Homan Emadifar

This study develops a comprehensive mathematical framework for analyzing the transmission dynamics of malaria, with particular emphasis on a four-dose vaccination strategy combined with treatment interventions as primary control mechanisms. The model stratifies the human population into nine compartments — susceptible, exposed, infected, under treatment, recovered, and four sequential vaccination classes ([Formula: see text] through [Formula: see text]) — together with susceptible and infected vector populations, all governed by a system of ordinary differential equations. Analysis of the disease-free equilibrium demonstrates local asymptotic stability when the basic reproduction number [Formula: see text], while a unique endemic equilibrium exists and is stable when [Formula: see text]. Sensitivity analysis identifies the mosquito biting rate, transmission probability, first-dose vaccination rate, and treatment rate as the most influential parameters governing malaria dynamics. Numerical simulations confirm that increasing vaccination coverage and completion rates across all four doses, in conjunction with optimized treatment, substantially reduces malaria incidence. To capture long-term memory effects inherent in malaria transmission — particularly non-exponential immunity waning and heterogeneous parasite development — a Caputo fractional-order version of the model is introduced, solved using the Adams–Bashforth predictor–corrector method. Comparison of the integer-order and fractional-order results reveals that memory effects slow the approach to equilibrium and may lead to higher endemic levels under identical intervention parameters, underscoring the value of the fractional extension. These findings emphasize that achieving high completion rates across all four vaccination doses, sustained treatment access, and integrated vector control are essential for meaningful progress toward malaria elimination in Sub-Saharan Africa.

Plate fin heat exchangers (PFHEs) are widely recognized for their compact design and high thermal efficiency, making them a popular choice in various heat transfer applications. Some common types of geometries which are used in the case of PFHEs are rectangular fins, triangular fins, wavy fins and offset strip fins. These geometries vary primarily in the arrangement of the fins, with rectangular and triangular fins arranged in-line, while offset and wavy fins are arranged in a staggered formation. These configurations help to increase the turbulence, which enhances the heat transfer. With a change in the height to width ratio, fin height and fin spacing (fin width) the heat exchanger’s performance changes. For wavy fins, different configurations were achieved by altering the amplitude and wavelength, while for triangular fins, modifications in the base and height were made to explore various designs. Over the past decades, many researchers have conducted both numerical and experimental analyses on these varieties of fins in the case of PFHEs for optimization of design parameters to improve efficiency. In the present analysis, a computational fluid dynamics (CFD) study has been conducted to find the heat transfer process that is affected by rectangular and offset strip fins. Using different combinations of geometrical parameters, numerical calculations for pressure drop and heat transfer analysis were performed using the ANSYS FLUENT solver. In this study, water has been used as a working medium and the operating conditions were varied, having Renolds numbers ranging from 250 to 8000. This study employs periodic boundary conditions to conduct the analysis and draw meaningful conclusions. In addition to this the impact of these parameters on the friction factor was studied with a change in Nusselt number with respect to Reynolds number.

Due to the increasing resistance to drug treatment among individuals infected with Mycoplasma Genitalium , it is essential to incorporate this factor into its mathematical modelling with a view to gaining insights into how to effectively combat the spread of the disease. To this extent, consequently, in this study, we developed a deterministic mathematical model that considers two strains of the disease: the drug-sensitive and drug-resistant strains. The analysis of the treatment model revealed that its disease-free equilibrium is globally asymptotically stable when a certain epidemiological threshold ( R 0 t ) is less than one, indicating disease control. However, when this threshold exceeds one, R 0 t > 1 , the model exhibits two co-existing endemic equilibria, and competitive exclusion occurs if the reproduction number of the drug-resistant strain is higher than that of the drug-sensitive strain. In contrast, the model without treatment shows infinitely many endemic equilibria when the epidemiological threshold exceeds one. Furthermore, it was discovered that widespread use of antibiotics to treat the wild strain of the disease can eliminate it from the community if the reduction in infectiousness does not exceed a certain value. Sensitivity analysis of the model helped identified key parameters influencing the disease transmission dynamics, which were supported by numerical simulations. Based on our findings, we recommend effective strategies to policymakers in the healthcare sector for combating the spread of M. Genitalium and mitigating its burden.

This study investigates the global post-effects of COVID-19 through mathematical modeling, focusing on the long-term impacts on health systems, economies, and societal structures. We developed an [ ] model, represented by a system of differential equations, to analyze the dynamics of COVID-19. The model includes compartments for susceptible (S), exposed (E), asymptomatic infected ([ ]), symptomatic infected ([ ]), deceased population (D), and recovered (R) individuals. Our analysis determined both local and global asymptotic stability conditions for the disease-free and endemic equilibria. The disease-free equilibrium is both locally and asymptotically stable when [ ] and unstable when [ ]. Sensitivity analysis revealed that parameters such as the contact rate between susceptible and infected individuals, and the progression rate from exposed to infected classes, significantly influence the spread of COVID-19, with positive sensitivity indices indicating increased transmission. Effective control measures aimed at reducing these parameters are crucial for disease mitigation. Conversely, parameters like the recovery rate of infected individuals have a negative sensitivity index, suggesting that increasing the recovery rate through effective treatment can reduce the prevalence of the disease. Numerical simulations and sensitivity analysis, performed using MATLAB, demonstrated that the number of susceptible individuals decreases over time, while exposed individuals initially rise and then decline to zero. The number of asymptomatic and symptomatic infections also increases rapidly before dropping to zero, which correlates with a decrease in the number of deaths due to a high recovery rate. The study emphasized the need for strengthening healthcare systems, enhancing emergency preparedness, and addressing health inequities. It also highlighted the importance of economic recovery plans, supporting low-income communities, and bridging digital divides. To prevent future outbreaks, it is essential to invest in disease surveillance, continue vaccine development, and foster global cooperation. These insights provide a foundation for strategic planning and decision-making, aiming to enhance global resilience and preparedness for future crises.

This study presents an approximate solution to Euler-Bernoulli beams on biparametric elastic foundation via Legendre polynomials and Newmark-beta method subject...