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Abstract Despite several studies conducted to investigate housing factors, the effects of housing construction materials on childhood ARI symptoms in Bangladesh remain unclear. Hence, the study aimed to measure such a correlation among children under the age of five. A hospital-based case–control study was conducted, involving 221 cases and 221 controls from January to April 2023. Bivariate and multivariate binary logistic regression was performed to measure the degree of correlation between housing construction materials and childhood ARI symptoms. Households composed of natural floor materials had 2.7 times (95% confidence interval 1.27–5.57) and households composed of natural roof materials had 1.8 times (95% confidence interval 1.01–3.11) higher adjusted odds of having under-five children with ARI symptoms than household composed of the finished floor and finished roof materials respectively. Households with natural wall type were found protective against ARI symptoms with adjusted indoor air pollution determinants. The study indicates that poor housing construction materials are associated with an increased risk of developing ARI symptoms among under-five children in Bangladesh. National policy regarding replacing poor housing materials with concrete, increasing livelihood opportunities, and behavioral strategies programs encouraging to choice of quality housing construction materials could eliminate a fraction of the ARI burden.

Researchers and engineers are actively working on enhancing the efficiency of heat exchangers in engineering applications by developing novel designs, exploring new materials, and utilizing nanofluids. Three kinds of nanofluids with varying concentrations are investigated in this paper. The objective is to assess the performance of N‐shaped double‐pipe heat exchanger used in thermoelectric power plants. The performance has been evaluated using COMSOL Multiphysics software. The findings show that higher nanofluid concentrations resulted in elevated heat transfer coefficients and improved efficiency of N‐shape double pipe heat exchanger. The analysis revealed that a mere 1% rise in the volume fraction of nanofluids enhanced the efficiency of the heat exchanger on average 23% when compared to the base fluid (water). In comparison to the N‐shape double pipe (Inconel 625) heat exchangers, the N‐shape double pipe (copper) heat exchangers appear to be more efficient. The introduction of nanoparticles has a notable impact on the heat transfer coefficients. Specifically, within an N‐shaped double pipe (copper) heat exchanger, the inclusion of a 1% volume fraction results in a 2.09% enhancement in the heat transfer coefficient for Al2O3/water, a 1.3% improvement for Fe3O4/water, and a 1.15% increase for TiO2/water. It also exposed that adding 1% Al2O3/water led to a significant 0.623% increase in effectiveness, while TiO2/water showed a 0.259% rise, and Fe3O4/water exhibited a 0.375% improvement. Moreover, increasing the Reynolds number enhances the Nusselt number for Al2O3/water and Fe3O4/water nanofluids by 55.22%, and for TiO2/water by 54.60% at a 6% volume concentration, leading to additional increases in exchanger efficiency. Therefore, the augmentation in nanofluid concentration leads to a reduction in the temperature pinch points both at the intake and outflow. This observation suggests that nanofluids exhibit a superior ability compared to conventional fluids when it comes to effectively lowering temperatures. [ABSTRACT FROM AUTHOR]

Multistage refrigeration system plays a vital role in industrial refrigeration for the chemical, petrochemical, pharmaceuticals and food industries. Modern chemical industries are complex, and the problems are commonly multi-dimensional, non-linear and time-consuming. This study presents the application of evolutionary computation techniques, namely PSO (particle swarm optimization), GA (Genetic Algorithm) and SA (Simulated Annealing) to solve a design problem of a two-stage vapor compression refrigeration system. Two objectives are evaluated, namely the minimization of total energy consumption and maximization of the coefficient of performance (COP) of the system. The basis of design for the two-stage refrigeration system is built from and validated against data from published literature. The mass flow ratio, evaporator and condenser temperature, parameters for subcooling and desuperheating, and the coefficient of performance for the basis of design show acceptable results. The errors are below 5% against the data from published literature, which are within errors of significant figures in the calculations. In this work, the optimum solutions show a reduction of the required amount of energy consumption by 30.8% and an increase of the COP by nearly 77% with respect to the basis of design. Further improvements are made to the optimization procedures to prevent early convergence and to increase the search efficiency for finding the global optima. The findings by PSO, GA and SA are in agreement, and all evolutionary techniques achieved proper convergence of the two objective functions. It is also found that PSO requires lower computational effort, less computation time and is also easier to implement compared to GA and SA.

Traditional methods for producing 316L steel with a desired roughness and a uniform thin coating for sufficient bioactivity and long-term durability are highly difficult and require post-processing. Electric discharge machining (EDM) is a nontraditional process that can accomplish both machining and coating the surface concurrently. This study provides a thorough examination of the impacts of EDM process parameters on the machining responses, which are significantly necessary for processing 316L steel to reach its full potential. The nano-hydroxyapatite particles added EDM method is used to improve the machining and the surface responses. EDM process parametric optimization is conducted using Taguchi's design to achieve the lowest surface roughness of 2.04 µm and a recast layer thickness of 6.11 µm. As a machinability metric, the greatest material erosion rate (MER) of 20.64 mg/min is achieved. Current intensity, discharge period and hydroxyapatite amount are identified to be important predictors for MER, surface roughness and recast layer thickness. Scanning electron microscope with the energy dispersive X-ray spectrums and atomic force microscopic images validate the machined surface morphology, elemental compositions and surface topography. Errors of the confirmatory tests are less than 10%, showing that the projected solution sets by the grey wolf optimizer are highly accurate. The study shows a thin recast layer with both nano and micro-surface roughness formation, which have a great importance in biomedical applications. [ABSTRACT FROM AUTHOR]