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JUDGING THE SUCCESS OF THE 401(K) PLAN How effective is your 401(k) plan To help evaluate the strength of your plan, Roger Gray, head of client services for Scudder Retirement Services, provides some key comparison benchmarks. This section discusses the critical benchmarks that Gray culled from the Profit Sharing/401(k) Council of America s (PSCA) 46th Annual Survey, as well as his own experience with plan sponsors. Plan Participation More than three-quarters (80.3%) of eligible employees participate in a 401(k) plan when given the option (see Exhibit 5.5). Smaller plans have better participation rates overall than do larger ones, the PSCA survey showed, lending credence to the impact a more personal touch can have. Highly compensated employees, Gray noted, who can better afford to make contributions and have more need for tax shelters, are significantly more likely than nonhighly compensated employees to participate in an employer-sponsored savings plan. In addition, he noted, participation rates tend to vary by industry, with the finance industry reporting the highest rate. Companies in the manufacturing and retail arenas, meanwhile, struggle more for participation. Participant Contributions For the most part, employers typically offer 401(k) plans on a pretax contribution basis only. According to PSCA data, on average, about 74% of 401(k) plans are designed this way, followed by about 19% that offer plans allowing both pretax and after-tax contributions (see Exhibit 5.6).

Changing Crisp But Uncertain Input Numbers to Fuzzy Numbers We have already covered the most important new rule feature, the creation of new rules from a database of expert knowledge Another new feature is the creation of fuzzy numbers from crisp numbers Consider this rule: rule block 0 (goal Moves input data to "R egi ", converts to on fuzzy numbers) IF (in Data frame = <F> AND rnum = <N> AND area = <A> AND xbar = <X> AND ybar = <Y> border = <B>) THEN make Region frame = <F> rnum = <N> area = <A> 10 0:1 xbar = <X> 20 0 ybar = <Y> 20 0 border = "<B>"; The antecedent of this rule assigns variable names to some crisp input numbers Of these, the frame and region number are inherently crisp, being known to complete precision.

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This adjustment is input in the form of a constraint that is added to the nodal problem, and the nodal problem is once again solved An iterative process between the nodal problem and ow analysis results in a least-cost feasible solution..

ARIA ELENA (35) AND Jose (37) have been married for 10 years and have two children, Rob (9) and Sylvia (7) Maria Elena is an elementary school teacher and Jose is a state government employee in a managerial position Jose complains that Maria Elena has not enjoyed sex during the entire time they have been married He has tried to be patient but he is really tired of it Maria Elena says she is always busy with her work and the children and sex just is not that important to her During an early session, Jose admits that lately he has been having problems with premature ejaculation, commenting that before marriage I could go all night Jose states that he thinks his problem is due to the fact that Maria Elena always has to have things her way, including when, where, and how they have sex.

However, this is not true of the area and centroid coordinates xbar and ybar; these are quite subject to errors of measurement, and should more properly be represented by fuzzy numbers The rule consequent takes care of this The instruction make Region area ,A10 01 creates an area in an instance of Region that is a fuzzy number The new area will be an s-shaped fuzzy number with absolute uncertainty 10 ( /2 10 pixels) and a relative uncertainty of 10% [0:1 ,A (area) pixels] Similarly for the centroid measurements; the crisp xbar and ybar in Data are transformed into fuzzy numbers in Region with absolute uncertainty 20 pixels and zero relative uncertainty These fuzzy numbers have uncertainties that are consistent with the process of feature extraction from noisy echocardiogram images Resolving Contradictions Of more importance are the rules to resolve contradictory preliminary classi cations.

We are virtually guaranteed to have contradictions; the rules for classifying as RV (right ventricle) and as RA RV (merged right atrium and right ventricle have identical antecedents Here, we cannot use the cavalier approach of taking the most likely classi cation that we used in irispar Instead, we use logic and an additional piece of information; the frame in which the Region appears Suppose that in the same frame we have a region classi ed both as RA RV and RV If in the same frame we also have a region classi ed as RA, then the classi cation as RV is correct and that as RA RV is wrong; the RA cannot appear twice in a single frame Our rule for this purpose is rule block 4 (goal Rule out RA+RV if RV and RA+RV and RA) (13:3) 13:2.

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