Resources analysis

Shenoy, U. V. (1995). Heat Exchange Network Synthesis Process Optimization by Energy and Resource Analysis. Gulf Phb. Co., Houston, TX. 

Resource analysis for the development of FCP1 suggested that 180 000 per annum would need to be expended on R D, for a two year time period, to achieve a 10% yield at Step 2. The R D spend would therefore total 360 000. The pay back period for a successful project would be only twelve months. The probability of success was thought to be good for a 10% and very good for a 5% improvement in the yield. On this basis the fall back position for the project would pay back the R D expenditure in just two years. A decision was made to go ahead with the project. 

GEORGE TOLLEY is a professor in the Department of Economics and Cochairman of the Resource Analysis Group at the University of Chicago. He is the author of numerous articles and books on benefit-cost analysis. During 1974 and 1975 he was the Deputy Assistant Secretary of Treasury for tax policy. 

National Energy Technology Laboratory (NETL). Carbon Capture and Sequestration System Analysis Guidelines. April 2005. http //www.netl.doe.gov/ technolo-gies/carbon seq/Resources/Analysis/pubs/C02CaptureGuidelines.pdf. Accessed May 2007. 

Next, it requires resource analysis. At local level, this may not be easy, e.g. the administration of an expensive drug at an early point in the process, may result in the re-engineering of the existing process of care, where an anticipated, subsequent admission to hospital for treatment becomes redundant. 

An example of an inferential task-analysis approach that is relatively new is nonlinear causal resource analysis (NCRA) [Kondraske, 1998,1999 Kondraske et al., 1997]. This method was motivated by human performance analysis situations where direct analysis is not possible (e.g., determination of the amount of visual information-processing speed required to drive safely on a highway). Quantitative task demands, in terms of performance variables that characterize the involved subsystems, are inferred from a population data set that includes measures of subsystem performance, resource availabilities (e.g., speed, accuracy, etc.), and overall performance on the task in question. This method is based on the simple observation that the individual with the least amount of the given resource (i.e., the lowest performance capacity) who is still able to accomplish a given goal (i.e., achieve a given level of performance in the specified high-level task) provides the key clue. That amount of availability is used to infer the amount of demand imposed by the task. 

Kondraske, G.V., Johnston, C., Pearson, A., and Tarbox, L. 1997. Performance prediction and limiting resource identification with nonlinear causal resource analysis. In Proceedings of the 19th Annual Engineering in Medicine and Biology Society Conference, pp. 1813-1816. 

Fischer, C.A., Kondraske, G.V., and Stewart, R.M. 2002. Prediction of driving performance using nonlinear causal resource analysis. CD-ROM Proceedings of the 24th International Conference on IEEE Engineering in Medicine and Biological Society, Houston, October 23-26, pp. 2473-2474. 

Vasta, P. J. and Kondraske, G.V. 1994. Performance prediction of an upper extremity reciprocal task using non-linear causal resource analysis. Proc. Int. Conf. IEEE Eng. Med. Biol. Soc. 16 (CD-ROM). 

The approach first quantifies task requirements by combining techniques that produce detailed task-decomposition analyses of goals, actions, and required procedures (e.g., GOMS, NGOMSL, TAG, keystroke-level task model, or Rasmussen s cognitive fi amework) with timeline analysis, where applicable, and the elementary resource analysis of tasks. The task-decomposition models are used to specify tasks at a level at which the elemental mental resources required to perform the tasks can be specified. The timehne provides a basis for specifying performance requirements. Other performance dimensions too should be used depending on the task. 

Nonlinear Causal Resource Analysis (NCRA) Human Performance Institute, The University of Texas at Arlington, P.O. Box 19180, Arlington, TX 76019, (817) 272-2335. 

Resource analysis During problem solving, resources play a major role in TRIZ. The proper use of available resources helps to obtain more eost-effeetive and ideal solutions without eomplicating a system and introducing new expensive eomponents and materials... 

A conceptually different and relatively new example of an inferential model, motivated by human performance problems specifically, is nonlinear causal resource analysis (NCRA) (Vasta and Kondraske, 1994 Kondraske, 1988). Quantitative task demands, in terms of performance variables... 

This work was supported by NSF (CH-0316806) and a fund from Michigan Life Science Corridor Fund (1632) to P. G. Wang. This work was partially supported by ACS grant IRG-98-278-04, Research Starter Grant from PhRMA Foundation, and NIP from AACP to D. Sun. The author thanks The Ohio State University for providing research resources, analysis instruments and funding support. 

Burrough, P.A. 1986. Principles of Geographical Information Systems for Land Resource Analysis. Oxford University Press, Oxford. 

Morgan PJ, Smith K. Potentiality of seaweed as a resource analysis of the pyrolysis products of Fucus serratus. Analyst 1978 103 1053-60. 

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