Explaining Rising Returns to Education in Urban China in the 1990s

Although theory predicts that international trade will decrease the relative demand for skilled workers in relatively skill-deficit countries, in recent decades many developing countries have experienced rising wage premiums for skilled workers. We examines this puzzle by quantifying the relative importance of different supply and demand factors in explaining the rapid increase in the returns to education experienced by China during the 1990s. Analyzing Chinese urban household survey and census data for six provinces, we find that although changes in the structure of demand did reduce the demand for skilled workers, consistent with trade theory, the magnitude of the effect was modest and more than offset by institutional reforms and technological changes that increased the relative demand for skill.education, earnings, inequality, China

Learning to Price Supply Chain Contracts against a Learning Retailer

The rise of big data analytics has automated the decision-making of companies and increased supply chain agility. In this paper, we study the supply chain contract design problem faced by a data-driven supplier who needs to respond to the inventory decisions of the downstream retailer. Both the supplier and the retailer are uncertain about the market demand and need to learn about it sequentially. The goal for the supplier is to develop data-driven pricing policies with sublinear regret bounds under a wide range of possible retailer inventory policies for a fixed time horizon. To capture the dynamics induced by the retailer's learning policy, we first make a connection to non-stationary online learning by following the notion of variation budget. The variation budget quantifies the impact of the retailer's learning strategy on the supplier's decision-making. We then propose dynamic pricing policies for the supplier for both discrete and continuous demand. We also note that our proposed pricing policy only requires access to the support of the demand distribution, but critically, does not require the supplier to have any prior knowledge about the retailer's learning policy or the demand realizations. We examine several well-known data-driven policies for the retailer, including sample average approximation, distributionally robust optimization, and parametric approaches, and show that our pricing policies lead to sublinear regret bounds in all these cases. At the managerial level, we answer affirmatively that there is a pricing policy with a sublinear regret bound under a wide range of retailer's learning policies, even though she faces a learning retailer and an unknown demand distribution. Our work also provides a novel perspective in data-driven operations management where the principal has to learn to react to the learning policies employed by other agents in the system

Aqua­(2,9-dimethyl-1,10-phenanthroline-κ2 N,N′)bis­(3-hydroxy­benzoato-κO)manganese(II)–2,9-dimethyl-1,10-phenanthroline–water (1/1/1)

In the title compound, [Mn(C7H5O3)2(C14H12N2)(H2O)]·C14H12N2·H2O, the MnII ion is coordinated by a bidentate 2,9-dimethyl-1,10-phenanthroline (dmphen) ligand, two monodentate 3-hydroxy­benzoate anions (3-HBA) and one water mol­ecule in a distorted trigonal-bipyramidal environment. An uncoordinated dmphen and an uncoordinated water mol­ecule cocrystallized with each complex mol­ecule. Intra- and inter­molecular O—H⋯N and O—H⋯O hydrogen bonds are also present between the coordinated 3-HBA and water mol­ecules and the uncoordinated dmphen and water mol­ecules in the crystal. The packing of the structure is further stabilized by π–π stacking inter­actions involving dmphen mol­ecules, with a centroid–centroid separation of 3.705 (3) Å

Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems

Research on Performance Degradation Assessment Method of Train Rolling Bearings under Incomplete Data

Abstract-This paper mainly discusses the performance degradation assessment of train rolling bearings under incomplete data, by using the support vector data description (SVDD) and dynamic particle swarm optimization (DPSO).The proposed method is based on the similarity weight for the assessment of the train rolling bearings under incomplete data. Firstly, to obtain effective features of bearing performance degradation from collected vibration data, the local mean decomposition (LMD) is employed to decompose the vibration data. Secondly, the high-dimensionality of features is reduced by the principal component analysis (PCA). And then, on the basis of choosing the kernel parameter and penalty weight, a degradation method based on SVDD is proposed. Finally, the experimental results verified that the proposed method has a better optimization performance than the traditional method and can assess the performance degradation of train rolling bearings under incomplete data

Fluorine-free and hydrophobic hexadecyltrimethoxysilane-TiO₂ coated mesh for gravity-driven oil/water separation

Superhydrophobic and superoleophilic meshes have attracted great attention in oil/water separating application. However, superhydrophobic surfaces are not only complicated in preparation but also easy to break in practical applications. In this paper, we prepared fluorine-free hydrophobic hexadecyltrimethoxysilane (HDTMS)-TiO2 coated meshes with properties of cost-effectiveness, easy to manufacture, and high separation efficiency by a liquid phase deposition method. The surface topography, composition, and functional groups of the meshes were characterized by field emission scanning electron microscope (FE-SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and Fourier transform microscopic infrared spectrometer (FT-IR) spectrum, respectively. A new gravity-driven oil/water separator was designed for the separation experiments. The separation efficiency of the hydrophobic HDTMS-TiO2 coated meshes maintained over 97.8% after 35 separating cycles. This study indicated that the superhydrophobicity of the separating mesh was nonessential for the highly efficient oil-water separation. The fluorine-free hydrophobic HDTMS-TiO2 coated meshes provided an economical and beneficial solution to treat industrial oily wastewater mixtures and environmental oil spills.</p

Ask Question First for Enhancing Lifelong Language Learning

Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks. Previous works based on the language model and following data-free constraint approaches have explored formatting all data as "begin token (\textit{B}) + context (\textit{C}) + question (\textit{Q}) + answer (\textit{A})" for different tasks. However, they still suffer from catastrophic forgetting and are exacerbated when the previous task's pseudo data is insufficient for the following reasons: (1) The model has difficulty generating task-corresponding pseudo data, and (2) \textit{A} is prone to error when \textit{A} and \textit{C} are separated by \textit{Q} because the information of the \textit{C} is diminished before generating \textit{A}. Therefore, we propose the Ask Question First and Replay Question (AQF-RQ), including a novel data format "\textit{BQCA}" and a new training task to train pseudo questions of previous tasks. Experimental results demonstrate that AQF-RQ makes it easier for the model to generate more pseudo data that match corresponding tasks, and is more robust to both sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. AQF-RQ can achieve only 0.36\% lower performance than multi-task learning.Comment: This paper has been accepted for publication at COLING 202

Generation of Multicellular Tumor Spheroids with Microwell-Based Agarose Scaffolds for Drug Testing

Three dimensional multicellular aggregate, also referred to as cell spheroid or microtissue, is an indispensable tool for in vitro evaluating antitumor activity and drug efficacy. Compared with classical cellular monolayer, multicellular tumor spheroid (MCTS) offers a more rational platform to predict in vivo drug efficacy and toxicity. Nevertheless, traditional processing methods such as plastic dish culture with nonadhesive surfaces are regularly time-consuming, laborious and difficult to provide uniform-sized spheroids, thus causing poor reproducibility of experimental data and impeding high-throughput drug screening. In order to provide a robust and effective platform for in vitro drug evaluation, we present an agarose scaffold prepared with the template containing uniform-sized micro-wells in commercially available cell culture plates. The agarose scaffold allows for good adjustment of MCTS size and large-scale production of MCTS. Transparent agarose scaffold also allows for monitoring of spheroid formation under an optical microscopy. The formation of MCTS from MCF-7 cells was prepared using different-size-well templates and systematically investigated in terms of spheroid growth curve, circularity, and cell viability. The doxorubicin cytotoxicity against MCF-7 spheroid and MCF-7 monolayer cells was compared. The drug penetration behavior, cell cycle distribution, cell apoptosis, and gene expression were also evaluated in MCF-7 spheroid. The findings of this study indicate that, compared with cellular monolayer, MCTS provides a valuable platform for the assessment of therapeutic candidates in an in vivo-mimic microenvironment, and thus has great potential for use in drug discovery and tumor biology research

Investigation of Current Returned Students’ Entrepreneurial Environment in China

One of the critical points of Chinese returned students’ entrepreneurship task is how to match distinguished external environment with their entrepreneurship development ability. In order to get the development status of Chinese returned students’ entrepreneurship, this paper investigates and analyzes the entrepreneurship environment and policies of various regions in China from the perspective of policy and environment. Through data compilation and coding analyses of the questionnaire survey of 357 different levels and departments’ returned students’ entrepreneurship policies, 1188 entrepreneurs, and 436 administrative staffs, we obtain returned students’ entrepreneurial team composition, status of their entrepreneurship development, cognition of entrepreneurship environment, and effectiveness of entrepreneurship policies, etc.. Based on existing problems, this paper gives some suggestions for further improving and optimizing Chinese returned students’ entrepreneurship environment.Key words: Returned students; Returned students’ entrepreneurship; entrepreneurship environmen