Cause and Effect Relationship

KDC has a cause and effect relationship between as the primary cause leading to secondary failures. Besides its drastic operational effects on redundant systems, the numerical etlects that reduce sy.stem reliability are pronounced Equation 2.4-5 shows that the probability ut failing a redundant. system composed of n components is the component probability raised to the n-th power. If a common clement couples the subsystems. Equation 2.4-5 is not correct and the failure rate is the failure rate of the common element. KDC is very serious because the time from primary failure to secondary failures may be too short to mitigate. The PSA Procedures Guide (NUREG,/CR-2.3(X)) cl.issities this type as "Type 2."... 

It is important to keep in mind that statistically based studies by themselves can never prove the e.xistence of a cause and effect relationship. However, such obseix ations may be used to generate or to test a hypothesis. Many possibilities exist for introducing bias in this type of investigation, and statistical correlations may be fortuitous. 

The distinction between machine conditions and fabricating variables is a necessary one to avoid mistakes in using problem-and-solution or cause-and-effect relationships to advantage. If the processing variables are properly defined and measured, not necessarily the machine settings, they can be directly... 

As a preamble to looking at some of these specific problems, it is worth reiterating effective control strategies are achieved through the planned avoidance of problems and understanding cause-and-effect relationships. 

With regard to cause-and-effect relationships, in practice, a problem originating from a single-source cause may have a lead-on effect that gives rise to multiple localized problems or becomes evident in several different areas of the boiler plant. Conversely, what appears to be a single localized problem may in fact be the combined effect of a number of smaller separate causes. 

A wide range of cause-and-effect relationship alternatives may arise, depending on the particular circumstances, especially in smaller residential and commercial properties. But combinations of poor system design,... 

Selected graphs in the text are available in interactive form. Students can manipulate parameters and see cause-and-effect relationships. 

The uncertainties associated with the data base of an individual river basin are compounded when the intent is to provide a global perspective. This point is made in a recent bound volume of UNEP data in which a number of data interpretation limitations are sited. Quality of data varies from one individual reporting entity to another and the precision of the data is usually not possible to ascertain. Thus direct comparisons between data from one country, or even one laboratory to the next are not always possible. Since uncertainties associated with the data (variability, accuracy, precision, etc.) are often not specified, the significance of the data may be difficult to determine and no valid interpretation of the data may therefore be possible. It comes as no surprise that these and similar data from other data bases are often, if not usually, inadequate to establish cause and effect relationships. 

Mechanistic Approaches. Adequate and appropriate river-quality assessment must provide predictive information on the possible consequences of water and land development. This requires an understanding of the relevant cause and effect relationships and suitable data to develop predictive models for basin management. This understanding may be achieved through qualitative, semi-quantitative or quantitative approaches. When quantitative or semi-quantitative methods are not available the qualitative approach must be applied. Qualitative assessments involve knowledge of how basin activities may affect river quality. This requires the use of various descriptive methods. An example of this kind of assessment is laboratory evaluation of the extent to which increases in plant nutrients, temperature or flow may lead to accelerated eutrophication with consequent reduction of water quality. 

Cause and effect relationships associated with erosion and river quality can be clearly established for many activities. For example construction activity at a site could be clearly responsible for a resulting landslide into a river. But other activities such as those related to agriculture and forestry may not be so apparent. Spatial and temporal linkages may not be so clearly established. 

What is needed is an alternative approach which permits development of valid cause and effect relationships. This strategy, one involving intensive surveys, is referred to here as mechanistic. The Willamette River, Oregon, USA, is used as a case study to illustrate quantitative, semi-quantitative and qualitative approaches to mechanistic assessment of river water quality using, respectively, dissolved oxygen depletion, erosion/deposition and potentially toxic trace elements as examples. 

Simulation is best described as the process of translating a real system into a working model in order to run experiments. A simulation does not duplicate a system rather it is an abstraction of reality using mathematics to express cause-and-effect relationships that determine the behavior of the system. Hence the representation displayed on a computer may not always be pictori-ally similar to the real system, and, if it is, then it must be regarded as an added bonus. Software for computer simulation is often customized and based on that developed in academia. There are not many commercial packages available for pharmaceutical formulation. 

A clear cause-and-effect relationship between vaccine administration and chronic diseases, such as diabetes melli-tus, multiple sclerosis, and chronic arthritis, has never been scientifically proven. 

The use of block diagrams to illustrate cause and effect relationship is prevalent in control. We use operational blocks to represent transfer functions and lines for unidirectional information transmission. It is a nice way to visualize the interrelationships of various components. Later, they are crucial to help us identify manipulated and controlled variables, and input(s) and output(s) of a system. 

A block diagram is a pictorial representation of the cause and effect relationship between the input and output of a physical system. A block diagram provides a means to easily identify the functional relationships among the various components of a control system. 

Although clinical observation suggests that ulcer patients are adversely affected by stressful life events, controlled studies have failed to document a cause-and-effect relationship. 

Although these cause-and-effect relationships may be difficult to establish at times ( God is subtle ), they nevertheless do exist ( he is not malicious ). 

If you can find one, you have a starting point to determine your future action, which will be detailed later. However, even if you can find some clusters, but can t find a simple cause-and-effect relationship, the presence of the clusters will suggest that something is wrong somewhere and it may need to be fixed. 

Importantly, this does not identify a cause and effect relationship but simply an association. 

Stock/flow structure combine xmcertainty wi cause-and-effect relationships which can be quantified through discrete event simulation. 

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