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(Due to these limitations, we strongly suggest a discontinuation of this type of design and instead recommend the creation of other comparison conditions that incorporate a greater number of potential causative factors in common with the treatment condition under investigation) It should be noted at this point that all of the comparison conditions described in the following sections do control for the variables ordinarily held constant by the no-treatment condition (ie, history, maturation, etc) They also control for other potentially causative factors inherent to a therapy, and the degree to which those factors are held constant is directly related to the strength of causal conclusions investigators may draw.

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Throughout this discussion, however, it is important to keep in mind the potential presence of unmeasured variables that may differ between groups differences that would limit the strength of causal conclusions (for an in-depth discussion of these issues, the reader is referred to this chapter s section entitled Random Assignment Within Waves ) Common (Nonspeci c) Factors or Placebo Comparison Design Whereas the no-treatment design allows researchers to reject the hypothesis that variables associated with history,.

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Johnson noted that the time constant for base charge storage is related to the transit time (e.g., ignoring recombination time terms and charge loss), thus the expression simpli es to Im V m Em v s Co t This expression has value in the relationship between the maximum current and capacitance ratio is equal to the maximum electric eld and saturation velocity product. In de ning a reactive impedance for the capacitance term, the Johnson Limit can be expressed as a function of maximum current and reactive impedance, as Im Xc fT where Xc 1 2pfT Co Em s 2p

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Users/customers In this model, users and customers are those that are already using and paying for the existing basic service, such as of a wellness or healthcare company. The customers may be individual consumers, enterprises buying services for their employees, hobby clubs acquiring services for their members, and government or municipal organisations providing services for citizens. The de nition of the user/customer component includes the issue of segmentation and deep, structured understanding of end-users and customers. Existing service providers (e.g. healthcare and training centres) are in the best positions in the market for having a sound knowledge of their customers, as they already have an established relationship with them and are most likely ready to pro t from the new learning allowed by this service extension. Products/services Applying mobile solutions for enhancing the existing service adds usefulness and convenience for the end-users and ef ciency for the customer. For example, a healthcare company can use mobile technology for appointment reservations. The information generated about training centre customers can be useful for planning proper health-related activities, increasing the service level and sophistication, augmenting the organisation knowledge base. When the service is provided to existing users/customers and it can be integrated with standard devices and platforms (mobile phones, PCs, web services, etc.), it will be distinctively convenient. Members of a training centre will experience an improved and better-than-average service, because wellness monitoring and data sharing add something new and speci c to the standard training service. In other words, the existing business leverages innovative mobile technologies to distinctively shape and differentiate its offering. The de nition of the service component in this business model may also be motivated by the reducing of costs. For a healthcare company or public healthcare institutions these new

This chapter provides an overview of data modeling concepts and techniques, examines data modeling, and discusses its purpose. We consider different ways to create and categorize data models and data modeling languages and next, focus on conceptual data modeling and present three different languages for creating conceptual data models and diagrams. We explore entity relationship modeling in detail and consider some other conceptual modeling techniques. We brie y discuss logical data modeling and how its purpose is different from conceptual data modeling. Similarly, we discuss physical data modeling and how its purpose is different from both conceptual and logical data modeling and then brie y consider some of the nomenclature and notations used in capturing these various types of data models. Finally, we examine how these different types of data modeling coexist within a heterogeneous database environment.

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