Farmers’ Choice and Informal Credit Markets in China

Informal credit markets are very active in many developing countries including China. Informal financial associations have become a major channel of borrowing. Using data from the 2006 Rural Household Survey, this paper investigates farmers’ borrowings from both formal and informal sources with higher/lower interest, by looking into both demand and supply of loan. Consistent with the theory and previous studies, age follows an inverted U-shaped pattern in its relationship with the probability of borrowing from informal loan with higher interest. Our study shows that the impact of age disappears for the formal loan participation. In addition, high income and saving imply lower credit constraints. Moreover, household and county characteristics and financial conditions have a large and varying influence on farmers’ borrowing behavior.informal credit, financial constraints, China, Agricultural Finance, Community/Rural/Urban Development, International Development, Q12, C5, G21,

Implementation and benchmarking of the local weight window generation function for OpenMC

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC\u27s usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future

Transition metal oxides for high performance sodium ion battery anodes

Sodium-ion batteries (SIBs) are attracting considerable attention with expectation of replacing lithium-ion batteries (LIBs) in large-scale energy storage systems (ESSs). To explore high performance anode materials for SIBs is highly desired subject to the current anode research mainly limited to carbonaceous materials. In this study, a series of transition metal oxides (TMOs) is successfully demonstrated as anodes for SIBs for the first time. The sodium uptake/extract is confirmed in the way of reversible conversion reaction. The pseudocapacitance-type behavior is also observed in the contribution of sodium capacity. For Fe2O3anode, a reversible capacity of 386 mAh g-1at 100 mA g-1 is achieved over 200 cycles; as high as 233 mAhg-1is sustained even cycling at a large current-density of 5 A g-1

Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition.

The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data