Model Builder

2021 ◽  
Keyword(s):  
Model Builder

A simplified homology-model builder toward highly protein-like structures: An inspection of restraining potentials

2012 ◽  
Vol 33 (24) ◽  
pp. 1927-1935 ◽  
Author(s):  
Tae-Rae Kim ◽  
Sangho Oh ◽  
Joshua SungWoo Yang ◽  
Sanghyuk Lee ◽  
Seokmin Shin ◽  
...  
Keyword(s):  
Homology Model ◽  
Model Builder

scholarly journals Investigation and Development of Post-Season Modelling of Arrow Squid in the Snares and Auckland Islands

2021 ◽  
Author(s):  
◽  
Vidette Louise McGregor
Keyword(s):  
Automatic Differentiation ◽  
Integrated Model ◽  
Current Data ◽  
Management Approach ◽  
Frequency Data ◽  
Catch Per Unit Effort ◽  
Length Frequency ◽  
Model Builder ◽  
Set Up ◽  
Length Frequency Data

<p>Squid fisheries require a different management approach to most fish species which are much longer living. Most squid live for around one year, spawn and then die. The result of this is an entirely new stock each year with little or no relationship of stock sizes between the years. Hence, it is difficult to set appropriate catch limits prior to the season. Currently, there is nothing set up for modelling the New Zealand squid fishery in-season or post-season. In-season management would allow for adjustments of catch limits during a season. Post-season management would provide information on how much the stock was exploited during a season (described as the escapement). I have produced an integrated model using ADMB (Automatic Differentiation Model Builder) (Fournier et al., 2011) which models length frequency data, CPUE (Catch Per Unit Effort) indices and catch weights from a season. It calculates escapement which indicates how much the fishery is currently being exploited. In running the model against data from four area and year combinations, I found the escapement calculation to be stable. The results suggest this modelling approach could be used with the current data collected for post-season modelling of the fishery. I am less confident about in-season modelling with the current data collected. The integrated model fits quite poorly to the CPUE data, suggesting some discrepancy either between the data or the assumptions made of them. Sampling from a greater number of tows is recommended to improve the length frequency data and this may also improve the ability of the model to fit both to these and the CPUE.</p>

scholarly journals Investigation and Development of Post-Season Modelling of Arrow Squid in the Snares and Auckland Islands

2021 ◽  
Author(s):  
◽  
Vidette Louise McGregor
Keyword(s):  
Automatic Differentiation ◽  
Integrated Model ◽  
Current Data ◽  
Management Approach ◽  
Frequency Data ◽  
Catch Per Unit Effort ◽  
Length Frequency ◽  
Model Builder ◽  
Set Up ◽  
Length Frequency Data

<p>Squid fisheries require a different management approach to most fish species which are much longer living. Most squid live for around one year, spawn and then die. The result of this is an entirely new stock each year with little or no relationship of stock sizes between the years. Hence, it is difficult to set appropriate catch limits prior to the season. Currently, there is nothing set up for modelling the New Zealand squid fishery in-season or post-season. In-season management would allow for adjustments of catch limits during a season. Post-season management would provide information on how much the stock was exploited during a season (described as the escapement). I have produced an integrated model using ADMB (Automatic Differentiation Model Builder) (Fournier et al., 2011) which models length frequency data, CPUE (Catch Per Unit Effort) indices and catch weights from a season. It calculates escapement which indicates how much the fishery is currently being exploited. In running the model against data from four area and year combinations, I found the escapement calculation to be stable. The results suggest this modelling approach could be used with the current data collected for post-season modelling of the fishery. I am less confident about in-season modelling with the current data collected. The integrated model fits quite poorly to the CPUE data, suggesting some discrepancy either between the data or the assumptions made of them. Sampling from a greater number of tows is recommended to improve the length frequency data and this may also improve the ability of the model to fit both to these and the CPUE.</p>

scholarly journals Suitable Site Selection for Rainwater Harvesting and Storage Case Study Using Dohuk Governorate

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 864 ◽  
Author(s):  
Gaylan Rasul Faqe Ibrahim ◽  
Azad Rasul ◽  
Arieann Ali Hamid ◽  
Zana Fattah Ali ◽  
Amanj Ahmad Dewana
Keyword(s):  
Rainwater Harvesting ◽  
Image Data ◽  
Research Area ◽  
Severe Drought ◽  
Water Harvesting ◽  
Water Crisis ◽  
Model Builder ◽  
Dry Zone ◽  
Elevation Model ◽  
Model Inverse

The Middle East is an inherently dry zone. It has experienced severe drought for the last seven years, and climate change has made the situation worse. The Dohuk governorate has been suffering from an appalling water crisis. One possible way of relieving this water crisis is by properly harvesting the rainwater. Rainwater harvesting is a widely used method of storing rainwater in the countries presenting with drought characteristics. Several pieces of research have derived and developed different criteria and techniques to select suitable sites for harvesting rainwater. The main aim of this research was to identify and select suitable sites for the potential erection of dams, as well as to derive a model builder in ArcMap 10.4.1. The model combined several parameters, such as slope, runoff potential, land cover/use, stream order, soil quality, and hydrology to determine the suitability of the site for harvesting rainwater. To compute the land use/cover categories, the study depended on Landsat image data from 2018. Supervised classification was applied using the ENVI 5 software, while the slope mapping and drainage order were extracted using a digital elevation model. Inverse distance weighting (IDW) was used for the spatial interpolation of the rain data. The results demonstrated that suitable areas for water harvesting, are located in the middle and northern part of the research area, and in intensively cultivated zones. The main soil texture in these suitable sites was loam, while the rainfall rate amounted to 750 to 900 mm. This research shows that 15% and 13% of the area studied can be categorized as having excellent and good suitability for water harvesting, respectively. Furthermore, 21% and 27% of the area studied were of moderate and poor suitability, while the remaining 24% were not suitable at all.

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