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A switch from solvent-borne to waterborne systems is one of the most frequent responses to government regulation of solvent emissions. One of the reasons that this switch has not gone even farther is that the strategies for making hydrophobic paint lms from water systems are only partially successful. Films are often not hydrophobic enough for many demanding end uses. Architectural paints, however, are mainly waterborne, as shown in Table 6.5. Waterborne systems represent 69.0% of the exterior architectural coating shipments and 88.0% of the interior shipments. Census gures [1] indicate that conversion of house paint to a waterborne system is very extensive except for a portion of oor enamels, primer, clear nishes, sealers, and stains for shingles or shakes. As recently as the 1970s and early 1980s, solvent-based primers were recommended for the do-it-yourself house painter for good adhesion over highly weathered (chalky) surfaces. Improvements in formulation have resulted in much improved adhesion of waterborne primers over chalky exterior walls. Now waterborne latex is used by nearly all do-it-yourself house painters for both primer and topcoat. As noted in Section 6.5.2, the hydrolysis resistance of latex lms from emulsion polymers prepared from the vinyl acetate (VA) monomer is marginal for outdoor use. In Europe and recently in the United States, vinyl versatate (II) (R1 = CH2 CH2 CH2 CH2 CH2 CH2 CH3 and R2 = R3 = CH3 ) has been introduced for use along with vinyl acetate for improved outdoor performance of latex coatings. Vinyl versatate is the vinyl ester of versatic acid, a 10-carbon carboxylic acid of the highly branched structure sometimes called neo :
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or even 0.75 in the higher microwave frequency range. This is a real problem, especially when characterising high-power transistors, whose optimum input and output impedances are very low and lie close to the edge of the Smith Chart. A simple solution includes pre-matching circuits, that is, impedance transformers, between the DUT and the tuners; this solution allows for a better accuracy within the transformed region. The pre-matching can also be included in the tuners. Obviously, the pre-matching circuits must be characterised in terms of S-parameters and replaced by different ones whenever the region of interest changes. A radical solution to the losses problem is the active-load approach. A possible scheme is the two-path technique [11]: the signal source is split and fed both to the input of the DUT and to the output of the DUT after ampli cation and phase shift (Figure 2.8). The output re ection coef cient seen by the DUT is the ratio of incident to re ected wave at its output port: aL (2.1) L = bL Now, bL is the wave coming out of the DUT, while aL is the wave injected from the output path; the amplitude and phase of the latter are easily set by means of the variable attenuator and phase shifter in the output path. In this way, any ratio can be synthesised, even greater than one in amplitude, since the ampli er in the output path overcomes all the losses. The value of the output re ection coef cient is checked on-site by means of the output directional couplers and a VNA. The two-path technique is an easy and stable technique for active-load synthesis. However, when simulating an actual power ampli er, the value of the output load must be kept constant for increasing input power levels and also for the associated increasing DUT temperature. This implies that, while bL changes because of the above, aL must
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For all the (2 NI x 3 N3 J sets of roots in step C '$,, s2,...., SN2 ' and 'tl,t2,....,ttl ' , w e can set up linear equations l,J2,...,lNt for rl,r2,...,rNt. Hence, we have unique values for r ] , r 2 , . . . , r N for each of the sets of ' s,, s2,...., SN^ ' and 't1, t2, ....,t N3 '. Hence, the total possible sets of r1, r2,...., rNi, s1, s2,...., SN2 , t1, t2,...., tN3 is given by:
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Our last relationship is more complex. For browsing purposes, the customer wants some products to appear in multiple product categories. This will make some products easier to find. At the database level, though, it means that we need to define a manyto-many relationship between the product table and the product category table. Drawing the ERD is easy it is shown in Figure 14.9. However, actually implementing the relationship isn t as simple. The problem is that you need a list of the different product categories to which a product should belong. However, as you know from the normalization discussion, you can t store the list in the product entity. To do so violates the first normal form, and the list would be hard to maintain. Likewise, you can t store a list of all of the products for a product category in the product category entity. The best solution is to create a join entity that resolves the many-to-many relationship: PRODUCT CATEGORY JOINER Product ID Product category ID You may look at this entity and say, But wait! There is no primary key. This is a good case for a composite primary key. When implemented, the composite primary key constraint will disallow a row that is exactly like an existing row, making a separate primary key redundant. A foreign key constraint will exist between the product ID in the joiner table and the product ID in the product table, as well as between the product category identifiers in the joiner table and the product category table. With the fourth entity in place, our complete ERD appears in Figure 14.10. The field names have been changed to valid database names, and the primary keys have been noted. If you have a tool such as Oracle Designer, you can plug in the ERD and it will generate a script for you that will create all of the tables. In the next section, we ll translate the ERD by hand to come up with our database.
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>>> [1,2,3] > [1,2] 1
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which is de ned as half the sampling frequency: fN Nyquist frequency  1 2xs 14:155
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the bond order, as well as partial s bond formation between C and the incoming base. Carried to the extreme, a substitution reaction with inversion of con guration at C (SN 2) ensues, as discussed in 9. If the center A is H rather than C, the corresponding reaction is a proton abstraction, that is, a Lowry Bronsted acid base reaction. If the interaction falls short of proton abstraction, the attractive interaction is called a hydrogen bond. Both aspects are discussed further in 10. Interhalogen bonds are accompanied by very low LUMOs (sXX ; see Figure 4.5) and thus can function as good s electron acceptors. The donor acceptor complexes between ammonia and F2 , Cl2 , and ClF have been investigated theoretically [93] and found to have linear structures, as expected on the basis of the above discussion. As a p Acceptor The s orbital associated with a s bond between C and a more electronegative element or group, X, is polarized toward carbon. Optimum interaction between the s orbital and an adjacent localized nonbonding orbital of an X:-type group will occur if the s orbital and the p (or sp n ) nonbonded orbital of the neighboring group are coplanar, as shown in Figure 4.9b. The two-orbital, two-electron interaction is accompanied by charge transfer into the s orbital and consequent reduction of the bond order, as well as partial p bond formation between C and the adjacent group. Carried to the extreme, an elimination reaction (E1cb) ensues, as discussed in 10. The tendency of the neighboring group to assist the departure of the leaving group is called anchimeric assistance or the neighboring group e ect. If the interaction falls short of elimination, the attractive interaction is called negative hyperconjugation [67]. The tendency to alter geometry or change conformation so as to maximize the interaction is called the anomeric e ect [94].
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and plants take much time to solve and cannot clearly be separated and controlled. Therefore, we decided to take the best of two and to use a real planning problem that can be solved in restricted time in a real practice environment. The planning or scheduling problem we used for the empirical research came from a hospital somewhere in the Netherlands and was slightly adjusted to serve our purposes. We even decided to keep the problem unsolvable, as it originally was. Unsolvable, of course, is relative, because every planning problem is solvable, provided that one adjusts the constraints or goal functions. We had two reasons for doing this. In practice, it is usually the case that a planner has to deal with problems that are not well-de ned. A planning problem is hardly ever solvable in its rst presentation. One has to adjust or release constraints or has to forget about certain goal functions. The second reason has to do with different planning domains. We wanted to know what types of constraints planners will evaluate if a planning problem is not solvable in its original formulation. We again realized the best of two that is, dealing with real scheduling problems and in semicontrolled settings to nd out about the cognitive strategies of planners in different domains. The study was conducted in the Netherlands and in Indonesia with the same problem, the same procedure, and organizations from similar branches. Therefore, we were also able to compare results of different countries with one another. The choice of countries is coincidental. The instruction, formulation, and requested solutions of the staff scheduling problem were as follows; in Appendix I the same problem is formulated for the production and transportation domain. The Staff Scheduling/Planning Problem
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you can use to separately move the attribute. If you want to include an attribute in the selection set of an action in a dynamic block, you must lock that attribute. You can then move them using the -ATTEDIT command on the command line.
I also suspect that my numbers for deleted unread and read and ignored are pretty conservative. Gartner estimates that 3 5% of phishing spams are answered. Even more depressing, Mailfrontier conducted a survey where adults in the U.S. evaluated emails to determine if they were fraudulent. The error rate was 28%. When the test was put online, the error rate held at about 30%. This is not perfectly representative for example, it doesn t take into account the fact that you probably won t have an account at all those institutions, so you would be able to filter for those you actually have accounts with but it does show how good phishing emails can be.
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