Online peer-to-peer (P2P) lending has emerged as an innovative financial technology (FinTech) platform that renders financial services that are potentially more inclusive and affordable than those offered by traditional financial institutions. A similar purpose is served by cryptocurrency markets, where transaction costs are reduced and financial accessibility is improved based on disruptive technologies such as blockchain and distributed ledgers. Despite these developments, however, in the operations management literature limited attention has been devoted to the contribution of online P2P lending to the promotion of financial inclusion (i.e., the availability and usage of financial services for all groups of people) and its dynamic interplay with cryptocurrency markets. The rise of cryptocurrency markets affects the composition and activity of borrowers and investors in P2P lending markets and hence the capacity of the latter to support financial inclusion, leading to an operations management challenge in online P2P lending. We examine how cryptocurrency markets influence P2P lending markets’ democratization of access to financial services, particularly P2P borrowing. To investigate these effects in depth, we develop a simple theoretical model to derive testable propositions, which are then empirically validated on the basis of unique datasets.We find that the growth in cryptocurrency markets is associated with increased loan requests and larger loan amounts in P2P markets, especially from borrowers who maintain good credit ratings, possess technical knowledge about cryptocurrencies, and intend to borrow for investment purposes. Our results suggest that cryptocurrency markets bring economic gains to the P2P lending market, at least in the short term. Nonetheless, the transfer of funds from P2P lending to cryptocurrency markets, particularly by highly creditworthy and tech-savvy investors, may provoke increased inequality in access to P2P lending markets. By scrutinizing the interdependence between two representative FinTech markets we uncover important operations management implications for theory and practice regarding the healthy growth and effective governance of crowdfunding platforms and the corresponding sustainability of their role in upholding financial inclusion.
An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
We have developed a manufacturing system by combination of high-pressure synthesis method using a multi-anvil press, and spark plasma sintering (SPS) method. By means of the system, we have succeeded in synthesizing new filled skutterudite-type thermoelectric materials Mm x Co 4 Sb 12 (Mm=mischmetal). The thermoelectric properties of partially filled skutterudite compounds Mm x Co 4 Sb 12 synthesized under high pressure have been investigated. The Seebeck coefficient of Mm x Co 4 Sb 12 shows negative value, which means n-type conductivity. The highest dimensionless figure of merit ZT value is 0.25 for Mm 0.6 Co 4 Sb 12 at 700 K.
The crystal structure of the double-layered ${\mathrm{Ca}}_{3}{\mathrm{Ru}}_{2}{\mathrm{O}}_{7}$ has been studied by convergent beam electron diffraction and powder neutron diffraction. The temperature dependence of the diffraction pattern reveals that all the lattice constants jump at the first-order metal-nonmetal transition at $48\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ without a change of the space group symmetry of $Bb{2}_{1}m$. In the neutron diffraction experiment, an additional magnetic reflection emerges below the N\'eel temperature, $56\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. A possible model for this antiferromagnetic ordering is proposed, in which model magnetic moments align ferromagnetically within the double layer and antiferromagnetically between the double layers. This model reasonably explains the characteristic field dependence of the magnetoresistance observed at around $6\phantom{\rule{0.3em}{0ex}}\mathrm{T}$.
We performed a Laser angle-resolved photoemission spectroscopy (ARPES) study on a wide doping range of Ba1-xKxFe2As2 (BaK) and precisely determined the doping evolution of the superconducting (SC) gaps in this compound. The gap size of the outer hole Fermi surface (FS) sheet around the Brillioun zone (BZ) center shows an abrupt drop with overdoping (for x > 0.6) while the inner and middle FS gaps roughly scale with Tc. This is accompanied by the simultaneous disappearance of the electron FS sheet with similar orbital character at the BZ corner. These results browse the different contributions of X2-Y2 and XZ/YZ orbitals to superconductivity in BaK and can be hardly completely reproduced by the available theories on iron-based superconductors.
Unoccupied electronic structure of iron-based superconductors NdFeAsO0.7 (Tc=51 K), BaFe2As2 and Ba(Fe0.89Co0.11)2As2 (Tc=23 K) has been investigated by means of inverse-photoemission spectroscopy (IPES). The unoccupied Fe 3d states are observed around 1 eV above the Fermi level for all compounds. The Fe 3p-3d resonant IPES suggests that the unoccupied Fe 3d states of Ba(Fe1−xCox)2As2 have more localized character compared with those of NdFeAsO0.7. The unoccupied Nd 4f states of NdFeAsO0.7 are located around 5 and 7 eV, and the Ba 5d and Ba 4f states of Ba(Fe1−xCox)2As2 are located around 5 and 12 eV, respectively.
Abstract BiCh 2 -based layered compounds have been extensively studied as potential thermoelectric and unconventional superconducting materials. For both functionalities, in-plane chemical pressure effects improve their thermoelectric or superconducting properties. In this study, we investigate the effects of in-plane chemical pressure on atomic vibrations of Bi by analyzing lattice specific heat measured at T = 1.9–300 K with multiple Debye and Einstein models for thermoelectric LaOBi(S,Se) 2 and superconducting LaO 0.5 F 0.5 Bi(S,Se) 2 . We reveal that in-plane chemical pressure enhances the oscillator number of the Einstein mode corresponding to large-amplitude Bi vibration along the c -axis in both the systems.
Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performance have not been well understood. In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. While they improve the scalability, their training times still depend on the implementations in the frameworks as sampling and its associated operations can introduce non-negligible overhead and computational cost. In addition, it is unknown how much the frameworks are 'eco-friendly' from a green computing perspective. In this paper, we provide an in-depth study of two mainstream GNN frameworks along with three state-of-the-art GNNs to analyze their performance in terms of runtime and power/energy consumption. We conduct extensive bench mark experiments at several different levels and present detailed analysis results and observations, which could be helpful for further improvement and optimization.
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