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About 10,000 papers for “modeling framework”
Andrew R. Spanjer (8795999), Andrew S. Gendaszek (5240750), Elyse J. Wulfkuhle (12575258) et al. · Figshare · 2022
<p>Conceptual diagram of modeling of inputs needed to run, parameterize, and link temperature and growth modeling.</p>
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Thorsten Arnold · 2014
verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. ii Contents
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Cheng Seong Khor · 2023
This chapter discusses superstructure optimization modeling by presenting a generic basic framework to perform model formulation and solution as well as its implementation. Such framework allows an efficient representation and handling of discrete decision variables within logical relationships (such as the selection of process units or equipment) besides the more common continuous decision types (such as flow rates of process units). Developing a suitable approach mainly involves the use of logic propositions that are reformulated as logical constraints using discrete binary (zero–one) variables and resides within a disjunctive optimization framework.
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Burcu Tepekule (4438969), Ai Ing Lim (22056678), Charlotte Jessica E. Metcalf (20664282) · Figshare · 2025
<b>A)</b> Key data sources and assumptions used during model fitting, including relative abundance (line plot) and IgA-index (bubble plot) data, each colored by taxon. Model predictions reconstruct ecological dynamics in the gut lumen and the germinal center (GC) reactions. 2Y: 2 years; mSIgA and eSIgA: maternal and endogenous secretory Immunoglobulin A. <b>B)</b> Model dynamics. <b>(i)</b> Breastfeeding and complementary feeding introduce human milk oligosaccharides (HMOs), plant-derived polysaccharides (PDPs), <b>(ii)</b> mSIgA, and <b>(iii)</b> commensal bacteria to the gut lumen as SIgA-bacteria complexes or bacteria alone, alongside with the bacteria seeding the infant’s gut during vaginal birth. The microbiome within the gut lumen (large light blue circle), contains subpopulations of commensals that exist either in an SIgA-bound (white circle, left) or unbound (white circle, right) state, and transition between the two is driven by SIgA coating or degradation. <b>(iv)</b> Bacterial populations modulate the inflammatory tone of the microenvironment of the gut lumen , <b>(v)</b> influencing epithelial cell-mediated communication with the organized gut-associated lymphoid tissue (GALT) inductive sites, and <b>(vi)</b> the antigenic sampling through barrier integrity. <b>(vii)</b> SIgA-bound and unbound antigens sampled by Microfold (M) cells <b>(viii)</b> activate the naïve B cells, and <b>(ix)</b> drive the balance between antigen-specific regulatory vs. helper T cells (indicated by the divide sign). <b>(x)</b> This balance and shape the selection threshold for taxon , <b>(xi)</b> which determines the BCR affinity ranges that lead to different B cell fates: <b>(xii)</b> apoptosis, <b>(xiii)</b> continued circulation, or <b>(xiv)</b> terminal differentiation into IgA-secreting plasma cells. <b>(xv)</b> IgA secreted by the plasma cells regulates the microbiome via <b>(xvi)</b> masking and <b>(xvii)</b> neutralization functions. Alongside the HMOs and PDPs, <b>(xviii)</b> the oxygen concentration (O<sub>2</sub>) also influences the ecological dynamics based on commensals’ metabolism. <b>C)</b> GC reactions (steps 1–7, materials and methods, corresponding to <b>(x)–(xiv)</b> on the previous panel)<i>.</i> Naïve B cells with a wide BCR affinity distribution migrate from the bone marrow to the GALT inductive sites, and their affinity distribution is combined with that of circulating B-cells. B-cells activate upon encountering bacterial antigens. Based on how close their BCR affinities are to the selection threshold , cells undergo positive selection, receiving adequate T cell help to continue circulating or differentiate to IgA-secreting plasma cells; and if not, become apoptotic. Circulating cells undergo somatic hypermutation (SHM), increasing the standard deviation of their BCR affinity distribution, continuing the cycle.
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One Tributary, Newport Bay System · 2002
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Xiangzheng Deng · 2011
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Jianghong Shi (13730953), Bryan Tripp (13730956), Eric Shea-Brown (175866) et al. · Figshare · 2022
<p>Framework for constructing MouseNet from biological constraints on anatomy, via publicly available data from large-scale experiments. The CNN architecture is set by the analysis of hierarchy [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010427#pcbi.1010427.ref025" target="_blank">25</a>] on the Allen Mouse Brain Connectivity Atlas (<a href="http://connectivity.brain-map.org" target="_blank">http://connectivity.brain-map.org</a>) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010427#pcbi.1010427.ref022" target="_blank">22</a>]; and the meta-parameters are mostly fixed by the combination of the 100-micrometer resolution interareal connectome [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010427#pcbi.1010427.ref023" target="_blank">23</a>] with detailed estimates of neuron density [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010427#pcbi.1010427.ref038" target="_blank">38</a>], and the statistics of connections between cortical layers from the literature [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010427#pcbi.1010427.ref039" target="_blank">39</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010427#pcbi.1010427.ref041" target="_blank">41</a>].</p>
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Jiawei Yin (8149467), A. Agung Julius (8149464), John T. Wen (3971912) · Figshare · 2021
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Michael Jastram, Andreas Graf · 2011
Im August 2011 hat das Requirements Modeling Framework (RMF) als ein neues Eclipse-Projekt das Licht der Welt erblickt. RMF besteht aus einem Kern, der Daten im Requirements Exchange Format (RIF/ReqIf) verarbeiten kann, und einem GUI namens ProR, mit dem sich diese Daten komfortabel bearbeiten lassen. ProR stellt einen Extension Point zur Verf\{u}gung, der die Integration mit anderen Werkzeugen erm\{o}glicht.
Cited by 2
Derek E. Moulton (7053563), Nathanaël Aubert-Kato (18086801), Axel A. Almet (3164238) et al. · Figshare · 2024
<p>A biochemical system, modeling an Activator-Substrate reaction (a), potentially creates a spatial pattern, depending on the choice of six parameters. This biochemical pattern is passed to the tissue scale (b) and defines the rate and width of mantle growth and secretion. From this mechanical process, a spine shape emerges, which is then compared with an image of an actual spine (c). For a given shell, we then seek the biochemical parameters such that the model output best matches the real spine shape, (d).</p>
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Sean M. Moore (8114876), Erica Rapheal (21807811), Sandra Mendoza Guerrero (6668096) et al. · Figshare · 2025
Our modeling framework involved seven sequential steps that result in a set of ensemble models of the FOI and annual incidence of LF in each of the 1<sup>st</sup> and 2<sup>nd</sup> administrative levels across West Africa. FOI estimates were projected and estimated for three different seroreversion rates, and LF incidence rates were estimated for 18 scenarios: 3 different seroreversion rates, 3 different assumptions about the susceptibility of seropositive individuals to disease, and 2 different assumptions about the susceptibility of seroreverted individuals to disease.
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Graeme Guthrie · 2009
Abstract If we are to analyze projects properly, we need to know how managers will exploit the flexibility embedded in those projects. To do this we need to understand managers’ objective functions. Section 2.1 explains why the manager of any firm should seek to maximize the market value of existing shareholders’ stake in the firm. The manager’s job is made more difficult by the uncertainty about the future consequences of any action. Modeling this uncertainty is a crucial part of real options analysis. Section 2.2 introduces the structure (called a binomial tree) that we use to model the risk associated with future cash flows. We use this structure in Section 2.3 to highlight the importance of dynamic decision making—that is, of using all available information to make future decisions.
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Kaveh Bakhsh Kelarestaghi (9761474), Alireza Ermagun (4002836), Kevin Heaslip (4896517) et al. · Figshare · 2020
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Edward H. Y. Lim, Simon James, Raymond Lee · Intelligent systems reference library · 2011
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Ryan G. Drum (827311), Christine A. Ribic (397272), Katie Koch (827312) et al. · Figshare · 2015
<p>Policies influence landowner decision-making, the effect of which depends on the different landowner types’ respective value systems. Landowner decisions affect habitat quantity and configuration, which function as inputs for the Grassland Bird Population Model.</p>
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Francesco Serafin, Olaf David, André Dozier et al. · ScholarsArchive (Brigham Young University) · 2018
Applications of physically-based environmental models originating from research should be ubiquitous to use in both research and planning/consulting environments. However, due to their complexity, data resolution requirements, parameter number, platform affinity, and other criteria they are rarely suited “out-of the box” for field applications. Results from physically-based models are considered the most accurate but operating an entire system requires dedicated knowledge, extensive set up, and sometimes significant computational time. Questions from field applications conversely require easy to get, quick and “accurate enough” results. The use of web-services might alleviate some of the implications for model users but ultimately shift the responsibility and workload to the hosting environment. To help closing the gap between research and field models we propose a machine learning (ML)-based meta-model approach aiming to capture the intrinsic knowledge of a physical model into an ensemble system of artificial neural networks and make it available for providing simplified answers to on the field problem-specific questions. A meta modeling approach was developed to help transitioning from research to field by enabling a modeling framework to interact with ML libraries to emerge model surrogates a(ny) modelling solution. The Cloud Services Integration Platform CSIP/OMS was extended and utilized to harvest data and derive the meta-model. Here, NeuroEvolution of Augmenting Topology (NEAT) techniques in an ensemble application, combined with ANN uncertainty quantification are the main methodologies used. Two examples applications have been prototyped and will be presented, a sheet and rill erosion model and a daily runoff model.
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2001
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Reimer, U., Froning, D. · JuSER (Forschungszentrum Jülich) · 2008
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Cuiying Yu (18437285), Lei Zhou (19808), Bushi Liu (18437288) et al. · Figshare · 2024
<div><p>Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.The method first introduces a lightweight feature extraction structure, E-GhostV2 network, into the trunk and neck of the model to achieve effective feature extraction while reducing the burden of the model. Additionally, the PConv is used in the model’s backbone to effectively reduce redundant computations and memory access, thereby enhancing efficiency and feature extraction capabilities. Furthermore, to address the problem of performance degradation caused by overemphasizing high-quality samples, the model adopts the WIoU loss function, which balances the training effect of high-quality and low-quality samples, enhancing the model’s robustness and generalization performance. Experimental results demonstrate that the improved model achieves satisfactory detection accuracy while reducing the number of model parameters, providing robust support for theoretical research and practical applications in the field of hazardous chemical vehicle object detection.</p></div>
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Sanjay Kulkarni (4726410), Isaac Hall (4726419), Richard Formica (4726413) et al. · Figshare · 2017
<p>CPRA i (i = 1 to 6) is one of the six CPRA categories: 1) 0%; 2) 1–79%, 3) 80–90%, 4) 90–95% 5) 95%-98%, 6) 99%-100%). CPRA j (j = 1 to 6) is another CPRA category, different from category i. There are 12 initial/intermediate states. (see supplemental materials for additional details).</p>
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