Identification knowledge description

Our knowledge about the forms and genesis of neoformed Fe " oxides in sediments and sedimentary rocks is still rather limited because the low concentrations and poor crystallinity of these oxides hinder their identification and description. Two groups of Fe-containing sediments, whose Fe oxides have, however, attracted more interest than usual, are red beds and sedimentary iron ores (Fiichtbauer, 1988). 

MORT excels in terms of organizational root cause identification, as factors such as functional responsibilities, management systems and policies are well covered, but this strength of the method requires an accurate description of the incident process, and an experienced MORT analyst who is knowledgeable and well-practiced in the methodology. 

An important aspect of hazards identification is a description of the pcn-asiveiicss of the hazard. For example, most environmental assessments require knowledge of the conccnlratioii of material in (he environment, weighted... 

Previous knowledge A fairly extensive description of cefaclor degradation was available in the literature including methodology for determination of degradation products (35) and identification of degradation products and pathways under different conditions (36). 

The description of small scale turbulent fields in confined spaces by fundamental approaches, based on statistical methods or on the concept of deterministic chaos, is a very promising and interesting research task nevertheless, at the authors knowledge, no fundamental approach is at the moment available for the modeling of large-scale confined systems, so that it is necessary to introduce semi-empirical models to express the tensor of turbulent stresses as a function of measurable quantities, such as geometry and velocity. Therefore, even in this case, a few parameters must be adjusted on the basis of independent measures of the fluid dynamic behavior. In any case, it must be underlined that these models are very complex and, therefore, well suited for simulation of complex systems but neither for identification of chemical parameters nor for online control and diagnosis [5, 6],... 

The success of MPC is based on a number of factors. First, the technique requires neither state space models (and Riccati equations) nor transfer matrix models (and spectral factorization techniques) but utilizes the step or impulse response as a simple and intuitive process description. This nonpara-metric process description allows time delays and complex dynamics to be represented with equal ease. No advanced knowledge of modeling and identification techniques is necessary. Instead of the observer or state estimator of classic optimal control theory, a model of the process is employed directly in the algorithm to predict the future process outputs. 

Under Art. 3, para. 2, of Annex I to PEPAT particular attention is paid to possible flaws in preparing the CEE by requesting a description of the methods and data used to forecast the impacts of the propo.ied activity, as well as an identification of the gaps in knowledge and uncertainties encountered in compiling the information required. This facilitates scrutiny and enquiries from both experts and non-experts, who must be informed on the name and address of the person or organization which prepared the CEE. The latter may also rely on a non-technical summary of the information provided. 

The statistical analyses suggest that the degree to which a student is able to use his or her abstract information is positively related to the student s success on the identification task. Those able to express mainly abstract knowledge apparently had the best understanding of the five concepts and were most easily able to identify them. Those for whom the abstract characterizations were somewhat incomplete (e.g., those who were able to give abstract description for some concepts but needed example details to describe others) performed less well but still were more successful than those who predominantly relied on example details. 

The first stage requires the completion of a detailed description of the concepts. This stage is also required for the identification of criteria and case studies that will be used to produce a small version of the expert system in order to demonstrate its overall feasibility. The specifications and concepts, which represent the knowledge that is to be captured, are established by describing the key concepts, interrelationships, and the flow of information that is needed to describe the problem-solving process of the expert system (12). 

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