The role of slurry TBM parameters on ground deformation: Field results and computational modelling

2016 ◽  
Vol 57 ◽  
pp. 257-264 ◽  
Author(s):  
Michael A. Mooney ◽  
Jacob Grasmick ◽  
Bernadette Kenneally ◽  
Yong Fang
Keyword(s):  
Ground Deformation ◽  
Computational Modelling ◽  
Deformation Field

scholarly journals Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc14 endocycles

2011 ◽  
Vol 8 (61) ◽  
pp. 1128-1141 ◽  
Author(s):  
P. K. Vinod ◽  
Paula Freire ◽  
Ahmed Rattani ◽  
Andrea Ciliberto ◽  
Frank Uhlmann ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Budding Yeast ◽  
Cell Cycle Control ◽  
Computational Modelling ◽  
Eukaryotic Cell ◽  
Mitotic Exit ◽  
Intuitive Reasoning ◽  
Systematic Analysis ◽  
Systems View

The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20–APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well.

scholarly journals What is the role of computational models in Cognitive Science? A quantitative and qualitative analysis of the history of the TRACE model of speech segmentation

2019 ◽  
Author(s):  
Manisha Chawla ◽  
Richard Shillcock
Keyword(s):  
Qualitative Analysis ◽  
Cognitive Science ◽  
Computational Models ◽  
Computational Modelling ◽  
Speech Segmentation ◽  
Cognitive Modelling ◽  
Trace Model ◽  
History Of ◽  
Central Paradigm

Implemented computational models are a central paradigm of Cognitive Science. How do cognitive scientists really use such models? We take the example of one of the most successful and influential cognitive models, TRACE (McClelland & Elman, 1986), and we map its impact on the field in terms of published, electronically available documents that cite the original TRACE paper over a period of 25 years since its publication. We draw conclusions about the general status of computational cognitive modelling and make critical suggestions regarding the nature of abstraction in computational modelling.

Causal evidence for a domain-specific role of left superior frontal sulcus in human perceptual decision-making

2021 ◽  
Author(s):  
Miguel Barretto Garcia ◽  
Marcus Grueschow ◽  
Marius Moisa ◽  
Rafael Polania ◽  
Christian Carl Ruff
Keyword(s):  
Decision Making ◽  
Sequential Sampling ◽  
Computational Modelling ◽  
Perceptual Decision Making ◽  
Domain Specific ◽  
Neural Processes ◽  
Sequential Sampling Model ◽  
Evidence Integration ◽  
Superior Frontal Sulcus

Humans and animals can flexibly choose their actions based on different information, ranging from objective states of the environment (e.g., apples are bigger than cherries) to subjective preferences (e.g., cherries are tastier than apples). Whether the brain instantiates these different choices by recruiting either specialized or shared neural circuitry remains debated. Specifically, domain-general theories of prefrontal cortex (PFC) function propose that prefrontal areas flexibly process either perceptual or value-based evidence depending on what is required for the present choice, whereas domain-specific theories posit that PFC sub- areas, such as the left superior frontal sulcus (SFS), selectively integrate evidence relevant for perceptual decisions. Here we comprehensively test the functional role of the left SFS for choices based on perceptual and value-based evidence, by combining fMRI with a behavioural paradigm, computational modelling, and transcranial magnetic stimulation. Confirming predictions by a sequential sampling model, we show that TMS-induced excitability reduction of the left SFS selectively changes the processing of decision-relevant perceptual information and associated neural processes. In contrast, value-based decision making and associated neural processes remain unaffected. This specificity of SFS function is evident at all levels of analysis (behavioural, computational, and neural, including functional connectivity), demonstrating that the left SFS causally contributes to evidence integration for  perceptual but not value-based decisions.

Bridging the Gap Between Qualitative, Semi-Quantitative and Quantitative Risk Assessment of Pipeline Geohazards: The Role of Engineering Judgment

2015 ◽  
Author(s):  
R. S. Rod Read ◽  
Moness Rizkalla
Keyword(s):  
Ground Deformation ◽  
Lateral Spreading ◽  
Quantitative Risk Assessment ◽  
Assessment Approach ◽  
Assessment Engineering ◽  
Hazard And Risk ◽  
Pipeline Route ◽  
Permanent Ground Deformation ◽  
Fault Displacement

Geohazards are threats of a geological, geotechnical, hydrological or seismic/tectonic nature that can potentially damage pipelines and other infrastructure. Depending on the physiographic setting of a particular pipeline, a broad range of geohazards may be possible along the pipeline route. However, only a limited number of geohazards such as landslides, fault displacement, mining-induced subsidence, liquefaction-induced lateral spreading, and hydrological scour, which can result in permanent ground deformation or exposure of the pipeline to direct impact, typically represent credible threats to pipeline integrity. Identifying potential geohazard occurrences and estimating the likely severity of each occurrence in relation to pipeline integrity is an integral part of geohazard management, and overall risk management of pipelines. Methods for identifying and assessing the potential likelihood and severity of geohazards vary significantly, from purely expert judgment-based approaches relying largely on visual observations of geomorphology to analytically-intense methods incorporating phenomenological or mechanistic models and data from monitoring and field characterization. Each of these methods can be used to assess hazard and risk associated with specific geohazards in terms of qualitative, semi-quantitative, or quantitative expressions as long as uncertainty and assumptions are understood and communicated as part of the assessment. Engineering judgment is highlighted as an essential component to varying degrees of each geohazard assessment approach.

scholarly journals Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

2014 ◽  
Vol 4 (5) ◽  
pp. 20140004 ◽  
Author(s):  
Yoram Vodovotz
Keyword(s):  
Inflammatory Response ◽  
Acute Inflammation ◽  
Computational Modelling ◽  
Data Driven ◽  
Patient Specific ◽  
Cell Tissue ◽  
Organ Systems ◽  
Forward Process

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

scholarly journals Intrinsic Flexibility of the EMT Zeolite Framework Under Pressure

2019 ◽  
Author(s):  
Antony Nearchou ◽  
Mero-Lee U. Cornelius ◽  
Jonathan M. Skelton ◽  
Zoe Jones ◽  
Andrew Cairns ◽  
...  
Keyword(s):  
Free Energy ◽  
Computational Modelling ◽  
Lattice Energy ◽  
Vibrational Entropy ◽  
Organic Additives ◽  
X Ray Diffraction ◽  
Zeolite Framework ◽  
Guest Molecules ◽  
Fau Zeolite

<p>The roles of organic additives in the assembly and crystallisation of zeolites is still not fully understood. This is important when attempting to prepare novel frameworks to produce new zeolites. We consider 18-crown-6 ether as an additive, which has previously been shown to differentiate between the EMT and FAU zeolite frameworks. However, it is unclear whether this distinction is dictated by influences on the metastable free-energy landscape or geometric templating. Using high pressure synchrotron X-ray diffraction, we have observed that the presence of 18C6 does not impact the EMT framework flexibility – agreeing with our previous geometric simulations and suggesting that 18C6 does not behave as a true geometric template. This was further studied with computational modelling, using first-principles comparative periodic DFT and lattice-dynamics calculations. It is shown that the lattice energy of FAU is more stable than EMT, however this is strongly impacted by the presence of solvent/guest molecules in the framework. Furthermore, the EMT topology possesses a greater vibrational entropy, being stabilised by free energy at finite temperature. Overall, these findings demonstrate that the role of the 18C6 additive is to influence the free-energy of crystallisation to assemble the EMT framework as opposed to FAU. </p>

scholarly journals The Mid-Twentieth Century Baby Boom and the Role of Social Influence. An Agent-Based Modelling Approach

2019 ◽  
Vol 8 ◽  
pp. 1-26
Author(s):  
Eli Nomes ◽  
André Grow ◽  
Jan Van Bavel
Keyword(s):  
Social Influence ◽  
Fertility Decline ◽  
Census Data ◽  
Formal Model ◽  
Baby Boom ◽  
Computational Simulation ◽  
Computational Modelling ◽  
Large Variability ◽  
Agent Based

Around the middle of the 20th century, most Western countries experienced a surge in birth rates, called the Baby Boom. This boom was unexpected at the time and the underlying mechanisms are still not entirely clear. It was characterized by high levels of inter- and intra-country variability in fertility, as some regions even experienced fertility decline during the Boom. In this paper, we suggest that social influence processes, propelling a shift towards two-child families, might have played an important role in the observed changes in fertility. Interactions in social networks can lead new types of childbearing behaviour to diffuse widely and thereby induce changes in fertility at the macro level. The emergence and diffusion of a two-child norm resulted in homogenization of fertility behaviour across regions. Overall, this led to a reduction of childlessness and thus an increase of fertility, as more people aspired to have at least two children. Yet, in those regions where larger family sizes were still common, the two-child norm contributed to a fertility decline. To explore the role of social influence with analytical rigor, we make use of agent-based computational modelling. We explicate the underlying behavioural assumptions in a formal model and assess their implications by submitting this model to computational simulation experiments. We use Belgium as a case study, since it exhibited large variability in fertility in a relatively small population during the Baby Boom years. We use census data to generate realistic starting conditions and to empirically validate the outcomes that our model generates. Our results show that the proposed mechanism could explain an important part of the variability of fertility trends during the Baby Boom era.

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