Determining Influencing Factors

A difficulty that should not be overlooked is that airborne particulates are rarely homogeneous. They vary greatly in size and shape, and their chemical composition is determined by factors specific to the source and location of the emissions. The combined effects and interactions of various substances mixed with particulates have not yet been established (except for sulfur dioxide), but they are believed to be significant, especially where long-term exposure occurs. Measurement techniques and their reliability may vary across regions and countries, and so may other factors, such as diet, lifestyle, and physical fitness, that influence the human health effects of exposure to particulates. 

Performance-influencing factors analysis is an important part of the human reliability aspects of risk assessment. It can be applied in two areas. The first of these is the qualitative prediction of possible errors that could have a major impact on plant or personnel safety. The second is the evaluation of the operational conditions under which tasks are performed. These conditions will have a major impact in determining the probability that a particular error will be committed, and hence need to be systematically assessed as part of the quantification process. This application of PIFs will be described in Chapters 4 and 5. 

A9-THC and its main metabolites are detected and quantified in forensic samples. Determination of these compounds in human beings is needed to make decision on abuse of A9-THC-containing drugs by individuals. A careful interpretation of the results is very important to avoid fallacies with regard to the behavior of individuals. The Cannabis influence factor (GIF), for example, is an useful tool for distinguishing between acute and chronic intake of A9-THC [98]. 

In an ideal case, the signal y A = f(zA), as shown in Fig. 3.6, is determined only by the analyte A (or the phenomenon of interest), namely both the position, zA = /(A), and intensity, yA = f(xA). But in real samples, matrix constituents are present which can principally interfere with the analyte signal. In structure analysis the same holds for the neighboring relationships (the environment of the species A of interest). Therefore, signal parameters are additionally influenced by the matrix (or the neighborhood , respectively), namely the species B,C,...,N, and follow then the complex relationships zA = /(A N), yA = /(xa xb,Xc,...,xN). Additionally, influencing factors a,b,...,m, background, y0, and noise (random deviations eA) may become relevant and have to be considered. 

Up to the present, there has been no systematic study of radon or radon daughters in Japanese homes. We have, therefore, measured the distribution of radon concentrations in houses to determine the factors that influence them in Japan. For this end, long-term measurements of radon concentrations in the home environment were undertaken as a joint-study of five reserch institutions. These preliminary observations are for 5 areas of Japan for which results are available. 

There is strong international competition in the area of domestic appliances, partly due to market saturation for many household appliances, as described in Chapter 2. The number of newly built houses and flats, the growing age of the population and the trend towards an increasing number of single households are major influencing factors for buying decisions of domestic appliances. Especially in Europe, it is not only the price that determines buying decisions, but also benefit to customers and their social environment need to be taken into account. Many trends in the technical improvement of household appliances were consumer-driven. In Europe in particular, customers wanted ... 

Nucleation and Growth (Round 1). Phase transformations, such as the solidification of a solid from a liquid phase, or the transformation of one solid crystal form to another (remember allotropy ), are important for many industrial processes. We have investigated the thermodynamics that lead to phase stability and the establishment of equilibrium between phases in Chapter 2, but we now turn our attention toward determining what factors influence the rate at which transformations occur. In this section, we will simply look at the phase transformation kinetics from an overall rate standpoint. In Section 3.2.1, we will look at the fundamental principles involved in creating ordered, solid particles from a disordered, solid phase, termed crystallization or devitrification. 

Impedance technology for determination of shrimp freshness, 248-260 applications, 253 influencing factors, 253-254 measurement temperature, 255,256/ monitoring procedure, 254 sample dilution, 254-256 shrimp preparation procedure, 254 storage temperature, 255-259 thermal processing, 254-257 Independent receptor mechanism, 32 Individual differences in sensitivity, taste, 19,2(y... 

Rutin extraction from buckwheat was optimally performed using between 50% and 60% ethanol with a 3-hr maceration (Kreft et al., 1999). Repeating the extraction two times was sufficient to extract most of the rutin. Lie et al. (2000) demonstrated the optimization of influencing factors such as extraction temperatures, extraction times, and solvent concentrations in a conventional extraction of dry powdered material of Hypericum perforatum. Applied extraction conditions caused variations to favor different components in the mixture. Higher extraction efficiency was achieved with moderately polar solvents to extract flavonoids such as rutin, isoquercitrin, and quercetin. The optimum extraction conditions for dry Hypericum perforatum leaf powder were determined as 44% to 69% ethanol in acetone with a 5.3- to 5.9-hr maceration at 55°C. 

Fig. 1 shows, at low magnification, particles of Gouda cheese expectorated after 5 chews. Clearly in this short time span the first subject (top half) has saturated the sample more completely with saliva and caused more extensive melting of the fat, than the second subject (lower half). Such factors may greatly influence the subjects perceptions of the textural character of the cheese. From examination of mastication patterns it may be possible to determine which factors are influential in assessment of food texture. 

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. 

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