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We studied classification of traumatic episodes by a large sample of service users and professionals. Participants were asked to classify the most severe injury they had sustained. They had previously participated in a randomized controlled trial of triage to secondary care. Three trained raters classified each episode. The underlying hypothesis that severe injuries had the most precise classification was supported. The degree to which two ratings of a class were concordant was strongly related to the frequency of responses. The number of responses for each injured body part was significantly higher than that expected by random guessing for 20 of the body parts, most of which have a protective function. No overall pattern of class frequencies was found when professional classifications were compared with those of service users. 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In addition to Notepad's .1. Technical Field The invention concerns data storage and retrieval in a wireless sensor network. More particularly, it concerns the distribution of storage in a wireless sensor network to minimize energy consumption and control read operations by minimizing low frequency information in response to queries. 2. Background Art Sensors are the new frontier for big data. While the Internet of Things (IoT) is a set of sensor technology to constantly and reliably collect data from physical objects, mobile sensor networks are a subset of wireless sensor networks (WSN) which have sensors deployed in the field. While many mobile sensor networks deploy sensors in mobile or static, networked devices, mobile sensor networks are typically deployed in the context of consumer or industrial devices such as automobiles, home/building infrastructure, or personal health-monitoring devices. Existing mobile sensor networks use RF (radio frequency) communications to efficiently share information and minimize the number of messages exchanged within the network. RF communications result in significant power consumption of devices in the network. Many mobile sensor networks are currently deployed in remote areas or are battery powered and thus, require minimal energy use. However, RF communications are expensive in terms of communication capacity and area and thus, reduce the network lifetime as more devices are deployed. It would be desirable to expand the network lifetime of a mobile sensor network by reducing unnecessary network traffic through reducing the overall level of communication within the network. Some mobile sensor networks rely on power lines to transfer information to a central processor. When coupled to a power line, a node in the mobile sensor network is simply plugged into a wall socket or other current source and utilizes the existing power line to communicate data. One example of the use of power lines for communications is the data-collection system used to monitor water and gas pipes in Canada, which employs seventeen nodes for each power line extending to a manhole. The nodes utilize ultrasonic signals on the power line to exchange information. Another example is the Data Transformer for Transmission of Data 1cdb36666d
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