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

The task of quickly bringing novel pharmaceutical products to market at reasonable cost is daunting. While both pharmaceutical sales and R&D expenses are on the rise, the latter is outpacing the former. In order to increase top line revenues, pharmaceutical firms have been employing a range of strategies to supplement the vitality of their R&D portfolios. While these strategies have the potential to increase top line revenues, they typically increase the demand for development resources that are already in short supply. We posit that the ability to accurately estimate and intelligently allocate scarce development resources is critical to controlling the cost of bringing new pharmaceuticals to market. In this paper, we consider development and implementation of steady-state and dynamic models to estimate resource requirements for the R&D process. Simulation results for a sample R&D pipeline will be considered.

ABSTRACT Large-scale Internet services require a computing infrastructure that can be appropriately described as a warehouse-sized computing system. The cost of building datacenter facilities capable of delivering a given power capacity to such a computer can rival the recurring energy consumption

, it is feasible to construct systems with aggregate capacities of tens of terabits per second and that e ciently support highly dynamic data communication applications with individual channel rates of 10 Gb/s. The integrated architectures allow greater scalability and superior performance, but their viability

as primal variables and congestion measures as dual variables, and congestion control as a distributed primal-dual algorithm carried out over the Internet to maximize aggregate utility subject to capacity constraints. The primal iteration is carried out by TCP algorithms such as Reno or Vegas

The proliferation of information on the Internet and access to fast computers with large storage capaci-ties has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private

simultaneously use all accessible APs and obtain the sum of their backhaul bandwidth. Past work can connect to multiple APs, but can neither aggregate AP backhaul bandwidth nor can it maintain concurrent TCPs across them. This paper introduces FatVAP, an 802.11 driver that aggregates the bandwidth available

Wireless sensor networks (WSNs) are ad-hoc networks composed of tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to the lifetime of a WSN to minimize the number of bits sent by each

effect of flow capacities on the burstiness of