scholarly journals Collaborative Governance Model in Managing International Borders in Riau Islands Province using Partial Least Squares Method

2017 ◽  
Vol 21 (2) ◽  
pp. 166
Author(s):  
Adji Suradji Muhammad

Despite the large number of border management agencies at both local and national level, there is no guarantee that borders of Riau Islands Province are well protected. Most illegal fishing, mining and human trafficking are attributable to the lack of collaboration among vari-ous border management agencies. It also indicates failure to implement effectively collabora-tive culture, leadership, team process, structure, and strategic vision. This study aims to eval-uate the implementation of Collaborative Governance Model in the management of Riau Islands Province borders. The study used a descriptive verification method, and collected data using an interview technique, while partial least squares method was used in analyzing the data. Re-sults swhowed that the collaborative team process (CTP), turned out to be the dependent varia-ble, while the other five principles, , inter alia, structural, cultural, leadership and strategic vision variables were established as the indepdendentindependent variables that influence CTP.

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987323 ◽  
Author(s):  
Marwa Chaabane ◽  
Majdi Mansouri ◽  
Kamaleldin Abodayeh ◽  
Ahmed Ben Hamida ◽  
Hazem Nounou ◽  
...  

A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.


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