Data for: Expected Stock Price Crash Risk and Bank Loan Pricing: Evidence from China's Listed Firms

Data for: Expected Stock Price Crash Risk and Bank Loan Pricing: Evidence from China's Listed Firm

Average run time of the algorithm with respect to DAG graph size.

Average run time of the algorithm with respect to DAG graph size.</p

Task nodes by grouping.

Task nodes by grouping.</p

DataSheet1_Will the Relaxation of COVID-19 Control Measures Have an Impact on the Chinese Internet-Using Public? Social Media-Based Topic and Sentiment Analysis.pdf

Objective: In December 2022, the Chinese government announced the further optimization of the implementation of the prevention and control measures of COVID-19. We aimed to assess internet-using public expression and sentiment toward COVID-19 in the relaxation of control measures in China.Methods: We used a user-simulation-like web crawler to collect raw data from Sina-Weibo and then processed the raw data, including the removal of punctuation, stop words, and text segmentation. After performing the above processes, we analyzed the data in two aspects. Firstly, we used the Latent Dirichlet Allocation (LDA) model to analyze the text data and extract the theme. After that, we used sentiment analysis to reveal the sentiment trend and the geographical spatial sentiment distribution.Results: A total of five topics were extracted according to the LDA model, namely, Complete liberalization, Resource supply, Symptom, Knowledge, and Emotional Outlet. Furthermore, sentiment analysis indicates that while the percentages of positive and negative microblogs fluctuate over time, the overall quantity of positive microblogs exceeds that of negative ones. Meanwhile, the geographical dispersion of public sentiment on internet usage exhibits significant regional variations and is subject to multifarious factors such as economic conditions and demographic characteristics.Conclusion: In the face of the relaxation of COVID-19 control measures, although concerns arise among people, they continue to encourage and support each other.</p

Average SLR and average Speedup with different DAG graph structures.

<p>Average SLR and average Speedup with different DAG graph structures.</p

Scheduling task graph in Fig 3 using the traditional level priority (right) and the predecessor-task layer priority strategy (left).

<p>Scheduling task graph in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159932#pone.0159932.g003" target="_blank">Fig 3</a> using the traditional level priority (right) and the predecessor-task layer priority strategy (left).</p

DAG graph with Non-Single-exit task node.

<p>DAG graph with Non-Single-exit task node.</p

Scheduling of the DAG graph in Fig 6 using HEFT algorithm.

Scheduling of the DAG graph in Fig 6 using HEFT algorithm.</p

DAG Graph and the computation cost of each task node on all servers.

<p>DAG Graph and the computation cost of each task node on all servers.</p

Cloud Computing Model.

<p>Cloud Computing Model.</p