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Autor: Grafiati
Publicado: 6 de septiembre de 2023

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1

Venturini, Chiara y Pablo Sáez. "A multi-scale clutch model for adhesion complex mechanics". PLOS Computational Biology 19, n.º 7 (14 de julio de 2023): e1011250. http://dx.doi.org/10.1371/journal.pcbi.1011250.

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Cell-matrix adhesion is a central mechanical function to a large number of phenomena in physiology and disease, including morphogenesis, wound healing, and tumor cell invasion. Today, how single cells respond to different extracellular cues has been comprehensively studied. However, how the mechanical behavior of the main individual molecules that form an adhesion complex cooperatively responds to force within the adhesion complex is still poorly understood. This is a key aspect of cell adhesion because how these cell adhesion molecules respond to force determines not only cell adhesion behavior but, ultimately, cell function. To answer this question, we develop a multi-scale computational model for adhesion complexes mechanics. We extend the classical clutch hypothesis to model individual adhesion chains made of a contractile actin network, a talin rod, and an integrin molecule that binds at individual adhesion sites on the extracellular matrix. We explore several scenarios of integrins dynamics and analyze the effects of diverse extracellular matrices on the behavior of the adhesion molecules and on the whole adhesion complex. Our results describe how every single component of the adhesion chain mechanically responds to the contractile actomyosin force and show how they control the traction forces exerted by the cell on the extracellular space. Importantly, our computational results agree with previous experimental data at the molecular and cellular levels. Our multi-scale clutch model presents a step forward not only to further understand adhesion complexes mechanics but also to impact, e.g., the engineering of biomimetic materials, tissue repairment, or strategies to arrest tumor progression.
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2

Nishimura, Satoshi, Koji Eto y Ryozo Nagai. "Thrombus Development Processes Are Dependent On Endothelial Injuries: Examined By In Vivo Molecular Imaging". Blood 122, n.º 21 (15 de noviembre de 2013): 1070. http://dx.doi.org/10.1182/blood.v122.21.1070.1070.

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Abstract The thrombotic cellular mechanisms associated with cardiovascular events remains unclear, largely because of an inability to visualize thrombus formation. In addition, the contribution of endothelial cell (EC) injuries to thrombus formation processes are unclear, and we developed in vivo imaging technique based on single- and multi-photon microscopy to revealed the multicellular processes during thrombus development (Figure a,b). We visualized the cell dynamics including single platelet behavior, and assessed dynamic cellular interplay in two thrombosis models. First, we visualized that rapidly developing thrombi composed of discoid platelets without EC disruption was triggered by ROS photochemically induced by moderate power laser irradiation (Figure c). In this model, thrombus consisted by discoid platelet aggregations without leukocyte recruitment. The second model is, thrombus with EC disruption. High power laser induced EC erosion and extravasations of circulating leukocytes with thrombus development. Inflammatory cytokine, adhesion molecules dynamically control these two processes. (Figure d)Figure.Figure. Using this technique, we elucidated that Lnk (adapter protein) regulates integrin signaling leading to stabilization of developing thrombus without EC disruption. Specifically, adhesion molecules dynamically control these processes. Thrombus formation was initiated by the binding of platelet GPIb-alpha to endothelial von Willebrand Factor in this model, and actin linker talin-dependent activation of alphaIIb-beta3 integrin in platelets was required for late phase thrombus stability. As for the thrombus formation with EC disruption, chemokine expressions in endothelium and leukocyte (especially neutrophils) recruitment played a significant role in these processes. TLR4 signaling also contributed to these steps. In sum, using our imaging system can be a powerful tool to analyze thrombus formation and evaluate the therapeutic strategies. Disclosures: No relevant conflicts of interest to declare.
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3

Zhou, Yatong, Taiyi Zhang y Xiaohe Li. "Multi-scale Gaussian Processes model". Journal of Electronics (China) 23, n.º 4 (julio de 2006): 618–22. http://dx.doi.org/10.1007/s11767-005-0209-4.

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4

Demin, I., F. Crauste, O. Gandrillon y V. Volpert. "A multi-scale model of erythropoiesis". Journal of Biological Dynamics 4, n.º 1 (enero de 2010): 59–70. http://dx.doi.org/10.1080/17513750902777642.

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5

YONEMOTO, Yukihiro y Tomoaki KUNUGI. "Multi-Scale Gas-Liquid Interfacial Model". Transactions of the Japan Society of Mechanical Engineers Series B 74, n.º 737 (2008): 96–101. http://dx.doi.org/10.1299/kikaib.74.96.

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6

Stepanov, R., P. Frick y D. Sokoloff. "A multi-scale disk dynamo model". Astronomische Nachrichten 327, n.º 5-6 (junio de 2006): 481–82. http://dx.doi.org/10.1002/asna.200610564.

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7

XIE, Wenjie, De XU, Yingjun TANG y Geng CUI. "Multi-Scale Multi-Level Generative Model in Scene Classification". IEICE Transactions on Information and Systems E94-D, n.º 1 (2011): 167–70. http://dx.doi.org/10.1587/transinf.e94.d.167.

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8

Chen, Shaopei, Jianjun Tan, Christophe Claramunt y Cyril Ray. "Multi-scale and multi-modal GIS-T data model". Journal of Transport Geography 19, n.º 1 (enero de 2011): 147–61. http://dx.doi.org/10.1016/j.jtrangeo.2009.09.006.

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9

Viaña, Raquel, Paola Magillo, Enrico Puppo y Pedro A. Ramos. "Multi-VMap: A Multi-Scale Model for Vector Maps". GeoInformatica 10, n.º 3 (septiembre de 2006): 359–94. http://dx.doi.org/10.1007/s10707-006-9832-y.

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10

Samadiani, Emad y Yogendra Joshi. "Multi-parameter model reduction in multi-scale convective systems". International Journal of Heat and Mass Transfer 53, n.º 9-10 (abril de 2010): 2193–205. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2009.12.013.

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11

Bachmann, Chris, Matthew J. Roorda y Chris Kennedy. "DEVELOPING A MULTI-SCALE MULTI-REGION INPUT–OUTPUT MODEL". Economic Systems Research 27, n.º 2 (11 de diciembre de 2014): 172–93. http://dx.doi.org/10.1080/09535314.2014.987730.

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12

Tavanti, Francesco y Valentina Tozzini. "A Multi-Scale–Multi-Stable Model for the Rhodopsin Photocycle". Molecules 19, n.º 9 (18 de septiembre de 2014): 14961–78. http://dx.doi.org/10.3390/molecules190914961.

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13

Greve, L. y S. Vlachoutsis. "Multi-scale and multi-model methods for efficient crash simulation". International Journal of Crashworthiness 12, n.º 4 (3 de octubre de 2007): 437–48. http://dx.doi.org/10.1080/13588260701483425.

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14

Seyedhosseini, M. y T. Tasdizen. "Multi-Class Multi-Scale Series Contextual Model for Image Segmentation". IEEE Transactions on Image Processing 22, n.º 11 (noviembre de 2013): 4486–96. http://dx.doi.org/10.1109/tip.2013.2274388.

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15

Musundi, Beryl, Johannes Müller y Zhilan Feng. "A Multi-Scale Model for Cholera Outbreaks". Mathematics 10, n.º 17 (30 de agosto de 2022): 3114. http://dx.doi.org/10.3390/math10173114.

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Cholera, caused by the pathogenic Vibrio cholerae bacteria, remains a severe public health threat. Although a lot of emphasis has been placed on the population-level spread of the disease, the infection itself starts within the body. As such, we formulated a multi-scale model that explicitly connects the within-host and between-host dynamics of the disease. To model the within-host dynamics, we assigned each susceptible individual with a pathogen load that increases through the uptake of contaminated food and water (booster event). We introduced minimal and maximal times when the booster events happen and defined a time since the last booster event. We then scaled the within-host dynamics to the population where we structured the susceptible population using the two variables (pathogen load and time since the last booster event). We analyzed the pathogen load’s invariant distribution and utilized the results and time scale assumptions to reduce the dimension of the multi-scale model. The resulting model is an SIR model whose incidence function has terms derived from the multi-scale model. We finally conducted numerical simulations to investigate the long-term behavior of the SIR model. The simulations revealed parameter regions where either no cholera cases happen, where cholera is present at a low prevalence, and where a full-blown cholera epidemic takes off.
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16

Lee, S. y A. Dobbins. "A Multi-scale model of Binocular combination". Journal of Vision 7, n.º 9 (30 de marzo de 2010): 815. http://dx.doi.org/10.1167/7.9.815.

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17

Stephanopoulos, George, Orhan Karsligil y Matthew Dyer. "Multi-Scale Aspects in Model-Predictive Control". IFAC Proceedings Volumes 31, n.º 11 (junio de 1998): 53–59. http://dx.doi.org/10.1016/s1474-6670(17)44906-8.

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18

Stephanopoulos, George, Orhan Karsligil y Matthew Dyer. "Multi-scale aspects in model-predictive control". Journal of Process Control 10, n.º 2-3 (abril de 2000): 275–82. http://dx.doi.org/10.1016/s0959-1524(99)00022-0.

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19

Broadbent, R. J., J. S. Spencer, A. G. Livingston, A. A. Mostofi y A. P. Sutton. "A Multi-Scale Model for Polymer Membranes". Procedia Engineering 44 (2012): 489–90. http://dx.doi.org/10.1016/j.proeng.2012.08.460.

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20

Vernerey, Franck, Wing Kam Liu, Brian Moran y Gregory Olson. "Multi-length scale micromorphic process zone model". Computational Mechanics 44, n.º 3 (20 de marzo de 2009): 433–45. http://dx.doi.org/10.1007/s00466-009-0382-7.

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21

Nie, Weizhi, Shu Xiang y Anan Liu. "Multi-scale CNNs for 3D model retrieval". Multimedia Tools and Applications 77, n.º 17 (19 de enero de 2018): 22953–63. http://dx.doi.org/10.1007/s11042-018-5641-1.

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22

Molchan, George, Tatiana Kronrod y Giuliano F. Panza. "Multi-scale seismicity model for seismic risk". Bulletin of the Seismological Society of America 87, n.º 5 (1 de octubre de 1997): 1220–29. http://dx.doi.org/10.1785/bssa0870051220.

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Abstract For a general use of the frequency-magnitude (FM) relation in seismic risk assessment, we formulate a multi-scale approach that relies on the hypothesis that only the ensemble of events that are geometrically small, compared with the elements of the seismotectonic regionalization, can be described by a log-linear FM relation. It follows that the seismic zonation must be performed at several scales, depending upon the self-similarity conditions of the seismic events and the linearity of the log FM relation, in the magnitude range of interest. The analysis of worldwide seismicity, using the Harvard catalog, where the seismic moment is recorded as the earthquake size, corroborates the idea that a single FM relation is not universally applicable. The multi-scale model of the FM relation is tested in the Italian region.
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23

Fager, Andrew, Hiroshi Otomo, Rafael Salazar-Tio, Ganapathi Balasubramanian, Bernd Crouse, Raoyang Zhang, Hudong Chen y Josephina Schembre-McCabe. "Multi-scale Digital Rock: Application of a multi-scale multi-phase workflow to a Carbonate reservoir rock". E3S Web of Conferences 366 (2023): 01001. http://dx.doi.org/10.1051/e3sconf/202336601001.

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In some of the challenging digital rock applications the trade-off between model resolution and representative elemental volume is not captured in a single resolution model satisfying the minimum requirements for both aspects. In the wide range of lithofacies found in carbonate reservoir rocks, some facies fall in this category, where large pores, ooids or vugs, are connected by small scale porous structures that could have orders of magnitude smaller pores. In these cases a multi-scale digital rock approach is needed. We recently developed an extension to a digital rock workflow that includes a way to handle sub-resolution pore structures in single phase and multi-phase flow scenarios in addition to regular resolvable pore structures. Here we present an application of this methodology to a multi-scale limestone carbonate rock. A microCT image captures the large pores for this sample, but does not resolve all the pores smaller than the pixel size. A three phase image segmentation that considers pore, solid and under-resolved pores or porous media (PM) is generated. A high resolution confocal image model is obtained for a representative region of the smaller pores or PM region. A set of constitutive relationships (namely permeability vs. porosity, capillary pressure vs saturation and relative permeability vs saturation) are obtained by simulation from the high resolution confocal model. The low resolution segmented image, a porosity distribution image, and the constitutive relationships for the PM are input in an extended LBM multi-scale multi-phase solver. First we present results for absolute permeability and show a parametric study on PM permeability. The model recovers the expected behaviour when the PM regions are considered pore or solid. A consistent value of permeability with experiments is obtained when we use the PM permeability from the high resolution model. To demonstrate the multi-phase behaviour, we present results for capillary pressure imbibition multi-scale simulations. Here a small model for a dual porosity system is created in order to compare single scale results with the multi-scale solver. Finally, capillary imbibition results for the whole domain are shown and different wettability scenario results are discussed. This application illustrates a novel multi-scale simulation approach that can address a long standing problem in digital rock.
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24

Cristiani, Emiliano y Elisa Iacomini. "An interface-free multi-scale multi-order model for traffic flow". Discrete & Continuous Dynamical Systems - B 24, n.º 11 (2019): 6189–207. http://dx.doi.org/10.3934/dcdsb.2019135.

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25

YUN, Ruide. "New Fractal Contact Model Considered Multi-scale Levels". Journal of Mechanical Engineering 55, n.º 9 (2019): 80. http://dx.doi.org/10.3901/jme.2019.09.080.

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26

Taylor, Shawn D. y Dawn M. Browning. "Multi-scale assessment of a grassland productivity model". Biogeosciences 18, n.º 6 (1 de abril de 2021): 2213–20. http://dx.doi.org/10.5194/bg-18-2213-2021.

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Abstract. Grasslands provide many important ecosystem services globally, and projecting grassland productivity in the coming decades will provide valuable information to land managers. Productivity models can be well calibrated at local scales but generally have some maximum spatial scale in which they perform well. Here we evaluate a grassland productivity model to find the optimal spatial scale for parameterization and thus for subsequently applying it in future productivity projections for North America. We also evaluated the model on new vegetation types to ascertain its potential generality. We find the model most suitable when incorporating only grasslands, as opposed to also including agriculture and shrublands, and only in the Great Plains and eastern temperate forest ecoregions of North America. The model was not well suited to grasslands in North American deserts or northwest forest ecoregions. It also performed poorly in agriculture vegetation, likely due to management activities, and shrubland vegetation, likely because the model lacks representation of deep water pools. This work allows us to perform long-term projections in areas where model performance has been verified, with gaps filled in by future modeling efforts.
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27

Torche, Paola C., Andrea Silva, Denis Kramer, Tomas Polcar y Ondrej Hovorka. "Multi‐scale model predicting friction of crystalline materials". Advanced Materials Interfaces 9, n.º 4 (13 de diciembre de 2021): 2100914. http://dx.doi.org/10.1002/admi.202100914.

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28

Xu, Qingfeng, Hèrm Hofmeyer y Johan Maljaars. "Multi‐scale bolt connection model for thermomechanical simulations". ce/papers 4, n.º 2-4 (septiembre de 2021): 1297–303. http://dx.doi.org/10.1002/cepa.1424.

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29

Li, Yu, Xiang Juan Li, Ya Sen Zhang, Xian Sun y Hong Qi Wang. "Multi-Scale Semantic Model for Unsupervised Object Segmentation". Advanced Materials Research 532-533 (junio de 2012): 859–64. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.859.

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It is difficult to segment instances of object classes accurately unsupervised in images, because of the complexity of structures, inter-class differences, background interference and so on. A multi-scale semantic model method is proposed to overcome the disadvantages existing in most of the relative methods. This method uses generative model to deal with the objects obtained by multi-scale segmentations instead of whole image, and calculates kinds of visual features to mine the topic information of every object. In the segmentation process, a semantic correlative function of every segment block based on KL divergence is built up and minimized to select the object correct regions. Experimental results demonstrate the effectiveness of the proposed method.
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30

Du, Shihong, Chen-Chieh Feng, Qiao Wang y Luo Guo. "Multi-Scale Qualitative Location: A Topology-Based Model". Transactions in GIS 18, n.º 4 (30 de septiembre de 2013): 604–31. http://dx.doi.org/10.1111/tgis.12058.

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31

Zhou, Haiying, Nicola Lai, Gerald M. Saidel y Marco E. Cabrera. "Multi-Scale Model of O2 Transport and Metabolism". Annals of the New York Academy of Sciences 1123, n.º 1 (19 de marzo de 2008): 178–86. http://dx.doi.org/10.1196/annals.1420.021.

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32

Haba, Naoyuki y Masaki Hirotsu. "TeV-scale seesaw from a multi-Higgs model". European Physical Journal C 69, n.º 3-4 (28 de agosto de 2010): 481–92. http://dx.doi.org/10.1140/epjc/s10052-010-1414-3.

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33

Song Haoze, 宋昊泽 y 吴小俊 Wu Xiaojun. "Deblurring Model of Image Multi-Scale Dense Network". Laser & Optoelectronics Progress 56, n.º 21 (2019): 211001. http://dx.doi.org/10.3788/lop56.211001.

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34

Kouznetsova, V. G. y M. G. D. Geers. "A multi-scale model of martensitic transformation plasticity". Mechanics of Materials 40, n.º 8 (agosto de 2008): 641–57. http://dx.doi.org/10.1016/j.mechmat.2008.02.004.

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35

Zha, Yufei, Duyan Bi y Yuan Yang. "Learning complex background by multi-scale discriminative model". Pattern Recognition Letters 30, n.º 11 (agosto de 2009): 1003–14. http://dx.doi.org/10.1016/j.patrec.2009.05.005.

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36

Contou-Carrere, Marie-Nathalie, Vassilios Sotiropoulos, Yiannis N. Kaznessis y Prodromos Daoutidis. "Model reduction of multi-scale chemical Langevin equations". Systems & Control Letters 60, n.º 1 (enero de 2011): 75–86. http://dx.doi.org/10.1016/j.sysconle.2010.10.011.

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37

Contucci, Pierluigi y Emanuele Mingione. "A Multi-scale Spin-Glass Mean-Field Model". Communications in Mathematical Physics 368, n.º 3 (30 de enero de 2019): 1323–44. http://dx.doi.org/10.1007/s00220-019-03308-8.

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38

Du, Shihong, Chen-Chieh Feng y Qiao Wang. "Multi-scale qualitative location: A direction-based model". Computers, Environment and Urban Systems 41 (septiembre de 2013): 151–66. http://dx.doi.org/10.1016/j.compenvurbsys.2013.05.005.

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39

Wang, C. H. "A multi-scale progressive damage model for laminates". Australian Journal of Mechanical Engineering 3, n.º 1 (enero de 2006): 73–78. http://dx.doi.org/10.1080/14484846.2006.11464496.

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40

Sarid, A. y M. Tzur. "The multi-scale generation and transmission expansion model". Energy 148 (abril de 2018): 977–91. http://dx.doi.org/10.1016/j.energy.2018.01.091.

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41

xu, Qiang, Jianyun Chen y Jing Li. "Multi-scale EFG model for Simulating Concrete Material". International Journal of Mechanics and Materials in Design 8, n.º 2 (18 de enero de 2012): 113–20. http://dx.doi.org/10.1007/s10999-012-9180-z.

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42

Labanda, Nicolás A., Sebastián M. Giusti y Bibiana M. Luccioni. "An objective multi-scale model with hybrid injection". International Journal of Non-Linear Mechanics 101 (mayo de 2018): 95–112. http://dx.doi.org/10.1016/j.ijnonlinmec.2018.01.009.

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43

Zhai, Aobo, Xianbin Wen, Haixia Xu, Liming Yuan y Qingxia Meng. "Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images". Remote Sensing 9, n.º 10 (24 de octubre de 2017): 1085. http://dx.doi.org/10.3390/rs9101085.

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44

Berg, Peter, Thomas Bosshard, Wei Yang y Klaus Zimmermann. "MIdASv0.2.1 – MultI-scale bias AdjuStment". Geoscientific Model Development 15, n.º 15 (5 de agosto de 2022): 6165–80. http://dx.doi.org/10.5194/gmd-15-6165-2022.

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Abstract. Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference data set, typically some sort of observations. There are numerous proposed methodologies to perform the adjustments – ranging from simple scaling approaches to advanced multi-variate distribution-based mapping. In practice, the actual bias adjustment method is a small step in the application, and most of the processing handles reading, writing, and linking different data sets. These practical processing steps become especially heavy with increasing model domain size and resolution in both time and space. Here, we present a new implementation platform for bias adjustment, which we call MIdAS (MultI-scale bias AdjuStment). MIdAS is a modern code implementation that supports features such as modern Python libraries that allow efficient processing of large data sets at computing clusters, state-of-the-art bias adjustment methods based on quantile mapping, and “day-of-year-based” adjustments to avoid artificial discontinuities, and it also introduces cascade adjustment in time and space. The MIdAS platform has been set up such that it will continually support development of methods aimed towards higher-resolution climate model data, explicitly targeting cases where there is a scale mismatch between data sets. The paper presents a comparison of different quantile-mapping-based bias adjustment methods and the subsequently chosen code implementation for MIdAS. A current recommended setup of the MIdAS bias adjustment is presented and evaluated in a pseudo-reference setup for regions around the world. Special focus is put on preservation of trends in future climate projections, and it is shown that the cascade adjustments perform better than the standard quantile mapping implementations and are often similar to methods that explicitly preserve trends.
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45

YAMAGUCHI, Atsushi, Kota Enoki, Takeshi ISHIHARA, Yukinari FUKUMOTO, Masamune OKINO, Shusaku IBA, Yuji OHYA et al. "Wind Power Forecasting with Physical Model and Multi Time Scale Model". Wind Engineers, JAWE 2007, n.º 111 (2007): 251–64. http://dx.doi.org/10.5359/jawe.2007.251.

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46

Schmidt, Adrian, Dieter Oehler, André Weber, Thomas Wetzel y Ellen Ivers-Tiffée. "A multi scale multi domain model for large format lithium-ion batteries". Electrochimica Acta 393 (octubre de 2021): 139046. http://dx.doi.org/10.1016/j.electacta.2021.139046.

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47

Hester, Susan D., Julio M. Belmonte, J. Scott Gens, Sherry G. Clendenon y James A. Glazier. "A Multi-cell, Multi-scale Model of Vertebrate Segmentation and Somite Formation". PLoS Computational Biology 7, n.º 10 (6 de octubre de 2011): e1002155. http://dx.doi.org/10.1371/journal.pcbi.1002155.

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48

Dauphin-Tanguy, G., O. Moreigne y P. Borne. "Optimal Control of Multi-time-scale Systems Through a Multi-model Representation". IFAC Proceedings Volumes 18, n.º 11 (septiembre de 1985): 473–78. http://dx.doi.org/10.1016/s1474-6670(17)60170-8.

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49

Cui, Yan, Wei Lu y Jun Teng. "Updating of structural multi-scale monitoring model based on multi-objective optimisation". Advances in Structural Engineering 22, n.º 5 (12 de octubre de 2018): 1073–88. http://dx.doi.org/10.1177/1369433218805235.

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Structural safety assessments are implemented based on measured data, but the limited number of sensors restricts the comprehensive acquisition of response information in large complex structures. A concurrent multi-scale model utilises global and local simulation characteristics to expand the insufficient measured data. Thus, good global and local simulation capability is necessary for structural health monitoring-oriented multi-scale model, and the updating of this monitoring model needs to consider the multi-type responses that are obtained from different structural scales. However, the existing methods usually integrate multi-type responses into a single-objective function, which cannot ensure the acquisition of the optimal parameters. Moreover, in common parameter screening method, the perturbation and threshold are set artificially, which causes a strong subjectivity, and the common polynomial response surface fits poorly for highly non-linear problem. Therefore, an updating method of the structural multi-scale monitoring model based on multi-objective optimisation is proposed. For the proposed method, a variance analysis based on the orthogonal experimental design is used to screen the unique significant influence parameters. The Kriging spatial interpolation technique is used to establish the approximate surrogate model between the response and its corresponding influence parameters. Simultaneously, the responses obtained from the global and local structural scales are used to define the sub-objectives of the multi-objective function vector in order to avoid the introduction of weight coefficients. Then, the multi-objective optimisation algorithm NSGA-II is used to obtain the optimal parameter values and realise the comprehensive updating of the initial multi-scale monitoring model. Finally, based on the health monitoring system of the large shell structure of the Zhuhai Opera House, the initial multi-scale monitoring model is updated using the proposed method. The structural dynamic characteristics and local stress obtained from the initial model, updated model and the real structure are compared to validate the effectiveness of the proposed method.
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Castaldo, Francesca, Francisco Páscoa dos Santos, Ryan C. Timms, Joana Cabral, Jakub Vohryzek, Gustavo Deco, Mark Woolrich, Karl Friston, Paul Verschure y Vladimir Litvak. "Multi-modal and multi-model interrogation of large-scale functional brain networks". NeuroImage 277 (agosto de 2023): 120236. http://dx.doi.org/10.1016/j.neuroimage.2023.120236.

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