Stable Zinc Metal Anode for High-performance Aqueous Zn-ion Batteries

Owing to the high capacity of metallic Zn anode and intrinsically safe aqueous electrolyte, aqueous Zn-ion batteries (AZIBs) have become advanced energy storage alternatives beyond the lithium-ion batteries by providing cost benefit, high safety, and competitive energy density. There has been a new wave of research interest across AZIBs, however, the state-of-the-art AZIBs are still far from satisfactory. One important reason is that Zn anode still suffers from low Coulombic efficiency (CE) and inferior cycling stability, due to its notorious dendrite formation and side reactions (e.g. corrosion, passivation, and H2 evolution) in aqueous electrolytes. Accordingly, my doctoral project is focused on improving the electrochemical performance of AZIBs via enabling highly stable and reversible Zn metal anode, including three subprojects (1) in situ construction of highly Zn2+-conductive solid electrolyte interphase for stable Zn anode; (2) an in situ polymeric interface on Zn anode towards high-performance aqueous Zn-ion batteries; (3) boosting advanced aqueous Zn/MnO2 batteries via electrolyte salt chemistry. In the first subproject, an in-situ formation of a dense, stable, and highly Zn2+- conductive SEI layer (hopeite) was demonstrated in aqueous Zn chemistry, by introducing Zn(H2PO4)2 salt into the electrolyte. The hopeite SEI enables uniform and rapid Zn-ion transport kinetics for dendrite-free Zn deposition, and restrains the side reactions via isolating active Zn from the bulk electrolyte. Under practical testing conditions with an ultrathin Zn anode, a low negative/positive capacity ratio, and lean electrolyte, the Zn/V2O5 full cell retained 94.4 % of its original capacity after 500 cycles. This work provides a simple yet practical solution to high-performance aqueous battery technology via building in-situ SEI layers

Impact of ownership type and firm size on organizational culture and on the organizational culture-effectiveness linkage

This paper aims to extend the extant (primarily Western) organizational culture literature to emerging economies by explicitly incorporating two key contextual variables-ownership type and firm size into organizational culture model. Based on the theoretical model developed by Denison and his colleagues, we examined the impact of ownership type and firm size on organizational culture, as well as the moderating effect of the two contextual variables on the linkage between organizational culture and firm effectiveness. Using survey data from foreign-invested and state-owned firms in China, we find that ownership type and firm size have significant influence on organizational culture. We also find that different ownership type and firm size result in different organizational cultural effect on performance

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.Comment: AAAI 2016 conferenc

Governor Celebrates Funding for Mattapan Community Health Center

BACKGROUND:There is no single standard chemotherapy regimen for elderly patients with advanced gastric cancer (AGC). A phase III trial has confirmed that both capecitabine monotherapy and capecitabine plus oxaliplatin are well tolerated for elderly patients with AGC, but their economic influence in China is unknown. OBJECTIVE:The purpose of this cost-effectiveness analysis was to estimate the effects of capecitabine monotherapy and capecitabine plus oxaliplatin in elderly patients with AGC on health and economic outcomes in China. METHODS:We created a Markov model based on data from a Korean clinical phase III trial to analyze the cost-effectiveness of the treatment of elderly patients in the capecitabine monotherapy (X) group and capecitabine plus oxaliplatin (XELOX) group. The costs were obtained from published reports and the local health system. The utilities were assumed on the basis of the published literature. Costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICER) were estimated. One-way and probabilistic sensitivity analyses (Monte Carlo simulations) were performed. RESULTS:In the cost-effectiveness analysis, X had a lower total cost ($45,731.68) and cost-effectiveness ratio ($65,918.93/QALY). The one-way sensitivity analysis suggested that the most influential parameter was the risk of requiring second-line chemotherapy in XELOX group. The probabilistic sensitivity analysis predicted that the X regimen was cost-effective 100% of the time, given a willingness-to-pay threshold of $26,598. CONCLUSIONS:Our findings show that the XELOX regimen is less cost-effective compared to the X regimen for elderly patients with AGC in China from a Chinese healthcare perspective

Estimating global cropland production from 1961 to 2010

Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17–45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation–Global Atmosphere–Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr−1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr−1 (∼  45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history

The influence of minimum wage regulation on labor income share and overwork: evidence from China

Minimum Wage Regulation (MWR) can raise wage rate, but its relation with labor income share is in controversy. We explore the influence of raising wage rate on labor income share and overwork in China. Panel data regressions are taken mainly based on China’s Industrial Enterprise Database and the International Labor Organization Database. Our findings show that raising wage rate can increase labor income share without leading to overwork. Factors that may significantly increase overwork are a higher proportion of male workers, a larger income gap and a lower percapita income. We point out that the neoclassical explanation for labor income share is not persuasive. We support policies of raising wage rate and believe MWR is an effective measure to increase labor income share

Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time

Scene Graph Parsing as Dependency Parsing

In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%. We further demonstrate the effectiveness of our learned parser on image retrieval applications.Comment: To appear in NAACL 2018 as oral. Code is available at https://github.com/Yusics/bist-parser/tree/sgparse