Long-run Effects of Market Risk Factors on Bank Performance in the SSA Banking System

The study assesses the long-term effects of market risk factors on bank performance in the Sub-Saharan Africa banking system. The article identifies the most influential market risk factor and the most affected bank performance factors in the long term. It covers 40 countries with 350 commercial banks for ten years. The analysis uses dynamic fixed-effects models (ARDL-DFE). The results demonstrated that non-performing loans are the most influencers affecting bank performance factors in the long run. Furthermore, the results show that return on average assets is the most bank performance factor affected mainly by market risks, especially the NPLs in the long run. Finally, the findings surprisingly proved mutual interactions and cointegration movements among bank market risk factors and bank performance measures in the long run. These findings can assist central banks in supervising and regulating SSA commercial banks and inspire regional bank managers in reducing market risks and sharpening long-run performance strategies through resource reallocating

An Image Encryption Scheme Based on DNA Computing and Cellular Automata

Networks have developed very quickly, allowing the speedy transfer of image information through Internet. However, the openness of these networks poses a serious threat to the security of image information. The field of image encryption has drawn attention for this reason. In this paper, the concepts of 1-dimensional DNA cellular automata and T-DNA cellular automata are defined, and the concept of reversible T-DNA cellular automata is introduced. An efficient approach to encryption involving reversible T-DNA cellular automata as an encryption tool and natural DNA sequences as the main keys is here proposed. The results of a simulation experiment, performance analysis, and comparison to other encryption algorithms showed this algorithm to be capable of resisting brute force attacks, statistical attacks, and differential attacks. It also enlarged the key space enormously. It meets the criteria for one-time pad and resolves the problem that one-time pad is difficult to save

Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling

Nowadays, gene chip technology has rapidly produced a wealth of information about gene expression activities. But the time-series expression data present a phenomenon that the number of genes is in thousands and the number of experimental data is only a few dozen. For such cases, it is difficult to learn network structure from such data. And the result is not ideal. So it needs to take measures to expand the capacity of the sample. In this paper, the Block bootstrap re-sampling method is utilized to enlarge the small expression data. At the same time, we apply “K2+T” algorithm to Yeast cell cycle gene expression data. Seeing from the experimental results and comparing with the semi-fixed structure EM learning algorithm, our proposed method is successful in constructing gene networks that capture much more known relationships as well as several unknown relationships which are likely to be novel

Does Fintech-Driven Inclusive Finance Induce Bank Profitability? Empirical Evidence from Developing Countries

This study explores the effect of fintech-driven inclusive finance on the profitability of banks using an unbalanced panel dataset from 660 banks across 40 developing countries between 2011 and 2021. We start with a fixed-effect estimate and subsequently validate our main findings using two-stage least squares (2SLS-IV), two-step system generalized method of moments (GMM), and generalized least squares (GLS) methodologies. Our analysis centers on three key profitability metrics: ROA, ROE, and NIM. Our findings suggest that fintech-backed inclusive finance boosts ROA by 9.10%, ROE by 18.87%, and NIM by 7.98%, highlighting the growing importance of mobile, internet, and agent banking in these nations. We also note that large banks benefit more from inclusive finance than small ones. Additionally, conventional banks see a more marked improvement in profitability than Islamic and savings banks. The relationship between inclusive finance and bank profitability is stronger in countries with higher GDP growth and those actively advancing financial inclusion through fintech, compared to countries with slower GDP growth and less emphasis on financial inclusion. When examining the interaction effects, the COVID-19 pandemic has further emphasized the positive connection between fintech and bank profitability. This suggests that fintech-driven inclusive finance can play a role in enhancing bank profitability, even in challenging times like the COVID-19 period. The transition towards fintech, however, mandates substantial investments, enhanced financial literacy, and heightened customer security, presenting persistent challenges for governments, policymakers, regulators, and financial institutions.</p

NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation

In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes the point cloud interpolation task more challenging. In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios. Furthermore, we construct a new multi-frame point cloud interpolation dataset called NL-Drive for large nonlinear motions in autonomous driving scenes to better demonstrate the superiority of our method. Ultimately, NeuralPCI achieves state-of-the-art performance on both DHB (Dynamic Human Bodies) and NL-Drive datasets. Beyond the interpolation task, our method can be naturally extended to point cloud extrapolation, morphing, and auto-labeling, which indicates its substantial potential in other domains. Codes are available at https://github.com/ispc-lab/NeuralPCI.Comment: Accepted by CVPR 2023. Project Page: https://dyfcalid.github.io/NeuralPC

LINC00174 Facilitates Cell Proliferation, Cell Migration and Tumor Growth of Osteosarcoma via Regulating the TGF-β/SMAD Signaling Pathway and Upregulating SSH2 Expression

Osteosarcoma (OS), a frequent malignant tumor which mainly occurs in the bone. The roles of long noncoding RNAs (lncRNAs) have been revealed in cancers, including OS. LncRNA long intergenic non-protein coding RNA (LINC00174) has been validated as an oncogene in several cancers. However, the role of LINC00174 in OS has not been explored. In our research, loss-of-function assays were conducted to explore the function of LINC00174 in OS cells. Then, we explored the downstream pathway of LINC00174 in OS cells. Bioinformatics, RNA pull-down and RIP experiments investigated the downstream mechanism of LINC00174 in OS cells. Finally, in vivo assays clarified the effect of LINC00174 on tumorigenesis. We found that LINC00174 was upregulated in OS tissues and cells. LINC00174 knockdown repressed OS cell growth. Mechanistically, LINC00174 knockdown suppressed the TGF-β/SMAD pathway. LINC00174 interacted with miR-378a-3p, and slingshot protein phosphatase 2 (SSH2) 3′UTR was targeted by miR-378a-3p in OS cells. Rescue assays showed that SSH2 upregulation or miR-378a-3p inhibition counteracted the inhibitory effect of LINC00174 depletion in OS cell growth. Additionally, LINC00174 depletion suppressed tumor growth in mice. In conclusion, LINC00174 promotes OS cellular malignancy and tumorigenesis via the miR-378a-3p/SSH2 axis and the TGF-β/SMAD pathway, which might provide a novel insight for OS treatment

Genome-wide analysis of a avirulent and reveal the strain induces pro-tective immunity against challenge with virulent Streptococcus suis Serotype 2

BACKGROUND: It was previously reported in China that two recent large-scale outbreaks of Streptococcus suis serotype 2 (S. suis 2) infections in human were caused by two highly virulent S. suis 2 strains, from which a novel genomic island (GEI), associated with disease onset and progression and designated 89 K, was identified. Here, an avirulent strain, 05HAS68, was isolated from a clinically healthy pig. RESULTS: By comparing the genomes of this avirulent strain with virulent strains, it was found that massive genomic rearrangements occurred, resulting in alterations in gene expression that caused enormous single gene gain and loss. Important virulent genes were lost, such as extracellular protein factor (ef) and suilysin (sly) and larger mutants, such as muramidase-released protein (mrp). Piglets vaccinated with the avirulent strain, 05HAS68, had increased TNF-α and IFN-γ levels in the peripheral blood and were fully protected from challenge infection with the most virulent S. suis 2 strain, 05ZYH33. Transfusion of T cells and plasma from vaccinated pigs resulted in protection of recipient animals against the 05ZYH33 challenge. CONCLUSION: These results suggest that analysis genome of the avirulent strains are instrumental in the development of vaccines and for the functional characterization of important of genetic determinants