Both Japan and Korea are trying to boost female labor force participation (FLFP) as they face the challenges of a rapidly aging population. Though FLFP has generally been on a rising trend, the female labor force in both countries is skewed towards non-regular employment despite women’s high education levels. This paper empirically examines what helps Japan and Korea to increase FLFP by type (i.e., regular vs. non-regular employment), using the SVAR model. In so doing, we compare these two Asian countries with two Nordic countries Norway and Finland. The main findings are: (i) child cash allowances tend to reduce the proportion of regular female employment in Japan and Korea, (ii) the persistent gender wage gap encourages more non-regular employment, (iii) a greater proportion of regular female employment is associated with higher fertility, and (iv) there is a need for more public spending on childcare for age 6-11 in Japan and Korea to help women continue to work.
This brief focuses on stochastic energy optimization for distributed energy resources in smart grids. Along with a review of drivers and recent developments towards distributed energy resources, this brief presents research challenges of integrating millions of distributed energy resources into the grid. The brief then proposes a novel three-level hierarchical architecture for effectively integrating distributed energy resources into smart grids. Under the proposed hierarchical architecture, distributed energy resource management algorithms at the three levels (i.e., smart home, smart neighborhood, and smart microgrid) are developed in this brief based on stochastic optimization that can handle the involved uncertainties in the system.
This book examines the changing nature of dating and mate selection in contemporary China, and addresses a wide array of both causes and consequences concerning mate selection, including economic change, traditional cultural norms, evolving gender roles, and both marriage and fertility aspirations.
Over the next few decades, millions of people, with varying backgrounds and levels of technical expertise, will have to effectively interact with robotic technologies on a daily basis. This means it will have to be possible to modify robot behavior without explicitly writing code, but instead via a small number of wearable devices or visual demonstrations. At the same time, robots will need to infer and predict humans’ intentions and internal objectives on the basis of past interactions in order to provide assistance before it is explicitly requested; this is the basis of imitation learning for robotics. This book introduces readers to robotic imitation learning based on human demonstration with wearable devices. It presents an advanced calibration method for wearable sensors and fusion approaches under the Kalman filter framework, as well as a novel wearable device for capturing gestures and other motions. Furthermore it describes the wearable-device-based and vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic knowledge of machine learning, and for researchers interested in wearable computing and robotic learning.
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