Preparation of Mn2+-Cu+ co-doped P2O5–ZnO–Al2O3 glasses and their red fluorescence properties

To make a Mn²⁺-doped red glass phosphor that can be excited with ultraviolet (UV) light of light-emitting diodes (LEDs), 60P₂O₅-35ZnO-5Al₂O₃-8MnO-xCu₂O glasses (x = 0-1.00) were prepared by a melt-quenching method at 1200-1400°C for 30-180 minutes in atmospheric air, and the redox of Mn and Cu as well as fluorescence properties were investigated. The Mn²⁺ ion was not reduced and oxidized in the melting, quenching, and annealing processes. The valence of Cu in the glasses changed in the order of 0, 1+, and 2+ with the increase in the amount of Cu₂O and in the melting temperature and time. In this study, a 60P₂O₅-35ZnO-5Al₂O₃-8MnO-0.10Cu₂O glass melted at 1250°C for 90 minutes, having the highest Cu⁺ concentration, showed the strongest Mn²⁺ red fluorescence under the UV light at 275 nm. This strong Mn²⁺ red fluorescence has been caused by the energy transfer from excited Cu⁺ ions to Mn²⁺ ions.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.