International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Artificial intelligence (AI) and machine learning techniques have revolutionized various fields, including 3D modelling of anatomical structures. One such area of research involves the use of AI and machine learning algorithms for approximating spherical harmonics functions in the realm of anatomical structure modelling.
Spherical harmonics functions are mathematical tools that describe functions on the surface of a sphere. In 3D modelling of anatomical structures, these functions are employed to represent complex surface details with accurate precision. However, calculating values of these functions for complex anatomical structures is time-consuming and prone to errors. This is where AI and machine learning come into play.
Using AI and machine learning algorithms, we have developed models that can automatically learn the inherent patterns and complexities of anatomical structures from vast amounts of training data. These models can then approximate the spherical harmonics functions that accurately represent the surface details of these structures. This automation significantly reduces the time and effort required in the 3D
modelling process.

The current publication presents an approach for the elaboration of a disposable microfluidic hollow micro-channel using 2 photon polymerization technology and Photonic Professional GT2 (Nanoscribe, Germany) equipment. The design of a 3D model of a microchannel is realized by the CAD analysis software – SOLIDWORKS. A suitable laminar flow is generated by using computational fluid dynamics (CFD) software. As a result, the critical points of the pressure, velocity, and wall shear stress into the microfluidic channel are obtained. A real prototype of the hollow microfluidic device is created, using a highly innovative technology of 3D nanoprinting by twophoton polymerization. Experimental studies with dilute erythrocyte suspensions are conducted to test the functionality of the developed prototype of nano 3D printed microchannel.

A spongy bone can be considered a multi-porous area with its fissures and pores as the most evident components of a double porous system. The work studies the stress-strain state of a spongy jawbone near the implant under occlusal loading. A mathematical model of the problem is the contact problem of the theory of elasticity between the implant and the jawbone. The problem is solved by using the boundary element methods, which are based on the solutions of Flamant’s (BEMF) and Boussinesq’s (BEMB) problems. The cases of various lengths of implant diameter are considered. Stressed contours (isolines) in the jawbone are drafted and the results obtained by BEMF and BEMB for the different diameter implants are compared.

The present study aims to model and analyze the images of induced in vitro blood clots from healthy donors and patients with type 2 diabetes mellitus (T2DM). By using the BioFlux microfluidic system, blood clots are induced at varying shear rates, and for different fibrinogen concentrations: native and highly modified. The fiber diameter of blood clots is analyzed by the scanning electron microscope (SEM). The obtained images of blood clots are imported as input data into the Image J software environment, after which obtained results for the area, number, and fibrin fiber diameter of blood clots, are further processed in a program developed in IntelliJ IDEA. It is found that patients with T2DM at the native concentration of fibrinogen at all studied shear rates form more blood clots having a larger total area, in comparison with the control group. At the higher modified fibrinogen concentration with increasing shear rate, the group with T2DM forms a smaller number of blood clots with a larger area, compared to healthy donors. From the SEM images, it is found, that denser fibrin networks are formed with increased fibrinogen concentration, which contains numerous thick fibrin fibеrs in healthy and diabetic individuals.

A dose assessment of external neutron irradiation at high-energy accelerating complexes, where neutron energy can be in a wide range of 10-8 to 103 MeV, is important for staff radiation safety. In this case, Bonner multi-sphere spectrometers can be used to register the neutron flow. The work proposes a method for unfolding the neutron spectrum by Bonner spectrometer readings, based on the use of the detector sensitivity functions as the basic functions of decomposition of the spectrum under consideration. To find the decomposition coefficients, the system of integral Fredholm equations of the 1st kind is used. From a mathematical point of view, this problem is the illposed one and is solved numerically using the A.N. Tikhonov regularization method. According to the received energy spectrum of neutrons, an assessment of the dose of irradiation is made at the detection locations. The results of unfolding the neutron spectrum at the JINR Phasotron and the dose to its personnel are presented.

Agent-based modeling proved to be a powerful tool for studying the complex multifactorial processes that take place in human population. In this paper we describe how this approach can be used to study the spread of airborne infections in a big city and the ways to control them. Agent-based modeling includes three main stages: a creating a synthetic population, simulation of disease spread in synthetic population during a fixed time period, and an analysis of the results. We created the population of 10 million agents and united them in a complex network according to their individual characteristics such as age, sex, marital status and occupation. In addition, each agent has a property that characterizes its state of health: susceptible, infected, and recovered. A susceptible agent can become infected if has an infected agent among its contacts and if an event of disease transmission occurs. Disease transmission is simulated as a random event with probability p which calculated for every pair susceptible-infected and depends on their individual characteristics and on the length of the contact. In so doing, the heterogeneity in a number of contacts and in resistance can be modelled. This approach was applied to model a dynamic of COVID-19 in Moscow during the period between October 2020 and December 2021.

The study aims to analyze the experimental haemorheological data with mathematical models and to analyze the interrelationship between the rheological parameters of the blood and blood rheology determinants. The study aims to develop this experimental basis of prognostic mathematical and hemorheological models for the interrelationship between the set of parameters. Two known models are used to describe the non-Newtonian rheological properties of blood – the shear stresses – shear rate dependences. The parameters of the simplest models – the power law and the Herschel-Bulkley law were used to analyse the differences between the hemorheological characteristics of blood from healthy individuals and a group of patients with diabetes mellitus type 2 (T2DM). The experimental data are statistically processed using the Mann-Whitney U-test for intergroup comparison of independent variables, as well as t-test. The Statistica program was used for statistical processing.

Overcrowding in the Emergency Department (ED) is one of the most important issues in healthcare systems. Two major causes of this congestion are identified, the first one is unjustified Emergency Department visits and the second one a lack of downstream beds. The lack of downstream beds can deteriorate the quality of care for patients who need hospitalization after an ED visit. In this paper a generic simulation model is developed in order to analyse patient pathways from the ED to hospital discharge.

The coagulation equation, proposed by Smoluchowski, has been widely used to describe aggregation phenomena in many fields of science since its inception. It considers a physical system of many particles, and each particle is characterized by a change of some nonnegative scalar quantity (e.g. volume). Assuming such a system to be spatially homogeneous and unbounded, considering only pairwise interactions and a balance relation of interacting particles, the Smoluchowski equation can be used to describe the evolution of a system of many particles. In this study, we construct an exact solution to this integro-differential equation containing an exponentially-decaying source term. This solution, in particular, describes the steady-state structural density of endosomes per cell carrying the nanoparticles (particles smaller than 100 nm). In addition, we derived an exact analytical solution to the unsteady-state coagulation equation in the Laplace transform image space. This solution can be inverted using numerical methods for Laplace transform inversion. For practical use, we derive an analytical solution to the non-stationary coagulation equation stitching the steady-state and initial distributions of structural density. Choosing the particular form of stitching functions, we demonstrate that the nonstationary solution evolves between the initial and steady-state distribution functions. Thus, analytical solutions obtained represent a general theoretical basis to describe the dynamics of cargo distributions in the endosomal network.

The rapid spread of COVID-19 disease worldwide has caused a frightening crisis in the health care system in many states. To prevent it, many states have taken various measures, including total blockades. In this paper we have used the logistic growth model to show the increase in the population of the number infected with the Covid-19 virus in Albania. The generalized logistic equation is used to interpret the COVID-19 epidemic data in Albania. The growth rate was calculated using two random data and the expected number of infected people. The predictions of the logistic model are as correct as the data are accurate, and that correct that they can imitate the dynamics of the epidemic. The model clearly shows that there is a correlation between real and projected data. When daily predictions of epidemic size begin to converge, we can say that the epidemic is under control. If we deviate from the forecast curve it may indicate that the  epidemic may get out of control. With a fully valid model, this type of information can be used as an example by policy makers to assess how to take appropriate action.

According to numerous studies, 76% of sudden cardiac arrest (SCA) are conditionally preventable, because occur due to ventricular fibrillation (VF) [1-3]. Immediate electrical impulse therapy (EIT) by external defibrillation is the main treatment for this type of arrhythmia [3-5]. Earlier, a comparative study of modern forms of impulses made it possible to reveal the concept of the development of this field of science [6]. The analysis of the literature and the results of recent developments made it possible to determine the main parameters of the pulse for the individual selection of the characteristics of the transthoracic defibrillation signal. Representation of the average current (I) and charge (Q) in the form of a certain function I (t) is the optimal way to dose the defibrillation pulse [7].

Monte Carlo methods have become popular computational device for problems in biology. In this work we implement and analyze the computational complexity of the Latin hypercube sampling algorithm. We compare the results with Importance sampling algorithm which is the most widely used variance reduction Monte Carlo method. We show that the Latin hypercube sampling has some advantageous over the importance sampling technique