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Improvements to air mass calculations for ground-based infrared measurements

High-resolution ground-based infrared solar spectra are routinely recorded at the Network for the Detection of Stratospheric Change (NDSC) stations. These data sets play a key role in providing a long-term record of atmospheric composition and their links to climate change. The analysis of observed infrared spectra involves comparison to a computer-modeled atmosphere where knowledge of the air mass distribution is an essential component. This note summarises improvements made to an existing and widely used computer code (FSCATM) to perform refractive ray-tracing and calculation of the air mass distribution. Changes were made towards higher vertical resolution in the troposphere and increased numerical precision. The revised FSCATM improves the analysis of infrared spectra mostly through the more accurate representation of the temperature profile. Air mass differences with respect to earlier versions are documented and are typically <0.7%, exceptions being extreme cases of inversion layers. The current version provides ray tracing and air mass calculations for any terrestrial observation site. The output files are reported in a format compatible with the SFIT and SFIT2 retrieval algorithms, which are widely used for NDSC infrared atmospheric studies. The improved computer code, documentation, reference profiles, and test cases are available electronically.     

Potential Benefits and Barriers to Integration

The rhetoric surrounding integration of mathematics and science abounds. Professional organizations’ standards and recommendations for reform in mathematics and science education each point out the need to make connections among various disciplines. However, some remain unconvinced, citing a lack of research supporting the assertion that integration improves student achievement. This article examines the current situation, discusses the growing body of related research, and examines the implementation issues related to integrated curriculum projects. The conclusion calls for mathematics and science educators to work collaboratively to address implementation issues surrounding reform of any kind and to explore further the possibilities of integration.     

Ultrafiltration of a polymer-electrolyte mixture

We present a mathematical model to describe the ultrafiltration behaviour of polymer-electrolyte mixtures. The model combines the proper thermodynamic forces (pressure, chemical potential and electrical potential differences) with multicomponent diffusion theory. The model is verified with experimental data on the ultrafiltration of aqueous solutions of PEG-4000 and potassium phosphate. The single solute rejection of PEG-4000 goes through a maximum as also found by others. The single solute rejection of potassium phosphate depends on the ionic strength of the solution. At low ionic strength rejections are found of 50%. Solutions containing a high concentration of PEG-4000 and potassium phosphate show a negative rejection for potassium phosphate. This is caused by the strongly non-ideal behaviour of these aqueous solutions. The model predicts the behaviour of single solute experiments quite well, but some deviations are found with the mixed solute experiments. However, negative rejections found in the mixed solute experiments are predicted by the model.     

Finding nonnormal bent functions

The question if there exist nonnormal bent functions was an open question for several years. A Boolean function in n variables is called normal if there exists an affine subspace of dimension n/2 on which the function is constant. In this paper we give the first nonnormal bent function and even an example for a nonweakly normal bent function. These examples belong to a class of bent functions found in [J.F. Dillon, H. Dobbertin, New cyclic difference sets with Singer parameters, in: Finite Fields and Applications, to appear], namely the Kasami functions. We furthermore give a construction which extends these examples to higher dimensions. Additionally, we present a very efficient algorithm that was used to verify the nonnormality of these functions.