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This study aims to identify health workforce challenges at Iranian hospitals during the COVID-19 pandemic.

This was a conventional content analysis study conducted in 2020. The population consisted of the managers (heads of hospitals, managers and matrons) and staff (nurses, physicians, etc.) of eligible hospitals. The participants were selected using purposive sampling, and data saturation was achieved after 28 interviews. The data were analyzed in MAXQDA10.

In total, 28 interviews were conducted with 10 women and 18 men. The challenges of hospital human resources were categorized into five main themes and 15 sub-themes. The main themes were the shortage of human resources, burnout, the need to acquire new knowledge and skills, the employees’ health and safety and the reward system.

Identification of challenges faced by human resources is the first step toward preventing human force shortage and psychological problems in the personnel. Implementing the recommendations of the present study would assist the proper management of hospitals’ human resources.

This paper aims to present a black-box fuzzy system identification method coupled with genetic algorithm optimization approach to predict the mixture thermal conductivity at dissimilar temperatures and nanoparticle concentrations, in the examined domains.

WO3 nanoparticles are dispersed in the deionized water to produce a homogeneous mixture at various nanoparticles mass fractions of 0.1, 0.5, 1.0 and 5.0 Wt.%.

The results depicted that the models not only have satisfactory precision, but also have acceptable accuracy in dealing with non-trained input values.

The transmission electron microscopy is applied to measure the mean diameters, shape and morphology of the dry nanoparticles. Moreover, the stability of nanoparticles inside the water is evaluated by using zeta potential and dynamic light scattering (DLS) tests. Then, the prepared nanofluid thermal conductivity is presented at different values of temperatures and concentrations.

This paper aims to propose a new nonlinear function estimation fuzzy system as a novel statistical approach to estimate nanofluids’ thermal conductivity.

A fuzzy system having a product inference engine, a singleton fuzzifier, a center average defuzzifier and Gaussian membership functions is proposed for this purpose.

Results indicate that the proposed fuzzy system can predict the thermal conductivity of Al₂O₃/paraffin nanofluid with appropriate precision and generalization and it also outperforms the classic interpolation methods.

A new nonlinear function estimation fuzzy system was introduced as a novel statistical approach to estimate nanofluids’ thermal conductivity for the first time.