scholarly journals Generalized co-sparse factor regression

2021 ◽  
Vol 157 ◽  
pp. 107127
Author(s):  
Aditya Mishra ◽  
Dipak K. Dey ◽  
Yong Chen ◽  
Kun Chen
Keyword(s):  
Factor Regression

scholarly journals Sequential Co-Sparse Factor Regression

2017 ◽  
Vol 26 (4) ◽  
pp. 814-825 ◽  
Author(s):  
Aditya Mishra ◽  
Dipak K. Dey ◽  
Kun Chen
Keyword(s):  
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.

scholarly journals Obtaining and using data of operational monitoring of atmospheric air

2019 ◽  
Vol 27 (4) ◽  
pp. 337-352
Author(s):  
Ekaterina S. Kulakova ◽  
Ayrat M. Safarov ◽  
Liliya A. Nasyrova ◽  
Dmitry S. Mizgirev
Keyword(s):  
Air Monitoring ◽  
Atmospheric Air ◽  
Marker Substances ◽  
Zone Of Influence ◽  
Using Data ◽  
Residential Quarters ◽  
Effective Use ◽  
Factor Regression ◽  
Air Control ◽  
The City

In order to control the chemical composition of atmospheric air in cities located in the zone of influence of petrochemical enterprises, automatic atmospheric air monitoring stations (ASKAV) are installed. For the effective use of experimental data, the authors of the article developed a data collection system with ASKAV, in which the air control in residential quarters and the gas mixture at the source of organized emissions are synchronized. The analysis of data on the concentration of pollutants in the atmospheric air of the Sterlitamak obtained from ASKAV, on the basis of which a list of marker substances for enterprises of the city was compiled. Priority sources of air pollution with marker substances during a different wind regime were identified. The authors developed models for changing the concentration of marker substances in the air using the method of factor regression. Based on the results of the cross-correlation function, the time of movement of the contaminated gas cloud from the source of emission to the residential area of the city is obtained. Conclusions are made about the effectiveness of the developed air monitoring system and the field of application of the acquired models.

scholarly journals The organizations. Justice, trust and retaliatory attitude: a study in a company in the metal-mechanic sector

2019 ◽  
Vol 34 (62) ◽  
pp. 33-49
Author(s):  
Carla Goergen ◽  
Jandir Pauli ◽  
Priscila Sardi Cerutti ◽  
Marcia Perin
Keyword(s):  
Organizational Justice ◽  
Mediating Effect ◽  
Moderator Effect ◽  
Confirmatory Factor ◽  
Negative Effect ◽  
Managerial Implications ◽  
Factor Regression ◽  
The Relationship ◽  
A Company

The working relationship between individuals and organizations has been based on the perspective of organizational justice due to the need for approaches that go beyond the formal-legal dimension that regulates such relationship. This study aimed to describe the role of employees’ trust in the organization according to the relationship between organizational justice and predisposition to retaliatory attitudes. To accomplish that, a survey was carried out, with 188 employees, chosen by convenience. Data analysis was performed using a confirmatory factor, regression, mediation and moderation in order to test the relationship between organizational justice with the other theoretical model constructs. The hypothesis of the negative effect of justice on the retaliation was confirmed (H1). The indirect mediating effect of organizational trust on justice and predisposition to employees’ retaliatory attitude (H2) and the moderating effect of trust on the relationship between justice and employees’ retaliatory attitude (H3) were also confirmed. The results suggested that employees’ trust in the organization has a mediating and moderator effect on the relationship between justice and employees’ retaliatory attitude. These findings contribute to a better understanding about the effects of justice on the relationship between employees and organizations, indicating objective managerial implications to mitigate the effects of retaliation in these organizations.

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