Risk parameter

QURESHI A A, BRADLOw B A, SALSER w A, BRACE L D (1997) Novel tocotiienols of rice bran modulate cardiovascular risk parameters of hypercholesterolemic humans. J Nutri Biochem, 8 290-8. 

In order to identify and then establish the risk level of a chemical, one uses a certain number of risk parameters. The purpose of this chapter is to consider these parameters in detail. It is also to enable the reader to submit these parameters to critical analysis if values are available, or to estimate them if they are unknown. The user of this book should then be able to offer an evaluation of the risk level of inflammability of a particular chemical. This is necessary even if the chemical is not in the tables included later in this book. 

Having obtained vapour pressure vaiues, which are of minimum reiiabiiity, the vapour equiiibrium concentration of the substance can be obtained and compared with various risk parameters or reguiatory criteria in order to estimate a potentiaiiy dangerous situation. Depending on the work to be done, one has to proceed to a seiection of appropriate units. 

In describing the main parameters used to estimate toxicity, the previous factors will be taken into account for some types of estimates. It will then be seen how difficult it is to interpret these estimates illustrating the difficulties raised by their use. Finally, a possible way of estimating toxic risk parameters will be suggested and applied to a few practical cases, emphasising how cautious one has to be when using it. 

In a transportation risk analysis (TRA), the risk parameters are more extensive, but more restrictive in some ways. Examples of risk parameters that could be considered are... 

Tables 6.3-6.5 show the computational results for Risk Model II over a range of values of risk parameter 02 with respect to different recourse penalty costs, for three representative cases of 0 = 1E — 10, IE — 7, and 1.55E — 5, respectively. An example of the detailed results is presented in Table 6.6 for 02 = 50 of the first case. Figure 6.2 illustrates the corresponding efficient frontier plot for Risk Model II while Figure 6.3 provides the plot of the expected profit for different levels of risk.

Kimmel GL Kochhar DM (1990) In vitro methods in developmental toxicology use in defining mechanisms and risk parameters. Boca Raton, Florida, CRC Press. 

Global specialty chemicals companies already operate plants in many different countries and have the expertise to navigate in the respective business environments. Therefore, typically political risks are not incorporated into quantitative models but considered when selecting the potential investment candidate countries. If desired, the model can be extended to include political risk based on an aggregate risk parameter to analyze risk/return profiles for alternative network configurations. For a more detailed discussion of the major elements of political risk see Appendix 3. 

In contrast to the use of a single (default) value for a risk parameter, a probability distribution can reflect the relative likelihood of the different possible values of the parameter. Thus, a probability distribution can reflect not only the largest and smallest possible values of a parameter, but also the probability of the occurrence of each of the values in its range. 

Kimmel, G.L. 1990. In vitro assays in developmental toxicology Their potential application in risk assessment. Pp.163-173 in In Vitro Methods in Developmental Toxicology Use in Defining Mechanisms and Risk Parameters, G.L. Kimmel, and D. M. Kochhar, eds. Boca Raton, FL CRC Press. 

THERdbASE contains two major modules, namely a Database Module and a Model Base Module. The Database Module relates information from exposure, dose and risk-related data files, and contains information about the following population distributions, location/activity patterns, food-consumption patterns, agent properties, agent sources (use patterns), environmental agent concentrations, food contamination, physiological parameters, risk parameters and miscellaneous data files. The Model Base Module provides access to exposure dose and risk-related models. The specific models included with the software are as follows Model 101, subsetting activity pattern data Model 102, location patterns (simulated) Model 103, source (time application) Model 104, source (instantaneous application) Model 105, indoor air (two zones) Model 106, indoor air (n zones) Model 107, inhalation exposure (BEAM) Model 108, inhalation exposure (multiple chemicals) Model 109, dermal dose (film thickness) Model 110, dose scenario (inhalation/dermal) Model 201, soil exposure (dose assessment). 

Aas E., Thierry B., Lennart B., Birgitta L., Ketil A.O., PAH metabolites in bile, cytochrome P4501A and DNA adducts as environmental risk parameters for chronic oil exposure a laboratory experiment with Atlantic cod, Aquat. Toxicol., 51, 241-258,... 

Metcalf M, Duke S. Valuing options—A case study. CTRS Technical Workshop on Benefit-Risk Framework for the Assessment of Medicines Valuing the Options and Determining the Relative Importance (Weighting) of Benefit and Risk Parameters, Philadelphia, PA, 2012. 

Proposed method of the SIL determination is based on modifiable risk graphs, which allows building any risk graph schemes with given number of the risk parameters and their ranges expressed qualitatively or preferably quantitatively. An example of modifiable risk graph is shown on Fig. 7. 

Proposed method allow buil ng architecture of modifiable risk graph based on specified risk parameters. This kind of analysis can be achieved for three main criteria orientated towards the consequence types ... 

Second part, related to the risk analysis and assessment, is based on risk graph method. The module consists of database storing risk parameters for each kind of saved graph and its description too. Its architecture is shown on Fig. 8. 

Having identified the maritime security risk parameters and their corresponding linguistic variables, fuzzy IF-THEN rules with a belief structure can... 

Table 1. Linguistic variables of risk parameters and their fuzzy memberships (Yang et al., 2009).

Risk parameters Linguistic variables Fuzzy memberships... 

The dependence of the risk parameters on process variables such as the concentrations of monomer, polymer, initiator or catalyst, solvent, water and particle size (in emulsion) and MWD are of paramount importance to establish the safe operation regions of polymerization reactors, and furthermore to develop optimal control strategies imder safe conditions. The maximum pressure, Pmax> and maximum temperature, Tmax achieved during the runaway depends on the process conditions (e.g., the higher the amount of monomer in the reactor and the process temperature, the higher Pmax and Tmax)- Also important is the rate at which the runaway reaches the maximum pressures and temperatures. This rate will provide an indication of the time that the operator/control system of the plant has to react in order to keep the polymerization imder safe conditions. 

A simplified fire safety evaluation of a building (see Table F.2). It consists of analyzing and scoring hazard and other related risk parameters to produce a rapid and simple estimate of relative fire risk. A detailed fire risk evaluation may not include attributes such as human behavior and attitudes. The structure of a risk index system facilitates quantification and inclusion of such factors. Where a quantitative fire safety evaluation is desirable, detailed fire risk assessment may not be cost-effective or appropriate. Fire risk indexing may provide a cost-effective means of fire safety... 

We assume M+ = 6960, a = 4000, (7 = 5000, Xg = 0, r = 10 and ri = 0.7 for the first candidate supplier S19. A low r value indicates that buyers will be adversely affected as soon as the supplier delivers defective items. A high ri value, on the other hand, models a relatively high tolerance to the amount of defective items before the maximum damage is faced by the buyers. In other words, ri models the level up to which buyers can absorb the particular MtT type risk. Parameters a, c+, and Xg are used to model the impact of defective items within the range [n, ri ]. M+ is the maximum damage that S19 can do to its buyers for this particular risk measure and it is reached once the defective level exceeds ri. 

The inverse frequency factor is then divided by the consequence parameter to arrive at a dimensioifless inverse risk parameter. Thus, a higher value of the resultant parameter indicates higher safety of a pipeline segment. 

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