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It is an instance of the Optimal Assignment Problem (OAP) [167], which is a wellknown problem originally studied in game theory, and then in operations research in the context of personnel assignment. Problem statement. Given m mobile nodes, each visiting one static node (a more general case will be studied later) and n prioritized static nodes, each require one mobile node to visit it for a given purpose (collect information, share energy, etc.). Also, given for each mobile node a non-negative estimate of the utility of visiting a mobile node, if a mobile node is incapable of undertaking a task (visiting a static node or performing a required operation after reaching a static node), then the mobile node is assigned a rating of zero for that task. The goal is to assign mobile nodes to static nodes so as to maximize overall expected performance, taking into account the priorities of the static nodes and the skill ratings of the mobile nodes. This problem can be cast as an ILP [167]: nd mn non-negative integers ij that maximize:

In the next step we split the product over all edges into individual factors w.r.t. the neighbors of the goal attribute: we write one factor for each neighbor. Each of these factors captures the part of the factorization that refers to the subgraph consisting of the attributes that can be reached from the goal attribute through this neighbor, including the factor potential of the edge that connects the neighbor to the goal attribute. That is, we write: P(Ag = ag )

Item Floor Walls Ceiling Windows Doors Lighting Electrical outlets Storage closets Carpet Paint Mold Other

It is obvious that imprecision, interpreted as set-valued information, can easily be handled by symbolic logic: for finite sets of alternatives we can simply write a disjunction of predicate expressions (for example, color(ball) = blue color(ball) = green color(ball) = turquoise to represent the first example statement given above). For intervals, we may introduce a predicate to compare values (for example, the predicate in 50 km/h velocity(car) velocity(car) 60 km/h to represent the second example statement). In other words: we can either list all alternatives in a disjunction or we can use a conjunction of predicates to bound the alternatives. Trivially, since imprecise statements can be represented in symbolic logic, we can draw inferences using the mechanisms of symbolic logic. However, w.r.t. the specific reasoning task we consider here, it is often more convenient to use relational algebra for inferences with imprecise statements. The reason is that the operations of relational algebra can be seen as a special case of logical inference rules that draw several inferences in one step. Consider a set of geometrical objects about which we know the rules x : x : x : x : color(x) = green color(x) = red color(x) = blue color(x) = yellow shape(x) = triangle, shape(x) = circle, shape(x) = circle, shape(x) = square.

Handbook of Applied Algorithms: Solving Scienti c, Engineering and Practical Problems Edited by Amiya Nayak and Ivan Stojmenovi Copyright 2008 John Wiley & Sons, Inc. c

You Make It Worth Their While, SMARTBUSINESSMAG.COM (May 2002), 17.

IMPROVING STAFF PRODUCTIVITY As part of its annual survey of credit and collections managers, IOMA asked about their best staff productivity approaches Many willingly shared their insights about the techniques that worked in their own organizations What is most constructive about these strategies is that they did not require any big investment of either money or IT resources something that credit managers often have a difficult time accessing Cross-train all employees in every aspect of the collection process The approach is rounded out by rotating branches, customers, and product lines so that eventually the entire staff knows about all accounts In this manner, unplanned absences or departures do not set the department back Set personal targets for each member of the staff This is especially important in a profit-sharing environment By setting the targets and discussing them quarterly, success criteria are easily identified and met.

[1] H. Holma and A. Toskala (eds) (2004), WCDMA for UMTS (3rd edn), John Wiley & Sons. [2] 3GPP, Technical Speci cation Group RAN, Physical channels and mapping of transport channels onto physical channels (FDD), 3GPP TS 25.211 version 6.7.0, Release 6, available at www.3gpp.org [3] 3GPP, Technical Speci cation Group RAN, Multiplexing and channel coding (FDD), 3GPP TS 25.212 version 6.7.0, Release 6, available at www.3gpp.org [4] 3GPP, Technical Speci cation Group RAN, Spreading and modulation (FDD), 3GPP TS 25.213 version 6.4.0, Release 6, available at www.3gpp.org [5] 3GPP, Technical Speci cation Group RAN, Physical layer procedures (FDD), 3GPP TS 25.214 version 6.7.1, Release 6, available at www.3gpp.org [6] 3GPP, Technical Speci cation Group RAN, Physical layer measurements (FDD), 3GPP TS 25.215 version 6.4.0, Release 6, available at www.3gpp.org [7] 3GPP, Technical Speci cation Group RAN, UE radio access capabilities de nition, 3GPP TS 25.306 version 6.7.0, Release 6, available at www.3gpp.org [8] 3GPP, Technical Speci cation Group RAN, FDD enhanced uplink; Overall description; Stage 2, 3GPP TS 25.309 version 6.4.0, Release 6, available at www.3gpp.org