On the base of the 3D seismic profile interpretations, combined with the previous findings, geometry and tectonic evolution across and along the Laojunmiao break-thrust belt in the northern margin of the Northern Qilian belt are deciphered. The Laojunmiao belt is a bi-layer thrust system, consisting of a trishear faultpropagation fold system in the upper part, wedge-shaped thrust in the lower part. The Laojunmiao thrust system is linked with the NE-SW striking-slip 134 fault in the western segment, which forms a unified system of fracture on the Laojunmiao belt. Thrusting sheet above the hanging wall of the 134-Laojunmiao fault system is folded under nearly E-S compressive stress field, which results in the N-S striking folding to superpose on the Cenozoic bedding.

In order to compare the response of community biomass to nutrient additions among different grassland ecosystems, the authors established nutrient addition experiments on three grassland ecosystems along the climate gradient, including meadow steppe, typical steppe and desert steppe in Inner Mongolia. On the basis of the threeyear experiment, the impacts of different nutrients (N, P) on different nutrient gradient (N: 10, 5 and 2.5 g/m2; P: 10 g/m2) on above- and belowground biomass of these grassland ecosystems was analyzed. The results show that nitrogen leads to a significant increase in the aboveground biomass of grassland in Inner Mongolia by 27% to 53.3%, an average increase of 37.8%. In addition, the response of biomass to N addition increases with the N gradients. Belowground biomass has a less response to fertilization than aboveground biomass. There is an average of 10.2% increase in the total biomass of grassland community in response to N addition. Biomass responds to P addition less deeply than N addition. Furthermore, the response of biomass to nutrient addition varies among three grassland ecosystems. In arid desert steppe, community biomass is more sensitive to N limitation than the typical steppe and meadow steppe. N addition reduces belowground biomass and R/S ratio in meadow steppe and desert steppe, whereas promotes the values in typical steppe. In contrast, nutrient additions have relatively less and insignificant impacts on the root distribution of temperate grasses. These observations indicate that nutrient addition has different effects on community biomass under different environmental conditions, which means it is necessary to consider the changed response of different grassland ecosystems to nutrient addition in the carbon cycle models budget and grassland management.

This paper proposes a new learning algorithm for process neural networks (PNNs) based on particle swarm optimization (PSO), called PSO-LM. After the orthogonal basis function expansion to the input functions and the weight functions of the PNN, the structure parameters and other parameters in the PNN will be formed as a particle, and globally optimized by PSO. This algorithm does not need any gradient calculations or the manual control of the network?s structure. The global learning capability and the convergence capability of the PNN can be guaranteed by the capabilities of PSO, so the PSO-LM can better develop and improve the approximation capability of the PNN. According to two practical prediction applications, PSO-LM can outperform the existing basis function expansion based learning algorithm ( BFE-LM) for PNNs, and the classic back propagation neural networks (BPNNs) on predictive accuracy.