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Low retention of retinol, thiamin, and ribo avin was seen in earth-oven-cooked meat compared with microwave-cooked meat. The difference in vitamin retention could be due to a higher cooking temperature of earthoven cooking compared with microwave cooking. Riccio et al. (2006) observed that cooking, in general, produced a decrease in the B vitamins content, both under mild (70 90 C) and severe (120 C) conditions. These researchers found that after 5 minutes of cooking at 100 C, all B vitamins analyzed were not detectable in the homogenized boiled ham without forti cation. Rhee et al. (1993) reported that samples lower in fat had a lower retention level of thiamin, ribo avin, and B12. These samples lost more of their weight in water, which resulted in the loss and destruction of more B vitamins then in ground meat that possessed higher fat contents. Overall mineral retention is better than vitamin retention (Rhee et al. 1993). The overall retention of minerals in cooked meat has been reported to be between 84% and 90% (Rhee et al. 1993).

Figure 3-6 shows a worksheet set up to display a calendar for any month. Believe it or not, the calendar is created with a single array formula that occupies 42 cells.

This can be broken down as follows:

Unfortunately, the ESD disadvantages of the GaAs HBT device are the size of the emitter, the emitter base sensitivity, and the interconnect metallurgical penetration. Figure 7.3 shows a plot of the HBM ESD robustness of a GaAs HBT. As the GaAs HBT emitter area increases from 0 to 100 mm2, the GaAs base collector HBM ESD results show an linear increase from 300 to 1250 V HBM; concurrently, the GaAs emitter base junction HBM ESD level increases from 200 to 600 V HBM. From the plot, it is evident that the GaAs HBT emitter base junction metric of ESD robustness per unit area is less than the base collector region. These results show that it is possible to utilize the GaAs HBT base collector region to provide RF ESD protection. Additionally, these results also show that as the GaAs HBT emitter is scaled to small dimensions, the ESD sensitivity of the GaAs HBT is below 250 V HBM levels, where the rst failure level will occur due to GaAs HBT emitter base ESD stress. An RF ESD design practice is as follows: GaAs HBT devices used in a base collector forward bias operation are suitable for ESD protection elements because of the GaAs HBT power design point: low resistance heavily doped collector region, low subcollector-to-substrate capacitance, high base doping concentration, and good interconnect structures. As GaAs HBT devices are scaled to small emitter area of less than 5 mm2, the ESD robustness of the GaAs HBT device will decrease below 250 V HBM levels, with the rst failure mechanism being the failure of the GaAs HBT emitter base region.

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delivered to a sink A hierarchical cluster structure [20,30,31] is better suited for this purpose The low-energy adaptation clustering hierarchy (LEACH) [20] is a twolevel clustering mechanism in which sensors are partitioned into clusters Each sensor volunteers to become a clusterhead (CH) with a certain probability such that the task of being CHs is evenly distributed and rotated among all sensors Once a sensor elects itself as the CH, it broadcasts a message to notify other nearby sensor nodes of the fact that it is willing to be a CH The remaining sensors then select a minimum transmission power to join their closest CHs Within the cluster, a CH uses time-division multiple access (TDMA) to allocate time slots to cluster members (so that the latter can relay their readings to the CH), compresses received data, and transmits a digested report directly to the base station (sink).

In this example, note an improvement over the previous technique as you create an insertion-based stego technique using .wav audio files by mimicking a sine wave.

Bandyopadhyay and Coyle [30] propose a multilevel hierarchical clustering algorithm Similar to LEACH, this approach aims to realize the objective of balancing the load of sensors and achieving energy ef ciency Chen et al [32] devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking A cluster is dynamically formed and a CH becomes active when the acoustic signal strength detected by the CH exceeds a predetermined threshold The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information.

(31)

With the use of a Voronoi diagram, they devise solution approaches to determine (I1) how CHs cooperate with one another to ensure that only one CH (preferably the CH that is closest to the target) is active with high probability; (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a suf cient number of sensors respond with nonredundant, essential information to determine the target location; and (I3) both.