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In this paper, we use provincial panel data on China for the 2002-19 period to conduct a spatial autocorrelation analysis of household saving rates as well as a dynamic panel analysis of the determinants of household saving rates using a spatial Durbin model. To summarize our main findings, we find that, in China, the household saving rate shows significant positive spatial autocorrelation with an overall “high-high” and “low-low” clustering pattern, that, as predicted by the life-cycle hypothesis, the youth dependency ratio and the old-age dependency ratio have a negative and significant impact on the household saving rate, and that the logarithm of per capita household disposable income, the regional economic growth rate, the share of the urban population, the industrialization rate, and the income disparity between urban and rural areas also have a significant impact on the household saving rate.

This systematic review aims to synthesize empirical studies investigating the relationship between father involvement and children’s school readiness in East Asia. Both “father involvement” and “school readiness” are multidimensional constructs and literature has been inconsistent in how both constructs should be measured. Due to the educational context in East Asia, “school readiness” has a different connotation in East Asia. This study plans to adopt an inclusive approach in defining father involvement and school readiness and aims to examine , in the context of East Asia, (1) the overall relationships between the two variables, (2) whether any components of father involvement are more strongly related to overall school readiness than other components, (3) whether father involvement is more strongly related to certain school readiness outcomes than others. Related individual, familial, extra-familial and cultural variables will be examined as moderators. Implications for future research, intervention and educational instructions will also be explored.

In this paper, we use provincial panel data on China for the 2002-19 period to conduct a spatial autocorrelation analysis of household saving rates as well as a dynamic panel analysis of the determinants of household saving rates using a spatial Durbin model. To summarize our main findings, we find that, in China, the household saving rate shows significant positive spatial autocorrelation with an overall "high-high" and "low-low" clustering pattern, that, as predicted by the life-cycle hypothesis, the youth dependency ratio and the old-age dependency ratio have a negative and significant impact on the household saving rate, and that the logarithm of per capita household disposable income, the regional economic growth rate, the share of the urban population, the industrialization rate, and the income disparity between urban and rural areas also have a significant impact on the household saving rate.

Both forgetting to unplug mobile phone chargers after using and generating large surge current when the charger is plugged into power, will accelerate the aging of high-voltage electronic components. It is also prone to failure and fire. In addition, the charger consumes 100~800 mW power when it is in standby mode for a long time. Therefore, a fireproof and energy saving mobile phone charger is proposed. This kind of charger can reduce the standby power consumption to 13 mW, so it is unnecessary to pull out the charger to meet the habit of the users. It can absorb the surge current, meanwhile, it also has the functions of timing charging, overcurrent and short circuit protection, so as to prevent the occurrence of fire.

In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR). This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real datasets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks., Comment: This paper is withdrawn by the authors for some errors

In order to compensate the threshold voltage variation while obtaining the sufficient data charging time, the AMOLED compensative pixel circuit needs to be driven by multi-pulse signals which have continuous high/low level pulses. By using traditional gate driver on array (GOA) with half duty-cycle delay of clock signals, the area of shift register is doubled, increasing the bezel width and power consumptions. In this paper, novel compact integrated gate driver circuit architectures for AMOLED pixel compensation purpose are presented. Outputs with programmable pulse width and number are achieved in these gate driving circuits without consuming extra large areas. Inner feedback technique is adopted in GOA circuit for leakage current cutting of depletion mode IGZO TFTs. The simulation result shows that these circuits work successfully with maximum ±3V threshold voltage shift. These GOA circuits can be used for internal or external Vth compensation techniques of AMOLED panel.

Glial cell line-derived neurotrophic factor (GDNF) is expressed at a high level in the human ovary and GDNF signaling is involved in the direct control of follicular activation and oocyte maturation. Transforming growth factor-β1 (TGF-β1) plays an important role in the regulation of various ovarian functions. Furin is an intracellular serine endopeptidase of the subtilisin family that is closely associated with the activation of multiple protein precursors. Despite the important roles of GDNF and TGF-β1 in the regulation of follicular development, whether TGF-β is able to regulate the expression and production of GDNF in human granulosa cells remains to be determined. The aim of this study was to investigate the effect of TGF-β1 on the production of GDNF and its underlying mechanisms in human granulosa-lutein (hGL) cells. We used two types of hGL cells (primary hGL cells and an established immortalized hGL cell line, SVOG cells) as study models. Our results show that TGF-β1 significantly induced the expression of GDNF and furin, which, in turn, increased the production of mature GDNF. Using a dual inhibition approach combining RNA interference and kinase inhibitors against cell signaling components, we showed that the TβRII type II receptor and ALK5 type I receptor are the principal receptors that mediated TGF-β1-induced cellular activity in hGL cells. Additionally, Sma- and Mad-related protein (SMAD)3 and SMAD4 are the downstream signaling transducers that mediate the biological response induced by TGF-β1. Furthermore, furin is the main proprotein convertase that induces the production of GDNF. These findings provide additional regulatory mechanisms by which an intrafollicular factor influences the production of another growth factor through a paracrine or autocrine interaction in hGL cells.