Fire Frequencies

Considerable effort has been spent in evaluating the fire events cataloged in licensee event reports and data from the American 

Nuclear Insurers (Hockenbury and Yeater, 1980 Fleming et al 1979). The nature and the frequency of fires at nuclear power plants change dramatically between construction, preoperational testing,, uiii plant operation, hence, data should be appropriate to the phase being evaluated. 

A number of issues arise in using the available data to estimate (he rates of location-dependent fire occurrence. These include the possible reduction in the frequency of fires due to increased awareness. Apostolakis and Kazarians (1980) use the data of Table 5.2-1 and Bayesian analysis to obtain the results in Table 5.2-2 using conjugate priors (Section 2.6.2), Since the data of Table 5.2-1 are binomially distributed, a gamma prior is used, with a and P being the parameters of the gamma prior as presented inspection 2.6.3.2. For example, in the cable- spreading room fromTable 5.2-2, the values of a and p (0.182 and 0.96) yield a mean frequency of 0.21, while the posterior distribution a and p (2.182 and 302,26) yields a mean frequency of 0.0072. 

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It was assumed fire frequency data from plants to Surry may be used. 

Wildfire is a very important factor in western forest ecosystems. In the San Bernardino Mountains, the fire frequencies were determined by McBride and Laven in two stand types before and after 1893, when the area was first set aside as a forest preserve and fire protection began. Before 1893, the average interval between fires in ponderosa stands was 12 yr after 1893, it was 24 yr. The comparable numbers for Jeffrey pine stands were 16 and 38 yr. The buildup of heavy fuels due to ozone-caused mortality and fire protection results in hotter fires, and the thinning of the tree canopy results in increased rates of fire spread. Hotter fires decrease tree survival. Moisture interception by condensation in living tree crowns would decrease as the stands became thinner, thus causing some sites to be drier. ... 

Figure 11.4. Comparison of data acquired for same sample at different laser firing frequencies (10 and 1400 Hz). The data acquisition time is much more rapid and measurable sensitivity is improved.

Adaptation/disadaptation rates determine how phasic a response is (Moore, 1994). Some Drosophila ORNs show amuch slower rise and fall in firing frequency (de Bruyne et al., 2001) and could be considered more tonic. These different responses to onset and duration of stimuli are mimicked by diversity in their response at the end of stimulation (de Bruyne et al., 2001). Some ORNs show an... 

It is also important to consider the dynamic surface tension in addition to the static surface tension since the firing frequencies experienced by the fluids are very rapid (up to 40 kHz on some print heads, or 40000 pulses per second ) and result in a dynamic environment. It has been theorized that an ideal ink would posses a high dynamic surface tension (to promote rapid meniscus recovery at high firing frequencies) and a low static surface tension (to achieve good substrate wet out). ... 

In summary, we know that pasture productivity is the key to predicting whether soil C stocks will increase or decrease following forest conversion to pasture, and we know that several factors, including native soil fertility, fertilization, climate, fire frequency, and grazing intensity, influence pasture productivity. We do not, however, know which of these factors has had the greatest influence in the past and which... 

Scholz A, Kuboyama N, Hempelmann G, Vogel W 1998 Complex blockade of TTX-resistant Na currents bv lidocaine and bupivacaine reduce firing frequency in DRG neurons. J Neurophysiol 79 1746-1754... 

The action of deltamethrin on the GABA response was more difficult to determine, because low concentrations of deltamethrin caused depolarization and rapid firing of action potentials, accompanied by a decrease in membrane resistance. Deltamethrin at 0.1 nM caused a massive increase in firing frequency, and a depolarization of 30mV, after perfusion for ten minutes. Application of TTX, which specifically blocks voltage dependent sodium channels, abolished the action potentials, and also reversed the pyrethroid-induced depolarization and decrease in membrane resistance. 

The normal orderly sequence of events in cardiac contraction is initiated by a primary pacemaker, the sinoatrial (SA) node,21 which is located near the surface at the junction of the right atrium and the superior vena cava. One of its properties is automaticity. The normal firing frequency is 60-100 impulses/minute. The established rhythm is conducted to the atrioventricular (AV) node. This node serves to slow the beat somewhat so that atrial contraction can occur before the ventricle is stimulated. The AV node is in the septum (dividing wall) between the atria. The impulse is conducted from the AV node to a common bundle of fibers (Bundle of His) that cross the right atrium to the left ventricle. From there a division of fibers directs impulses along the septum dividing the ventricles, down to the lateral walls and the apex of the heart. The branching of the common bundle leads into the Purkinje fibers that innervate the heart musculature of the ventricles. 

Olfactory receptor neurons not only have to be selective to a spectrum of molecules, they must also report the quantity or concentration of molecules in the environment and also temporal properties. Receptor neurons respond to fluctuations in stimulus concentration with changes in action potential firing frequency. We also see very clear diversity in the temporal responses of ORNs responsible for pheromone detection in S. litoralis (J. Mackenzie et al, unpublished) which may suggest that the Ifont end of the pheromone detection system in the moth is tuned to different frequency information which is presumably rich in turbulent odour plumes (see Section 4). In solving the problem of chemieal source localisation the capture of temporal information has already been demonstrated to be important [15]. 

In order to use models of neurons that are biologically plausible, we have first simulated the conductance based models of the PNs and LNs used in Bazhenov et al. [6]. Both neurons were found to be type I neurons and we chose to model them with the theta neuron model [11]. We then fitted the parameters of the theta models so as to match the instantaneous firing frequency vs. applied current curves (see Figure Al). Note however that these instantaneous frequency curves do not take into account a possible frequency adaptation leading to a decrease of the frequency over time. Therefore, the two parameters involved in the adaptation current of the theta models have been fitted independently so that the time responses to applied constant current correspond to the ones obtained with the conductance based model. Figures A2 and A3 clearly indicate a close match between the time responses of the two models. In particular, the frequency adaptation seen in the conductance based model of the LN is similar to the one of the theta model (see Figure A3). The parameters for the fitted theta models are given below. 

Figure Al. Instantaneous firing frequency vs. applied current for a PN (left) and a LN (right). Plain curves are for the simulations of the conductance based models from [6] and dotted curves are for the simulations of the corresponding fitted theta models.

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