ALBERT

Description: Fe3O4/CdWO4 and Fe3O4/CdWO4/PrVO4 magnetic nanoparticles were prepared at different molar ratios of PrVO4 to previous layers (Fe3O4/CdWO4) via the co-precipitation method assisted by a sonochemical procedure, in order to investigate the photocatalytic performance of these systems and their cytotoxicity properties. The physico-chemical properties of these magnetic nanoparticles were determined via several experimental methods: X-ray diffraction, energy dispersive X-ray spectroscopy, Fourier transformation infrared spectroscopy and ultraviolet-visible diffuse reflection spectroscopy, using a vibrating sample magnetometer and a scanning electron microscope. The average sizes of these nanoparticles were found to be in the range of 60–100 nm. The photocatalytic efficiency of the prepared nanostructures was measured by methylene blue degradation under visible light (assisted by H2O2). The magnetic nanosystem with a 1:2:1 ratio of three oxide components showed the best performance by the degradation of ca. 70% after 120 min of exposure to visible light irradiation. Afterwards, this sample was used for the photodegradation of methyl orange, methyl violet, fenitrothion, and rhodamine-B pollutants. Finally, the mechanism of the photocatalytic reaction was examined by releasing •OH under UV light in a system including terephthalic acid, as well as O2−, OH, and hole scavengers. Additionally, the cytotoxicity of each synthesized sample was assessed using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay against the human cell line PANC1 (cancer), and its IC50 was approximately 125 mg/L.

Description: Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.