scholarly journals Research on key influence factors of creativity development of applied undergraduates based on PCA

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Xiangmin Ren ◽  
Jingwei Yu
Keyword(s):  
Principal Component Analysis ◽  
Influence Factors ◽  
Principal Component ◽  
Heilongjiang Province ◽  
Key Factors ◽  
Factors Affecting ◽  
The Core ◽  
Creativity Development ◽  
Teachers And Students ◽  
Core Characteristics

Abstract Creativity is one of the core characteristics of talent; for this reason, the creativity development of applied undergraduates should be one of the basic components of their education. This article gives an overview of the meaning of the creativity of applied undergraduates and makes a literature knowledge-mining and expert investigation on the factors affecting the creativity development. We obtained more than 100 influencing factors, filtered out the duplicative factors, and formed the remaining factors into a questionnaire. A survey was conducted among 1460 teachers and students of some applied undergraduates in Heilongjiang Province. By using principal component analysis (PCA) to analyse the questionnaire, the key factors that affect the creativity development of applied undergraduates are obtained, and the key factors are systematically analysed. According to the results of the analysis, the specific ways and methods of the creativity development of applied undergraduates are put forward.

Data Processing in the Key Factors Affecting China's Endowment Real Estate Enterprises Financing

2015 ◽  
Vol 730 ◽  
pp. 349-352
Author(s):  
Hua Liu ◽  
Nan Zhang
Keyword(s):  
Real Estate ◽  
Data Processing ◽  
Influence Factors ◽  
Principal Component ◽  
Key Factors ◽  
Factors Affecting ◽  
Real Estate Enterprises ◽  
Financing Problem ◽  
Statistical Analysis Software ◽  
General Influence

Financing problem is one of the main reasons for restricting the development of endowment real estate enterprises in China. By analyzing the present situation of endowment real estate enterprises financing and researching relevant literatures, we sum up 20 general influence factors. Using the data processing model of the principal component analysis method to analyze the 20 general influence factors under the help of SPSS 19.0 statistical analysis software, we can find out the key influence factors which affect endowment real estate enterprises financing. Aiming at the key influence factors, we put forward some specific measures to promote the smooth development of endowment real estate enterprises financing in China.

Key Factors Analysis of Port Logistics Development Based on Principal Component Analysis

2012 ◽  
Vol 253-255 ◽  
pp. 1522-1526
Author(s):  
Shou Wen Ji ◽  
Ling Shan Zhao
Keyword(s):  
Principal Component Analysis ◽  
Condition Factor ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Factors Affecting ◽  
Factors Analysis ◽  
Port Logistics ◽  
Spss Software

The factors affecting the modern port logistics development have become more numerous and complex, which is hard to verify the key factors. Principal component analysis which is short for PCA is used to analyze these factors, aiming at finding out the key factors of them. Lianyungang Port was chosen for case study, and nine factors were selected to analyze. By applying PCA, combined with expert scoring and SPSS software, the cumulative contribution rate could be calculated and the principal component was determined. At last the infrastructure factor, collecting and distributing condition factor, information factor and policy factor are found out as key factors. It provides a basis for Lianyungang Port’s planning and policy-making.

scholarly journals Fuzzy Cluster Analysis on Influencing Factors of College Student Scores

2020 ◽  
Vol 34 (5) ◽  
pp. 607-616
Author(s):  
Jia Wen ◽  
Xiaochong Wei ◽  
Haipeng Liu ◽  
Yiyuan Rong
Keyword(s):  
Cluster Analysis ◽  
College Student ◽  
Principal Component ◽  
Teaching Quality ◽  
Fuzzy Cluster ◽  
Key Factors ◽  
Fuzzy Cluster Analysis ◽  
Factors Affecting ◽  
Family Factor ◽  
Teachers And Students

In the age of the Internet, the learning environment is increasingly diversified. It is of great importance to explore the factors that truly affect the college student scores. Focusing on 13 factors that potentially influence college student scores, this paper carries out a questionnaire survey on students of different grades from different colleges, conducts fuzzy processing of the collected data, randomly selects the processed data for initialization of attribute values. Then, the initialized data were subject to principal component analysis (PCA), fuzzy cluster analysis (FCA), and analysis of variance (ANOVA). Through the analysis, six factors were identified as the key factors affecting college student scores: family factor, Exam factor, exchange factor, learner factor, classmate factor, and campus factor. On this basis, the authors called for the concerted efforts from the school, teachers, and students for improving the teaching quality in colleges.

Reliability Parameters Forecasting for Transmission Lines Based on Principal Component Regression

2013 ◽  
Vol 291-294 ◽  
pp. 2381-2386 ◽  
Author(s):  
Wen Xia Liu ◽  
Ji Kai Xu ◽  
Hong Yuan Jiang ◽  
Yong Tao Shen
Keyword(s):  
Principal Component Analysis ◽  
Transmission Lines ◽  
Principal Component Regression ◽  
Principal Component ◽  
Forecasting Accuracy ◽  
Factors Affecting ◽  
Reliability Parameters ◽  
Impacting Factors ◽  
Simulation Results

It is the foundation for evaluating the reliability of transmission lines to obtain and analyze the original reliability parameters. However, these parameters depend on long- term statistic and calculation. In the case of lacking such parameters in a new project , this paper proposes a method of Principal Component Analysis to obtain the principal component of the impacting factors ,in which various factors affecting reliability parameters are taken into account. Through this method, we can use PCR to obtain the failure rate of the unknown transmission lines on the base of the known credible lines’ rates. The simulation results show that the proposed approach possesses higher forecasting accuracy and provides references for the power system dispatching departments and transmission lines maintenance departments.

scholarly journals Factors underlying treatment decision-making for localized prostate cancer in the U.S. and Canada: A scoping review using principal component analysis

2018 ◽  
Vol 13 (7) ◽  
Author(s):  
Mustafa Andkhoie ◽  
Desneige Meyer ◽  
Michael Szafron
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Principal Component Analysis ◽  
Scoping Review ◽  
Principal Component ◽  
Treatment Decision ◽  
Localized Prostate Cancer ◽  
Key Factors ◽  
Treatment Decision Making ◽  
Word Cloud

Introduction: The purpose of this research is to gather, collate, and identify key factors commonly studied in localized prostate cancer (LPC) treatment decision-making in Canada and the U.S.Methods: This scoping review uses five databases (Medline, EMBASE, CINAHL, AMED, and PsycInfo) to identify relevant articles using a list of inclusion and exclusion criteria applied by two reviewers. A list of topics describing the themes of the articles was extracted and key factors were identified using principal component analysis (PCA). A word cloud of titles and abstracts of the relevant articles was created to identify complementary results to the PCA.Results: This review identified 77 relevant articles describing 32 topics related to LPC treatment decision-making. The PCA grouped these 32 topics into five key factors commonly studied in LPC treatment decision-making: 1) treatment type; 2) socioeconomic/demographic characteristics; 3) personal reasons for treatment choice; 4) psychology of treatment decision experience; and 5) level of involvement in the decision-making process. The word cloud identified common phrases that were complementary to the factors identified through the PCA.Conclusions: This research identifies several possible factors impacting LPC treatment decision-making. Further research needs to be completed to determine the impact that these factors have in the LPC treatment decision-making experience.

Development of a New Tool for Managing Performance Nutrition: The Athlete Food Choice Questionnaire

2019 ◽  
Vol 29 (6) ◽  
pp. 620-627 ◽  
Author(s):  
Rachael L. Thurecht ◽  
Fiona E. Pelly
Keyword(s):  
Principal Component Analysis ◽  
Nutrition Education ◽  
Food Choice ◽  
Weight Control ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Dietary Interventions ◽  
Frequency Scale ◽  
The Individual

This study aimed to develop and refine an Athlete Food Choice Questionnaire (AFCQ) to determine the key factors influencing food choice in an international cohort of athletes. A questionnaire that contained 84 items on a 5-point frequency scale was developed for this study. Athletes at the 2017 Universiade, in Taiwan, were invited to participate. Principal component analysis was utilized to identify key factors and to refine the questionnaire. Completed questionnaires were received from 156 athletes from 31 countries and 17 sports. The principal component analysis extracted 36 items organized into nine factors explaining 68.0% of variation. The nine factors were as follows: nutritional attributes of the food, emotional influences, food and health awareness, influence of others, usual eating practices, weight control, food values and beliefs, sensory appeal, and performance. The overall Kaiser–Meyer–Olkin measure was 0.75, the Bartlett test of sphericity was statistically significant, χ2(666) =2,536.50, p < .001, and all of the communalities remained >0.5. Intercorrelations were detected between performance and both nutritional attributes of the food and weight control. The price of food, convenience, and situational influences did not form part of the factorial structure. This research resulted in an AFCQ that includes factors specific to athletic performance and the sporting environment. The AFCQ will enable researchers and sports dietitians to better tailor nutrition education and dietary interventions to suit the individual or team. The next phase will test the accuracy and reliability of the AFCQ both during and outside of competition. The AFCQ is a useful tool to assist with management of performance nutrition for athletes.

scholarly journals Diversity of entomopathogenic fungi associated with Mediterranean fruit fly (Ceratitis capitata (Diptera: Tephritidae)) in Moroccan Argan forests and nearby area: impact of soil factors on their distribution

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ayoub Hallouti ◽  
Mohamed Ait Hamza ◽  
Abdelaziz Zahidi ◽  
Rachid Ait Hammou ◽  
Rachid Bouharroud ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Entomopathogenic Fungi ◽  
Ceratitis Capitata ◽  
Chemical Properties ◽  
Principal Component ◽  
Fruit Fly ◽  
Component Analysis ◽  
Organic Content ◽  
Pathogenicity Test ◽  
Factors Affecting

Abstract Background Studying the ecology of biocontrol-agents is a prerequisite to effectively control medfly (Ceratitis capitata (Diptera: Tephritidae)) with entomopathogenic fungi. In this context, factors affecting the occurrence and distribution of medfly-associated entomopathogenic-fungi were studied. Soil samples (22) were collected from natural and cultivated areas of Souss-region Morocco. Results A total of 260 fungal isolates belonging to 22 species and 10 genera were obtained by using medfly pupae as bait. Medfly-associated fungi were detected in all studied soils and pupae infection percentages ranged from 3.33% to 48%. Two genera, Fusarium and Beauveria were the most frequent with 83 isolates (32%) and 50 isolates (19.23%) respectively. Pathogenicity test of isolated species against medfly pupae showed high mortality rates up to 91% for some strains. Principal component analysis (PCA) demonstrated a strong influence of origin, physical, and chemical properties of soil on the abundance of these fungi. In general, medfly-associated fungi were more abundant in soils with moderate pH (7.5 to 8) having high sand and organic content. High relative humidity negatively influenced the abundance of these fungi. Both factors directly affected the fungal infection percentages in pupae. The response of fungi to these parameters varied among species. According to principal component analysis (PCA), the soils of argan fields and forests were more suitable for the development of medfly-associated fungi than citrus orchards. Conclusions These results guide identifying suitable soils for the effective application of entomopathogenic fungi as biological control agents. In summary, isolated indigenous strains seem to be a promising option to control C. capitata.

Evaluation Study on the Network Impact Index of WeChat Based on Principal Component Analysis

2016 ◽  
Vol 13 (10) ◽  
pp. 7676-7679 ◽  
Author(s):  
Yi Liu
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Influence Factors ◽  
Principal Component ◽  
Modern Society ◽  
Component Analysis ◽  
Evaluation Study ◽  
Positive Energy ◽  
Impact Index ◽  
And Function

WeChat software is an important social tool in modern society. This paper discusses the network impact of WeChat from ten aspects including WeChat popularity, attention, video observability, network reputation, function usability, dissemination speed of information, transmission ratio of positive energy and impact of WeChat on network economy, politics and culture, and questionnaires on these ten influence factors are distributed to college students for investigation. Principal component analysis is used to deal with the survey results, the principal components of the ten factors are extracted, and the results show that WeChat popularity, attention, video observability, network reputation and function usability are the main components, in which WeChat popularity, attention and video observability are the factors having the greatest impact on the calculation. And this paper presents the function relationship between the main principal components of WeChat network impact index and these ten influence factors, to evaluate the network impact index of WeChat.

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