Visible luminescence mechanism in nano ZnO under weak confinement regime

We describe the structure of luminescence spectrum in the visible region in nano-ZnO in colloidal and thin film forms under weak confinement regime by modeling the transition from excited state energy levels of excitons to their ground state. Measurements on nanocrystallites indicate the presence of luminescence due to excitonic emissions when excited with 255 nm. The relevant energy levels showing the transitions corresponding to the observed peaks in the emission spectrum of ZnO of particle size 18 nm are identified.

Overtone spectra of styrene and polystyrene in the visible and near infrared regions

Vibrational overtone spectra of styrene (liquid) and polystyrene (solid), studied by the laser-induced thermal lens (for ΔV=6) and the conventional near infrared absorption (for ΔV=3–5) techniques, are reported. For polystyrene, the overtone energy-bond length correlation predicts that the aryl CH bonds are ∼0.0005 Å longer than that in benzene, while no such conclusions could be drawn on styrene. Thesp 3 CH overtones in polystyrene are observed on the low energy side of the aryl CH overtones.

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.