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The Huashandong W deposit (7.7 Mt @ 0.26% WO3) is a newly discovered Neoproterozoic scheelite-dominated deposit in the world-class Jiangnan tungsten belt, South China. It occurs largely in metasedimentary rocks near the western margin of the Neoproterozoic Jiuling batholith and mainly consists of W-bearing exogreisen, hydrothermal breccias, and quartz veins. Two episodes of Neoproterozoic granite magmatism are identified with zircon U-Pb ages of ca. 830–823 and 807–804 Ma, respectively. According to cross-cutting relationships and mineral assemblages, two periods of hydrothermal alteration and W mineralization, corresponding to the two episodes of granite magmatism, are recognized in the Huashandong deposit. The early period is dominated by greisenization and associated scheelite mineralization whereas the late period is characterized by sericitization, hydrothermal brecciation, quartz veining, and associated scheelite and minor wolframite mineralization. Molybdenite Re-Os dating indicates that the early- and late-period W mineralization took place at ca. 828 and 800 Ma, respectively. Zircon trace element characteristics suggest that the Jiuling granite crystallized from reduced granitic magmas and has the potential for W mineralization. The petrologic and geochemical features of the Jiuling granite, coupled with the zircon U-Pb ages and Hf isotopic signatures and molybdenite Re-Os ages, imply that the Huashandong W deposit is genetically related to two phases of evolved granodiorites in the Jiuling batholith. The magmatic-hydrothermal nature of both metallogenic events is confirmed by H-O isotopes on hydrothermal quartz. [ABSTRACT FROM AUTHOR]

Accurate and reliable monthly runoff forecasting plays an important role in making full use of water resources. In recent years, long short-term memory neural networks (LSTM), as a deep learning technology, has been successfully applied in forecasting monthly runoff. However, the hyperparameters of LSTM are predetermined, which has a significant influence on model performance. In this study, given that the decomposition of monthly runoff series may provide a more accurate prediction, as revealed by many previous studies, a hybrid model, namely, VMD-GWO-LSTM, is proposed for monthly runoff forecasting. The proposed hybrid model comprises two main components, namely, variational mode decomposition (VMD) coupled with the gray wolf optimizer (GWO)-based LSTM. First, VMD is utilized to decompose raw monthly runoff series into several subsequences. Second, GWO is implemented to optimize the hyperparameters of the LSTM for each subsequence on the condition that the inputs are determined. Finally, the total output of all subsequences is aggregated as the final forecast result. Four quantitative indices are employed to evaluate the model performance. The proposed model is demonstrated using 73 and 62 years of monthly runoff series data derived from the Xinfengjiang and Guangzhao Reservoirs in China's Pearl River system, respectively. To identify the feasibility and superiority of the proposed model, backpropagation neural networks (BPNN), support vector machine (SVM), LSTM, EMD-LSTM, VMD-LSTM and GWO-LSTM are also utilized for comparison. The results indicate that the proposed hybrid model can yield best forecast accuracy among these models, making it a promising new method for monthly runoff forecasting. [ABSTRACT FROM AUTHOR]

Based on a survey conducted after the reform of the residential registration system (hukou in Chinese), this paper explores housing tenure change among China's rural–urban migrants in a medium-size city. It emphasizes the long-term effect of their initial hukou status which encapsulates the conditions in their place of origin and the social capital these represent. Our study presents two major findings: One, the initial housing tenures of rural migrants differ because their tenure choice reflects their socio-demographic characteristics and their migration record; the latter expresses their moving history as well as their short- and long-term objectives. Two, the current tenure choice of rural migrants is path-dependent—once they choose the initial tenure, they will be more (or less) likely to choose a specific subsequent one, leading ultimately to the current tenure. The national reform of the hukou system is fundamental to the understanding of their housing careers. This reform made it possible to change the hukou obtained at birth to a local one, giving the migrant access to a wide range of facilities and social services. Homeownership became the usual key to unlock these benefits. Migrants who intend to settle permanently at their destination may therefore be expected to purchase a dwelling later in their housing career. Adopting a housing policy that sets migrants on the path to homeownership, allows a city to bind desirable migrants. That path appears to be conditioned by the early steps in the migration experience. Therefore, the local housing policy should be concerned with accommodating the housing needs of newly arriving migrants. [ABSTRACT FROM AUTHOR]

The theory of cooperation and competition, though developed in the West, may be useful to develop and analyze productive teamwork in China. Three hundred and sixty-eight employees from a call center participated in a cooperative teamwork 1-day workshop followed by daily practice. Results support the hypothesis that cooperative teams strengthen relationships, reduce turnover, and improve productivity of individual team members. During the 2-month period, team members increased the number of phones answered by nearly 40%, reduced customer complaints by over 55%, and lowered the turnover by over 20%, while the control call centers had either minor increases or decreases in these measures. These results support that the theory of cooperation and competition can guide the development of effective teams in China and that cooperative teams can improve the performance of individual employees. [ABSTRACT FROM AUTHOR]