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treatment for cancer patients, for example, involves more than treating tumor cells. An individual s quality of life is powerfully affected by the characteristics of the disease and its treatment, but it is also shaped by the personal and social resources and coping strategies that the person brings to the experience. The biopsychosocial model provides a framework for understanding how psychological approaches can be interwoven with medical care to help patients adapt to their illness. Moreover, it has helped to guide an exciting field of research that is exploring how psychosocial factors may affect immune activity, neuroendocrine functioning, and disease outcomes among individuals with cancer or other illnesses. Typical Schedule:

, 125, 144 Spokane, A R, 576, 588 Sprafka, J M, 361, 563 Spratt, K F, 313 Spraul, M, 195, 216 Spring, B, 125, 143, 157, 168 Spring, S B, 389 Springs, F E, 399, 412 Spritzer, K, 107, 116 Spruell, M, 426, 432 Squier, C, 73, 243 Squires, K E, 241 Srivastava, E D, 412 Staal, J A, 358 Staats, H, 91, 360 Stabler, B, 196, 216.

In a distributed system, a transaction can either be a local transaction or a global transaction. A local transaction is a transaction that performs all its work at the site where it originates. In other words, a local transaction does not leave its originating site. A global transaction, on the other hand, is a transaction that has to perform work at one or more sites different from its originating site. The originating site is the site where the transaction rst enters the system. That is where the transaction is assigned to a transaction monitor, TM, as mentioned before. A global transaction is carried out as a set of local transactions each running as an agent of the global transaction at a given site. Each local transaction is a subtransaction of a global transaction. This introduces a new degree of dif culty in committing a global transaction. Using this model, we can write the equity adjustment transaction we discussed in Example 5.5 as global transaction T1 consisting of two local transactions T1LA and T1NY:

The number of elements of a eld is called the order of that eld. A eld with a nite number of elements is usually called a nite eld, or Galois eld GF. The inverse for the addition operation of an element of the eld a F is denoted as a, and inverse for the multiplication operation of an element of the eld is denoted as a 1 . Subtraction and division operations are de ned as a function of the inverse elements as a b = a + ( b) a/b = a(b 1 ) (8)

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section 14.4. The data in all experiments were normalized based on the estimated range as described in Hastie et al. (2001) and Angelov and Zhou (2006). Both experiments are conducted in a simulated online manner. eSensor starts with an empty fuzzy rule base and generates it on-the- y based on the data that are provided sample-by-sample and disregarded from the memory once processed. Based on the data spatial distribution, eSensor evolves the structure of the fuzzy rule base on-the- y and optimizes the parameters as detailed in 2. The output prediction is calculated for every data sample and can be used at any time instant. Training samples are provided when they are available. The experimental results indicate that the selection of most important input variables signi cantly improves the performance of the inferential sensor in both precision and interpretability (simplicity) terms.