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Статті в журналах з теми "Models of adaptation"
Novikov, D. A. "Team adaptation models." Automation and Remote Control 71, no. 5 (May 2010): 882–93. http://dx.doi.org/10.1134/s0005117910050164.
Повний текст джерелаYoung, Laurence R. "Models for neurovestibular adaptation." Journal of Vestibular Research 13, no. 4-6 (December 28, 2003): 297–307. http://dx.doi.org/10.3233/ves-2003-134-614.
Повний текст джерелаCowin, Stephen C. "Bone Stress Adaptation Models." Journal of Biomechanical Engineering 115, no. 4B (November 1, 1993): 528–33. http://dx.doi.org/10.1115/1.2895535.
Повний текст джерелаSCHULZKE, JORG-DIETER, HEINS SCHMITZ, MICHAEL FROMM, CARL J. BENTZEL, and ERNST OTTO RIECKEN. "Clinical Models of Intestinal Adaptation." Annals of the New York Academy of Sciences 859, no. 1 INTESTINAL PL (November 1998): 127–38. http://dx.doi.org/10.1111/j.1749-6632.1998.tb11117.x.
Повний текст джерелаBeckmann, Aike, C.-Elisa Schaum, and Inga Hense. "Phytoplankton adaptation in ecosystem models." Journal of Theoretical Biology 468 (May 2019): 60–71. http://dx.doi.org/10.1016/j.jtbi.2019.01.041.
Повний текст джерелаTERRIER, A., R. L. RAKOTOMANANA, A. N. RAMANIRAKA, and P. F. LEYVRAZ. "Adaptation Models of Anisotropic Bone." Computer Methods in Biomechanics and Biomedical Engineering 1, no. 1 (January 1997): 47–59. http://dx.doi.org/10.1080/01495739708936694.
Повний текст джерелаMill, Robert, Martin Coath, Thomas Wennekers, and Susan L. Denham. "Abstract Stimulus-Specific Adaptation Models." Neural Computation 23, no. 2 (February 2011): 435–76. http://dx.doi.org/10.1162/neco_a_00077.
Повний текст джерелаSu, Xun, and Minpeng Chen. "Econometric Approaches That Consider Farmers’ Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review." Sustainability 14, no. 21 (October 22, 2022): 13700. http://dx.doi.org/10.3390/su142113700.
Повний текст джерелаJia, Huicong, Fang Chen, and Enyu Du. "Adaptation to Disaster Risk—An Overview." International Journal of Environmental Research and Public Health 18, no. 21 (October 25, 2021): 11187. http://dx.doi.org/10.3390/ijerph182111187.
Повний текст джерелаGorban, A. N., T. A. Tyukina, L. I. Pokidysheva, and E. V. Smirnova. "Dynamic and thermodynamic models of adaptation." Physics of Life Reviews 37 (July 2021): 17–64. http://dx.doi.org/10.1016/j.plrev.2021.03.001.
Повний текст джерелаДисертації з теми "Models of adaptation"
Pennings, Pleuni. "Models of adaptation and speciation." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-66567.
Повний текст джерелаWallin, Johan. "Dose Adaptation Based on Pharmacometric Models." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-100569.
Повний текст джерелаXu, Jiaolong. "Domain adaptation of deformable part-based models." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/290266.
Повний текст джерелаLa detección de peatones es crucial para los sistemas de asistencia a la conducción (ADAS). Disponer de un clasificador preciso es fundamental para un detector de peatones basado en visión. Al entrenar un clasificador, se asume que las características de los datos de entrenamiento siguen la misma distribución de probabilidad que las de los datos de prueba. Sin embargo, en la práctica, esta asunción puede no cumplirse debido a diferentes causas. En estos casos, en la comunidad de visión por computador cada vez es más común utilizar técnicas que permiten adaptar los clasificadores existentes de su entorno de entrenamiento (dominio de origen) al nuevo entorno de prueba (dominio de destino). En esta tesis nos centramos en la adaptación de dominio de los detectores de peatones basados en modelos deformables basados en partes (DPMs). Como prueba de concepto, usamos como dominio de origen datos sintéticos (mundo virtual) y adaptamos el detector DPM entrenado en el mundo virtual para funcionar en diferentes escenarios reales. Comenzamos explotando al máximo las capacidades de detección del DPM entrenado en datos del mundo virtual pero, aun así, al aplicarlo a diferentes conjuntos del mundo real, el detector todavía pierde poder de discriminaci ón debido a las diferencias entre el mundo virtual y el real. Es por ello que nos centramos en la adaptación de dominio del DPM. Para comenzar, consideramos un único dominio de origen para adaptarlo a un único dominio de destino mediante dos métodos de aprendizaje por lotes, el A-SSVM y SA-SSVM. Después, lo ampliamos a trabajar con múltiples (sub-)dominios mediante una adaptación progresiva usando una jerarquía adaptativa basada en SSVM (HA-SSVM) en el proceso de optimización. Finalmente, extendimos HA-SSVM para conseguir un detector que se adapte de forma progresiva y sin intervención humana al dominio de destino. Cabe destacar que ninguno de los métodos propuestos en esta tesis requieren visitar los datos del dominio de origen. La evaluación de los resultados, realizadas con el sistema de evaluación de Caltech, muestran que el SA-SSVM mejora ligeramente respecto al A-SSVM y mejora en 15 puntos respecto al detector no adaptado. El modelo jerárquico entrenado mediante el HA-SSVM todavía mejora más los resultados de la adaptación de dominio. Finalmente, el método secuencial de adaptación de domino ha demostrado que puede obtener resultados comparables a la adaptación por lotes pero sin necesidad de etiquetar manualmente ningún ejemplo del dominio de destino. La adaptación de domino aplicada a la detección de peatones es de gran importancia y es un área que se encuentra relativamente sin explorar. Deseamos que esta tesis pueda sentar las bases del trabajo futuro en esta área.
On-board pedestrian detection is crucial for Advanced Driver Assistance Systems (ADAS). An accurate classi cation is fundamental for vision-based pedestrian detection. The underlying assumption for learning classi ers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classi ers. However, in practice, there are di erent reasons that can break this constancy assumption. Accordingly, reusing existing classi ers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset. We start by exploiting the maximum detection accuracy of the virtual-world trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still su er from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area.
Pirrotta, Elizabeth. "Testing chromatic adaptation models using object colors /." Online version of thesis, 1994. http://hdl.handle.net/1850/11674.
Повний текст джерелаNikolaidis, Stefanos. "Mathematical Models of Adaptation in Human-Robot Collaboration." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1121.
Повний текст джерелаAcosta, Padilla Francisco Javier. "Self-adaptation for Internet of things applications." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S094/document.
Повний текст джерелаThe Internet of Things (IoT) is covering little by little every aspect on our lives. As these systems become more pervasive, the need of managing this complex infrastructure comes with several challenges. Indeed, plenty of small interconnected devices are now providing more than a service in several aspects of our everyday life, which need to be adapted to new contexts without the interruption of such services. However, this new computing system differs from classical Internet systems mainly on the type, physical size and access of the nodes. Thus, typical methods to manage the distributed software layer on large distributed systems as usual cannot be employed on this context. Indeed, this is due to the very different capacities on computing power and network connectivity, which are very constrained for IoT devices. Moreover, the complexity which was before managed by experts on several fields, such as embedded systems and Wireless Sensor Networks (WSN), is now increased by the larger quantity and heterogeneity of the node’s software and hardware. Therefore, we need efficient methods to manage the software layer of these systems, taking into account the very limited resources. This underlying hardware infrastructure raises new challenges in the way we administrate the software layer of these systems. These challenges can be divided into: intra-node, on which we face the limited memory and CPU of IoT nodes, in order to manage the software layer and ; inter-node, on which a new way to distribute the updates is needed, due to the different network topology and cost in energy for battery powered devices. Indeed, the limited computing power and battery life of each node combined with the very distributed nature of these systems, greatly adds complexity to the distributed software layer management. Software reconfiguration of nodes in the Internet of Things is a major concern for various application fields. In particular, distributing the code of updated or new software features to their final node destination in order to adapt it to new requirements, has a huge impact on energy consumption. Most current algorithms for disseminating code over the air (OTA) are meant to disseminate a complete firmware through small chunks and are often implemented at the network layer, thus ignoring all guiding information from the application layer. First contribution: A models@runtime engine able to represent an IoT running application on resource constrained nodes. The transformation of the Kevoree meta-model into C code to meet the specific memory constraints of an IoT device was performed, as well as the proposition of modelling tools to manipulate a model@runtime. Second contribution: Component decoupling of an IoT system as well as an efficient component distribution algorithm. Components decoupling of an application in the context of the IoT facilitates its representation on the model@runtime, while it provides a way to easily change its behaviour by adding/removing components and changing their parameters. In addition, a mechanism to distribute such components using a new algorithm, called Calpulli is proposed
Gurdamar, Emre. "Adaptation Of Turbulence Models To A Navier-stokes Solver." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606568/index.pdf.
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models, having different correlations, constants and boundary conditions are selected to be adapted into the base solver. The basic equations regarding the base Navier-Stokes solver to which the turbulence models are implemented presented by briefly explaining the outputs obtained from the solver. Numerical work regarding the implementation of turbulence models into the base solver is given in steps of non-dimensionalization, transformation of equations into generalized coordinate system, numerical scheme, discretization, boundary and initial conditions and limitations. These sections of implementation are investigated and presented in detail with providing every steps of work accomplished. Certain trial problems are solved and outputs are compared with experimental data. Solutions for fluid flow over flat plate, in free shear, over cylinder and airfoil are demonstrated. Airfoil validation test cases are analyzed in detail. For three dimensional applications, computation of flow over a wing is accomplished and pressure distributions from certain sections are compared with experimental data.
Clarkson, P. R. "Adaptation of statistical language models for automatic speech recognition." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597745.
Повний текст джерелаBaptista, Adérito Herculano Sarmento. "Dynamic adaptation of interaction models for stateful web services." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/12042.
Повний текст джерелаWireless Sensor Networks (WSNs) are accepted as one of the fundamental technologies for current and future science in all domains, where WSNs formed from either static or mobile sensor devices allow a low cost high-resolution sensing of the environment. Such opens the possibility of developing new kinds of crucial applications or providing more accurate data to more traditional ones. For instance, examples may range from large-scale WSNs deployed on oceans contributing to weather prediction simulations; to high number of diverse Sensor devices deployed over a geographical area at different heights from the ground for collecting more accurate data for cyclic wildfire spread simulations; or to networks of mobile phone devices contributing to urban traffic management via Participatory Sensing applications. In order to simplify data access, network parameterisation, and WSNs aggregation, WSNs have been integrated in Web environments, namely through high level standard interfaces like Web services. However, the typical interface access usually supports a restricted number of interaction models and the available mechanisms for their run-time adaptation are still scarce. Nevertheless, applications demand a richer and more flexible control on interface accesses – e.g. such accesses may depend on contextual information and, consequently, may evolve in time. Additionally, Web services have become increasingly popular in the latest years, and their usage led to the need of aggregating and coordinating them and also to represent state in between Web services invocations. Current standard composition languages for Web services (wsbpel,wsci,bpml) deal with the traditional forms of service aggregation and coordination, while WS-Resource framework (wsrf) deals with accessing services pertaining state concerns (relating both executing applications and the runtime environment). Subjacent to the notion of service coordination is the need to capture dependencies among them (through the workflow concept, for instance), reuse common interaction models, e.g. embodied in common behavioural Patterns like Client/Server, Publish/- Subscriber, Stream, and respond to dynamic events in the system (novel user requests, service failures, etc.). Dynamic adaptation, in particular, is a pressing requirement for current service-based systems due to the increasing trend on XaaS ("everything as a service") which promises to reduce costs on application development and infrastructure support, as is already apparent in the Cloud computing domain. Therefore, the self-adaptive (or dynamic/adaptive) systems present themselves as a solution to the above concerns. However, since they comprise a vast area, this thesis only focus on self-adaptive software. Concretely, we propose a novel model for dynamic interactions, in particular with Stateful Web Services, i.e. services interfacing continued activities. The solution consists on a middleware prototype based on pattern abstractions which may be able to provide (novel) richer interaction models and a few structured dynamic adaptation mechanisms, which are captured in the context of a "Session" abstraction. The middleware was implemented and uses a pre-existent framework supporting Web enabled access to WSNs, and some evaluation scenarios were tested in this setting. Namely, this area was chosen as the application domain that contextualizes this work as it contributes to the development of increasingly important applications needing highresolution and low cost sensing of environment. The result is a novel way to specify richer and dynamic modes of accessing and acquiring data generated by WSNs.
Este trabalho foi parcialmente financiado pelo Centro de Informática e Tecnologias da Informação (CITI), e pela Fundação para a Ciência e a Tecnologia (FCT / MCTES) em projectos de investigação
Ahadi-Sarkani, Seyed Mohammad. "Bayesian and predictive techniques for speaker adaptation." Thesis, University of Cambridge, 1996. https://www.repository.cam.ac.uk/handle/1810/273100.
Повний текст джерелаКниги з теми "Models of adaptation"
Roy, Callista. The Roy adaptation model. 3rd ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2008.
Знайти повний текст джерелаA, Andrews Heather, ed. The Roy adaptation model. Stamford, Conn: Appleton & Lange, 1999.
Знайти повний текст джерела1920-, Cowan G. A., Pines David 1924-, Meltzer David, and Santa Fe Institute (Santa Fe, N.M.), eds. Complexity: Metaphors, models, and reality. Reading, Mass: Addison-Wesley, 1994.
Знайти повний текст джерелаSohail, Siddiqui Khawar, and Thomas Torsten, eds. Protein adaptation in extremophiles. New York: Nova Biomedical Books, 2008.
Знайти повний текст джерелаTreur, Jan, and Laila Van Ments, eds. Mental Models and Their Dynamics, Adaptation, and Control. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85821-6.
Повний текст джерелаCallista Roy: An adaptation model. Newbury Park, Calif: Sage Publications, 1991.
Знайти повний текст джерелаAdaptation in dynamical systems. Cambridge: Cambridge University Press, 2011.
Знайти повний текст джерелаCallista, Roy, ed. The Roy adaptation model: The definitive statement. Norwalk, Conn: Appleton & Lange, 1991.
Знайти повний текст джерелаEnvironmental and Water Resources Institute (U.S.), ed. Climate change modeling, mitigation, and adaptation. Reston, Virginia: American Society of Civil Engineers, 2013.
Знайти повний текст джерелаHidden order: How adaptation builds complexity. Reading, Mass: Addison-Wesley, 1995.
Знайти повний текст джерелаЧастини книг з теми "Models of adaptation"
Fink, Gernot A. "Model Adaptation." In Markov Models for Pattern Recognition, 201–9. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_11.
Повний текст джерелаPonting, Keith M. "Channel Adaptation." In Computational Models of Speech Pattern Processing, 112–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_12.
Повний текст джерелаMill, Robert. "Stimulus-Specific Adaptation, Models." In Encyclopedia of Computational Neuroscience, 2883–88. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_109.
Повний текст джерелаMill, Robert. "Stimulus-Specific Adaptation, Models." In Encyclopedia of Computational Neuroscience, 1–7. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_109-2.
Повний текст джерелаSomogyi, Zoltán, Dóra Hidy, Györgyi Gelybó, Zoltán Barcza, Galina Churkina, László Haszpra, László Horváth, Attila Machon, and Balázs Grosz. "Models and Their Adaptation." In Atmospheric Greenhouse Gases: The Hungarian Perspective, 201–28. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9950-1_9.
Повний текст джерелаHester, Todd, and Peter Stone. "Learning and Using Models." In Adaptation, Learning, and Optimization, 111–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3_4.
Повний текст джерелаDeMori, Renato, and Marcello Federico. "Language Model Adaptation." In Computational Models of Speech Pattern Processing, 280–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_26.
Повний текст джерелаFleurey, Franck, Vegard Dehlen, Nelly Bencomo, Brice Morin, and Jean-Marc Jézéquel. "Modeling and Validating Dynamic Adaptation." In Models in Software Engineering, 97–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01648-6_11.
Повний текст джерелаLehmann, Janette, Mounia Lalmas, Elad Yom-Tov, and Georges Dupret. "Models of User Engagement." In User Modeling, Adaptation, and Personalization, 164–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4_14.
Повний текст джерелаSantana, Roberto, and Siddhartha Shakya. "Probabilistic Graphical Models and Markov Networks." In Adaptation, Learning, and Optimization, 3–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28900-2_1.
Повний текст джерелаТези доповідей конференцій з теми "Models of adaptation"
Uzunov, Anton V., Mohan Baruwal Chhetri, and John Wondoh. "GOURMET: A Methodology for Realizing Goal-Driven Self-Adaptation." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00034.
Повний текст джерелаVogel, Thomas, and Holger Giese. "Adaptation and abstract runtime models." In the 2010 ICSE Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1808984.1808989.
Повний текст джерелаMohammed, Mufasir Muthaher. "Dynamic adaptation for distributed systems in model-driven engineering." In MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3550356.3558505.
Повний текст джерела"Naïve Bayes Domain Adaptation for Biological Sequences." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004245500620070.
Повний текст джерелаMoisan, Sabine, Jean-Paul Rigault, and Mathieu Acher. "A feature-based approach to system deployment and adaptation." In 2012 Models in Software Engineering (MiSE). IEEE, 2012. http://dx.doi.org/10.1109/mise.2012.6226019.
Повний текст джерелаConnell, S. D., and N. K. Jain. "Writer adaptation of online handwriting models." In Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318). IEEE, 1999. http://dx.doi.org/10.1109/icdar.1999.791817.
Повний текст джерелаZhuo Han and Kumpati S. Narendra. "Second level adaptation using multiple models." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5991086.
Повний текст джерелаKitza, Markus, Pavel Golik, Ralf Schlüter, and Hermann Ney. "Cumulative Adaptation for BLSTM Acoustic Models." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2162.
Повний текст джерелаBoulanger, Frederic, Cecile Hardebolle, Christophe Jacquet, and Dominique Marcadet. "Semantic Adaptation for Models of Computation." In 2011 11th International Conference on Application of Concurrency to System Design (ACSD). IEEE, 2011. http://dx.doi.org/10.1109/acsd.2011.17.
Повний текст джерелаZahabi, Samira Tofighi, Somayeh Bakhshaei, and Shahram Khadivi. "Using topic models in domain adaptation." In 2014 7th International Symposium on Telecommunications (IST). IEEE, 2014. http://dx.doi.org/10.1109/istel.2014.7000763.
Повний текст джерелаЗвіти організацій з теми "Models of adaptation"
Lei, Xin, Wen Wang, and Andreas Stolcke. Unsupervised Domain Adaptation with Multiple Acoustic Models. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada630345.
Повний текст джерелаKolar, Jachym, Yang Liu, and Elizabeth Shriberg. Speaker Adaptation of Language Models for Automatic Dialog Act Segmentation of Meetings. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada469307.
Повний текст джерелаYe, Nong. Models of Quality of Service and Quality of Information Assurance Towards Their Dynamic Adaptation. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada541993.
Повний текст джерелаWyndham, Amber, Emile Elias, Joel Brown, Michael Wilson, and Albert Rango. Drought Vulnerability Assessment to Inform Grazing Practices on Rangelands of Southeastern Colorado’s Major Land Resource Area 69. USDA Southwest Climate Hub, July 2018. http://dx.doi.org/10.32747/2018.6947062.ch.
Повний текст джерелаWyndham, Amber, Emile Elias, Joel R. Brown, Michael A. Wilson, and Albert Rango. Drought Vulnerability Assessment to Inform Grazing Practices on Rangelands in Southeast Arizona and Southwest New Mexico’s Major Land Resource Area 41. United States. Department of Agriculture. Southwest Climate Hub, August 2018. http://dx.doi.org/10.32747/2018.6818230.ch.
Повний текст джерелаWyndham, Amber, Emile Elias, Joel R. Brown, Michael A. Wilson, and Albert Rango. Drought Vulnerability Assessment to Inform Grazing Practices on Rangelands of Southeastern Colorado’s Major Land Resource Area 69. United States. Department of Agriculture. Southwest Climate Hub, January 2018. http://dx.doi.org/10.32747/2018.6876399.ch.
Повний текст джерелаWyndham, Amber, Emile Elias, Joel Brown, Michael Wilson, and Albert Rango Rango. Drought Vulnerability Assessment to Inform Grazing Practices on Rangelands in Southeast Arizona and Southwest New Mexico’s Major Land Resource Area 41. USDA Southwest Climate Hub, August 2018. http://dx.doi.org/10.32747/2018.6947060.ch.
Повний текст джерелаWyndham, Amber, Emile Elias, Joel Brown, Michael Wilson, and Albert Rango. Drought Vulnerability Assessment to Inform Grazing Practices on Rangelands of Southeastern Colorado’s Major Land Resource Area 69. USDA Southwest Climate Hub, March 2018. http://dx.doi.org/10.32747/2018.6965584.ch.
Повний текст джерелаErulkar, Annabel, and Erica Chong. Evaluation of a savings and micro-credit program for vulnerable young women in Nairobi. Population Council, 2005. http://dx.doi.org/10.31899/pgy19.1010.
Повний текст джерелаPradhananga, Saurav, Arthur Lutz, Archana Shrestha, Indira Kadel, Bikash Nepal, and Santosh Nepal. Selection and downscaling of general circulation model datasets and extreme climate indices analysis - Manual. International Centre for Integrated Mountain Development (ICIMOD), 2020. http://dx.doi.org/10.53055/icimod.4.
Повний текст джерела