Identification tasks description

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. 

SPS exercises. The same exercise described above for Experiment I was used in Experiment II. For this study, a set of 10 items was selected a priori for presentation in the identification task, and all students responded to the set. The order of item presentation within the set was randomly determined for each student. This exercise was undertaken by each student at the end of initial instruction - either specific instruction using only examples or abstract instruction using only general descriptions. 

The identification task. There was no statistically significant difference in the mean number of items recognized by the two groups on the first computer task. Students who saw only the specific instruction (i.e., concrete examples) performed slightly better than those who saw only the abstract instruction (i.e., the general descriptions of the situations), with both groups identifying correctly about one half of the items (MSI = 5.7 and MAI = 5.2, t < 1). 

The classical approach to species identification is that of qualitative morphological and quantitative morphometric analysis. Thus, taxonomists perform the identification task by visual observation and measurement of morphological and morphometric features (i.e. the colour of feathers, the shape of wings, the distance between the eyes, the angle between two veins). A natural way to automate the traditional taxonomic ways of species identification by visual observation and measurement is to employ methods of image analysis. Furthermore, since taxonomic descriptions rely on morphological and morphometric features, a feature-based approach seems appropriate. 

The reason for an Exposition is so that there is a description of the system showing how it works and how it controls the achievement of quality. This is different from the policies and procedures. The policies are a guide to action and decision and as such are prescriptive. The procedures are the methods to be used to carry out certain tasks and as such are task related. They need to be relatively simple and concise. A car maintenance manual, for example, tells you how to maintain the car but not how the car works. Some requirements, such as those on traceability and identification, cannot be implemented by specific procedures although you can have specific policies covering such topics. There is no sequence of tasks you can perform to achieve traceability and identification. These requirements tend to be implemented as elements of many procedures which when taken as a whole achieve the traceability and identification requirements. In order that you can demonstrate achievement of such requirements and educate your staff, a description of the system rather than a separate procedure would be an advantage. The Exposition can be structured around the requirements of ISO/TS 16949 and other governing standards. It is a guide or reference document and not auditable. 

The above problems of fabrication and performance present a challenging task of identification of the governing material mechanisms. Use of nonlinear finite element analysis enables close simulation of actual thermal and mechanical loading conditions when combined with measurable geometrical and material parameters. As we continue to investigate real phenomena, we need to incorporate non-linearities in behavior into carefully refined models in order to achieve useful descriptions of structural responses. 

As a starting point in the description of the solid intermetallic phases it is useful to recall that their identification and classification requires information about their chemical composition and structure. To be consistent with other fields of descriptive chemistry, this information should be included in specific chemical and structural formulae built up according to well-defined rules. This task, however, in the specific domain of the intermetallic phases, or more generally in the area of solid-state chemistry, is much more complicated than for other chemical compounds. This complexity is related both to the chemical characteristics (formation of variable composition phases) and to the structural properties, since the intermetallic compounds are generally non-molecular in nature, while the conventional chemical symbolism has been mainly developed for the representation of molecular units. As a consequence there is no complete, or generally accepted, method of representing the formulae of intermetallic compounds. 

Frequently in theoretical work on the subject, whether dealing with the steady or nonsteady state, the mathematical development of an adopted model is followed by a descriptive summary of the results which is rarely traced back clearly to the assumptions inherent in the model. This has often resulted in extravagant identification of the model with the actual phenomenon and has been an obstacle in the task of reconciling conflicting views. There is need, therefore, of a purely descriptive exposition, stripped as completely as possible of mathematical language, to clarify the physical concepts of the combustion wave phenomenon. 

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 systematization of biological data through mathematical modeling is not a trivial task and requires the intensive use of material and human resources. Moreover, the models often incorporate experimental uncertainties that do not always allow the identification of the trends of the culture, with the desired precision. Figure 8.1 summarizes the main types of models applied to the description of cellular metabolism, according to the classification proposed by Tsuchiya et al., (1966). 

Efforts in engineering science reinforce these observations (Stephano-poulos, 1987). Many tasks are not achievable if representational expressivity isn t sufficiently rich to allow the description of the necessary concepts (Brachman and Levesque, 1985). Concepts must be manipulated directly if powerful reasoning is to be achieved. The success of any advanced computer-aided tool for enhancing the identification of hazards requires (1) the development of a representational language sufficiently rich to embody advanced scientific concepts and (2) a means for manipulating these concepts and reasoning about them, directly. 

From the viewpoint of scientific methodology there are three main tasks in CAPE representation of the problem, generation of several alternative solutions, and selection of the best one. These tasks correspond to the activities realized in four phases of any scientific method analysis (description of the problem and identification of the objectives), hypothesis (generation of solutions), synthesis (comparing the solutions), and validation (formulation of conclusions). The activities realized in the last two phases correspond to the selection task in CAPE. 

Based on the emphasis on in silico modeling or SAR extraction, the tasks of data mining in these areas are different. Regarding SAR extraction and identification, the task is to derive a comprehensive description of the SAR in the data. With regard to virtual screening and model building, the task is clearly to establish predictive models. We will briefly review methods and applications in both task areas. 

In [16, 23-25] as a criterion critical state of the reaction system the extremal behavior of the total concentration of reaction species is suggested. Such a description of the critical state has obvious advantages. It permits to use mathematical tools on finding the extremal conditions of a reaction. It is well-known that such mathematical approaches are well developed. In this case we have used the calculus of variations, namely the Pontryagin method of maximum with value identification of species and steps under critical conditions of a reaction. Thus, simultaneously two important tasks are solved ... 

The conceptual modeling step produced the formal descriptive representation of the supply chain configuration problem. In order to proceed with further evaluation, an experimental plan is developed. The purpose of experimental planning is to define procedures for modeling and analysis of the supply chain configuration problem. The tasks of experimental planning are (1) selection of appropriate modeling methods (2) definition of performance measures (3) identification of relevant experimental scenarios and experimental factors and (4) definition of individual experiments to be conducted as well as their properties. 

Computing in Education [Hi-ce] (http // www.hi-ce.org). The software uses three basic components objects, the actual physical entities of the phenomenon under study variables, either qualitative or quantitative descriptions of the objects and relationships that describe how variables affect one another (Jackson, Krajcik Soloway, 2000). To support students in model construction, Model-It scaffolds the learner in transitioning from what s/he already knows of the world to building a computerized model representation. The process of modelling the pond ecosystem consisted of identification of critical objects, variables, and relationships. The final task involved identifying the correct causal assumptions. (See Figure 1 for a screenshot of the Model-It software.)... 

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