January to August temperature variability since 1776 inferred from tree-ring width of Pinus tabulaeformis in Helan Mountain

IEECAS SKLLQG

Tree-ring-derived precipitation records from inner Mongolia；China；since a.d. 1627

IEECAS SKLLQG

Tree-ringbased drought reconstruction (a.d. 1855-2001) for the Qilian mountains；northwestern China

IEECAS SKLLQG

Seasonal precipitation in the south-central Helan Mountain region, China, reconstructed from tree-ring width for the past 224 years

IEECAS SKLLQG

Magnets for HIRFL-CSR rings

The magnet design, fabrication, and measurement of HIRFL-CSR (Heavy Ion Research Facility in Lanzhou Cooling Storage Ring) are presented. All magnets will be laminated And welded with an armor-coated surface between two big endplates made of sticking glue 0.5 mm-thick sheets. The dipole of CSRm was chosen an H type with an air circle on the pole to improve the field uniformity. The dipole of CSRe was chosen the C type with an air circle and two air slots on the pole to improve the field homogeneity. Its reproducibility of magnet to magnet was adjusted with inserting small laminating pieces before demountable pole ends to reach less than +/- 2 x 10(-4) at optimized field level. CSRm quadrupoles diameter is 170 mm and has two different lengths, and its endplates were made with punching pieces after coating with epoxy glue, there is chamfered directly on the pole ends to reduce 12th-order contribution of field and without the demountable pole ends. CSRe main quadrupoles diameter is 240 mm and has two different lengths, and its endplates were also made with punching pieces coated with epoxy glue, there is also chamfered directly on the pole ends to reduce 12th-order contribution of field like CSRm.

DIRECT MASS MEASUREMENTS OF EXOTIC NUCLEI IN STORAGE RINGS

Mass measurements of exotic nuclei is a fast, developing field which is essential for basic nuclear physics and a wide range of applications. The method of storage ring mass spectrometry has many advantages: (1) a large amount of nuclides can be simultaneously measured; (2) very short-lived (T-1/2 greater than or similar to 50 mu s) and very rare species (yields down to single ions) can be accessed; (3) nuclides in several atomic charge states can be investigated, (4) half-life measurements can be performed with time-resolved mass spectrometry. In this contribution we concentrate on some recent achievements and future perspectives of the storage ring mass spectrometry.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.