We present a high performance computing framework for finite element simulation of blood flow in the left ventricle of the human heart. The mathematical model is described together with the discretization method and t...
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Prior knowledge of the expected steady state as well as the dynamic response of a machine is useful for hardware operation. In this work, the study of the electromechanical behaviour of an induction machine is carried...
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ISBN:
(纸本)9781728120683
Prior knowledge of the expected steady state as well as the dynamic response of a machine is useful for hardware operation. In this work, the study of the electromechanical behaviour of an induction machine is carried out through MATLAB, wherein the dynamics between a high rating induction machine and a low rating induction machine is presented. A generic model in combination with the dynamic model is represented for the analytical research purposes. Critical parameters for the transient-stability performances of the two types of induction machines are perused with the aid of mathematical equations.
ACT-R has been used to study human-computer interaction. By default, ACT-R models can only interact with interfaces written in Common Lisp. JSegMan has allowed ACT-R models to interact with external interfaces without...
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In the rapidly evolving field of emerging technologies, machine learning has the ability to advance the material science field significantly. Although there are many techniques to determine the material properties and...
In the rapidly evolving field of emerging technologies, machine learning has the ability to advance the material science field significantly. Although there are many techniques to determine the material properties and behavior, we used machine learning as a key technique, which allows the machine to make data-driven decisions for carrying out certain tasks. There exist tremendous traditional methods and computational data modelling systems that consume a lot of time and require a lot of resources. Thus, it is mandatory to develop a mathematicalmodelling system that reduces time and works more efficiently than usual. Here we build upon the machine learning approaches to predict the nanoscopic changes, which include strain instabilities and variations in the structural changes of graphite. In this paper, we primarily focus on the material behaviour of graphite plates and use deep learning techniques to investigate the microstructural material properties and compare them to other techniques for predicting the full results. Here we have obtained nearly 8000 organized values from 29 different data sets. Raman spectroscopy was used to conduct experiments on graphite material for data sets. We used K-Nearest Neighbor (KNN) clustering algorithm to determine the changes in graphite material properties.
This article proposes a model based on biological epidemic model in order to comprehend the behaviours of infectious codes in a computer network. Further, we establish the local and global stability of the model at th...
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ISBN:
(数字)9781728170343
ISBN:
(纸本)9781728170350
This article proposes a model based on biological epidemic model in order to comprehend the behaviours of infectious codes in a computer network. Further, we establish the local and global stability of the model at their equilibrium points and find the threshold parameter R 0 which gives the idea about epidemic character, i.e. all the infected nodes die out, if R 0 0> 1 implies that the infected nodes have the potential to transmit harmful codes in a network. For the purpose of accessing the spread of malicious codes in computer network MATLAB tools are used for solving and simulating the model.
In this paper we analyse the kinematics and dynamics of the human upper limb to determine the position and the orientation of the shoulder and elbow joints in accordance with the forward and reverse kinematic mathemat...
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Due to the harsh environment of the Arctic region, ship structures must be designed to withstand ice loads in case of a collision between a ship and ice takes place. Although experimental studies can give invaluable i...
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Neurotransmitters play a crucial role in neurological functions. Neural activity is significantly impacted by the balance of neurotransmitters in different parts of the brain. In order to comprehend many complex multi...
Neurotransmitters play a crucial role in neurological functions. Neural activity is significantly impacted by the balance of neurotransmitters in different parts of the brain. In order to comprehend many complex multi-factorial neurological diseases, mathematicalmodelling can be helpful. Neurotransmitter release, diffusion, and intake are studied rigorously nowadays in various computational models of brain disorders. The main focus of this work is on dopamine, a neurotransmitter that plays an important role associated with the brain’s reward system. Dopamine-mediated neurodegenerations like Parkinson’s disease (PD) and Schizophrenia have been studied recently using computational approaches. The current work, presented here uses mathematical models to analyse the impact of dopamine levels on specific brain networks leading to diseases such as PD. In this work, neural dynamics in the normal and parkinsonian states are studied in detail with the help of biologically inspired Basal Ganglia (BG) models. The simulations illustrate the behaviour of BG neurons under altered dopamine levels leading to normal or disease states. An extended study with the inclusion of learning and action selection may also help to model the reinforcement learning capability of the BG network.
human actors are seen as the main capability to enhance the resilience of road transport systems against disturbing scenarios. This paper compares different approaches to introduce users' behaviour into the resili...
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