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Journal articles on the topic 'Models of adaptation'

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Published: 10 December 2022
Last updated: 29 January 2023

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1

Novikov, D. A. "Team adaptation models." Automation and Remote Control 71, no. 5 (May 2010): 882–93. http://dx.doi.org/10.1134/s0005117910050164.

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2

Young, 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.

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Dynamic models have played a more prominent role in the vestibular and oculomotor field than in any other branch of physiology. The ease of identification of input and output variables and the challenge of multi-loop, multi-axis adaptive control has attracted numerous modelers from engineering and shaped behavioral and neurophysiological experimental programs. In particular, the adaptive characteristics of the neurovestibular system have generated continuing speculation as to mechanisms. This treatment of adaptation and multi-sensor integration covers the development and application of such models, principally in the author's laboratory. It emphasizes the continuing relevance of both "model reference" and "error pattern recognition" notions of adaptive control.
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3

Cowin, 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.

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The basic concepts employed in formulating models of the process of stress adaptation in living bone tissue are reviewed. A purpose of this review is to define and separate issues in the formulation of bone remodeling theories. After discussing the rationale and objective of these models, the concepts and techniques involved in the modeling process are reviewed one by one. It is concluded that some techniques will be more successful than others in achieving the goals of computational bone remodeling. In particular, rationale is given for the preference of surface bone remodeling approaches over internal bone remodeling approaches, and for interactive multi-scale level, rather than mono-scale level, computational strategies.
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4

SCHULZKE, 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.

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5

Beckmann, 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.

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6

TERRIER, 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.

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7

Mill, 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.

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Many neurons that initially respond to a stimulus stop responding if the stimulus is presented repeatedly but recover their response if a different stimulus is presented. This phenomenon is referred to as stimulus-specific adaptation (SSA). SSA has been investigated extensively using oddball experiments, which measure the responses of a neuron to sequences of stimuli. Neurons that exhibit SSA respond less vigorously to common stimuli, and the metric typically used to quantify this difference is the SSA index (SI). This article presents the first detailed analysis of the SI metric by examining the question: How should a system (e.g., a neuron) respond to stochastic input if it is to maximize the SI of its output? Questions like this one are particularly relevant to those wishing to construct computational models of SSA. If an artificial neural network receives stimulus information at a particular rate and must respond within a fixed time, what is the highest SI one can reasonably expect? We demonstrate that the optimum, average SI is constrained by the information in the input source, the length and encoding of the memory, and the assumptions concerning how the task is decomposed.
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8

Su, 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.

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The question of whether and to what extent farmers can adapt to climate change has recently gained academic interest. This paper reviews contemporary econometric approaches that assess the impacts of climate change on agriculture and consider farmer adaptation, complementing previous methodological reviews with this distinctive adaptation perspective. The value of adaptation can be measured by comparing the differences between the long-term climate change effect and the short-term weather shock effect. However, this theoretical model has not yet been well supported by empirical evidence, as it is difficult to identify true adaptation, incorporating adaptation cost, and estimated adaptation rate. Quasi-natural experiments, cost-benefit analysis, and Bayesian models are effective tools to address these methodological drawbacks. Two methods dominate in the estimation of climate effects, but each has its own advantages. A good estimate provides a trade-off between the incorporation of farmers’ adaptive behavior and the reduction in omitted variables bias. Cross-sectional data models based on climate variability can capture farmers’ long-term adaptations but are prone to bias due to omitted variables. Panel data models are more effective at mitigating omitted variable bias by applying fixed effects, but do not consider farmers’ adaptative behavior to long-term climate change. To address this dilemma, several cutting-edge approaches have been developed, including integration with the weather and climate model, the long differences approach, and the long- and short-term hybrid approach. We found three key challenges, namely: (1) exploring adaptation mechanisms, (2) the CO2 fertilization effect, and (3) estimating the distributional effects of climate impacts. We also recommend future empirical studies to incorporate satellite remote sensing data, examine the relationship between different adaptation measures, model farmers’ future climate expectations, and include adaptation costs.
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9

Jia, 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.

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The role of natural disaster adaptation is increasingly being considered in academic research. The Paris Agreement and Sustainable Development Goal 13 require measuring the progress made on this adaptation. This review summarizes the development stages of adaptation, the multiple attributes and analysis of adaptation definitions, the models and methods for adaptation analysis, and the research progress of natural disaster adaptation. Adaptation research methods are generally classified into two types: case analysis and mathematical models. The current adaptive research in the field of natural disasters focuses primarily on the response of the social economy, especially the adaptive decision making and risk perception at farm-level scales (farmer households). The evaluation cases of adaptation in the field of disasters exist mostly as a part of vulnerability evaluation. Adaptation and adaptive capacity should focus on four core issues: adaptation to what; who or what adapts; how does adaptation occur; what is adaptation; and how good is the adaptation. The main purpose of the “spatial scale–exposure–vulnerability” three-dimensional scales of adaptation assessment is to explore the differences in index system under different scenarios, the spatial pattern of adaptations, and the geographical explanation of its formation mechanism. The results of this study can help and guide future research on integrating climate change and disaster adaptations especially in regional sustainable development and risk reduction strategies.
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10

Gorban, 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.

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11

Pires, C. T. A., H. C. Ferreira, D. Uehara, and R. M. Sales. "ADAPTATION FOR TANDEM COLD MILL MODELS." IFAC Proceedings Volumes 40, no. 11 (2007): 293–98. http://dx.doi.org/10.3182/20070821-3-ca-2919.00044.

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12

Blackie, Caroline, and Howard Howland. "ACCOMMODATION AND CONVERGENCE ADAPTATION MODELS REVIEWED." Optometry and Vision Science 79, Supplement (December 2002): 290. http://dx.doi.org/10.1097/00006324-200212001-00539.

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13

Cheng, H., M. Fill, H. Valdivia, and W. Lederer. "Models of Ca2+ release channel adaptation." Science 267, no. 5206 (March 31, 1995): 2009–10. http://dx.doi.org/10.1126/science.7701326.

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14

Sachs, F., F. Qin, and P. Palade. "Models of Ca2+ release channel adaptation." Science 267, no. 5206 (March 31, 1995): 2010–11. http://dx.doi.org/10.1126/science.7701327.

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15

Morin, Brice, Olivier Barais, Jean-Marc Jezequel, Franck Fleurey, and Arnor Solberg. "Models@ Run.time to Support Dynamic Adaptation." Computer 42, no. 10 (October 2009): 44–51. http://dx.doi.org/10.1109/mc.2009.327.

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16

Atack, P. A., and I. S. Robinson. "Adaptation of hot mill process models." Journal of Materials Processing Technology 60, no. 1-4 (June 1996): 535–42. http://dx.doi.org/10.1016/0924-0136(96)02383-7.

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17

Bauer, Marianne, and Erwin Frey. "Delayed adaptation in stochastic metapopulation models." EPL (Europhysics Letters) 122, no. 6 (July 30, 2018): 68002. http://dx.doi.org/10.1209/0295-5075/122/68002.

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18

Gupta, Rahul, and Sunita Sarawagi. "Domain adaptation of information extraction models." ACM SIGMOD Record 37, no. 4 (March 20, 2009): 35–40. http://dx.doi.org/10.1145/1519103.1519109.

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19

Pires, Carlos Thadeu de Ávila, Henrique Cezar Ferreira, and Roberto Moura Sales. "Adaptation for tandem cold mill models." Journal of Materials Processing Technology 209, no. 7 (April 2009): 3592–96. http://dx.doi.org/10.1016/j.jmatprotec.2008.08.020.

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20

Hayhoe, M. M. "Spatial interactions and models of adaptation." Vision Research 30, no. 6 (January 1990): 957–65. http://dx.doi.org/10.1016/0042-6989(90)90061-o.

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21

Miller, Thomas W. "Stress adaptation in children: Theoretical models." Journal of Contemporary Psychotherapy 25, no. 1 (March 1995): 5–14. http://dx.doi.org/10.1007/bf02308665.

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22

Il?ichev, V. G. "Adaptation of parameters in ecology models." Automation and Remote Control 66, no. 2 (February 2005): 281–94. http://dx.doi.org/10.1007/s10513-005-0051-z.

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23

Forsberg-Wärleby, Gunilla, and Anders Moller. "Models of Adaptation—An Adaptation Process After Stroke Analysed from Different Theoretical Perspectives of Adaptation." Scandinavian Journal of Occupational Therapy 3, no. 3 (January 1996): 114–22. http://dx.doi.org/10.3109/11038129609106694.

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24

Duffin, James, and Safraaz Mahamed. "Adaptation in the respiratory control system." Canadian Journal of Physiology and Pharmacology 81, no. 8 (August 1, 2003): 765–73. http://dx.doi.org/10.1139/y03-049.

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Exposure to hypoxia, whether for short or prolonged periods or for repeated episodes, produces alterations in the ventilatory responses. This review presents evidence that these adaptations are likely to be mediated by adaptations in the respiratory chemoreflexes, particularly the peripheral chemoreflex, and proposes models of respiratory control explaining the observed changes in ventilation. After a brief introduction to the respiratory control system, a graphical model is developed that illustrates the operation of the system in the steady state, which will be used later. Next, the adaptations in ventilatory responses to hypoxia that have been observed are described, and methods of measuring the alterations in the chemoreflexes that might account for them are discussed. Finally, experimental data supporting the view that changes in the activity of the peripheral chemoreflex can account for the ventilatory adaptations to hypoxia are presented and incorporated into models of chemoreflex behaviour during exposures to hypoxia of various durations.Key words: respiration, chemoreflexes, hypoxia, adaptation, models.
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25

GANECHKO, Iryna, and Oksana TRUBEI. "RETAIL BUSINESS MODELS: ADAPTATION TO NEW CHALLENGES." Herald of Kyiv National University of Trade and Economics 133, no. 5 (October 20, 2020): 77–88. http://dx.doi.org/10.31617/visnik.knute.2020(133)07.

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Background. The retail sector in Ukraine is in crisis due to COVID-19. New risks and challenges have emerged that have put at risk the functioning of the trading industry. Therefore, there is a need to find ways to adapt and transform business models in retail to new, crisis conditions. Analysis of recent researches and publications. Peculiarities of trade business functio­ning in conditions of uncertainty, risks, and crises are actively studied by many scientists and practitioners. However, the issue of changing retail business models in the face of instability, which would allow companies to adapt to today’s challenges, remains insufficiently studied. The aim of the article is to study the process of transformation and adaptation of business models in retail to new economic realities, challenges, and threats associated with the situation around COVID-19. Materials and methods. The information base of this study was scientific work on the adaptation of business to modern conditions of instability and rapid change. The research methodology is based on a system of general scientific and special methods, in particular: analy­sis and synthesis; systematic and integrated approach. Results. The peculiarities of the instability of the modern world according to the VUCA concept, in particular its manifestations related to the COVID-19 pandemic, have been studied. The main threats to business development in the field of retail trade in the current crisis situation have been identified. Changes in business models in retail are proposed, which will help in adapting to difficult economic conditions and maintaining the competitiveness of retail enterprises in the consumer goods market. Conclusion. The necessity of transformational changes of business models in retail in order to adapt them to crisis conditions and ensure the survival of retail enterprises in today’s unstable world is proved.
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26

Bertoldi, Nicola. "Dynamic Models in Moses for Online Adaptation." Prague Bulletin of Mathematical Linguistics 101, no. 1 (April 1, 2014): 7–28. http://dx.doi.org/10.2478/pralin-2014-0001.

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Abstract A very hot issue for research and industry is how to effectively integrate machine translation (MT) within computer assisted translation (CAT) software. This paper focuses on this issue, and more generally how to dynamically adapt phrase-based statistical machine translation (SMT) by exploiting external knowledge, like the post-editions from professional translators. We present an enhancement of the Moses SMT toolkit dynamically adaptable to external information, which becomes available during the translation process, and which can depend on the previously translated text. We have equipped Moses with two new elements: a new phrase table implementation and a new LM-like feature. Both the phrase table and the LM-like feature can be dynamically modified by adding and removing entries and re-scoring them according to a time-decaying scoring function. The final goal of these two dynamically adaptable features is twofold: to create additional translation alternatives and to reward those which are composed of entries previously inserted therein. The implemented dynamic system is highly configurable, flexible and applicable to many tasks, like for instance online MT adaptation, interactive MT, and context-aware MT. When exploited in a real-world CAT scenario where online adaptation is applied to repetitive texts, it has proven itself very effective in improving translation quality and reducing post-editing effort.
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27

Vinken, Kasper, and Gabriel Kreiman. "Adaptation in models of visual object recognition." Journal of Vision 19, no. 10 (September 6, 2019): 210a. http://dx.doi.org/10.1167/19.10.210a.

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28

West, Mike, and P. Jeff Harrison. "Monitoring and Adaptation in Bayesian Forecasting Models." Journal of the American Statistical Association 81, no. 395 (September 1986): 741–50. http://dx.doi.org/10.1080/01621459.1986.10478331.

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29

Jiaolong Xu, Sebastian Ramos, David Vazquez, and Antonio M. Lopez. "Domain Adaptation of Deformable Part-Based Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 12 (December 1, 2014): 2367–80. http://dx.doi.org/10.1109/tpami.2014.2327973.

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30

González-Rivera, Gloria. "A note on adaptation in garch models." Econometric Reviews 16, no. 1 (January 1997): 55–68. http://dx.doi.org/10.1080/07474939708800372.

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31

Makous, W. L. "Fourier models and the loci of adaptation." Journal of the Optical Society of America A 14, no. 9 (September 1, 1997): 2323. http://dx.doi.org/10.1364/josaa.14.002323.

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32

D'Hulst, R., and G. J. Rodgers. "Efficiency and persistence in models of adaptation." Physica A: Statistical Mechanics and its Applications 324, no. 1-2 (June 2003): 323–29. http://dx.doi.org/10.1016/s0378-4371(02)01847-2.

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33

Stuart, C. I. J. M. "Physical models of biological information and adaptation." Journal of Theoretical Biology 113, no. 3 (April 1985): 441–54. http://dx.doi.org/10.1016/s0022-5193(85)80032-1.

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34

Pan, Wanbin, Xiang Chen, and Shuming Gao. "Automatic shape adaptation for parametric solid models." Computer-Aided Design 62 (May 2015): 78–97. http://dx.doi.org/10.1016/j.cad.2014.11.001.

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35

Turner, C. H., M. R. Forwood, D. M. Raab-Cullen, M. P. Akhter, D. B. Kimmel, R. R. Recker, A. G. Torrance, and L. E. Lanyon. "On animal models for studying bone adaptation." Calcified Tissue International 55, no. 4 (October 1994): 316–18. http://dx.doi.org/10.1007/bf00310412.

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36

Denker, John S. "Neural network models of learning and adaptation." Physica D: Nonlinear Phenomena 22, no. 1-3 (October 1986): 216–32. http://dx.doi.org/10.1016/0167-2789(86)90242-3.

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37

Frankel, Joe, and Simon King. "Observation process adaptation for linear dynamic models." Speech Communication 48, no. 9 (September 2006): 1192–99. http://dx.doi.org/10.1016/j.specom.2006.05.001.

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38

Sastre‐Vazquez, P., J. L. Usó‐Domènech, and J. Mateu. "Adaptation of linguistic laws to ecological models." Kybernetes 29, no. 9/10 (December 2000): 1306–23. http://dx.doi.org/10.1108/03684920010346392.

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39

Datta, Saheli, Raquel Prado, and Abel Rodríguez. "Bayesian factor models in characterizing molecular adaptation." Journal of Applied Statistics 40, no. 7 (July 2013): 1402–24. http://dx.doi.org/10.1080/02664763.2013.785652.

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40

Tanaka, Hirokazu, John W. Krakauer, and Terrence J. Sejnowski. "Generalization and Multirate Models of Motor Adaptation." Neural Computation 24, no. 4 (April 2012): 939–66. http://dx.doi.org/10.1162/neco_a_00262.

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When subjects adapt their reaching movements in the setting of a systematic force or visual perturbation, generalization of adaptation can be assessed psychophysically in two ways: by testing untrained locations in the work space at the end of adaptation (slow postadaptation generalization) or by determining the influence of an error on the next trial during adaptation (fast trial-by-trial generalization). These two measures of generalization have been widely used in psychophysical studies, but the reason that they might differ has not been addressed explicitly. Our goal was to develop a computational framework for determining when a two-state model is justified by the data and to explore the implications of these two types of generalization for neural representations of movements. We first investigated, for single-target learning, how well standard statistical model selection procedures can discriminate two-process models from single-process models when learning and retention coefficients were systematically varied. We then built a two-state model for multitarget learning and showed that if an adaptation process is indeed two-rate, then the postadaptation generalization approach primarily probes the slow process, whereas the trial-by-trial generalization approach is most informative about the fast process. The fast process, due to its strong sensitivity to trial error, contributes predominantly to trial-by-trial generalization, whereas the strong retention of the slow system contributes predominantly to postadaptation generalization. Thus, when adaptation can be shown to be two-rate, the two measures of generalization may probe different brain representations of movement direction.
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41

Il’ichev, V. G., and O. A. Il’icheva. "Spatial Adaptation of Populations in Ecological Models." Biophysics 63, no. 2 (March 2018): 274–81. http://dx.doi.org/10.1134/s0006350918020112.

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42

Boukhedouma, Saida, Zaia Alimazighi, and Mourad Oussalah. "Adaptation and Evolution Frameworks for Service Based Inter-Organizational Workflows." International Journal of E-Business Research 13, no. 2 (April 2017): 28–57. http://dx.doi.org/10.4018/ijebr.2017040103.

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This paper describes pattern-based frameworks for adaptation and evolution of service-based workflow process models meeting specific cooperation patterns widely recognized in the B2B area. In the authors' previous works, they focused on restructuring and interconnecting workflows using the SOA paradigm so as to deal with more flexible inter-organizational workflow (IOWF) models. The main issue of this paper is the question of flexibility of IOWF process models where the authors distinguish two main aspects: adaptability and evolutivity affecting different levels of the IOWF process. For that, they define three categories of adptation called functional, behavioral and interactional adaptations and two categories of evolution called functional evolution and organizational evolution; then, they conceptualize some adaptation and evolution patterns which can be applied on the IOWF process models. The authors' adaptation and evolution frameworks are developed according to the MVC pattern and operate on process models specified with BPEL. Thanks to their high modularity, the frameworks are easily extensible and allow the reusing of patterns to implement other ones.
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43

Pei, Xiaodong, Jing Wu, Junbo Xue, Jincai Zhao, Changxin Liu, and Yuan Tian. "Assessment of Cities’ Adaptation to Climate Change and Its Relationship with Urbanization in China." Sustainability 14, no. 4 (February 15, 2022): 2184. http://dx.doi.org/10.3390/su14042184.

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Global climate change has led to more frequent occurrences of extreme, dangerous events; therefore, it is urgent to enhance cities’ adaptation to climate change. Focusing on the impact of high temperature, low temperature, drought, and flooding, we established multi-dimensional assessment systems covering natural, economic, and social elements for cities’ climate change adaptations. Based on the assessment systems, studies on adaptation to climate change were conducted in 248 cities in China using the entropy weight method, coefficient of variation method, and exploratory spatial data analysis; with the help of geographically weighted regression (GWR), the relationships between four types of urbanization and cities’ adaptation to climate events were explored. The results showed the following: (1) High-administrative-level cities had higher adaptation than ordinary prefecture-level cities. (2) The differences in adaptation to the four types of climate events between cities within each of the seven regions in China presented significantly different spatial patterns. (3) Under the four types of climate events, the global spatial correlations of cities’ adaptations in China were positive. The agglomeration characteristics of adaptation were mainly H–H and L–L agglomerations. (4) When analyzing the impacts of four types of urbanization on cities’ adaptation to climate events, the fitting effects of GWR models were far better than those of OLS models. Population urbanization, economic urbanization, land urbanization, and industrial urbanization had different impacts on adaptation. Under the influence of social and economic development, the urbanization regression coefficients of different cities had significant spatial differences.
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44

Shelestova, O. "COPING STRATEGIES AND MODELS OF OVERCOMING POST-STRESS CONDITIONS." Herald of Kiev Institute of Business and Technology 43, no. 1 (March 12, 2020): 70–75. http://dx.doi.org/10.37203/kibit.2020.43.09.

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The article is devoted to the research of coping strategies on the adaptation process. Actions associated with the COVID 19 pandemic touched many areas of life. They provided increased requirements for human mental activity, creating additional stresses, which led to an increase in neuropsychiatric disorders. Models of overcoming post-stress states are considered. The role of external and internal factors in the formation and overcoming of post-stress states and disorders of adaptation is analyzed in the article. In the process of studying the underlying patterns and mechanisms of adaptation to normal life, two psychological models of overcoming post-stress states were identified: psychodynamic and cognitive. The psychodynamic model notes that successful adaptation of personality requires awareness of all aspects of traumatic experience and integration through the scheme "traumatic stress - negation - relapse - acceptance". The cognitive model is a cognitive theory of stress, that is, a cognitive assessment of a person's most traumatic event, and himself in it. Three types of assessment were proposed to determine a person's response to a traumatic event: primary, secondary stress assessment, and reassessment. Many researchers consider the impact of stress adaptation to be one of the leading causes of disorders of adaptation. Also, the manifestations of adaptation disorders are greatly exacerbated by the simultaneous complex action of several stressors. The severity of active stressors does not always correlate with the severity of adaptation disorders, as personality traits and cultural and social norms and values contribute to the response to stress. Also, the features of the development of these disorders of adaptation disorders depend on the features of cognitive processes, personal resources of the individual involved in their regulation.
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45

St-Cyr, Amik, Christiane Jablonowski, John M. Dennis, Henry M. Tufo, and Stephen J. Thomas. "A Comparison of Two Shallow-Water Models with Nonconforming Adaptive Grids." Monthly Weather Review 136, no. 6 (June 1, 2008): 1898–922. http://dx.doi.org/10.1175/2007mwr2108.1.

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Abstract In an effort to study the applicability of adaptive mesh refinement (AMR) techniques to atmospheric models, an interpolation-based spectral element shallow-water model on a cubed-sphere grid is compared to a block-structured finite-volume method in latitude–longitude geometry. Both models utilize a nonconforming adaptation approach that doubles the resolution at fine–coarse mesh interfaces. The underlying AMR libraries are quad-tree based and ensure that neighboring regions can only differ by one refinement level. The models are compared via selected test cases from a standard test suite for the shallow-water equations, and via a barotropic instability test. These tests comprise the passive advection of a cosine bell and slotted cylinder, a steady-state geostrophic flow, a flow over an idealized mountain, a Rossby–Haurwitz wave, and the evolution of a growing barotropic wave. Both static and dynamics adaptations are evaluated, which reveal the strengths and weaknesses of the AMR techniques. Overall, the AMR simulations show that both models successfully place static and dynamic adaptations in local regions without requiring a fine grid in the global domain. The adaptive grids reliably track features of interests without visible distortions or noise at mesh interfaces. Simple threshold adaptation criteria for the geopotential height and the relative vorticity are assessed.
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46

GOTTSCHALK, FLORIAN, WIL M. P. VAN DER AALST, MONIQUE H. JANSEN-VULLERS, and MARCELLO LA ROSA. "CONFIGURABLE WORKFLOW MODELS." International Journal of Cooperative Information Systems 17, no. 02 (June 2008): 177–221. http://dx.doi.org/10.1142/s0218843008001798.

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Workflow modeling languages allow for the specification of executable business processes. They, however, typically do not provide any guidance for the adaptation of workflow models, i.e. they do not offer any methods or tools explaining and highlighting which adaptations of the models are feasible and which are not. Therefore, an approach to identify so-called configurable elements of a workflow modeling language and to add configuration opportunities to workflow models is presented in this paper. Configurable elements are the elements of a workflow model that can be modified such that the behavior represented by the model is restricted. More precisely, a configurable element can be either set to enabled, to blocked, or to hidden. To ensure that such configurations lead only to desirable models, our approach allows for imposing so-called requirements on the model's configuration. They have to be fulfilled by any configuration, and limit therefore the freedom of configuration choices. The identification of configurable elements within the workflow modeling language of YAWL and the derivation of the new "configurable YAWL" language provide a concrete example for a rather generic approach. A transformation of configured models into lawful YAWL models demonstrates its applicability.
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Mulyasari, Gita, P. Prawito, and R. Yuristia. "Adaptation to climate variability of rainfed farmers in Seluma Regency, Bengkulu, Indonesia." IOP Conference Series: Earth and Environmental Science 1016, no. 1 (April 1, 2022): 012020. http://dx.doi.org/10.1088/1755-1315/1016/1/012020.

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Abstract Farmers are synonymous with poverty because of the great uncertainty about the impact and magnitude of climate variability. Adaptation efforts, in principle, minimize vulnerability to impacts caused by climate variability. This study aims to see the level of adaptation of farmers and the factors that influence it. A randomly selected 100 rainfed farmers survey was conducted in Seluma Regency, Bengkulu, from June to September 2020. The analysis used to measure the level of adaptation was to compare the number of adaptations adopted by farmers with the total adaptation used in this study and to analyze the factors that affect the level of adaptation using multiple regression models. The results showed that rainfed farmers have a low of adaptation to climate variability. Most farmers make adjustments to planting time, replace climate-resistant varieties, and take advantage of government assistance as a form of adaptation to climate variability. The level of adaptation of rainfed farmers to climate variability is influenced by experience, income, and farmer group membership.
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JEONG, Yongwon. "Speaker Adaptation in Sparse Subspace of Acoustic Models." IEICE Transactions on Information and Systems E96.D, no. 6 (2013): 1402–5. http://dx.doi.org/10.1587/transinf.e96.d.1402.

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Klevtsov, S. I., and A. V. Maksimov. "Adaptation Parameters of Time Series Models for Forecasting." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012050. http://dx.doi.org/10.1088/1742-6596/2096/1/012050.

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Abstract Prospects for using time series to predict changes in technical parameters in real time are considered. The task is to assess the trend dynamics of the parameter. Adaptive polynomial models of the first and second order, based on the method of multiple exponential smoothing, were selected for forecasting. The models have been modified to adapt to the peculiarities of the computing process in the microcontroller. The initial data, the acceleration values in three axes, were obtained using a three-axis accelerometer installed on the vehicle. Comparison of the forecasting results showed that the second-order adaptive polynomial model is generally more preferable from the point of view of the reduced error. Both models can be used to estimate the change in a parameter for an arbitrary number of prediction intervals. The efficiency of using the models for the forecasting problem largely depends on the determination of the adaptation parameters, such as the smoothing constant and the initial estimates of the coefficients of the time series model. The paper considers the features of the behavior of the models and defines the rules for the selection of adaptation parameters depending on the nature of the change in the technical parameter over time.
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Ngargueudedjim, Kimtangar, Guy Anago, and Jean-Louis Robert. "Adaptation of cumulative damage models to multiaxial fatigue*." Materials Testing 48, no. 4 (April 2006): 160–63. http://dx.doi.org/10.3139/120.100721.

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