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The aim of this study was to develop a transdermal gel formulation of Nigella Sativa L. seed extract using phytosomes for the management of acne vulgaris. The optimized formulation was prepared and evaluated for various parameters. Nigella Sativa-loaded phytosomes were created using the rotary vacuum evaporator method by mixing Nigella Sativa L. extract and lecithin in a 1:1 ratio, with a stirring speed ranging from 25 to 150 rpm. Design Expert software was employed for optimization, utilizing a central composite factorial design. Spreadability and viscosity were selected as dependent variables, while Carbopol 934P and PEG 400 were independent variables. The optimized gel formulation was further characterized through in vitro skin permeation studies, skin irritation tests, antimicrobial assays and stability studies. The Nigella Sativa-loaded phytosomal formulation had a particle size of 202.5 nm with a zeta potential of -2.43. Scanning electron microscopy revealed a uniform structure, and confocal laser microscopy demonstrated enhanced penetration of the phytosomes into deeper skin layers. MTT assay results confirmed that the optimized gel is non-irritant to the skin. Antibacterial studies showed that the Nigella Sativa phytosomal gel effectively reduced the growth of Propionibacterium acnes, an acne-causing bacterium, in both MIC and MBC tests. [ABSTRACT FROM AUTHOR]

The accurate estimation of the power number for closed clearance impellers holds significant importance in industries such as chemical, biochemical, paper and pulp, as well as paints, pigments, and polymers. Existing state‐of‐the‐art correlations for predicting power numbers, however, are inaccurate for impeller Reynolds number ReI>100. In this study, we compiled a dataset of 1470 data points from 15 research articles in the open literature, covering five types of impellers: (i) anchor; (ii) gate; (iii) single helical ribbon; (iv) double helical ribbon; and (v) helical ribbon with screw. Six machine learning models, namely artificial neural networks (ANN), CatBoost regressor, extra tree regressor, support vector regressor, random forest, and XGBoost regressor, were developed and compared. The results revealed that ANN emerged as the most efficient model, demonstrating the highest testing R2 value of 0.99 and the lowest testing MAPE of 7.3%. Further, we used the ANN model to develop a novel set of process correlations to estimate impeller power numbers for the industrially important anchor and double helical ribbon impellers: which significantly outperform the existing state‐of‐the‐art correlations available in literature. [ABSTRACT FROM AUTHOR]

We model three factors that affect effectiveness of trade sanctions: a country's endowment, distance between countries, and network connectivity. Our model explains several empirical observations: (i) sanctions impose costs on both sender and target; (ii) sanctions are often unsuccessful; and (iii) import sanctions, and export plus import sanctions, are more effective than export sanctions alone. We also offer extensions of our benchmark including retaliation by target, incentives of the third country to participate in multilateral sanctions or sanction‐busting, and the consequences of different centralities of sender and target in a trade network. [ABSTRACT FROM AUTHOR]