External Shocks, Household Consumption and Fertility in Indonesia

This paper examines the impact of idiosyncratic income shocks on household consumption, educational expenditure and fertility in Indonesia, and assesses whether the investment in human capital of children and fertility are used to smooth household consumption. Using six different kinds of self-reported economic hardships, our findings indicate that coping mechanisms are rather efficient for Indonesian households that perceive an economic hardship. Only in case of unemployment we find a significant decrease in consumption spending and educational expenditure while fertility increases. Theses results indicate that households that perceive an unemployment shock use children as a means for smoothing consumption. Regarding the death of a household member or natural disaster we find that consumption even increases. These results are consistent with the argument that coping mechanisms even over-compensate the actual consumption loss due to an economic hardship. One important lesson from our findings is that different types of income shock may lead to different economic and demographic behavioral adjustments and therefore require specific targeted social insurance programs.Consumption, Insurance, Fertility and Indonesia

The Environmental Consequences of Economic Growth Revisited

Although numerous studies on the economic growth-environment nexus exist, relatively little attention has been paid to model the effect of income on the environment, controlling for other relevant factors. The primary contribution of this paper is to examine the environmental consequences of economic growth for developed and developing countries in a dynamic cointegration framework by incorporating energy consumption and foreign direct investment (FDI). For this purpose, an autoregressive distributed lag (ARDL) approach to cointegration is applied to annual data for the period 1971-2005. Results show that economic growth improves environmental quality for developed countries in the long-run, but worsen the environment in developing economies. We also find that energy consumption has a detrimental long-run effect on environmental quality for both developed and developing countries. FDI, however, is found to have little long-run effect on the environment in both developed and developing countries. Finally, it is found that, in the short-run, income and energy play key roles in affecting the environment in developed and developing countries, but FDI does not.

Fabrication of three-dimensional suspended, interlayered and hierarchical nanostructures by accuracy-improved electron beam lithography overlay

Nanofabrication techniques are essential for exploring nanoscience and many closely related research fields such as materials, electronics, optics and photonics. Recently, three-dimensional (3D) nanofabrication techniques have been actively investigated through many different ways, however, it is still challenging to make elaborate and complex 3D nanostructures that many researchers want to realize for further interesting physics studies and device applications. Electron beam lithography, one of the two-dimensional (2D) nanofabrication techniques, is also feasible to realize elaborate 3D nanostructures by stacking each 2D nanostructures. However, alignment errors among the individual 2D nanostructures have been difficult to control due to some practical issues. In this work, we introduce a straightforward approach to drastically increase the overlay accuracy of sub-20 nm based on carefully designed alignmarks and calibrators. Three different types of 3D nanostructures whose designs are motivated from metamaterials and plasmonic structures have been demonstrated to verify the feasibility of the method, and the desired result has been achieved. We believe our work can provide a useful approach for building more advanced and complex 3D nanostructures.114sciescopu

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics