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In addition to the identity of the selected device, the Type 3 message also includes timing information. SMACS and EAR employ a superframe of length Tframe; however, unlike conventional TDMA, the superframes are not synchronous between devices. Rather, the phase of their superframes are independent (i.e., the superframe of each device begins at an independent time). To establish the communication link, device B sends device C in its Type 3 message its schedule of existing links in its superframe, plus the time until the start of its next superframe. Device C receives the Type 3 message, and compares the link schedule of device B with its own device schedule, taking into consideration the differing times at which their superframes start. It then identi es two (ideally sequential) time slots that are available to both devices. It transmits the location of these time slots, together with a proposed frequency for the new communication link (frequency Y), in a nal Type 4 message. Device B receives the Type 4 message and, at the appropriate time, moves to frequency Y and exchanges a pair of test messages with device C to ensure that the wireless link on that frequency is in fact open. Devices B and C now have a communication link established with each other. This link establishment process can be repeated a number of times, creating a dense network among many devices. After the network is formed, as part of network maintenance the devices periodically transmit a broadcast invitation (BI) message (a beacon) to announce their presence to other network devices, encouraging them to form connections. These invitation messages need not be sent every Tframe , but may be sent on some multiple of Tframe , to trade off connection formation latency with energy ef ciency. Following the BI messages there is a small period (collection of slots) during which the device monitors the frequency for the replies of any invitees. Since coordination is not performed with all devices within range of devices B and C to establish a noninterfering pair of time slots for this communication link, if the SMACS procedure were performed in an ad hoc TDMA network on a single frequency, frame collisions with neighboring devices would be unavoidable. By employing multiple frequencies for its communication links, SMACS employs a distributed form of dynamic FDMA to avoid these otherwise inevitable frame collisions. Sohrabi et al. [53] note that SMACS can be generalized to de ne the communication links to be speci c frequency-hopping patterns, rather than xed frequencies. This extension moves SMACS from a TDMA/FDMA hybrid to a TDMA/CDMA hybrid, and offers the advantage of protection against channel degradations, such as multipath at fading and the presence of xed interfering signals, at the cost of increased complexity. Since SMACS already requires a frequency-agile transceiver, the additional complexity is due largely to the increased complexity of the protocol, which must identify, select, and synchronize the orthogonal hopping patterns. EAR extends SMACS for use with mobile devices. The assumptions made by the EAR protocol are that there are only a few mobile devices in a randomly distributed, much larger collection of stationary devices, and that, as before, energy consumption, rather than connectivity, is of primary importance. A mobile device begins the EAR algorithm by searching (monitoring) for BI messages sent by stationary devices. It may receive several; if so, it selects one
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6.3 Fluvial ecosystems and the hyporheic zone 6.3.1 Spatial and temporal hierarchies of groundwater effects on in-stream ecology A hierarchy of scale governs the degree to which groundwater affects in-stream ecology (Cannan and Armitage 1999): this hierarchy is governed by the magnitude of the base ow index (BFI). For instance, where BFI is high (so that the ow regime is volumetrically dominated by groundwater discharge), the ecology of the stream is obviously dependent on groundwater discharge at every feasible scale of observation, from a small patch of streambed to the catchment as a whole (cf. Sear et al. 1999). It hardly needs stating that if virtually all of the water in the channel is groundwater, the in-stream ecology owes its very existence to aquifer out ows. Even in streams where BFI is more modest, in-stream ecology can still be powerfully in uenced by groundwater discharge at the scales of individual reaches and/or channel perimeters. For instance, if groundwater in ows are restricted to one or two localized patches on the streambed, the ecological niches which correspond to those patches will tend to be strongly in uenced by small-scale mixing of groundwaters and surface waters (e.g. Jones and Mulholland 2000; Malcolm et al. 2003). This hierarchy can equally be expressed in terms of time-scales. Where BFI is high, groundwater discharge will be the predominant in uence on in-stream ecology at almost all times (with the possible exceptions occurring during periods of ooding). In medium- to low-BFI streams, dynamic changes in groundwater/stream water exchanges might well occur over very short timescales (e.g. Maddock et al. 1995; Alden and Munster 1997; Malcolm et al. 2003), such that relatively modest uctuations in stream stage give rise to profound changes in the direction and velocity of groundwater movement near the channel (e.g. Alden and Munster 1997). Nowhere are the interactions between surface and subsurface waters more dynamic than in the hyporheic zone, in which various hydrogeo-
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Hamilton, W. (1859). Lectures on metaphysics and logic. Edinburgh, Scotland: William Blackwood & Sons. Hardy, L., Mullen, R., & Jones, G. (1996). Knowledge and conscious control of motor actions under stress. British Journal of Psychology, 87, 621 636. Hart, S. G., & Staveland, L. E. (1987). Development of NASATLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139 183). Amsterdam: Elsevier. Hatfield, B. D., & Hillman, C. H. (2001). The psychophysiology of sport: A mechanistic understanding of the psychology of superior performance. In R. N. Singer, H. A. Hausenblas, & C. M. Janelle (Eds.), Handbook of sport psychology (2nd ed., pp. 362 388). New York: Wiley. Hatfield, B. D., Landers, D. M., & Ray, W. J. (1984). Cognitive processes during self-paced motor performance: An electroencephalographic profile of skilled marksmen. Journal of Sport Psychology, 6, 42 59. Hayes, N. A., & Broadbent, D. E. (1988). Two modes of learning for interactive tasks. Cognition, 28, 249 276. Heath, M., Rival, C., & Neely, K. (2006). Visual feedback schedules influence visuomotor resistance to the M llerLyer figures. Experimental Brain Research, 168, 348 356. Heuer, H., & Wing, A. M. (1984). Doing two things at once: Process limitations and interactions. In M. M. Smyth & A. M. Wing (Eds.), The psychology of human movement (pp. 183 213). London: Academic Press. Hill, H., & Raab, M. (2005). Analyzing a complex visuomotor tracking task with brain-electrical event related potentials. Human Movement Science, 24, 1 30. Hodges, N. J., & Franks, I. M. (2002). Modelling coaching practice: The role of instruction and demonstration. Journal of Sports Sciences, 20, 793 811. Holding, D. H. (1970). Repeated errors in motor learning. Ergonomics, 13, 727 734. Hunkin, M. H., Squires, E. J., Parkin, A. J., & Tidy, J. A. (1998). Are the benefits of errorless learning dependent on implicit memory Neuropsychologia, 36, 25 36. Jackson, R. C. (2003). Evaluating the evidence for implicit perceptual learning: A re-analysis of Farrow and Abernethy. Journal of Sports Sciences, 21, 503 509. Jackson, R. C., & Farrow, D. (2005). Implicit perceptual training: How, when, and why Human Movement Science, 24, 308 325. James, W. (1890). Principles of psychology. New York: Holt. Janelle, C. M., Duley, A. R., & Coombes, S. A. (2004). Psychophysiological and related indices of attention during skill acquisition. In A. M. Williams & N. J. Hodges (Eds.), Skill acquisition in sport: Research, theory, and practice (pp. 282 308). London: Routledge.
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Table 3.1. Populations (log10 CFU/cm2) of indicator organisms and pathogens before and after application of decontamination interventions on hides, beef carcasses and beef or pork trimmings. (cont.) Surface Interventions in sequence 1st Ozonated water (1%, 15 min, 7.2 C) 2nd CPCe (0.5%, 15 min, 7.2 C) NA 3rd NA 4th E. coli Coliforms APC Salmonella Typhimurium ClO2 (200 ppm, 15 min, 7.2 C) Trisodium phosphate (10%, 15 min, 7.2 C) NA NA E. coli Microorganism(s) Log10 CFU/cm2 or g Before 6.8 6.0 7.1 5.8 6.8 After 5.1 4.1 5.6 4.0 6.2 Reference Acetic acid (5%, 3 min) CPC (0.5%, 3 min) CPC (0.5%, 3 min) Trisodium phosphate (10%, 3 min) NA NA Coliforms APC Salmonella Typhimurium APC Salmonella Typhimurium APC Salmonella Typhimurium APC Salmonella Typhimurium 6.0 7.1 5.8 7.1 5.8 7.1 5.8 7.1 5.8 5.7 6.8 5.5 5.3 3.8 5.9 4.4 6.2 4.6 Pohlman et al. (2002b) ClO2 (200 ppm, 3 min) NA NA CPC (0.5%, 3 min) NA NA
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Does the animal model provide a valid measure of the reinforcing effects of nicotine
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Some of the differences between ActionScript and JavaScript are as follows:
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using animal models; and the extent to which genetic variation in uences response to treatment for tobacco dependence. These previously disparate lines of research, which are beginning to converge through multidisciplinary, collaborative research efforts, will lead to an improved understanding of the complexities of tobacco dependence and will provide key information to the development of medications and behavioral treatments for curtailing tobacco dependence worldwide.
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This chapter provided several examples of how to create and modify pivot tables using VBA code. In the next chapter, I present VBA techniques to manipulate charts.
Listing 15-6 Continued
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