A carbon oxidation factor regression model of coal-fired power plants in China

2017 ◽  
Vol 142 ◽  
pp. 4403-4411 ◽  
Author(s):  
Handong Wu ◽  
Wei Han ◽  
Dandan Wang ◽  
Lin Gao
Keyword(s):  
Regression Model ◽  
Power Plants ◽  
Carbon Oxidation ◽  
Factor Regression

scholarly journals Approaches to assessing the level of innovation development in manufacturing industries

2020 ◽  
Vol 220 ◽  
pp. 01027
Author(s):  
Vitaly Makoveev ◽  
Liliya Mukhametova
Keyword(s):  
Regression Model ◽  
Socioeconomic Factors ◽  
Manufacturing Industry ◽  
Energy Sector ◽  
Machine Building ◽  
Innovative Activity ◽  
Manufacturing Industries ◽  
Innovation Development ◽  
Factor Regression

Sustainable long-term development of the energy sector is impossible without a developed manufacturing industry and especially machine-building enterprises. The article offers a method for assessing the level of innovation development in the manufacturing industry and identifies the factors that have the greatest impact on the development of the process of creating and implementing innovations in this sector. A multi-factor regression model is constructed to determine the degree of influence of various socioeconomic factors on the level of development of innovative activity in manufacturing industries, as well as to develop proposals and recommendations for its activation.

scholarly journals Improvement of the innovation management system in the manufacturing industry

2020 ◽  
Vol 220 ◽  
pp. 01048
Author(s):  
Vitaly Makoveev ◽  
Liliya Mukhametova
Keyword(s):  
Regression Model ◽  
Manufacturing Industry ◽  
Innovation Management ◽  
Machine Building ◽  
Manufacturing Industries ◽  
Economic Factors ◽  
Innovation Development ◽  
Factor Regression ◽  
Socio Economic Factors

Sustainable long-term development of the energy sector is impossible without a developed manufacturing industry and especially machine-building enterprises. The article identifies the factors that have the greatest impact on the development of innovation in the manufacturing industry. A multi-factor regression model is constructed to determine the degree of influence of various socio-economic factors on the level of innovation development in manufacturing industries. An organizational and economic mechanism aimed at enhancing innovation in the manufacturing industry and increasing the competitiveness of the products of enterprises in this sector is proposed.

Prediction of Nuclear Power Project Cost Based on Improved Non-Equidistant GM (1,1) Model and Exponential Regression Model

2014 ◽  
Vol 644-650 ◽  
pp. 3489-3492
Author(s):  
Bing Yi Liu ◽  
Dong Xiao Niu ◽  
Jin Peng Qiu ◽  
Hui Xu ◽  
Yue Wang
Keyword(s):  
Regression Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Construction Cost ◽  
Exponential Regression ◽  
Power Construction ◽  
Exponential Regression Model ◽  
The Cost ◽  
Power Project

Today, faced with the problem of international energy supply difficulties, due to the nuclear power’s fewer of its resources consumption, little environmental pollution and other advantages, it has become one of the essential pillars of the electricity supply. This paper analyzed from a comprehensive localization of nuclear power plants, then use the non-equidistant GM (1,1) model and the exponential regression model to predict the construction cost of nuclear power, and do the error analysis. The conclusion that by increasing the degree of localization of domestic nuclear power construction, which can effectively reduce the cost of building nuclear power plants conclusions.

scholarly journals Use of the multi-factor regression model for temperature control in structurally isolated rooms of the railway stations

2021 ◽  
Vol 1151 (1) ◽  
pp. 012044
Author(s):  
V S Tynchenko ◽  
I V Markevich ◽  
A R Ogol ◽  
O V Baryshnikova ◽  
D V Rogova ◽  
...  
Keyword(s):  
Regression Model ◽  
Temperature Control ◽  
Railway Stations ◽  
Factor Regression

scholarly journals A-64 The Relationship between Big-5 Personality Factors and Cognitive Health

2019 ◽  
Vol 34 (6) ◽  
pp. 924-924
Author(s):  
J Beach ◽  
H Ricketts ◽  
V McCaskey ◽  
S Taylor ◽  
M Harrell ◽  
...  
Keyword(s):  
Regression Model ◽  
Eating Habits ◽  
Multiple Regression Model ◽  
Institutional Setting ◽  
Personality Factors ◽  
Health Questionnaire ◽  
Cognitive Health ◽  
Significant Regression ◽  
Factor Regression ◽  
The Relationship

Abstract Objective The purpose of the current study was to examine the relationship between factors of personality and cognitive health. Methods Two hundred and two participants (M age = 19.51, SD = 3.33; M education = 12.40, SD = .75; 72.3% Female, 55.3% White, 36.0% African American, 4.6% Asian, 4.1% Other) completed the cognitive health questionnaire (CHQ) and a 120-item International Personality Item Pool Representation of the NEO-PI-R (IPIP-NEO) as a part of a larger battery in an institutional setting. A CHQ total score was calculated based on items of four positive factors of cognitive health including social/intellectual activities, nutrition, exercise, and eating habits. Results A multiple linear regression using backwards elimination was calculated to predict scores on the Cognitive Health Questionnaire utilizing the five personality factors of the IPIP-NEO. The overall five-factor regression model yielded a significant regression equation (F(5,196) = 7.76, p < .001), with an R2 of .165. The final three-factor regression model consisting of extraversion, openness, and consciousness yielded significant results (F(3,198) = 12.70, p < .001), with an R2 of .161. Conclusions This exploratory study investigated the relationship between factors of personality and cognitive health. Although a multiple regression model involving all five factors of personality were significantly predictive of cognitive health, the results of this study indicate that greater variance of cognitive health is predicted by extraversion, openness and conscientiousness than neuroticism and agreeableness. Further research should investigate each factor of cognitive health and how these components are predicted by features of personality.

Estimation of operational efficiency and its determinants using DEA

2013 ◽  
Vol 7 (4) ◽  
pp. 409-429 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Vijay Kumar Bajpai
Keyword(s):  
Sensitivity Analysis ◽  
Power Plant ◽  
Regression Model ◽  
Power Plants ◽  
Research Work ◽  
Regression Result ◽  
Truncated Regression ◽  
Specific Knowledge ◽  
Content Type

Purpose – The purpose of this study is to benchmark the performance of state-owned coal-fired power plants (CFPPs) and test whether plant-specific knowledge in terms of quality of coal, size, age and make of plant contribute to an improvement in plant efficiency. Design/methodology/approach – The methodology that is utilized in the study follows a nonparametric approach of data envelopment analysis (DEA) with sensitivity analysis and Tobit regression model. The input-oriented DEA models are applied to evaluate the overall, pure technical and scale efficiencies of the CFPPs. Further, slack analysis is conducted to identify modes to improve the efficiency of the inefficient plants. Sensitivity analysis based on peer count and the removal of variables is carried out to identify the benchmark power plant. Through Tobit and bootstrap-truncated regression model, the paper investigates whether a plant's specific knowledge influences its efficiency. Findings – The DEA analysis demonstrates that nine plants are technically purely efficient.The slack analysis reveals that reducing the consumption of oil is the most effective way to improve the efficiency of inefficient plants. Mattur plant is the benchmark for most of the inefficient plants. Regression result suggests that quality of coal and size of plant significantly affect the inefficiency of the sample plants. Bharat Heavy Electrical Limited MAKE plant achieved higher efficiency in comparison to mixed MAKE. Originality/value – This study is one of the few published studies that benchmark the performance of state-owned CFPPs. This research carried out taking some new uncontrollable parameters of power plant utilities of India. Research work also identifies the possible causes of inefficiency and provides measures to improve the efficiency of the inefficient power plant.

scholarly journals Analysis and Prediction of the Cybersports Match Outcome in DOTA 2 Discipline Based on Multi-factor Regression on the Example of the Team Virtus Pro

2019 ◽  
Vol 5 (10) ◽  
pp. 19-24
Author(s):  
I. Zhigarev
Keyword(s):  
Regression Model ◽  
Analysis Of Variance ◽  
Microsoft Excel ◽  
Sports Industry ◽  
Sports Organizations ◽  
Traditional Sports ◽  
The Law ◽  
Object Of Study ◽  
Factor Regression ◽  
Statistical Indicators

This article is devoted to the question of constructing a regression model, which will make it possible to analyze and predict the outcome of e-sports meetings in the DOTA 2 discipline using the Virtus Pro team as an example. In addition, throughout the text, a brief analysis of the e-sports industry these days, as well as a brief explanation of the essence of the chosen e-sports discipline are taken into consideration. Convincing arguments are presented that make it possible for the reader to understand that e-sports today can equal traditional sports in the following indicators: the number of followers, peak views as well as the amount of money circulating in the industry. The law introduced during the research is intended for the following external users: e-sports organizations, potential investors, breeders as well as for the players themselves. The resulting model was analyzed and described using various statistical indicators. A correlation grid was obtained, indicators of regression statistics were described, analysis of variance was made with a detailed description of its results. Factor signs considered during the research are described in detail for reader’s understanding. It is important to note that the resulting law makes it possible to apply it not only to the object of study, but also to other e-sports organizations having a team in this discipline, provided that their styles of playing the game have similarities. The tool for deriving the regression law was Microsoft Excel. The source of data is the team’s statistics, which were collected during real e-sports meetings by the leading site of statistics and the gaming community DOTA 2 — Dotabuff.

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