vation and performance in an elementary physical education running program. These researchers directly assessed parents perception of the value of the running program for their child using both the attainment ( It is important that my child does well in this program ) and the utility ( It is useful for my child to do well in this program ) dimensions of the value construct. Children s motivation was assessed by calculating the number of laps that they ran or walked over the school year as part of the running program (thus measuring persistence and level of effort). Furthermore, the timed mile run was used to assess children s cardiovascular fitness level. Correlational and regression procedures indicated that parents value beliefs were significantly and positively related to children s mile run performance and to their motivation level (as assessed via the number of laps completed). That is, children whose parents placed higher value (importance and utility) on their children s participation in the running program were more likely to exert greater effort in the program and to attain better scores on the mile run. The somewhat mixed results found in these physical activity studies likely are due to differences in the way researchers have measured both the value construct (relative versus absolute; attainment, utility, intrinsic, or cost) and the parents endorsement of this value (i.e., through assessment of parents directly or through children s perceptions of their parents value beliefs). Furthermore, as the research discussed next shows, other parental beliefs and attitudes may be as or even more important predictors of children s perceptions, beliefs, values, and behavior than are measures of the value parents place on sport and physical activity participation. Parental Attitudes regarding Sport Ethics and Behavior A few studies have been conducted to examine the link between parents and their children in their attitudes and behaviors regarding antisocial or aggressive behaviors in competitive contexts. A series of studies by Michael Smith and his colleagues (see review of this research in Morra & Smith, 2002) have documented the role of parents in encouraging their sons both to perceive highly aggressive acts in hockey as legitimate and to exhibit higher frequencies of such aggressive behavior in hockey games. Similar links between parental attitudes and values concerning the legitimacy of aggressive, assaultive, and antisocial behaviors and their children s corresponding attitudes and values have been found by other researchers (e.g., Guivernau & Duda, 2002; Mugno & Feltz, 1985).
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the DBE, and external from the DBE. In the rst approach, we use components or applications to encrypt the data prior to sending it to the DBE for storage and then use similar components or applications to decrypt the data after it is retrieved from the DBE. Since the DB is storing the encrypted version of the data, the data types used inside our DBs must be modeled to hold the encrypted ciphertext, not the plaintext. This often requires extensive use of binary data types in the database, such as variable-length binary (varbinary) types or binary large objects (BLOBs). In the second approach, because the DBE directly supports encryption, the DDBE users can simply read and write their data normally while the DBE handles the encryption under the covers. This is generally the most ef cient approach to data encryption for centralized DBMS. With any type of encryption, we must be careful with key handling, because a compromised key means compromised data. Often, the key is protected by a password. This password must be stronger than the key it is protecting. In this context, stronger has a very speci c, technical meaning, called entropy, that refers to more than just the literal password length. A long password consisting of words easily found in a dictionary and using only a subset of possible characters (such as all lowercase letters) has lower entropy than a password that is the same length but uses random, uniformly distributed characters. Users must never store their passwords directly on a machine unless the passwords are also encrypted using a suf ciently strong encryption mechanism. Software providing password vaulting, a technique where a master password is used to decrypt a list of other passwords, is a reasonably safe way to store passwords. The master password must be at least as strong as the contained passwords. The Mac OS X keychain is an example of a password vault. We must also be careful when the password is supplied by a machine or process instead of a person. Hard-coding passwords in command scripts can make them vulnerable to both snooping and to system utilities that list the command lines of running processes. Hard-coding passwords in source code can make the passwords vulnerable to string extraction utilities. The best options use operating system provided keyprotection constructs such as keychains and ACL protected registries. In the future, secure hardware module key storage will probably become increasingly popular.
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The difference with respect to the de nition given for a binary BCH code is that coef cients gi of this generator polynomial belong to the extended Galois eld GF(q). On the other hand, the minimal polynomials are of the simplest form, i (X ) = X i . The most useful codes, in practice, of this class are de ned over Galois elds of the form GF(2m ), that is, nite elds with elements that have a binary representation in the form of a vector with elements over GF(2). Each element i is root of the minimal polynomial X i so that X i is a factor of X n 1. As a consequence of this, g(X ) is also a factor of X n 1 and hence it is the generator polynomial of a cyclic code with elements taken from GF(2m ). Since operations are de ned over GF(2m ), where the additive inverse of a given element is that same element, addition and subtraction are the same operation, and so to say that i is a root of X i is the same as to say that it is a root of X + i . An RS code can be equivalently de ned as the set of code polynomials c(X ) over GF(q) of degree deg{c(X )} n 1 that have , 2 , . . . , n k as their roots [3]. Therefore c(X ) CRS if and only if c( ) = c( 2 ) = c( 3 ) = = c( 2t ) = 0 where deg {c(X )} n 1 (3)
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because there is trade-off between them. Let us consider two extreme cases. One is known as always-update, in which each cell is a location area. Under always-update, a mobile station needs to update its location whenever it enters a new cell. Obviously, the cost of location update is very high, but there is no paging cost because the cellular system can just route an incoming call to the last reported cell without paging. The other is known as never-update, in which the whole service area is a location area. Therefore there is no cost of location update. However, the paging cost is very high because the cellular system needs to page every cell in the service area to find out the cell in which the mobile is currently located so an incoming call can be routed to the base station of that cell. Various approaches for location area planning in a city environment, the worst-case environment, are discussed in [25]. The simplest approach is the use of heuristic algorithms for approximating the optimal location area configuration. The approach collects a high number of location area configurations and picks up the best one. Although the approach does not guarantee the optimal location area configuration, the optimal solution can be approximated when the number of experiments is high. A more complex approach is based on area zones and highway topology. A city can have area zones such as the city center, suburbs, etc. Moreover, population movements between area zones are usually routed through main highways that connect the area zones. Naturally, the size of a location area is determined by the density of mobile subscribers, and the shape is determined by the highway topology. Location updates due to zig-zag movement can be avoided if location areas with overlapping borders are defined. The most complex approach is to create dynamic location area configurations based on the time-varying mobility and traffic conditions. For example, when the network detects a high-mobility and low-traffic time zone, it decides to reduce the number of location areas to reduce location updates. When the network detects an ordinary mobility and high-traffic time zone, it decides to increase the number of location areas to reduce paging. The above approaches are based on the mobility characteristics of the subscriber population. The authors in [25] also discussed location area planning based on the mobility characteristics of each individual mobile subscriber or a group of mobile subscribers. Another per-user dynamic location area strategy has been proposed in [43]. Their strategy uses the subscriber incoming call arrival rate and mobility to dynamically determine the size of a subscriber s location area, and their analytical results show their strategy is better than static ones when call arrival rates are subscriber- or time-dependent. In the classical location area strategy, the most recently visited location area ID is stored in a mobile station. Whenever the mobile station receives a new location area ID, it initiates a location update. In [19], the author has proposed a two location algorithm (TLA). The two location algorithm allows a mobile station to store the IDs of two most recently visited location areas. When a mobile station moves into a new location area, it checks to see if the new location is in the memory. If the new location is not found, the most recently visited location is kept and the other location is replaced by the new location. In this case, a location update is required to notify the cellular system of the change that has been made. If the new location is already in the memory, no location update is performed. When an incoming call arrives for a mobile station, two location areas are used to find the cell in which the mobile station is located. The order of the locations selected to locate the mobile station affects the performance of the algorithm. The possible
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The test against zero results in a Boolean value, effectively doing a conversion. With this correction and using defines, the test for left and right bumps become Listing 6-4.
system becomes, the less effective it may be as far as overall control is concerned. For example, if a food inventory control system has to be supported by a separate food cost control system, then two packages of software will be required, two different computer hardware systems may be needed, and two sets of user-operator systems will have to be learned. Initially, most microcomputer applications in the hospitality industry were stand-alone applications, but as the power and memory capacity of microcomputers increased, available software packages rapidly became more integrated and their use as stand-alone applications quickly diminished. Three of the common application-oriented software packages are word processing, databases, and spreadsheets. Word Processing Word processing refers to software that is programmed to manipulate words (text). Almost all microcomputers have a word-processing program installed, as it is quite versatile in the creation of written communications in a business environment. The purchase of a low-cost microcomputer to be used primarily for word processing is a good way to introduce computers into a business. Word processors can be very useful when a large amount of standard correspondence is handled, as in a hotel reservation department where a form letter is used to confirm reservations, or in a catering operation where a standard banquet contract is used. Only certain information, such as the number of expected guests, the menu selected, and the price of the meal, has to be inserted. The main purpose of a word processor is to facilitate text creation and editing, and the ease with which this may be done is a major factor in selection of word processing software. One of the major advantages of using computers rather than typewriters for word processing is that documents can be printed more attractively. For example, some computer printers have a variety of type styles that can be used in the same document, as well as allowing text editing; most word processing software contains spelling and grammar checkers. Database Applications A database is a collection of records such as addresses of regular customers, a food or beverage inventory listing, personnel data, or a file of recipes. These are all records that form a database. A database application allows quick access to, and ready manipulation of, the records that are in that database. In other words, it is much like an office filing system where records (files) can be randomly accessed, used as required, and then restored in the same order or rearranged in some other order before storing. For example, for one query, a database of recipes can be stored in alphabetic sequence according to the main recipe ingredient, and only recipes containing that ingredient can be printed out.
Note that we need to standardize the variables involved in the composite, to avoid the possibility that the greater variability of one of the variables will overwhelm that of the other variable. For example, the standard deviation of ber among all cereals is 2.38 grams, and the standard deviation of potassium is 71.29 milligrams. (The grams/milligrams scale difference is not at issue here. What is relevant is the difference in variability, even on their respective scales.) Figure 3.11 illustrates the difference in variability. We therefore proceed to perform the regression of nutritional rating on the variables sugarsz and shelf 2, and W = berz + potassiumz /2. The results are provided in Table 3.13. Note rst that the multicollinearity problem seems to have been resolved, with the VIF values all near 1. Note also, however, that the regression results are rather 2 disappointing, with the values of R 2 , Radj , and s all underperforming the model results found in Table 3.7, from the model y = 0 + 1 (sugars) + 2 ( ber) + 4 (shelf 2) + , which did not even include the potassium variable. What is going on here The problem stems from the fact that the ber variable is a very good predictor of nutritional rating, especially when coupled with sugar content, as we shall see later when we perform best subsets regression. Therefore,
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