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Polythiophene-based organic transistors are excellent candidates for gas sensor arrays in electronic nose applications, but there has not yet been a thorough understanding of the relevant factors that contribute to the sensing response. Experimental and theoretical investigation of polythiophenes show that relevant factors include: the functional group of the polythiophene side-chain, the length of the side chain, the functional group of the analyte, and the length of the analyte. Side-chains on the polythiophene can enhance discrimination of the analyte, while analyte functional groups are largely responsible in determining the sensitivity of the sensor response. Theoretical density functional theory (DFT) calculations show that the analyte polythiophene interaction is energetically favorable with energetic differences between different analytes.

Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls.

Polythiophene-based organic transistors are excellent candidates for gas sensor arrays in electronic nose applications, but there has not yet been a thorough understanding of the relevant factors that contribute to the sensing response. Experimental and theoretical investigation of polythiophenes show that relevant factors include: the functional group of the polythiophene side-chain, the length of the side chain, the functional group of the analyte, and the length of the analyte. Side-chains on the polythiophene can enhance discrimination of the analyte, while analyte functional groups are largely responsible in determining the sensitivity of the sensor response. Theoretical density functional theory (DFT) calculations show that the analyte polythiophene interaction is energetically favorable with energetic differences between different analytes.

Large-scale continuous media (CM) system implementations require scalable servers most likely built from clusters of storage nodes. Across such nodes, random data placement is an attractive alternative to the traditional round-robin striping. One benefit of random placement is that additional nodes can be added with low data-redistribution overhead such that the system remains load balanced. One of the challenges in this environment is the implementation of a retransmission-based error control (RBEC) technique. Because data is randomly placed, a client may not know which server node to ask for a lost packet retransmission. We design and implement three RBEC techniques that utilize the benefits of random data placement in a cluster server environment while enabling a client to efficiently identify the correct server node for lost packet requests. We implement and evaluate our techniques with a one-, two-, four-, and eight-way server cluster and across local and wide-area networks. Our results show the feasibility and effectiveness of our approaches in a real-world environment and also identify one solution as generally superior to the other two. © 2005 SPIE and IS&T.