LOFAR, the LOw-Frequency ARray, is a new-generation radio interferometer constructed in the north of the Netherlands and across europe. Utilizing a novel phased-array design, LOFAR covers the largely unexplored low-frequency range from 10-240 MHz and provides a number of unique observing capabilities. Spreading out from a core located near the village of Exloo in the northeast of the Netherlands, a total of 40 LOFAR stations are nearing completion. A further five stations have been deployed throughout Germany, and one station has been built in each of France, Sweden, and the UK. Digital beam-forming techniques make the LOFAR system agile and allow for rapid repointing of the telescope as well as the potential for multiple simultaneous observations. With its dense core array and long interferometric baselines, LOFAR achieves unparalleled sensitivity and angular resolution in the low-frequency radio regime. The LOFAR facilities are jointly operated by the International LOFAR Telescope (ILT) foundation, as an observatory open to the global astronomical community. LOFAR is one of the first radio observatories to feature automated processing pipelines to deliver fully calibrated science products to its user community. LOFAR's new capabilities, techniques and modus operandi make it an important pathfinder for the Square Kilometre Array (SKA). We give an overview of the LOFAR instrument, its major hardware and software components, and the core science objectives that have driven its design. In addition, we present a selection of new results from the commissioning phase of this new radio observatory.

Spitzer Space Telescope and Herschel Space Observatory imaging of M31 is used, with a physical dust model, to construct maps of dust surface density, dust-to-gas ratio, starlight heating intensity, and polycyclic aromatic hydrocarbon (PAH) abundance, out to R ≈ 25 kpc. The global dust mass is M d = 5.4 × 10 7 M , the global dust/H mass ratio is M d /M H = 0.0081, and the global PAH abundance is q PAH = 0.039. The dust surface density has an inner ring at R = 5.6 kpc, a maximum at R = 11.2 kpc, and an outer ring at R ≈ 15.1 kpc. The dust/gas ratio varies from M d /M H ≈ 0.026 at the center to ∼0.0027 at R ≈ 25 kpc. From the dust/gas ratio, we estimate the interstellar mediu (ISM) metallicity to vary by a factor ∼10, from Z/Z ≈ 3 at R = 0 to ∼ 0.3 at R = 25 kpc. The dust heating rate parameter U peaks at the center, with U ≈ 35, declining to U ≈ 0.25 at R = 20 kpc. Within the central kiloparsec, the starlight heating intensity inferred from the dust modeling is close to what is estimated from the stars in the bulge. The PAH abundance reaches a peak q PAH ≈ 0.045 at R ≈ 11.2 kpc. When allowance is made for the different spectrum of the bulge stars, q PAH for the dust in the central kiloparsec is similar to the overall value of q PAH in the disk. The silicate-graphite-PAH dust model used here is generally able to reproduce the observed dust spectral energy distribution across M31, but overpredicts 500µm emission at R ≈ 2-6 kpc, suggesting that at R = 2-6 kpc, the dust opacity varies more steeply with frequency (with β ≈ 2.3 between 200 and 600µm) than in the model. Subtraction of foreground and background emission has been carried out following methods described in Aniano et al. (2012), with automatic identification of background pixels and fitting of a "tilted plane" background model (with three parameters -zero point, tilt, and tilt orientation) for 3 IRAC images in bands 1-4 were multiplied by extended source calibration factors 0.91, 0.94, 0.66, 0.74 (Reach et al. 2005). 4 MIPS images were generated by the Mips enhancer v3.10 pipeline on 2007 Jul 3. 5 The PACS and SPIRE images were processed by HIPE v9, and the Level 1 HIPE images were then processed by Scanamorphos v18.0 (Roussel 2013). We used the calibration files in HIPE v9 (version 42 for PACS, and version 10.0 for SPIRE). Intensities in the SPIRE bands were obtained by dividing the HIPE v9 flux density per beam by effective beam solid angles Ω = (1.103, 1.944, 4.183) × 10 −8 sr for SPIRE250, 350, and 500, as recommended by Griffin et al. (2013).

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A variety of global kinetic models are reviewed, including first-order, nth-order, nucleation, and sequential models as well as models having Gaussian, Weibull, and discrete activation-energy distributions. The important characteristics of these various models are outlined, with guidance in how to select the correct model. Some of the models have similar characteristics, and the parameter relationships among similar models are discussed. The comparison includes the relationship between conversion-dependent parameters determined by modified Friedman and Coats−Redfern isoconversion methods and reactivity distribution parameters determined by nonlinear regression of rate or fraction-reacted profiles. A new method for deriving discrete activation-energy distribution parameters having ln(A) = a + bE is also presented. Data accuracy requirements are discussed briefly. Kinetic analyses are given for a variety of materials, including synthetic polymers (polyethylene, polystyrene, polydimethylenenaphthalene, polysulfone, and polyvinyl acetate), petroleum sources rocks (including well-preserved algal kerogens and the Bakken and Monterey shales), oil shales (including kukersite), and the Illinois and Pittsburgh Premium coal samples.