests were conducted between December 2013 and February 2014 with a microbial air sampler (MAS-100NT). TSA was used for bacteria collection and DG18 for fungi collection. The overall average bacterial and fungal concentrations were 83CFU/m3 and 69CFU/m3, respectively. The ranges were between 8-354 CFU/m3 and 0-1468 CFU/m3, respectively. A significant difference was found in the bacterial concentration (p=.005) between different room locations. The highest concentration was found in the rooms located at the front end of the circulation (99 CFU/m3), while the lowest was found in the rooms located at the rear end of the circulation (55CFU/m3). Differences in fungal concentrations for different room locations did not reach statistical significance. In addition, differences in bacterial and fungal concentrations for rooms with different sink locations did not reach statistical significance. Even though the microbial concentrations generally complied with standards, the results may help designers and hospital administrators develop a healthier environment for patients

Design and Estimation of an AUV Portable Intelligent Rescue System Based on Attitude Recognition Algorithm

This research is based on the attitude sensing algorithm to design a portable intelligent rescue system for autonomous underwater vehicles (AUVs). To lower the possibility of losing the underwater vehicle and reduce the difficulty of rescuing, when an AUV intelligent rescue system (AIRS) detects the fault of AUVs and they could not be reclaimed, AIRS can pump carbon dioxide into the airbag immediately to make the vehicle resurface. AIRS consists of attitude sensing module, double-trigger inflator mechanism, and activity recognition algorithm. The sensing module is an eleven-DOF sensor that is made up of a six-axis inertial sensor, a three-axis magnetometer, a barometer, and a thermometer. Furthermore, the signal calibration and extended Kalman filter (SC-EKF) is proposed to be used subsequently to calibrate and fuse the data from the sensing module. Then, the attitude data are classified with the principle of feature extraction (FE) and backpropagation network (BPN) classifier. Finally, the designed double-trigger inflator can be triggered not only by electricity but also by water damage when the waterproof cabin is severely broken. With the AIRS technology, the safety of detecting and investigating the use AUVs can be increased since there is no need to send divers to engage in the rescue mission under water

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

Microbial Air Contamination in an Intensive Care Unit

Unit layout affects every aspect of intensive care services, including patient safety. A previous study has shown that patients admitted to beds adjacent to the sink and to the door of a large bayroom had the highest number of positive blood cultures and the highest blood culture incidence density, respectively. The present study measures microbial air contamination in a medical intensive care unit of a medical center in central Taiwan. Of the 17 rooms, 8 rooms with distinct physical environmental characteristics were selected. Sampling tests were conducted between December 2013 and February 2014 with a microbial air sampler (MAS-100NT). TSA was used for bacteria collection and DG18 for fungi collection. The overall average bacterial and fungal concentrations were 83CFU/m3 and 69CFU/m3, respectively. The ranges were between 8-354 CFU/m3 and 0-1468 CFU/m3, respectively. A significant difference was found in the bacterial concentration (p=.005) between different room locations. The highest concentration was found in the rooms located at the front end of the circulation (99 CFU/m3), while the lowest was found in the rooms located at the rear end of the circulation (55CFU/m3). Differences in fungal concentrations for different room locations did not reach statistical significance. In addition, differences in bacterial and fungal concentrations for rooms with different sink locations did not reach statistical significance. Even though the microbial concentrations generally complied with standards, the results may help designers and hospital administrators develop a healthier environment for patients