Task observation comparing observations

This observation of Parr and Berk provides the basis for a simple approach to molecular shape analysis and molecular similarity analysis, described below. Although the molecular shapes, as defined by the electronic density, differ somewhat from the shapes of the nuclear potentials, their similarity can be exploited the nuclear potential contour surfaces provide a simple approximation of the shape of molecules. We shall refer to the isopotential surfaces of the nuclear potential contours as NUPCO surfaces. These surfaces have a major advantage the computation of NUPCO s is a trivially simple task as compared to the calculation of electronic densities. Furthermore, nuclear potential is a useful molecular property in its own right, without any reference to electronic density a comparison of NUPCO s of various molecules can provide a valid tool for evaluating molecular similarity. The superposition of potentials of different sets of nuclei can result in similar composite potentials, consequently, the comparison of NUPCO s is better... 

The use of multivariate reference regions usually requires the assistance of a computer program, which takes a set of results obtained by several laboratory tests on the same clinical specimen and calculates an index. The interpretation of a multivariate observation in relation to reference values is then the task of comparing the index with a critical value estimated from the reference values. This, obviously, is much simpler than comparing each result with its proper reference interval. 

In patients with moderate Alzheimer-type dementia, chronic treatment with selegiline (10 mg daily) had some beneficial effects such as improvements in congnitive functions, learning, alertness and task performance . These observations are in line with results obtained in rats chronically treated with selegiline, in which active treatment significantly improved the learning capacity as compared to saline treated controls. ... 

ABSTRACT A proper JHA requires much more than observing a job to see if it looks hard . The purpose of this study is to break down a task into its component steps, determine the hazards associated with each step, and focus on the ergonomic connection of those hazards. In order to quantify the risks of musculoskeletal disorders, we calculated lifting tasks and compared those to minimal averages. The results indicated that multiple job tasks were in excess of the recommended weight limits for those tasks. Implementing controls is necessary for reduction of these injuries. 

Step 1. Develop a standard observation procedure. Identify who should do formal observations. The author generally makes task observation a two-phase process. Both a quaUfied trainer (if available) and the front-line supervisor conduct the observation. (Not at the same time. Their notes and/ or work sheet are compared later.)... 

Identify when task(s) are to be observed. Task observation should be required, and it should be part of the supervisor s and trainer s evaluation. Tasks should be observed on a regular basis. (A schedule should be followed, and it should be flexible but firm.) In some cases several tasks can be observed at the same time. Those conducting the observation (the frontline supervisor and the trainer) should coordinate their activities to coincide with each other, so the results can be compared at nearly the same time. 

Using a JSA as a training tool is important because of its relevance to the task at hand. Because the JSA was developed by analyzing and observing the task, the information within the JSA should be immediately relevant to that task when compared to a textbook situation. The terms used in a JSA should be easily understood by the worker, again keeping the JSA relevant to the task at hand. These two concepts, relevance and understandability, add to the effectiveness of your training and build the safety culture. 

This paper is structured as follows in section 2, we recall the statement of the forward problem. We remind the numerical model which relates the contrast function with the observed data. Then, we compare the measurements performed with the experimental probe with predictive data which come from the model. This comparison is used, firstly, to validate the forward problem. In section 4, the solution of the associated inverse problem is described through a Bayesian approach. We derive, in particular, an appropriate criteria which must be optimized in order to reconstruct simulated flaws. Some results of flaw reconstructions from simulated data are presented. These results confirm the capability of the inversion method. The section 5 ends with giving some tasks we have already thought of. 

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