Multi-stage dynamic evolution of green financial system from the perspective of bilateral moral hazard.

In order to improve the regulatory efficiency of the government in the construction process of the green financial system, the multi-stage dynamic evolution game model of green financial system from the perspective of bilateral moral risk is constructed and analyzed. It is found that cost-controllable and profitable collaborative innovation are the fundamental to realize the sustainable cooperation of green innovation between financial institutions and carbon emission enterprises. The introduction of reward and punishment mechanism and transfer payment mechanism for government is conducive to promoting the willingness of financial institutions and carbon emission enterprises to cooperate in green innovation.

How to improve the function of government carbon tax in promoting enterprise carbon emission reduction: from the perspective of three-stage dynamic game.

Flexible strategy of carbon tax is an important chip for the government to promote the implementation of carbon emission reduction. However, low-carbon technological innovation of enterprises is bound to produce market competition problems with traditional production technology. Based on this, in order to explore the relationship between heterogeneous objectives, carbon tax pricing strategy of government and decision-making of heterogeneous enterprise in the market, and how the government can make better use of carbon tax to promote the implementation of carbon emission reduction, this paper constructs and analyzes a three-stage dynamic game model between the government and heterogeneous enterprises.

Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding.

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites.

Hourly associations between ambient temperature and emergency ambulance calls in one central Chinese city: Call for an immediate emergency plan.

Background: Most studies examining the short-term effects of temperature on health were based on the daily scale, few were at the hourly level. Revealing the relationship between unfavorable temperatures on an hourly basis and health is conducive to the development of more accurate extreme temperature early warning systems and reasonable dispatch of ambulances.

Methods: Hourly data on temperature, air pollution (including PM, O, SO and NO) and emergency ambulance calls (EACs) for all-cause, cardiovascular and respiratory diseases from January 16, 2014 to December 31, 2016 were obtained from Luoyang, China.

Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards.

Background: Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear.

Methods: We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM, PM, SO, and NO) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors.

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction.