Chinese Interest in Thyroid Related Diseases: Evidence from Baidu Index

Background: Common thyroid diseases are hyperthyroidism, hypothyroidism, thyroiditis, thyroid tumor and so on. Baidu is currently the most widely used online search tool in China, has developed an internet search trends collection and analysis tool called the Baidu Index. The aim of the present study was to understand the trend and characteristics of public’s online attention to thyroid diseases, and to explore the value of Baidu Index in monitoring online retrieval behavior of thyroid related information.Methods: Taking the period from January 1, 2011 to December 31, 2019 as the time range into consideration, we used the big data analysis tool of Baidu Index and took “thyroid nodules”, “thyroid cancer”, “thyroiditis” “hyperthyroidism” and “hypothyroidism” as the keywords, the data of “search index” and “media index” were recorded on a weekly basis, and all information were aggregated into quarterly and annual to generate the final data which was carried out for secondary analysis. Pearson correlation analysis was used to analyze the correlation between the search index of keywords and the year. One-way Analysis of Variance was used to analyze the differences between search index and media index.Results: Among the five keywords, thyroid nodule search index had the highest growth rate (640%), followed by thyroid cancer (298%). The media’s attention to thyroid diseases had been declining year by year. Unlike the public’s attention, the media index of hyperthyroidism was significantly higher than other keywords.Conclusion: Over the past nine years, the public's attention to thyroid related diseases has been increasing gradually. Baidu Index is an effective tool to track the health information query behavior of Chinese internet users, which can provide a cost-effective supplement to traditional monitoring system.

On the factors of Bitcoin’s value at risk

This study investigates the factors of Bitcoin’s tail risk, quantified by Value at Risk (VaR). Extending the conditional autoregressive VaR model proposed by Engle and Manganelli (2004), I examine 30 potential drivers of Bitcoin’s 5% and 1% VaR. For the 5% VaR, quantity variables, such as Bitcoin trading volume and monetary policy rate, were positively significant, but these effects were attenuated when new samples were added. The 5% VaR responds positively to the Internet search index and negatively to the fluctuation of returns on commodity variables and the Chinese stock market index. For the 1% VaR, variables related to the macroeconomy play a key role. The consumer sentiment index exerts a strong positive effect on the 1% VaR. I also find that the 1% VaR has positive relationships with the US economic policy uncertainty index and the fluctuation of returns on the corporate bond index.

Separation of stellar properties using SDSS, 2MASS magnitudes

В работе описан и апробирован на выборке размером ∼ 300 звезд теоретико-вероятностный подход, который на основе блеска в полосах ugriz и JHK s позволяет вынести суждение о значениях атмосферных характеристик звезды и полном поглощении. Подход основан на методе максимального правдоподобия с использованием поискового индекса по типу k - d дерева, для создания которого использовались результаты вычислений моделей звездных атмосфер Куруца. We present a probability-theoretic approach to separation of stellar properties using SDSS, 2MASS magnitudes. The approach is based on the maximum likelihood method using a search index of the type k - d tree. Using synthetic photometry of Kurucz model spectra, the search index is constructed and the approach is tested on sample of ∼ 300 stars.

Regional disparities in information-seeking response to the COVID-19 pandemic in China

The emergence of the COVID-19 virus and the subsequent official announcement of human-to-human transmission of COVID-19 alarmed the public and initiated the uptake of preventive measures. We conducted interrupted time-series analyses using Baidu Search Index from Jan 1, 2017 to Mar 15, 2021 to investigate how information seeking changed over time and how changes in information seeking varied across regions in China. Our findings show that changes in the patterns of search interest in COVID-19 in each province of China occurred in a synchronous fashion during the first wave of the COVID-19 pandemic and subsequent local outbreaks, irrespective of the location and severity of each outbreak. However, inequalities in the magnitude of public response to and awareness of COVID-19 were evident, with lower-levels of information seeking regarding COVID-19 in less developed areas compared with developed areas.

Analyzing Levels of Concern About Joint Punishment for Dishonesty Using the Visibility Graph Network

Joint punishment for dishonesty is an important means of administrative regulation. This research analyzed the dynamic characteristics of time series data from the Baidu search index using the keywords “joint punishment for dishonesty” based on a visibility graph network. Applying a visibility graph algorithm, time series data from the Baidu Index was transformed into complex networks, with parameters calculated to analyze the topological structure. Results showed differences in the use of joint punishment for dishonesty in certain provinces by calculating the parameters of the time series network from January 1, 2020 to May 27, 2021; it was also shown that most of the networks were scale-free. Finally, the results of K-means clustering showed that the 31 provinces (excluding Hong Kong, Macao and Taiwan) can be divided into four types. Meanwhile, by analyzing the national Baidu Index data from 2020 to May 2021, the period of the time series data and the influence range of the central node were found.

An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.

An Approach in Big Data Analytics to improve the velocity of unstructured data Using Map Reduce

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.

An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.