Isolation, spectroscopic characterization, X-ray, theoretical studies as well as in vitro cytotoxicity of Samarcandin

Samarcandin 1, a natural sesquiterpene-coumarin, was isolated as well as elucidated from F. assa-foetida which has significant effect in Iranian traditional medicine because of its medicinal attitudes. The crystal structure of samarcandin was determined by single-crystal X-ray structure analysis. It is orthorhombic, with unit cell parameters a = 10.8204 (5) Å, b = 12.9894 (7) Å, c = 15.2467 (9) Å, V = 2142.9 (2) Å3, space group P212121 and four symmetry equivalent molecules in the unit cell. Samarcandin was isolated in order to study for its theoretical studies as well as its cellular toxicity as anti-cancer drug against two cancerous cells. In comparison with controls, our microscopic and MTT assay data showed that samarcandin suppresses cancer cell proliferation in a dose-dependent manner with IC50 = 11 μM and 13 for AGS and WEHI-164 cell lines, respectively. Density functional theory (DFT) and time-dependent density functional theory (TD-DFT) of the structure was computed by three functional methods and 6-311++G∗∗ standard basis set. The optimized molecular geometry and theoretical analysis agree closely to that obtained from the single crystal X-ray crystallography. To sum up, the good correlations between experimental and theoretical studies by UV, NMR, and IR spectra were found. © 2016 Elsevier Inc. All rights reserved

Mixed Integer Neural Inverse Design

In computational design and fabrication, neural networks are becoming important surrogates for bulky forward simulations. A long-standing, intertwined question is that of inverse design: how to compute a design that satisfies a desired target performance? Here, we show that the piecewise linear property, very common in everyday neural networks, allows for an inverse design formulation based on mixed-integer linear programming. Our mixed-integer inverse design uncovers globally optimal or near optimal solutions in a principled manner. Furthermore, our method significantly facilitates emerging, but challenging, combinatorial inverse design tasks, such as material selection. For problems where finding the optimal solution is not desirable or tractable, we develop an efficient yet near-optimal hybrid optimization. Eventually, our method is able to find solutions provably robust to possible fabrication perturbations among multiple designs with similar performances

Mixed Integer Neural Inverse Design

In computational design and fabrication, neural networks are becoming important surrogates for bulky forward simulations. A long-standing, intertwined question is that of inverse design: how to compute a design that satisfies a desired target performance? Here, we show that the piecewise linear property, very common in everyday neural networks, allows for an inverse design formulation based on mixed-integer linear programming. Our mixed-integer inverse design uncovers globally optimal or near optimal solutions in a principled manner. Furthermore, our method significantly facilitates emerging, but challenging, combinatorial inverse design tasks, such as material selection. For problems where finding the optimal solution is not desirable or tractable, we develop an efficient yet near-optimal hybrid optimization. Eventually, our method is able to find solutions provably robust to possible fabrication perturbations among multiple designs with similar performances

Gas recognition based on the physicochemical parameters determined by monitoring diffusion rates in microfluidic channels

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.Monitoring the diffusion progress rates of different gases in a microfluidic channel affords their discrimination by the comparison of their temporal profiles in a high-dimensional feature space. Here, we demonstrate gas recognition by determination of their three important physicochemical parameters via a model-based examination of the experimentally determined diffusion rates in two different cross-section channels. The system utilized comprises two channels with respective cross-sectional diameters of 1000 μm and 50 μm. The open end of both channels are simultaneously exposed to the analyte, and the temporal profiles of the diffusion rates are recorded by continuous resistance measurements on the chemoresistive sensors spliced to the channels at their other ends. Fitting the solutions of the diffusion equation to the experimental profiles obtained from the large cross-section channel results in the diffusivity of the analyte. The results of small cross-section channel, however, fit the solutions of a modified diffusion equation which accounts for the adsorption of the analyte molecules to the channel walls, as well. The latter fitting process results in the adsorption parameter for the analyte-channel wall interactions and the population of the effective adsorption sites on the unit area of the walls. The allocation of these three meaningful parameters to an unknown gaseous analyte affords its recognition

Equivalence relations in semihypergroups and the corresponding quotient structures

AbstractIn this paper, we introduce and study two equivalence relations in semihypergroups, for which the corresponding quotient structures are monoids and commutative monoids

Sliver® modules - a crystalline silicon technology of the future

A new technique has been devised for the manufacture of thin (<60µm) highly efficient single crystalline solar cells. Novel methods of encapsulating these Sliver® solar cells have also been devised. Narrow grooves are formed through a 1-2mm thick wafer. Device processing (diffusion, oxidation, deposition) is performed on the wafer, so that each of the narrow strips becomes a solar cell. The strips are then detached from the wafer and laid on their sides, which greatly increases the surface area of solar cell that can be obtained from the wafer. Further gains of a factor of two can be obtained by utilising a simple method of static concentration. Large decreases in processing effort (up to 30-fold) and silicon usage (up to 10-fold) per m2 of module are possible. The size, thickness and bifacial nature of the cells create the opportunity for a wide variety of module architectures and applications