UPPER GREAT LAKES TRANSPORTATION IMPACT FORECASTING SYSTEM

Community/Rural/Urban Development,

Measuring nitrate and nitrite concentrations in vegetables, fruits in Shiraz

Nitrosamine is derived from nitrate and it seems as one of the factors and causes of gastrointestinal cancer in adults and Methemoglobinemia (blue baby syndrome). Eighty percent of nitrate enters to the body through vegetables and fruits, so in this study nitrate concentration in available vegetables and fruits at Shiraz was determined and compared with standard limit. At first, Shiraz city was divided to several regions in geographical terms, then samples were purchased; these samples were used by citizens more during spring and winter, then samples were extracted. Nitrate reading was done using a spectrophotometer Palin test (Photometer 7100) and accuracy was measured by a conventional spectrophotometers. SAS and Excel were used to draw diagrams and statistical calculations. Statistical comparisons showed value of nitrate and nitrite in leafy vegetables is more than fruits and cucurbits. Average concentration of nitrate is more in potato and is low in onion among cucurbits. Among fruit and vegetables, highest concentration of nitrate is allocated to eggplant and lowest concentration of nitrate is allocated to tomato. Among leafy vegetables, highest concentration is allocated to mint and lowest concentration is allocated to savory. Generally, concentration of nitrate in all samples was lower than WHO limit. © JASE

Grain growth competition during melt pool solidification -- Comparing phase-field and cellular automaton models

A broad range of computational models have been proposed to predict microstructure development during solidification processing but they have seldom been compared to each other on a quantitative and systematic basis. In this paper, we compare phase-field (PF) and cellular automaton (CA) simulations of polycrystalline growth in a two-dimensional melt pool under conditions relevant to additive manufacturing (powder-bed fusion). We compare the resulting grain structures using local (point-by-point) measurements, as well as averaged grain orientation distributions over several simulations. We explore the effect of the CA spatial discretization level and that of the melt pool aspect ratio upon the selected grain texture. Our simulations show that detailed microscopic features related to transient growth conditions and solid-liquid interface stability (e.g. the initial planar growth stage prior to its cellular/dendritic destabilization, or the early elimination of unfavorably oriented grains due to neighbor grain sidebranching) can only be captured by PF simulations. The resulting disagreement between PF and CA predictions can only be addressed partially by a refinement of the CA grid. However, overall grain distributions averaged over the entire melt pools of several simulations seem to lead to a notably better agreement between PF and CA, with some variability with the melt pool shape and CA grid. While further research remains required, in particular to identify the appropriate selection of CA spatial discretization and its link to characteristic microstructural length scales, this research provides a useful step forward in this direction by comparing both methods quantitatively at process-relevant length and time scales

A comparison of the effect of single and multiple cavities on base flows

The paper represents a novel approach to understand the effect of single and multiple cavities on base pressure. We considered a control plate of 1 mm thick between a square nozzle of the cross-sectional area of 100 mm 2 and square duct of the cross-sectional area of 625 mm 2 . Both single and multiple cavities results are compared for a different level of expansion. The nozzle pressure ratio taken are 1.27, 1.33, 1.53 and 1.7. The high-speed compressible subsonic nozzle is being used with internal flow apparatus to achieve flows ranging between Mach 0.6 to Mach 0.9. The comparison between single and multiple cavities are shown graphically with and without control. The multiple cavities were found to be more effective as compared to a single cavity for controlling the base pressure

2-{(1E)-1-[(3-{(E)-[1-(2-Hy­droxy-4-meth­oxy­phen­yl)ethyl­idene]amino}-2,2-di­methyl­prop­yl)imino]­eth­yl}-5-meth­oxy­phenol

Mol­ecules of the title compound, C23H30N2O4, are located on a crystallographic mirror plane. The mol­ecule has a curved shape with the dihedral angle formed between the two benzene rings being 55.26 (5)°. Intra­molecular O—H⋯N hydrogen bonds are noted. In the crystal, supra­molecular layers are formed in the ac plane owing to the presence of C—H⋯π inter­actions

Jeans that fit : weighing the mass of the Milky Way analogues in the ΛCDM universe

The spherical Jeans equation is a widely used tool for dynamical study of gravitating systems in astronomy. Here, we test its efficacy in robustly weighing the mass of Milky Way analogues, given they need not be in equilibrium or even spherical. Utilizing Milky Way stellar haloes simulated in accordance with Λ cold dark matter (ΛCDM) cosmology by Bullock and Johnston and analysing them under the Jeans formalism, we recover the underlying mass distribution of the parent galaxy, within distance r/kpc ∈ [10, 100], with a bias of ∼ 12 per cent and a dispersion of ∼ 14 per cent. Additionally, the mass profiles of triaxial dark matter haloes taken from the surfs simulation, within scaled radius 0.2 < r/rmax < 3, are measured with a bias of ∼ − 2.4 per cent and a dispersion of ∼ 10 per cent. The obtained dispersion is not because of Poisson noise due to small particle numbers as it is twice the later. We interpret the dispersion to be due to the inherent nature of the ΛCDM haloes, for example being aspherical and out-of-equilibrium. Hence, the dispersion obtained for stellar haloes sets a limit of about 12 per cent (after adjusting for random uncertainty) on the accuracy with which the mass profiles of the Milky Way-like galaxies can be reconstructed using the spherical Jeans equation. This limit is independent of the quantity and quality of the observational data. The reason for a non-zero bias is not clear, hence its interpretation is not obvious at this stage.Publisher PDFPeer reviewe