We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

Please cite this article in press as: Saleem, M., et al., Geochemical speciation, anthropogenic contamination, risk assessment and source identification of selected metals in freshwater sediments-A case study from Mangla Lake, Pakistan. Environ. Nanotechnol. Monit. Manag. (2015), http://dx. a b s t r a c tGeochemical speciation of selected metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) using modified BCR sequential extraction procedure was performed in the sediments collected from Mangla Lake, Pakistan. The extraction method was optimized using certified reference material (BCR-701) which showed significant recoveries (95-106%). Among the metals, Cd, Co, Ni, Pb and Sr exhibited relatively higher mobility and bioavailability, while Cu, Fe, Mn and Zn were found mainly in the residual fractions. Principal component analysis (PCA) revealed four groups of the metals in the sediments; I (Cd-Cr-Ni-Sr), II (Co-Pb), III (Fe-Zn), and IV (Mn-Cu); first two groups were mostly contributed by anthropogenic sources. The pollution indicators (individual contamination factor, enrichment factor and geoaccumulation index) revealed significant anthropogenic contamination of Cd, Co, Ni, Pb, Sr and Zn in the sediments. Risk assessment code (RAC) indicated medium risk for Co and Ni; high risk for Cd and Pb; and very high risk for Sr in the sediments. Cluster analysis (CA) and global contamination factor (GCF) were used to identify the pollution hotspots, which indicated more severe metal contamination at sites near to Mirpur city and other urban/semi-urban areas (S3-S5), especially for Cd, Co, Ni, Pb and Sr.

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A series of eighteen 1,3,4-oxadiazole derivatives have been synthesized by treating aromatic acid hydrazides with carbon disulfide in ethanolic potassium hydroxide yielding potassium salts of 1,3,4-oxadiazoles. Upon neutralization with 1 N hydrochloric acid yielded crude crystals of 1,3,4-oxadiazoles, which were purified by recrystallization in boiling methanol. The synthesized 1,3,4-oxadiazoles derivatives were evaluated in vitro for their urease inhibitory activities, most of the investigated compounds were potent inhibitors of Jack bean urease. The molecular docking studies were performed by docking them into the crystal structure of Jack bean urease to observe the mode of interaction of synthesized compounds. The synthesized compounds were also tested for antibacterial and antioxidant activities and some derivatives exhibited very promising results.

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