EVN detection of the newly-discovered black hold candidate MAXI J1836-194

The X-ray transient MAXI J1836-194 is most likely a Galactic stellar-mass black hole. It has been shown to harden in the X-rays and brighten in the infrared. Here, we report on the detection of MAXI J1836-194 at 5 GHz with the European VLBI Network, in real-time e-VLBI observations on 2011 October 17. The transient source was detected with a flux density of 5.4 +- 0.3 mJy at RA 18h35m43.44555s, Dec. -19d19'10.4921" (J2000, 1 sigma formal uncertainty ~0.5 mas, note that the systematic error may be much larger due to the low elevation.)

Utility greedy discrete bit loading for interference limited multi-cell OFDM system

In this contribution we present the solution of the utility greedy discrete bit loading for interference limited multicell OFDM networks. Setting the utility as the sum of consumed power proportions, the algorithm follows greedy way to achieve the maximum throughput of the system. Simulation has shown that the proposed algorithm has better performance and lower complexity than the traditional optimal algorithm. The discussion of the results is provided

1,3-Dioxo-2,3-dihydro-1H-isoindol-2-yl 2,3,4-tri-O-acetyl-β-d-xyloside

The title compound, C19H19NO10, was obtained from the reaction of α-d-1-bromo-2,3,4-tri-O-acetylxylose with N-hy­droxy­phthalimide in the presence of potassium carbonate. The asymmetric unit contains two independent mol­ecules, in which the O—CH—O—N torsion angles are 73.0 (4) and 65.0 (4)°. The hexa­pyranosyl rings adopt chair conformations and the substituent groups are in equatorial positions. In the crystal, mol­ecules are linked by nonclassical C—H⋯O hydrogen bonds

Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV power generation is constructed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.Department of Electrical Engineerin