Improving AOSE with an Enriched Modelling Framework

2006 ◽  
pp. 94-108
Author(s):  
Richard Hill ◽  
Simon Polovina ◽  
Martin D. Beer
Keyword(s):  
Modelling Framework

A Measurement Modelling Framework for Complex Working Memory Models

2011 ◽  
Author(s):  
Klaus Oberauer ◽  
Jarrold Chris ◽  
Farrell Simon ◽  
Lewandowsky Stephan
Keyword(s):  
Working Memory ◽  
Memory Models ◽  
Modelling Framework ◽  
Working Memory Models

IRC and CRM: Modelling Framework for the "Basel 2.5" Risk Measures

2012 ◽  
Author(s):  
Sascha Wilkens ◽  
Jean-Baptiste C. Brunac ◽  
Vladimir Chorniy
Keyword(s):  
Risk Measures ◽  
Modelling Framework

Habitability: a process versus a state variable framework with observational tests and theoretical implications

2021 ◽  
pp. 1-8
Author(s):  
A. Lenardic ◽  
J. Seales
Keyword(s):  
Process Variable ◽  
State Variable ◽  
Modelling Framework ◽  
Meta Level ◽  
Planetary Habitability

The term habitable is used to describe planets that can harbour life. Debate exists as to specific conditions that allow for habitability but the use of the term as a planetary variable has become ubiquitous. This paper poses a meta-level question: What type of variable is habitability? Is it akin to temperature, in that it is something that characterizes a planet, or is something that flows through a planet, akin to heat? That is, is habitability a state or a process variable? Forth coming observations can be used to discriminate between these end-member hypotheses. Each has different implications for the factors that lead to differences between planets (e.g. the differences between Earth and Venus). Observational tests can proceed independent of any new modelling of planetary habitability. However, the viability of habitability as a process can influence future modelling. We discuss a specific modelling framework based on anticipating observations that can discriminate between different views of habitability.

scholarly journals The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

Oecologia ◽  
2021 ◽  
Author(s):  
Peng He ◽  
Pierre-Olivier Montiglio ◽  
Marius Somveille ◽  
Mauricio Cantor ◽  
Damien R. Farine
Keyword(s):  
Population Level ◽  
R Package ◽  
Habitat Configuration ◽  
Modelling Framework ◽  
Alternative Habitat ◽  
Animal Populations ◽  
Research Questions ◽  
Habitat Networks ◽  
Animal Habitat

AbstractBy shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

Radiative sky cooling of solar cells: fundamental modelling and cooling potential of single-junction devices

2021 ◽  
Vol 5 (7) ◽  
pp. 2085-2096
Author(s):  
Jérémy Dumoulin ◽  
Emmanuel Drouard ◽  
Mohamed Amara
Keyword(s):  
Solar Cells ◽  
Thermal Properties ◽  
Potential Benefit ◽  
Single Junction ◽  
Modelling Framework

A fundamental modelling framework of solar cells is presented in order to quantify the potential benefit of enhanced radiative sky cooling for different single-junction technologies, according to their basic electrical and thermal properties.

scholarly journals Active control of liquid film flows: beyond reduced-order models

2021 ◽  
Vol 104 (1) ◽  
pp. 267-287
Author(s):  
Radu Cimpeanu ◽  
Susana N. Gomes ◽  
Demetrios T. Papageorgiou
Keyword(s):  
Liquid Film ◽  
Information Transfer ◽  
Analytical Models ◽  
Feedback Controls ◽  
Reduced Order ◽  
Modelling Framework ◽  
Theoretical Approaches ◽  
Active Feedback ◽  
Feedback Strategies ◽  
Set Up

AbstractThe ability to robustly and efficiently control the dynamics of nonlinear systems lies at the heart of many current technological challenges, ranging from drug delivery systems to ensuring flight safety. Most such scenarios are too complex to tackle directly, and reduced-order modelling is used in order to create viable representations of the target systems. The simplified setting allows for the development of rigorous control theoretical approaches, but the propagation of their effects back up the hierarchy and into real-world systems remains a significant challenge. Using the canonical set-up of a liquid film falling down an inclined plane under the action of active feedback controls in the form of blowing and suction, we develop a multi-level modelling framework containing both analytical models and direct numerical simulations acting as an in silico experimental platform. Constructing strategies at the inexpensive lower levels in the hierarchy, we find that offline control transfer is not viable; however, analytically informed feedback strategies show excellent potential, even far beyond the anticipated range of applicability of the models. The detailed effects of the controls in terms of stability and treatment of nonlinearity are examined in detail in order to gain understanding of the information transfer inside the flows, which can aid transition towards other control-rich frameworks and applications.

Robotic mobile fulfillment systems: a mathematical modelling framework for e-commerce applications

2021 ◽  
pp. 1-17
Author(s):  
Adrien Rimélé ◽  
Michel Gamache ◽  
Michel Gendreau ◽  
Philippe Grangier ◽  
Louis-Martin Rousseau
Keyword(s):  
Mathematical Modelling ◽  
Modelling Framework

scholarly journals Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

2021 ◽  
Vol 10 (4) ◽  
pp. 570
Author(s):  
María A Callejon-Leblic ◽  
Ramon Moreno-Luna ◽  
Alfonso Del Cuvillo ◽  
Isabel M Reyes-Tejero ◽  
Miguel A Garcia-Villaran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Modelling Framework ◽  
Visual Analog Scales ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
Taste Disorders ◽  
Polymerase Chain ◽  
Fold Cross Validation ◽  
Validation Scheme ◽  
Control Study

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.

scholarly journals A copula-based multivariate hidden Markov model for modelling momentum in football

2021 ◽  
Author(s):  
Marius Ötting ◽  
Roland Langrock ◽  
Antonello Maruotti
Keyword(s):  
Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
State Dependence ◽  
Change Points ◽  
Sports Fans ◽  
Modelling Framework ◽  
State Dependent ◽  
Different Levels

AbstractWe investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state dependence of the variables, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga data considered, we find that the fitted HMMs comprise states which can be interpreted as a team showing different levels of control over a match. Our modelling framework enables inference related to causes of momentum shifts and team tactics, which is of much interest to managers, bookmakers, and sports fans.

scholarly journals Thermo-mechanical forging of 708M40 steel ring samples: experiments and modelling

2021 ◽  
Author(s):  
Steven Hill ◽  
Richard P.Turner
Keyword(s):  
Finite Element ◽  
Experimental Parameter ◽  
Compression Tests ◽  
Molybdenum Disulphide ◽  
Heating Unit ◽  
Modelling Framework ◽  
Process Understanding ◽  
Ring Compression ◽  
Water Based ◽  
Temperature Variable

AbstractA series of ring compression tests using BS970:708M40 alloy steel samples were studied. These tests were conducted using a 2-factor soak-temperature variable, namely 1030 °C and 1300 °C, and a 4-factor lubricant variable consisting of unlubricated samples, synthetic water-based, graphite water-based, and graphite and molybdenum disulphide viscous grease. The lubricant agents were all applied to the tool/billet interface. Process variables such as blow force and heating were controlled with the use of a gravitationally operated drop hammer and an automated programmable induction-heating unit. This matrix of the experimental parameters offered a sound base for exploring dominant factors impacting upon bulk deformation. This deformation was measured using fully calibrated equipment and then systematically recorded. A finite element modelling framework was developed to further improve the thermo-mechanical deformation process understanding, with finite element (FE) predictions validated through experimental measurement. Through the combined experimental and FE work, it was shown that temperature variation in the experimental parameter matrix played a larger role in determining deformation than the lubrication agent. Additionally, the use of synthetic and graphite water-based lubricants does not necessarily produce greater deformation when used in high-temperature forgings due to the lubricants breaking down, evaporating, or inducing rapid billet cooling as a result of the carrier used (water). Graphite-molybdenum disulphate grease far outperforms the other lubricants used in this trial in reducing friction and allowing deformation to occur across a die-face.

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