The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST, also called the Guo Shou Jing Telescope) is a special reflecting Schmidt telescope. LAMOST's special design allows both a large aperture (effective aperture of 3.6 m-4.9 m) and a wide field of view (FOV) (5 • ). It has an innovative active reflecting Schmidt configuration which continuously changes the mirror's surface that adjusts during the observation process and combines thin deformable mirror active optics with segmented active optics. Its primary mirror (6.67 m×6.05 m) and active Schmidt mirror (5.74 m×4.40 m) are both segmented, and composed of 37 and 24 hexagonal sub-mirrors respectively. By using a parallel controllable fiber positioning technique, the focal surface of 1.75 m in diameter can accommodate 4000 optical fibers. Also, LAMOST has 16 spectrographs with 32 CCD cameras. LAMOST will be the telescope with the highest rate of spectral acquisition. As a national large scientific project, the LAMOST project was formally proposed in 1996, and approved by the Chinese government in 1997. The construction started in 2001, was completed in 2008 and passed the official acceptance in June 2009. The LAMOST pilot survey was started in October 2011 and the spectroscopic survey will launch in September 2012. Up to now, LAMOST has released more than 480 000 spectra of objects. LAMOST will make an important contribution to the study of the large-scale structure of the Universe, structure and evolution of the Galaxy, and cross-identification of multiwaveband properties in celestial objects.

The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of stars, galaxies and QSOs. Objects in both the pilot survey and the first year regular survey are included in the LAMOST DR1. The pilot survey started in October 2011 and ended in June 2012, and the data have been released to the public as the LAMOST Pilot Data Release in August 2012. The regular survey started in September 2012, and completed its first year of operation in June 2013. The LAMOST DR1 includes a total of 1202 plates containing 2 955 336 spectra, of which 1 790 879 spectra have observed signalto-noise ratio (SNR) ≥ 10. All data with SNR ≥ 2 are formally released as LAMOST DR1 under the LAMOST data policy. This data release contains a total of 2 204 696 spectra, of which 1 944 329 are stellar spectra, 12 082 are galaxy spectra and 5017 are quasars. The DR1 not only includes spectra, but also three stellar catalogs with measured parameters: late A,FGK-type stars with high quality spectra (1 061 918 entries), A-type stars (100 073 entries), and M-type stars (121 522 entries). This paper introduces the survey design, the observational and instrumental limitations, data reduction and analysis, and some caveats. A description of the FITS structure of spectral files and parameter catalogs is also provided.

No abstract

Climate change is conventionally recognised as a large-scale issue resolved through regional or national policy initiatives. However, little research has been done to directly evaluate local climate change action plans. This study examines 40 recently adopted local climate change action plans in the US and analyses how well they recognise the concepts of climate change and prepare for climate change mitigation and adaptation. The results indicate that local climate change action plans have a high level of 'awareness', moderate 'analysis capabilities' for climate change, and relatively limited 'action approaches' for climate change mitigation. The study also identifies specific factors influencing the quality of these local jurisdictional plans. Finally, it provides policy recommendations to improve planning for climate change at the local level.climate change, local jurisdiction, action plan, quality,

Floods, the most common natural hazard in the world, cause serious loss in terms of lives, buildings, and infrastructures. As a consequence, the need for flood risk assessment has become critical. Using a semi-quantitative model and fuzzy analytic hierarchy process (FAHP) weighting approach, this paper assessed flood risk in the Dongting Lake region, Hunan Province, Central China, an area where flood hazards frequently occur. The model was designed using spatial multi-criteria analysis (SMCA) techniques in a Geographic Information System (GIS). A GIS database of indicators for the evaluation of hazard and vulnerability was created. Each indicator was analyzed, standardized, and weighted; after which, the weights of the indicators were combined to obtain the final flood risk index map. Using the flood risk index, the study area was classified into five categories of flood risk: very low, low, medium, high, and very high. The high and very high risk zones are mainly concentrated in the northern and central plains. The results obtained can provide useful information for decision makers and insurance companies.