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Review of systems, evaluation, lab tests and treatment Symptoms of starvation and purging Cold intolerance Amenorrhea, delayed menarch Light-headedness/lack of concentration Abdominal bloating Fatigue Constipation/diarrhoea Sore throat and chest pain Face and extremity oedema Physical exam Dry skin, brittle hair and nails Decreased subcutaneous fat Hypothermia Bradycardia Lanugo Cold and discolored hands and feet Orthostatic blood pressure changes Parotid gland enlargement Erosion of dental enamel Laboratory evaluation Urine analysis Complete blood count and sedimentation rate Chemistry panel including electrolytes, calcium, magnesium and renal, thyroid and liver function tests Indication for an electrocardiogram Pulse is less than 50 bpma (dependent on sport participation) Electrolyte abnormality Frequent purging behaviour Treatment Multidisciplinary team approach: Physician Nutritionist Psychologist/mental health professional Coach ( )b Trainer ( )b Criteria for hospitalisation: Weight 30% below normal Hypotension/dehydration Electrolyte abnormalities

A surface is the common name ascribed to a model of a multivariabled function. In the interest of brevity, we restrict our attention to modelling functions in just two variables f = f (x, y). The ppf.market.surface module offers the class surface for their representation, which is associated with the commonly encountered bilinear interpolation scheme that relies on the ppf.utility.bound function (refer to section 4.3):

Summary

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We have programmed three problems of the What type, none trivial, and all arising from real-world problems. Iris.par for speciating iris plants used 7 rules; echo.par and schizo.par used 25 programmer-written rules each, although echo.par automatically generated another dozen or so rules from its expert knowledge database. We think this demonstrates that a well-written fuzzy expert system can solve well-de ned nontrivial tasks with a relatively few number of rules. Of course, in these programs we are not trying to solve dif cult intellectual tasks like theorem proving. (As a matter of fact, few humans can prove theorems!) But when we add the ability to learn, we are really well on our way to emulating human thought. All these programs input data, and if the data are numeric we put it in word form as soon as possible. All our data words will have truth values attached, whether computed or assigned subjectively by humans. We are very quickly led to ambiguities, which we treasure; good ambiguities lead to robustness of our program. If it is necessary to give a crisp output, we use resolution of contradictions with as much smartness as we can muster, either looking at existing data more critically or acquiring new data. But it is not always advisable to give crisp answers, particularly if we have not embodied suf cient knowledge and skills into our program, and very seldom with medical diagnoses. If a program can signi cantly narrow down the range of uncertainty, it has done a good job. 13.6 13.1 QUESTIONS What fuzzy operation is almost always carried out in problems where the answer is a number, but seldom carried out if the answer is a word

The first part of this section analyzes the naming problem in more detail. The analysis yields requirements for a distributed object name server that state what a designer expects from a name server. We use these requirements to present the naming services of the objectoriented middleware systems that we introduced in 4. We present the naming primitives of the C O RBA Naming service and discuss C O M Monikers and the RMI Registry.

Query Case retrieval Refine query and retrieve again Analysis of retrieval Inappropriate case further retrievals not possible FAILURE Case appropriate to adapt Case(s) Case base

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where pij|k = P(C = ci , A = aj | B = bk ) and pi.|k and p.j|k likewise. That is, Igain (C, AB) = Igain (C, B) only holds if the attributes C and A are conditionally independent given attribute B.

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Therefore, as regards both price ceilings and price floors, remember that great mischief is typically caused when you interfere with the markets. This reasoning doesn t mean that governments should never intervene in the operation of a market, but rather that they need to be smart enough not to intervene in ways that they know are going to lead to perverse results. (Of course, from our own individual, selfish and short-sighted perspective, we actually hope that the government decides to support the salaries of academic economists. We d love to end up getting paid not to teach.)

H(Shannon) (Ai )

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