scholarly journals Towards a computationally efficient free-surface groundwater flow boundary condition for large-scale hydrological modelling

2019 ◽  
Vol 123 ◽  
pp. 225-233 ◽  
Author(s):  
M. Rahman ◽  
R. Rosolem ◽  
S.J. Kollet ◽  
T. Wagener
Keyword(s):  
Boundary Condition ◽  
Free Surface ◽  
Groundwater Flow ◽  
Large Scale ◽  
Hydrological Modelling ◽  
Computationally Efficient ◽  
Flow Boundary

DYNAMIC SIMILARITY IN GROUNDWATER FLOW WITH A FREE SURFACE

1963 ◽  
Vol 41 (1) ◽  
pp. 90-94
Author(s):  
A. E. Scheidegger
Keyword(s):  
Free Surface ◽  
Groundwater Flow ◽  
Large Scale ◽  
Small Error ◽  
Small Scale ◽  
Drainage Ditches ◽  
Scaling Relationships ◽  
Large Length ◽  
River Valleys ◽  
Laboratory Models

The scaling relationships applicable for the construction of laboratory models representing groundwater flow with a free surface are deduced. It is found that, for large length reductions, these scaling relationships are very difficult to satisfy. The scaling relationships, however, can generally be used for transferring results from small-scale systems (such as drainage ditches) to large-scale systems (such as river valleys). However, if one is willing to allow a small error by assuming that the boundary conditions may be correctly scaled if the pressure at the free surface in the prototype and in the model is taken as zero, much simpler scaling relationships can be obtained, which can be easily satisfied in practice.

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

Network effect in shared supply chain platform value co-creation behavior in evolutionary game1

2021 ◽  
pp. 1-12
Author(s):  
Zou Xiaohong ◽  
Chen Jinlong ◽  
Gao Shuanping
Keyword(s):  
Boundary Condition ◽  
Supply Chain ◽  
Large Scale ◽  
Simulation Analysis ◽  
Supply And Demand ◽  
Evolutionary Game ◽  
Dynamic Game ◽  
Network Effect ◽  
Chain Model ◽  
The Cost

The shared supply chain model has provided new ideas for solving contradictions between supply and demand for large-scale standardized production by manufacturers and personalized demands of consumers. On the basis of a platform network effect perspective, this study constructs an evolutionary game model of value co-creation behavior for a shared supply chain platform and manufacturers, analyzes their evolutionary stable strategies, and uses numerical simulation analysis to further verify the model. The results revealed that the boundary condition for manufacturers to participate in value co-creation on a shared supply chain platform is that the net production cost of the manufacturers’ participation in the platform value co-creation must be less than that of nonparticipation. In addition, the boundary condition for the shared supply chain platform to actively participate in value co-creation is that the cost of the shared supply chain platform for active participation in value co-creation must be less than that of passive participation. Moreover, value co-creation behavior on the shared supply chain platform is a dynamic game interaction process between players with different benefit perceptions. Finally, the costs and benefits generated by the network effect can affect value co-creation on shared supply chain platforms.

An Efficient Preconditioner for Linear System Solution in Multi-Domain Modeling of the Circulatory System

2013 ◽  
Author(s):  
Mahdi Esmaily Moghadam ◽  
Yuri Bazilevs ◽  
Tain-Yen Hsia ◽  
Alison Marsden
Keyword(s):  
Strong Coupling ◽  
Large Scale ◽  
Computational Cost ◽  
Circulatory System ◽  
Flow Simulation ◽  
Global Dynamics ◽  
Domain Modeling ◽  
Computationally Efficient ◽  
Lumped Parameter ◽  
System Solution

A closed-loop lumped parameter network (LPN) coupled to a 3D domain is a powerful tool that can be used to model the global dynamics of the circulatory system. Coupling a 0D LPN to a 3D CFD domain is a numerically challenging problem, often associated with instabilities, extra computational cost, and loss of modularity. A computationally efficient finite element framework has been recently proposed that achieves numerical stability without sacrificing modularity [1]. This type of coupling introduces new challenges in the linear algebraic equation solver (LS), producing an strong coupling between flow and pressure that leads to an ill-conditioned tangent matrix. In this paper we exploit this strong coupling to obtain a novel and efficient algorithm for the linear solver (LS). We illustrate the efficiency of this method on several large-scale cardiovascular blood flow simulation problems.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

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