Global Stability of a MERS-CoV Infection Model with CTL Immune Response and Intracellular Delay

In this paper, we propose and study a Middle East respiratory syndrome coronavirus (MERS-CoV) infection model with cytotoxic T lymphocyte (CTL) immune response and intracellular delay. This model includes five compartments: uninfected cells, infected cells, viruses, dipeptidyl peptidase 4 (DPP4), and CTL immune cells. We obtained an immunity-inactivated reproduction number (Formula presented.) and an immunity-activated reproduction number (Formula presented.). By analyzing the distributions of roots of the corresponding characteristic equations, the local stability results of the infection-free equilibrium, the immunity-inactivated equilibrium, and the immunity-activated equilibrium were obtained. Moreover, by constructing suitable Lyapunov functionals and combining LaSalle's invariance principle and Barbalat's lemma, some sufficient conditions for the global stability of the three types of equilibria were obtained. It was found that the infection-free equilibrium is globally asymptotically stable if (Formula presented.) and unstable if (Formula presented.) ;the immunity-inactivated equilibrium is locally asymptotically stable if (Formula presented.) and globally asymptotically stable if (Formula presented.) and condition (H1) holds, but unstable if (Formula presented.) ;and the immunity-activated equilibrium is locally asymptotically stable if (Formula presented.) and is globally asymptotically stable if (Formula presented.) and condition (H1) holds. © 2023 by the authors.

Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry

Featured Application: Collapsing cavitation bubbles can be used in material surface cleaning, the medical field, and so on. By adjusting the micro-jet intensity of the collapsing bubbles, the cavitation phenomenon can be employed to clean irregular material surfaces, such as sections, cracks, and vegetable leaves. In the medical field, cavitation bubbles can be used as microbubble contrast agents for ultrasound diagnostic imaging or vehicles for drug or gene delivery. The growth and violent collapse of cavitation bubbles can also be employed in sterilization or killing viruses such as COVID-19. The interaction mechanism between the cavitation bubble and a solid wall is a basic problem in bubble collapse prevention and application. In particular, when bubble collapse occurs near solid walls with arbitrarily complex geometries, it is difficult to efficiently establish a model and quantitatively explore the interaction mechanism between bubbles and solid walls. Based on the advantages of the lattice Boltzmann method, a model for cavitation bubble collapse close to a solid wall was established using the pseudopotential multi-relaxation-time lattice Boltzmann model. Solid walls with arbitrarily complex geometries were introduced in the computational domain, and the fractal dimension was used to quantify the complexity of the solid wall. Furthermore, owing to the lack of periodicity, symmetry, spatial uniformity and obvious correlation in this process, the Minkowski functionals-based morphological analysis method was introduced to quantitatively describe the temporal evolution of collapsing bubble profiles and acquire effective information from the process. The interaction mechanism between the bubble and solid wall was investigated using evolutions of physical fields. In addition, the influences of the solid walls' surface conditions and the position parameter on collapsing bubbles were discussed. These achievements provide an efficient tool for quantifying the morphological changes of the collapsing bubble. © 2023 by the authors.

Comparing Feature Sets of Pseudo Zernike Moment and Minkowski Functionals to Classify Normal Subjects from COVID Subjects

Aim: Objective of this study is to analyze the efficiency of Pseudo Zernike Moment in differentiating COVID subjects from controls compared to Minkowski Functionals. Materials and Methods: The data for this study is obtained from a publicly available dataset. By fixing predefined values to the parameters such as effect size and algorithm power as 0.3 and 0.80 in G power tool provides the required sample size as 176. Pseudo Zernike moments and Minkowski features are extracted from the binary lung CT scans. Result: Pseudo Zernike moment feature (M2) is found to have a mean value of 0.63 for normal subjects and 0.56 for COVID subjects. Minkowski area feature is found to have the ability to differentiate COVID subject compared to its other features. Pseudo Zernike features exhibit better statistical significance (p<0.05) in differentiating normal and COVID subjects. Neural network classifier shows better classification ability with 91% classification accuracy in separating COVID subjects from normal controls. Conclusion: Compared to Minkowski features, pseudo-Zernike moments has better classification ability to differentiate normal and COVID subjects. © 2022 IEEE.

Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.

Formation of the Structure of Multilayer Polyagent Functionals

The processes of intellectual monitoring in emergencies are studied. The intelligent monitoring system is an environment for creating and using intelligent agents to provide knowledge of decision-making processes. In emergencies, objects acquire new properties quickly, and the informativeness of the results of previous observations decreases. To increase the power of data mining tools, monitoring agents are combined into agent functionalities with a multi-tier structure. The paper presents the results of research on the processes of formation of multi-echelon polyagent functionals. The efficiency of construction of a multi-echelon polyagent functional in solving the problem of predicting the incidence of the population of Ukraine on Covid-19 in conditions of low informativeness of the results of observations has been experimentally confirmed. © 2021 Copyright for this paper by its authors

Formation of feedback in the structures of poly-agent functionals

The research aim is to improve the efficiency of poly-agent functional monitoring information system by feedback creation. The signs list at the entrance functionality varies according to the characteristics of the signal at the output. The signal characteristics at the functional output are improved by changing the features list of the results of observations at its input. Building poly-agent functionalities by the monitoring information system (MIS) is improved. The MIS is a software implementation of the information technology of intelligent monitoring (ITLM). This paper describes the use of ITLM for forecasting the incidence of COVID-19 disease in the Ukrainian population. The information technology is designed to work under conditions of crisis monitoring. During the pandemic, the properties of the monitoring objects change, and the informativeness of the accumulated results of monitoring decreases. It is proposed to adapt the list of features of the array of input data (AID) to change the informativeness of the observation results. A method for informativeness identifying the AID features of a poly-agent functional based on the results of constructing agents with structural tasks is proposed. AID increasing informativeness by signs list optimizing according to signals' characteristics at the agents' output with the structural tasks of MIS is experimentally confirmed. © 2021 Elsevier B.V.. All rights reserved.