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Mainland Chinese grow up in a nation with Confucianism, Daoism and Buddhism as their cultural heritage, and are educated with atheism, materialism, and scientism in contemporary China. However, the high rate of conversion to Christianity among Chinese immigrants in Anglo-Saxon countries constitutes a distinctive feature in studies of migration. This paper aims to investigate the reasons for becoming Christian and the development of spirituality of a group of first-generation Chinese Australians from mainland China. All the seven participants are highly educated women who migrated to Australia as adults and had young children at the time of conversion. Data were collected mainly through open-ended in-depth interviews, and triangulated with private conversations, observations, and WeChat messaging. This ethnographic qualitative research found that these immigrants' Christian attempts were prominently triggered by settlement crisis as new immigrants and as immigrant parents. They see Christianity and church community as a strong vehicle to resolve integration difficulties in a new society, such as economic and career insecurities, social isolation, language barriers, marital crises, and parenting dilemmas. Their Christian movement is facilitated by identified ideological congruence but hindered by cultural conflicts between their newly acquired Christian doctrines and their previously instructed values. The findings have implications for immigrant families, secular institutions, and religious organizations, as to the psychosocial well-being of new migrants. [ABSTRACT FROM AUTHOR]

Small Shelly Fossils (SSFs) from the Cambrian are widely distributed and well known across different paleocontinents of the world. However, middle Cambrian SSFs from North China Platform have only rarely been documented until now. In this paper, we presented the first report on SSFs from bioclastic and oolitic limestones of the Zhangxia and Hsuzhuang formations of Henan province, North China. The carbonate-hosted fauna includes brachiopods (Micromitra sp., M. modesta, Eoobolus sp., and Schizopholis sp.), helcionellids (Oelandiellaaccordionata and O. aliciae), hyolithids, Hyolithellus sp., Chancelloriaeros, sponge spicules, echinoderm ossicles, and chancelloriid sclerites. In terms of preservation, the brachiopod shell valves of M. modesta appeared to be homogeneous, consisting of tightly packed phosphate grains. Eoobolus sp. is composed of primary layer and secondary baculate, both of which consist of tightly compacted phosphate grains. Schizopholis sp. has multiple-lamellar phosphatized microstructures that distinctly differ from the other brachiopods recovered from the Longwanggou section. A similar multiple-lamellar microstructure was also revealed in conchs of Hyolithellus, with tightly compacted phosphate grains. The argillaceous shell of Oelandiellaaccordionata and O. aliciae, and the calcitic inner molds of hyolith did not preserve any shell structure. The helcionellids O. accordionata and O. aliciae and the brachiopod M. modesta were reported for the first time from North China. The fauna is most similar to the middle Cambrian faunas of Australia, in the brachiopod and mollusk components; it is also similar in composition of brachiopods and mollusks to coeval faunas from South China. The new fauna of SSFs in the Yiyang Longwanggou Section indicated that the Hsuzhuang and Zhangxia formations are late Drumian to middle Guzhuangian in age, most likely correlating with the Murrawong Creek Formation of Australia. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them. [ABSTRACT FROM AUTHOR]