What causes Chinese listed firms to switch bank loan provider? Evidence from a survival analysis

This paper analyses the duration of firm-bank relationships and examines what drives firms in China to change from one bank loan provider to another. Matched data of firm-loan-duration to bank provides a unique panel data set of relationship between China's listed firms and their lending banks consisting of 2102 firms listed on both the Shanghai Stock Exchange and Shenzhen Stock Exchange in the period of 1996–2016. The Cox proportional hazard model is used to allow for a semiparametric hazard function after parametrically controlling for firm-specific financial factors, industry factors, ownership characteristics, internal management changes, and external macroeconomic changes. In addition, we explore the impact of the 2008 financial crisis, bank-financial and ownership characteristics. The main finding of this study is that in an environment of growing commercialisation of relationships the firm-bank relationship between state-owned enterprises (SOEs) and state-owned banks (SOBs) in China remains super-stable. However, a change in the CEO of a firm even of a SOE increases the probability of the loan-provider being changed

Compare Artificial Neural Networks Model with ARIMA model for Stock Price Prediction – Take Chinese A-shares Stocks as Example

Stock price prediction will continue to be an attractive topic because stock is an investment tool with high returns along with high risk. Many researches have already applied different forecasting models on predict the developed market stock price, and conclude that the neural networks have a superior performance over the statistical models. As the largest emerging market in the world, the Chinese stock market has experienced a rapidly expansion since its establishment. As the market is fluctuant with less efficiency when compared with other mature markets, the stock price prediction in the Chinese stock market may be more challenging and significant. However, there are not many works focus on the comparison of two types of prediction models when forecasting the Chinese stock market. This paper will compare the performance of two popular prediction models, auto-regressive integrated moving average model (ARIMA) and artificial neural networks (ANNs), on forecasting the Chinese A-shares stocks price. The experiment will forecast the price of seven representative stocks from December 2, 2019 to December 27, 2019, and then use some accuracy measures to estimate the prediction efficiency. The test results indicate that both prediction models have a good performance on forecasting the sample stock prices, but the results cannot prove that one forecasting model is superior than another

Students' Intention of Visiting Urban Green Spaces after the COVID-19 Lockdown in China.

This study addresses students' perceptions of using urban green spaces (UGSs) after the easing of COVID-19 lockdown in China. We questioned whether they are still mindful of the risks from the outdoor gathering, or conversely, starting to learn the restoration benefits from the green spaces. Online self-reported surveys were distributed to the Chinese students aging from 14 to 30 who study in Hunan and Jiangsu Provinces, China. We finally obtained 608 complete and valid questionnaire forms from all participants. Their intentions of visiting UGSs were investigated based on the extended theory of planned behavior model. Structural equation modeling was employed to test the hypothesized psychological model. The results have shown good estimation performance on risk perception and perceived knowledge to explain the variances in their attitudes, social norms, and perceived behavior control. Among these three endogenous variables, the perceived behavior control owns the greatest and positive influence on the behavioral intention, inferring that controllability is crucial for students to make decisions of visiting green spaces in a post-pandemic context

From Knowing to Doing: Learning Diverse Motor Skills through Instruction Learning

Recent years have witnessed many successful trials in the robot learning field. For contact-rich robotic tasks, it is challenging to learn coordinated motor skills by reinforcement learning. Imitation learning solves this problem by using a mimic reward to encourage the robot to track a given reference trajectory. However, imitation learning is not so efficient and may constrain the learned motion. In this paper, we propose instruction learning, which is inspired by the human learning process and is highly efficient, flexible, and versatile for robot motion learning. Instead of using a reference signal in the reward, instruction learning applies a reference signal directly as a feedforward action, and it is combined with a feedback action learned by reinforcement learning to control the robot. Besides, we propose the action bounding technique and remove the mimic reward, which is shown to be crucial for efficient and flexible learning. We compare the performance of instruction learning with imitation learning, indicating that instruction learning can greatly speed up the training process and guarantee learning the desired motion correctly. The effectiveness of instruction learning is validated through a bunch of motion learning examples for a biped robot and a quadruped robot, where skills can be learned typically within several million steps. Besides, we also conduct sim-to-real transfer and online learning experiments on a real quadruped robot. Instruction learning has shown great merits and potential, making it a promising alternative for imitation learning

Automatic Generation of Electronic Medical Record Based on GPT2 Model

Writing Electronic Medical Records (EMR) as one of daily major tasks of doctors, consumes a lot of time and effort from doctors. This paper reports our efforts to generate electronic medical records using the language model. Through the training of massive real-world EMR data, the CMedGPT2 model provided by us can achieve the ideal Chinese electronic medical record generation. The experimental results prove that the generated electronic medical record text can be applied to the auxiliary medical record work to reduce the burden on the compose and provide a fast and accurate reference for composing work

An Word2vec based on Chinese Medical Knowledge

Introducing a large amount of external prior domain knowledge will effectively improve the performance of the word embedded language model in downstream NLP tasks. Based on this assumption, we collect and collate a medical corpus data with about 36M (Million) characters and use the data of CCKS2019 as the test set to carry out multiple classifications and named entity recognition (NER) tasks with the generated word and character vectors. Compared with the results of BERT, our models obtained the ideal performance and efficiency results

Disease Diagnosis Prediction of EMR Based on BiGRL-Att-CapsNetwork Model

Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance

Life Cycle Assessment of A Hydrocarbon-based Electrified Cleaning Agent

The electrified cleaning agent requires a moderate volatilization rate, low ozone-depleting substances value, non-flammable, non-explosive and other characteristics. This study performed a whole life cycle assessment on a hydrocarbon-based electrified cleaning agent. The life cycle model is cradle-to-grave, and the background data sets include power grid, transportation, high-density polyethylene, chemicals, etc. The analysis shows that the global warming potential (GWP) of the life cycle of 1 kg of electrified cleaning agent is 2.08 kg CO2 eq, acidification potential (AP) is 9.49E-03 kg SO2 eq, eutrophication potential (EP) is 1.18E-03 kg PO43-eq, respirable inorganic matter (RI) is 2.13E- 03 kg PM2.5 eq, ozone depletion potential (ODP) is 4.91E-05 kg CFC-11 eq, photochemical ozone formation potential (POFP) is 2.89E-02 kg NMVOC eq, ionizing radiation-human health potential (IRP) is 3.16E-02 kg U235 eq, ecotoxicity (ET) is 2.69E-01 CTUe, human toxicity-carcinogenic (HT-cancer) is 4.32E-08 CTUh, and human toxicity-non-carcinogenic (HT-non cancer) is 2.31E-07 CTUh. The uncertainty of the results is between 3.46-9.95%.The four processes of tetrachloroethylene production, D40 solvent oil production, tetrachloroethylene environmental discharge during product use, and electricity usage during product disposal have substantial effects on each LCA indicator, so they are the focus of process improvement. Changes in power consumption during production and transportation distance of raw materials have little effect on total carbon emissions. Compared with the production process of single-solvent electrified cleaning agent tetrachloroethylene and n-bromopropane, the production of the electrified cleaning agent developed in this study has its own advantages in terms of carbon footprint and other environmental impact indicators. Carbon emissions mainly come from the power consumption of each process, natural gas production and combustion, and other energy materials for heating. It is recommended to use renewable raw materials instead of crude oil to obtain carbon credits based on geographical advantages, and try to use production processes with lower carbon emissions, while the exhaust gas from the traditional production process is strictly absorbed and purified before being discharged

A Joint Model of Clinical Domain Classification and Slot Filling Based on RCNN and BiGRU-CRF

The task of the Intent Classification & Slot Filling serves as a key joint task in the voice assistant, which also plays the role of the pre-work in the construction of the medical consultation assistant system. How to distribute a doctor-patient conversation into a formatted electronic medical record to an accurate department (Intent Classification) to extract the key named entities or mentions (Slot Filling) through a specialized domain knowledge recognizer is one of the key steps of the entire system. In real cases, the medical vocabulary and clinical entities in different departments of the hospital often differ to some extent. Therefore, we propose a comprehensive model based on CMed-BERT, RCNN and BiGRU-CRF for a joint task of department identification and slot filling of the specific domain. Experimental results confirmed the competitiveness of our model