scholarly journals Alluvial cover on bedrock channels: applicability of existing models

2020 ◽  
Vol 8 (3) ◽  
pp. 695-716 ◽  
Author(s):  
Jagriti Mishra ◽  
Takuya Inoue
Keyword(s):  
Mathematical Models ◽  
Sediment Accumulation ◽  
Theoretical Models ◽  
Sediment Supply ◽  
Model Parameters ◽  
Hydraulic Roughness ◽  
Bed Topography ◽  
Bed Roughness ◽  
The Relationship ◽  
Bedrock Channels

Abstract. Several studies have demonstrated the importance of alluvial cover; furthermore, several mathematical models have also been introduced to predict the alluvial cover on bedrock channels. Here, we provide an extensive review of research exploring the relationship between alluvial cover, sediment supply and bed topography of bedrock channels, describing various mathematical models used to analyse the deposition of alluvium. To test one-dimensional theoretical models, we performed a series of laboratory-scale experiments with varying bed roughness under simple conditions without bar formation. Our experiments show that alluvial cover is not merely governed by increasing sediment supply and that bed roughness is an important controlling factor of alluvial cover. A comparison between the experimental results and the five theoretical models shows that (1) two simple models that calculate alluvial cover as a linear or exponential function of the ratio of the sediment supplied to the capacity of the channel produce good results for rough bedrock beds but not for smoother bedrock beds; (2) two roughness models which include changes in roughness with alluviation and a model including the probability of sediment accumulation can accurately predict alluvial cover in both rough and smooth beds; and (3), however, except for a model using the observed hydraulic roughness, it is necessary to adjust model parameters even in a straight channel without bars.

scholarly journals State of the Art Study of Influence of Bed Roughness and Alluvial Cover on Bedrock Channels and Comparisons of Existing Models with Laboratory Scale Experiments

2020 ◽  
Author(s):  
Jagriti Mishra ◽  
Takuya Inoue
Keyword(s):  
Mathematical Models ◽  
State Of The Art ◽  
Theoretical Models ◽  
Sediment Supply ◽  
Laboratory Scale ◽  
Experimental Results ◽  
Bed Topography ◽  
Bed Roughness ◽  
The Relationship ◽  
Bedrock Channels

Abstract. Several studies have implied towards the importance of bed roughness on alluvial cover, besides, several mathematical models have also been introduced to mimic the effect bed roughness may project on alluvial cover. Here, we provide a state of the art review of research exploring the relationship between alluvial cover, sediment supply and bed topography, thereby, describing various mathematical models used to analyse deposition of alluvium. In the interest of analysing the efficiency of various available mathematical models, we performed laboratory-scale experiments and compared the results with various models. Our experiments show that alluvial cover is not merely governed by increasing sediment supply, and, bed topography is an important controlling factor of alluvial cover. Testing experimental results with various theoretical models suggest a fit of certain models for a particular bed topography and inefficiency in predicting higher roughness topography. Three models efficiently predict the experimental observations, albeit their limitations which we discuss here in detail.

scholarly journals Interrogating theoretical models of neural computation with deep inference

2019 ◽  
Author(s):  
Sean R. Bittner ◽  
Agostina Palmigiano ◽  
Alex T. Piet ◽  
Chunyu A. Duan ◽  
Carlos D. Brody ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ad Hoc ◽  
Neural Mechanism ◽  
Theoretical Models ◽  
Generative Models ◽  
Neural Computation ◽  
Machine Learning Techniques ◽  
Model Parameters ◽  
The Relationship ◽  
Computational Properties

1AbstractA cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon – whether behavioral or in terms of neural activity – and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choices of model parameters. Historically, the gold standard has been to analytically derive the relationship between model parameters and computational properties. However, this enterprise quickly becomes infeasible as biologically realistic constraints are included into the model increasing its complexity, often resulting in ad hoc approaches to understanding the relationship between model and computation. We bring recent machine learning techniques – the use of deep generative models for probabilistic inference – to bear on this problem, learning distributions of parameters that produce the specified properties of computation. Importantly, the techniques we introduce offer a principled means to understand the implications of model parameter choices on computational properties of interest. We motivate this methodology with a worked example analyzing sensitivity in the stomatogastric ganglion. We then use it to go beyond linear theory of neuron-type input-responsivity in a model of primary visual cortex, gain a mechanistic understanding of rapid task switching in superior colliculus models, and attribute error to connectivity properties in recurrent neural networks solving a simple mathematical task. More generally, this work suggests a departure from realism vs tractability considerations, towards the use of modern machine learning for sophisticated interrogation of biologically relevant models.

scholarly journals Statistical Approach to Incorporating Experimental Variability into a Mathematical Model of the Voltage-Gated Na+ Channel and Human Atrial Action Potential

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1516
Author(s):  
Daniel Gratz ◽  
Alexander J Winkle ◽  
Seth H Weinberg ◽  
Thomas J Hund
Keyword(s):  
Action Potential ◽  
Mathematical Models ◽  
Cardiac Myocyte ◽  
Population Level ◽  
Na Channel ◽  
Model Parameters ◽  
Healthy Population ◽  
Experimental Measurements ◽  
Voltage Gated ◽  
Experimental Variability

The voltage-gated Na+ channel Nav1.5 is critical for normal cardiac myocyte excitability. Mathematical models have been widely used to study Nav1.5 function and link to a range of cardiac arrhythmias. There is growing appreciation for the importance of incorporating physiological heterogeneity observed even in a healthy population into mathematical models of the cardiac action potential. Here, we apply methods from Bayesian statistics to capture the variability in experimental measurements on human atrial Nav1.5 across experimental protocols and labs. This variability was used to define a physiological distribution for model parameters in a novel model formulation of Nav1.5, which was then incorporated into an existing human atrial action potential model. Model validation was performed by comparing the simulated distribution of action potential upstroke velocity measurements to experimental measurements from several different sources. Going forward, we hope to apply this approach to other major atrial ion channels to create a comprehensive model of the human atrial AP. We anticipate that such a model will be useful for understanding excitability at the population level, including variable drug response and penetrance of variants linked to inherited cardiac arrhythmia syndromes.

scholarly journals Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoshi Miyamoto ◽  
Zu Soh ◽  
Shigeyuki Okahara ◽  
Akira Furui ◽  
Taiichi Takasaki ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Neurological Complications ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Perfusion Flow ◽  
Postsurgical Complication ◽  
Tenfold Cross Validation ◽  
Suction Flow ◽  
The Relationship

AbstractThe need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

scholarly journals Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency

2021 ◽  
Vol 11 (9) ◽  
pp. 3827
Author(s):  
Blazej Nycz ◽  
Lukasz Malinski ◽  
Roman Przylucki
Keyword(s):  
Electromagnetic Levitation ◽  
Optimization Methods ◽  
Calculation Model ◽  
Model Parameters ◽  
Starting Point ◽  
Low Efficiency ◽  
The Relationship ◽  
Reactive Metals ◽  
Electromagnetic Levitation Melting ◽  
Metal Melting

The article presents the results of multivariate calculations for the levitation metal melting system. The research had two main goals. The first goal of the multivariate calculations was to find the relationship between the basic electrical and geometric parameters of the selected calculation model and the maximum electromagnetic buoyancy force and the maximum power dissipated in the charge. The second goal was to find quasi-optimal conditions for levitation. The choice of the model with the highest melting efficiency is very important because electromagnetic levitation is essentially a low-efficiency process. Despite the low efficiency of this method, it is worth dealing with it because is one of the few methods that allow melting and obtaining alloys of refractory reactive metals. The research was limited to the analysis of the electromagnetic field modeled three-dimensionally. From among of 245 variants considered in the article, the most promising one was selected characterized by the highest efficiency. This variant will be a starting point for further work with the use of optimization methods.

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