scholarly journals Nuclear rocket shielding methods, modification, updating, and input data preparation. Volume VI. Point kernel techniques. Final progress report

1970 ◽  
Author(s):  
R.K. Disney ◽  
S.L. Zeigler
Keyword(s):  
Input Data ◽  
Progress Report ◽  
Data Preparation ◽  
Nuclear Rocket ◽  
Shielding Methods

scholarly journals From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

2019 ◽  
Vol 8 (9) ◽  
pp. 376 ◽  
Author(s):  
Zhi-Wei Hou ◽  
Cheng-Zhi Qin ◽  
A-Xing Zhu ◽  
Peng Liang ◽  
Yi-Jie Wang ◽  
...  
Keyword(s):  
Service Composition ◽  
Input Data ◽  
Knowledge Bases ◽  
Future Research ◽  
Knowledge Representation And Reasoning ◽  
Data Preparation ◽  
Preparation Methods ◽  
Construction Of Knowledge ◽  
Task Oriented ◽  
Geographic Modeling

One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.

scholarly journals ENVIRONMENTAL STUDIES: RADIOLOGICAL SIGNIFICANCE OF NUCLEAR ROCKET DEBRIS. Semiannual Progress Report, January 1--June 30, 1965.

1966 ◽  
Author(s):  
B.R. Fish ◽  
T.G. Clark ◽  
R.H. Boyett ◽  
W.H. Jr. Wilkie ◽  
W.C. McWhorter ◽  
...  
Keyword(s):  
Environmental Studies ◽  
Progress Report ◽  
Nuclear Rocket

Molecular replacement withMOLREP

2009 ◽  
Vol 66 (1) ◽  
pp. 22-25 ◽  
Author(s):  
Alexei Vagin ◽  
Alexei Teplyakov
Keyword(s):  
Rigid Body ◽  
Electron Density ◽  
Input Data ◽  
Molecular Replacement ◽  
Data Preparation ◽  
X Ray ◽  
Search Models ◽  
Automatic Mode ◽  
Structural Superposition ◽  
Fully Automatic

MOLREPis an automated program for molecular replacement that utilizes a number of original approaches to rotational and translational search and data preparation. Since the first publication describing the program,MOLREPhas acquired a variety of features that include weighting of the X-ray data and search models, multi-copy search, fitting the model into electron density, structural superposition of two models and rigid-body refinement. The program can run in a fully automatic mode using optimized parameters calculated from the input data.

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