Quantitation of agmatine by liquid chromatography with laser-induced fluorescence detection

A high performance liquid chromatography (HPLC) method is described for the determination of agmatine, an endogenous neuromodulator. The method involves pre-column derivatization of the sample with a fluorescent tagging reagent, 7-fluoro-4-nitrobenzoxadiazole (NBD-F). The resulting agmatine derivative is stable and can be readily extracted into ethyl acetate at pH 8.5. The extraction enhances the quantification of low level agmatine because it eliminates chromatographic peaks caused by endogenous amino acids. The HPLC separation is carried out on a C₈ reversed phase column and completed in less than 10 min. With laser-induced fluorescence (LIF) detection, the detection limit is 5×10⁻⁹ M agmatine. Method precision (coefficient of variation) is 5% for agmatine in human plasma at the sub-μM level. This method has been validated by determination of agmatine in biological samples including human plasma and rat brain and stomach tissues.     

Enhanced ethanol sensing properties of WO3 modified TiO2 nanorods

Pristine and WO₃ decorated TiO₂ nanorods (NRs) were synthesised to investigate n-n-type heterojunction gas sensing properties. TiO₂ NRs were fabricated via hydrothermal method on fluorine-doped tin oxide coated glass (FTO) substrates. Then, tungsten was sputtered on the TiO₂ NRs and thermally oxidised to obtain WO₃ nanoparticles. The heterostructure was characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) spectroscopy. Fabricated sensor devices were exposed to VOCs such as toluene, xylene, acetone and ethanol, and humidity at different operation temperatures. Experimental results demonstrated that the heterostructure has better sensor response toward ethanol at 200 °C. Enhanced sensing properties are attributed to the heterojunction formation by decorating TiO₂ NRs with WO₃.     

Scheduling programs with repetitive projects: A comparison of a simulated annealing,a genetic and a pair-wise swap algorithm

We address the problem of scheduling in programs involving the production of multiple units of the same product. Our study was motivated by a construction program for fast naval patrol boats. Other applications of this problem include procurement of multiple copies of aircraft, spacecraft, and weapon systems. In this problem we must decide how many units of the product to assign to each of a number of available crews (individuals, teams, subcontractors, etc.). These types of problems are characterized by two potentially conflicting considerations: 1) the need to complete each unit by its contractual due date, and 2) learning effects. Because of the first consideration, there is a tendency to use multiple crews for simultaneous production, so that meeting due dates is assured. However, the second consideration encourages assigning many units to a single crew so that learning effects are maximized. We study this scheduling problem with two different penalty cost structures and develop models for both versions. The models trade-off the penalty associated with late deliveries and the savings due to learning (and possibly incentive payments for early completion). We discuss different heuristic algorithms — simulated annealing, a genetic algorithm, and a pair-wise swap heuristic — as well as an exhaustive search to determine a baseline for comparisons. Our computational results show that the pair-wise swap algorithm is the most efficient solution procedure for these models.