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The essential elements of any radio system are: (1) a transmitter for generating and modulating a high-frequency carrier wave with an information baseband6; (2) a transmitting antenna that will radiate the maximum amount of signal energy of the modulated carrier in the desired direction; (3) a receiving antenna that will intercept the maximum amount of the radiated energy after its transmission through space; and (4) a receiver to select the desired carrier wave, amplify the signal, detect it, or separate the signal from the carrier. Although the basic principles are the same in all cases, there are many different designs of radio systems. These differences depend upon the types of signals to be transmitted, type of modulation (AM, FM, or PM or a hybrid), where in the frequency spectrum (see Figure 2.6) in which transmission is to be affected, and licensing restrictions. Figure 2.14 is a generalized model of a radio link. The information-transport capacity of a radio link depends on many factors. The rst factor is the application. The following is a brief list of applications with some relevant RF bandwidths:

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2 See the USEPA site http://www.epa.gov/ozone/geninfo/numbers.html for details on the naming convention for ozone-depleting substances, such as CFCs and their bromine-containing cousins, the halons. 3 See Refs. [21] ( 3) and [24] ( 2), and for a discussion of energy levels in atoms and molecules and their importance in atmospheric chemistry. 4 In the atmospheric science community, the term heterogeneous chemistry is generally taken to include any reactions occurring on or in particles in the atmosphere. Reactions occurring in liquid particles are, of course, homogeneous liquid-phase reactions, sensu stricto, but atmospheric chemistry models usually fold the gas particle partitioning into the rate expression (see below), so there is some justi cation for the terminology.

context) These examples of imperfect historical t could easily be expanded upon The ve factors owe their consolidation and impact to analyses of large data matrices that did not become possible until the last decades of the twentieth century This section starts with setting out the strongest possible case for PCA by presenting a classical (see Horst, 1965) rationale for it Next, it examines the grounds for the magical number ve It then considers the so-named person-centered approach as an alternative to PCA in certain contexts The Case for Principal Component Analysis Applying PCA to a scores matrix is the logical consequence of performing item analysis In the general case, the aim of item analysis is to maximize the internal consistency of one or more scales based on the items; the exception whereby items are weighted by their predictive validity is outside the present scope.

The promotion and maintenance of health involves psychosocial processes at the interface between the individual, the health care system, and society (Marks et al, 2000) Health psychology is concerned with the psychological aspects of the promotion, improvement, and maintenance of health The ecological context of these psychological aspects of health includes the many in uential social systems within which human beings exist: families, workplaces, organizations, communities, societies, and cultures (Marks, 1996; Marks et al, 2000; Whitehead, 1995) Any psychological activity, process, or intervention that enhances well-being is of interest to health psychology Equally, any activity, process, or circumstance which has psychological components and which threatens well-being is of concern to health psychology Interventions need to be considered in the light of the prevailing environmental conditions that contain the contextual cues for health-related behaviors A behavioral change resulting from an intervention delivered in one speci c environment (eg.

The basic idea of item analysis may be expressed as follows: The investigator is aware that each single item, carefully chosen as it may be, is an imperfect operationalization of whatever construct it represents But the investigator has no better criterion against which to gauge the validity of the item than the total score on the set of equivalent items Item analysis is thus a bootstrapping operation Carrying this basic idea to its logical consequence proceeds as follows: At the rst step, items are weighted according to their association with the total score Discarding items on that basis would amount to arbitrarily assigning a zero weight That may be defensible in extreme cases where it is evident for substantive reasons albeit post hoc that the item does not belong in the set In the general case, however, all items would be retained.