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Books on the topic 'Optimum configuration'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Powe, Neil. Is there an optimum configuration of settlement sizes? Countryside Change Unit, Dept. of Agricultural Economics & Food Marketing, University of Newcastle upon Tyne, 1993.

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2

Bejan, Adrian, and Giuseppe Grazzini, eds. Shape and Thermodynamics. Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-836-9.

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Shape and Thermodynamics is a two-day international Workshop focused on the Constructal Theory of generation of configuration in nature and engineering. From the early developments related to tree configurations for the cooling of electronics, today Constructal theory is being applied to conceptual design of transportation net-works, river basins, living bodies, building materials and many other flow systems. Constructal theory is also enriching thermo-dynamics, from basic theory to design and optimization. This theory approaches design "as science", with the generation of configuration regarded as a phenomenon of all physics, based on principle (the Constructal law). For example, Constructal Theory contributes to the evolution of fuel cells, in the design of cooling channels, the optimal feeding of reactants, etc. Important applications are also found in the design of heat exchangers, district heating networks, etc. The growing scientific literature on Constructal Theory has an important Italian component, although further dissemination is timely. Moreover, the relation with other thermodynamic research areas deserves to be explored. Website: Shape and Thermodinamics

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3

Sommerfeldt, Carl. Optimal hypertorus configurations for water-scale multiprocessor interconnection networks. National Library of Canada, 1990.

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4

Kumar, R. A novel multireceiver communications system configuration based on optimal estimation theory. Jet Propulsion Laboratory, 1990.

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5

Nemec, Marian. Optimal shape design of aerodynamic configurations: A Newton-Krylov approach. University of Toronto, Institute for Aerospace Studies, 2003.

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6

Center, Langley Research, ed. Method for optimal configuration of an ECLSS on the Space Station Freedom. National Aeronautics and Space Administration, Langley Research Center, 1991.

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7

Street, Deborah J. The construction of optimal stated choice experiments: Theory and methods. Wiley, 2007.

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8

Street, Deborah J. The construction of optimal stated choice experiments: Theory and methods. Wiley, 2007.

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9

Iollo, Angelo. Pseudo-time method for optimal shape design using the Euler equations. Institute for Computer Applications in Science and Engineering, 1995.

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10

Geojoe, Kuruvila, Ta'asan Shlomo, and Institute for Computer Applications in Science and Engineering., eds. Pseudo-time method for optimal shape design using the Euler equations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1995.

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11

Geojoe, Kuruvila, Ta'asan Shlomo, and Institute for Computer Applications in Science and Engineering., eds. Pseudo-time method for optimal shape design using the Euler equations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1995.

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12

Geojoe, Kuruvila, Ta'asan Shlomo, and Institute for Computer Applications in Science and Engineering., eds. Pseudo-time method for optimal shape design using the Euler equations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1995.

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13

Biezad, Daniel J. Designing low bandwidth propulsive-only flight controllers. National Aeronautics and Space Administration, 1996.

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14

Optimum Choice of Energy System Configuration and Storages for a Proper Match between Energy Conversion and Demands. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03928-037-7.

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15

Optimum aerodynamic design via boundary control. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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16

National Aeronautics and Space Administration (NASA) Staff. Novel Multireceiver Communications System Configuration Based on Optimal Estimation Theory. Independently Published, 2018.

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17

National Aeronautics and Space Administration (NASA) Staff. Optimal Configuration of Human Motion Tracking Systems: A Systems Engineering Approach. Independently Published, 2018.

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18

National Aeronautics and Space Administration (NASA) Staff. Method for Optimal Configuration of an Eclss on the Space Station Freedom. Independently Published, 2018.

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19

Street, Deborah J., and Leonie Burgess. Construction of Optimal Stated Choice Experiments: Theory and Methods. Wiley & Sons, Incorporated, John, 2007.

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20

Street, Deborah J., and Leonie Burgess. Construction of Optimal Stated Choice Experiments: Theory and Methods. Wiley & Sons, Incorporated, John, 2007.

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21

Montgomery, Erwin B. Algorithm for Selecting Electrode Configurations and Stimulation Parameters. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.003.0014.

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Chapter 9, Approaches to Programming, provided a general discussion regarding the approaches to DBS programming. The focus of Chapter 9 was on the underlying electroneurophysiological principles rather an explicit algorithm that addressed every possible circumstance. Chapters 11, Chapter 12, and Chapter 13 discussed approaches in the context of specific DBS targets. These approaches emphasized interpreting the DBS responses to visualize the location of the DBS contacts in the unique regional anatomy of the individual patient. For example, the production of paresthesias at stimulation currents insufficient to produce clinical benefit with DBS in the vicinity of the STN indicates that the DBS lead position is probably too posterior. This chapter gives an algorithm that takes the programmer step by step through the process of positioning DBS leads and contacts, and determining stimulation levels for optimal results.

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22

Street, Deborah J., and Leonie Burgess. The Construction of Optimal Stated Choice Experiments: Theory and Methods (Wiley Series in Probability and Statistics). Wiley-Interscience, 2007.

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23

Beck, Tobias Alexander. Optimal Operation, Configuration and Sizing of Energy Storage and Energy Conversion Technologies for Residential House Energy Systems. Logos Verlag Berlin, 2020.

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24

Montgomery, Erwin B. Approaches to Programming. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.003.0009.

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Persistence and patience are the keys to successful DBS programming. Several studies show that one of the most common causes of unsuccessful DBS therapy is an inadequately programmed IPG. Note that with the rapid increase in the types and manufacturers of DBS systems now and anticipated, it is difficult to make precise statements about stimulation parameters. The responsibility for choice of stimulation parameters, electrode configurations, and pulse trains lies solely with the programmer. Specific examples of stimulation parameters, electrode configurations, and pulse trains provided in this text are for informational and educational purposes only. Covered in this chapter are issues of battery life, the optimal time to program DBS, monopolar surveys, and programming to prevent adverse effects.

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25

Montgomery, Erwin B. Introduction. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.003.0001.

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Even by the standards at the time of the first edition, programming for some patients can still be a challenge. Even with the old systems, there were literally thousands of possible combinations of stimulator parameters which often intimidates programmers. The increase in functionality, such as multiple stimulation patterns and interleaved electrode configurations, has exponentially increased the number of combinations of DBS settings. Fortunately, most patients respond to a similar and narrow range of combinations, provided that the DBS stimulating leads are optimally placed. For other patients, however, dedicated effort is required to identify the optimal combination. The greatest danger for patients is that programmers will give up too soon. The premise of this text is that post-operative DBS programming can be made more effective and efficient by knowing some basic electrophysiological principles and some details of neuroanatomy.

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26

Laver, Michael, and Ernest Sergenti. Benchmarking the Baseline Model. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0005.

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This chapter begins the investigation of multiparty competition using the baseline model specified in Chapter 3 and methods and procedures specified in Chapter 4. The most significant results concern the representativeness of evolved configurations of party policy positions. In symmetric populations, the ideal points of voters are not best represented by a set of (Hunter) parties who compete for their support by trying to find popular policy positions. Instead, voter preferences are better represented by a set of (Aggregator) parties that do not compete with each other on policy at all but instead seek to represent the policy preferences only of their current supporters. This happens because the dynamics of vote-seeking competition in this setting cause parties to set policy positions closer to the center of the policy space than would be needed for optimal representation—while at the same time avoiding the dead center of the space.

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