scholarly journals Decision Support to Sustainable Management of Bottom Trawl Fleet

2016 ◽  
Vol 8 (3) ◽  
pp. 204 ◽  
Author(s):  
Irena Bitunjac ◽  
Nikša Jajac ◽  
Ivan Katavić
Author(s):  
Vera Van Lancker ◽  
Frederic Francken ◽  
Lars Kint ◽  
Nathan Terseleer ◽  
Dries Van den Eynde ◽  
...  

For sustainable management of marine geological resources, a geological knowledge base is being built for the Belgian and southern Netherlands part of the North Sea. Voxel models of the subsurface are used for predictions on sand and gravel quantities and qualities, to ensure long-term resource use. The voxels are filled with geological data from boreholes and seismic lines, but other information can be added also. The geology provides boundary conditions needed to run environmental impact models that calculate resource depletion and regeneration under various scenarios of aggregate extraction. Such analyses are important in monitoring progress towards good environmental status, as outlined in the Marine Strategy Framework Directive. By including uncertainty, data products can be generated with confidence limits, which is critical for assessing the significance of changes in the habitat or in any other resource-relevant parameter. All of the information is integrated into a cross-domain, multi-criteria decision support system optimised for user-friendliness and online visualisation.


2019 ◽  
Vol 11 (11) ◽  
pp. 2998 ◽  
Author(s):  
Fawad Ahmed ◽  
Yuan Jian Qin ◽  
Luis Martínez

Technology brings green sustainable management practices to the workplace. It is important to ascertain the factors that enable or inhibit employees’ perceptions towards technology adoption. Corporate sustainability and sustainable management practices partially depend on employees for the successful implementation of technological changes in the workplace. This study aims at applying the technology acceptance model (TAM) from an employees’ user-perspective. It addresses those factors that form employee readiness for e-business and enable their intention to use e-business technologies such as decision support systems (DSS). It focuses on technology intensive firms while combining Davis’ technology acceptance model and Lai and Ong’s employee readiness for e-business (EREB) model. A survey questionnaire was used to collect the data for this cross-sectional study from 331 employees of 28 well-established small and medium-sized e-businesses located in the United Kingdom. The outcomes show that the four dimensions of EREB explain the 58.2% of variance in perceived ease of use and the 50.2% of variance in perceived usefulness. Together, perceived usefulness and perceived ease of use explain the 51.8% of variance in intention to use while fully mediating the relationship between higher order EREB construct and intention to use DSS.


2005 ◽  
Vol 52 (12) ◽  
pp. 189-198 ◽  
Author(s):  
D. DeSilva ◽  
S. Burn ◽  
G. Tjandraatmadja ◽  
M. Moglia ◽  
P. Davis ◽  
...  

Wastewater pipeline leakage is an emerging concern in Europe, especially with regards to the potential effect of leaking effluent on groundwater contamination and the effects infiltration has on the management of sewer reticulation systems. This paper describes efforts by Australia, in association with several European partners, towards the development of decision support tools to prioritize proactive rehabilitation of wastewater pipe networks to account for leakage. In the fundamental models for the decision support system, leakage is viewed as a function of pipeline system deterioration. The models rely on soil type identification across the service area to determine the aggressiveness of the pipe environment and for division of the area into zones based on pipe properties and operational conditions. By understanding the interaction between pipe materials, operating conditions, and the pipe environment in the mechanisms leading to pipe deterioration, the models allow the prediction of leakage rates in different zones across a network. The decision support system utilizes these models to predict the condition of pipes in individual zones, and to optimize the utilization of rehabilitation resources by targeting the areas with the highest leakage rates.


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