scholarly journals Parameter Synthesis for Cardiac Cell Hybrid Models Using δ-Decisions

Author(s):  
Bing Liu ◽  
Soonho Kong ◽  
Sicun Gao ◽  
Paolo Zuliani ◽  
Edmund M. Clarke
2010 ◽  
Vol 58 (S 01) ◽  
Author(s):  
B Nasseri ◽  
M Kukucka ◽  
SJ Kim ◽  
YH Choi ◽  
KS Kang ◽  
...  

2012 ◽  
Vol 60 (S 01) ◽  
Author(s):  
R Roy ◽  
M Kukucka ◽  
D Messroghli ◽  
A Brodarac ◽  
M Becher ◽  
...  

Author(s):  
Sonia Stefanovic ◽  
Brigitte Laforest ◽  
Jean-Pierre Desvignes ◽  
Fabienne Lescroart ◽  
Laurent Argiro ◽  
...  
Keyword(s):  

2020 ◽  
Author(s):  
Jeremy H.M. Wong ◽  
Yashesh Gaur ◽  
Rui Zhao ◽  
Liang Lu ◽  
Eric Sun ◽  
...  

Author(s):  
Shahzad Khan ◽  
Syed S. Ahmad ◽  
Mohammad A. Kamal

: Diabetic cardiomyopathy (DCM) is a significant complication of diabetes mellitus characterized by gradual failing heart with detrimental cardiac remodellings such as fibrosis and diastolic and systolic dysfunction, which is not directly attributable to coronary artery disease. Insulin resistance and resulting hyperglycemia is the main trigger involved in the initiation of diabetic cardiomyopathy. There is a constellation of many pathophysiological events such as lipotoxicity, oxidative stress, inflammation, inappropriate activation of the renin-angiotensin-aldosterone system, dysfunctional immune modulation promoting increased rate of cardiac cell injury, apoptosis, and necrosis which ultimately culminates into interstitial fibrosis, cardiac stiffness, diastolic dysfunction initially and later systolic dysfunction too. These events finally lead to clinical heart failure of DCM. Herein, we have briefly discussed the pathophysiology of DCM. We have also briefly mentioned potential therapeutic strategies currently used for DCM.


2021 ◽  
pp. 126373
Author(s):  
Yeditha Pavan Kumar ◽  
Rathinasamy Maheswaran ◽  
Ankit Agarwal ◽  
Bellie Sivakumar

2021 ◽  
pp. 1-22
Author(s):  
Ha Thi Hang ◽  
Hoang Tung ◽  
Pham Duy Hoa ◽  
Nguyen Viet Phuong ◽  
Tran Van Phong ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.


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