Factor Levels defined

O Intravenous factor replacement with recombinant or plasma-derived products to treat or prevent bleeding is the primary treatment hemophilia. Primary prophylaxis is defined as the regular administration of factor concentrates with the intention of preventing joint bleeds.4 The rationale for primary prophylaxis is that individuals with factor levels of greater than 0.02 unit/mL (2 IU/dL) rarely suffer from spontaneous bleeds and arthropathy. Therefore, to maintain a trough level above this might convert severe hemophilia to moderate disease, with the abolition of joint bleeds and the associated arthropathy.5... 

The factor k (the transfer factor) was defined as a crop- and task-specific factor and is defined as the slope of the line that fits dermal exposure levels (g/hr) and corresponding levels of DFR (g/m2) on the crop (i.e., the regression coefficient of DFR). The DFR, according to the procedures described by Iwata et al. (1977), was considered to be a good estimate of source strength for re-entry exposure. 

In robustness tests, usually the factors are examined at two extreme levels.For mixture-related and quantitative factors, these levels usually are chosen symmetrically around the nominal. The range between the extreme levels is selected so that it represents the variability that can occur when transferring the method.However, specifications to estimate such variability are not given in the ICH guidelines. Often the levels are chosen based on personal experience, knowledge, or intuition. Some define the extreme levels as nominal level +x%. However, this relative variation in factor levels is not an appropriate approach, since the absolute variation then depends on the value of the nominal level. Another possibility is to define the levels based on the precision or the uncertainty, with which... 

After selection of the experimental design, the experiments can be defined. For this purpose, the level symbols, —1 and +1, as given in Tables 6 and 7, are replaced by the real factor values, as for instance shown in Tables 2 and 3, respectively, yielding the factor level combinations to be performed. Dummy factors in PB designs can be neglected during the execution of the experimental work. 

This model allows us to estimate a response inside the experimental domain defined by the levels of the factors and so we can search for a maximum, a minimum or a zone of interest of the response. There are two main disadvantages of the complete factorial designs. First, when many factors were defined or when each factor has many levels, a large number of experiments is required. Remember the expression number of experiments = replicates x Oevels) " (e.g. with 2 replicates, 3 levels for each factor and 3 factors we would need 2 x 3 = 54 experiments). The second disadvantage is the need to use ANOVA and the least-squares method to analyse the responses, two techniques involving no simple calculi. Of course, this is not a problem if proper statistical software is available, but it may be cumbersome otherwise. 

A Level C IVIVC establishes a single point relationship between a dissolution parameter, for example, t5o%, percent dissolved in 4 hours and a pharmacokinetic parameter (e.g., AUC, C ax. I max)- A Level C correlation does not reflect the complete shape of the plasma concentration time curve, which is the critical factor that defines the performance of ER products. 

The parameter K is the stress intensity factor, whose level defines the stress field around the crack tip. In the case of a mode I loading, it is denoted as Kj. 

If the number of alternatives identified is too large to perform a detailed evaluation for each, a pre-selection step should reduce the number of alternatives to 5-10 sites. A practical approach commonly used is a checklist approach to eliminate sites that do not meet a certain level defined for important location factors (cf. Wardrep 1985, p. 10). 

Factors may have associated values called levels of variations. Each state of a black box has a definite combination of factor levels. The more different states of the black box that exist, the more complex is the research subject. Formalization of preliminary information includes analysis of reference data, expert opinions and use of direct data, which enables correct selection of response, factors and null point or center of experiment. Factor limitations are also defined at this stage. If the research is linked with several following responses, then response limitations also have to be analyzed. The next phase refers to defining the research problem. When defining this problem one must keep in mind the research-subject model, and in a general case it is Eq. (2.1) that defines the link between the inlet and outlet of the black box. Defining the research problem is possible only now when its aim has been determined, the criteria established, the factors, limitations and null point defined. The problem is a simple one when only one response or optimization criterion is in... 

Problems of choosing responses of complex research subjects have been analyzed. The optimization parameter is, in fact, a reaction or response to factor level changes that define the status of a research subject. Responses may be economic, technoeco-nomic, technical-technological, statistical, psychological, etc. A response should be quantitative, singular, statistically effective, universal, physically real, simple and easily measurable. For responses with no quantitative measurement, the ranking method is used. Out of all responses typical for a research subject, only one or a general response is taken. Other responses are used as constraints. 

Having selected the system response, we start choosing factors, levels of the factors and center point of the design (basic level or the null point). By factor we understand the controllable independent variable that corresponds to one possibility of influence on the object of research. A factor is considered defined if its name and domain of factors are determined. A factor may take several values in this field. The chosen factor values, both qualitative and quantitative, are called factor variation levels. Factor variation levels in the design of experiments are coded values. Under factor interval of variation we understand the difference between two factor levels, which in their coded form have value one. When selecting the factors one should pay attention to the conditions they must meet. 

Let us stick to response geometrical interpretation of black box with two input factors. A simple graphic system with x-y coordinates is sufficient for this. One may insert values of variation levels of one factor on one axis, and those of the other factor on another axis. Each black box status will have a corresponding point in the surface. As has been said in Sect. 2.1.3, factors are defined by their domains. This means that each factor is defined by its minimal and maximal values where it may be changed continuously or discontinuously. If the factors are concordant then those limits in the plane form a rectangle within which are the points that coincide with black box statuses. Dashed lines in Fig. 2.28 mark the limit values of the domain of factors and full lines the limits of concordant domain of factors. To present graphically the response values, we use the third axis of the coordinate system, so that the response surface has the shape given in Fig. 2.29. 

The way in which the spin factor modifies the wave-mechanical description of the hydrogen electron is by the introduction of an extra quantum number, ms = Electron spin is intimately linked to the exclusion principle, which can now be interpreted to require that two electrons on the same atom cannot have identical sets of quantum numbers n, l, mi and rns. This condition allows calculation of the maximum number of electrons on the energy levels defined by the principal quantum number n, as shown in Table 8.2. It is reasonable to expect that the electrons on atoms of high atomic number should have ground-state energies that increase in the same order, with increasing n. Atoms with atomic numbers 2, 10, 28 and 60 are... 

An orthogonal array is called regular if it can be constructed by using a defining relation . For example, the array displayed in Table 1 is regular and is constructed by the following method. Denote a row (factor-level combination) of the array by (xi,..., Xe). The first four columns of the array consist of all the 16 level combinations of factors 1 through 4, and the levels of factors 5 and 6 are defined via... 

Three levels of risk are suggested here (low, medium, and high) although some pharmaceutical and healthcare companies may like to consider five levels of risk to match the system integrity levels defined by lEC/ISO 61508 for safety critical systems. Each system should be rated against a number of weighted risk factors to determine an overall level of risk. Seven example risk factors are considered in Table 14.4 ... 

The levels selected in a robustness test are different from those at which factors are evaluated in method optimization. For optimization purposes the variables are examined in a broad interval. In robustness testing the levels are much less distant. They represent the (somewhat exaggerated) variations in the values of the variables that could occur when a method is transferred. For instance, in optimization the levels for pH would be several units apart, while in robustness testing the difference could be 0.2 pH units. The levels can for instance be defined based on the uncertainty with which a factor level can be set and re.set 36 and usually they are situated around the method (nominal) conditions if the method specifies pH 4.0, the levels would be 3.9 and 4.1. The experimental designs used are in both situations the same and comprise fractional factorial and Plackett-Burman designs. 

A schematic of a batch parametric pumped adsorption process is sketched in Figure 15.22(a), and Figure 15.22(b) shows the synchronized temperature levels and flow directions. At the start, the interstices of the bed and the lower reservoir are filled with liquid of the initial composition and with the same amount in both. The upper reservoir is empty. The bed is kept cold while the liquid is displaced from the interstices into the upper reservoir by liquid pumped from the lower reservoir. Then the temperature of the bed is raised and liquid is pumped down through the bed. Adsorption occurs from the cold liquid and desorption from the hot liquid. For the system of Figure 15.22(c), the separation factor is defined as the ratio of concentrations of the aromatic component in the upper and lower reservoirs very substantial values were obtained in this case. Data of partial desalination of a solution with an ion exchange resin are in Figure 15.22(d), but here the maximum separation ratio is only about 10. 

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