Computational and Mathematical Biophysics

Abstract In this article, we present a novel approach for robust state estimation in linear uncertain descriptor systems, with a specific focus on the cardiovascular system (CVS) as a case study. Descriptor systems, which extend traditional state-space representations by incorporating algebraic constraints, present significant challenges in observer design, especially under conditions of uncertainty. To address these challenges, we propose the design of a functional observer using the <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>H</m:mi> </m:mrow> <m:mrow> <m:mi>∞</m:mi> </m:mrow> </m:msub> </m:math> {H}_{\infty } filter approach, capable of managing uncertainties arising from both external disturbances and variations in system parameters. The observer is designed using a unique linear matrix inequality framework, which ensures the stability and performance of the estimation error system. The convergence of the estimation error is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed method is demonstrated through simulations on a CVS model, highlighting its potential to reliably estimate unknown states in the presence of uncertainties.

Pabitra K. Tunga

Juhi Jaiswal

Nutan K. Tomar

Robust H ∞ filter-based functional observer design for descriptor systems: An application to cardiovascular system monitoring

基于鲁棒H∞滤波的描述系统函数观测器设计：心血管系统监测中的应用

Abstract During the recent Coronavirus disease (COVID-19) pandemic, different parts of the globe faced indefinite lockdowns. To maintain the lockdown measures, government authorities deployed security forces and police. The primary role of the police is to monitor the movement of the people and advise/guide them to follow the lockdown. In the current work, we propose a nonlinear mathematical model to study the role of police and security personnel in controlling COVID-19. It is observed that the proposed model possesses two equilibria, namely, trivial and non-trivial. We performed the stability analysis of the non-trivial equilibrium point by using the compound matrix technique. Finally, we perform a detailed numerical simulation to investigate the feasibility of the theoretical results. The current study demonstrates that police forces played a significant role in controlling the pandemic.

Vikram Singh

Shikha Kapoor

Sandeep kumar Gupta

Sandeep Sharma

Modelling the leadership role of police in controlling COVID-19

警察在控制COVID-19中的领导角色建模

Abstract Close-contact places such as long-term facilities have been found to be high-risk and high-morbidity places in the United States during COVID-19 outbreaks. This could be due to the presence of vulnerable resident population, frequent contacts of residents with visitors, staff working at multiple facilities, and potential contaminated surfaces at these facilities. Here, we model close-contacts places to evaluate the role of different transmission pathways of COVID-19 in the presence of adaptable interventions. The model captures a coupled dynamics between three subpopulations (staff, residents, and visitors) and incorporates infection from infectious individuals and through the environment. By using parameterization of the models via real data from facilities in the United States, we identify and quantify the impact of duration and choice of interventions in real time for subpopulations to mitigate disease burden. We study the trade-off between disease burden and prevention cost using cost-effectiveness analysis. It was found that the specific time interval in which an intervention is included has an important effect when maximizing the effectiveness while minimizing the costs. We find that the presence of super-spreader healthcare workers contribute to a significantly higher peak of the number of infected cases compared to similar situation in the presence of super-spreader residents. Considering two nonpharmaceutical interventions: the use of face masks and cleaning surfaces, we observe that the later is an optimum intervention at the beginning of the system’s evolution, while wearing masks becomes an optimum strategy once the infection is widely established and population behavior changes.

Aditi Ghosh

Yair Antonio Castillo Castillo

Domenica Nicole Garzon Armendariz

Leon Arriola

Anuj Mubayi

COVID-19 transmission dynamics in close-contacts facilities: Optimizing control strategies

COVID-19在密切接触者设施中的传播动力学：优化控制策略

Abstract The objective of this article is to study the compartmental modeling approach for the prediction of unreported cases of coronavirus disease 2019 by considering six compartments. Our model is described by a system of six ordinary differential equations with initial conditions. The basic properties of solution of the model are established. The model is shown to have two equilibrium points, i.e., the disease-free and endemic equilibrium points. The basic reproduction number <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>R</m:mi> </m:mrow> <m:mrow> <m:mn>0</m:mn> </m:mrow> </m:msub> </m:math> {R}_{0} is derived by the next-generation matrix method. Stability analysis is carried out in the study. Furthermore, sensitivity analysis is also performed to identify the impact of important parameters that significantly affect <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>R</m:mi> </m:mrow> <m:mrow> <m:mn>0</m:mn> </m:mrow> </m:msub> </m:math> {R}_{0} . Numerical simulations provide a good approximation model for COVID-19, which will be utilized to investigate future pandemic with similar nature of spread as COVID-19 and estimate the number of unreported cases worldwide.

Abhishekh Singh

Vikash Rana

Vijai Shanker Verma

Compartmental modeling approach for prediction of unreported cases of COVID-19 with awareness through effective testing program

基于分室模型的COVID-19未报告病例预测方法及通过有效检测提高公众意识

Abstract A new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.

Raulia R. Syrlybaeva

Marat R. Talipov

CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network

CBSF：一种新的基于神经网络权重参数化的对接经验评分函数

Abstract We observed the existence of periodic orbits in immune network under transitive solvable Lie algebra. In this article, we focus to develop condition of maximal Lie algebra for immune network model and use that condition to construct a vector field of symmetry to study nonlinear pathogen model. We used two methods to obtain analytical structure of solution, namely normal generator and differential invariant function. Numerical simulation of analytical structure exhibits correlated periodic pattern growth under spatiotemporal symmetry, which is similar to the linear dynamical simulation result. We used Lie algebraic method to understand correlation between growth pattern and symmetry of dynamical system. We employ idea of using one parameter point group of transformation of variables under which linear manifold is retained. In procedure, we present the method of deriving Lie point symmetries, the calculation of the first integral and the invariant solution for the ordinary differential equation (ODE). We show the connection between symmetries and differential invariant solutions of the ODE. The analytical structure of the solution exhibits periodic behavior around attractor in local domain, same behavior obtained through dynamical analysis.

Ruma Dutta

Aurel Stan

Correlated dynamics of immune network and sl(3, R) symmetry algebra

免疫网络与sl(3, R)对称代数的关联动力学

ISSN	2544-7297
h-index	13

Publication Information	Publisher: Walter de Gruyter GmbH，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2012，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Journal Fees	$Details
Average review cycle	30 weeks

Computational and Mathematical Biophysics

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Robust H _∞ filter-based functional observer design for descriptor systems: An application to cardiovascular system monitoring

Modelling the leadership role of police in controlling COVID-19

COVID-19 transmission dynamics in close-contacts facilities: Optimizing control strategies

Compartmental modeling approach for prediction of unreported cases of COVID-19 with awareness through effective testing program

CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network

Robust H _∞ filter-based functional observer design for descriptor systems: An application to cardiovascular system monitoring

Modelling the leadership role of police in controlling COVID-19

COVID-19 transmission dynamics in close-contacts facilities: Optimizing control strategies

Compartmental modeling approach for prediction of unreported cases of COVID-19 with awareness through effective testing program

Correlated dynamics of immune network and sl(3, R) symmetry algebra