This study explored the structural, optical, and dielectric properties of Pure and Mn+2 doped ZnO nano-particles (Zn1−xMnxO) with x ≥ 20%, synthesized by co-precipitation method followed by annealing at 4500C. Different characterization techniques were conducted to characterize the as-prepared nano-particles. X-ray Diffraction analysis of the pure and Mn+2 doped presented a hexagonal wurtzite structure and a decreased crystallite size with increasing doping concentration. Morphological analysis from SEM revealed finely dispersed spherical nanoparticles with particle size of 40–50 nm. Compositional analysis from EDX confirmed the incorporation of Mn+2ions in ZnO structure. The Results of UV spectroscopy showed that changing the doping concentration affects the band gap, and a red shift is observed as the doping concentration is increased. The band gap changes from 3.3 to 2.75 eV. Dielectric measurements exhibited decrease in the relative permittivity, dielectric loss factor and ac conductivity by increasing Mn concentration.

Due to numerous applications, the study of hybrid nanofluids is a hot topic of research, which enables us to improve thermal performance. The current work is carried out to inspect thermal and solutal transportation in the Prandtl model toward a heated stretched plate. The flow analysis has been developed in Cartesian coordinates considering variable thermal conductivity and non-uniform diffusion coefficient. Furthermore, the modeling of physical phenomena is carried out considering the porous stretched surface under Soret and Dufour effects and heat generation. The principle of boundary layer theory was used to simplify the model partial differential equations (PDEs). The derived PDEs have been transformed into a set of coupled nonlinear ordinary differential equations (ODEs) after utilizing the appropriate transformation. The converted ODEs are coupled and nonlinear. So, the exact solution is not possible. Thus, the derived ODEs have been solved numerically via the finite element scheme. The impact of numerous emerging parameters have been displayed and explained by observing the underlying physics behind them. Moreover, a comparative study is also established. A grid independent survey is established for the convergence of the used numerical approach.

Hybrid nanofluids are extensively analyzed in recent studies due to their better performance in numerous areas such as heat and mass transfer enhancement, biological fluid movement, medical equipment, heat exchangers, electronic cooling and automotive industry. In current study the nanoparticle concentration utilized is much important in biomedical industry. Major applications include drug delivery, radio-pharmaceuticals, centrifuging blood to obtain red blood cells and plasma, medical implants, onco therapeutics and photo thermal cancer therapy. In this regard, the primary focus of this study is to simulate a blood based unsteady hybrid nanofluid flow between two rotating, stretching disks and convective boundaries. The two nanoparticles in this study are uranium dioxide $$UO_{2}$$ U O 2 and multi-walled carbon nanotubes MWCNTs. The hybrid nanofluid is under the influence of magnetohydrodynamic effects and chemical reaction with activation energy. The governing partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using suitable similarity transform. Homotopy analysis method is used to solve the non-linear system of ODEs and $$\hbar $$ ħ -curves are plotted to find suitable region of $$\hbar _{i}$$ ħ i for convergent series solution. Velocity profile is examined for axial, radial and tangential direction against various fluid parameters. Temperature and concentration profiles are analyzed for both convective and non-convective cases. It is observed that convective boundaries result in elevated temperature when compared with non-convective case. Moreover, skin friction, heat and mass transfer rates are also examined with respect to changing volume fraction $$\varphi _{UO_{2}}$$ φ U O 2 .The results revealed that skin friction and rate of heat transfer increases with increase in volume fraction of both nanoparticles $$UO_{2}$$ U O 2 and MWCNTs while the mass transfer rate depicts contrasting behavior.

Due to the frequent occurrence of numerous emergency events that have significantly damaged society and the economy, the need for emergency decision-making has been manifest recently. It assumes a controllable function when it is critical to limit property and personal catastrophes and lessen their negative consequences on the natural and social course of events. In emergency decision-making problems, the aggregation method is crucial, especially when there are more competing criteria. Based on these factors, we first introduced some basic concepts about SHFSS, and then we introduced some new aggregation operators such as the spherical hesitant fuzzy soft weighted average, spherical hesitant fuzzy soft ordered weighted average, spherical hesitant fuzzy weighted geometric aggregation, spherical hesitant fuzzy soft ordered weighted geometric aggregation, spherical hesitant fuzzy soft hybrid average, and spherical hesitant fuzzy soft hybrid geometric aggregation operator. The characteristics of these operators are also thoroughly covered. Also, an algorithm is developed within the spherical hesitant fuzzy soft environment. Furthermore, we extend our investigation to the Evaluation based on the Distance from Average Solution method in multiple attribute group decision-making with spherical hesitant fuzzy soft averaging operators. And a numerical illustration for “supply of emergency aid in post-flooding the situation” is given to show the accuracy of the mentioned work. Then a comparison between these operators and the EDAS method is also established in order to further highlight the superiority of the established work.

In this study, a time series modeling approach is used to determine an ARIMA model and advance counterfactual forecasting at a point of policy intervention. We consider monthly data of HIV/AIDS cases from the Ministry of Health (Copperbelt province) of Zambia, for the period 2010 to 2019 and have a total of 120 observations. Results indicate that ARIMA (1, 0, 0) is an adequate model which best fits the HIV/AIDS time series data and is, therefore, suitable for forecasting cases. The model predicts a reduction from an average of 3500 to 3177 representing 14.29% in HIV/AIDS cases from 2017 (year of policy activation) to 2019, but the actual recorded cases dropped from 3500 to 1514 accounting for 57.4% in the same time frame.