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Готові списки джерел за темами / Evolution Analysis

Добірка наукової літератури з теми "Evolution Analysis"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Evolution Analysis"

1

Leenu Singh, Leenu Singh, and Syed Imtiyaz Hassan. "Virtualization Evolution For Transparent Malware Analysis." International Journal of Scientific Research 2, no. 6 (June 1, 2012): 101–4. http://dx.doi.org/10.15373/22778179/june2013/33.

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2

Raiyani, Dr Jagdish R., and Dr Gaurav Lodha. "Evolution of Non-Performing Assets: An Empirical Analysis." Indian Journal of Applied Research 1, no. 3 (October 1, 2011): 1–2. http://dx.doi.org/10.15373/2249555x/dec2011/1.

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3

Rau, René T. "Hyperbolic evolution groups and dichotomic evolution families." Journal of Dynamics and Differential Equations 6, no. 2 (April 1994): 335–50. http://dx.doi.org/10.1007/bf02218534.

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4

Facchinelli, Fabio, and Andreas P. M. Weber. "Analysis of Cyanophora paradoxa tells important lessons on plastid evolution." Perspectives in Phycology 2, no. 1 (May 1, 2015): 3–10. http://dx.doi.org/10.1127/pip/2015/0018.

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5

Kloeden, Peter E., and Thomas Lorenz. "Stochastic morphological evolution equations." Journal of Differential Equations 251, no. 10 (November 2011): 2950–79. http://dx.doi.org/10.1016/j.jde.2011.03.013.

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6

Igbida, Noureddine. "Evolution Monge–Kantorovich equation." Journal of Differential Equations 255, no. 7 (October 2013): 1383–407. http://dx.doi.org/10.1016/j.jde.2013.04.020.

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7

Stefanelli, Ulisse. "Nonlocal quasivariational evolution problems." Journal of Differential Equations 229, no. 1 (October 2006): 204–28. http://dx.doi.org/10.1016/j.jde.2006.05.004.

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8

Zhang, Ming Qin, Da Ming Sun, and Qing Wang. "Analysis on Evolution of Car Carrier Based on Theory of Technology Evolution." Applied Mechanics and Materials 365-366 (August 2013): 463–67. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.463.

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Анотація:

In order to predict the development direction of stereo garage car carrier to accommodate the present need of transportation, combined technology system of car carrier with evolution routes belongs to evolution patterns in theory of technology evolution. Relationships between total system and evolution routes, relationships between subsystem and evolution routes were built, on the basis of researching the structural feature of stereo garage car carrier, analyzed the evolution rules of total system andsubsystems along different evolutional routes, indicated its present evolutionary state and predicted the future development direction. It is helpful for enterprise to accurately predict the next generation of product and greatly improve market competitiveness.

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9

Naito, Toshiki, and Nguyen Van Minh. "Evolution Semigroups and Spectral Criteria for Almost Periodic Solutions of Periodic Evolution Equations." Journal of Differential Equations 152, no. 2 (March 1999): 358–76. http://dx.doi.org/10.1006/jdeq.1998.3531.

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10

Alsulaimy, Muteb Abdullaziz, Ibrahim Abduallh Almalki, and Mohanned Khaled Almarzook. "Analysis on Evolution of Cardiac Rehabilitation, Participation, Risks and Benefits Involved." International Journal of Psychosocial Rehabilitation 23, no. 3 (September 20, 2019): 821–27. http://dx.doi.org/10.37200/ijpr/v23i3/pr190370.

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Дисертації з теми "Evolution Analysis"

1

Shin, Gary Thomas. "Analysis of rotavirus evolution." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1790313851&sid=3&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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2

Shorten, David. "Spectral analysis of neutral evolution." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27420.

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It has been argued that much of evolution takes place in the absence of fitness gradients. Such periods of evolution can be analysed by examining the mutational network formed by sequences of equal fitness, that is, the neutral network. It has been demonstrated that, in large populations under a high mutation rate, the population distribution over the neutral network and average mutational robustness are given by the principal eigenvector and eigen- value, respectively, of the network's adjacency matrix. However, little progress has been made towards understanding the manner in which the topology of the neutral network influences the resulting population distribution and robustness. In this work, we build on recent results from spectral graph theory and utilize numerical methods to enhance our understanding of how populations distribute themselves over neutral networks. We demonstrate that, in the presence of certain topological features, the population will undergo an exploration catastrophe and become confined to a small portion of the network. We further derive approximations, in terms of mutational biases, for the population distribution and average robustness in networks with a homogeneous structure. The applicability of these results is explored, first, by a detailed review of the literature in both evolutionary computing and biology concerning the structure of neutral networks. This is extended by studying the actual and predicted population distribution over the neutral networks of H1N1 and H3N2 influenza haemagglutinin during seasons between 2005 and 2016. It is shown that, in some instances, these populations experience an exploration catastrophe. These results provide insight into the behaviour of populations on neutral networks, demonstrating that neutrality does not necessarily lead to an exploration of genotype/phenotype space or an associated increase in population diversity. Moreover, they provide a plausible explanation for conflicting results concerning the relationship between robustness and evolvability.

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3

Pei, Breivold Hongyu. "Software Architecture Evolution through Evolvability Analysis." Doctoral thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-13087.

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In this thesis, we study evolution of software architecture and investigate ways to support this evolution. The central theme of the thesis is how to analyze software evolvability, i.e., a system’s ability to easily accommodate changes. We focus on two main aspects: (i) what software characteristics are necessary for an evolvable software system; and (ii) how to assess evolvability of long-lived proprietary systems in a systematic manner. A secondary focus is to investigate how evolvability is addressed in open source software evolution. We have performed a systematic review of architecture evolution research, and proposed a software evolvability model, in which subcharacteristics of software evolvability and corresponding measuring attributes are identified. Based on this model, we have proposed the softwarearchitectureevolvabilityanalysis (AREA) process which provides repeatable techniques for supporting software architecture evolution: a) Qualitative evolvability analysis method that focuses on improving the capability of being able to understand and analyze systematically the impact of change stimuli on software architecture evolution; b) Quantitative evolvability analysis method that provides quantifications of stakeholders’ evolvability concerns and potential architectural solutions’ impacts on evolvability. These techniques have been validated in industrial settings of different domains, and can be used as an integral part of software development and evolution process. This is to ensure that the implications of the potential improvement strategies and evolution path of software architectures are analyzed with respect to the evolvability subcharacteristics. As a supplementary research contribution, we have conducted a systematic review of the existing studies in open source software (OSS) evolution, and performed a comprehensive analysis which describes how software evolvability is addressed during the development and evolution of OSS, and identified challenges and future research directions in OSS evolution.

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4

Telford, Maximilian John. "A molecular analysis of chaetognath evolution." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260778.

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5

Chotard, Alexandre. "Markov chain Analysis of Evolution Strategies." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112230/document.

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Cette thèse contient des preuves de convergence ou de divergence d'algorithmes d'optimisation appelés stratégies d'évolution (ESs), ainsi que le développement d'outils mathématiques permettant ces preuves.Les ESs sont des algorithmes d'optimisation stochastiques dits ``boîte noire'', i.e. où les informations sur la fonction optimisée se réduisent aux valeurs qu'elle associe à des points. En particulier, le gradient de la fonction est inconnu. Des preuves de convergence ou de divergence de ces algorithmes peuvent être obtenues via l'analyse de chaînes de Markov sous-jacentes à ces algorithmes. Les preuves de convergence et de divergence obtenues dans cette thèse permettent d'établir le comportement asymptotique des ESs dans le cadre de l'optimisation d'une fonction linéaire avec ou sans contrainte, qui est un cas clé pour des preuves de convergence d'ESs sur de larges classes de fonctions.Cette thèse présente tout d'abord une introduction aux chaînes de Markov puis un état de l'art sur les ESs et leur contexte parmi les algorithmes d'optimisation continue boîte noire, ainsi que les liens établis entre ESs et chaînes de Markov. Les contributions de cette thèse sont ensuite présentées:o Premièrement des outils mathématiques généraux applicables dans d'autres problèmes sont développés. L'utilisation de ces outils permet d'établir aisément certaines propriétés (à savoir l'irreducibilité, l'apériodicité et le fait que les compacts sont des small sets pour la chaîne de Markov) sur les chaînes de Markov étudiées. Sans ces outils, établir ces propriétés était un processus ad hoc et technique, pouvant se montrer très difficile.o Ensuite différents ESs sont analysés dans différents problèmes. Un (1,\lambda)-ES utilisant cumulative step-size adaptation est étudié dans le cadre de l'optimisation d'une fonction linéaire. Il est démontré que pour \lambda > 2 l'algorithme diverge log-linéairement, optimisant la fonction avec succès. La vitesse de divergence de l'algorithme est donnée explicitement, ce qui peut être utilisé pour calculer une valeur optimale pour \lambda dans le cadre de la fonction linéaire. De plus, la variance du step-size de l'algorithme est calculée, ce qui permet de déduire une condition sur l'adaptation du paramètre de cumulation avec la dimension du problème afin d'obtenir une stabilité de l'algorithme. Ensuite, un (1,\lambda)-ES avec un step-size constant et un (1,\lambda)-ES avec cumulative step-size adaptation sont étudiés dans le cadre de l'optimisation d'une fonction linéaire avec une contrainte linéaire. Avec un step-size constant, l'algorithme résout le problème en divergeant lentement. Sous quelques conditions simples, ce résultat tient aussi lorsque l'algorithme utilise des distributions non Gaussiennes pour générer de nouvelles solutions. En adaptant le step-size avec cumulative step-size adaptation, le succès de l'algorithme dépend de l'angle entre les gradients de la contrainte et de la fonction optimisée. Si celui ci est trop faible, l'algorithme convergence prématurément. Autrement, celui ci diverge log-linéairement.Enfin, les résultats sont résumés, discutés, et des perspectives sur des travaux futurs sont présentées
In this dissertation an analysis of Evolution Strategies (ESs) using the theory of Markov chains is conducted. Proofs of divergence or convergence of these algorithms are obtained, and tools to achieve such proofs are developed.ESs are so called "black-box" stochastic optimization algorithms, i.e. information on the function to be optimized are limited to the values it associates to points. In particular, gradients are unavailable. Proofs of convergence or divergence of these algorithms can be obtained through the analysis of Markov chains underlying these algorithms. The proofs of log-linear convergence and of divergence obtained in this thesis in the context of a linear function with or without constraint are essential components for the proofs of convergence of ESs on wide classes of functions.This dissertation first gives an introduction to Markov chain theory, then a state of the art on ESs and on black-box continuous optimization, and present already established links between ESs and Markov chains.The contributions of this thesis are then presented:o General mathematical tools that can be applied to a wider range of problems are developed. These tools allow to easily prove specific Markov chain properties (irreducibility, aperiodicity and the fact that compact sets are small sets for the Markov chain) on the Markov chains studied. Obtaining these properties without these tools is a ad hoc, tedious and technical process, that can be of very high difficulty.o Then different ESs are analyzed on different problems. We study a (1,\lambda)-ES using cumulative step-size adaptation on a linear function and prove the log-linear divergence of the step-size; we also study the variation of the logarithm of the step-size, from which we establish a necessary condition for the stability of the algorithm with respect to the dimension of the search space. Then we study an ES with constant step-size and with cumulative step-size adaptation on a linear function with a linear constraint, using resampling to handle unfeasible solutions. We prove that with constant step-size the algorithm diverges, while with cumulative step-size adaptation, depending on parameters of the problem and of the ES, the algorithm converges or diverges log-linearly. We then investigate the dependence of the convergence or divergence rate of the algorithm with parameters of the problem and of the ES. Finally we study an ES with a sampling distribution that can be non-Gaussian and with constant step-size on a linear function with a linear constraint. We give sufficient conditions on the sampling distribution for the algorithm to diverge. We also show that different covariance matrices for the sampling distribution correspond to a change of norm of the search space, and that this implies that adapting the covariance matrix of the sampling distribution may allow an ES with cumulative step-size adaptation to successfully diverge on a linear function with any linear constraint.Finally, these results are summed-up, discussed, and perspectives for future work are explored

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6

Simmering, Volker. "The evolution of standards /." Wiesbaden : Deutscher Universitäts-Verlag, 2003. http://www.loc.gov/catdir/toc/fy0602/2005424021.html.

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7

Sorensen, Julian Karl. "White noise analysis and stochastic evolution equations." Title page, contents and abstract only, 2001. http://web4.library.adelaide.edu.au/theses/09PH/09phs713.pdf.

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8

Guerra, Martins dos Santos Assunção José Afonso. "Evolutionary analysis of animal microRNAs." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244240.

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In recent years, microRNAs (miRNAs) have been recognised as important genetic regulators of gene expression in Animals and Plants. They can potentially target a large fraction of the cellular transcriptome, having been shown to be important for diverse biological processes such as development, cell differentiation, proliferation and metabolism. The publication of the Human genome in 2001 marked the start of a great community effort to sequence a variety of other species. These data have great potential for comparative genomics, that can lead to better biological understanding. Some miRNA families are known to be highly conserved, across long evolutionary distances, many found in co-transcribed clusters across the genome. While these phenomena have been previously reported, a large-scale analysis of evolutionary patterns was still lacking. Furthermore, the rate at which new relevant data is being made available makes it challenging to keep up and many of the evolutionary studies performed before are now significantly out of date. This thesis describes a number of approaches taken to analyse miRNA datasets, harnessing the full potential of currently available data for comparative genomics. These were used, not only to revisit many of the notions in the field with a larger and updated dataset, but also to develop novel strategies that enable a coherent view of miRNA evolution at different evolutionary time-scales. A new tool, described within this thesis, was developed for large-scale, species independent miRNA mapping. An assessment of the evolution of the miRNA reper- toire across species was performed, together with detailed sequence conservation analysis and miRNA family clustering. Phylogenetic profile analysis uncovered in- teresting co-evolution between miRNAs and protein coding genes. The genomic organisation of miRNAs and their conservation across species was also studied, pro- viding detailed conserved synteny maps for miRNAs and proteins across more than 80 species. Finally, at the intra-specific level, I analysed the occurrence of single nucleotide polymorphisms affecting miRNA loci or their predicted target sites. All the tools built and integrated in this research were made available to the community and designed to be easily updated, making it easier to keep up with the data that is constantly being made available. Many aspects of miRNA biology are still being uncovered, and the ability to easily put these findings into an evolutionary context will potentially be useful for the community.

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9

Grant, Alastair Donald. "Comparative genome analysis to reveal protein evolution." Thesis, University College London (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429170.

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10

Buckler, Edward St Clair. "Maize domestication and molecular evolution /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842511.

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Книги з теми "Evolution Analysis"

1

1949-, Prestini Elena, ed. The evolution of applied harmonic analysis. Boston: Birkhauser, 2004.

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2

1962-, Herron Jon C., ed. Evolutionary analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2007.

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3

1962-, Herron Jon C., ed. Evolutionary analysis. 3rd ed. Upper Saddle River, NJ: Pearson Education, 2004.

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4

Scott, Freeman. Evolutionary analysis. 3rd ed. Upper Saddle River, NJ: Pearson Education, 2004.

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5

Scott, Freeman. Evolutionary analysis. Upper Saddle River, N.J: Prentice Hall, 1998.

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6

Amann, Herbert, Wolfgang Arendt, Matthias Hieber, Frank M. Neubrander, Serge Nicaise, and Joachim von Below, eds. Functional Analysis and Evolution Equations. Basel: Birkhäuser Basel, 2008. http://dx.doi.org/10.1007/978-3-7643-7794-6.

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7

Bu, Lei, and Yingfei Xiong, eds. Software Analysis, Testing, and Evolution. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04272-1.

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8

Pitts, Clarke. Structured products: Evolution and analysis. London: Risk Books, 2013.

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9

Urban morphometry: Evolution and analysis. New Delhi, India: Research India Press, 2015.

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10

M, Miyamoto Michael, and Cracraft Joel, eds. Phylogenetic analysis of DNA sequences. New York: Oxford University Press, 1991.

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Частини книг з теми "Evolution Analysis"

1

O’Neil, Michael, and Conor Ryan. "Analysis of Grammatical Evolution." In Grammatical Evolution, 63–77. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_6.

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2

Yoshida, Norihiro, and Eunjong Choi. "Clone Evolution and Management." In Code Clone Analysis, 197–208. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4_14.

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3

Koonin, Eugene V., and Michael Y. Galperin. "Genome Annotation and Analysis." In Sequence — Evolution — Function, 193–226. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3783-7_6.

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4

Fromm, Christian. "Light Curve Analysis." In Spectral Evolution in Blazars, 37–58. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10768-4_3.

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5

Fromm, Christian. "VLBI Kinematic Analysis." In Spectral Evolution in Blazars, 59–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10768-4_4.

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6

Saganowski, Stanisław, Piotr Bródka, and Przemysław Kazienko. "Community Evolution." In Encyclopedia of Social Network Analysis and Mining, 1–14. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_223-1.

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7

Bródka, Piotr, Stanisław Saganowski, and Przemysław Kazienko. "Community Evolution." In Encyclopedia of Social Network Analysis and Mining, 220–32. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_223.

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Saganowski, Stanisław, Piotr Bródka, and Przemysław Kazienko. "Community Evolution." In Encyclopedia of Social Network Analysis and Mining, 357–70. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_223.

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9

Ishikawa, Yoji, Kensei Kobayashi, and Takeshi Saito. "Martian Soil Analysis." In Chemical Evolution: Physics of the Origin and Evolution of Life, 389–95. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1712-5_35.

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Ratner, Vadim A., Andrey A. Zharkikh, Nikolay Kolchanov, Sergey N. Rodin, Viktor V. Solovyov, and Andrey S. Antonov. "Dynamic Properties of Self-Reproducing Molecular Systems: Theoretical Analysis." In Molecular Evolution, 11–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-12530-4_2.

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Тези доповідей конференцій з теми "Evolution Analysis"

1

Mobus, George. "The evolution of gaia's brain." In Systems Analysis in Economics - 2020. Moscow, "Science" Publishing House, 2021. http://dx.doi.org/10.33278/sae-2020.book1.060-061.

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2

Parsamyan, Bakur. "SIDIS Transverse Spin Asymmetries at COMPASS: Multy-Dimensional Analysis." In QCD Evolution 2015. Trieste, Italy: Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.249.0007.

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3

Alsmadi, Izzat, and Kenneth Magel. "Open Source Evolution Analysis." In 2006 22nd IEEE International Conference on Software Maintenance. IEEE, 2006. http://dx.doi.org/10.1109/icsm.2006.45.

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4

Pisano, Christian, Alessandro Bacchetta, Filippo Delcarro, Marco Radici, and Andrea Signori. "A first determination of the unpolarized quark TMDs from a global analysis." In QCD Evolution 2017. Trieste, Italy: Sissa Medialab, 2018. http://dx.doi.org/10.22323/1.308.0010.

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Nilsson, Emma, Jonas Lukasczyk, Wito Engelke, Talha Bin Masood, Gunilla Svensson, Rodrigo Caballero, Christoph Garth, and Ingrid Hotz. "Exploring Cyclone Evolution with Hierarchical Features." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00016.

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6

Wang, Bo, and Chirag Patel. "Differential Evolution for Design Optimization." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5616.

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7

Acar, Hülya, and Alexey L. Lukashov. "Löwner evolution as Itô diffusion." In INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040353.

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8

Hindle, Abram, Zhen Ming Jiang, Walid Koleilat, Michael W. Godfrey, and Richard C. Holt. "YARN: Animating Software Evolution." In 2007 4th IEEE International Workshop on Visualizing Software for Understanding and Analysis. IEEE, 2007. http://dx.doi.org/10.1109/vissof.2007.4290711.

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9

Wang, Peipei, Gina R. Bai, and Kathryn T. Stolee. "Exploring Regular Expression Evolution." In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2019. http://dx.doi.org/10.1109/saner.2019.8667972.

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10

Müller, Julian, and Ulrik Brandes. "The evolution of roles." In ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341161.3342889.

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Звіти організацій з теми "Evolution Analysis"

1

Stuart, Andrew M. Numerical Analysis of Evolution Equations. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada324354.

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2

Strubel, Jessica, and Sanjukta Pookulangara. The Evolution of Styletribes: A Netnographic Analysis. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-538.

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3

Ervin, Vincent J., William J. Layton, and Monika Neda. Numerical Analysis of Filter Based Stabilization for Evolution Equations. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada512969.

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4

Alabarda, Yusuf, and Rafal Lisowiec. The Private Military Firms: Historical Evolution and Industry Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 2007. http://dx.doi.org/10.21236/ada473255.

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5

Garlan, David, Mary Shaw, and Jeannette Wing. A Technology Investigation Supporting Software Architecture and Analysis for Evolution. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada387473.

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6

Finley, P. D., and J. Krason. Geological evolution and analysis of confirmed or suspected gas hydrate localities. Office of Scientific and Technical Information (OSTI), October 1988. http://dx.doi.org/10.2172/7262976.

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7

Templeton, D. C. Evolution of Induced Fracture Networks Using Machine Learning Correlation Image Analysis. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1572626.

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8

Guvenen, Fatih, and Burhanettin Kuruscu. Understanding the Evolution of the U.S. Wage Distribution: A Theoretical Analysis. Cambridge, MA: National Bureau of Economic Research, May 2007. http://dx.doi.org/10.3386/w13096.

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9

Cople, Shelley D. Pentachlorophenol Hydroxylase: Analysis of Catalytic Abilities and Evolution of a Better Enzyme. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada422642.

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10

Leonard, Hugh R., and Jr. Handling the Hot Potato: Evolution and Analysis of the Base Closing Decision Process. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada262623.

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