Podcasting Acceptance on Campus: An extension of the UTAUT Model

This research developed and empirically tested a theoretical model on the acceptance of podcasting in the context of learning in higher education. The model integrated key variables from the TAM and UTAUT model, hypothesized and tested the effects of their antecedents found in literature concerning technology acceptance in higher education. The result confirmed the effects of UTAUT’s four key antecedents on behavioral intention (intention to use): facilitating conditions, social influence, performance expectancy, and effort expectancy. Our findings suggest that facilitating factors pertinent to podcasting include technical support and copyright clearance. The inter-relationships among the four UTAUT antecedents are explicitly specified and relevant antecedents for podcasting are proposed and tested. The overall results are expected to contribute to theoretical development and industry practitioner in promoting the acceptance of podcasting in classrooms

Modified empirical fitting of the discharge behavior of LiFePO4_4 batteries under various conditions

A mathematical model is developed by fitting the discharge curve of a new LiFePO$_4$ battery and then used to investigate the relationship between the discharge time and the closed-circuit voltage. This model consists of exponential and polynomial terms where the exponential term dominates the discharge time of a battery and the polynomial term dominates the change in the closed-circuit voltage. Time shift and time scale processes modify the exponential and polynomial terms, respectively, so that the model is suitable for batteries under various conditions. References W. Su, H. Eichi, W. Zeng and M.-Y. Chow, A survey on the electrification of transportation in a smart grid environment, IEEE Intl. Conf. Ind. I. 8:1–10, 2012. doi:10.1109/TII.2011.2172454 J. Wang, Z. Sun and X. Wei, Performance and characteristic research in LiFePO$_4$ battery for electric vehicle applications, IEEE Vehicle Power 1657–1661, 2009. doi:10.1109/VPPC.2009.5289664 A. Shafiei, A. Momeni and S. S. Williamson, Battery modeling approaches and management techniques for plug-in hybrid electric vehicles, IEEE Vehicle Power 1–5, 2011. doi:10.1109/VPPC.2011.6043191 P. Bai, D. A. Cogswell and M. Z. Bazant, Suppression of phase separation in LiFePO$_4$ nanoparticles during battery discharge, Nano Lett. 11:4890–4896, 2011. doi:10.1021/nl202764f H. L. Chan and D. Sutanto, A new battery model for use with battery energy storage systems and electric vehicle power systems, IEEE Power Eng. Soc. 1:470–475, 2000. doi:10.1109/PESW.2000.850009 T. Kim and W. Qiao, A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects, IEEE T. Energy Conver. 26:1172–1180, 2011. doi:10.1109/TEC.2011.2167014 D. N. Rakhmatov and S. B. K. Vrudhula, An analytical high-level battery model for use in energy management of portable electronic systems, IEEE ICCAD 488–493, 2001. doi:10.1109/ICCAD.2001.968687 V. Srinivasan and J. Newman, Discharge model for the lithium iron-phosphate electrode, J. Electrochem. Soc. 151:A1517–A1529, 2004. doi:10.1149/1.1785012 V. Rao, G. Singhal, A. Kumar and N. Navet, Battery model for embedded systems, VLSI Des. 105–110, 2005. doi:10.1109/ICVD.2005.61 S. Dargavillez and T. W. Farrell, Predicting active material utilization in LiFePO$_4$ electrodes using a multiscale mathematical model, J. Electrochem. Soc. 157:A830–A840, 2010. doi:10.1149/1.3425620 R. Rao, S. Vrudhula and D. N. Rakhmatov, Battery modeling for energy-aware system design, Computer 36:77–87, 2003. doi:10.1109/MC.2003.1250886 M. Chen and G. A. Rincon-Mora, Accurate electrical battery model capable of predicting runtime and i-v performance, IEEE T. Energy Conver. 21:504–511, 2006. doi:10.1109/TEC.2006.874229 L. Gao, S. Liu and R. A. Dougal, Dynamic lithium-ion battery model for system simulation, IEEE T. Compon. Pack. T. 25:495–505, 2002. doi:10.1109/TCAPT.2002.803653 V. Agarwal, K. Uthaichana, R. A. DeCarlo and L. H. Tsoukalas, Development and validation of a battery model useful for discharging and charging power control and lifetime estimation, IEEE T. Energy Conver. 25:821–835, 2010. doi:10.1109/TEC.2010.2043106 B. Schweighofer, K. M. Raab and G. Brasseur, Modeling of high power automotive batteries by the use of an automated test system, IEEE T. Instrum. Meas. 52:1087–1091, 2003. doi:10.1109/TIM.2003.81482

Detecting T-cell Reactivity to Whole Cell Vaccines

BCR-ABL$^+$ K562 cells hold clinical promise as a component of cancer vaccines, either as bystander cells genetically modified to express immunostimulatory molecules, or as a source of leukemia antigens. To develop a method for detecting T-cell reactivity against K562 cell-derived antigens in patients, we exploited the dendritic cell (DC)-mediated cross-presentation of proteins generated from apoptotic cells. We used UVB irradiation to consistently induce apoptosis of K562 cells, which were then fed to autologous DCs. These DCs were used to both stimulate and detect antigen-specific CD8+ T-cell reactivity. As proof-of-concept, we used cross-presented apoptotic influenza matrix protein-expressing K562 cells to elicit reactivity from matrix protein-reactive T cells. Likewise, we used this assay to detect increased anti-CML antigen T-cell reactivity in CML patients that attained long-lasting clinical remissions following immunotherapy (donor lymphocyte infusion), as well as in 2 of 3 CML patients vaccinated with lethally irradiated K562 cells that were modified to secrete high levels of granulocyte macrophage colony-stimulating factor (GM-CSF). This methodology can be readily adapted to examine the effects of other whole tumor cell-based vaccines, a scenario in which the precise tumor antigens that stimulate immune responses are unknown