Hopping transport in the space-charge region of p-n structures with InGaN/GaN QWs as a source of excess 1/f noise and efficiency droop in LEDs

Semiconductors - It is shown that the emission efficiency and the 1/f noise level in light-emitting diodes with InGaN/GaN quantum wells correlate with how the differential resistance of a diode...     

Operational Safety of the Zagorsk PSP After Leveling of Its Powerhouse

Power Technology and Engineering - The work done on the test site to ensure the operational safety of the powerhouse of the Zagorsk PSP-2 after its leveling is described. The controlled...     

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.