7 resultados para Orleans University.

em Indian Institute of Science - Bangalore - Índia


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Background: There was a low adherence to influenza A (H1N1) vaccination program among university students and health care workers during the pandemic influenza in many parts of the world. Vaccination of high risk individuals is one of the recommendations of World Health Organization during the post-pandemic period. It is not documented about the student's knowledge, attitude and willingness to accept H1N1 vaccination during the post-pandemic period. We aimed to analyze the student's knowledge, attitude and willingness to accept H1N1 vaccination during the post-pandemic period in India. Methods: Vaccine against H1N1 was made available to the students of Vellore Institute of Technology, India from September 2010. The data are based on a cross-sectional study conducted during October 2010 to January 2011 using a self-administered questionnaire with a representative sample of the student population (N = 802). Results: Of the 802 respondents, only 102/802 (12.7%) had been vaccinated and 105/802 (13%) planned to do so in the future, while 595/802 (74%) would probably or definitely not get vaccinated in the future. The highest coverage was among the female (65/102, 63.7%) and non-compliance was higher among men in the group (384/595; 64.5%) (p < 0.0001). The representation of students from school of Bio-sciences and Bio-technology among vaccinees is significantly higher than that of other schools. Majority of the study population from the three groups perceived vaccine against H1N1 as the effective preventive measure when compared to other preventive measures. 250/595 (42%) of the responders argued of not being in the risk group. The risk perception was significantly higher among female (p < 0.0001). With in the study group, 453/802 (56.4%) said that they got the information, mostly from media. Conclusions: Our study shows that the vaccination coverage among university students remains very low in the post-pandemic period and doubts about the safety and effectiveness of the vaccine are key elements in their rejection. Our results indicate a need to provide accessible information about the vaccine safety by scientific authorities and fill gaps and confusions in this regard.

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[1] D. Tse and P. Viswanath, Fundamentals of Wireless Communication.Cambridge University Press, 2006. [2] H. Bolcskei, D. Gesbert, C. B. Papadias, and A.-J. van der Veen, Spacetime Wireless Systems: From Array Processing to MIMO Communications.Cambridge University Press, 2006. [3] Q. H. Spencer, C. B. Peel, A. L. Swindlehurst, and M. Haardt, “An introduction to the multiuser MIMO downlink,” IEEE Commun. Mag.,vol. 42, pp. 60–67, Oct. 2004. [4] K. Kusume, M. Joham,W. Utschick, and G. Bauch, “Efficient tomlinsonharashima precoding for spatial multiplexing on flat MIMO channel,”in Proc. IEEE ICC’2005, May 2005, pp. 2021–2025. [5] R. Fischer, C. Windpassinger, A. Lampe, and J. Huber, “MIMO precoding for decentralized receivers,” in Proc. IEEE ISIT’2002, 2002, p.496. [6] M. Schubert and H. Boche, “Iterative multiuser uplink and downlink beamforming under SINR constraints,” IEEE Trans. Signal Process.,vol. 53, pp. 2324–2334, Jul. 2005. [7] ——, “Solution of multiuser downlink beamforming problem with individual SINR constraints,” IEEE Trans. Veh. Technol., vol. 53, pp.18–28, Jan. 2004. [8] A. Wiesel, Y. C. Eldar, and Shamai, “Linear precoder via conic optimization for fixed MIMO receivers,” IEEE Trans. Signal Process., vol. 52,pp. 161–176, Jan. 2006. [9] N. Jindal, “MIMO broadcast channels with finite rate feed-back,” in Proc. IEEE GLOBECOM’2005, Nov. 2005. [10] R. Hunger, F. Dietrich, M. Joham, and W. Utschick, “Robust transmit zero-forcing filters,” in Proc. ITG Workshop on Smart Antennas, Munich,Mar. 2004, pp. 130–137. [11] M. B. Shenouda and T. N. Davidson, “Linear matrix inequality formulations of robust QoS precoding for broadcast channels,” in Proc.CCECE’2007, Apr. 2007, pp. 324–328. [12] M. Payaro, A. Pascual-Iserte, and M. A. Lagunas, “Robust power allocation designs for multiuser and multiantenna downlink communication systems through convex optimization,” IEEE J. Sel. Areas Commun.,vol. 25, pp. 1392–1401, Sep. 2007. [13] M. Biguesh, S. Shahbazpanahi, and A. B. Gershman, “Robust downlink power control in wireless cellular systems,” EURASIP Jl. Wireless Commun. Networking, vol. 2, pp. 261–272, 2004. [14] B. Bandemer, M. Haardt, and S. Visuri, “Liner MMSE multi-user MIMO downlink precoding for users with multple antennas,” in Proc.PIMRC’06, Sep. 2006, pp. 1–5. [15] J. Zhang, Y. Wu, S. Zhou, and J. Wang, “Joint linear transmitter and receiver design for the downlink of multiuser MIMO systems,” IEEE Commun. Lett., vol. 9, pp. 991–993, Nov. 2005. [16] S. Shi, M. Schubert, and H. Boche, “Downlink MMSE transceiver optimization for multiuser MIMO systems: Duality and sum-mse minimization,”IEEE Trans. Signal Process., vol. 55, pp. 5436–5446, Nov.2007. [17] A. Mezghani, M. Joham, R. Hunger, and W. Utschick, “Transceiver design for multi-user MIMO systems,” in Proc. WSA 2006, Mar. 2006. [18] R. Doostnejad, T. J. Lim, and E. Sousa, “Joint precoding and beamforming design for the downlink in a multiuser MIMO system,” in Proc.WiMob’2005, Aug. 2005, pp. 153–159. [19] N. Vucic, H. Boche, and S. Shi, “Robust transceiver optimization in downlink multiuser MIMO systems with channel uncertainty,” in Proc.IEEE ICC’2008, Beijing, China, May 2008. [20] A. Ben-Tal and A. Nemirovsky, “Selected topics in robust optimization,”Math. Program., vol. 112, pp. 125–158, Feb. 2007. [21] D. Bertsimas and M. Sim, “Tractable approximations to robust conic optimization problems,” Math. Program., vol. 107, pp. 5–36, Jun. 2006. [22] P. Ubaidulla and A. Chockalingam, “Robust Transceiver Design for Multiuser MIMO Downlink,” in Proc. IEEE Globecom’2008, New Orleans, USA, Dec. 2008, to appear. [23] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004. [24] G. H. Golub and C. F. V. Loan, Matrix Computations. The John Hopkins University Press, 1996.

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There are multiple goals of a technology transfer office (TTO) based in a university system. Whilst commercialization is a critical goal, maintenance and cleaning of the TTO's database needs detailing. Literature in the area is scarce and only some researchers make reference to TTO data cleaning. During an attempt to understand the commercial strategy of a university TTO in Bangalore the challenge of data cleaning was encountered. This paper describes a case study of data cleaning at an Indian university based TTO. 382 patent records were analyzed in the study. The case study first describes the back ground of the university system. Second, the method to clean the data and the experiences encountered are highlighted. Insights drawn indicate that patent data cleaning in a TTO is a specialized area which needs attention. Overlooking this activity can have legal implications and may result in an inability to commercialize the patent. Two levels of patent data cleaning are discussed in this case study. Best practices of data cleaning in academic TTOs are discussed.

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This paper describes a university based system relevant to doctoral students who have problems with themselves, their peers and research supervisors. Doctoral students have various challenges to solve and these challenges contribute to delays in their thesis submission. This tool aims at helping them think through their problem in a pre-counseling stage. The tool uses narratives and hypothetical stories to walk a doctoral student through options of responses he or she can make given the situation in the narrative. Narratives were developed after a preliminary survey (n=57) of doctoral students. The survey indicated that problems they experienced were: busy supervisors, negative competition from peers and laziness with self. The narrative scenarios in the tool prompt self-reflection and provide for options to chose from leading to the next scenario that will ensue. The different stages of the stimulus-response cycles are designed based on Thomas-Kilmann conflict resolution techniques (collaboration and avoidance). Each stimulus-response cycle has a score attached that reflects the student's ability to judge a collaborative approach. At the end of all the stages a scorecard is generated indicating either a progressive or regressive outcome of thesis submission.