Geochronological,geochemical,and Nd-Hf isotopic studies of the Qinling Complex,central China:Implications for the evolutionary history of the North Qinling Orogenic Belt

The Qinling Complex of central China is thought to be the oldest rock unit and the inner core of the North Qinling Orogenic Belt （NQOB）. Therefore, the Qinling Complex is the key to understanding the pre- Paleozoic evolution of the NQOB. The complex, which consists of metagraywackes and marbles, un- derwent regional amphibolite-facies metamorphism. In this study, we constrained the formation age of the Qinling Complex to the period between the late Mesoproterozoic and the early Neoproterozoic （ca. 1062-962 Ma）, rather than the Paleoproterozoic as previously thought. The LA-ICP-MS data show two major metamorphic ages （ca. 499 and ca. 420-400 Ma） for the Qinling Complex. The former age is consistent with the peak metamorphic age of the high- and ultra-high pressure （HP-UHP） rocks in the Qinling Complex, indicating that both the HP-UHP rocks and their country rocks experienced intensive regional metamorphism during the Ordovician. The latter age may constrain the time of partial melting in the NQOB between the late Silurian and early Devonian. The Qinling Complex is mostly affiliated with subduction-accretion processes along an active continental margin, and should contain detritus deposited in a forearc basin.

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.