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complex array of changing, medically plausible connections Further, the design and timing of risk-reducing interventions would clearly depend on a more re ned understanding of the speci c processes involved Epidemiological Studies of Psychosocial Risk Reliable associations between psychosocial risk factors and disease are the empirical cornerstone of research in this area Without such evidence, there is little justi cation to assert that personality, stress, emotions, or characteristics of the social environment affect health Studies of this type examine characteristics that might in uence the development of disease, but also of interest are the associations of these characteristics with the prognosis of established disease Studies of both types have common potential methodological limitations, as well as unique ones The Limitations of Cross-Sectional Designs The potential association between psychosocial risk factors and health outcomes has often been examined with very limited research designs and methods of assessment.

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on the clique packing number, where f (X, M) is a lower bound on pM(X) that can be computed efficiently. A simple method is to employ the sum bound f (X, M) =

For example, cross-sectional comparisons of participants levels of a given risk factor in groups with and without a given disease have been common However, many psychosocial variables (eg, negative emotions, coping strategies or styles) could plausibly both in uence and be in uenced by physical illness (Cohen & Rodriguez, 1995) Hence, the direction of causality is a very serious concern in cross-sectional designs Further, studies of this type (ie, cross-sectional case-control designs) often compare patients suffering from a speci c condition recruited from a medical clinic of some sort with controls recruited from a convenient population (eg, hospital employees or medical outpatients on routine visits) Selection processes differ across such groups, and those processes may be associated with the psychosocial risk factor of interest.

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For example, patients with early stages of asymptomatic cancer undergoing treatment in a specialty clinic may be characterized by a greater degree of health worry than are individuals with a similar level or stage of cancer who have not yet sought medical attention Hence, health worries and associated variables (eg, negative emotionality) are likely to be overrepresented in the clinic sample Therefore, psychosocial differences between cases and controls could re ect selection processes rather than the presence versus absence of the disease Given the maturing status of the eld, cross-sectional designs should be used only in initial investigations, and they should be interpreted cautiously and replicated with more.

Data aggregation and query dissemination are important issues in wireless sensor networks [43]. Sensor nodes are typically energy constrained. Therefore, it is desirable to minimize the number of messages relayed, because radio transmissions can quickly consume battery power. A naive approach to reporting sensed phenomena is one where all (raw) sensor readings are relayed to a base station for off-line analysis and processing. However, since sensor nodes within the same vicinity often detect the same, common phenomena, it is likely some redundancy in sensor readings will occur [44]. Local collaboration allows nearby sensor nodes to lter and process sensor readings before transmitting them to a base station. Consequently, this process can reduce the number of messages relayed to the base station. Figure 1.8 represents an animal-tracking application where several sensor nodes are randomly deployed in a forest. When an animal, represented by the solid square, passes through the area being monitored, individual sensor nodes detect the presence of the animal and relay their ndings, in a multihop fashion, to the base station located some distance away. In suf ciently dense sensor networks, overlapping areas of coverage are possible. Thus, the animal may be detected by several sensors. In the scenario presented in Figure 1.8, nodes A, B, C, D, and E sense the presence of a nearby animal. Nodes B E each send a message to node A with their observed sensor data. Node A forwards the received messages, along with its own set of sensor readings, to the next node along the path to the base station. Thus, node A sends a total

Now you have to get ready to call the script. You need to instantiate an NSTask for the execution and two pipes to handle reading and writing data with the script.

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