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Given the phenomenon of "financing is difficult and expensive" for MSEs, this paper empirically investigated the influencing mechanism of the credit demand side characteristics on the financing constraints of MSEs based on the information conveyance perspective. The conclusions show that MSEs in China are severely suffering from financing constraints and 57.17% and 50.00% of MSEs with credit demand have not applied for loans from formal and informal financing channels, respectively. In terms of enterprise characteristics, MSEs have low asset size, short establishment history, weak profitability, and lack of tools such as fixed assets, complete financial management system, professional technicians, and private brands to convey risk information to financing institutions, which are key factors resulting in their financing constraints. In terms of owner characteristics, young owners lack financing experience and convey higher risk information to financing institutions; therefore, owners' age negatively influences the financing constraints of MSEs. These findings suggest that banks can use big data credit technology as a tool to obtain risk information about MSEs, and the government should implement diversified interventions to improve the information environment in financial markets. These findings provide empirical evidence for banks and governments to address the financing constraints of MSEs. [ABSTRACT FROM AUTHOR]

Credit rationing hindered the development of MSEs. Big data credit technology creates a great opportunity to address this issue. Then, how does big data credit technology affect and to what extent can it alleviate the credit rationing of MSEs? Based on the bounded rationality economic man hypothesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology are constructed, respectively, in this paper, and the evolutionary trajectory of bank-enterprise credit strategies under the two modes are comparatively analyzed. The results show that it is hard to alleviate the credit rationing of MSEs under the traditional mode. However, under big data credit technology, when the overall credit level of MSEs is high, the credit rationing of MSEs will be effectively alleviated. When the overall credit level of MSEs is too low, it is difficult to determine whether big data credit technology can alleviate the credit rationing of MSEs. In order to verify the feasibility of big data credit technology in alleviating the credit rationing of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit levels under the two credit modes. We found that the credit rationing of MSEs is always lower under big data credit technology than under the traditional mode. Therefore, big data credit technology can effectively alleviate the credit rationing of MSEs under any circumstances. The research provides theoretical support for banks to apply big data credit technology to achieve a win-win situation for both parties. [ABSTRACT FROM AUTHOR]

Aiming at the strategic behaviour in 'technology make-or-buy decisions' in the real economy, this study develops a Cournot competition model with endogenous technological level, by comparing the technology acquisition cost of technologically backward firms under different technology acquisition strategies, we theoretically analysed the 'anti-extortion' mechanism of indigenous innovation by technologically backward firms, and the relationship between indigenous innovation and technology imports is empirically tested by using the data of Chinese industrial enterprises above designated size. We show that indigenous innovation can significantly enhance a firm's bargaining power, and the technology acquisition strategy of simultaneous technology imports and indigenous innovation can not only reduce the technology imports cost but also reduce the indigenous innovation cost. Therefore, only by indigenous innovation based on technology imports, can firms enhance their market competitiveness. [ABSTRACT FROM AUTHOR]

Discontinuity generally induces the characteristic of strong nonlinear and singularity for vibro-impact systems. Chaos in a vibro-impact system under harmonic and white noise excitations is investigated. Based on dynamical theory and Melnikov method, the homoclinic orbits are studied and the critical value of onset of Smale chaos is given analytically. Additively, this analytical results are verified by numerical simulations, including Phase diagram, Poincare section and safe basins. The results reveals that the conclusion via Melnikov method is the necessary condition for onset of chaos motion, and the necessary and sufficient condition for the erosion of safe basin. [ABSTRACT FROM AUTHOR]

In order to reflect the long memory property of the financial assets,and eliminate financial arbitrage in fractional Brownian motion environment,the asset price changes behavior of the gap options is depicted with bifractional Brownian motion. It is assumed that asset price process follows stochastic differential equations driven by bifractional Brownian motion, and the interest rate satisfied the Vasicek model, using the stochastic analysis theory for bifractional Brownian motion and the method of actuarial mathematics, the mathematical model of financial market was built. And the pricing formula of gap option under bifractional stochastic interest rate was obtained. The result of the gap option pricing formula in fractional Brownian motion environment is extended. [ABSTRACT FROM AUTHOR]

Exploiting long-range semantic contexts and geometric information is crucial to infer salient objects from RGB and depth features. However, existing methods mainly focus on excavating local features within fixed regions by continuously feeding forward networks. In this article, we introduce Dynamic Message Propagation (DMP) to dynamically learn context information within more flexible regions. We integrate DMP into a Siamese-based network to process the RGB image and depth map separately and design a multi-level feature fusion module to explore cross-level information between refined RGB and depth features. Extensive experiments show clear improvements of our method over 17 methods on six benchmark datasets for RGB-D salient object detection (SOD). Additionally, our method outperforms its competitors for the video SOD task. [ABSTRACT FROM AUTHOR]

The effects of planting ryegrass combined with sludge addition and arbuscular mycorrhizal fungi (AMF) inoculation on organic carbon accumulation in reclaimed mine substrates (RMS) were investigated. A 6-year field experiment was carried out; in the 0–15-cm edaphic layer, a mixed matrix was constructed according to the ratio of fly ash to coal gangue to sludge=1∶6∶3 , with 0.5 kg/m2 of AMF inoculated and ryegrass sowed annually. The particle size, carbon fraction, enzyme activity, AMF-related protein and ryegrass rhizosphere effect of RMS aggregates were monitored regularly. The cumulative contribution of the above factors to organic carbon in RMS was quantitatively explored by path analysis. After reclamation, the texture of reclamation matrix in mining area was improved, the stability of aggregates augmented significantly with the proportion of large aggregates increased gradually. Organic carbon in RMS increased by 1.96 times to 38.12 g/kg ; the contents of total N and available P increased significantly year by year, mainly concentrated in aggregates. Path analysis showed that the underground biomass of ryegrass and 0.5–2-mm aggregates were the two factors with the strongest direct positive effect on organic carbon accumulation in RMS. In addition, available phosphatase and easily extractable glomalin affected the process through complex interaction with each other. A combination of ryegrass-AMF-sludge on a reclamation mining field could improve the quality of RMS through aggregates by promoting the process of soil formation, and ryegrass root growth conducive to organic carbon accumulation. [ABSTRACT FROM AUTHOR]