Numerical investigation of heat transfer and fluid flow characteristics of ternary nanofluids through convergent and divergent channels.

The characteristics of nanomaterials have garnered significant attention in recent research on natural and forced convection. This study focuses on the forced convection characteristics of ternary nanofluids within convergent and divergent channels. The ternary nanofluid comprises titanium oxide (TiO2), zinc oxide (ZnO), and silver suspended in water, which serves as the base fluid. Using COMSOL Multiphysics 6.0, a reliable software for finite element analysis, numerical simulations were conducted for steady and incompressible two-dimensional flow. Reynolds numbers varying from 100 to 800 were employed to investigate forced convection. Additionally, we explored aspect ratios (channel height divided by the height of the convergent or divergent section) of -0.4, -0.2, 0, 0.2, and 0.4. Our findings revealed that only at aspect ratio a = 0.4 did the average outlet temperature increase as the Reynolds number rose, while other aspect ratios exhibited decreasing average temperatures with declining Reynolds numbers. Moreover, as the Reynolds number increased from 100 to 800 and the total volume fraction of the ternary nanofluids ranged from 0.003 to 0.15, there was a significant 100% enhancement in the average Nusselt number. For clarity, this article briefly presents essential information, such as the study's numerical nature, fluid properties (constant-property fluid), and the methodology (COMSOL Multiphysics 6.0, finite element analysis). Key conclusions are highlighted to enable readers to grasp the main outcomes at a glance. These details are also adequately covered in the manuscript to facilitate a comprehensive understanding of the research. The utilization of this emerging phenomenon holds immense potential in various applications, ranging from the development of highly efficient heat exchangers to the optimization of thermal energy systems. This phenomenon can be harnessed in scenarios in which effective cost management in thermal production is a critical consideration.

Three dimensional study for entropy optimization in nanofluid flow through a compliant curved duct: A drug delivery and therapy application.

This research explores the three-dimensional characteristics of nanofluid dynamics within curved ducts, in contrast to earlier studies that mainly focus on two-dimensional flow. By using this ground-breaking method, we can capture a more accurate depiction of fluid behavior that complies with the intricate duct design. In this study, we investigate the three dimensional flow and entropic analysis of peristaltic nanofluid flows in a flexible curved duct, comparing the effects of silver and copper nanoparticles. To obtain accurate results, we assume physical constraints such as long wavelength and low Reynolds number and used a perturbation technique through NDSolve commands for finding exact solutions of the obtained differential equations. A comprehensive error analysis is provided through residual error table and figures to estimate a suitable range of the physical factors. Our findings indicate that the velocity of the nanofluid is directly proportional to the elasticity of the walls, while the mass per unit volume inversely affects velocity. We show that reducing the aspect ratio of the duct rectangular section can decrease entropy generation by raising magnitudes of damping force exerted by to the flexible walls of the enclosure. Additionally, using a larger height of the channel than the breadth can reduce stream boluses. The practical implications of this study extend beyond turbines and endoscopy to biomedical processes such as drug delivery and microfluidic systems.

Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function.

The LINEX loss function, which climbs exponentially with one-half of zero and virtually linearly on either side of zero, is employed to analyze parameter analysis and prediction problems. It can be used to solve both underestimation and overestimation issues. This paper explained the Bayesian estimation of mean, Gamma distribution, and Poisson process. First, an improved estimator for µ 2 is provided (which employs a variation coefficient). Under the LINEX loss function, a better estimator for the square root of the median is also derived, and an enhanced estimation for the average mean in such a negatively exponential function. Second, giving a gamma distribution as a prior and a likelihood function as posterior yields a gamma distribution. The LINEX method can be used to estimate an estimator λ B L ^ using posterior distribution. After obtaining λ B L ^ , the hazard function h B L ^ and D B L ^ the function of survival estimators are used. Third, the challenge of sequentially predicting the intensity variable of a uniform Poisson process with a linear exponentially (LINEX) loss function and a constant cost of production time is investigated using a Bayesian model. The APO rule is offered as an approximation pointwise optimal rule. LINEX is the loss function used. A variety of prior distributions have already been studied, and Bayesian estimation methods have been evaluated against squared error loss function estimation methods. Finally, compare the results of Maximum Likelihood Estimation (MLE) and LINEX estimation to determine which technique is appropriate for such information by identifying the lowest Mean Square Error (MSE). The displaced estimation method under the LINEX loss function was also examined in this research, and an improved estimation was proposed.

Impact of pangolin bootleg market on the dynamics of COVID-19 model.

In this paper we consider ant-eating pangolin as a possible source of the novel corona virus (COVID-19) and propose a new mathematical model describing the dynamics of COVID-19 pandemic. Our new model is based on the hypotheses that the pangolin and human populations are divided into measurable partitions and also incorporates pangolin bootleg market or reservoir. First we study the important mathematical properties like existence, boundedness and positivity of solution of the proposed model. After finding the threshold quantity for the underlying model, the possible stationary states are explored. We exploit linearization as well as Lyapanuv function theory to exhibit local stability analysis of the model in terms of the threshold quantity. We then discuss the global stability analyses of the newly introduced model and found conditions for its stability in terms of the basic reproduction number. It is also shown that for certain values of R 0 , our model exhibits a backward bifurcation. Numerical simulations are performed to verify and support our analytical findings.

Sensitivity analysis and optimal control of COVID-19 dynamics based on SEIQR model.

It is of great curiosity to observe the effects of prevention methods and the magnitudes of the outbreak including epidemic prediction, at the onset of an epidemic. To deal with COVID-19 Pandemic, an SEIQR model has been designed. Analytical study of the model consists of the calculation of the basic reproduction number and the constant level of disease absent and disease present equilibrium. The model also explores number of cases and the predicted outcomes are in line with the cases registered. By parameters calibration, new cases in Pakistan are also predicted. The number of patients at the current level and the permanent level of COVID-19 cases are also calculated analytically and through simulations. The future situation has also been discussed, which could happen if precautionary restrictions are adopted.

Numerical study of fractional order COVID-19 pandemic transmission model in context of ABO blood group.

The worldwide association of health (WHO) has stated that COVID-19 (the novel coronavirus disease-2019) as a pandemic. Here, the common SEIR model is generalized in order to show the dynamics of COVID-19 transmission taking into account the ABO blood group of the infected people. Fractional order Caputo derivative are used in the proposed model. Our study is guided by the results that have been obtained by Chen J, Fan H, Zhang L, et al. from three unique medical clinics in Wuhan and Shenzhen, China. In this study, the feasibility region of the proposed model are calculated plus the points of equilibrium. Also, the equilibrium points stability is examined. A unique solution existence for the proposed paradigm is proved via utilizing the fixed point theory with regards to Caputo fractional derivative. Numerical experiments of the proposed paradigm is done and we show its sensitivity to the fractional order.

Mathematical analysis of COVID-19 via new mathematical model.

We develop a new mathematical model by including the resistive class together with quarantine class and use it to investigate the transmission dynamics of the novel corona virus disease (COVID-19). Our developed model consists of four compartments, namely the susceptible class, S ( t ) , the healthy (resistive) class, H ( t ) , the infected class, I ( t ) and the quarantine class, Q ( t ) . We derive basic properties like, boundedness and positivity, of our proposed model in a biologically feasible region. To discuss the local as well as the global behaviour of the possible equilibria of the model, we compute the threshold quantity. The linearization and Lyapunov function theory are used to derive conditions for the stability analysis of the possible equilibrium states. We present numerical simulations to support our investigations. The simulations are compared with the available real data for Wuhan city in China, where the infection was initially originated.

Theoretical and numerical analysis of novel COVID-19 via fractional order mathematical model.

In the work, author's presents a very significant and important issues related to the health of mankind's. Which is extremely important to realize the complex dynamic of inflected disease. With the help of Caputo fractional derivative, We capture the epidemiological system for the transmission of Novel Coronavirus-19 Infectious Disease (nCOVID-19). We constructed the model in four compartments susceptible, exposed, infected and recovered. We obtained the conditions for existence and Ulam's type stability for proposed system by using the tools of non-linear analysis. The author's thoroughly discussed the local and global asymptotical stabilities of underling model upon the disease free, endemic equilibrium and reproductive number. We used the techniques of Laplace Adomian decomposition method for the approximate solution of consider system. Furthermore, author's interpret the dynamics of proposed system graphically via Mathematica, from which we observed that disease can be either controlled to a large extent or eliminate, if transmission rate is reduced and increase the rate of treatment.

A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments.

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.