Issues for further study

The topics covered include the following: a lunar outpost map, lunar resource utilization, asteroid resource utilization, space energy utilization, and space 'real estate' utilization

Lunar exploration for resource utilization

The strategy for developing resources on the Moon depends on the stage of space industrialization. A case is made for first developing the resources needed to provide simple materials required in large quantities for space operations. Propellants, shielding, and structural materials fall into this category. As the enterprise grows, it will be feasible to develop additional sources - those more difficult to obtain or required in smaller quantities. Thus, the first materials processing on the Moon will probably take the abundant lunar regolith, extract from it major mineral or glass species, and do relatively simple chemical processing. We need to conduct a lunar remote sensing mission to determine the global distribution of features, geophysical properties, and composition of the Moon, information which will serve as the basis for detailed models of and engineering decisions about a lunar mine

Effective Resource Utilization in Arkansas Public Schools

Teacher pay in Arkansas public schools varies widely from district to district across the state. This pay discrepancy is driven by both the funds available to a district and by how these funds are allocated. There is a standard per student budget given to districts across the state, but this budget can be supplemented by additional property taxes collected on property within a district. This leaves districts with more highly valued property at an advantage. Districts are free to allocate their budget for teacher pay as they see fit, with constraints on number of students per teacher and minimum teacher salary. This research has two main objectives: 1) investigate what variables affect student performance in Arkansas public schools and 2) determine the cost-effectiveness associated with changing possible decision variables in terms of improving student performance. The objectives were achieved by using public data available through the Arkansas Department of Education. Objective 1 was accomplished using feature selection and predictive modeling. Objective 2 integrated the results found from the first objective with district budget information in order to analyze the cost-effectiveness of different district budget policies. Results from this study are valuable to districts trying to improve student performance in the most cost-effective way

Modelling Energy Consumption based on Resource Utilization

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the best case.Comment: Submitted to Journal of Supercomputing on 14th June, 201

Integrated Generation Management for Maximizing Renewable Resource Utilization

Two proposed methods to reduce the effective intermittency and improve the efficiency of wind power generation in the grid are spatial smoothing of wind generation and utilization of short term electrical storage to deal with lulls in production. In this thesis, based on a concept called integrated generation management (IGM), we explore the impact of spatial smoothing and the use of emerging plug-in hybrid electric vehicles (PHEVs) as a potential storage resource to the smart-grid. IGM combines nuclear, slow load-following coal, fast load-following natural gas, and renewable wind generation with an optimal control method to maximize the renewable generation and minimize the fossil generation. With the increasing penetration of PHEVs, the power grid is seeing new opportunities to make itself smarter than ever by utilizing those relatively large batteries. Based on current projections of PHEV market penetration and various wind generation scenarios, we demonstrate the potential for efficient wind integration at levels of approaching 30% of the aver- age electrical load with utilization efficiency exceeding 65%. At lower levels of integration (e.g. 15%), efficiencies are possible exceeding 85%