scholarly journals Analysis on Influencing Factors of College Students' Information Ability in Big Data Environment

2021 ◽  
Vol 2 (3) ◽  
pp. 36-42
Author(s):  
Xiaomei Hu ◽  
Yuan Yuan ◽  
Mengjie Wang
Keyword(s):  
College Students ◽  
Big Data ◽  
Information Society ◽  
Rapid Development ◽  
Current Situation ◽  
Actual Situation ◽  
Factors Affecting ◽  
Main Factors ◽  
Data Environment ◽  
Constituent Elements

Information ability is the basis and premise of college students' survival and career development, the condition of their lifelong learning, and the necessary ability of innovative talents. In order to adapt to the rapid development of the current information society, college students, as an important force of social construction, must cultivate good information ability. Firstly, this paper analyzes the position of college students' information ability in the ability structure. Secondly, it analyzes the constituent elements of college students' information ability in the big data environment. Thirdly, it analyzes the current situation of information ability training of economic and management college students under the big data environment. Finally, combined with the actual situation of Anhui University of Finance and Economics, through the questionnaire, this paper investigates and analyzes the current situation of economic and management college students' information ability, in order to explore the main factors affecting college students' information ability.

E-Commerce Security Research in Big Data Environment

2021 ◽  
pp. 1476-1490
Author(s):  
Mei Zhang ◽  
Huan Liu ◽  
Jinghua Wen
Keyword(s):  
Big Data ◽  
Electronic Commerce ◽  
Rapid Development ◽  
Security System ◽  
Information Storage ◽  
New Era ◽  
Security Research ◽  
Distributed Platform ◽  
Data Environment ◽  
Electronic Commerce Security

Rapid development of e-commerce and mobile communication opens a new era of big data. In this article, the authors put big data and e-commerce security together. They construct electronic commerce security system from these aspects: the creation of database, the security of information storage, the mining of information based on big data environment thoroughly. The second-generation product distributed platform- Apache Hadoop which is more popular and instant has been brought in. what's more, this article expounds the structure and working process. On the base of this platform, this article analyses the certainty and security of e-commerce transactions data developed on the condition of big data. It puts forward a construction view that people should guide and monitor the behavior of e-commerce, and improve the security system of electronic commerce on the base of data.

scholarly journals Risk factors for Keshan disease: a prospective cohort study protocol of gut flora

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenzhen Li ◽  
Jin Wei ◽  
Yanping Zhang ◽  
Gaopeng Li ◽  
Huange Zhu ◽  
...  
Keyword(s):  
Big Data ◽  
Cohort Study ◽  
Rapid Development ◽  
Keshan Disease ◽  
Molecular Pathways ◽  
Gut Flora ◽  
Factors Affecting ◽  
New Biomarkers ◽  
The Relationship

Abstract Background Keshan disease is an endemic cardiomyopathy of undefined causes. Being involved in the unclear pathogenesis of Keshan disease, a clear diagnosis, and effective treatment cannot be initiated. However, the rapid development of gut flora in cardiovascular disease combined with omics and big data platforms may promote the discovery of new diagnostic markers and provide new therapeutic options. This study aims to identify biomarkers for the early diagnosis and further explore new therapeutic targets for Keshan disease. Methods This cohort study consists of two parts. Though the first part includes 300 participants, however, recruiting will be continued for the eligible participants. After rigorous screening, the blood samples, stools, electrocardiograms, and ultrasonic cardiogram data would be collected from participants to elucidate the relationship between gut flora and host. The second part includes a prospective follow-up study for every 6 months within 2 years. Finally, deep mining of big data and rapid machine learning will be employed to analyze the baseline data, experimental data, and clinical data to seek out the new biomarkers to predict the pathogenesis of Keshan disease. Discussion Our study will clarify the distribution of gut flora in patients with Keshan disease and the abundance and population changes of gut flora in different stages of the disease. Through the big data platform analyze the relationship between environmental factors, clinical factors, and gut flora, the main factors affecting the occurrence of Keshan disease were identified, and the changed molecular pathways of gut flora were predicted. Finally, the specific gut flora and molecular pathways affecting Keshan disease were identified by metagenomics combined with metabonomic analysis. Trial registration: ChiCTR1900026639. Registered on 16 October 2019.

scholarly journals Investigation on Preoperative Interview of Standardized Training for Anesthesiologists

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Yifan Xue ◽  
Rui Xia ◽  
Dan Qiao
Keyword(s):  
Questionnaire Survey ◽  
Evaluation System ◽  
Current Situation ◽  
Hebei Province ◽  
Basic Knowledge ◽  
Online Questionnaire ◽  
Factors Affecting ◽  
Know How ◽  
Main Factors

Objective — To investigate the current situation of preoperative interview of standardized training anesthesiologists in Hebei province by online questionnaire. To understand the current situation of preoperative interview of standardized training anesthesiologists and screen out the main factors affecting the preoperative interview of standardized training anesthesiologists. Methods — The questionnaire is designed by the authors and is distributed through Wechat Mini Program. Questionnaire survey was conducted among standardized training anesthesiologists in several training bases in Hebei Province. Results — 313 valid questionnaires were collected. The standardized training anesthesiologists had problems such as lack of basic knowledge, lack of teacher's evaluation on their preoperative interview, and don't know how to improve. In addition, the number of preoperative interviews in neurosurgery and cardiothoracic surgery was relatively small, and the ability of preoperative interviews in specialized surgery was insufficient. Conclusion — To improve the quality of preoperative interviews, standardized training anesthesiologists need to improve the training and evaluation system and enhance teaching.

scholarly journals Strategies for the Application of Computer Software Technology in Big Data

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Zhengxuan Li
Keyword(s):  
Big Data ◽  
Computer Technology ◽  
Rapid Development ◽  
Computer Software ◽  
Development Trend ◽  
Developed Countries ◽  
Current Situation ◽  
Software Technology ◽  
The Development Trend

The rapid development of computer software technology has a significant impact on China. However, compared with developed countries, the development of computer software technology in China is still quite different, and it is necessary to explore and research computer technology continuously. This paper analyzes the current situation of computer software technology and the development trend of big data, explains the related techniques of computing and big data, and how to apply computer technology to big data.

scholarly journals RESEARCH ON EARLY WARNING SYSTEM OF COLLEGE STUDENTS’ BEHAVIOR BASED ON BIG DATA ENVIRONMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 659-665
Author(s):  
C. Y. Yang ◽  
J. Y. Liu ◽  
S. Huang
Keyword(s):  
College Students ◽  
Big Data ◽  
Early Warning ◽  
Behavioral Problems ◽  
Early Warning System ◽  
Warning System ◽  
Safety Control ◽  
Education Management ◽  
Data Environment ◽  
Behavior Based

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.

Analysis on the Main Factors Influencing the High-Speed Railway Passenger Flow

2014 ◽  
Vol 919-921 ◽  
pp. 1085-1090
Author(s):  
Yuan Huang ◽  
He Song ◽  
Chun Yan Jia
Keyword(s):  
High Speed ◽  
Rapid Development ◽  
Service Level ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Factors Affecting ◽  
Passenger Flow ◽  
Survey Statistics ◽  
Main Factors ◽  
The Times

With the rapid development of our national economy, the fast process of city, regional passenger demand is increasing rapidly, high-speed railway emerging as the times requiring. High-speed railway has many advantages in essence and also some disadvantages. How to use its advantages, how to avoid their disadvantages, is important for the advantageous to improve the interests of the company on high speed railway transport department and railway sector, as well as the social development. Through the way of questionnaire survey, statistics and analyzes the main factors affecting the High-speed Rail passenger flow, provides more active mode of operation for the High-speed Rail department. This paper raised the urgent and realistic basis to improve the service level.

scholarly journals The Role of Trade Infrastructure of the Consumer Market in Improving the Quality of Life of the Population of the Irkutsk Region

2020 ◽  
Author(s):  
Andrey Fedotov ◽  
◽  
Svetlana Schepina ◽  
◽  
Keyword(s):  
Quality Of Life ◽  
Standard Of Living ◽  
Consumer Market ◽  
Factors Affecting ◽  
Irkutsk Region ◽  
Main Factors ◽  
Constituent Elements ◽  
The Impact

One of the fundamental indicators that characterize the state of the socio-economic situation in the region is the quality of life of the population. To this end, it is necessary to analyze and evaluate the volume of consumption of goods per capita, as well as the logistics component of the development of the consumer market. The article considers the impact of the trade infrastructure of the consumer market and its constituent elements on the quality of life of the population of the Irkutsk region. The analysis of the main factors affecting the standard of living of the population is given. Ways to improve the quality of life in the region are proposed.

scholarly journals A Survey on Unsupervised K-Means Algorithm in Big Data Environment

2021 ◽  
pp. 1-8
Author(s):  
Fatama Sharf Al-deen ◽  
Fadl Mutaher Ba-Alwi
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Literature Review ◽  
Relational Databases ◽  
Rapid Development ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Prominent Feature ◽  
Data Environment

Due to the rapid development in information technology, Big Data has become one of its prominent feature that had a great impact on other technologies dealing with data such as machine learning technologies. K-mean is one of the most important machine learning algorithms. The algorithm was first developed as a clustering technology dealing with relational databases. However, the advent of Big Data has highly effected its performance. Therefore, many researchers have proposed several approaches to improve K-mean accuracy in Big Data environment. In this paper, we introduce a literature review about different technologies proposed for k-mean algorithm development in Big Data. We demonstrate a comparison between them according to several criteria, including the proposed algorithm, the database used, Big Data tools, and k-mean applications. This paper helps researchers to see the most important challenges and trends of the k-mean algorithm in the Big Data environment.

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