The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ( ∆ T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine hours, average daily minimum and maximum temperatures, and cloudiness have been used. The accuracy of the models has been tested by statistical indicators that include mean percentage error (MPE), coefficient of determination (R 2 ), mean absolute relative error (MARE), mean bias error (MBE), and root mean square error (RMSE). The error analysis revealed that the proposed models are suitable for the estimation of the clearness index. It is also concluded that multiple regression models give better estimates of clearness index for all the stations (0.80 ≤ R 2 ≤ 0.86) compared to single parameter model and therefore are recommended. The study indicated that clear sky conditions prevail throughout the months at all the investigated sites (0.58 ≤ K T ≤ 0.68), which is a good indicator for solar energy utilization.The statistical indicators also suggest that multilinear regression model M-3 gives a better representation of the climate system and using three parameters reduces the uncertainties in the developed model.

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Background: Given the crucial role of gut microbiota in animal and human health, studies on modulating the intestinal microbiome for therapeutic purposes have absorbed significant attention, of which the role of fecal microbiota transplantation (FMT) has been emphasized. Methods: In the current study, we evaluated the effect of FMT on Escherichia coli (E.coli) infected mice from the perspective of analysis of body weight loss, mortality, intestinal histopathology and immunohistochemistry, and the gut microbiome. Results: Results showed that FMT effectively decreased weight loss and mortality in infected mice to a certain extent, relieving the damaged structure of the intestinal villi driven by infection. Furthermore, the abundance of bacteria health-threatening, such as phylum Proteobacteria, family Enterobacteriaceae, Tannerellaceae, genus Escherichia-Shigella, Sphingomonas, Collinsella etc., were significantly increased, whereas those of beneficial bacteria (phylum Firmicutes, family Lactobacillaceae, genus Lactobacillus) were decreased in gut of infected mice. Moreover, we sought to investigate if the amelioration of clinical symptoms with FMT treatment in infected mice was associated with modulation in disordered gut microbiota. According to beta diversity, the microbial community results reflected the similarities between non-infected and FMT mice’s gut microbiota. The improvement of the intestinal microbiota following FMT was characterized by the significantly increased beneficial microorganisms and the synergistic decrease of Escherichia-Shigella, Acinetobacter, etc. Conclusion: These findings suggest a beneficial host-microbiome correlation might be built following FMT to relieve gut infections and pathogens-associated diseases.

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