Two new 1D chains of Ni(2)Na(2) heterometallic double half-cubane building units: Synthesis, structures and variable temperature magnetic study

An equimolar mixture of Ni(NO(3))(2)center dot 6H(2)O and pyridine-2-aldehyde with two equivalents of NaN(3) in methanol in the presence of NaOMe resulted in the formation of light green precipitate which upon crystallization from dimethylformamide (DMF) yielded light green single crystals [{Ni(2)Na(2)(pic)(4)(N(3))(2)(H(2)O)(2)(MeOH)}center dot MeOH center dot 3H(2)O](n) (1) and [{Ni(2)Na(2)(pic)(4)(N(3))(2)(H(2)O)(4)}center dot 2DMF center dot H(2)O](n) (2) (pic = pyridine-2-carboxylate) at room temperature and high temperature (100 degrees C), respectively. Variable temperature magnetic studies revealed the existence of overall ferromagnetic behaviour with J approximate to + 10 cm(-1) and D approximate to -2 to -7 cm(-1) for 1 and 2, respectively. Negative D values as well as variation of D upon slight distortion of structure by varying reaction temperature were observed. The X-band Electron Paramagnetic Resonance (EPR) spectra of both 2 and 3 were recorded below 50 K. The structural distortion was also implicated from the EPR spectra. Density Functional Theory (DFT) calculations on both complexes were performed in two different ways to corroborate the magnetic results. Considering only Ni(2)(II) dimeric unit, results were J = + 20.65 cm(-1) and D = -3.16 cm(-1) for 1, and J = +24.56 cm(-1) and D = -4.67 cm(-1) for 2. However, considering Ni(2)(II)Na(2)(I) cubane as magnetic core the results were J = +16.35 cm(-1) (1), +19.54 cm(-1) (2); D = -3.05 cm(-1) (1), -4.25 cm(-1) (2).

Reinforcement learning controllers for automatic generation control in power systems having re-heat units with GRC and dead-band

A new automatic generation controller (AGC) design approach, adopting reinforcement learning (RL) techniques, was recently pro- posed [1]. In this paper we demonstrate the design and performance of controllers based on this RL approach for automatic generation control of systems consisting of units having complex dynamics—the reheat type of thermal units. For such systems, we also assess the capabilities of RL approach in handling realistic system features such as network changes, parameter variations, generation rate constraint (GRC), and governor deadband.