TASK 1, characterization

Characterizing Tasks Characterizing Assistive Devices Characterizing Overall Systems in High-Level-Task Situations... 

Task characterization or task analysis, like the organization of human system parameters, is facilitated with a hierarchical perspective. A highly objective, algorithmic approach could be delineated for task analysis... 

Compared to the commercial availability of took that characterize the performance of various human subsystems, instruments that quantify task attributes are much less prevalent. However, the perception that increased emphasis on this area will increase the utility of measures that characterize the human will likely motivate a substantial increase in the number and variety of products available for task characterization. In addition, factors such as the Americans with Disabilities Act (ADA), which encourages worksite evaluations and modifications to facilitate employment of individuals with disabilities and the increase in work-site related injuries such as carpal tunnel syndrome have led to an increased demand for such tools. 

What would seem to be a hopeless task characterizing fibers according to the aggregation of the amorphous and crystalline regions has been greatly simplified by the fact that the fiber properties are related to the amounts and the orientations of the two phases. Another simplification arises with the manner in which these orientations are related to the spinning parameters, at least for the relatively low spinning speeds that have been used in most of the studies presented in the open literature. 

In the case of the Superphenix probes we were asked to provide a 100% characterization of the probes, that meant to verify all acoustics characteristics over the 160 groups of element multiplexed around the probe. This task has required the development of an automatic acquisition and analysis system which is described below. 

As we have mentioned, the particular characterization task considered in this work is to determine attenuation in composite materials. At our hand we have a data acquisition system that can provide us with data from both PE and TT testing. The approach is to treat the attenuation problem as a multivariable regression problem where our target values, y , are the measured attenuation values (at different locations n) and where our input data are the (preprocessed) PE data vectors, u . The problem is to find a function iy = /(ii ), such that i), za jy, based on measured data, the so called training data. 

The characterization of surfaces undergoing corrosion phenomena at liquid-solid and gas-solid interfaces remains a challenging task. The use of STM for in situ studies of corrosion reactions will continue to shape the atomic-level understanding of such surface reactions. 

Characterization of zeolites is primarily carried out to assess tire quality of materials obtained from syntliesis and postsyntlietic modifications. Secondly, it facilitates tire understanding of tire relation between physical and chemical properties of zeolites and tlieir behaviour in certain applications. For tliis task, especially, in situ characterization metliods have become increasingly more important, tliat is, techniques which probe tire zeolite under actual process conditions. 

All three tasks are generally too complicated to be solved from first principles. They are, therefore, tackled by making use of prior information, and of information that has been condensed into knowledge. The amount of information that has to be processed is often quite large. At present, more than 41 million different compounds are known all have a series of properties, physical, chemical, or biological all can be made in many different ways, by a wide range of reactions all can be characterized by a host of spectra. This immense amount of information can be processed only by electronic means, by the power of the computer. 

A very important data mining task is the discovery of characteristic descriptions for subsets of data, which characterize its members and distinguish it from other subsets. Descriptions can, for example, be the output of statistical methods like average or variance. 

A most important task in the handling of molecular data is the evaluation of "hidden information in large chemical data sets. One of the differences between data mining techniques and conventional database queries is the generation of new data that are used subsequently to characterize molecular features in a more general way. Generally, it is not possible to hold all the potentially important information in a data set of chemical structures. Thus, the extraction of relevant information and the production of reliable secondary information are important topics. 

Characterization. The proper characterization of coUoids depends on the purposes for which the information is sought because the total description would be an enormous task (27). The foUowiag physical traits are among those to be considered size, shape, and morphology of the primary particles surface area number and size distribution of pores degree of crystallinity and polycrystaUinity defect concentration nature of internal and surface stresses and state of agglomeration (27). Chemical and phase composition are needed for complete characterization, including data on the purity of the bulk phase and the nature and quaHty of adsorbed surface films or impurities. 

Unstable gas condensate (UGC) is a large-tonnage raw material for producing of liquefied gases, motor fuels and other products. It is characterized with very complicated composition being under pressure up to 10 MPa. That is why analysis of UGC samples is rather complicated task. 

There are several reasons that Newton-Raphson minimization is rarely used in mac-romolecular studies. First, the highly nonquadratic macromolecular energy surface, which is characterized by a multitude of local minima, is unsuitable for the Newton-Raphson method. In such cases it is inefficient, at times even pathological, in behavior. It is, however, sometimes used to complete the minimization of a structure that was already minimized by another method. In such cases it is assumed that the starting point is close enough to the real minimum to justify the quadratic approximation. Second, the need to recalculate the Hessian matrix at every iteration makes this algorithm computationally expensive. Third, it is necessary to invert the second derivative matrix at every step, a difficult task for large systems. 

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