Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

On the Design of Prefetching Strategies in a Peer-Driven Video on-Demand System

Abstract: In this paper, we examine the prefetching strategies in a peer-driven video on-demand system. In our design, each video is encoded into multiple low bit-rate substreams and copies of the substreams are distributed to the participating peers. When a peer streams in a substream of rate r, it instead streams at rate rcirc, where r>rcirc. In this manner, if one of the peer's suppliers disconnects, the client peer can tap the reservoir of prefetched bits while searching for a replacement server, thereby avoiding any glitches or reduced visual quality. We examine how to assign prefetching rates to each of substreams as a function of their importance. Our studies show that appropriate prefetching strategies can bring significant performance improvements for both multiple description and layered videos

Incentivization for Health Crowdsensing

Abstract: Crowdsensing in the case of health-related information can enable a new mode of health data collection, able to collect more detailed and real-world health and physiological data than possible via traditional means. Various incentivization schemes for crowdsensing in general have been proposed, but there are characteristics specific to the health scenario that have been less well explored. Health crowdsensing can also be considered on a health condition-centric basis as 1) those with specific health conditions more often inherently collect data relevant to that condition; and 2) they are more motivated to see knowledge and research developments relevant to that condition and hence potentially more willing to take the steps needed to be a participant. In this paper we introduce a taxonomy of inventive mechanisms for health crowdsensing, and review the specific nature of incentivization for the case of health-related data. Finally, we present an incentive mechanism for collecting health-related data based on the well known best response dynamics in which participants have incomplete information on other participant’s costs. A game theoretical multi-window framework is introduced where a stable solution is achieved after a number of iterations.

A mechanistic study of the synthesis, single crystal X-ray data and anticarcinogenic potential of bis(2-pyridyl)selenides and -diselenides

Abstract: The reaction of bis(organyl)diselenide with a reducing agent, such as LiAlH4, NaBH4, Li(C2H5)3BH, etc., generally leads to cleavage of the Se–Se bond resulting in the formation of the corresponding organylselenols/selenolates. However, this work for the first time demonstrates the scisson of the C(pyridine)–Se bond in bis(2-pyridyl)diselenides with LiAlH4. The reaction affords analytically pure bis(2-pyridyl)selenides in near quantitative yields. The reaction pathway involves the formation of a selenated aluminato complex followed by the scission of the C(pyridine)–Se bond and generation of LiAlSeH2. The generation of LiAlSeH2 was established by experimental and NMR analysis. The mechanism of the reaction has been supported by theoretical analysis. Single crystal X-ray structure determination of bis(3-methyl-2-pyridyl)selenide (2e) was performed and it shows that the molecules are self-assembled in a 2D-network of C–H⋯N hydrogen bonds and π⋯π stacking interactions. The synthesized compounds were also evaluated against the Raji cancer cell line (acute lymphoid leukemia).

A framework for big data technology in health and healthcare

Abstract: Big data technologies can have particular importance in the field of healthcare. The volume of data available in digital form pertaining to the health of individuals has already become huge and is rapidly growing. It includes both the traditional clinically collected data, administrative data and increasingly other health and contextual sensor data collected in non-clinical settings. While the importance of analytics to healthcare is increasingly recognized, the healthcare industry is yet to fully harness the emergence of big data technologies specifically for carrying out analytics and to fully recognize the implications for healthcare. In this paper, we introduce a framework to help organize and guide the understanding of the application of big data technologies for processing health and healthcare data.