The impact of acute and chronic aerobic and resistance exercise on stem cell mobilization: A review of effects in healthy and diseased individuals across different age groups.

Stem cells (SCs) play a crucial role in tissue repair, regeneration, and maintaining physiological homeostasis. Exercise mobilizes and enhances the function of SCs. This review examines the effects of acute and chronic aerobic and resistance exercise on the population of SCs in healthy and diseased individuals across different age groups. Both acute intense exercise and moderate regular training increase circulating precursor cells CD34+ and, in particular, the subset of angiogenic progenitor cells (APCs) CD34+/KDR+. Conversely, chronic exercise training has conflicting effects on circulating CD34+ cells and their function, which are likely influenced by exercise dosage, the health status of the participants, and the methodologies employed. While acute activity promotes transient mobilization, regular exercise often leads to an increased number of progenitors and more sustainable functionality. Short interventions lasting 10-21 days mobilize CD34+/KDR + APCs in sedentary elderly individuals, indicating the inherent capacity of the body to rapidly activate tissue-reparative SCs during activity. However, further investigation is needed to determine the optimal exercise regimens for enhancing SC mobilization, elucidating the underlying mechanisms, and establishing functional benefits for health and disease prevention. Current evidence supports the integration of intense exercise with chronic training in exercise protocols aimed at activating the inherent regenerative potential through SC mobilization. The physical activity promotes endogenous repair processes, and research on exercise protocols that effectively mobilize SCs can provide innovative guidelines designed for lifelong tissue regeneration. An artificial neural network (ANN) was developed to estimate the effects of modifying elderly individuals and implementing chronic resistance exercise on stem cell mobilization and its impact on individuals and exercise. The network's predictions were validated using linear regression and found to be acceptable compared to experimental results.

Dynamic stability of the euler nanobeam subjected to inertial moving nanoparticles based on the nonlocal strain gradient theory.

This research studied the dynamic stability of the Euler-Bernoulli nanobeam considering the nonlocal strain gradient theory (NSGT) and surface effects. The nanobeam rests on the Pasternak foundation and a sequence of inertial nanoparticles passes above the nanobeam continuously at a fixed velocity. Surface effects have been utilized using the Gurtin-Murdoch theory. Final governing equations have been gathered implementing the energy method and Hamilton's principle alongside NSGT. Dynamic instability regions (DIRs) are drawn in the plane of mass-velocity coordinates of nanoparticles based on the incremental harmonic balance method (IHBM). A parametric study shows the effects of NSGT parameters and Pasternak foundation constants on the nanobeam's DIRs. In addition, the results exhibit the importance of 2T-period DIRs in comparison to T-period ones. According to the results, the Winkler spring constant is more effective than the Pasternak shear constant on the DIR movement of nanobeam. So, a 4 times increase of Winkler and Pasternak constants results in 102 % and 10 % of DIR movement towards higher velocity regions, respectively. Furthermore, the effect of increasing nonlocal and material length scale parameters on the DIR movement are in the same order regarding the magnitude but opposite considering the motion direction. Unlike nonlocal parameter, an increase in material length scale parameter shifts the DIR to the more stable region.

A generalized Chebyshev operational method for Volterra integro-partial differential equations with weakly singular kernels.

Volterra integro-partial differential equations with weakly singular kernels (VIPDEWSK) are utilized to model diverse physical phenomena. A matrix collocation method is proposed for determining the approximate solution of this functional equation category. The method employs shifted Chebyshev polynomials of the fifth kind (SCPFK) to construct two-dimensional pseudo-operational matrices of integration, avoiding the need for explicit integration and thereby speeding up computations. Error bounds are examined in a Chebyshev-weighted space, providing insights into approximation accuracy. The approach is applied to several experimental examples, and the results are compared with those obtained using the Bernoulli wavelets and Legendre wavelets methods.

Examination of the vessel's shape, resistive force and volumetric-aqueous efficiencies to optimize the vessels' foil under noise propagation.

There are several parameters in designing undersurface vessel forms, the most important of which is the hull's total strength, which includes the strength of the hull and its attachments. According to studies, 70 % of the total strength of the vessels is related to their hull only without attachments. The hull has three major parts: nose, cylinder, and heel. The advanced vessels' architecture has a parallel shape (cylinder shape). This cylindrical part is important in examining the used volume by pilots and vessel equipment. This paper uses the CFD method to examine the vessel's shape, and the resistive force and volumetric-aqueous efficiencies are extracted. An optimum profile is extracted by the values of resistive force and volumetric-aqueous efficiencies. The results indicate the significant effect of the hull form on the hydro-acoustic noise of the hull. In other words, by optimizing the hydrodynamic form of the hull, the noise propagation can be reduced as much as possible. Also, the linear slope of the optimized hull is not optimized more than the hull. This means that the turbulence caused by the optimized hull has a higher damping potential.

A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis.

OBJECTIVES: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respiratory illnesses, posing diagnostic challenges. Therefore, the primary goal of this study is to design a novel decision-support system under a bipolar intuitionistic fuzzy environment to examine an effective TB diagnosing method. METHODS: To achieve the aim, a novel fuzzy decision support system is derived by integrating PROMETHEE and ARAS techniques. This technique evaluates TB diagnostic methods under the bipolar intuitionistic fuzzy context. Moreover, the defuzzification algorithm is proposed to convert the bipolar intuitionistic fuzzy score into crisp score. RESULTS: The proposed method found that the sputum test (T3) is the most accurate in diagnosing TB. Additionally, comparative and sensitivity analyses are derived to show the proposed method's efficiency. CONCLUSION: The proposed bipolar intuitionistic fuzzy sets, combined with the PROMETHEE-ARAS techniques, proved to be a valuable tool for assessing effective TB diagnosing methods.

The effect of the initial temperature, pressure, and shape of carbon nanopores on the separation process of SiO2 molecules from water vapor by molecular dynamics simulation.

Today, with the advancement of science in nanotechnology, it is possible to remove dust nanostructures from the air breathed by humans or other fluids. In the present study, the separation of SiO2 molecules from H2O vapor is studied using molecular dynamics (MD) simulation. This research studied the effect of initial temperature, nanopore geometry, and initial pressure on the separation of SiO2 molecules. The obtained results show that by increasing the temperature to 500 K, the maximum velocity (Max-Vel) of the samples reached 2.47 Å/fs. Regarding the increasing velocity of particles, more particles pass via the nanopores. Moreover, the shape of the nanopore could affect the number of passing particles. The results show that in the samples with a cylindrical nanopore, 20 and 40% of SiO2 molecules, and with the sphere cavity, about 32 and 38% of SiO2 particles passed in the simulated structure. So, it can be concluded that the performance of carbon nanosheets with a cylindrical pore and 450 K was more optimal. Also, the results show that an increase in initial pressure leads to a decrease in the passage of SiO2 particles. The results reveal that about 14 and 54% of Silica particles passed via the carbon membrane with increasing pressure. Therefore, for use in industry, in terms of separating dust particles, in addition to applying an EF, temperature, nanopore geometry, and initial pressure should be controlled.

A mathematical model of coronavirus transmission by using the heuristic computing neural networks.

In this study, the nonlinear mathematical model of COVID-19 is investigated by stochastic solver using the scaled conjugate gradient neural networks (SCGNNs). The nonlinear mathematical model of COVID-19 is represented by coupled system of ordinary differential equations and is studied for three different cases of initial conditions with suitable parametric values. This model is studied subject to seven class of human population N(t) and individuals are categorized as: susceptible S(t), exposed E(t), quarantined Q(t), asymptotically diseased IA (t), symptomatic diseased IS (t) and finally the persons removed from COVID-19 and are denoted by R(t). The stochastic numerical computing SCGNNs approach will be used to examine the numerical performance of nonlinear mathematical model of COVID-19. The stochastic SCGNNs approach is based on three factors by using procedure of verification, sample statistics, testing and training. For this purpose, large portion of data is considered, i.e., 70%, 16%, 14% for training, testing and validation, respectively. The efficiency, reliability and authenticity of stochastic numerical SCGNNs approach are analysed graphically in terms of error histograms, mean square error, correlation, regression and finally further endorsed by graphical illustrations for absolute errors in the range of 10-05 to 10-07 for each scenario of the system model.

Limit properties in the metric semi-linear space of picture fuzzy numbers.

The picture fuzzy set (PFS) just appeared in 2014 and was introduced by Cuong, which is a generalization of intuitionistic fuzzy sets (Atanassov in Fuzzy Sets Syst 20(1):87-96, 1986) and fuzzy sets (Zadeh Inf Control 8(3):338-353, 1965). The picture fuzzy number (PFN) is an ordered value triple, including a membership degree, a neutral-membership degree, a non-membership degree, of a PFS. The PFN is a useful tool to study the problems that have uncertain information in real life. In this paper, the main aim is to develop basic foundations that can become tools for future research related to PFN and picture fuzzy calculus. We first establish a semi-linear space for PFNs by providing two new definitions of two basic operations, addition and scalar multiplication, such that the set of PFNs together with these two operations can form a semi-linear space. Moreover, we also provide some important properties and concepts such as metrics, order relations between two PFNs, geometric difference, multiplication of two PFNs. Next, we introduce picture fuzzy functions with a real domain that is also known as picture fuzzy functions with time-varying values, called geometric picture fuzzy function (GPFFs). In this framework, we give definitions about the limit of GPFFs and sequences of PFN. The important limit properties are also presented in detail. Finally, we prove that the metric semi-linear space of PFNs is complete, which is an important property in the classical mathematical analysis.

Modeling the dynamics of COVID-19 using fractal-fractional operator with a case study.

This research study consists of a newly proposed Atangana-Baleanu derivative for transmission dynamics of the coronavirus (COVID-19) epidemic. Taking the advantage of non-local Atangana-Baleanu fractional-derivative approach, the dynamics of the well-known COVID-19 have been examined and analyzed with the induction of various infection phases and multiple routes of transmissions. For this purpose, an attempt is made to present a novel approach that initially formulates the proposed model using classical integer-order differential equations, followed by application of the fractal fractional derivative for obtaining the fractional COVID-19 model having arbitrary order Ψ and the fractal dimension Ξ . With this motive, some basic properties of the model that include equilibria and reproduction number are presented as well. Then, the stability of the equilibrium points is examined. Furthermore, a novel numerical method is introduced based on Adams-Bashforth fractal-fractional approach for the derivation of an iterative scheme of the fractal-fractional ABC model. This in turns, has helped us to obtained detailed graphical representation for several values of fractional and fractal orders Ψ and Ξ , respectively. In the end, graphical results and numerical simulation are presented for comprehending the impacts of the different model parameters and fractional order on the disease dynamics and the control. The outcomes of this research would provide strong theoretical insights for understanding mechanism of the infectious diseases and help the worldwide practitioners in adopting controlling strategies.

Analytical and qualitative investigation of COVID-19 mathematical model under fractional differential operator.

In the current article, we aim to study in detail a novel coronavirus (2019-nCoV or COVID-19) mathematical model for different aspects under Caputo fractional derivative. First, from analysis point of view, existence is necessary to be investigated for any applied problem. Therefore, we used fixed point theorem's due to Banach's and Schaefer's to establish some sufficient results regarding existence and uniqueness of the solution to the proposed model. On the other hand, stability is important in respect of approximate solution, so we have developed condition sufficient for the stability of Ulam-Hyers and their different types for the considered system. In addition, the model has also been considered for semianalytical solution via Laplace Adomian decomposition method (LADM). On Matlab, by taking some real data about Pakistan, we graph the obtained results. In the last of the manuscript, a detail discussion and brief conclusion are provided.

Investigation of fractal-fractional order model of COVID-19 in Pakistan under Atangana-Baleanu Caputo (ABC) derivative.

This manuscript addressing the dynamics of fractal-fractional type modified SEIR model under Atangana-Baleanu Caputo (ABC) derivative of fractional order y and fractal dimension p for the available data in Pakistan. The proposed model has been investigated for qualitative analysis by applying the theory of non-linear functional analysis along with fixed point theory. The fractional Adams-bashforth iterative techniques have been applied for the numerical solution of the said model. The Ulam-Hyers (UH) stability techniques have been derived for the stability of the considered model. The simulation of all compartments has been drawn against the available data of covid-19 in Pakistan. The whole study of this manuscript illustrates that control of the effective transmission rate is necessary for stoping the transmission of the outbreak. This means that everyone in the society must change their behavior towards self-protection by keeping most of the precautionary measures sufficient for controlling covid-19.

Fractional order mathematical modeling of novel corona virus (COVID-19).

In this manuscript, the mathematical model of COVID-19 is considered with eight different classes under the fractional-order derivative in Caputo sense. A couple of results regarding the existence and uniqueness of the solution for the proposed model is presented. Furthermore, the fractional-order Taylor's method is used for the approximation of the solution of the concerned problem. Finally, we simulate the results for 50 days with the help of some available data for fractional differential order to display the excellency of the proposed model.

Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology.

The outburst of the pandemic Coronavirus disease since December 2019, has severely impacted the health and economy worldwide. The epidemic is spreading fast through various means, as the virus is very infectious. Medical science is exploring a vaccine, only symptomatic treatment is possible at the moment. To contain the virus, it is required to categorize the risk factors and rank those in terms of contagion. This study aims to evaluate risk factors involved in the spread of COVID-19 and to rank them. In this work, we applied the methodology namely, Fuzzy Analytic Hierarchy Process (FAHP) to find out the weights and finally Hesitant Fuzzy Sets (HFS) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to identify the major risk factor. The results showed that "long duration of contact with the infected person" the most significant risk factor, followed by "spread through hospitals and clinic" and "verbal spread". We showed the appliance of the Multi Criteria Decision Making (MCDM) tools in evaluation of the most significant risk factor. Moreover, we conducted sensitivity analysis.

An analysis of a nonlinear susceptible-exposed-infected-quarantine-recovered pandemic model of a novel coronavirus with delay effect.

In the present study, a nonlinear delayed coronavirus pandemic model is investigated in the human population. For study, we find the equilibria of susceptible-exposed-infected-quarantine-recovered model with delay term. The stability of the model is investigated using well-posedness, Routh Hurwitz criterion, Volterra Lyapunov function, and Lasalle invariance principle. The effect of the reproduction number on dynamics of disease is analyzed. If the reproduction number is less than one then the disease has been controlled. On the other hand, if the reproduction number is greater than one then the disease has become endemic in the population. The effect of the quarantine component on the reproduction number is also investigated. In the delayed analysis of the model, we investigated that transmission dynamics of the disease is dependent on delay terms which is also reflected in basic reproduction number. At the end, to depict the strength of the theoretical analysis of the model, computer simulations are presented.

Prediction modelling of COVID using machine learning methods from B-cell dataset.

Coronavirus is a pandemic that has become a concern for the whole world. This disease has stepped out to its greatest extent and is expanding day by day. Coronavirus, termed as a worldwide disease, has caused more than 8 lakh deaths worldwide. The foremost cause of the spread of coronavirus is SARS-CoV and SARS-CoV-2, which are part of the coronavirus family. Thus, predicting the patients suffering from such pandemic diseases would help to formulate the difference in inaccurate and infeasible time duration. This paper mainly focuses on the prediction of SARS-CoV and SARS-CoV-2 using the B-cells dataset. The paper also proposes different ensemble learning strategies that came out to be beneficial while making predictions. The predictions are made using various machine learning models. The numerous machine learning models, such as SVM, Naïve Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random forest, ensembles, and neural networks are used in predicting and analyzing the dataset. The most accurate result was obtained using the proposed algorithm with 0.919 AUC score and 87.248% validation accuracy for predicting SARS-CoV and 0.923 AUC and 87.7934% validation accuracy for predicting SARS-CoV-2 virus.

Threshold conditions for global stability of disease free state of COVID-19.

This article focus the elimination and control of the infection caused by COVID-19. Mathematical model of the disease is formulated. With help of sensitivity analysis of the reproduction number the most sensitive parameters regarding transmission of infection are found. Consequently strategies for the control of infection are proposed. Threshold condition for global stability of the disease free state is investigated. Finally, using Matlab numerical simulations are produced for validation of theocratical results.

A 6-point subdivision scheme and its applications for the solution of 2nd order nonlinear singularly perturbed boundary value problems.

In this paper, we first present a 6-point binary interpolating subdivision scheme (BISS) which produces a C2 continuous curve and 4th order of approximation. Then as an application of the scheme, we develop an iterative algorithm for the solution of 2nd order nonlinear singularly per-turbed boundary value problems (NSPBVP). The convergence of an iterative algorithm has also been presented. The 2nd order NSPBVP arising from combustion, chemical reactor theory, nuclear engi-neering, control theory, elasticity, and fluid mechanics can be solved by an iterative algorithm with 4th order of approximation.

Fractal-Fractional Mathematical Model Addressing the Situation of Corona Virus in Pakistan.

This work is the consideration of a fractal fractional mathematical model on the transmission and control of corona virus (COVID-19), in which the total population of an infected area is divided into susceptible, infected and recovered classes. We consider a fractal-fractional order SIR type model for investigation of Covid-19. To realize the transmission and control of corona virus in a much better way, first we study the stability of the corresponding deterministic model using next generation matrix along with basic reproduction number. After this, we study the qualitative analysis using "fixed point theory" approach. Next, we use fractional Adams-Bashforth approach for investigation of approximate solution to the considered model. At the end numerical simulation are been given by matlab to provide the validity of mathematical system having the arbitrary order and fractal dimension.

Modeling the effect of delay strategy on transmission dynamics of HIV/AIDS disease.

In this manuscript, we investigate a nonlinear delayed model to study the dynamics of human-immunodeficiency-virus in the population. For analysis, we find the equilibria of a susceptible-infectious-immune system with a delay term. The well-established tools such as the Routh-Hurwitz criterion, Volterra-Lyapunov function, and Lasalle invariance principle are presented to investigate the stability of the model. The reproduction number and sensitivity of parameters are investigated. If the delay tactics are decreased, then the disease is endemic. On the other hand, if the delay tactics are increased then the disease is controlled in the population. The effect of the delay tactics with subpopulations is investigated. More precisely, all parameters are dependent on delay terms. In the end, to give the strength to a theoretical analysis of the model, a computer simulation is presented.

Certain inequalities involving generalized Erdélyi-Kober fractional q-integral operators.

In recent years, a remarkably large number of inequalities involving the fractional q-integral operators have been investigated in the literature by many authors. Here, we aim to present some new fractional integral inequalities involving generalized Erdélyi-Kober fractional q-integral operator due to Gaulué, whose special cases are shown to yield corresponding inequalities associated with Kober type fractional q-integral operators. The cases of synchronous functions as well as of functions bounded by integrable functions are considered.