DETERMINANTS OF BANK PROFITABILITY AND RISK-TAKING IN CHINA

By using the ordinary lease squares estimation technique, this paper examines the relationship between bank-specific characteristics together with macroeconomic factors, and profitability in Chinese banking sector. Therefore, to find out the how each factor affects the bank’s profitability. Moreover, this paper also uses three risk measures to analyze the banks’ business condition. The regression analysis is based on a panel data set consisting of 152 observations of 30Chinese banks over a 6-year period from 2011 to 2016.We found that for profitability, capital ratio and GDP growth rate have significant positive impacts and inflation rate has significant negative impact. Other independent variables do not have significant relationship with bank’s profitability. For risk, none of the independent variable has significant impact on Z-score and non-performing ratio; however, total deposits to total assets ratio and bank size have significant impact on non-interest income ratio

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs' idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs' participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.Comment: 10 pages, 9 figure

Influence of frost damage on water penetration into neat and air entrained concrete

In service life, concrete can be damaged either by mechanical or environmental loads or by combined ones. These damages will strongly influence water movement in concrete which could later lead to more serious deteriorations. This paper applies neutron radiography to investigate the influence of frost damage on water penetration into concrete. In addition, the improvement of frost resistance by addition of air entrainment was investigated. The results indicate that it is possible to visualize penetration of water into the porous structure of concrete by neutron radiography. Further evaluation of the test data allows determining time-dependent moisture profiles quantitatively with high resolution. After concrete is damaged by freeze-thaw cycles water penetration into ordinary concrete is accelerated. It can be shown that frost damage is not equally distributed in specimens exposed to freeze-thaw cycles. Thermal gradients lead to more serious damage near the surface. The beneficial effect of air entrainment on frost resistance has been demonstrated. After 50 freeze-thaw cycles, air entrained concrete showed no measurable increase in water absorption. But layers near the surface of concrete absorbed slightly more water after 200 freeze-thaw cycles although the dynamic elastic modulus remained constant. Results presented in this paper help us to better understand mechanisms of frost damage of concrete

Future Climate Change Will Have a Positive Effect on Populus Davidiana in China

Since climate change significantly affects global biodiversity, a reasonable assessment of the vulnerability of species in response to climate change is crucial for conservation. Most existing methods estimate the impact of climate change on the vulnerability of species by projecting the change of a species’ distribution range. This single-component evaluation ignores the impact of other components on vulnerability. In this study, Populus davidiana (David’s aspen), a tree species widely used in afforestation projects, was selected as the research subject under four future climate change scenarios (representative concentration pathway (RCP)2.6, RCP4.5, RCP6.0, and RCP8.5). Exposure components of range change as well as the degree of fragmentation, degree of human disturbance, and degree of protection were considered simultaneously. Then, a multicomponent vulnerability index was established to assess the effect of future climate change on the vulnerability of P. davidiana in China. The results show that the distribution range of P. davidiana will expand to the northwest of China under future climate change scenarios, which will lead to an increased degree of protection and a decreased degree of human disturbance, and hardly any change in the degree of fragmentation. The multicomponent vulnerability index values of P. davidiana under the four emission scenarios are all positive by 2070, ranging from 14.05 to 38.18, which fully indicates that future climate change will be conducive to the survival of P. davidiana. This study provides a reference for the development of conservation strategies for the species as well as a methodological case study for multicomponent assessment of species vulnerability to future climate change

Associations Between Genetic Variants in Mrna Splicing-Related Genes and Risk of Lung Cancer: a Pathway-Based Analysis from Published Gwass

mRNA splicing is an important mechanism to regulate mRNA expression. Abnormal regulation of thisprocess may lead to lung cancer. Here, we investigated the associations of 11,966 single-nucleotide polymorphisms (SNPs) in 206 mRNA splicing-related genes with lung cancer risk by using the summarydata from six published genome-wide association studies (GWASs) of Transdisciplinary Research in Cancerof the Lung (TRICL) (12,160 cases and 16,838 controls) and another two lung cancer GWASs of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls). We found that a total of 12 significant SNPs with false discovery rate (FDR) ≤0.05 were mapped to one novel gene PRPF6and two previously reported genes (DHX16 and LSM2) that were also confirmed in this study. The sixnovel SNPs in PRPF6 were in high linkage disequilibrium and associated with PRPF6 mRNA expression inlymphoblastoid cells from 373 Europeans in the 1000 Genomes Project. Taken together, our studies shednew light on the role of mRNA splicing genes in the development of lung cancer

Selecting a semantic similarity measure for concepts in two different CAD model data ontologies

Semantic similarity measure technology based approach is one of the most popular approaches aiming at implementing semantic mapping between two different CAD model data ontologies. The most important problem in this approach is how to measure the semantic similarities of concepts between two different ontologies. A number of measure methods focusing on this problem have been presented in recent years. Each method can work well between its specific ontologies. But it is unclear how accurate the measured semantic similarities in these methods are. Moreover, there is yet no evidence that any of the methods presented how to select a measure with high similarity calculation accuracy. To compensate for such deficiencies, this paper proposes a method for selecting a semantic similarity measure with high similarity calculation accuracy for concepts in two different CAD model data ontologies. In this method, the similarity calculation accuracy of each candidate measure is quantified using Pearson correlation coefficient or residual sum of squares. The measure with high similarity calculation accuracy is selected through a comparison of the Pearson correlation coefficients or the residual sums of squares of all candidate measures. The paper also reports an implementation of the proposed method, provides an example to show how the method works, and evaluates the method by theoretical and experimental comparisons. The evaluation result suggests that the measure selected by the proposed method has good human correlation and high similarity calculation accuracy