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The value of group term life insurance paid for by the employer is excluded from income for the first $50,000 of life insurance purchased The excess value of life insurance coverage over this amount must be included in employee income This income is only subject to Social Security and Medicare taxes Use the following IRS table to determine the fair market value of group term life insurance per $1,000 of insurance for a range of age brackets:

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In this case a particle is attracted more to its better neighbors A disadvantage of the FIPS is that it does not consider the fact that the in uences of multiple particles may cancel each other For example, if two neighbors respectively contribute the amount of a and a to the velocity update of a speci c particle, then the sum of their in uences is zero Consider the e ect when all the neighbors of a particle are organized approximately symmetrically around the particle The change in weight due to the FIPS velocity term will then be approximately zero, causing the change in position to be determined only by vi (t) Barebones PSO Formal proofs [851, 863, 870] have shown that each particle converges to a point that is a weighted average between the personal best and neighborhood best positions If it is assumed that c1 = c2 , then a particle converges, in each dimension to yij (t) + yij (t) 2 (1656)

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This behavior supports Kennedy s proposal to replace the entire velocity by random numbers sampled from a Gaussian distribution with the mean as de ned in equation (1656) and deviation, (1657) = |yij (t) yij (t)|

Exhibit 61 Age Bracket Under Age 25 Age 25 29 Age 30 34 Age 35 39 Age 40 44 Age 45 49 Age 50 54 Age 55 59 Age 60 64 Age 65 69 Age 70 and above

165 Single-Solution Particle Swarm Optimization The velocity therefore changes to vij (t + 1) N yij (t) + yij (t) , 2

This text makes smart data strategy explicit, understandable, and actionable to those entities capable of leading their unique contribution to enterprise performance Proven examples are provided of how government and business enterprises can achieve their missions and goals more effectively so:

(1658)

Fair Market Value Multiplier for Group Term Life Insurance Value per $1,000 per Month $005 006 008 009 010 015 023 043 066 127 206

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In the above, yij can be the global best position (in the case of gbest PSO), the local best position (in the case of a neighborhood-based algorithm), a randomly selected neighbor, or the center of a FIPS neighborhood The position update changes to xij (t + 1) = vij (t + 1) (1659)

Leaders discover unique performance improvement advantages from adopting smart data strategy Government enterprise executives improve ability to achieve results more quickly Commercial enterprise participants discover how to maximize demonstrated value to government enterprise participants and discover how to maximize demonstrated value to government enterprise as members of the supply chain as prime contractors, subcontractors, and suppliers All bene t from more cost-effective strategy that changes information management economics

Kennedy also proposed an alternative to the barebones PSO velocity in equation (1658), where vij (t + 1) = yij (t) if U (0, 1) < 05 yij (t)+ ij (t) y N( , ) otherwise 2 (1660)

employee, who contributes $2 per month to this benefit The first $50,000 of this amount is excluded from the employee s gross income To calculate the value of the remaining $30,000, divide it by 1,000 and multiply the result by $023 (as taken from the above table for the 50-54 age bracket), which yields a fair value of $690 per month Then subtract her $200 monthly contribution to arrive at a net monthly value received of $490 Next, multiply the monthly

Based on equation (1660), there is a 50% chance that the j-th dimension of the particle dimension changes to the corresponding personal best position This version can be viewed as a mutation of the personal best position

value of $490 by 12 in order to obtain the full-year value of the life insurance, which is $5880 The $5880 should be reported as her gross income

Selection-Based PSO Angeline provided the rst approach to combine GA concepts with PSO [26], showing that PSO performance can be improved for certain classes of problems by adding a selection process similar to that which occurs in evolutionary algorithms The selection procedure as summarized in Algorithm 165 is executed before the velocity updates are calculated Algorithm 165 Selection-Based PSO Calculate the tness of all particles; for each particle i = 1, , ns do Randomly select nts particles; Score the performance of particle i against the nts randomly selected particles; end Sort the swarm based on performance scores; Replace the worst half of the swarm with the top half, without changing the personal best positions;

By surveying government and commercial enterprise customers for a number of years, and from having been directly engaged in related topics for more than 15 years, we observed the following:

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