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  1. Artikel ; Online: Project Governance and Project Performance

    Muhammad Zeeshan Fareed / Qin Su

    Sustainability, Vol 14, Iss 2516, p

    The Moderating Role of Top Management Support

    2022  Band 2516

    Abstract: Project governance (PG) has been primarily acknowledged as critical by researchers and practitioners in regard to successfully executing projects. However, project governance of public projects has received less attention from researchers. Therefore, in ... ...

    Abstract Project governance (PG) has been primarily acknowledged as critical by researchers and practitioners in regard to successfully executing projects. However, project governance of public projects has received less attention from researchers. Therefore, in this study, we studied the effects of project governance and top management support (TMS) on project performance (PP) and their interactions in public sector projects. Using the lens of resource dependence theory (RDT), we hypothesize whether TMS moderates the impact of PG on PP. A quantitative deductive approach was employed to examine this relationship. Quantitative data were collected using a structured questionnaire from 346 project managers, team members, and stakeholders. Our results indicated that PG and TMS are positively significantly correlated with project performance. Moreover, we found that TMS acts as a quasi-moderator in the relationship between PG and PP.
    Schlagwörter project performance ; project governance ; top management support ; megaprojects ; Belt and Road Initiative (BRI) ; Pakistan ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Thema/Rubrik (Code) 690 ; 650
    Sprache Englisch
    Erscheinungsdatum 2022-02-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Salient region detection through salient and non-salient dictionaries.

    Mian Muhammad Sadiq Fareed / Qi Chun / Gulnaz Ahmed / Adil Murtaza / Muhammad Rizwan Asif / Muhammad Zeeshan Fareed

    PLoS ONE, Vol 14, Iss 3, p e

    2019  Band 0213433

    Abstract: Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are ... ...

    Abstract Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are compromised. To deal with this issue, we regularize the low-rank representation through a local graph-regularization and a maximum mean-discrepancy regularization terms. Firstly, we introduce a novel feature space that is extracted by combining the four feature spaces like CIELab, RGB, HOG and LBP. Secondly, we combine a boundary metric, a candidate objectness metric and a candidate distance metric to compute the low-level saliency map. Thirdly, we extract salient and non-salient dictionaries from the low-level saliency. Finally, we regularize the low-rank representation through the Laplacian regularization term that saves the structural and geometrical features and using the mean discrepancy term that reduces the distribution divergence and connections among similar regions. The proposed model is tested against seven latest salient region detection methods using the precision-recall curve, receiver operating characteristics curve, F-measure and mean absolute error. The proposed model remains persistent in all the tests and outperformed against the selected models with higher precision value.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2019-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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