approaches are available for solving the aggregate capacity planning problem now. One of the mathematical programming techniques is linear programming. This complex mathematical algorithm is not easily understood and implemented. However, it can provide management with improved acumen to see the situation

and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including

A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like

This paper considers the relationship between aggregate productivity and stock and flow government-spending variables. The empirical results indicate that (i) the nonmilitary public capital stock is dramatically more important in determining productivity than is either the flow of nonmilitary

We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm

as model construction. An operational semantics is provided for PEPA and its use to generate an underlying Markov process for any PEPA model is explained and demonstrated. Model simplification and state space aggregation have been proposed as means to tackle the problems of large performance models

(but decreasing marginal) productivity of labor, reduced growth rates of labor income, excess capacity, and the requirement for downsizing and exit. The last two decades indicate corporate internal control systems have failed to deal effectively with these changes, especially slow growth

—without subject control, without stressing the capacity limitations of the system, and without necessarily demanding attention. Controlled processing is a temporary activation of a se-quence of elements that can be set up quickly and easily but requires attention, is capacity-limited (usually serial in nature

State of the art, real-time, rate-adaptive, multimedia applications adjust their transmission rate to match the available network capacity. Unfortunately, this source-based rate-adaptation performs poorly in a heterogeneous multicast environment because there is no single target rate

evidence of within plant productivity improvements that can be attributed to a liberalized trade for the plants in the import-competing sector. In many cases, aggregate productivity improvements stem from the reshuffling of resources and output from less to more efficient producers.