The Influence of Green Credit on the Commercial Banks’ Financial Performance: Evidence from China

This study examines the impact of green credit on commercial banks’ financial performance in China, factoring in both profits and risk respectively. The development of the green economy has brought about emergent green credit market since the government became determined to address environmental issues. Both environmental management and the green production method innovation require substantial financial support, and green credit from bank is their main source. The main purpose of this study is to explore whether there is a correlation among the unfolding of green credit business, the profitability of banks and the credit risk of loans. The study in this paper selects 19 Chinese listed banks and collects 9 years of panel data covering the period from 2013 to 2021. Two robust standard error fixed-effects panel regression models based on the principle of linear estimates are established. The empirical results show that when the proportion of green credit (as a percentage of total credit) increases, it brings about a decrease in ROA, slightly hurting the profits of commercial banks in China. But as the green credit expands, the NPL of unwanted loans decreases, moderating the risk faced by banks and optimising asset quality. And the model results are significant. The paper concludes that the promotion of green credit has temporarily been a hindrance to banks' earning and the long-term impact is unclear so far. And green credit is less likely to default and therefore lower the non-performing ratio of banks. Chinese commercial banks are still relatively passive in the development of green credit. There are still some imperfections in the implementation of China's green credit policy, which, if updated and improved, would have a more significant benefits to banks in the future. This study contributes to the literature on the correlation between green credits and banks' financial performance, supplement the empirical case in the area of green credit. Additionally, it serves as a resource and reference for future government policymaking and bank development

A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector

To assess operational performance of air traffic control sector, a multivariate detection index system consisting of 5 variables and 17 indicators is presented, which includes operational trafficability, operational complexity, operational safety, operational efficiency, and air traffic controller workload. An improved comprehensive evaluation method, is designed for the assessment by optimizing initial weights and thresholds of back propagation (BP) neural network using genetic algorithm. By empirical study conducted in one air traffic control sector, 400 sets of sample data are selected and divided into 350 sets for network training and 50 sets for network testing, and the architecture of genetic algorithm-based back propagation (GABP) neural network is established as a three-layer network with 17 nodes in input layer, 5 nodes in hidden layers, and 1 node in output layer. Further testing with both GABP and traditional BP neural network reveals that GABP neural network performs betterthan BP neural work in terms of mean error, mean square error and error probability, indicating that GABP neural network can assess operational performance of air traffic control sector with high accuracy and stable generalization ability. The multivariate detection index system and GABP neural network method in this paper can provide comprehensive, accurate, reliable and practical operational performance assessment of air traffic control sector, which enable the frontline of air traffic service provider to detect and evaluate operational performance of air traffic control sector in real time, and trigger an alarm when necessary.</p

Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey

Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area

Ganoderma lucidum Protects Dopaminergic Neuron Degeneration through Inhibition of Microglial Activation

Abundant evidence has suggested that neuroinflammation participates in the pathogenesis of Parkinson's disease (PD). The emerging evidence has supported that microglia may play key roles in the progressive neurodegeneration in PD and might be a promising therapeutic target. Ganoderma lucidum (GL), a traditional Chinese medicinal herb, has been shown potential neuroprotective effects in our clinical trials that make us to speculate that it might possess potent anti-inflammatory and immunomodulating properties. To test this hypothesis, we investigated the potential neuroprotective effect of GL and possible underlying mechanism of action through protecting microglial activation using co-cultures of dopaminergic neurons and microglia. The microglia is activated by LPS and MPP+-treated MES 23.5 cell membranes. Meanwhile, GL extracts significantly prevent the production of microglia-derived proinflammatory and cytotoxic factors [nitric oxide, tumor necrosis factor-α (TNF-α), interlukin 1β (IL-1β)] in a dose-dependent manner and down-regulate the TNF-α and IL-1β expressions on mRNA level as well. In conclusion, our results support that GL may be a promising agent for the treatment of PD through anti-inflammation

Study on the composition optimization method for improving the fluidity of cast Ti2_2AlNb alloy and its mechanism

In this paper, the effects of Al, Nb main elements, Fe, Mo, W, Co, B, Si and their contents on the fluidity of Ti-22Al-25Nb alloy were investigated. The composition that was beneficial to improve the fluidity was screened through the thermodynamic software calculating thermophysical parameters affecting the fluidity of Ti$_2$AlNb alloy, the numerical simulation test of its fluidity and the verification test of the fluidity of optimized alloys. Finally, the improvement mechanism of the alloy fluidity was discussed. Results showed that the appropriate reduction of Nb element was better than Al element for the improvement of fluidity. The addition of trace Fe, B and Si elements were beneficial to the improvement of fluidity, the improvement effect of B element was best, while the addition of trace Mo, W, Co were not conducive to the improvement of fluidity. The cessation mechanism of Ti$_2$AlNb alloy is the cessation mechanism of the alloy with a wide crystallization temperature range. The composition which was most beneficial to improve the fluidity was Ti-22Al-24Nb-0.1B. The main reasons for the improvement of the fluidity had two sides: on the one hand, the reduction of 1at% Nb and the addition of 0.1at% B not only increased the superheat and crystallization latent heat of the alloy, but also reduced the melt viscosity and thermal conductivity, thus improving the fluidity. On the other hand, the TiB phase refined the grains, the fine grains prevented the dendrite from growing into developed dendrite networks, inhibited the adverse effect of the increase in the width of the solidification zone on the fluidity, reduced the flow resistance of the molten metal, and further improved the fluidity of the alloy.Comment: 23 pages, 14 figures, research pape