Creating Function Procedures in Java

Draw PDF417 in Java Creating Function Procedures

Note: Angular is appropriate only for blending shapes with sharp corners and straight lines. If the shapes you choose do not have corners, Flash reverts to distributive shape tweening.

4. Goals should also be expressed relative to desired results to be achieved within a specified time frame. For example, a restaurant manager might state that her objective is to increase sales by adding a special entr e to the menu each day. This is not an objective because no results have been stated in quantitative terms and no time frame has been established. Instead, the restaurant manager has stated how she is going to achieve something. Stating how something is to be achieved is not an objective. The restaurant manager in this situation would not find it difficult to implement what she says the objective is, but there is no way to measure the results. A more comprehensive objective for this restaurant might be to increase sales by increasing customer average check by $1 by the end of a 90-day period. Research has shown that the more objective a performance measure is, the more likely it is that supervisors and those they supervise will work with effort. Accounting systems play a key role in this because they can provide relatively objective performance evaluations. Research further shows that unless those being measured think that their behavior can influence the performance measure, they are unlikely to invest effort to achieve goals. The measurement criteria, or standards, motivate the individuals to perform according to a clear understanding of expectations. An important aspect of MBO is that the department heads are not judged on a personal basis, but rather, against the mutually agreed upon standards. If standards are not achieved, the employee is not penalized, but, rather, is assisted by the supervisor in locating problem areas and identifying the cause of the problem. This investigation is then used to assist the department head in future performance or, if necessary, in reestablishing performance standards if the standards are the fault.

consider athletic career demands together with (a) developmental tasks related to childhood, adolescence, or adulthood; ( b) changes in relationships and support that athletes get from their significant others, including coaches, parents, siblings, peers, and partners (Wylleman, De Knop, Ewing, & Cumming, 2000); and (c) athletes progress through the steps in a corresponding educational system or in a vocational career. Transitions in different spheres of life might overlap, and thus might create difficult life situations for athletes. It is really important to be able to predict such overlaps. Chronological age markers for the athletic and nonathletic transitions, suggested by the model, are averaged over many athletes and several sports (Wylleman & Lavallee, 2004, p. 516) and need to be specified in research for different sports, genders, and cultures. This model has a sound applied potential and directs applied sport psychologists to create a holistic view of the athlete and his or her social environment that is particularly important in individual counseling interventions. Career Transition Explanatory Models Career transition models (Schlossberg, 1981; Stambulova, 1997, 2003; Taylor & Ogilvie, 1994, 2001a) have explanatory potential and focus on reasons and demands, coping

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relaxation (Millman et al. 1981; Millman et al. 1983). This would indicate that in living muscle the amount of water within the lamentous structure of the cell would not necessarily change. However, the location of this water can be affected by changes in volume as muscle undergoes rigor. As muscle goes into rigor, cross-bridges form between the thick and thin laments, thus reducing available space for water to reside (Offer and Trinick 1983). It has been shown that as the pH of porcine muscle is reduced from physiological values to 5.2 5.6 (near the isoelectric point of myosin), the distance between the thick laments declines an average of 2.5 nm (Diesbourg et al. 1988). This decline in lament spacing may force sarcoplasmic uid from between the myo laments to the extramyo brillar space. Indeed, it has been hypothesized that enough uid may be lost from the intramyo brillar space to increase the extramyo brillar volume by as much as 1.6 times more than its pre-rigor volume (Bendall and Swatland 1988). During the development of rigor, the diameter of muscle cells decreases (Hegarty 1970; Swatland and Belfry 1985) and is likely the result of transmittal of the lateral shrinkage of the myo brils to the entire cell (Diesbourg et al. 1988). Additionally, during rigor development, sarcomeres can shorten; this also reduces the space available for water within the myo bril. In fact, it has been shown that drip loss can increase linearly with a decrease in the length of the sarcomeres in muscle cells (Honikel et al. 1986). More recently, highly sensitive low- eld nuclear magnetic resonance (NMR) studies have been used to gain a more complete understanding of the relationship between muscle cell structure and water distribution (Bertram et al. 2002). These studies have suggested that within the myo bril, a higher proportion of water is held in the I-band than in the more protein-dense A-band. This observation may help explain why shorter sarcomeres (especially in cold-shortened

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Every year, more than 1 exabyte (1018 bytes) of data is generated worldwide, most of it in digital form [1]. Data-driven methodologies for automatic generation of computational models are probably one of the most important tools needed to be developed to properly process and use that immense quantity of information [2]. In most applications (industry, medicine, nance, business, etc.), these methodologies should be iterative, to process the data as it is being reported in real time, and transparent, building a linguistic model clearly interpretable by humans. eTS (evolving Takagi-Sugeno systems) is one of those methodologies [3]. In the present work, the eTS developed by [4] is improved, increasing the rule-span in state space, and used to build fuzzy systems from data. Traditionally, the most important property of a fuzzy system has been its accuracy in representing the real system (for simulation, prediction, decision making, etc.). However, the obtained fuzzy systems frequently are without practical utility because it is impossible to give some semantic meaning to the rules due to fuzzy sets superposition, and rules that are sometimes redundant and sometimes contradictory, frequently with high complexity. In [5] a comprehensive collection of papers present several approaches for evolving the fuzzy systems to more interpretability (mainly of ine trained or expert-based ones). In recent years, interpretability has been considered to be the key feature of fuzzy models [6, 7], continuing previous important works [8, 9], and can be pursued by rule base simpli cation and reduction methods. There is actually considerable activity concerning this challenging problem. Several perspectives are being developed, for example, by fuzzy set merging using entropy measures [10], by genetic optimization [11 13], by multiobjective evolutionary algorithms [14 16], by manipulating the cost index [17, 18], or by Radial Basis Function Networks [19]. For a more detailed review and treatment, see [22] or [23]. Most of the known methods have been developed for batch processing for an already-existing rule base. Kernel-based learning methods, such as Support Vector Machines (SVMs), Relevance Vector Machines (RVMs), or Kernel PCA, have been studied extensively in the machine-learning community, being among the best learning techniques for many benchmark data sets and real-world applications [24]. The basic idea of these methods is to preprocess the data by some nonlinear mapping (using the kernel trick) and then apply a linear algorithm in a usually higher-dimensional feature space. Such large feature spaces are handled with the simple computational cost of the kernel function. This approach indicates an alternative point of view to the parameters learning of a fuzzy system, with the membership functions being those examples that are critical to solving the given task. In the traditional forms of automatic learning, the functions are simply regarded as clusters. The objective of the present chapter is online learning, for developing models in real time for classi cation problems and dynamic systems. Computational time is a critical issue and simple, ef cient techniques are searched for. A pruning technique is developed and tested to reduce the degree of redundancy and unnecessary complexity arising in the automated building of fuzzy rules. This improves the human semantic interpretability and as a consequence the usefulness of the results, allowing the merging of compatible fuzzy sets and possibly reduction of the number of rules and features. The fuzzy system is based on an improved version of the eTS algorithm of [4], strengthening its capability to