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To do this, you'll need to request a PayPal debit card which operates on the MasterCard network. You can use that card number with any merchant who accepts MasterCard, and the funds will be deducted from the PayPal account. This service is free but has a daily spending limit of $3,000. In the next section, we'll see how both personal users and merchants can use PayPal to accept payments. If you give someone the email address associated with your PayPal account, that person can send you money from their own PayPal account. If you're selling items on eBay, you can select PayPal as an option for accepting payment. When you're signed in to PayPal, click the "merchant services" tab to see all the options available to you as a seller. Cost and availability of these services depend on which website payments type you've selected for your account. You'll have the "standard" type by default as a recipient, but you can upgrade to the "pro" type for a $30 monthly subscription fee.

The theory is that the higher the side mold seam the later the bottle was made - at least in the era from the early/mid 19th century until the first few decades of the 20th century. This dating tool was apparently devised by Grace Kendrick in her seminal 1963 book "The Antique Bottle Collector." This book was pioneering and reprinted many times into the 1970s and is probably the most common and widely quoted bottle book (by collectors and archaeologists alike) ever written, containing a wealth of generally good information. Although there are examples of bottles having mold seams that fit these date ranges properly, the issue of dating bottles is vastly more complicated than the simple reading of side mold seams. If it were that simple much of this website would be unnecessary! For example, the process that produces a tooled finish frequently erases traces of the side mold seam an inch or more below the base of the finish whereas the typical applied finish has the seam ending higher - right at the base of the finish (Lockhart et.

This indicates that the simulated hydrological model for Wadi El-Sheikh and consequently WDB requires significant adjustment. The curve number (CN) value was considered a critical parameter for the calibration correction. After running the simulation with different CN range values, the calibrated model of Wadi El-Sheikh matched with the field measured hydrograph with a CN value of 86 (Fig. 6a). After assigning the adjusted CN, the simulation results in (Table 3) showed that Wadi El-Sheikh yielded a peak discharge of 3.1 m3/s which produces a satisfactory approximate to the field measured hydrograph with a peak discharge of 2.7 m3/s showing that the time of peak is about 14.30 h. Field measured hydrograph data compared to model simulation results over Wadi El Shiekh outlet in October 1993. Blue and red lines show the measured and modeled discharge in (m3/s), respectively. The calibrated model was successfully applied to the WDB, particularly on the predefined risky sub-basins (i.e., high risk category). The model yielded different results compared to the non-calibrated model results (Table 4). Results show that Wadi Zaghraa is ranked first according to the peak discharge both before and after the calibration.

There are two main strategies for training a Bayesian classifier: train everything and train-on-error. In the train everything strategy, you train the classifier with every message that you receive. This strategy is highly responsive to changes in spam patterns but may change too quickly in response to unrelated variability in messages. In addition, it is resource intensive to scan every message. In the train-on-error "http://East2Westmovers.com/__media__/js/netsoltrademark.php?d=Healthcare-Industry.Sblinks.net%2Fuser%2Fholliejess%2F strategy, you train the classifier only with messages that it has previously classified incorrectly (i.e., false positives and false negatives). This strategy is resource efficient but may not train the classifier as quickly when spam patterns change. Once a suitable number of messages have been learned, however, switching to a train-on-error approach saves resources, because many fewer messages must be trained. Louis suggests that switching to train-on-error after 10,000 spam and 10,000 non-spam message have been learned may be reasonable. You can train SpamAssassinâs Bayesian classifier with either strategy.